From 3fef5adb3fd9810d27c4f0f89fb1b514c9eef313 Mon Sep 17 00:00:00 2001 From: Eugene Berson Date: Sat, 15 Oct 2016 13:07:06 -0700 Subject: [PATCH 01/12] update --- eugenetest.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 eugenetest.md diff --git a/eugenetest.md b/eugenetest.md new file mode 100644 index 0000000..87a03f3 --- /dev/null +++ b/eugenetest.md @@ -0,0 +1 @@ +hello 123 \ No newline at end of file From 3af56bd074e728d59722d6753594851bc37ad64f Mon Sep 17 00:00:00 2001 From: Eugene Berson Date: Sat, 22 Oct 2016 20:31:23 -0700 Subject: [PATCH 02/12] changes --- Eugene project ideas.md | 40 + Yelp_Eugene.ipynb | 861 ++ eugenetest.md | 1 - .../Titanic_Eugene-checkpoint.ipynb | 1620 ++++ labs/Titanic_Eugene.ipynb | 1620 ++++ labs/untitled | 0 .../02_pandas-checkpoint.ipynb | 7213 +++++++++++++++++ .../Yelp_Eugene-checkpoint.ipynb | 861 ++ notebooks/02_pandas.ipynb | 4 +- notebooks/05_bias_variance_tradeoff.ipynb | 4 +- notebooks/05_linear_regression.ipynb | 4 +- notebooks/05_model_evaluation.ipynb | 11 +- 12 files changed, 12227 insertions(+), 12 deletions(-) create mode 100644 Eugene project ideas.md create mode 100644 Yelp_Eugene.ipynb delete mode 100644 eugenetest.md create mode 100644 labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb create mode 100644 labs/Titanic_Eugene.ipynb create mode 100644 labs/untitled create mode 100644 notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/Yelp_Eugene-checkpoint.ipynb diff --git a/Eugene project ideas.md b/Eugene project ideas.md new file mode 100644 index 0000000..1534a96 --- /dev/null +++ b/Eugene project ideas.md @@ -0,0 +1,40 @@ +Idea #1 Slack integrations +Parameters: +1) Uses a slash command? +2) Is a bot? +3) Is listed in App Directory? +4) Featured in the app directory (at any point in time)? + +Trying to predict +1) Is it used by paid team or a free team? + + +Idea #2 Strava segment data +Parameters: +1) Number of times riding per week +2) Average distance ridden per week +3) Longest ride ever + +Trying to predict +1) Rider's percentile on the leader board for most popular segment in their area + + +Idea #3: Google Finance +Parameters: +1) P/E ratio average LTM +2) P/E ratio % change LTM +3) Industry sector + +Trying to predict +1) LTM change in stock price + + +Idea #4: Slack credit card payments data from Stripe +Parameters: +1) Country of credit card issue +2) Type of credit card (Visa, MC, etc.) +3) Issuing bank (Chase vs Wells etc) +4) Transaction size (bucketed by size) + +Trying to predict: +1) Will the transaction attempt be successful or will it fail? \ No newline at end of file diff --git a/Yelp_Eugene.ipynb b/Yelp_Eugene.ipynb new file mode 100644 index 0000000..7ab0ba3 --- /dev/null +++ b/Yelp_Eugene.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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business_iddatereview_idstarstexttypeuser_idcoolusefulfunny
09yKzy9PApeiPPOUJEtnvkg2011-01-26fWKvX83p0-ka4JS3dc6E5A5My wife took me here on my birthday for breakf...reviewrLtl8ZkDX5vH5nAx9C3q5Q250
1ZRJwVLyzEJq1VAihDhYiow2011-07-27IjZ33sJrzXqU-0X6U8NwyA5I have no idea why some people give bad review...review0a2KyEL0d3Yb1V6aivbIuQ000
26oRAC4uyJCsJl1X0WZpVSA2012-06-14IESLBzqUCLdSzSqm0eCSxQ4love the gyro plate. Rice is so good and I als...review0hT2KtfLiobPvh6cDC8JQg010
3_1QQZuf4zZOyFCvXc0o6Vg2010-05-27G-WvGaISbqqaMHlNnByodA5Rosie, Dakota, and I LOVE Chaparral Dog Park!!...reviewuZetl9T0NcROGOyFfughhg120
46ozycU1RpktNG2-1BroVtw2012-01-051uJFq2r5QfJG_6ExMRCaGw5General Manager Scott Petello is a good egg!!!...reviewvYmM4KTsC8ZfQBg-j5MWkw000
\n", + "
" + ], + "text/plain": [ + " business_id date review_id stars \\\n", + "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", + "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", + "2 6oRAC4uyJCsJl1X0WZpVSA 2012-06-14 IESLBzqUCLdSzSqm0eCSxQ 4 \n", + "3 _1QQZuf4zZOyFCvXc0o6Vg 2010-05-27 G-WvGaISbqqaMHlNnByodA 5 \n", + "4 6ozycU1RpktNG2-1BroVtw 2012-01-05 1uJFq2r5QfJG_6ExMRCaGw 5 \n", + "\n", + " text type \\\n", + "0 My wife took me here on my birthday for breakf... review \n", + "1 I have no idea why some people give bad review... review \n", + "2 love the gyro plate. Rice is so good and I als... review \n", + "3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!... review \n", + "4 General Manager Scott Petello is a good egg!!!... review \n", + "\n", + " user_id cool useful funny \n", + "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 \n", + "1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 \n", + "2 0hT2KtfLiobPvh6cDC8JQg 0 1 0 \n", + "3 uZetl9T0NcROGOyFfughhg 1 2 0 \n", + "4 vYmM4KTsC8ZfQBg-j5MWkw 0 0 0 " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.cross_validation import cross_val_score\n", + "from sklearn import metrics\n", + "import statsmodels.formula.api as smf\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# visualization\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "data = pd.read_csv('http://localhost:8888/files/data/yelp.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DOoxpmppPZ+UPRmUYnbUL1+qWfvjWnH789rwalt00Pj4U09kzk7p9grLDdnEc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5+f5u1TE6Rzjcntz2+k+PxGip39QY4Nb2NXPLTlv0xxulw4l0wNTU3M4XYalCpB7JqBw9H+lVO3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\np7P8wfdG6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Zlkgf5O36ap8tnqC/K2Ap2jlekcrU6rnicVJVhZq1T5nE0V6QrfrvKZypYJ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\n9ecHG/qSO/uHx+NhvvfhtcW+5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7vKR0MqrwpXmFTFOGEVAmW1Sx7GhqLquhZEwHh2LqMa4cmxsz7WZ9s5qtR1Jb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\niagiYVO5fElL2WX1RntkGAGVy44uz+W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yf4x2j2+pCl1nJfufK7vHJ9k/RuIrHSv+Nsqw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S7g103TfCab2xQRERERERHJhmwm4EPA7xmG8TzQBBwwDGPY6o2maS54HnDDMD4K3El68/YngB8D\nNwL3AI8bhtFmmubZha5fREREREREJJeymYB/GvgC8HEgBnwxw/tiwIIScMMwqoDPA/+esux2oAV4\nj2maYeBzhmG8f2b7v7/g6EVERERERERyKGsJuGmaXwG+AmAYRhRYbZpmd5ZW/wXiSXtDyrLtwLGZ\n5DvhBeLN0UVEREREREQcJVeDpTUDPQCGYfgWs6KZmu6dwGdmvbQaOD9rWTewdjHbExEREREREcmF\nnCTgpmmeAn7JMIyTwJhhGC2GYfyFYRi/s5D1zCTv/xs4aJrmxKyX/cDsZRPAohJ+ERERERERkVzI\nyTzghmEcAD4H/CnwqZnFbwJ/aBhGyDTNP57nqn4POGqa5r9avBYGArOW+YDxhcbr8eR31rTE9hWH\nM2JwShxOiMEJ20/ldrtwu11ZW59T9nEmim/xnB6j0+NbrGxesy7X4tbjdrvxenO3n5fCsXR6jIov\n/3L53ezYf8tlG3ZtR9u4uu0sVk4ScOA3gP9smubfGobxMIBpml80DGMU+E1gvgn4R4B6wzBGZv72\nARiG8SHgs8DmWe9fBVxYaLDl5cUL/UhOKA5nxQDOiMMJMThFIFCy6JtwK07fx4pv8Zweo9Pju1rZ\nvGaLfAWL+nxxcQFVVSVZieVylsKxdHqMii9/7Phu2obztqNt2CtXCbgBPGex/N+AP1/AejqA1BL3\n88RHUf8U8anOftMwDF9K8/QdwPMLDXZ4OEQkEl3ox7LG43FTXl6sOBwSQyQa48JAiODoBFWlPlYH\nivFksdZ1vpywL1LjcIKBgbGs14A7YR9n4qT4ItEY5/vH6RsKUVNRzJpqP4UFHsfEl4mT9qGVXMRn\nR5I5X9m8ZsMTU4v6fCg0RTA4lpVYrDj9XAPnx7gS43PS9Qq5vSe24/gul23YtZ2lsg2re5DUe3O7\nj8li5SoBv0g8CT85a/l7mTtwWkamaZ5J/XumJjxmmuZJwzBOAWeARw3D+AywB9gGPLjQYCORKNPT\n+f+hVxz5jyESi/HssfM89oyZXLZ/l8Ht7Wvw5KDmdV4xOeB4OEU0GiMajWV9vU7fx/mOL9N1sXtb\nfMzLfMc3H06P0enxXa1sXrOx2OLWE43as4+XwrF0eoyKL3/s+G7ahvO24+RtLOTefKlcm7lqKP+X\nwJ8bhrEHcAGGYRi/DPwvZqYqWyzTNKPAXuLNzn8EHAD2maZ5Nhvrl5WpOxhOu8ABHnvGpHswnOET\nIstfpuvi/MCCh9wQERERmbfleG+ekxpw0zQ/bxhGJfANoAj4DjBNfETzzy5ivR+b9fcJ4H2LCFUk\nTd9QyHr5YIg1Vc5ohi1it8zXxdIt/ERERMT5luO9ea6aoGOa5m8bhvH/Eh8ozQ10mqY5nKvtiWRD\nTYX1hVxTuTQvcJFsyHxdFNkciYiIiKwky/HePGdjtRuGsR7wmKb5I6AE+KxhGPtztT2RbKivKmL/\nLiNt2f5dBvVKNGQFy3RdrAn48xSRiIiIrATL8d48V/OA30O8+fndhmGcAP4FOA58zDCMgGmaCxkJ\nXcQ2HpeL29vXcF1LgMGxSSpLC6mrKMrbAGwiTpC4LjY3V9E/FKamspj6yqK8zA4gIiIiK0fGe5Al\nfG+eqybovwt8Afgu8GngFHAt8CHgf7CwqchEbOVxuVhXW8KWjXUEg2NLYjRFkVzzuFw0BPw0qNZb\nREREbLTc7kFy1QR9E/DlmZHKdwPfmfn3D4nP3y0iIiIiIiKyouQqAR8EKg3DqAC2A/86s7wV6M/R\nNkVEREREREQcK1dN0L9DfC7wEeLJ+DOGYfwM8BfAkznapoiIiIiIiIhj5aoG/FeBF4FRYI9pmhPA\nDuAHwG/kaJsiIiIiIiIijpWTGnDTNEPAw7OW/V4utiUiIiIiIiKyFORqGrIHLve6aZpfzcV2RURE\nRERERJwqV33AH82wPAycBZSAi1xGJBbjQu8YP+kKUllSSN0Sn+9Q7BOJxegOhukbClFTUUx9lc4d\nERERu6k8lkxy1QQ9rW+5YRgeYCPwJeDLudimyHIRicV49th5HnvGTC7bv8vg9vY1+uGWy9K5IyIi\nkn8qj+VyclUDnsY0zQjwpmEYDwHfBB6zY7siS1F3MMyLr51nX0crk9NRCr1uXnztPNe2BFhTVZzv\n8MTBuoPhtMIe4LFnTMtzR0/mRUREFma+LRQXUh7LymNLAp4iCqyxeZsiS8rI+CRtTQGeOHI8uWz3\n9kZGxiZBP9pyGX1DIevlg6G0Al9P5kVERBZmIWXnfMtjWZnsHIStHPgE8FIutimyXHi9Hg6/dCpt\n2eGXTvHuzfV5ikiWipoK60K9pjJ9uZ7Mi4iILMxCys75lseyMuVqHvBHLf77YyAIHMzRNkWWhdHx\nyQUtF0morypi/y4jbdn+XQb1lUVpyy73ZF5ERETmWkjZOd/yWFYmWwZhE5H5q83wdFRPTeVKPC4X\nt7evYXNzFf1DYWoqi6m36J+mJ/MiIiILs5Cyc77lsaxMSpRFHKa+qoiP7tqYtuyjuzbqqanMi8fl\noiHg59qmKojBT7uCnB8IEYnFku/Rk3kREZGFWWjZmSiPtzTHm6g7IfmOxGKcHwjx2smB+L1BNHbl\nD0nW2T0Im4jMQ2lxIXtvbWUqEh8FvbS4MN8hyRJypYFi9GReRERkYRJl53UtAQbHJqksLaSuYumU\nnVb3Bgd2G3zw9g15jGplUgIu4jDdwTB//e3X5yxvWn2zBsiSeZnPQDGJJ/MNAX8+QhQREVlyPC4X\n62pL2LKxjmBwjOnpaL5Dmjere4OvHzbZatRRX+HLU1Qrk5qgiziMBsiSxdI5JCIiIqky3Rv0BMdt\njkSUgIs4jAbIksXSOSQiIiKpMt0b1FWpJZzdlICLOIwGyJLF0jkkIiIiqazuDQ7sNmhuKM9TRCuX\n+oCLOMxSH+RD8k+DrImIiEgqq3uDhmo/vgIv40zkO7wVRQm4iAMt5UE+xBk0yJqIiIikmn1v4HHr\nwXw+KAEXcaBILMaF3jF+0hWksqSQOtVeyoxILEZ3MEzfUIiaimLqq3RuiIiIpIpE4/Ndq6wUJ1IC\nLuIwV5rDWVYunRsiIiKXNzE1zTM/OsvXD6usFGfSIGwis0RiMc70jvHcK2c50ztGJBazdfuZ5nDu\nHgzbGoc4SyQWo6t7TOeGiIjIZZw4N5yWfIPzyspILF5D/9rJAc4PhGy/15T8Ug24SAon1DBebg7n\nNVWaRmolikTj5+XQ+KTl6zo3RERE4nozzGvtlLLSCfeakl+qARdJ4YTaZ83hLLOd7x/nsWdMCr3W\nP9k6N0REROJqM8xr7ZSy0gn3mpJfSyIBNwxjjWEY/2gYRr9hGGcMw/hjwzAKZ15rMgzjGcMwRg3D\neN0wjF35jleWrsvVPttFczjLbInz8lhnD7u3N6a9pnNDRETkkpaGcg7sdu59lBPuNSW/lkoT9G8B\n/cAtQDXwFWAaeAQ4BLwK3AjcAzxuGEabaZpn8xSrLGFOqH3WPOAyW+K8PN09AsDeW1uZikRp31hL\nY12Jzg0REZEZvgIvu25ay6amS/Nd1ztoNhkn3GtKfjm+BtwwDAN4N/CgaZqdpmm+CPw34IBhGO8D\nmoFfMuM+B/wA+Hj+IpalzCm1z4l5wG/dupZ1NUqwVro11f7keXm6e4RDzx2nwl+o5FtERMSCxx2f\n73pLc4A1VcWOKiudcq8p+bMUasAvAh8wTbNv1vIK4D3AMdM0UztNvADcbFdwi6X5np3FKbXPOi9W\njqlolNM94/QEx6mr8rO+zk+BO/3ZqMcdPy83Nzvzab6IiEi+LZV7p8S95uXK9KXyXeTqOD4BN01z\nCHgm8bdhGC7gPwHfBVYD52d9pBtYa1uAi6BREJ0pUfu8ZWMdweAY09NRW7ev82LlmIpG+eeXzvD4\nkePJZfd0tPKB7evmJuGu+NP8hoD14DIiIiIr1VK7d7pcmb7UvossnOMTcAt/BGwFtgEPAROzXp8A\nfAtZoceTn5b4F3qt5/S9rjXAupoS2+NJ7Id87Q+nxJDvOJx6XjiB2+3C7c5e4ZPv8+3EudG05Bvg\n8SPHubalmo0N5XmP70qcHh84P0anx7dY2bxmXYu88XS73XgzzCSQDUvhWDo9RsWXf7n8brncf3be\nO+X6PFhO32U5bSOb619SCbhhGH8I/Brwc6ZpvmEYRhgIzHqbD7CeADCD8vL8DHrwk64g6+vLaG+r\nY3I6SqHXzbHOHgZHJ9lyTV1eYoL87Y+JqWlOnBvm1eP91Fb5aWkox1dg/yk6OBzm9a4BugfOUR/w\ns7kpQGW5ff1ynHpeOEEgULLom3AruTjnE+dz98AY/qICopEYFWW+5Hk9Oj7JWGiKe27bwOpqP8GR\nCUIT0xzr7KFnMMQNRq0jrof5yNdvxkI4PUanx3e1snnNFvkKFvX54uICqqpy/xBzKRxLp8eo+PLH\nju82320kytHe4PgVy8GfdAUtl4+HI1wcmrjiOqy2BVx2+77igsu+vpD45/Ndcnkf6KTj7vRtZIMz\n7+YsGIbxZ8AvAfeZpvnEzOJzwOZZb10FXFjIuoeHQ0Qi9jYzBgiU+WhrCvBESg3Y7u2NBMp9BINj\ntsfj8bgpLy/Oy/6IRGM886OzfP3wpSd+B3Yb7LppLZ4s1nheSXg6wlM/OMUTR04kl+3raOGumxsp\n8npsicGp54UTDAyMZb0GPBfnvNX5vHt7I51dA+y4fg23ta/h6R+cTqv9Trze1hRgfX0J//TsO3m/\nHq4kn78Z8+X0GHMRnx1J5nxl85oNT0wt6vOh0FROf0Odfq6B82NcifE56XqF3N4TL2T/LfS+sLKk\ncM6y9fVlvH0myD989+3LrsNqW/t3G5QWF/BXh16f89nCAg++4oLLltOLua+1+i4AlaWFWf8Ns+Oa\nWy7bSN3OYi2JBNwwjP8OfBL4iGmaj6e89EPgEcMwfKZpJpqi7wCeX8j6I5Go7f18AaYjUQ6/dCpt\n2eGXTrHz+jV5iSchH/vj/EAo7UcK4OuHTTbPjF5plxPnRznXO8bBe7cwPD5Jub+Qo292c/LCKNes\nLrMlhulIlM6uAfZ1tKbVgOf7vHCCaDRGNBrL+nqzfc6fHwjxwo/Ppx3Dc72j3L2jmQv94xw/PzKn\n6fnhl06xr6OVJ44c56ZNdZbXQ0tDhSNHPs/Xb+hCOD1Gp8d3tbJ5zcZii1tPNGrPPl4Kx9LpMSq+\n/LHju81nG1bl6As/Pm95XzgVjTI+Mc3H7t5Msc/L0Te7OfpGN3s7Wvmzf3g17b2z7y0jsRhd3WMM\njk2yr6OVY509nO4e4bHDJvs6Wud8tqGuhMoSH54C92XvWxdzX1tXGR8lfXYf8LqKopwdG6cc96Ww\njWxwfAJuGMYm4HeAzwLfNwyjPuXlI8AZ4FHDMD4D7CHeN/xBu+O8Gv1D4QzLQzQEnFHjaJe+oZD1\n8sGQrQn41HSE1dUlfOlbryWX7dnZwtR0xLYYRsamLGvAR8cnYYWdF0vVyPhk2jFcX1/GDRtrk+fV\nXbc0W35ucqbQ6A1aXw/H3url+NkhDcQiIiLL2uxyFOL3QiNjk5ByX2g1mOm+jlY+9P9s4HyvdW1x\n4t7SarCz3dsbgfiUn5MWidxPTwZ56sWTPPizsxvgpq97Mfe1TpmRR3JnKYwisYd4nL9DfMTz88Sb\nmJ83TTMK7CPe7PxHwAFgn2maZ/MU64LUVFhfgDWV9idZkViMM71jPPfKWc70jhFZZE3DQjllX3i9\nbr79/Im0Zd9+/gReGwdccXvcli0j3Mt40Jflxuv1JI/h+voy7t7RnHZeFWYYCKrQ62Z9fRmBCusx\nBwq9bh57xqR70PrhnYiIyHKQWo4mHH7pFAUF6d0BT/eMz2lR9sSR44yMT1Gb4R4ycW/ZHQzPGezs\n8EunaG+L97NOLavX15exr6OV6vIi9nW0Eola17L6Cr0cvziaccDH+d7XJmbkuXXrWtbVOK/lmyyO\n42vATdP8Q+APL/P6ceB99kWUPfVV1k1M6ivtG/ALnDHdQW2lj3s6WudMx1RbsUG9N6UAACAASURB\nVKAB7Retb9D6iWXvYIiNa8ptiWFwxDq5Cg6HwaZm8LI4o+OTQLzAbmsK0HVxJO31Y5097N7emHZz\nsXt7I+d6R7lhYy2P/Ytp+fqxzh7A/pYhIiIidkqUo1da3hO0Hne5e2Ccd7fVXvY+O1Mt9eR0lDvf\n20TBTMVHoixPrY2/871NPHj3Jh598s20ZSfODfHNZ99mfX3ZnHI8H/f44kyOT8CXM6c0MbF6AvjY\nMybXttjX/7p3cIKXO3vYe2srU5F4X5+XO3u4sa3O1kSjprLYcgTyTE9Rc6GizGcZQ2WZvQ8j5OpM\nRqIUFcV/Wtvb6jjW2cPdO5rZv9ugqszHhf5xYrEY53pHOXjvFroujnBdSzVnukfYsLYirftD4npo\nWlXOky+c4HR3PJHPRysZERERu2S676quiPev7h0MUeovpDpDC8rVNX66g2Hqqor59IPbmJ6OUuov\nxONx8dOuIDUVxVRXFFnebyXKXIiXw02ry/hff5/el/zp73fx2w9u4zOffA8X+sc53zdGmb+Av3u6\nEyBZXu+9tZWG2hLW1JRQX6lm5BKnBDzPEk1MtmysIxgcy8vAAU7of903FOJ090jyBysfMQBUlBSw\nta027Snn3o4WyksWN/3NQhS43ZYxeN1qgu50k5Eo//zvpznW2cvu7Y0U+7y0NQV48oWTtDUFeCxl\nQJY9O1s4+mY3VWVFnJkZ8CW1b3jq9XDXLc3Jf+sJuoiILHdWrUQ/umsjpy6O8NffvjQy+QN3tiUH\nME247w6DN7uCfDNl9POP7tpIaXFh2md/9cM3cGNb3Zz+40ffvJgsc093j3DPbRssY3z17T4q/IWs\nry/liSPH54zvkijH/8tHblCrNUmjBDzPIrEYF3rH+ElXkMqSQury8HTMCf2vnRADwPDYNIeOpPcB\nP3TkBJubqllVYU8MU9Eor3T2zhkFfVPj7CnvJZcisRjdwTB9QyFqKoqpr4pfm5mWA3R1j3Gss5f2\ntjpcLhdr60qTI6ke6+yZc0z37zaYmIrgcbuSNeRW2jfWsnFtBTWVxXqCLiIiy8LlylOPy8VtW1fT\n0lBB31CIqrIi3G747KNH09bx1ac7+R+f2M7G9ZX0DYaorfTjcrn43FfT3/eNZ95Kjmq+bXM92zbV\n43KTTL5Ta8I7tjZwo1HL6Z4xCr1uigov9TtPfV/TqjKefOEkrXdtAjKP76JWazKbEvA8ckLfa3BG\nX3QnxAAwOGrd/3pwxL7+1xMTEcuRP8OT9o3EvtJlujZv27qa771yIeM1OxZKH7U18dQ8URM++5ie\n7h5hLDTFhf4xqsqKeOfsIHt2tqQN2HbfB9poXV1GLAfTr4mIiOTDle6BI7EY33vlAi++dp62pgB/\n+dJPLGcQ2ba5nmNv9aZVntx/Z5vlNn0FHrZtrk/OdpNY3+w+3k+9eJI9O1voDY5z9I1uHrizjY/u\n2sj3X7tgWZZPTUXYv8vgxdfOq9+3zIsS8DzqDoZ58bX0OQ5ffO28rX2v4dJTxta1FfQEx6mr8rOu\n1m/rQwAnxABQUWpdA5lpeS74fNYjf7YbtbbFsNJlGhehZW3FZcdL8BcXpB271dV+1teXsaamJK35\nOcSP6cF7r+exwyYH772eL33rx8ma8kceuImJiWlqq4oxmgKMj04wrQRcRESWiSuNP5S4R757RzOT\n01EePtDOwHCYg/duSc7zDbBtU33a2CkAI+NTlttcW1dKdUVR8v2JGuv2trq0pBriM+AcvPd6jr7R\nzVef7uSzv/JemlZXzKlZP/zSKd69uZ7b29ewubmK0fEptm2qZyw0qVZrkpES8Dya7xyHuZZ4ypjP\nmngnxAAQicTm1EDu2dlCJGJf8tOfYYqp/qEw2DQS+0qXaVyE3gyjrSbGKhgcmUhbPjkd5YaNtZw4\nP2z5uQv98TlKx8JTyfef7h5hYmKaLc0BvF43vgIv40xYfl5ERGQputL4Q4l75KNvdrO6uoS/PnSp\n7/aenS1s21zP0Te6LVsHHuvs4YO3beCfvvdO2mdisRjDKaOon+sdZc/OFsv5vgGGxy+VvT0D42Sa\noXdkbBKPq5SGgB/UW1DmQQl4Hnm9HoIj8ad5w+OTlPsLOfpm95w5DnPNCaOgO6Y1gMfFhf6xlGPi\n4+ibF9ncbN8vanWGpkrVGeaGlsWZmJrmTO8YvYOX+qCljkmQ2t+rqjw+Yurp7pG05UU+L+eD41SW\n+bjrluZkH+/CmXnlE/3OZovNlOb1gXhNeeJpvPqLiYjIcpZp7J+CAjfnB0IUF3kJjoS5rX0tPz05\nkGwhdrp7hG8/f4KHDrTTUFtKoPxSC8XUcnlTU4D9uw3CkxFWV5cQHAnj9bpZV3epO2FDbWlyphIr\ngZk5vyeno5T4CynwWFcIlZUUXrY/u8hsSsDzyAXJfigJe3a2gM0tTZ0wCrpTWgOEwtNUlRWlHZPd\n2xsZD0/bFkNRoYe9HS1p/Zn2drSkDQIi2RGJxnj8e8f5P//cmVyW6Oud6M9l1S+svtpPVVlRcvnr\n7/Sxta027Zjt3t7IaChes514yj67ZcW53lF2b2/kTPcIN2ys5VzvqPqLiYjIsmc19s/u7Y38/TNv\nc7p7hAfv3kxDbSl/9LWX016H+OjiweEwTxw5zi/+h2vZvb2Rzq6BOeX1ne9torSogC9968fJddz7\nvg08cGcbX326M9nq7MkXTs7pu72vo5WegfG09X3o9mv48O3X8M1n306LaWoq4ogxnWTpUAKeR9PR\nWNoNOcT7nFzbUm1rHDUV1nNf21kL5/Va93t+9+Z622IAKC7y0tk1MGe0ajv7X4cnI5zvnVsLr1HQ\ns+98/3ha8g3x1h+tayuoqyrmwB0Gn/vqj9Je//bzJ3jk/puSo5unjoSa6vBLp3j4QDvr68vYtqme\n4MgEB+/dkpwHPP7UvYUnXzhBe1sd337+BL/zsXfTWFeiAltERJY1j8uV7Dd9oX+ckfFJpiMxrttQ\nQ3tbHROTkWTym3qPeveOZp584SS1VX4euf8mJqcjdP77AHfvaE6rPFlfX4avwEORz5tWe/6tf3uH\nR+6/id984CYmpyI89WL6nN1TkShbWqsp9nn473/1UlrM//js2xzYbSTfl7hHfPfm+ry3JJWlRQl4\nHvVfpub5GptG3AaorfTNmQfxno5WaivsG3hsJKVPTqrhMevluTI1ZT0C+eS0fSOQT0xELGvhwxMa\nBT3bMrX+OPZWL0+9eNJyxFWIj5ZvdZ4AaXPZ9w6G2NpWO+dYdnYNcLp7hK6Lw7Q1BTjW2QPA6Hi8\nH5mIiMhy53G5aAj4GRmb4p2zQ2kVMffdER/JfPYI5RBvFTgWmuTP//G15OtdFy+VvVafSS2jf3Ki\nn6dePMmvfvj65BziiTm793W00FRfSufpQcuYCwo8HDqcXtM9NWV9f2ZnS1JZWpSA51G1Q+a+7h2c\n4OVZcxS/3NnDjW11tv1wFBdZn4r+DMtzpaAg/yOQaxR0+2Tqg5boi13odVu2DiktLrQ8Rvs6WtMS\n8NrKYr761JsZ39e0qpwnXziR/Iz6fouIyEqR6DftcrvmlKlj4SnW15fNqdkGOHTk0tgqifIztR+3\n1ajmqWVvooz/s2/+mF++5zp+5d4tjKS0OOwbnsh4f7ChoYI/+KWbGRybpLK0kLqKIrqD1oPnqkyX\nTKxnjBdbeN2ueJ/vFHt2ttje/DS1//VTL57kiSPHaWsKxPtf22R6Omq5L6Yj1iNT5srQqPVo05mW\n58LwmPW27G4NsBKsqfZz3wfS5wvdvb0xWSMdKPexta027dq4YWMtFwesR0NPHUl1b0cLfYPWNeyT\n01H2drSkJd/q+y0iIitFYh7w3/nyDwiOzE1gz/WOsrWtNq1mO5UvZcDi090jHH2zm70d8fvITKOa\nT05H08p4gNM9Y/zFt17ja0938qVv/Zijb3TTNxhK9lFPtX+XwaqqItbVlnDr1rWsq4l3Gcv0XpXp\nkolqwPOoqNDDhf6xOU/e7O737IT+116vO++jj4Mz5gEvL7HeVnlJoW0xrBQet4t7bmtlc2MVvYMh\nSv2FHO3s5u4dzYQmpqmt9PM3//eNtM/E5wbdYrm+61qqqassprqiiNdP9lO3zm/5vk2NVbx+sp+t\nRl28v9vGWvX9FhGRFWF8apqTF0YpLy3g4QPtuN1zy76G2lKeOHI84ywizWvK02YRikajPPX9U+y9\ntZXVNSWWn9nUWMVzr55La6mWqA1PVV0Rn4d8fX0pn37w3Vec0zu1P3v/UFjzf8sVKQHPo5oKHw21\nJfxFStOafR0t1JTbl+wBaXMipi23scY1nGH08VDY3n7PUQfMA+52YTkKun7Hc8NX4GVdbQmrq4qZ\nmI7yk0JP8jzM1Ac8ODIx5zzZ29HCNw6byYJ9z84WPB6XxciqLXzzu2+n1Xwr+RYRkZWgb3Ccp394\nmlc6e2lrCnD4pVPs323MKSsTlQ7HOnvmvHb3jmbO9Y7yd09fGkR1X0d8hpJDzx1nfX3ZnM/s3t7I\nN7/7Nm1NgeR0oh/dtZHS4vTKjY/u2sipiyP89bcvzTu+f5fBtU1Vly2nE/3ZGwLWD95FUikBz6Mz\nveOEJiM8fKCd/qFwstbsbN84LfX2DcSUqZ+1nf2vi2bme5w9J3pxkb1Tb7kdMA94NMasUdDj+0Kj\noOdWJBajq2eUqekY+zpaCU1Os6mxikC5L3kMjr7RDcCamhJeP9nPQwfa6TwV5LqWav7t5TO0t8Vr\ntBN9xTc1BaiuKOJTP38jb58dwlhfSWNdKTcadXpKLiIiK8ZUNMqJc6P0BMfx+wr40O0b+J+PvQJA\naGKazq4B9t7air/IS1WZj8npKPff2cZ0JEZ4MsLBe7cQHJmgqqyIokJ38rMJTxw5wUP743ODT05H\nWVNTwq/93A2MjE9S7Cvg6JsXkwOtPbR/K/6iAtbV+nG7XDSueg/9Q2FK/YVEYzE+++jRtHVrRHPJ\nNiXgeTQ5FcHn9fDHXz+WXLZnZwsTk/bW+ib6X8+u9Z3K0IcmJ2IZ5kS3mRPmAR8PTVnGMBayL4aV\nJhKNpc3huW1zPWtqS+Zcm9s211NVVsSZnhGKCj10ngry1IsnWV9fmjYvOCSO2RSPHTa557YNPP69\nd/iFn93MqQsj3N6+Rk/JRURkRZiKRvnnl86kzbaz99aWZE30sc4e2poCvGLG///YYTM5knlqLfae\nnS28c3aQa5vnTte7vr6MrgvDc8rhxKwju7c3JrfXMxjia0+/kpyre1VVMW+cDPKnf/9qxpZvGtFc\nskmDsOWR2+OynAfc47G3NszrdfPqW73svbWVu25pZl9HK6++1UuBRb+YnHFhuS/sVlzktewPb2dr\nAH9xgWUMJcV6XpYr5/vH0+bw3LapPq0LAMTPx9tvXEdn1wA1FcUcOnIi2XespMj6mPmLCwCIxeJd\nGGoqinjsGZPuQesRU0VERJab0z3jack3wKHnTtDeVhd/vXuEzq74XN6JsrS9rW5Oufrt509w6w0N\nlFmMidPeVsc/fe+dtGWHXzqV3Ebqv8v98a6eifK4OxhO3gNY9QkHjWgu2aU7+jzqDVqPkNw7GGLj\nmnLb4ggOh6mv9tNQW5Js8nyu2s/A8ASssSeG3mCIbZvr2bapPq3ZdU/Q/n1hxc59kTmGMNi4L5a7\nSCzGhd4xzLPDhCenuee2Dayu9hMcmSCUocVD72CIu25pwl9UwAN3baKi1McDd21iwGIEV4iP4poY\ncXX39kZ6Z0ZF15N0ERFZKXqC6TOHJKb3DJQXJZuWF/k8RKKXxtvJNJL52Z5RmhsqkvN3J5T5Cyzf\nn7qeyZkWn0ffvJhcNnu2Eqs+5xrRXLJNCXge1WVoglpXZW/T1IbaEi72j89p/t1Qa18cjatK6RsM\nzYmhabV9feEB6jIkRXUB+354M80Pn2m5LFxi+pMXXzs/p4nb7u2NGI1Vlp8rLS6g81SQzq4B2poC\nyTm+M43Sur6+jKICDxvWVvLkCyeSLSn0JF1ERFaK1HurRNNyq6biqc2/KzLM/FJf7edzf3uUbZvr\n+ZV7txCemKai1MdQhilcU2u0E6OgJ8ZzgZnyOGWc3cQAqXtvbaWhtoQ1NSUaq0WyTk3Q86i40D3n\nxn1fRytFhfYeltBExLL5d9jGvuihiUhy8LOfv7ONg/du4UL/GOM2j4KOKz5qdard2xtxYd8Pb2GB\ni7t3pPdBuntHM4UF+vHPlu5gmBdfO5/W3G19fRn7Olrxet2UFHnZe2tL2vIH7tqEv7iAG66p4cPv\nvyYtaU88MU+1e3sjfYMhzvSM8uQLJ2hrCnCss0dP0kVEZEUp8xfy0IGtPHygnXtu20Bn1wD7Oi51\ne+zsGqCjvYGh0Ql++Z7rOHjvFgJlvuS83gm7tzdSXlLIb9zXzrXN1fh9Xs73j1Hs8zI9HU2W26nv\nT8z5fc9tG5iOxOjuv1QbnyiPZ8/jfbp7BL/Py9YN1aypKlbyLVmnGvA86g2GKCr0sPfWVqYiUQq9\nbooKPfQGw6yrtp7DMBf6h6ybz/YNhrlmtT1Nnqemo5aDsE1HbBwIjnhTpMRInIljcqyzh9aGcprr\n7KmNn5iMUlFSmBZDSZGXiSl798VyNjw2SVtTgK6L8Sfds5/IP/XiST5292Z+9+Pv5vUTAzye0q9s\nb0cL6+vTr4vEE/Nf/uC7ON09yoa1FTzxveO0NJSz9Zoatm2uY3oqyq03rNGTdBERWVEKvHD83BCH\njpxg/25jTg34h2+/htHxKV6eGYwt9cH4L93zLnqCIWKxGOd6R/npiX4e/17KYG4dLYQnpvm7pztZ\nX1/G3ltbqSgtpLq8iN7BEF5vDe1tdXjdLh7/3jtsv24V+3dtpKykMK081jzeYicl4HlU4i/k//vH\n1+Ys/80HbrI1jkBFET/3M9fQuKqcvsEQNZXFnLo4THWFfbV0Xq/bshbe7n1RUepjz85mSooL6Rkc\np67Sz9raEkr91k2hciEG9A2Hua65Om16unX1ZbbFsNy5PC4Ov3SKfR2trK8v4+4dzfQPhXn4QDvF\nRV6mpqL0Do4zORXlbM8I+zpaKfbFp0a50D+O3+dl2+b6tGZsp7tHuNg/zlMvnuTgvddzunuEqrIi\nWlaV0h0M0zceosy+6eRFREQcoWdwglc6e9nXEZ9mrMhXwkP7txKLQWGBh97BEA21JVyzvoqh0XhZ\nfLZnlImpCE9/v4u7d7ZALEbT6nL+9Bvp048dOnKChw+0AySnGQPm9BFPLPvmd9/mM5+8GWLw064g\nNRXF1FfF73eTrR1VVkuOKQHPo9HxqQUtz5WyYi8TUxH+6GsvJ5ft7Wih1MZRtwcy1ML3D9k78FhN\nuY/OUwNpI2Dv7Whhx7tW2xaD1x1vCZE6BdbejhY8bvUYyZbgcLyvWGhymq1ttcmWF+vry9jaVpt2\n/PfsbOFc7yhVZUU8dvjSSOl7O1rSkvBEU7fEAC+7tzcyPR1Nm94MSE57oifrIiKyEoyNT86p9b57\nRzMVJYX8n38xuePmRvqHQ7zS2Ws5LktwOMxjh820qctSDVgMXms1iFti2Tvnhvjb77yRXP4f91zH\naGiSbzzzVnKZymrJJd3R51Gpv4A7bm7k4QPtPPizm3n4QDt33NxIaYaRHHNlJDQ9Z8qlQ0dOMGrj\nvNOBiqJkX9tEn6D19WW21sID9A1PWO6L/mHrwT1yYToatYwhElUT9GypKvdxx82NXL8hPdlub6uz\nnH7s1hsa5kyHcuhIfFqyj/+HzTxy/020ri3nw++/hrqAn/fNTFfm8bjTkm9A05CJiMiKUuIvnFOG\nPvnCSfxFBRy8dwubmwIcOnLCcuqxwy+doqosfi946LkT3L0jnoSnqq3yz1nWtKp8zrLEgGxDo+n3\ndH1DobTkGzKX1ZFYjDO9Yzz3ylnO9I4Riam6XBZONeB5VFTosazp9BV6bI0jmKH22c5prwo8bra2\n1aY9Hd3b0YLXY+8zot5ZU2Uk9ATHbZsOzQkxLHd+X/zae/1Ef9ryTNOedGc4Jr2D4/QPh/nJ8f5k\nTfienS14PS5ubKsjFLZuzaJpyEREZKUYs2jZub6+jL6hEI9/73hy9PNMZfCF/rHkv7suDtPWFADi\nTc53b2/kG4fNOcsSg5+mLjvW2cO+jlbO9Y6mrT/TdmeX1YkZVNSqTRZLCXgehScjljWdmxoDtsZR\nlaGWOVBuX+3zVCTK+d74KOip84DbvS8ST1Hb2+qYnL40CJudU8PVZtiW3dPTLWfjE/FrL9HSInG8\nm1aVWTZvK88wBkBtpZ/R0BS33biOzc3VlBQVcPTNi2xuDrB1QzW9g9YtJzQNmYiIrBQls1p2bttc\nz603NPDmqSD7OlqTc3inThmWKpZSy1zodfPUiyf5zx+5gVMXRzjW2ZPs+/3LH9zCxf6xtGUHP3Q9\nxCA4Eqa9rY5jnT20t9VxlO60dVqZXVZ3B8OWrdqubQnoobosiBLwPOoNhiyX9wRDttZ0hsLT7N7e\nOKfPzXjYvibo0UjMchT0SMTepj01FT7LmvjqCp9tMVSUFLC3o2VOP/TyEnu7JixnfYPxa+9c7yg3\nbJx7vOHSyOZ7drZw/PzQnGtkz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C4NTaZciAF8/SmTbZv8rK92Rkc4VyKRKJFI\nNOPrdcr5ls5S47P7HHL6/gPnx+j0+JYrG23W6fvK6e0V8m8f2s3p8a1ENt/bSjpG0Uh0SXHZcYzs\nOg/y5b3kyzYywdEdcNM0zyf/PTcSHjVN84xhGGeB88CXDcP4LHAA2A180PZARTJoYHTSevnI5Jrv\ngMvi6BwSWT3UXkVE1pZVm0XCNM0IcJDYtPOfAA8Ah0zTvJDTwERWqLbS+oKrtkoXYrI4OodEVg+1\nVxGRtcXRI+Dzmab5oXl/nwbelqNwRLKiobqYI/sMHnn6ypTEI/sMGqqWn7xE1hadQyKrh9qriMja\nsqo64CJrgcflYu+u9Wxv8zMSCFFVVkh9ZTGeLGT/lvwUP4e2bapmcHSK2qoSGqp0Dok4kdqriMja\nog64iAN5XC421pWyY0s9w8OBVZFQQpzF43LR5PfR5PflOhQRuQa1VxGRtWPV3gMuIiIiIiIispqo\nAy4iIiIiIiJigyV3wA3DqMpGICIiIiIiIiL5bDn3gPcYhnEU+BLwlGma0QzHJCIiIiIiIpJ3ljMF\n/RAQBr4DnDcM478YhrEls2GJiIiIiIiI5Jclj4Cbpvl94PuGYZQD7wEeBH7PMIwfExsV/1+maY5n\nNkwRERERERGR1W3ZSdhM0xw3TfOLwG8A/wm4Cfhb4JJhGH9pGEZFhmIUERERERERWfWWVQfcMIwi\nYlPR3w/sA3qAPwe+DGwE/gL4FrA/I1GKiIiIiIiIrHJL7oAbhvFF4N1AMXAUeBepydhOGYbxR8Sm\no4uIiIiIiIgIyxsB3wX8AfA10zSH0jznVeC9y45KREREREREJM8sJwnbrkU8xwTMZUUkIiIiIiIi\nkoeWMwW9FPgYcDtQCLiSHzdNc29mQhMRERERERHJH8uZgv7/EUvA9hRwObPhWDMMox34H8Q6/YPA\nfzdN88/mHmsFvgDcCnQDHzNN82k74hIRERERERFZrOV0wN8FvNc0zScyHYwVwzBcwD8BLxErdbYZ\n+IZhGBdM0/wGsURwPwNuBg4DjxqG0WGa5gU74hMRERERERFZjOV0wCPAyUwHchUNwMvAw6ZpBohl\nWf8B8BbDMHqBTcAe0zSngD82DOPtwIeBz9gYo4iIiIiIiMhVLacD/m3gg8QyoWedaZqXgSPxvw3D\nuB24A3gY+FXgxFznO+4FYtPRRURERERERBxjOR3wfuD3DMO4B+gCppMfNE3zw5kIzIphGN3ARuAJ\n4DvAfwMuzXtaL7AhWzFkWjgapac/wGvdw1SVFlJfVYzH5br2C0UkL4SjUXqHpxgYnaS2soSGan0G\niKw2+i4XEZHFWk4H/FeBf5n7//UZjGUx7gUagb8G/gLwMe8HgLm/i2yOa1nC0SjPnLjEI09fqdh2\nZJ/B3l3r9cUtsgboM0Bk9VM7FhGRpVhOHfC3ZSOQRW77BIBhGB8Hvgb8HVA972lFQHAp6/V43BmJ\nb6l6+gMpX9gAjzxtsr3dz8baUtvjie+HXO0Pp8TglDicEIMTtp/M7XbhdmfugjbX+/hanwG5ju9a\nnB4fOD9Gp8e3Uplss07dV077Lr8ap+7DOMWXe9l8bytZt8vtwuu99uvtOEZ2nQf58l7yZRuZXP9y\nRsAxDMMH3MDCOuBR0zSfz0RgSduqB241TfNo0uI35rbdA2yd95LGueWLVlFRsqIYl+u17mHL5SMT\nIXZsrrc5mitytT+cFgM4Iw4nxOAUfn8priyMKDn9M8Dp54DT4wPnx+j0+JYrG23WafvKqd/lV+O0\nfTif4sudbL63srLiZb+2sMBLdfXif9Cy4xjZdR7ky3vJl21kwpI74IZhHAD+AaggtfMNEAU8GYgr\n2SbgO4ZhbDBNM96xvgXoI5Zw7ZOGYRSZphmfiv4WYEk/AoyNTRIORzIW8GJVlRZaLy8rZHg4YHM0\nsV91KipKcrY/nBKDU+JwQgzJcTjB0FAg4yPgudzH1/oMyHV81+L0+MD5MWYjvqVcpGZbJtusU4+l\n077Lr8ap+zBuLcbnpPYK2b0mnpiYuvaT0gjNzC6qPdlxDtl1nubLe8mXbSRvZ6WWMwL+J8A/A58F\nRlccwbUdB34C/P3c1PNNwOeAPwSeA84DXzYM47PAAWA3sSztixYOR5idtf+Dvr6qmCP7jAX3jdVX\nFucknrhc7Q+nxeCUOJwQg1NEIlEikWjG1+v0zwCnnwNOjw+cH6PT41uubLRZp+0rp36XX43T9uF8\nii93svneVtIxikaiS4rLjmNk13mQL+8lX7aRCcvpgG8C3mma5qlMB2PFNM2IYRgHgf8O/BAIAP/N\nNM3/DokR+b8j1kl/EzhkmuYFO2JbKY/Lxd5d69ne5mckEKKqrJD6SmVOFVkr4p8B2zZVMzg6RW1V\nCQ3Kniyyqui7XERElmI5HfBfEivzZUsHHBK1wN+d5rHTQM4Sw62Ux+ViY10pO7bUMzwcWBW/2uQ7\nlZMRO3lcLpr8Ppr8vqs+T+XKRJwr3Xe52q2IiMy3nA74p4C/Mgzj32NdB/xcJgITyQWVkxEnCkd0\nXoqsNvo+ERERK8vJpf44sB04SqwDfmbuX/fcf0VWrd7hKctyMr0jy08cIrJSlwaDOi9FVhl9n4iI\niJXljID/WsajEHGIgdFJ6+Ujk6yvdkYmcFl7dF6KrD5qtyIiYmXJHXDTNI9lIxARJ6ittL4oqq3S\nxZLkjs5LkdVH7VZERKwspw7431/tcdM0P7z8cERyq6HaupxMQ1VxDqOStW59jU/npcgqo+8TERGx\nstwyZPPX0Q5UAo+sOCKRHFI5GXEij1vlykRWG5UZFBERK8uZgr6g5JdhGC7gr4DxTAQlkksqDSdO\ntNhyZSLiHGq3IiIy33KyoC9gmmYU+AvgX2VifbI2haNRzvcHeO7lC5zvDxCORnMdUs5oX+S/cDTK\npaFJXj0zxKWhyYwd42ytV0QWSm5v5/sDTM/MZm39as8iIvlhOVPQ07kOKMrg+mQNUb3UK7Qv8l+2\njrHOHRH7WLW3B+/q4Nd2NWVt/WrPIiKrX6aSsFUA+4jVBhdZsnT1Uq9v86+5ci3aF/kvW8dY546I\nfaza29e+18W21mrWZSDTudqziEh+WtQUdMMwPmcYRvXcn28jNtq9KelfObEp6P86G0FK/rtavdS1\nRvsi/13tGK9kSqvOHZHsSpkSPhCwfM7AyFRGtqX2LCKSnxY7Av67wOeBYaAF2GOaZl/WopI1R/VS\nr6iptC5RU5NmH8nqk+58r6ksWdGUVrUjkeyZPyX8UGe75fNqM1RmTO1ZRCQ/LTYJWzfwqGEYX5r7\n+/81DOPvrf5lJ0zJd/F6qcnWar1Uj8fN/j0tKcv272nB49E9f/ki3fnu8bgsp7ReGgquaL1rsR2J\nZNr8KeEnuvoWfFY/eFcH6zOU8VztWUQkPy12BPx9wL8jNvodBZqBULaCkrXH43Jx5851tDVV0j86\nSX1VCRvrfGsy0UzfUJDh8Skevm8HY8EQFb4ijp+8TN9QkMY0o+OyuiSf733DQRr8PjbU+nj11JDl\n8wdGphZ1T6nqDousTDgapXd4ioHRSWorS2iovtJ+5k8JP9cbq7zdEhOlAAAgAElEQVT6ew/uYnY2\nQl11CUarn+DENLORlWcrV3sWEclPi+qAm6b5U+A+AMMwzgAHTNMczGZgyQzDWA/8JbH7z4PA/wb+\nrWmaIcMwWoEvALcSG6n/mGmaT9sVm2RGOBrl2Zd7lO0VqPf7qC4v5vPffjWxbP+eFupVRzZvWJ3v\nhzvbKfMVWD5/KVNaVXdYZHmulXXc6vagc73jVJYW0eQvwet1U1TgJch0xmJSexYRyT9LrgNumuYm\nOzvfc74NFAO3A+8F3gV8du6xo8Al4Gbgq8Smym+wOb5lU73nmHTZXnszlMxmNQmHIzz10tmUZU+9\ndJZweG2eG6vV1er3Wp3vjx47RTgczeqUVhFJ71rfQ1a3B92/dzPTM+Gs1QEXEZH8k8k64FlhGIYB\n/ArQYJrmwNyy/wj8qWEY3yOWhX2PaZpTwB8bhvF24MPAZ3IV82KpxucVV8v2utbKrQyOWv/oMDg6\nSZN/be2L1epabTvd+T4SCNHVPcTBt7YzE45w/SY/e7Y3ZmxKq4ikd63vob6hYEr7rKkoYmB0ij/8\n0o8Tz81kHXAREclPju+AA5eBu+Kd7ySVwK8CJ+Y633EvEJuO7niq8XmFsr1eoX2x+l2rbac7xoVe\nN+d6xxP3lt5+Q2PGp7SKiLVrffbWVpaktM9Dne1894fdKc/NZB1wERHJT47vgJumOQok7uk2DMMF\n/A7wA2AdsennyXqBVTEF3UmjvuFolJ7+AK91D1NVWki9zYle4tle548Y5iLbqxP2xe/efxPhSGQu\nCVshHrdbmW9tcrUkTIt9vVV94OaGckYmphkPhPB63Xz4XdczNDbFia4+zvWOc7iznZ92XanueGSf\nkdWp5yt9nyJOtJLzOvl7qLmhnF0d9ayr9TE5Pcsrp4co8xXw2/fdQM9AkBNdfYRmI5brWWzSxGy9\nDxERcTbHd8At/CmwE9gNfBwWDA1NA0V2B7UcTqn37ISp8PFsr9vb/IwEQlSVFVJfaf8FhxP2RSQa\n5ULfOI8eO5VYdrizne2bqnQBlmUrPf7x10+GUu8DbW4op6PVz//+51/S0epPucf/3Xuv46P33kBN\neSG7jLrUbMfu7BxvJ5znIpm20vM6/j10w3V+Tpj9/PjnvXS0+vnrl15LPGf/nha6uof4lesb6Gjx\n8+SLZxasZ6V1wNU+RUTy26rqgBuG8SfAvwF+wzTNNwzDmAL8855WRCxT+qJ5PEvORZcR3rmELskX\n4/v3tOD1uvB67Yuppz9gOV12e7ufjbWltsXhBVoby6moKGFsbJJw2Hp0IZucsC9OX5xI6XxDLEHX\n9W01bGmqsCWGZLlqH1bcbhfuDHZK4+8t/t+VHv/465sbyhNtu7mhnHe+ZROf//arHOps57F5x/Zb\nz7zJzi11lBR6aakvo6W+LCW+6ZlZLgwE6R8JUltZwvoa34o75pk6z+fvPydyeoxOj2+lMtlmr7Wv\nMnFee4FoJNYurdrrUy+d5eH7bqT78hiRaJQPvnMrX37iZOLxB+/qYENdGa4VJFTN5veQ0883xZd7\n2XxvK1m3y724a2M7jpFd50G+vJd82UYm179qOuCGYfwV8FvAg6ZpPja3+CKwbd5TG4Gepay7oiI3\n92q91j2cktCl0OvmRFcfN26uZXt7nW1xvHLGuvbw8Pg0OzbX2xZHslwdEyfsi77XL1svH5lkz/Z1\ntsTgVH5/Ka4sjADFz7fXuoctHx+ZCC3q+MdfH79H9H13dTAeDNF9OfZ3uimr6dY/PTPLo8+e4mvf\n60ose/CuDg7f2U5RwfI/vlf6PufLVXtdCqfH6PT4lisbbTbdvsrUeR1fT7r22n15jCdfPMOTL57h\n7ttaed9dHXg9brZu8tO+oXJFbTN5+/Mtt31acfr5pvhyJ5vvraxs+bNDCgu8VFcv/gcoO46RXedB\nvryXfNlGJqyKDrhhGP8J+E3gPaZpPpr00L8AnzYMo8g0zfhU9LcAzy9l/bkaba0qLUxJ6JJYXlbI\n8PDCe0izpaTQ+jQoLvTaGgfEflnK5Qi4E/ZFfbX1fb/1VSW2Hw+4ckycYGgokPER8OTzraq00PJ5\ni22Tya8/1zvOxOQMR587zaHOdiCWZG0p678wEEzpfMOVJE8rGQlb6fuMy3V7XQynx5iN+JZykZpt\nmWyz19pXmTqv4+tJ116Tl3/3h92JkfL/8tu3UVTgXfGxzNT7sLIW20Mm5Xt7hexeE09MLL+0bGhm\ndlHnvx3nkF3nab68l3zZRvJ2VsrxHXDDMLYC/wH4I+CHhmE0JD18DDgPfNkwjM8CB4jdG/7BpWwj\nHI4wm+aX7myqr7JOPFZfWWxrPDMzYcup8DMz4ZzsF8jdMXHCvthY5+PeO6/jO8++mVh2753XsbHO\nl7Pj4RSRSJRIFspxxc+3lbbJ+a+Pj6Cd6Orj/r2b8XrdPPiODgJTM4nka+/dtwWXy8WJXw4sSLbU\nP2J9N03/8OSKkjxl+rMnV+11KZweo9PjW65stNl0+yoT53U4GiUSjfKBX99KaCbC/Xs3881nfpl4\nfP+eFk4kJUsEqCgt5J7bNzE0Ps30zOyKj6Ud1wZOP98UX+5k872tpGMUjUSXFJcdx8iu8yBf3ku+\nbCMTHN8BJ9apdhPrhP+HuWUuIGqapscwjEPAF4GfAG8Ch0zTvJCTSJfIKYnHyn2FllPh33rTelvj\ncAKn7ItyX0FKDOW+Alu3v1bF2+S2TdWpydAW2Sbnv77UV5hI0jQxNZNSsiiefO30xTH+3V//MLE8\nOdlStkrSrfR9ijjRSs9rq+Rn7967mf/8kT0Mj01TUuzl6983F8xaGwuEElPSM1EHXO1TRCS/Ob4D\nbprmnwB/cpXHTwFvsy+izPK4XGysK2XHlnqGhwM5+dWmobqY23esd0QJsFxzwr441xfkH548uWD5\nxoZy2hrKLF4hmeRxuWjy+2haZgmw5NeHo1GO7DOYDM1aJl/raPHzxcdfT1meXC98fY2PB+/qSJmG\nnqnzcaXvU8SJVnJe9w5PLUh+9q1nfslNm2/lxjY/4WiU23es51zSc+aPiGeqDrjap4hI/nJ8Bzzf\nzUQinL44Qd/rl6mv9rGxzkeB297smk4ZiXcCj8tF503raFlXQd9IkPoqH60Npbbui75h62nHvUNB\ndcAzKNv13uN1fJsbyhgNhCyf0zNofT/bwMgk66tL8LhdHL6znW0t1fSPTGokTNasTLdXqzrbA6OT\nls+9NBBgYCT2vM6b1tHWVMnA6CSlxQV865lfLhgRz0QdcBERyV/qgOfQTCTC9146v6De8117Nuak\nE57rkXgnmIlE+P6Pc3tM0iVha9BISMZku87u/PXHk7DNly5bcvIU86ICLxvrSllXrQt6WZsy3V7T\nrW/bpmrL518aCCRmsBzubOenc/kbDnW2L+h8w8rrgIuISH5TBzyHzvUF09Z71khnbpzrC3Khf4KH\n79vBWDBEha+Q4yd7Od9v3+hzc72PD9yzldFAiNBs7B7wytJCNtapA54pVlNNk6d+X43VyNn8TkDy\n+psbyin3FfC+uwwmJmcTx7TA4+b4ycsLkv7Fp5hne4ReZLVYTntN107D0SjdvdZ1tv/ot29bkPzs\nvfu2UO/38b67OxLfB7s66jnXO55IrjgTjiTadb2/hPV+H9EsJIsUEZH8oA54DmmqsfOEZsKsqynl\n899+NbHswB1tTIfCtsYxHpxJuWf43juvs3X7+S7dVNP41O90FjsSF19/c0M5Ha1+jp24yPXtNSlJ\n2O6+rZXewSC9g0E+eu8NnOud4PpN1WxpqgTI6gi9yGqy1Paarp3euXMdz77cw2jQ+paQvqFgSvKz\nMl8hr58e5C//188SzzlwR1vKDJX5yRUfvMtY6tsTEZE1xt55zpJCU42dx+1x8fjzp1OWPf78aTwe\n+zo95/qCKSXIAL7z7Juc77f+wUaWbrnZxdONxPWOpNY2ja9/V0c9T710ll0d9SkX6RCrHxwfSbs8\nGOTJF89QWVqEx+Va9HZE1oKlttd07ed8f5BHnjbT1veurSpJJD/bscnPbDi6IHni48+fpnKuTrdV\nu/7a90wuDemzWkRE0lMHPIea630cnndv6OHO9jU71TgcjXK+P8BzL1/gfH+AcNT+KXwDI9YjLf1p\nlmfD1WZGSGY0VMfq7CZbTHbxq43ExYWjUaLE6giX+wrnpqAXWr6uprKED71zGye6+lK2v5jtiKwV\nS22v6dpP/DP0RFcf+/e0pDz2kQPbiUajvHpmiEtDk4TCEYbGrdczMhFi97YGGmtKuef2TRzqbKe5\nofzK9vVDmYiIXIWmoOdQgdvNXXs2sm1TDf2jk9RXleQkC7oTZDsp1mI11lj/+LEuzXI7Y0i3XJZu\nuZn/rzUSN/88/sjB7XS0+hlPM+V1cK6j8PB9O6gpL0xsP1v1v0VWo6W213TtJz67LJ447eBb25kJ\nR9izrZ6T3cP8wd/+CwC7tzXQVFcKWK9/cHSSproynnzxTGJd8Q79ud5xJWETEZGrWns9PYdxu1z4\nijxUlhZSUujBvUbv73TKlFuvx8Xdt7WmLLv7tla8Nk5B97hZMDqzf0+LrdPg14J45v+37tzAxtrF\nlZprqC7mvfu2pCx7774tiZG4+eexr9jLUy+dtRxxi9cPfuzYqQVlypY7Qi+Sr5bSXtO10411vkS7\nOtc7ztHnTlHpK8TtcvGNp3+ReO7urQ08duz0Ndvtro76xPL4rSYP3tXBet1GJiIiV6ER8Bxyyqiv\nEyw3KVamDY+FKCsuSIyMxLNVD42FaK61J4aL/UG6uodSYjjR1UdLYznNNaX2BCFplZUUphybspIr\n08vnn8ejE9PAwhG35obylNGz833jdF8aS7T95Y7Qi0iMVTt1z7WreJK12qoSGqqK+Xn3cMprx+Zm\nrMTbZzxJYvyzOL48NK9c5/raUvbu3khwYppZZUEXEZE01AHPoZWUQso3Tply6ysu4JvP/HLB8n/7\ngd22xVBTWcK53vEF9WU1/Tj3eoen+OLjry9Y3rruVtZXlyw4j5P/Tj6m8+sHV/iK+Py3X0lp+/ER\nvx1b6hkeDjA772JfRKxdq502+X00JY1Sz2+3FUk5G5KTJM43P5lbU10pRQVegkyv9C2IiEge0xT0\nHBoYnaS5oZxDne0piVxykWgp1wnQnDLldnJqxnJ5cNJ6eTZ43S4O3NGWsuzAHW0a/cyxcDTKSGA6\nTdKlWJudfx73j0wumMJ64I42TnT1pfx9/ORlAC4N5Cb5oEg+udqMqnA0yqWhyUSytXA0SkN1MR85\nsD3xXRyJRDiUlCD1RFffgs/kw53tKe34yD5DU89FRGRRNAKeQ/V+Hx2t/pQyJ/v3tFBv85e4E6bC\nO2XKbV2aUeY6G2ckFBW66RkM8Nv37WA8GKLCV8Txk5fZvbXBthgklVUbSU26dGXUOj7F9c2LY5QW\nexfcTnCxf4Ij+w0uDkwkju3xN3qBWAd8eGx6Td6GIpIp6WZU1ft9aeuDT0yGUr6Lf/f+G/n0Q7fw\n2unBRLuNt+OtLdWEZiLsMupSprJ73GqzIiJybRoBz6FwOMJTL51NWfbUS2cJh+0dAXNKArTlJMXK\nNCeMxHs8bqrLi/nrb7/KV7/bxee//QrV5cVKwpZDVm0knnRp/vnhcblw4eIf/ukNnnjhDB2tfo4+\nd4onXzzDY8dOcV1TFW3rygjPRvn8t19JdL7jyZ1U71tkZdJ9jofDEcvvunP9wZQkbAB/9c1XqCwr\npMjr5rFjpzj+Ri9HnzuF1+3iuZ9d5K+++TNcLhc7NsVuG9EPZiIislgaAc+hwVHri+zB0Uma/PaN\nuManwu/qqCc0eyXpl90J0GYiEU5fnKDv9cvUV/tyUpLN43LxlhvX0dxYTv9IkLpqH831ZbZeXPUN\nBRken+Lh+3YwFgxR4Svk+Mle+oaCNFYqC3a2haNReoenGBidpLayhIbqYssprc0N5bStr2BicoY3\ne8aZmpqlpLgAr9vFTDjCB359K6XFBQyMTiaO5fraMtrXlVHgdrN313qa6kt5o3t4QXInu9ueyGo1\nv73WVRXRPzJNfXUJ//6Du3HhIuqCiUCI0UCI5oZyzvWOs3tbA7u3NjAWDBGcmkksT/4uHBqbYv/u\njVy3sYqewcCCGSsDI5M0VBUntl9XVYKvrCjHe0RERJxuVXXADcMoAn4C/GvTNJ+bW9YKfAG4FegG\nPmaa5tO5inEp/BXWnal0y7PFCVPhZyIRvvfSeR5NiuFwZzt37dloayd8ajbCU8fPcfTY6cSyg51t\nvGN3M8Vee+Ko9/uoLi/m899+NbEsF7cmrEXpbsfYtqk65XnNDeV0tPr580deTizbv6eF4fEp1tWU\n8vjzqefP8ZO9iYv25Ns7qkqLLJM7KeGeyLXNb6/NDeXc3FGf+B6Z/zfE2unWNj9FXs+Cz9iGmthn\nb/y78MkXz3C4s50yXwFf/W7Xgu1bTWl/8K4Ofm1XU1ber4iI5IdV0wGf63w/Amyb99BjwCvAzcBh\n4FHDMDpM07xgc4hLFolGuX/vZmbCkcTIc4HHTRR7p6CHwxG6uoc41NmeMgJ+x43rbYvhXF8w5SIJ\n4NFjp7i+rYa2hjLb4jjbN8Gl/sCC0edzfRNsWV9hSwxOOB5rldVU8xdfvcSmpgref89WxgIhTnT1\nsaujPuUHK4hNSX/4vhv5/LdfSVn+clc/79m3hbb1ldRUFjM8Pk13X4DW+tJE8qeB0cnEsa6tLLnq\nLQ9WI/Sa/ipr0fz2umteZ3tXRz0/7epL+Sy92D/Bnbs28PMzsc/Y+MyTru4h7n/7Zk6eHU5Z/uix\nU3zqoVv4+JGduFwuLvRNMD0TprayxHJK+9e+18W21mrWzfsRTe1WRETiVkUH3DCMrcDXLZbvBdqA\nXzVNcwr4Y8Mw3g58GPiMvVEu3fDYNBNTM3z3h92JZXff1srQWIiNNtZ7ngjOWI6ATwRDYNNU+L7h\noOXy3qGgrR3w2dkI62pKU0ZGDtzRxoyNJaDGA9bHYzxg3/FYq+ZPNY+PdP/Rl48nlh18axs1aW4F\nCMzLoh9//Z9+9aeJZfv3tPDiK5e4fcd6y+RP7923JW18TkiYKOIU89vr/LrcJUXelM/S5oZybtpS\nt6A9xke+/+vXT6Qsh1iSxfO94wQmZ1Jmtrx33xYmgtbVMQZGplI64Gq3IiKSbLUkYesEfkBsmnny\nt9Ue4MRc5zvuhbnnOV5xsTel8w3w3R92U1LksTUOr9djmQyuoMC+OGocUgfc63WnXGQBPP78aQps\nmn4O4PK4LI+HW0nYsm5+9uRdHfULjsXR505TleY+z9p5HXOr18eTtz3ytMl5i+RP33j6F2mTsDkl\nYaKIE8xvr/PrcleXF6W0v10d9Qs+35966Sy7tzakbafx9cx/3Tee/kXa78jaeTNY1G5FRCTZqhgB\nN03zb+L/bxgpmU3XAZfmPb0X2GBDWCs2PDaVkggmPt15eHza1jgmgqElLc8Gr9vFoc72lJHAQ53t\nto8O9A9b14/tH5m0bQr60OiUZVK8wdEpsCmGtSqePfmRp02aG8pprCnlnts3JaauNtWVEZqNMDMb\nYf+elpSL9vv3biYKfOjXt1FS7OX4yd4FI3Jx8eV9w8FE8qdk6ZKwXa2+sZK2yVqT3F4hVq/7wXcY\nBKZmY23M5UppX6HZiOVn6+T0rOX6K0oLuX/vZnoGrWdoTQRDKdsHePfezbhdLsLRaOL7S+1WRESS\nrYoO+FX4gPm91WlgVaQh3dhQxuXB4ILpzhsb7Jt+DulrX9s5+uwr8VJc6Empl1xc6MFXYu8pWp/m\nYijdPsqGpnrrpHgb6pWELdvidbxvuM7PCbOfv/lOatuM3xd6qLM9pb53TUURA6NT/HnSFNaDnW20\nr6/kyRcXbic+Utc3PElHqx8gpROedkaIQ2aKiDhBvL1u21TNmxfHqKko5hfnh3nihSuJDZOnktdU\nFFl+tpaXFlqufywQYnomnH7GS1UJ17dWJ7Y/OjHNj39+mW8988uUKeZqtyIikmy1d8CnAP+8ZUWA\n9c/VaXg8uZmJPxWKWE53vuG6Wrw2TnluqvXxwH6Drz915Vf8B/YbNNX48LjtGYGORKILpuICbG+r\nsXVf+Io9HLijLeW4HLijDV+xx7Y4pkNRy+mQN3fU27ov4nLVPqy43S7cGTwn4+8t+T16gWgEvvXM\nmynPffz50xzqbOdc7zgnuvoS9b0hNltj/u0kR4+d5uNHdi0YKY/X+47/N96hj3fA9+9pwet14fW6\nF8TnhLaazGr/OY3TY3R6fCuVyTabrr26XS7+4Z/e4Mh+I6XzDbHPznj7mg1bf7a2NJanbafnesd5\n6O6OBd8Lye0uvv1kjzxtsr3dz8baUke1W6efb4ov97L53laybpfbtahrIDuOkV3nQb68l3zZRibX\nv9o74BdZmBW9EehZykoqKnLzK3T/65etl49M8qvb19kay749zWxsLKd/OEh9tQ+jpZraKvtGXF/r\nHracjj8aCFG9pd62OJ595RLFRR4++b6bGRydpLbKR3fPKN094+w0Gm2JofeVS5bTJHuHAty2Y21n\nQvf7S3Fl4baEiooSpmdmOX1xjP7hIMGp9FNSk6e0fuzILvqGg/iKrD9KRyam2bbJz02baxmZmKa6\nvJjAZIibttTR3TOaWE+5rzAx1f1EVx83bq5le3tdSnxx9+69jp1GPX1zbXVTUwVFBbn9KM/VZ+hS\nOD1Gp8e3XNlos8n7anpmltGzI9xz+ya8FhdGzQ3lbFpXwQd/fduCe8TjLg4E6Ooe4qP33sC53olE\nO4y3T19xAVVlxXzqoZvpHQpSW1VCVVkhv7g4xuDoJNXlxeze1pAoNRg3MhFix+bY95fT2q3TzzfF\nlzvZfG9lZcsvs1tY4KW6evEzRO04RnadB/nyXvJlG5mw2jvg/wJ82jCMItM041PR3wI8v5SVjI1N\nEg7bl+U6Lt30s7qqEoaHA7bFMTUb5skfneWxpNrXhzrbuOfWFoq99iRiq6kotsw+XlNRbOu+aFlX\nzk9P9qdkyT1wRxvb2/y2xdHSWG45TbKlsdzWfRHn8bgd84E2NBTI+Ah4RUUJQyNBvv/SucQI1aHO\ndsvnjwVCKVPGz1waJUqUjfXWmfob/D5+fnqQn/2in45WP3/72OuJxw52tvGOW1v4/o/OMh4MpdQD\nryorZHg4kIhv/mdUQ2URDZWxabHBiWmCC+7EsUe6+JzE6TFmI76lXKRmWybb7Px9FY5EefonFxLt\n9sj+lBwxiSoEf/GNl4H07brQ6+Zc7ziXB4Mp7TCudyiY+Dw+2NnGxrpyjr/Rt2Cm1PxOeLwdxzmh\n3a7F9pBJ+d5eIbvXxBMTy088GJqZXdQ1kB3nkF3nab68l3zZRvJ2Vmq1d8CPAeeBLxuG8VngALAb\n+OBSVhIOR5i1scxUXFGB23K6c1GBy9Z4Tl+aSOl8Azx27DRbW2vYvK7clhhGJkKW0/Gvb6uhvsK+\nW/onp8KWcWzb5LftmEyGIrjc8IkHdjE4OkVNZTGvnxlkKpSb89RJIpEokUg0o+ucCIY4eXaEkUCI\nQ53tXOyfoNxXwOHO9pSawnff1poyZbyj1Z/4e9f/VcdHD2/H7XYnZnCEIxEKvK7E1PX5dcOPHjvN\nJx7YRZHXw4muvsTyI/sM6iuLU451rj6jFsvp8YHzY3R6fMuVjTYbDkeYDM1y5nIgpd2uq/Hx0N0d\njAdnCM1GaG0sT5mSHr/1I3mq+b13XkfrunJ2b2vgRFcfBzvbOJr0fRifih539NhpOpr9lt8TD993\nY6IDbtWOncTp55viy51svreVdIyikeiS4rLjGNl1HuTLe8mXbWTCauyAJ77JTdOMGIZxEPg74CfA\nm8Ah0zQv5Cq4pbjQF6BnMMBv37eD8WCICl8Rx09e5nyvjw1++34RHRm3/kVyeHwK7OqAp4thzL4Y\nIH098r7hoG1Z0F1EKS70pNSkPdjZZsu215pQOMKjP/gFjz6bWif42ImL7Lm+MSUpYEHS9NaK0sKU\nKapDY9NcHppckMU/fq2RLhv68PgUPYMBdhr1bL+ulus3VbOlqVK1gUWuIhSO8L2Xzid+IIu32289\n8ybXt9ek5GNITsKWuHXkvTsZGJ1iPBjiJyd7+c6zb3Kos52bjTp+avYnpqI3N5Tz5ItnFlQp6B+x\n/p6YngnzyffdTFVZIfWVxWrHIiJiadV1wE3T9Mz7+zTwthyFsyL+ymKOv9G74L6xt9+y0dY4Ksut\nR5jTZX61NYY0y7Olrtr6vvf6NMuzIRIlZQQGYn9vbZmfb1BWqvvyRKLzDbE6wY8dO8Whzna++cwv\nFzw/nsxpLBBKuSgvLPAsGOF+7NgpPvHArtjjae49ravycfyNN4hVT4Tbtjfqol3kGrovT6TMTklu\nt/PbYXISNoh1xN1uF//zuydTnvfYsVOJEeymujKefPFMyuuS1aXJj7Kuxsee7esYHg6sihEYERHJ\njVXXAc8n0XCUD71zGyVF3sS01cnpWSLhzE7Xu5ZAMGSZAdbOOuCB4AwfOXA9hQWexL4IzYSZCM7Y\nFgOAx+3i8J3tKZ2yw3e243bbl/G0f6429PwkbHaOwq8F4WiUnsEr95PF634fvvM6/Em3PSQfi5bG\ncj567w0UFng4st9gcnqW+uqStHV++0cmU7KeJ7exg51tKSNpR/YZNFQtP0GNyFrRO3Sl3eze1kDb\n+kred3cHRQULc5YkJ2GrqSwmEokyPG597/VYMLY8uUqBVbv1FXkWdPYPd7bT2midC0JERCSZOuA5\n5K8swjw/zNHnrox2HnxrG0Zzta1xlPoKU2oaxzt8u4y6a784QxprSzh+si9l5PdgZxu7t9qXAR2g\nqNBNha8wZV9U+AopLrRvVHJ9XZllEramOl3cZUo4GuWZE5eonav7Hk/WFK/7HU/WFF8+/1h0dQ/R\n0eqnq3uInUYdzY3WP4yUlRTS1X2BnUY9lWWFfPqhWxgaiysX1VQAACAASURBVN3Xv7G+jKGxKf6f\n99xEbVUJDVWasiqyGA3+2Aj07m0NrKsp5c8fid2uMz/J2vwkbBD7Xrl+U63leit8sR/e4qPeR95h\nMDA8ySce3MXI+DRFBV6On7zM0WOnef/dHfzbD9zCwMgUDX4fG+t8FOZxaSoREckcdcBzaGRihpfN\nfg51tqeMdG7bZG/isXKfl988dD3jwVn6RoLUV/m4paMObOwLjAdneblr4b7Y2uKHKvvimJwK4/G4\nMFqq6R8JUlflY2AkSHAqbFsM0WiUru6hBfviV7bZ+2NEPusdnuKRp00+eng7D77DoLKsiCdeOJPY\n5yVF3sTslO7LsaRr8Xu+n3rpLJ966Ba6e0Z551s20X15HF+xl4fuNvif371S5/dQZzuRSJiGGh8u\nF4TDUSLRKMWFXtrXleNxuWjy+2jy23d7g8hqNz0zS1Ghh/ffs5W6qhK++YMrt4qc6Orj/r2bmQlH\ncLlctK2vSHkcYrfzbNlYvSAB6kN3d1BeWsB7fm0L0zNhmupKcQGRaBSi8OqbAym3i33lu1189jdv\n5datmpUkIiJLow54Dk1OzViOrgUn7Z12Xejx8OLrPQtGn2+3sRb59HTYcl9MTdvX8QUo83k5eXaK\nv//HNxLLDna20bbBvousiYD1eTEesPe8yGfxKePB6TCjgRDFhd4F+/zgW9t42exPjIYlJ3M63zvO\n9Ew4UTbvyRfPcO+d1/GxIzvpH5mkwlfE5PQMPzX7WV9XumCq6vZNVRrtFlmicCTKo8+e4mvf60os\nS26XABNTM2mTsMUNjEzSMxjgEw/uYnBkCn9FMb84P8z//O6V9R7sbOPlrivt36rM2ODoJE1+Z5Ro\nFBGR1UPzpXKouNibcm8ZxBLGlBTb+7vI4Pi0ZdKvoTT3yWVDUZHHcl8UF9lThzxuPDhruS8mArO2\nxeCU8yKf1VbGLprrqkp44oUz+CuLF+zzo8+dZlfHlVkHT710NvF3dXnRgvPkO8++yZlLY3z1u118\n/tuv8KUn3mD31sYFz3v02CnO91tnURaR9C4NBlM635DaLnd11Kd0vuc/HldX7eP4G71MTs3y5X96\ng9OXRlPKlUHscz/5dY8/f5rdWxtTnlNbpc63iIgsna7oc2hwxLr01uDoFNiYbKvfAaW3Bkesk1gN\njEzZmnisfzjI7m0N7N7akEgGd/xkr837YsoyCZvd50U+a6gu5nfvv4lINMpH772B8YB1wsHQbCTl\nWDTWlHL/3s30DC5sM/EkbvfcvilxzOJJnebrHQrSUl9K7/AUA6OT1FaW0FCte8BFriZdssN4mb/k\ncn/z221zQznnesd5/z0duIAPvWsbBV43u7c1UO4rtFxvRWlh4nVASnv+yIHtRKNRXjk9RJmvkNnZ\nMBWlhfhsrB4iIiKrkzrgOVSbJuNxTaW9mZCdUHqrPs19sPU2T+9rXVfOwMhUYmoxxKYeblpvXy3y\nDQ0+6yRs9bpXOFMi0SgX+sa50D/BuppS3G7rjm9VaeGCY3H3ba0LSvTNT+IGsWNWn6ZcUb3fxzMn\nLvHI01fuGT+yz2DvrvXqhIukUV1h/d3Y2ljBPbdvoqO5iidftE6eePCtbTzwjg5eebOfrzx5ZRT9\nwB1tgHXlkbFAiI7WWPnHc73jNPp93HP7Jn5lWz1d3cP8wd/+S+K58eSMd+xs4td2NWXg3YqISL7S\nFPQcKin2zH35X3HgjjZ8xfZOu64s9XKwMzWOg51tVJTa9/uMx+NK3KsXt39PC16PvZ2R4FQ4JTEP\nxKYeBibtuxd9OhS1nII+HbK3PF0+O9cX5NFjp9i9tYHHnz+dKDeU7MAdbXg8rgXH4rs/7MbrcaW0\n3V0d9ZbHbHhiakEbP9zZTnlJQUrnG+CRp01608yKERGYmY1Yfk888cJpnnzxTOJvq/Z49LnTBObd\nHw6xz/dwOGq53hNdfYkp7AfuaGMiGGJ2NsLsbJRvPP2LlOfHn/e173VxaUi3mIiISHoaAc+hsz3j\nFBd5+OT7bmZwdJLaKh/dPaN094yzwV9qWxxvXhhjKhTm4w/sYngslpDm9TODvHlhjMZKe0ZdL/ZN\nMDw+xcP37UiZ+n2hb4JWG8tv9TlgOn7vUCDt8s3r7BuJz2d9w0F+5907KCjw8JGD26ksLWRobIpP\nPBArN+SvLKasxMvFgSs12WurSqgsLaR/ZJIGv4/pmXCiXF26Kaxet5uewUDieVtbqtmyoYKTZ0cs\nnz8wMsn6at1XKjLf5GyEyVCYG66rYeeWOvpGgtRWlvDsiQuc6x3nw+/aRoHXw8aGMipKC9m9rYGm\nujJcLhfranwMj08TnLJOZDkSCKWU4mxuKOfJF88kpp7XV5fw8i/6qSovoqt7iJZG68/h+BT4gZEp\n1un+cBERSUMd8BzatL6c42/086df/Wli2YE72rjhOr+tcayvK+Pv//ENvv+j1BGD//DhX7Ethqa6\nMi70BVKmfuei9nW67dkZx7pa6x9f1qdZLku3pbmSF17t4VJ/gHU1pXzx6OuJxw7c0cbm5kp+9Ppl\n6qpitwNc7J8gEokueF7PYIDjb/QuqD8cV11RPJc1OZY5+bbtjRS43YkkcPMpqZPIQpOzEZ46fo6X\nu/rpaPWnjG7v39PC77x7B+f7JxLVK5obyrlpS92C23jAegp7VWkh53rHEx3uQ53tKVnTvR4Px9/o\npamujHO942lvmSr0xiYVpru9TEREBNQBz6nAZJif/cKqDri9ta8Bfv8DtxCJkKh97XbbWgaccMS6\n9vVum2tfR4H37tvCVCiciKO40N5bAlyu2HT8+ReZLt0bnDEDo9O83NXP/W/fzH/9+omUx3oGAwyP\nh6ipKKamspixwDQ3tNcseN7jz5/m4ftu5PgbvZzo6ltQV/hgZxuu6JXbBo7sM2iYuzBvqC7myD5j\nwT3gDbpwF1ngXN8ER4+d5lBne0qnGmJTv3duuYXK0iIevm8HPYNB1tX4Un7MjT/vobs7LD9bPUm3\nOh3sbONEV1/i70Od7Rw/eTkxJf3IPoPmOt+C9ht//MG7Oljv9xGN6JYhERGxpg54DoVmrGtfh2bs\nrX1d6HXx2qmhBXXAb+mosy2GyelZy30xOWVf+S+AqekwU6FwShyHOtttrUfePzyZMh0y/mNE2/oK\nNtXbOyMgXwWCsVrrF/omUpbv3tbAuppS/uQrP0ksO9jZxuU093ROTs9wz+2buG5DJW6Xi0+//5ZY\nXeHKYrp7Rrk0GOQDv76V65qqaEzKcu5xudi7az3bNlUzODpFbVUJDVXKgi5iZWCuSkZpSYHl4z2D\nAS4PBhMd68N3Xmf5vNBMhK7uIf7v9+zkXO840WiUE119tDS28IF7tjIaCHGxf4KdRj133NRENBql\nsNDDTVtqmJwM89ab1ifaaXL7LfUVMDsToXPneoxWP8GJaWbVARcRkTTUAc+hggLr2te7DPs6vgCB\nqbBl7eutLX6otieGQofsC4/HtWCE5bFjp/j9999iWwxV5UUp0yGTl0tmlPoKeOqls3zigV0py3dv\nbVgwcnb02Gk++b6bLdfjryjmS0+8kRiZe/i+G/ni41emqceX/+Fv3bqgc+1xuWjy+2hKM51VRGLi\nt2akuw2nrqqErzx5MvH3uhrrNrWhPjaFfGY2wqPPvplYXuD18IWk20vit4wc6mzn60+Z/OFv3Yqx\nIfXHT6v26/W6KSrwEsS6/KCIiAioA55TgyOTfOY39xCYDNM3EqS+ykdpiYdzlwO21762qju9NuuA\nT6bZF5O2xREIhrh/72ZmwpFEDAUeNxNB61rVsnTDo1N84oGduFzw4Ds6CEzNcLF/Aq/XnVLHO/4j\nyOTUDHff1pqSQflgZxslhV4+9M5t/OD4eSBWJzh+/pQWFxCJRmluKFdyNZEVmJ0N85nf3MPAyPSC\nz8bq8iJmw5GUdjs8Pm051XxiMsT+PS0Mj0+lLE/+O1loNpaQbWRimoGRSWorS2io1kwVERFZGXXA\nc2j7pir+z896Fkz9fttN62yNo6muzLrutI2Jx9Iln7I7mU1Lo/W+aGm0b19UVxRz6tJYSmfv7tta\n2Vpp03SENcBoqeS5V660veaGcnZ21PGX/+tniefEyxKd6x2nuMhLWXEBR/YbjAZC3NBWwzM/Pc/R\nY6c5+NY2Gmp8nOsdp6m2jJ3GwuRP6ZI2ici1bazz8X9+1kNdlY+JpFJizQ3l7DTq+NITbySeGy9f\naXUbz84tdXR1n+Wdb2njfXd1sKG+jK9/32RXh3WukarSQjpa/fzZ167kfziyz2DvrvXqhIuIyLKt\n+jrghmEUGYbxd4ZhDBuGcdEwjI/nOqbF6hmetpz6fXnY3ulr4Yh13elI1L572HxpaqKXltibAG0y\nFLHcF1OhiG0xhCPRBbVqv/vDbmbDuqcwU/pGUtvero76BW0xXtf3YGcbs7MRvvnML5mcnqXA6+JM\nz+hcdvNYfeHdWxs52NlGcHqWo88tXE9w2t5cBiL5JP5d6Sv2pnw27uqot2xv4XCUm7bUcfS5Uzz5\n4hkeO3aKnR11FBa46Wj188QLp/nq97oYnpimo9XPia4+yzrgHo9rwffBI0+b9I5Yj5iLiIgsRj6M\ngP8ZsAu4E2gFvmIYRrdpmt/JZVCL0e+AmtMAlwcD/MavbaalsSI2za6qhLOXx+gZCNDeYE/d6e6e\ncTZvrOTTD92SmI4/Gw5z5tI4TdX2ld/qG7LeF3bW4L48GLCcBn95MMB1aerPytIkt73d2xpobiy3\nnHpe7/cxMj7FLy6MAtDg9zEzG2ZobDrl+dMzs0yFwmnrDPcOBmlTAj2RZekfDvLhd21jKhROfDaW\nFHmpqy6xbLdlJQUMjk8lRsCN5ire6B7iYn+A5sZyfMWxS5/zvRN0dQ+x06jHV+zlk++7mdFAiOqy\nIqKRKBNp2rNuKRERkZVY1R1wwzB8wL8C3mGa5ivAK4ZhfA74HcDxHfD6autpqemWZ8t1Gyt56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starscoolusefulfunny
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std1.2146362.0678612.3366471.907942
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" + ], + "text/plain": [ + " stars cool useful funny\n", + "count 10000.000000 10000.000000 10000.000000 10000.000000\n", + "mean 3.777500 0.876800 1.409300 0.701300\n", + "std 1.214636 2.067861 2.336647 1.907942\n", + "min 1.000000 0.000000 0.000000 0.000000\n", + "25% 3.000000 0.000000 0.000000 0.000000\n", + "50% 4.000000 0.000000 1.000000 0.000000\n", + "75% 5.000000 1.000000 2.000000 1.000000\n", + "max 5.000000 77.000000 76.000000 57.000000" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 10)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 3.794700\n", + "useful -0.012205\n", + "dtype: float64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### STATSMODELS ###\n", + "\n", + "# create a fitted model\n", + "lm = smf.ols(formula='stars ~ useful', data=data).fit()\n", + "\n", + "# print the coefficients\n", + "lm.params" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 3.804871\n", + "funny -0.039029\n", + "dtype: float64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### STATSMODELS ###\n", + "\n", + "# create a fitted model\n", + "lm = smf.ols(formula='stars ~ funny', data=data).fit()\n", + "\n", + "# print the coefficients\n", + "lm.params" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 5, 0],\n", + " [0, 0, 0],\n", + " [0, 1, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]], dtype=int64)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "features = np.array([data.cool, data.useful, data.funny]).T\n", + "features # 2D array" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5],\n", + " [5],\n", + " [4],\n", + " ..., \n", + " [4],\n", + " [2],\n", + " [5]], dtype=int64)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = np.array([data.stars]).T \n", + "response # 1D array\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# step 1: split data into training set and test set\n", + "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n", + "# the random_state allows us all to get the same random numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 6, 4],\n", + " [1, 2, 1],\n", + " [0, 1, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [2, 3, 2],\n", + " [0, 2, 0]], dtype=int64)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_train" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 3, 1],\n", + " [0, 1, 0],\n", + " [0, 0, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]], dtype=int64)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_test" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3],\n", + " [5],\n", + " [5],\n", + " ..., \n", + " [4],\n", + " [2],\n", + " [1]], dtype=int64)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response_train" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5],\n", + " [5],\n", + " [5],\n", + " ..., \n", + " [3],\n", + " [4],\n", + " [3]], dtype=int64)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/plain": [ + "0.35320000000000001" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(features_train, response_train) # Note that I fit to the training\n", + "knn.score(features_test, response_test) # and scored on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = data[['cool', 'useful', 'funny']]\n", + "y = data['stars']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import cross_val_score\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.28657014, 0.24787606, 0.22011006, 0.17558779, 0.24274274])" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/eugenetest.md b/eugenetest.md deleted file mode 100644 index 87a03f3..0000000 --- a/eugenetest.md +++ /dev/null @@ -1 +0,0 @@ -hello 123 \ No newline at end of file diff --git a/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb new file mode 100644 index 0000000..2dc8b4a --- /dev/null +++ b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb @@ -0,0 +1,1620 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AEhERERFpgGQqRSpTxKP5PnWlACQiIiIiUkeVSoVQOEbJ5tSStwZQABIRERERqZNCocB0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\nlVtdC7vGZTIZkpkMZRx4vY1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h/nu20t+1+vOWvD4/RR0NEhFpG9FYnNmihd+v5VrFUpmX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(data, x_vars=['Pclass','Parch'], y_vars='Survived', size=6, aspect=0.7, kind='reg')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 3],\n", + " [0, 1],\n", + " [0, 3],\n", + " ..., \n", + " [2, 3],\n", + " [0, 1],\n", + " [0, 3]], dtype=int64)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "features = np.array([data.Parch, data.Pclass]).T\n", + "features # 2D array" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " 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[1],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [1],\n", + " [0]], dtype=int64)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = np.array([data.Survived]).T \n", + "response # 1D array" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# step 1: split data into training set and test set\n", + "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n", + "# the random_state allows us all to get the same random numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 2],\n", + " [0, 3],\n", + " [0, 3],\n", + " ..., \n", + " [0, 2],\n", + " [0, 1],\n", + " [0, 2]], dtype=int64)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_train" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/plain": [ + "0.63228699551569512" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(features_train, response_train) # Note that I fit to the training\n", + "knn.score(features_test, response_test) # and scored on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = data[['Parch', 'Pclass']]\n", + "y = data['Survived']\n", + "from sklearn.cross_validation import cross_val_score\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.61452514, 0.68156425, 0.70786517, 0.75280899, 0.70621469])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/labs/Titanic_Eugene.ipynb b/labs/Titanic_Eugene.ipynb new file mode 100644 index 0000000..2dc8b4a --- /dev/null +++ b/labs/Titanic_Eugene.ipynb @@ -0,0 +1,1620 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
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AEhERERFpgGQqRSpTxKP5PnWlACQiIiIiUkeVSoVQOEbJ5tSStwZQABIRERERqZNCocB0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\nlVtdC7vGZTIZkpkMZRx4vY1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h/nu20t+1+vOWvD4/RR0NEhFpG9FYnNmihd+v5VrFUpmX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(data, x_vars=['Pclass','Parch'], y_vars='Survived', size=6, aspect=0.7, kind='reg')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 3],\n", + " [0, 1],\n", + " [0, 3],\n", + " ..., \n", + " [2, 3],\n", + " [0, 1],\n", + " [0, 3]], dtype=int64)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "features = np.array([data.Parch, data.Pclass]).T\n", + "features # 2D array" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " 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us all to get the same random numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 2],\n", + " [0, 3],\n", + " [0, 3],\n", + " ..., \n", + " [0, 2],\n", + " [0, 1],\n", + " [0, 2]], dtype=int64)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_train" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/plain": [ + "0.63228699551569512" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(features_train, response_train) # Note that I fit to the training\n", + "knn.score(features_test, response_test) # and scored on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = data[['Parch', 'Pclass']]\n", + "y = data['Survived']\n", + "from sklearn.cross_validation import cross_val_score\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.61452514, 0.68156425, 0.70786517, 0.75280899, 0.70621469])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/labs/untitled b/labs/untitled new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb new file mode 100644 index 0000000..b826e3f --- /dev/null +++ b/notebooks/.ipynb_checkpoints/02_pandas-checkpoint.ipynb @@ -0,0 +1,7213 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nCLASS: Pandas for Data Exploration, Analysis, and Visualization\\n\\nWHO alcohol consumption data:\\n article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/ \\n original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\\n files: drinks.csv (with additional 'continent' column)\\n\"" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + "CLASS: Pandas for Data Exploration, Analysis, and Visualization\n", + "\n", + "WHO alcohol consumption data:\n", + " article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/ \n", + " original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\n", + " files: drinks.csv (with additional 'continent' column)\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", + " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + ] + } + ], + "source": [ + "# imports\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + "Pandas Basics: Reading Files, Summarizing, Handling Missing Values, Filtering, Sorting\n", + "'''\n", + "\n", + "# read in the CSV file from a URL\n", + "drinks = pd.read_csv('../data/drinks.csv')\n", + "\n", + "type(drinks) # Use the type method to check python type\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
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1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
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189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
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beer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcohol
count193.000000193.000000193.000000193.000000
mean106.16062280.99481949.4507774.717098
std101.14310388.28431279.6975983.773298
min0.0000000.0000000.0000000.000000
25%20.0000004.0000001.0000001.300000
50%76.00000056.0000008.0000004.200000
75%188.000000128.00000059.0000007.200000
max376.000000438.000000370.00000014.400000
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" + ], + "text/plain": [ + " beer_servings spirit_servings wine_servings \\\n", + "count 193.000000 193.000000 193.000000 \n", + "mean 106.160622 80.994819 49.450777 \n", + "std 101.143103 88.284312 79.697598 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 20.000000 4.000000 1.000000 \n", + "50% 76.000000 56.000000 8.000000 \n", + "75% 188.000000 128.000000 59.000000 \n", + "max 376.000000 438.000000 370.000000 \n", + "\n", + " total_litres_of_pure_alcohol \n", + "count 193.000000 \n", + "mean 4.717098 \n", + "std 3.773298 \n", + "min 0.000000 \n", + "25% 1.300000 \n", + "50% 4.200000 \n", + "75% 7.200000 \n", + "max 14.400000 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.describe() # describe any numeric columns" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'country', u'beer_servings', u'spirit_servings', u'wine_servings',\n", + " u'total_litres_of_pure_alcohol', u'continent'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.columns # get series of column names" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " a b\n", + "0 1 3\n", + "1 2 4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note: the dataframe is like a more complicated dictionary where the \n", + "# column names are the keys and the column itself is the value\n", + "\n", + "d = {'a':[1,2], 'b':[3,4]}\n", + "pd.DataFrame(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(193, 6)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.shape # tuple of (#rows, #cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "country 0\n", + "beer_servings 0\n", + "spirit_servings 0\n", + "wine_servings 0\n", + "total_litres_of_pure_alcohol 0\n", + "continent 23\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# find missing values in a DataFrame\n", + "drinks.isnull() # DataFrame of booleans\n", + "drinks.isnull().sum() # convert booleans to integers and add" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
5Antigua & Barbuda102128454.9NaN
11Bahamas122176516.3NaN
14Barbados143173366.3NaN
17Belize26311486.8NaN
32Canada2401221008.2NaN
41Costa Rica14987114.4NaN
43Cuba9313754.2NaN
50Dominica52286266.6NaN
51Dominican Republic19314796.2NaN
54El Salvador526922.2NaN
68Grenada1994382811.9NaN
69Guatemala536922.2NaN
73Haiti132615.9NaN
74Honduras699823.0NaN
84Jamaica829793.4NaN
109Mexico2386855.5NaN
122Nicaragua7811813.5NaN
130Panama285104187.2NaN
143St. Kitts & Nevis194205327.7NaN
144St. Lucia1713157110.1NaN
145St. Vincent & the Grenadines120221116.3NaN
174Trinidad & Tobago19715676.4NaN
184USA249158848.7NaN
\n", + "
" + ], + "text/plain": [ + " country beer_servings spirit_servings \\\n", + "5 Antigua & Barbuda 102 128 \n", + "11 Bahamas 122 176 \n", + "14 Barbados 143 173 \n", + "17 Belize 263 114 \n", + "32 Canada 240 122 \n", + "41 Costa Rica 149 87 \n", + "43 Cuba 93 137 \n", + "50 Dominica 52 286 \n", + "51 Dominican Republic 193 147 \n", + "54 El Salvador 52 69 \n", + "68 Grenada 199 438 \n", + "69 Guatemala 53 69 \n", + "73 Haiti 1 326 \n", + "74 Honduras 69 98 \n", + "84 Jamaica 82 97 \n", + "109 Mexico 238 68 \n", + "122 Nicaragua 78 118 \n", + "130 Panama 285 104 \n", + "143 St. Kitts & Nevis 194 205 \n", + "144 St. Lucia 171 315 \n", + "145 St. Vincent & the Grenadines 120 221 \n", + "174 Trinidad & Tobago 197 156 \n", + "184 USA 249 158 \n", + "\n", + " wine_servings total_litres_of_pure_alcohol continent \n", + "5 45 4.9 NaN \n", + "11 51 6.3 NaN \n", + "14 36 6.3 NaN \n", + "17 8 6.8 NaN \n", + "32 100 8.2 NaN \n", + "41 11 4.4 NaN \n", + "43 5 4.2 NaN \n", + "50 26 6.6 NaN \n", + "51 9 6.2 NaN \n", + "54 2 2.2 NaN \n", + "68 28 11.9 NaN \n", + "69 2 2.2 NaN \n", + "73 1 5.9 NaN \n", + "74 2 3.0 NaN \n", + "84 9 3.4 NaN \n", + "109 5 5.5 NaN \n", + "122 1 3.5 NaN \n", + "130 18 7.2 NaN \n", + "143 32 7.7 NaN \n", + "144 71 10.1 NaN \n", + "145 11 6.3 NaN \n", + "174 7 6.4 NaN \n", + "184 84 8.7 NaN " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# So we have 23 missing continent values, that's a no bueno situation\n", + "\n", + "''' SELECT * from drinks where continent is NULL '''\n", + "drinks[drinks['continent'].isnull()] # DataFrame of values with null continent value\n", + "\n", + "# Anyone have any thoughts?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NA
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NA
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NA
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NA
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NA
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NA
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", + "

193 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "5 Antigua & Barbuda 102 128 45 \n", + "6 Argentina 193 25 221 \n", + "7 Armenia 21 179 11 \n", + "8 Australia 261 72 212 \n", + "9 Austria 279 75 191 \n", + "10 Azerbaijan 21 46 5 \n", + "11 Bahamas 122 176 51 \n", + "12 Bahrain 42 63 7 \n", + "13 Bangladesh 0 0 0 \n", + "14 Barbados 143 173 36 \n", + "15 Belarus 142 373 42 \n", + "16 Belgium 295 84 212 \n", + "17 Belize 263 114 8 \n", + "18 Benin 34 4 13 \n", + "19 Bhutan 23 0 0 \n", + "20 Bolivia 167 41 8 \n", + "21 Bosnia-Herzegovina 76 173 8 \n", + "22 Botswana 173 35 35 \n", + "23 Brazil 245 145 16 \n", + "24 Brunei 31 2 1 \n", + "25 Bulgaria 231 252 94 \n", + "26 Burkina Faso 25 7 7 \n", + "27 Burundi 88 0 0 \n", + "28 Cote d'Ivoire 37 1 7 \n", + "29 Cabo Verde 144 56 16 \n", + ".. ... ... ... ... \n", + "163 Suriname 128 178 7 \n", + "164 Swaziland 90 2 2 \n", + "165 Sweden 152 60 186 \n", + "166 Switzerland 185 100 280 \n", + "167 Syria 5 35 16 \n", + "168 Tajikistan 2 15 0 \n", + "169 Thailand 99 258 1 \n", + "170 Macedonia 106 27 86 \n", + "171 Timor-Leste 1 1 4 \n", + "172 Togo 36 2 19 \n", + "173 Tonga 36 21 5 \n", + "174 Trinidad & Tobago 197 156 7 \n", + "175 Tunisia 51 3 20 \n", + "176 Turkey 51 22 7 \n", + "177 Turkmenistan 19 71 32 \n", + "178 Tuvalu 6 41 9 \n", + "179 Uganda 45 9 0 \n", + "180 Ukraine 206 237 45 \n", + "181 United Arab Emirates 16 135 5 \n", + "182 United Kingdom 219 126 195 \n", + "183 Tanzania 36 6 1 \n", + "184 USA 249 158 84 \n", + "185 Uruguay 115 35 220 \n", + "186 Uzbekistan 25 101 8 \n", + "187 Vanuatu 21 18 11 \n", + "188 Venezuela 333 100 3 \n", + "189 Vietnam 111 2 1 \n", + "190 Yemen 6 0 0 \n", + "191 Zambia 32 19 4 \n", + "192 Zimbabwe 64 18 4 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU \n", + "2 0.7 AF \n", + "3 12.4 EU \n", + "4 5.9 AF \n", + "5 4.9 NA \n", + "6 8.3 SA \n", + "7 3.8 EU \n", + "8 10.4 OC \n", + "9 9.7 EU \n", + "10 1.3 EU \n", + "11 6.3 NA \n", + "12 2.0 AS \n", + "13 0.0 AS \n", + "14 6.3 NA \n", + "15 14.4 EU \n", + "16 10.5 EU \n", + "17 6.8 NA \n", + "18 1.1 AF \n", + "19 0.4 AS \n", + "20 3.8 SA \n", + "21 4.6 EU \n", + "22 5.4 AF \n", + "23 7.2 SA \n", + "24 0.6 AS \n", + "25 10.3 EU \n", + "26 4.3 AF \n", + "27 6.3 AF \n", + "28 4.0 AF \n", + "29 4.0 AF \n", + ".. ... ... \n", + "163 5.6 SA \n", + "164 4.7 AF \n", + "165 7.2 EU \n", + "166 10.2 EU \n", + "167 1.0 AS \n", + "168 0.3 AS \n", + "169 6.4 AS \n", + "170 3.9 EU \n", + "171 0.1 AS \n", + "172 1.3 AF \n", + "173 1.1 OC \n", + "174 6.4 NA \n", + "175 1.3 AF \n", + "176 1.4 AS \n", + "177 2.2 AS \n", + "178 1.0 OC \n", + "179 8.3 AF \n", + "180 8.9 EU \n", + "181 2.8 AS \n", + "182 10.4 EU \n", + "183 5.7 AF \n", + "184 8.7 NA \n", + "185 6.6 SA \n", + "186 2.4 AS \n", + "187 0.9 OC \n", + "188 7.7 SA \n", + "189 2.0 AS \n", + "190 0.1 AS \n", + "191 2.5 AF \n", + "192 4.7 AF \n", + "\n", + "[193 rows x 6 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pandas isn't brilliant, it saw the \"NA\" string and assumed it meant a null value\n", + "\n", + "# handling missing values\n", + "drinks.dropna() # drop a row if ANY values are missing\n", + "drinks.fillna(value='NA') # fill in missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "country 0\n", + "beer_servings 0\n", + "spirit_servings 0\n", + "wine_servings 0\n", + "total_litres_of_pure_alcohol 0\n", + "continent 0\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fix the original import, this time using a live url!\n", + "drinks = pd.read_csv('https://raw.githubusercontent.com/sinanuozdemir/sfdat28/master/data/drinks.csv', na_filter=False)\n", + "drinks.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 AS\n", + "1 EU\n", + "2 AF\n", + "3 EU\n", + "4 AF\n", + "5 NA\n", + "6 SA\n", + "7 EU\n", + "8 OC\n", + "9 EU\n", + "10 EU\n", + "11 NA\n", + "12 AS\n", + "13 AS\n", + "14 NA\n", + "15 EU\n", + "16 EU\n", + "17 NA\n", + "18 AF\n", + "19 AS\n", + "20 SA\n", + "21 EU\n", + "22 AF\n", + "23 SA\n", + "24 AS\n", + "25 EU\n", + "26 AF\n", + "27 AF\n", + "28 AF\n", + "29 AF\n", + " ..\n", + "163 SA\n", + "164 AF\n", + "165 EU\n", + "166 EU\n", + "167 AS\n", + "168 AS\n", + "169 AS\n", + "170 EU\n", + "171 AS\n", + "172 AF\n", + "173 OC\n", + "174 NA\n", + "175 AF\n", + "176 AS\n", + "177 AS\n", + "178 OC\n", + "179 AF\n", + "180 EU\n", + "181 AS\n", + "182 EU\n", + "183 AF\n", + "184 NA\n", + "185 SA\n", + "186 AS\n", + "187 OC\n", + "188 SA\n", + "189 AS\n", + "190 AS\n", + "191 AF\n", + "192 AF\n", + "Name: continent, dtype: object" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# selecting a column ('Series')\n", + "drinks['continent']\n", + "drinks.continent # equivalent\n", + "\n", + "# Note the dictionary like selection" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(drinks.continent) # Series if pandas equivalent to list" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 193\n", + "unique 6\n", + "top AF\n", + "freq 53\n", + "Name: continent, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# summarizing a non-numeric column\n", + "drinks.continent.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NA 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.continent.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servings
0Afghanistan0
1Albania89
2Algeria25
3Andorra245
4Angola217
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" + ], + "text/plain": [ + " country beer_servings\n", + "0 Afghanistan 0\n", + "1 Albania 89\n", + "2 Algeria 25\n", + "3 Andorra 245\n", + "4 Angola 217" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "'''\n", + "note the double square bracket\n", + "the outer pair is used like in a python dictionary\n", + " to select\n", + "the inner pair is a list!\n", + "\n", + "so in all, the double use of square brackets is telling\n", + "the dataframe to select a list!\n", + "'''\n", + "# selecting multiple columns\n", + "''' SELECT country,beer_servings from drinks '''\n", + "drinks[['country', 'beer_servings']].head()\n", + "\n", + "\n", + "# remember the .head() just shows us the first 5 rows" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servings
0Afghanistan0
1Albania89
2Algeria25
3Andorra245
4Angola217
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" + ], + "text/plain": [ + " country beer_servings\n", + "0 Afghanistan 0\n", + "1 Albania 89\n", + "2 Algeria 25\n", + "3 Andorra 245\n", + "4 Angola 217" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_cols = ['country', 'beer_servings']\n", + "drinks[my_cols].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
0Afghanistan0000.0AS0
1Albania89132544.9EU275
2Algeria250140.7AF39
3Andorra24513831212.4EU695
4Angola21757455.9AF319
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "0 0.0 AS 0 \n", + "1 4.9 EU 275 \n", + "2 0.7 AF 39 \n", + "3 12.4 EU 695 \n", + "4 5.9 AF 319 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add a new column as a function of existing columns\n", + "drinks['total_servings'] = drinks.beer_servings + drinks.spirit_servings + drinks.wine_servings\n", + "drinks.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
1Albania89132544.9EU275
3Andorra24513831212.4EU695
7Armenia21179113.8EU211
9Austria279751919.7EU545
10Azerbaijan214651.3EU72
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "1 Albania 89 132 54 \n", + "3 Andorra 245 138 312 \n", + "7 Armenia 21 179 11 \n", + "9 Austria 279 75 191 \n", + "10 Azerbaijan 21 46 5 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "1 4.9 EU 275 \n", + "3 12.4 EU 695 \n", + "7 3.8 EU 211 \n", + "9 9.7 EU 545 \n", + "10 1.3 EU 72 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# logical filtering and sorting\n", + "\n", + "'''\n", + "How it works:\n", + " drinks.continent=='EU' by itself returns a bunch\n", + " of Trues and Falses\n", + " \n", + "drinks.continent=='EU'\n", + "\n", + "See?\n", + "\n", + "\n", + "when you wrap drinks around it with square brackets\n", + "you're telling the drinks dataframe to select\n", + "only those that are True, and not the False ones\n", + "\n", + "drinks[drinks.continent=='EU']\n", + "'''\n", + "drinks.continent=='EU' # this is a series of T and F\n", + "\n", + "# we put tht series of Trues and Falses directly into the square brackets of justice\n", + "drinks[drinks.continent=='EU'] .head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrytotal_servings
5Antigua & Barbuda275
11Bahamas349
14Barbados352
17Belize385
32Canada462
41Costa Rica247
43Cuba235
50Dominica364
51Dominican Republic349
54El Salvador123
68Grenada665
69Guatemala124
73Haiti328
74Honduras169
84Jamaica188
109Mexico311
122Nicaragua197
130Panama407
143St. Kitts & Nevis431
144St. Lucia557
145St. Vincent & the Grenadines352
174Trinidad & Tobago360
184USA491
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" + ], + "text/plain": [ + " country total_servings\n", + "5 Antigua & Barbuda 275\n", + "11 Bahamas 349\n", + "14 Barbados 352\n", + "17 Belize 385\n", + "32 Canada 462\n", + "41 Costa Rica 247\n", + "43 Cuba 235\n", + "50 Dominica 364\n", + "51 Dominican Republic 349\n", + "54 El Salvador 123\n", + "68 Grenada 665\n", + "69 Guatemala 124\n", + "73 Haiti 328\n", + "74 Honduras 169\n", + "84 Jamaica 188\n", + "109 Mexico 311\n", + "122 Nicaragua 197\n", + "130 Panama 407\n", + "143 St. Kitts & Nevis 431\n", + "144 St. Lucia 557\n", + "145 St. Vincent & the Grenadines 352\n", + "174 Trinidad & Tobago 360\n", + "184 USA 491" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# North American countries with total servings\n", + "# drinks[['country', 'total_servings']][drinks.continent=='NA']\n", + "\n", + "new_df = drinks[['country', 'total_servings']] # selection of two columns\n", + "new_df[drinks.continent=='NA']" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/html": [ + "
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countrytotal_servings
54El Salvador123
69Guatemala124
74Honduras169
84Jamaica188
122Nicaragua197
43Cuba235
41Costa Rica247
5Antigua & Barbuda275
109Mexico311
73Haiti328
51Dominican Republic349
11Bahamas349
14Barbados352
145St. Vincent & the Grenadines352
174Trinidad & Tobago360
50Dominica364
17Belize385
130Panama407
143St. Kitts & Nevis431
32Canada462
184USA491
144St. Lucia557
68Grenada665
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" + ], + "text/plain": [ + " country total_servings\n", + "54 El Salvador 123\n", + "69 Guatemala 124\n", + "74 Honduras 169\n", + "84 Jamaica 188\n", + "122 Nicaragua 197\n", + "43 Cuba 235\n", + "41 Costa Rica 247\n", + "5 Antigua & Barbuda 275\n", + "109 Mexico 311\n", + "73 Haiti 328\n", + "51 Dominican Republic 349\n", + "11 Bahamas 349\n", + "14 Barbados 352\n", + "145 St. Vincent & the Grenadines 352\n", + "174 Trinidad & Tobago 360\n", + "50 Dominica 364\n", + "17 Belize 385\n", + "130 Panama 407\n", + "143 St. Kitts & Nevis 431\n", + "32 Canada 462\n", + "184 USA 491\n", + "144 St. Lucia 557\n", + "68 Grenada 665" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same thing, sorted by total_servings\n", + "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrytotal_servings
68 Grenada 665
144 St. Lucia 557
184 USA 491
32 Canada 462
143 St. Kitts & Nevis 431
130 Panama 407
17 Belize 385
50 Dominica 364
174 Trinidad & Tobago 360
14 Barbados 352
145 St. Vincent & the Grenadines 352
51 Dominican Republic 349
11 Bahamas 349
73 Haiti 328
109 Mexico 311
5 Antigua & Barbuda 275
41 Costa Rica 247
43 Cuba 235
122 Nicaragua 197
84 Jamaica 188
74 Honduras 169
69 Guatemala 124
54 El Salvador 123
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" + ], + "text/plain": [ + " country total_servings\n", + "68 Grenada 665\n", + "144 St. Lucia 557\n", + "184 USA 491\n", + "32 Canada 462\n", + "143 St. Kitts & Nevis 431\n", + "130 Panama 407\n", + "17 Belize 385\n", + "50 Dominica 364\n", + "174 Trinidad & Tobago 360\n", + "14 Barbados 352\n", + "145 St. Vincent & the Grenadines 352\n", + "51 Dominican Republic 349\n", + "11 Bahamas 349\n", + "73 Haiti 328\n", + "109 Mexico 311\n", + "5 Antigua & Barbuda 275\n", + "41 Costa Rica 247\n", + "43 Cuba 235\n", + "122 Nicaragua 197\n", + "84 Jamaica 188\n", + "74 Honduras 169\n", + "69 Guatemala 124\n", + "54 El Salvador 123" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#sorted in reverse order\n", + "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings', ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
3Andorra24513831212.4EU695
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "3 Andorra 245 138 312 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "3 12.4 EU 695 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# contries with wine servings over 300 and total liters over 12\n", + "drinks[drinks.wine_servings > 300][drinks.total_litres_of_pure_alcohol > 12]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
3Andorra24513831212.4EU695
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "3 Andorra 245 138 312 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "3 12.4 EU 695 " + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Same result but using python logic operators\n", + "# Note the paranthesis around each filter when using logical operators\n", + "drinks[(drinks.wine_servings > 300) & (drinks.total_litres_of_pure_alcohol > 12)]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
3Andorra24513831212.4EU695
6Argentina193252218.3SA439
35Chile1301241727.6SA426
40Cook Islands0254745.9OC328
42Croatia2308725410.2EU571
48Denmark2248127810.4EU583
55Equatorial Guinea9202335.8AF325
61France12715137011.8EU648
64Georgia521001495.4EU301
67Greece1331122188.3EU463
83Italy85422376.5EU364
92Laos6201236.2AS185
94Lebanon2055311.9AS106
99Luxembourg23613327111.4EU640
113Montenegro311141284.9EU273
136Portugal1946733911.0EU600
137Qatar14270.9AS50
148Sao Tome & Principe56381404.2AF234
156Slovenia2705127610.6EU597
165Sweden152601867.2EU398
166Switzerland18510028010.2EU565
167Syria535161.0AS56
171Timor-Leste1140.1AS6
177Turkmenistan1971322.2AS122
178Tuvalu64191.0OC56
185Uruguay115352206.6SA370
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "3 Andorra 245 138 312 \n", + "6 Argentina 193 25 221 \n", + "35 Chile 130 124 172 \n", + "40 Cook Islands 0 254 74 \n", + "42 Croatia 230 87 254 \n", + "48 Denmark 224 81 278 \n", + "55 Equatorial Guinea 92 0 233 \n", + "61 France 127 151 370 \n", + "64 Georgia 52 100 149 \n", + "67 Greece 133 112 218 \n", + "83 Italy 85 42 237 \n", + "92 Laos 62 0 123 \n", + "94 Lebanon 20 55 31 \n", + "99 Luxembourg 236 133 271 \n", + "113 Montenegro 31 114 128 \n", + "136 Portugal 194 67 339 \n", + "137 Qatar 1 42 7 \n", + "148 Sao Tome & Principe 56 38 140 \n", + "156 Slovenia 270 51 276 \n", + "165 Sweden 152 60 186 \n", + "166 Switzerland 185 100 280 \n", + "167 Syria 5 35 16 \n", + "171 Timor-Leste 1 1 4 \n", + "177 Turkmenistan 19 71 32 \n", + "178 Tuvalu 6 41 9 \n", + "185 Uruguay 115 35 220 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "3 12.4 EU 695 \n", + "6 8.3 SA 439 \n", + "35 7.6 SA 426 \n", + "40 5.9 OC 328 \n", + "42 10.2 EU 571 \n", + "48 10.4 EU 583 \n", + "55 5.8 AF 325 \n", + "61 11.8 EU 648 \n", + "64 5.4 EU 301 \n", + "67 8.3 EU 463 \n", + "83 6.5 EU 364 \n", + "92 6.2 AS 185 \n", + "94 1.9 AS 106 \n", + "99 11.4 EU 640 \n", + "113 4.9 EU 273 \n", + "136 11.0 EU 600 \n", + "137 0.9 AS 50 \n", + "148 4.2 AF 234 \n", + "156 10.6 EU 597 \n", + "165 7.2 EU 398 \n", + "166 10.2 EU 565 \n", + "167 1.0 AS 56 \n", + "171 0.1 AS 6 \n", + "177 2.2 AS 122 \n", + "178 1.0 OC 56 \n", + "185 6.6 SA 370 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# contries with more wine servings than beer servings\n", + "drinks[drinks.wine_servings > drinks.beer_servings]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "6 True\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "163 False\n", + "164 False\n", + "165 True\n", + "166 True\n", + "167 True\n", + "168 False\n", + "169 False\n", + "170 False\n", + "171 True\n", + "172 False\n", + "173 False\n", + "174 False\n", + "175 False\n", + "176 False\n", + "177 True\n", + "178 True\n", + "179 False\n", + "180 False\n", + "181 False\n", + "182 False\n", + "183 False\n", + "184 False\n", + "185 True\n", + "186 False\n", + "187 False\n", + "188 False\n", + "189 False\n", + "190 False\n", + "191 False\n", + "192 False\n", + "dtype: bool" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# to reiterate how the filter works:\n", + "drinks.wine_servings > drinks.beer_servings # is a series of T and F" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinenttotal_servings
98 Lithuania 343 244 56 12.9 EU 643
135 Poland 343 215 56 10.9 EU 614
65 Germany 346 117 175 11.3 EU 638
62 Gabon 347 98 59 8.9 AF 504
45 Czech Republic 361 170 134 11.8 EU 665
117 Namibia 376 3 1 6.8 AF 380
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "98 Lithuania 343 244 56 \n", + "135 Poland 343 215 56 \n", + "65 Germany 346 117 175 \n", + "62 Gabon 347 98 59 \n", + "45 Czech Republic 361 170 134 \n", + "117 Namibia 376 3 1 \n", + "\n", + " total_litres_of_pure_alcohol continent total_servings \n", + "98 12.9 EU 643 \n", + "135 10.9 EU 614 \n", + "65 11.3 EU 638 \n", + "62 8.9 AF 504 \n", + "45 11.8 EU 665 \n", + "117 6.8 AF 380 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# last 5 elements of the dataframe sorted by beer servings\n", + "drinks.sort_index(by='beer_servings').tail(6)\n", + "# note tail took in an optional input of 6, showing us the last 6 rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Quiz\n", + "\n", + "# 1. List countries that drink more spirits than beer on average\n", + "\n", + "# 2. What are the top three beer drinking countries?\n", + "\n", + "# 3. What are the top three beer drinking countries in Europe?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Answers below don't peak!\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 Albania\n", + "5 Antigua & Barbuda\n", + "7 Armenia\n", + "10 Azerbaijan\n", + "11 Bahamas\n", + "12 Bahrain\n", + "14 Barbados\n", + "15 Belarus\n", + "21 Bosnia-Herzegovina\n", + "25 Bulgaria\n", + "30 Cambodia\n", + "36 China\n", + "38 Comoros\n", + "40 Cook Islands\n", + "43 Cuba\n", + "49 Djibouti\n", + "50 Dominica\n", + "54 El Salvador\n", + "61 France\n", + "64 Georgia\n", + "68 Grenada\n", + "69 Guatemala\n", + "71 Guinea-Bissau\n", + "72 Guyana\n", + "73 Haiti\n", + "74 Honduras\n", + "77 India\n", + "82 Israel\n", + "84 Jamaica\n", + "85 Japan\n", + " ... \n", + "89 Kiribati\n", + "91 Kyrgyzstan\n", + "94 Lebanon\n", + "96 Liberia\n", + "101 Malawi\n", + "112 Mongolia\n", + "113 Montenegro\n", + "119 Nepal\n", + "122 Nicaragua\n", + "125 Niue\n", + "134 Philippines\n", + "137 Qatar\n", + "139 Moldova\n", + "141 Russian Federation\n", + "143 St. Kitts & Nevis\n", + "144 St. Lucia\n", + "145 St. Vincent & the Grenadines\n", + "149 Saudi Arabia\n", + "155 Slovakia\n", + "161 Sri Lanka\n", + "162 Sudan\n", + "163 Suriname\n", + "167 Syria\n", + "168 Tajikistan\n", + "169 Thailand\n", + "177 Turkmenistan\n", + "178 Tuvalu\n", + "180 Ukraine\n", + "181 United Arab Emirates\n", + "186 Uzbekistan\n", + "Name: country, dtype: object" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1. \n", + "drinks['country'][drinks.spirit_servings>drinks.beer_servings]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/html": [ + "
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countrybeer_servings
62Gabon347
45Czech Republic361
117Namibia376
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" + ], + "text/plain": [ + " country beer_servings\n", + "62 Gabon 347\n", + "45 Czech Republic 361\n", + "117 Namibia 376" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2. \n", + "drinks[['country', 'beer_servings']].sort_index(by='beer_servings').tail(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/html": [ + "
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countrybeer_servings
45Czech Republic361
65Germany346
135Poland343
\n", + "
" + ], + "text/plain": [ + " country beer_servings\n", + "45 Czech Republic 361\n", + "65 Germany 346\n", + "135 Poland 343" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 3. \n", + "drinks[drinks.continent=='EU'][['country', 'beer_servings']].sort_index(by='beer_servings', ascending=False).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "145.434782609\n" + ] + } + ], + "source": [ + "# average North American beer consumption\n", + "'''\n", + "Note the procedure:\n", + "drinks Dataframe\n", + "drinks.beer_servings one column (Series)\n", + "drinks.beer_servings[drinks.continent=='NA'] logical filtering\n", + "drinks.beer_servings[drinks.continent=='NA'].mean() mean of that filtered column\n", + "'''\n", + "\n", + "print drinks.beer_servings[drinks.continent=='NA'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# average European beer consumption\n", + "drinks.beer_servings[drinks.continent=='EU'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Split Apply Combine**\n", + "\n", + "\n", + "\n", + "Pandas uses the \"groupby\" command to split apply and combine" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 61.471698\n", + "AS 37.045455\n", + "EU 193.777778\n", + "NA 145.434783\n", + "OC 89.687500\n", + "SA 175.083333\n", + "Name: beer_servings, dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each continent, calculate mean beer servings\n", + "drinks.groupby('continent').beer_servings.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NA 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each continent, count number of occurrences\n", + "drinks.groupby('continent').continent.count()\n", + "drinks.continent.value_counts() #same thing" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 94.075472\n", + "AS 106.954545\n", + "EU 468.555556\n", + "NA 335.695652\n", + "OC 183.750000\n", + "SA 352.250000\n", + "Name: total_servings, dtype: float64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can apply any function using .apply\n", + "drinks.groupby('continent').total_servings.apply(lambda x: x.mean()) # mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# on lambda functions:\n", + "\n", + "'''\n", + "Lamda Functions are ananonymous functions\n", + "\n", + "f = lambda x: x + 1 IS THE SAME AS \n", + "def f(x): return x + 1\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 114.040622\n", + "AS 132.630446\n", + "EU 176.921857\n", + "NA 134.437696\n", + "OC 175.450848\n", + "SA 81.923606\n", + "Name: total_servings, dtype: float64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# note x here is an entire series\n", + "drinks.groupby('continent').total_servings.apply(lambda x: x.std()) # standard deviation" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "continent\n", + "AF 0\n", + "AS 0\n", + "EU 0\n", + "NA 123\n", + "OC 0\n", + "SA 216\n", + "Name: total_servings, dtype: int64\n", + "continent\n", + "AF 504\n", + "AS 646\n", + "EU 695\n", + "NA 665\n", + "OC 545\n", + "SA 439\n", + "Name: total_servings, dtype: int64\n" + ] + } + ], + "source": [ + "# for each continent, calculate the min, max, and range for total servings\n", + "print drinks.groupby('continent').total_servings.min()\n", + "print drinks.groupby('continent').total_servings.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 504\n", + "AS 646\n", + "EU 695\n", + "NA 542\n", + "OC 545\n", + "SA 223\n", + "Name: total_servings, dtype: int64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What does this do?\n", + "drinks.groupby('continent').total_servings.apply(lambda x: x.max() - x.min())" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "'''\n", + "Plotting\n", + "'''\n", + "\n", + "# bar plot of number of countries in each continent\n", + "drinks.continent.value_counts().plot(kind='bar', title='Countries per Continent')\n", + "plt.xlabel('Continent')\n", + "plt.ylabel('Count')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# bar plot of average number of beer servings by continent\n", + "drinks.groupby('continent').beer_servings.mean().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# histogram of beer servings\n", + "drinks.beer_servings.hist(bins=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0SqASbOF7hMglSfgMIRJEL7hBVlGnu2yW6rLpccadCcAg8GpJv5R0TtOxG4CP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\nKYpSXTYdp5/pZnFWPO9Lkh6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# grouped histogram of beer servings\n", + "drinks.beer_servings.hist(by=drinks.continent)\n", + "\n", + "# stop and think, does this make sense\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# same charts with the same scale for x and y axis\n", + "drinks.beer_servings.hist(by=drinks.continent, sharex=True, sharey=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# boxplot of beer servings by continent\n", + "drinks.boxplot(column='beer_servings', by='continent')" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatterplot of beer servings versus wine servings\n", + "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', alpha=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['b', 'r', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b',\n", + " 'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n", + " 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n", + " 'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b', 'r', 'b', 'b', 'b',\n", + " 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'r', 'r', 'b', 'b', 'r',\n", + " 'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b',\n", + " 'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n", + " 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b',\n", + " 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b',\n", + " 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b',\n", + " 'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'r', 'r', 'b', 'b',\n", + " 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n", + " 'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b',\n", + " 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'b',\n", + " 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], \n", + " dtype='|S1')" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same scatterplot, except all European countries are colored red\n", + "colors = np.where(drinks.continent=='EU', 'r', 'b')\n", + "colors # is a series of 'r' and 'b' that \n", + " # correspond to countries" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['a', 'b', 'b'], \n", + " dtype='|S1')" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + "np.where is like a condensed if statement\n", + "it's like a list comprehension for pandas!\n", + "\n", + "it will loop through drinks.continent which is a series\n", + "for each element:\n", + " if it is \"EU\":\n", + " make it 'r'\n", + " else:\n", + " make it 'b'\n", + "\n", + "More in depth:\n", + " drinks.continent=='EU' is a logical statement\n", + " It will return a bunch of Trues and Falses\n", + " and np.where makes the True ones 'r' and\n", + " the False ones 'b'\n", + " \n", + " Recall logical filtering!\n", + "\n", + "'''\n", + "\n", + "# Side quest\n", + "np.where([True, False, False], 'a', 'b')\n", + "\n", + "# 10 gold coins earned" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DeObSykWH6hbblEmTePbRR3FmZVGldiPWb/wNEeja9Qpmzpxaalc7U0qVjEAm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\nR45IvXpnSWTkZRIRcb1ER8eVyN9RWab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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', c=colors)\n", + "# passing colors into the chart makes the european dots, red!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n----UFO data----\\nScraped from: http://www.nuforc.org/webreports.html\\n'" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + "----UFO data----\n", + "Scraped from: http://www.nuforc.org/webreports.html\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ufo = pd.read_csv('../data/ufo.csv') " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", + "3 Abilene NaN DISK KS 6/1/1931 13:00\n", + "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head() # Look at the top 5 observations" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTime
80538NelighNaNCIRCLENE9/4/2014 23:20
80539UhrichsvilleNaNLIGHTOH9/5/2014 1:14
80540TucsonRED BLUENaNAZ9/5/2014 2:40
80541Orland parkREDLIGHTIL9/5/2014 3:43
80542LoughmanNaNLIGHTFL9/5/2014 5:30
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "80538 Neligh NaN CIRCLE NE 9/4/2014 23:20\n", + "80539 Uhrichsville NaN LIGHT OH 9/5/2014 1:14\n", + "80540 Tucson RED BLUE NaN AZ 9/5/2014 2:40\n", + "80541 Orland park RED LIGHT IL 9/5/2014 3:43\n", + "80542 Loughman NaN LIGHT FL 9/5/2014 5:30" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.tail() # Look at the bottom 5 observations" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ufo['Location'] = ufo['City'] + ', ' + ufo['State']" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTimeLocation
0IthacaNaNTRIANGLENY6/1/1930 22:00Ithaca, NY
1WillingboroNaNOTHERNJ6/30/1930 20:00Willingboro, NJ
2HolyokeNaNOVALCO2/15/1931 14:00Holyoke, CO
3AbileneNaNDISKKS6/1/1931 13:00Abilene, KS
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00New York Worlds Fair, NY
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time \\\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00 \n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00 \n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00 \n", + "3 Abilene NaN DISK KS 6/1/1931 13:00 \n", + "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00 \n", + "\n", + " Location \n", + "0 Ithaca, NY \n", + "1 Willingboro, NJ \n", + "2 Holyoke, CO \n", + "3 Abilene, KS \n", + "4 New York Worlds Fair, NY " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# rename some columns\n", + "ufo.rename(columns={'Colors Reported':'Colors', 'Shape Reported':'Shape'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColorsShapeStateTimeLocation
0IthacaNaNTRIANGLENY6/1/1930 22:00Ithaca, NY
1WillingboroNaNOTHERNJ6/30/1930 20:00Willingboro, NJ
2HolyokeNaNOVALCO2/15/1931 14:00Holyoke, CO
3AbileneNaNDISKKS6/1/1931 13:00Abilene, KS
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00New York Worlds Fair, NY
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" + ], + "text/plain": [ + " City Colors Shape State Time \\\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00 \n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00 \n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00 \n", + "3 Abilene NaN DISK KS 6/1/1931 13:00 \n", + "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00 \n", + "\n", + " Location \n", + "0 Ithaca, NY \n", + "1 Willingboro, NJ \n", + "2 Holyoke, CO \n", + "3 Abilene, KS \n", + "4 New York Worlds Fair, NY " + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "del ufo['City'] # delete a column (permanently)\n", + "del ufo['State'] # delete a column (permanently)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LIGHT 16332\n", + "TRIANGLE 7816\n", + "CIRCLE 7725\n", + "FIREBALL 6249\n", + "OTHER 5506\n", + "SPHERE 5231\n", + "DISK 5226\n", + "OVAL 3721\n", + "FORMATION 2405\n", + "CIGAR 1983\n", + "VARIOUS 1957\n", + "FLASH 1329\n", + "RECTANGLE 1295\n", + "CYLINDER 1252\n", + "DIAMOND 1152\n", + "CHEVRON 940\n", + "EGG 733\n", + "TEARDROP 723\n", + "CONE 310\n", + "CROSS 241\n", + "DELTA 7\n", + "CRESCENT 2\n", + "ROUND 2\n", + "HEXAGON 1\n", + "FLARE 1\n", + "PYRAMID 1\n", + "DOME 1\n", + "Name: Shape, dtype: int64" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.Shape.value_counts() # excludes missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LIGHT 16332\n", + "NaN 8402\n", + "TRIANGLE 7816\n", + "CIRCLE 7725\n", + "FIREBALL 6249\n", + "OTHER 5506\n", + "SPHERE 5231\n", + "DISK 5226\n", + "OVAL 3721\n", + "FORMATION 2405\n", + "CIGAR 1983\n", + "VARIOUS 1957\n", + "FLASH 1329\n", + "RECTANGLE 1295\n", + "CYLINDER 1252\n", + "DIAMOND 1152\n", + "CHEVRON 940\n", + "EGG 733\n", + "TEARDROP 723\n", + "CONE 310\n", + "CROSS 241\n", + "DELTA 7\n", + "CRESCENT 2\n", + "ROUND 2\n", + "HEXAGON 1\n", + "PYRAMID 1\n", + "DOME 1\n", + "FLARE 1\n", + "Name: Shape, dtype: int64" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.Shape.value_counts(dropna=False) # includes missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8402" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.Shape.isnull().sum() # count the missing values in the shape column" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Colors 63509\n", + "Shape 8402\n", + "Time 0\n", + "Location 47\n", + "dtype: int64" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.isnull().sum() # returns a count of missing values in all columns" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ColorsShapeTimeLocation
12REDSPHERE6/30/1939 20:00Belton, SC
19REDOTHER4/30/1943 23:00Bering Sea, AK
36REDFORMATION7/10/1945 1:30Portsmouth, VA
44GREENSPHERE6/30/1946 19:00Blairsden, CA
82BLUECHEVRON7/15/1947 21:00San Jose, CA
84BLUEDISK8/8/1947 22:00Modesto, CA
91REDSPHERE5/10/1948 19:00Scipio, IN
111ORANGECIRCLE8/15/1949 22:00Tarrant City, AL
129GREENDISK6/10/1950 0:00Napa, CA
138ORANGECIGAR7/2/1950 13:00Coeur d'Alene, ID
152BLUEDISK4/15/1951 0:30Irving, KS
157GREENDISK6/15/1951 20:30Greenville, MS
163GREENSPHERE7/3/1951 12:00Green River, WY
164BLUEDISK7/10/1951 23:30Provo, UT
174ORANGETRIANGLE4/15/1952 16:00Greenville, TX
178REDFIREBALL6/1/1952 22:00Norfolk, VA
202GREENOVAL7/13/1952 21:00Arlington, VA
226REDSPHERE4/1/1953 18:00Cambridge, MA
229YELLOWFIREBALL4/15/1953 16:00Midwest City, OK
238REDFIREBALL6/30/1953 0:00Cleveland, OH
241BLUEDISK7/4/1953 14:00NaN
249ORANGEOTHER8/15/1953 19:00Artesia, NM
256GREENDISK11/21/1953 22:30Pendleton, IN
288REDOVAL7/1/1954 21:00St. Louis Airport, MO
289REDCIRCLE7/1/1954 22:00Los Angeles, CA
304REDDISK9/9/1954 12:30Beaumont, TX
311ORANGECIRCLE12/15/1954 23:10Red Bank, NJ
314YELLOWEGG5/1/1955 15:00Holbrook, MA
323ORANGECIRCLE6/15/1955 0:00Terre Haute, IN
354ORANGECYLINDER6/1/1956 20:00Memphis, TN
...............
80429REDCIRCLE8/31/2014 20:50Bettendorf, IA
80430REDSPHERE8/31/2014 21:00Pompton Lakes, NJ
80436ORANGESPHERE8/31/2014 21:45Montrose, MI
80438ORANGECYLINDER8/31/2014 22:00Brunswick, OH
80441REDTEARDROP8/31/2014 22:30Scribner, NE
80453RED ORANGE GREEN BLUETRIANGLE9/1/2014 11:56Minersville, PA
80457ORANGECIRCLE9/1/2014 16:05Syracuse, NY
80458ORANGEFIREBALL9/1/2014 20:00Parma, OH
80459ORANGECIRCLE9/1/2014 20:00Roswell, NM
80465GREENVARIOUS9/1/2014 21:30South Londonderry, VT
80467ORANGELIGHT9/1/2014 22:00Gold Hill, NC
80469ORANGECIRCLE9/1/2014 22:19Milford, DE
80473BLUETRIANGLE9/2/2014 5:50Trevose, PA
80482ORANGEFIREBALL9/2/2014 22:30Spearfish, SD
80489ORANGESPHERE9/3/2014 5:00Pleasure Island, NC
80494RED GREENCIRCLE9/3/2014 19:00Boca Raton, FL
80495YELLOWFLASH9/3/2014 19:00Kingston, NH
80496REDFLASH9/3/2014 19:00New York City, NY
80500RED BLUELIGHT9/3/2014 21:00Johnsburg, IL
80503ORANGEVARIOUS9/3/2014 21:15Bethel Park, PA
80505YELLOWTRIANGLE9/3/2014 21:30Clarence, NY
80509RED BLUECIRCLE9/3/2014 23:30Chesapeake Beach, MD
80510REDOVAL9/4/2014 0:00Rogersville, TN
80519REDCIRCLE9/4/2014 20:20Glen Ellyn, IL
80522ORANGELIGHT9/4/2014 20:30Lawrence, MA
80524REDLIGHT9/4/2014 21:10Olympia, WA
80525BLUELIGHT9/4/2014 21:11Iowa City, IA
80528REDTRIANGLE9/4/2014 21:30North Royalton, OH
80536REDDISK9/4/2014 23:00Wyoming, PA
80541REDLIGHT9/5/2014 3:43Orland park, IL
\n", + "

15516 rows × 4 columns

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" + ], + "text/plain": [ + " Colors Shape Time \\\n", + "12 RED SPHERE 6/30/1939 20:00 \n", + "19 RED OTHER 4/30/1943 23:00 \n", + "36 RED FORMATION 7/10/1945 1:30 \n", + "44 GREEN SPHERE 6/30/1946 19:00 \n", + "82 BLUE CHEVRON 7/15/1947 21:00 \n", + "84 BLUE DISK 8/8/1947 22:00 \n", + "91 RED SPHERE 5/10/1948 19:00 \n", + "111 ORANGE CIRCLE 8/15/1949 22:00 \n", + "129 GREEN DISK 6/10/1950 0:00 \n", + "138 ORANGE CIGAR 7/2/1950 13:00 \n", + "152 BLUE DISK 4/15/1951 0:30 \n", + "157 GREEN DISK 6/15/1951 20:30 \n", + "163 GREEN SPHERE 7/3/1951 12:00 \n", + "164 BLUE DISK 7/10/1951 23:30 \n", + "174 ORANGE TRIANGLE 4/15/1952 16:00 \n", + "178 RED FIREBALL 6/1/1952 22:00 \n", + "202 GREEN OVAL 7/13/1952 21:00 \n", + "226 RED SPHERE 4/1/1953 18:00 \n", + "229 YELLOW FIREBALL 4/15/1953 16:00 \n", + "238 RED FIREBALL 6/30/1953 0:00 \n", + "241 BLUE DISK 7/4/1953 14:00 \n", + "249 ORANGE OTHER 8/15/1953 19:00 \n", + "256 GREEN DISK 11/21/1953 22:30 \n", + "288 RED OVAL 7/1/1954 21:00 \n", + "289 RED CIRCLE 7/1/1954 22:00 \n", + "304 RED DISK 9/9/1954 12:30 \n", + "311 ORANGE CIRCLE 12/15/1954 23:10 \n", + "314 YELLOW EGG 5/1/1955 15:00 \n", + "323 ORANGE CIRCLE 6/15/1955 0:00 \n", + "354 ORANGE CYLINDER 6/1/1956 20:00 \n", + "... ... ... ... \n", + "80429 RED CIRCLE 8/31/2014 20:50 \n", + "80430 RED SPHERE 8/31/2014 21:00 \n", + "80436 ORANGE SPHERE 8/31/2014 21:45 \n", + "80438 ORANGE CYLINDER 8/31/2014 22:00 \n", + "80441 RED TEARDROP 8/31/2014 22:30 \n", + "80453 RED ORANGE GREEN BLUE TRIANGLE 9/1/2014 11:56 \n", + "80457 ORANGE CIRCLE 9/1/2014 16:05 \n", + "80458 ORANGE FIREBALL 9/1/2014 20:00 \n", + "80459 ORANGE CIRCLE 9/1/2014 20:00 \n", + "80465 GREEN VARIOUS 9/1/2014 21:30 \n", + "80467 ORANGE LIGHT 9/1/2014 22:00 \n", + "80469 ORANGE CIRCLE 9/1/2014 22:19 \n", + "80473 BLUE TRIANGLE 9/2/2014 5:50 \n", + "80482 ORANGE FIREBALL 9/2/2014 22:30 \n", + "80489 ORANGE SPHERE 9/3/2014 5:00 \n", + "80494 RED GREEN CIRCLE 9/3/2014 19:00 \n", + "80495 YELLOW FLASH 9/3/2014 19:00 \n", + "80496 RED FLASH 9/3/2014 19:00 \n", + "80500 RED BLUE LIGHT 9/3/2014 21:00 \n", + "80503 ORANGE VARIOUS 9/3/2014 21:15 \n", + "80505 YELLOW TRIANGLE 9/3/2014 21:30 \n", + "80509 RED BLUE CIRCLE 9/3/2014 23:30 \n", + "80510 RED OVAL 9/4/2014 0:00 \n", + "80519 RED CIRCLE 9/4/2014 20:20 \n", + "80522 ORANGE LIGHT 9/4/2014 20:30 \n", + "80524 RED LIGHT 9/4/2014 21:10 \n", + "80525 BLUE LIGHT 9/4/2014 21:11 \n", + "80528 RED TRIANGLE 9/4/2014 21:30 \n", + "80536 RED DISK 9/4/2014 23:00 \n", + "80541 RED LIGHT 9/5/2014 3:43 \n", + "\n", + " Location \n", + "12 Belton, SC \n", + "19 Bering Sea, AK \n", + "36 Portsmouth, VA \n", + "44 Blairsden, CA \n", + "82 San Jose, CA \n", + "84 Modesto, CA \n", + "91 Scipio, IN \n", + "111 Tarrant City, AL \n", + "129 Napa, CA \n", + "138 Coeur d'Alene, ID \n", + "152 Irving, KS \n", + "157 Greenville, MS \n", + "163 Green River, WY \n", + "164 Provo, UT \n", + "174 Greenville, TX \n", + "178 Norfolk, VA \n", + "202 Arlington, VA \n", + "226 Cambridge, MA \n", + "229 Midwest City, OK \n", + "238 Cleveland, OH \n", + "241 NaN \n", + "249 Artesia, NM \n", + "256 Pendleton, IN \n", + "288 St. Louis Airport, MO \n", + "289 Los Angeles, CA \n", + "304 Beaumont, TX \n", + "311 Red Bank, NJ \n", + "314 Holbrook, MA \n", + "323 Terre Haute, IN \n", + "354 Memphis, TN \n", + "... ... \n", + "80429 Bettendorf, IA \n", + "80430 Pompton Lakes, NJ \n", + "80436 Montrose, MI \n", + "80438 Brunswick, OH \n", + "80441 Scribner, NE \n", + "80453 Minersville, PA \n", + "80457 Syracuse, NY \n", + "80458 Parma, OH \n", + "80459 Roswell, NM \n", + "80465 South Londonderry, VT \n", + "80467 Gold Hill, NC \n", + "80469 Milford, DE \n", + "80473 Trevose, PA \n", + "80482 Spearfish, SD \n", + "80489 Pleasure Island, NC \n", + "80494 Boca Raton, FL \n", + "80495 Kingston, NH \n", + "80496 New York City, NY \n", + "80500 Johnsburg, IL \n", + "80503 Bethel Park, PA \n", + "80505 Clarence, NY \n", + "80509 Chesapeake Beach, MD \n", + "80510 Rogersville, TN \n", + "80519 Glen Ellyn, IL \n", + "80522 Lawrence, MA \n", + "80524 Olympia, WA \n", + "80525 Iowa City, IA \n", + "80528 North Royalton, OH \n", + "80536 Wyoming, PA \n", + "80541 Orland park, IL \n", + "\n", + "[15516 rows x 4 columns]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shows how many rows has a not null shape AND a not null color\n", + "ufo[(ufo.Shape.notnull()) & (ufo.Colors.notnull())]" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15510, 4)\n", + "(80543, 4)\n" + ] + } + ], + "source": [ + "ufo.dropna() # drop a row if ANY values are missing\n", + "print ufo.dropna().shape\n", + "ufo.dropna(how='all') # drop a row only if ALL values are missing\n", + "print ufo.dropna(how='all').shape" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ColorsShapeTimeLocation
0NaNTRIANGLE6/1/1930 22:00Ithaca, NY
1NaNOTHER6/30/1930 20:00Willingboro, NJ
2NaNOVAL2/15/1931 14:00Holyoke, CO
3NaNDISK6/1/1931 13:00Abilene, KS
4NaNLIGHT4/18/1933 19:00New York Worlds Fair, NY
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" + ], + "text/plain": [ + " Colors Shape Time Location\n", + "0 NaN TRIANGLE 6/1/1930 22:00 Ithaca, NY\n", + "1 NaN OTHER 6/30/1930 20:00 Willingboro, NJ\n", + "2 NaN OVAL 2/15/1931 14:00 Holyoke, CO\n", + "3 NaN DISK 6/1/1931 13:00 Abilene, KS\n", + "4 NaN LIGHT 4/18/1933 19:00 New York Worlds Fair, NY" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head() # Without an inplace=True, the dataframe is unaffected!" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ufo.Colors.fillna(value='Unknown', inplace=True) # Permanent" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ColorsShapeTimeLocation
0UnknownTRIANGLE6/1/1930 22:00Ithaca, NY
1UnknownOTHER6/30/1930 20:00Willingboro, NJ
2UnknownOVAL2/15/1931 14:00Holyoke, CO
3UnknownDISK6/1/1931 13:00Abilene, KS
4UnknownLIGHT4/18/1933 19:00New York Worlds Fair, NY
5UnknownDISK9/15/1934 15:30Valley City, ND
6UnknownCIRCLE6/15/1935 0:00Crater Lake, CA
7UnknownDISK7/15/1936 0:00Alma, MI
8UnknownCIGAR10/15/1936 17:00Eklutna, AK
9UnknownCYLINDER6/15/1937 0:00Hubbard, OR
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" + ], + "text/plain": [ + " Colors Shape Time Location\n", + "0 Unknown TRIANGLE 6/1/1930 22:00 Ithaca, NY\n", + "1 Unknown OTHER 6/30/1930 20:00 Willingboro, NJ\n", + "2 Unknown OVAL 2/15/1931 14:00 Holyoke, CO\n", + "3 Unknown DISK 6/1/1931 13:00 Abilene, KS\n", + "4 Unknown LIGHT 4/18/1933 19:00 New York Worlds Fair, NY\n", + "5 Unknown DISK 9/15/1934 15:30 Valley City, ND\n", + "6 Unknown CIRCLE 6/15/1935 0:00 Crater Lake, CA\n", + "7 Unknown DISK 7/15/1936 0:00 Alma, MI\n", + "8 Unknown CIGAR 10/15/1936 17:00 Eklutna, AK\n", + "9 Unknown CYLINDER 6/15/1937 0:00 Hubbard, OR" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ufo.fillna(value = 'Unknown', inplace = True) # Permanent for the entire dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTimeMonthDayYear
0IthacaNaNTRIANGLENY6/1/1930 22:00611930
1WillingboroNaNOTHERNJ6/30/1930 20:006301930
2HolyokeNaNOVALCO2/15/1931 14:002151931
3AbileneNaNDISKKS6/1/1931 13:00611931
4New York Worlds FairNaNLIGHTNY4/18/1933 19:004181933
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time \\\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00 \n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00 \n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00 \n", + "3 Abilene NaN DISK KS 6/1/1931 13:00 \n", + "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00 \n", + "\n", + " Month Day Year \n", + "0 6 1 1930 \n", + "1 6 30 1930 \n", + "2 2 15 1931 \n", + "3 6 1 1931 \n", + "4 4 18 1933 " + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "''' Fun Stuff '''\n", + "\n", + "ufo = pd.read_csv('../data/ufo.csv')\n", + "\n", + "# Make a new month column\n", + "ufo['Month'] = ufo['Time'].apply(lambda x:int(x.split('/')[0]))\n", + "\n", + "'''\n", + "the apply function applys the lambda funciton to every element in the Series\n", + "lambda x:x.split('/')[0] will take in x and split it by '/' and return the first element\n", + "so if we pass in say 9/3/2014 01:22 into the function we would get:\n", + "9 i.e. the month\n", + "'''\n", + "\n", + "# similar for day\n", + "ufo['Day'] = ufo['Time'].apply(lambda x:int(x.split('/')[1]))\n", + "\n", + "# for year, I need the [:4] at the end to remove the time\n", + "ufo['Year'] = ufo['Time'].apply(lambda x:int(x.split('/')[2][:4]))\n", + "ufo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1KAd6s0zSN0lXh+wNbIqI/yPpImAS0IN0tuz/ynV/BJxEupT27RHx\nb7l8DfAz0uWYr46IX7b/KzHbXYdd1MysAn2D1JN/Czgl9/I/Cbw/Iuol/UjShIiYAfxTRGzI12X6\nvaRfRMRTeT91EXFyx7wEsz050JtlEbFF0u3A6xGxTdJZpKtDLsgXkupFuvYIwIWSJpL+h95GuulG\nIdDf3s5NN2uSA73Z7urzA9LVA38aEVOKK0g6lnRhr1Mi4nVJN5M+BAreaJeWmpXJs27MGjcPGC/p\nMNh54+YhwMGkKwtulvQ28rXZzSqVe/RmjYiIP0u6CpiXr72+lXRry0clLSVd5nY16cYfOzfrgKaa\nNcmzbszMqpxTN2ZmVc6B3sysyjnQm5lVOQd6M7Mq50BvZlblHOjNzKqcA72ZWZVzoDczq3L/H+Og\nUSnRC3mjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sightings_per_year = ufo.groupby('Year').City.count()\n", + "\n", + "sightings_per_year.plot(kind='line', \n", + " color='r', \n", + " linewidth=1, \n", + " title='UFO Sightings by year')" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -----Analysis-----\n", + "# Clearly, Aliens love the X-Files (which came out in 1993).\n", + "# Aliens are a natural extension of the target demographic so it makes sense.\n", + "\n", + "# Well hold on Sinan, the US population is always increasing\n", + "# So maybe there's a jump in population which would make sense!\n", + "# US Population data from 1930 as taken from the Census\n", + "\n", + "us_population = pd.read_csv('../data/us_population.csv')\n", + "us_population.plot(x = 'Date', y = 'Population', legend = False)\n", + "# Seems like a steady increase to me..\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the sightings in in July \n", + "ufo[(ufo.Year==2014) & (ufo.Month == 7)].groupby('Day').City.count().plot( kind='bar',\n", + " color='b', \n", + " title='UFO Sightings in July 2014')\n", + " \n", + "\n", + "# -----Analysis-----\n", + "# Aliens are love the 4th of July. The White House is still standing. Therefore\n", + "# it follows that Aliens are just here for the party.\n", + "\n", + "# Well maybe it's just 2014?" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ,\n", + " ], dtype=object)" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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HlbgOWD8QrBIrgMXJ8/OA28psw2qglENW9Xoep1Gu2avXv+9IaJT3sBTNvO9DlTNL8K3A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\niZ9NyszMzEpW8LR2SRPI3HX40ojYLml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"outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "'''\n", + "Further Exploration\n", + "'''\n", + "\n", + "'''\n", + "Joining Data\n", + "\n", + "MovieLens 100k data:\n", + " main page: http://grouplens.org/datasets/movielens/\n", + " data dictionary: http://files.grouplens.org/datasets/movielens/ml-100k-README.txt\n", + " files: u.user, u.data, u.item\n", + "'''\n", + "\n", + "# read 'u.data' into 'ratings'\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n", + "ratings = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.data', header=None, names=r_cols, sep='\\t')\n", + "\n", + "# read 'u.item' into 'movies'\n", + "m_cols = ['movie_id', 'title']\n", + "movies = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.item', header=None, names=m_cols, sep='|', usecols=[0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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11Toy Story (1995)2875875334088
21Toy Story (1995)1484877019411
31Toy Story (1995)2804891700426
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" + ], + "text/plain": [ + " movie_id title user_id rating unix_timestamp\n", + "0 1 Toy Story (1995) 308 4 887736532\n", + "1 1 Toy Story (1995) 287 5 875334088\n", + "2 1 Toy Story (1995) 148 4 877019411\n", + "3 1 Toy Story (1995) 280 4 891700426\n", + "4 1 Toy Story (1995) 66 3 883601324" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# merge 'movies' and 'ratings' (inner join on 'movie_id')\n", + "movie_ratings = pd.merge(movies, ratings)\n", + "movie_ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Star Wars (1977) 583\n", + "Contact (1997) 509\n", + "Fargo (1996) 508\n", + "Return of the Jedi (1983) 507\n", + "Liar Liar (1997) 485\n", + "English Patient, The (1996) 481\n", + "Scream (1996) 478\n", + "Toy Story (1995) 452\n", + "Air Force One (1997) 431\n", + "Independence Day (ID4) (1996) 429\n", + "Raiders of the Lost Ark (1981) 420\n", + "Godfather, The (1972) 413\n", + "Pulp Fiction (1994) 394\n", + "Twelve Monkeys (1995) 392\n", + "Silence of the Lambs, The (1991) 390\n", + "Jerry Maguire (1996) 384\n", + "Chasing Amy (1997) 379\n", + "Rock, The (1996) 378\n", + "Empire Strikes Back, The (1980) 367\n", + "Star Trek: First Contact (1996) 365\n", + "Back to the Future (1985) 350\n", + "Titanic (1997) 350\n", + "Mission: Impossible (1996) 344\n", + "Fugitive, The (1993) 336\n", + "Indiana Jones and the Last Crusade (1989) 331\n", + "Willy Wonka and the Chocolate Factory (1971) 326\n", + "Princess Bride, The (1987) 324\n", + "Forrest Gump (1994) 321\n", + "Monty Python and the Holy Grail (1974) 316\n", + "Saint, The (1997) 316\n", + " ... \n", + "Brother's Kiss, A (1997) 1\n", + "Coldblooded (1995) 1\n", + "Silence of the Palace, The (Saimt el Qusur) (1994) 1\n", + "Century (1993) 1\n", + "Homage (1995) 1\n", + "Hana-bi (1997) 1\n", + "Symphonie pastorale, La (1946) 1\n", + "They Made Me a Criminal (1939) 1\n", + "Brothers in Trouble (1995) 1\n", + "Girls Town (1996) 1\n", + "Touki Bouki (Journey of the Hyena) (1973) 1\n", + "Marlene Dietrich: Shadow and Light (1996) 1\n", + "Sleepover (1995) 1\n", + "Sunchaser, The (1996) 1\n", + "Nothing Personal (1995) 1\n", + "Niagara, Niagara (1997) 1\n", + "Further Gesture, A (1996) 1\n", + "Great Day in Harlem, A (1994) 1\n", + "Eye of Vichy, The (Oeil de Vichy, L') (1993) 1\n", + "Daens (1992) 1\n", + "Woman in Question, The (1950) 1\n", + "Someone Else's America (1995) 1\n", + "Man from Down Under, The (1943) 1\n", + "Jupiter's Wife (1994) 1\n", + "Spanish Prisoner, The (1997) 1\n", + "Next Step, The (1995) 1\n", + "I, Worst of All (Yo, la peor de todas) (1990) 1\n", + "Two Friends (1986) 1\n", + "Gate of Heavenly Peace, The (1995) 1\n", + "Crude Oasis, The (1995) 1\n", + "Name: title, dtype: int64" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each movie, count number of ratings\n", + "movie_ratings.title.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: order is deprecated, use sort_values(...)\n", + " if __name__ == '__main__':\n" + ] + }, + { + "data": { + "text/plain": [ + "title\n", + "Marlene Dietrich: Shadow and Light (1996) 5.000000\n", + "Prefontaine (1997) 5.000000\n", + "Santa with Muscles (1996) 5.000000\n", + "Star Kid (1997) 5.000000\n", + "Someone Else's America (1995) 5.000000\n", + "Entertaining Angels: The Dorothy Day Story (1996) 5.000000\n", + "Saint of Fort Washington, The (1993) 5.000000\n", + "Great Day in Harlem, A (1994) 5.000000\n", + "They Made Me a Criminal (1939) 5.000000\n", + "Aiqing wansui (1994) 5.000000\n", + "Pather Panchali (1955) 4.625000\n", + "Anna (1996) 4.500000\n", + "Everest (1998) 4.500000\n", + "Maya Lin: A Strong Clear Vision (1994) 4.500000\n", + "Some Mother's Son (1996) 4.500000\n", + "Close Shave, A (1995) 4.491071\n", + "Schindler's List (1993) 4.466443\n", + "Wrong Trousers, The (1993) 4.466102\n", + "Casablanca (1942) 4.456790\n", + "Wallace & Gromit: The Best of Aardman Animation (1996) 4.447761\n", + "Shawshank Redemption, The (1994) 4.445230\n", + "Rear Window (1954) 4.387560\n", + "Usual Suspects, The (1995) 4.385768\n", + "Star Wars (1977) 4.358491\n", + "12 Angry Men (1957) 4.344000\n", + "Third Man, The (1949) 4.333333\n", + "Letter From Death Row, A (1998) 4.333333\n", + "Bitter Sugar (Azucar Amargo) (1996) 4.333333\n", + "Citizen Kane (1941) 4.292929\n", + "Some Folks Call It a Sling Blade (1993) 4.292683\n", + " ... \n", + "Symphonie pastorale, La (1946) 1.000000\n", + "Baton Rouge (1988) 1.000000\n", + "Boys in Venice (1996) 1.000000\n", + "Tigrero: A Film That Was Never Made (1994) 1.000000\n", + "Office Killer (1997) 1.000000\n", + "T-Men (1947) 1.000000\n", + "Terror in a Texas Town (1958) 1.000000\n", + "Nobody Loves Me (Keiner liebt mich) (1994) 1.000000\n", + "The Courtyard (1995) 1.000000\n", + "King of New York (1990) 1.000000\n", + "Liebelei (1933) 1.000000\n", + "Stefano Quantestorie (1993) 1.000000\n", + "Hostile Intentions (1994) 1.000000\n", + "Pharaoh's Army (1995) 1.000000\n", + "New Age, The (1994) 1.000000\n", + "Hungarian Fairy Tale, A (1987) 1.000000\n", + "Death in the Garden (Mort en ce jardin, La) (1956) 1.000000\n", + "Promise, The (Versprechen, Das) (1994) 1.000000\n", + "I, Worst of All (Yo, la peor de todas) (1990) 1.000000\n", + "Power 98 (1995) 1.000000\n", + "Police Story 4: Project S (Chao ji ji hua) (1993) 1.000000\n", + "Low Life, The (1994) 1.000000\n", + "Somebody to Love (1994) 1.000000\n", + "Invitation, The (Zaproszenie) (1986) 1.000000\n", + "Lotto Land (1995) 1.000000\n", + "Touki Bouki (Journey of the Hyena) (1973) 1.000000\n", + "JLG/JLG - autoportrait de d�cembre (1994) 1.000000\n", + "Daens (1992) 1.000000\n", + "Butterfly Kiss (1995) 1.000000\n", + "Eye of Vichy, The (Oeil de Vichy, L') (1993) 1.000000\n", + "Name: rating, dtype: float64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movie_ratings.groupby('title').rating.mean().order(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + 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business_iddatereview_idstarstexttypeuser_idcoolusefulfunny
09yKzy9PApeiPPOUJEtnvkg2011-01-26fWKvX83p0-ka4JS3dc6E5A5My wife took me here on my birthday for breakf...reviewrLtl8ZkDX5vH5nAx9C3q5Q250
1ZRJwVLyzEJq1VAihDhYiow2011-07-27IjZ33sJrzXqU-0X6U8NwyA5I have no idea why some people give bad review...review0a2KyEL0d3Yb1V6aivbIuQ000
26oRAC4uyJCsJl1X0WZpVSA2012-06-14IESLBzqUCLdSzSqm0eCSxQ4love the gyro plate. Rice is so good and I als...review0hT2KtfLiobPvh6cDC8JQg010
3_1QQZuf4zZOyFCvXc0o6Vg2010-05-27G-WvGaISbqqaMHlNnByodA5Rosie, Dakota, and I LOVE Chaparral Dog Park!!...reviewuZetl9T0NcROGOyFfughhg120
46ozycU1RpktNG2-1BroVtw2012-01-051uJFq2r5QfJG_6ExMRCaGw5General Manager Scott Petello is a good egg!!!...reviewvYmM4KTsC8ZfQBg-j5MWkw000
\n", + "
" + ], + "text/plain": [ + " business_id date review_id stars \\\n", + "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", + "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", + "2 6oRAC4uyJCsJl1X0WZpVSA 2012-06-14 IESLBzqUCLdSzSqm0eCSxQ 4 \n", + "3 _1QQZuf4zZOyFCvXc0o6Vg 2010-05-27 G-WvGaISbqqaMHlNnByodA 5 \n", + "4 6ozycU1RpktNG2-1BroVtw 2012-01-05 1uJFq2r5QfJG_6ExMRCaGw 5 \n", + "\n", + " text type \\\n", + "0 My wife took me here on my birthday for breakf... review \n", + "1 I have no idea why some people give bad review... review \n", + "2 love the gyro plate. Rice is so good and I als... review \n", + "3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!... review \n", + "4 General Manager Scott Petello is a good egg!!!... review \n", + "\n", + " user_id cool useful funny \n", + "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 \n", + "1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 \n", + "2 0hT2KtfLiobPvh6cDC8JQg 0 1 0 \n", + "3 uZetl9T0NcROGOyFfughhg 1 2 0 \n", + "4 vYmM4KTsC8ZfQBg-j5MWkw 0 0 0 " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.cross_validation import cross_val_score\n", + "from sklearn import metrics\n", + "import statsmodels.formula.api as smf\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# visualization\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "data = pd.read_csv('http://localhost:8888/files/data/yelp.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DOoxpmppPZ+UPRmUYnbUL1+qWfvjWnH789rwalt00Pj4U09kzk7p9grLDdnEc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5+f5u1TE6Rzjcntz2+k+PxGip39QY4Nb2NXPLTlv0xxulw4l0wNTU3M4XYalCpB7JqBw9H+lVO3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\np7P8wfdG6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Zlkgf5O36ap8tnqC/K2Ap2jlekcrU6rnicVJVhZq1T5nE0V6QrfrvKZypYJ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\n9ecHG/qSO/uHx+NhvvfhtcW+5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7vKR0MqrwpXmFTFOGEVAmW1Sx7GhqLquhZEwHh2LqMa4cmxsz7WZ9s5qtR1Jb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\niagiYVO5fElL2WX1RntkGAGVy44uz+W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yf4x2j2+pCl1nJfufK7vHJ9k/RuIrHSv+Nsqw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S7g103TfCab2xQRERERERHJhmwm4EPA7xmG8TzQBBwwDGPY6o2maS54HnDDMD4K3El68/YngB8D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\nbJx7vOHSyOZ7drZw/PzQnGtkz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C4NTaZciAF8/SmTbZv8rK92Rkc4VyKRKJFI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starscoolusefulfunny
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std1.2146362.0678612.3366471.907942
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" + ], + "text/plain": [ + " stars cool useful funny\n", + "count 10000.000000 10000.000000 10000.000000 10000.000000\n", + "mean 3.777500 0.876800 1.409300 0.701300\n", + "std 1.214636 2.067861 2.336647 1.907942\n", + "min 1.000000 0.000000 0.000000 0.000000\n", + "25% 3.000000 0.000000 0.000000 0.000000\n", + "50% 4.000000 0.000000 1.000000 0.000000\n", + "75% 5.000000 1.000000 2.000000 1.000000\n", + "max 5.000000 77.000000 76.000000 57.000000" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 10)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 3.794700\n", + "useful -0.012205\n", + "dtype: float64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### STATSMODELS ###\n", + "\n", + "# create a fitted model\n", + "lm = smf.ols(formula='stars ~ useful', data=data).fit()\n", + "\n", + "# print the coefficients\n", + "lm.params" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 3.804871\n", + "funny -0.039029\n", + "dtype: float64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### STATSMODELS ###\n", + "\n", + "# create a fitted model\n", + "lm = smf.ols(formula='stars ~ funny', data=data).fit()\n", + "\n", + "# print the coefficients\n", + "lm.params" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 5, 0],\n", + " [0, 0, 0],\n", + " [0, 1, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]], dtype=int64)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "features = np.array([data.cool, data.useful, data.funny]).T\n", + "features # 2D array" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5],\n", + " [5],\n", + " [4],\n", + " ..., \n", + " [4],\n", + " [2],\n", + " [5]], dtype=int64)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = np.array([data.stars]).T \n", + "response # 1D array\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# step 1: split data into training set and test set\n", + "features_train, features_test, response_train, response_test = train_test_split(features, response, random_state=4)\n", + "# the random_state allows us all to get the same random numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 6, 4],\n", + " [1, 2, 1],\n", + " [0, 1, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [2, 3, 2],\n", + " [0, 2, 0]], dtype=int64)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_train" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 3, 1],\n", + " [0, 1, 0],\n", + " [0, 0, 0],\n", + " ..., \n", + " [0, 0, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]], dtype=int64)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_test" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3],\n", + " [5],\n", + " [5],\n", + " ..., \n", + " [4],\n", + " [2],\n", + " [1]], dtype=int64)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response_train" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5],\n", + " [5],\n", + " [5],\n", + " ..., \n", + " [3],\n", + " [4],\n", + " [3]], dtype=int64)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\ipykernel\\__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/plain": [ + "0.35320000000000001" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(features_train, response_train) # Note that I fit to the training\n", + "knn.score(features_test, response_test) # and scored on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = data[['cool', 'useful', 'funny']]\n", + "y = data['stars']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import cross_val_score\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.28657014, 0.24787606, 0.22011006, 0.17558779, 0.24274274])" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/02_pandas.ipynb b/notebooks/02_pandas.ipynb index 722e3b2..b826e3f 100644 --- a/notebooks/02_pandas.ipynb +++ b/notebooks/02_pandas.ipynb @@ -7191,9 +7191,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/05_bias_variance_tradeoff.ipynb b/notebooks/05_bias_variance_tradeoff.ipynb index 82fc5ab..f41c77b 100644 --- a/notebooks/05_bias_variance_tradeoff.ipynb +++ b/notebooks/05_bias_variance_tradeoff.ipynb @@ -589,9 +589,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/05_linear_regression.ipynb b/notebooks/05_linear_regression.ipynb index 2a46cce..6902367 100644 --- a/notebooks/05_linear_regression.ipynb +++ b/notebooks/05_linear_regression.ipynb @@ -1627,9 +1627,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/05_model_evaluation.ipynb b/notebooks/05_model_evaluation.ipynb index e2cb051..2f2ac4d 100644 --- a/notebooks/05_model_evaluation.ipynb +++ b/notebooks/05_model_evaluation.ipynb @@ -96,7 +96,8 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, "outputs": [ { @@ -126,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -137,7 +138,7 @@ "['even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd']" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -698,9 +699,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { From 82df89d71a60cc4fab1abbb700c1a03403cc3d3e Mon Sep 17 00:00:00 2001 From: Eugene Berson Date: Tue, 1 Nov 2016 20:16:41 -0700 Subject: [PATCH 03/12] update --- hw/.ipynb_checkpoints/hw1-checkpoint.ipynb | 729 +++- hw/hw1.ipynb | 729 +++- .../06_titanic_lab_solutions-checkpoint.ipynb | 453 +++ .../Titanic_Eugene-checkpoint.ipynb | 159 +- labs/06_titanic_lab_solutions.ipynb | 9 +- labs/Titanic_Eugene.ipynb | 159 +- .../06_logistic_regression-checkpoint.ipynb | 3581 +++++++++++++++++ .../07_nlp-checkpoint.ipynb | 2836 +++++++++++++ .../Untitled-checkpoint.ipynb | 6 + notebooks/06_logistic_regression.ipynb | 64 +- notebooks/07_nlp.ipynb | 209 +- notebooks/Untitled.ipynb | 64 + 12 files changed, 8806 insertions(+), 192 deletions(-) create mode 100644 labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 notebooks/Untitled.ipynb diff --git a/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb b/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb index 7d018b1..90ba1de 100644 --- a/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb +++ b/hw/.ipynb_checkpoints/hw1-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -27,24 +27,161 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " business_id name address city postal_code \\\n", + "0 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "1 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "2 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "3 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "4 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "\n", + " latitude longitude phone_number TaxCode business_certificate \\\n", + "0 37.791116 -122.403816 NaN H24 779059.0 \n", + "1 37.791116 -122.403816 NaN H24 779059.0 \n", + "2 37.791116 -122.403816 NaN H24 779059.0 \n", + "3 37.791116 -122.403816 NaN H24 779059.0 \n", + "4 37.791116 -122.403816 NaN H24 779059.0 \n", + "\n", + " ... owner_city \\\n", + "0 ... San Francisco \n", + "1 ... San Francisco \n", + "2 ... San Francisco \n", + "3 ... San Francisco \n", + "4 ... San Francisco \n", + "\n", + " owner_state owner_zip Score date_x type date_y \\\n", + "0 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "1 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "2 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "3 CA 94104 NaN 20140807 Reinspection/Followup 20140729 \n", + "4 CA 94104 NaN 20140807 Reinspection/Followup 20140729 \n", + "\n", + " ViolationTypeID risk_category \\\n", + "0 103154 Low Risk \n", + "1 103119 Moderate Risk \n", + "2 103145 Low Risk \n", + "3 103129 Moderate Risk \n", + "4 103144 Low Risk \n", + "\n", + " description \n", + "0 Unclean or degraded floors walls or ceilings \n", + "1 Inadequate and inaccessible handwashing facili... \n", + "2 Improper storage of equipment utensils or linens \n", + "3 Insufficient hot water or running water \n", + "4 Unapproved or unmaintained equipment or utensils \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 1 Combine the three dataframes into one data frame called restaurant_scores\n", - "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html" + "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html\n", + "\n", + "restaurant_scores_draft = pd.merge(businesses, inspections, on='business_id', how='inner')\n", + "restaurant_scores = pd.merge(restaurant_scores_draft, violations, on='business_id', how='inner')\n", + "restaurant_scores.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(restaurant_scores.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20140603 985\n", + "20140514 982\n", + "20140206 965\n", + "20140204 953\n", + "20140410 922\n", + "20140227 816\n", + "20140416 806\n", + "20130416 795\n", + "20140611 791\n", + "20150112 784\n", + "20140402 778\n", + "20131030 758\n", + "20160329 751\n", + "20140520 733\n", + "20141021 727\n", + "20130424 722\n", + "20140602 718\n", + "20130916 701\n", + "20140408 698\n", + "20140327 695\n", + "20141113 690\n", + "20140610 685\n", + "20160222 684\n", + "20140507 681\n", + "20140917 677\n", + "20140617 674\n", + "20140527 672\n", + "20140806 670\n", + "20140604 670\n", + "20140909 664\n", + " ... \n", + "20160218 26\n", + "20160115 25\n", + "20151128 25\n", + "20130705 24\n", + "20131224 24\n", + "20140607 24\n", + "20130518 22\n", + "20150415 21\n", + "20131013 20\n", + "20150725 18\n", + "20140628 15\n", + "20140517 15\n", + "20150724 15\n", + "20140301 15\n", + "20140426 14\n", + "20141218 12\n", + "20150702 11\n", + "20140614 11\n", + "20150920 10\n", + "20130907 10\n", + "20131110 9\n", + "20160116 8\n", + "20140322 7\n", + "20131214 7\n", + "20151205 6\n", + "20151010 6\n", + "20160305 4\n", + "20150502 4\n", + "20160227 4\n", + "20131116 3\n", + "Name: date_y, dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "restaurant_scores.groupby(['business_id'])\n", + "restaurant_scores.date_y.value_counts()" ] }, { @@ -81,19 +617,154 @@ "collapsed": false }, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1Reinspection/Followup67793
2Complaint18157
3New Ownership8646
4Non-inspection site visit7683
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" + ], + "text/plain": [ + " insp_type count\n", + "0 Routine - Unscheduled 128015\n", + "1 Reinspection/Followup 67793\n", + "2 Complaint 18157\n", + "3 New Ownership 8646\n", + "4 Non-inspection site visit 7683" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 3 Group and count the inspections by type" + "# 3 Group and count the inspections by type\n", + "newframe1 = pd.DataFrame(restaurant_scores.type.value_counts(ascending=False)).reset_index()\n", + "newframe1.rename(columns={'index': 'insp_type','type':'count'}, inplace=True)\n", + "newframe1.head()" ] }, { @@ -103,10 +774,34 @@ "collapsed": false }, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 4 Create a plot that shows number of inspections per month\n", "# Bonus for creating a heatmap\n", - "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap" + "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "restaurant_scores.plot(x='date_y', y='business_id', kind='scatter', alpha=0.3)" ] }, { @@ -535,9 +1230,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/hw/hw1.ipynb b/hw/hw1.ipynb index 7d018b1..90ba1de 100644 --- a/hw/hw1.ipynb +++ b/hw/hw1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -27,24 +27,161 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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business_idnameaddresscitypostal_codelatitudelongitudephone_numberTaxCodebusiness_certificateapplication_dateowner_nameowner_addressowner_cityowner_stateowner_zip
010Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0NaNTiramisu LLC33 Belden StSan FranciscoCA94104
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" + ], + "text/plain": [ + " business_id name address city postal_code \\\n", + "0 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "\n", + " latitude longitude phone_number TaxCode business_certificate \\\n", + "0 37.791116 -122.403816 NaN H24 779059.0 \n", + "\n", + " application_date owner_name owner_address owner_city owner_state \\\n", + "0 NaN Tiramisu LLC 33 Belden St San Francisco CA \n", + "\n", + " owner_zip \n", + "0 94104 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "businesses = pd.read_csv('./data/businesses_plus.csv', parse_dates=True, dtype={'phone_number': str})\n", - "businesses.head()\n", + "businesses.head(1)\n", "# dtype casts the column as a specific data type" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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210NaN20140124Reinspection/Followup
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business_iddateViolationTypeIDrisk_categorydescription
01020140114103154Low RiskUnclean or degraded floors walls or ceilings
11020140114103119Moderate RiskInadequate and inaccessible handwashing facili...
21020140114103145Low RiskImproper storage of equipment utensils or linens
31020140729103129Moderate RiskInsufficient hot water or running water
41020140729103144Low RiskUnapproved or unmaintained equipment or utensils
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" + ], + "text/plain": [ + " business_id date ViolationTypeID risk_category \\\n", + "0 10 20140114 103154 Low Risk \n", + "1 10 20140114 103119 Moderate Risk \n", + "2 10 20140114 103145 Low Risk \n", + "3 10 20140729 103129 Moderate Risk \n", + "4 10 20140729 103144 Low Risk \n", + "\n", + " description \n", + "0 Unclean or degraded floors walls or ceilings \n", + "1 Inadequate and inaccessible handwashing facili... \n", + "2 Improper storage of equipment utensils or linens \n", + "3 Insufficient hot water or running water \n", + "4 Unapproved or unmaintained equipment or utensils " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "violations = pd.read_csv('./data/violations_plus.csv', parse_dates=True)\n", "violations.head()" @@ -64,14 +282,332 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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business_idnameaddresscitypostal_codelatitudelongitudephone_numberTaxCodebusiness_certificate...owner_cityowner_stateowner_zipScoredate_xtypedate_yViolationTypeIDrisk_categorydescription
010Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0...San FranciscoCA94104NaN20140807Reinspection/Followup20140114103154Low RiskUnclean or degraded floors walls or ceilings
110Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0...San FranciscoCA94104NaN20140807Reinspection/Followup20140114103119Moderate RiskInadequate and inaccessible handwashing facili...
210Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0...San FranciscoCA94104NaN20140807Reinspection/Followup20140114103145Low RiskImproper storage of equipment utensils or linens
310Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0...San FranciscoCA94104NaN20140807Reinspection/Followup20140729103129Moderate RiskInsufficient hot water or running water
410Tiramisu Kitchen033 Belden PlSan Francisco9410437.791116-122.403816NaNH24779059.0...San FranciscoCA94104NaN20140807Reinspection/Followup20140729103144Low RiskUnapproved or unmaintained equipment or utensils
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" + ], + "text/plain": [ + " business_id name address city postal_code \\\n", + "0 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "1 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "2 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "3 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "4 10 Tiramisu Kitchen 033 Belden Pl San Francisco 94104 \n", + "\n", + " latitude longitude phone_number TaxCode business_certificate \\\n", + "0 37.791116 -122.403816 NaN H24 779059.0 \n", + "1 37.791116 -122.403816 NaN H24 779059.0 \n", + "2 37.791116 -122.403816 NaN H24 779059.0 \n", + "3 37.791116 -122.403816 NaN H24 779059.0 \n", + "4 37.791116 -122.403816 NaN H24 779059.0 \n", + "\n", + " ... owner_city \\\n", + "0 ... San Francisco \n", + "1 ... San Francisco \n", + "2 ... San Francisco \n", + "3 ... San Francisco \n", + "4 ... San Francisco \n", + "\n", + " owner_state owner_zip Score date_x type date_y \\\n", + "0 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "1 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "2 CA 94104 NaN 20140807 Reinspection/Followup 20140114 \n", + "3 CA 94104 NaN 20140807 Reinspection/Followup 20140729 \n", + "4 CA 94104 NaN 20140807 Reinspection/Followup 20140729 \n", + "\n", + " ViolationTypeID risk_category \\\n", + "0 103154 Low Risk \n", + "1 103119 Moderate Risk \n", + "2 103145 Low Risk \n", + "3 103129 Moderate Risk \n", + "4 103144 Low Risk \n", + "\n", + " description \n", + "0 Unclean or degraded floors walls or ceilings \n", + "1 Inadequate and inaccessible handwashing facili... \n", + "2 Improper storage of equipment utensils or linens \n", + "3 Insufficient hot water or running water \n", + "4 Unapproved or unmaintained equipment or utensils \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 1 Combine the three dataframes into one data frame called restaurant_scores\n", - "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html" + "# Hint: http://pandas.pydata.org/pandas-docs/stable/merging.html\n", + "\n", + "restaurant_scores_draft = pd.merge(businesses, inspections, on='business_id', how='inner')\n", + "restaurant_scores = pd.merge(restaurant_scores_draft, violations, on='business_id', how='inner')\n", + "restaurant_scores.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(restaurant_scores.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20140603 985\n", + "20140514 982\n", + "20140206 965\n", + "20140204 953\n", + "20140410 922\n", + "20140227 816\n", + "20140416 806\n", + "20130416 795\n", + "20140611 791\n", + "20150112 784\n", + "20140402 778\n", + "20131030 758\n", + "20160329 751\n", + "20140520 733\n", + "20141021 727\n", + "20130424 722\n", + "20140602 718\n", + "20130916 701\n", + "20140408 698\n", + "20140327 695\n", + "20141113 690\n", + "20140610 685\n", + "20160222 684\n", + "20140507 681\n", + "20140917 677\n", + "20140617 674\n", + "20140527 672\n", + "20140806 670\n", + "20140604 670\n", + "20140909 664\n", + " ... \n", + "20160218 26\n", + "20160115 25\n", + "20151128 25\n", + "20130705 24\n", + "20131224 24\n", + "20140607 24\n", + "20130518 22\n", + "20150415 21\n", + "20131013 20\n", + "20150725 18\n", + "20140628 15\n", + "20140517 15\n", + "20150724 15\n", + "20140301 15\n", + "20140426 14\n", + "20141218 12\n", + "20150702 11\n", + "20140614 11\n", + "20150920 10\n", + "20130907 10\n", + "20131110 9\n", + "20160116 8\n", + "20140322 7\n", + "20131214 7\n", + "20151205 6\n", + "20151010 6\n", + "20160305 4\n", + "20150502 4\n", + "20160227 4\n", + "20131116 3\n", + "Name: date_y, dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "restaurant_scores.groupby(['business_id'])\n", + "restaurant_scores.date_y.value_counts()" ] }, { @@ -81,19 +617,154 @@ "collapsed": false }, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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insp_typecount
0Routine - Unscheduled128015
1Reinspection/Followup67793
2Complaint18157
3New Ownership8646
4Non-inspection site visit7683
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" + ], + "text/plain": [ + " insp_type count\n", + "0 Routine - Unscheduled 128015\n", + "1 Reinspection/Followup 67793\n", + "2 Complaint 18157\n", + "3 New Ownership 8646\n", + "4 Non-inspection site visit 7683" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 3 Group and count the inspections by type" + "# 3 Group and count the inspections by type\n", + "newframe1 = pd.DataFrame(restaurant_scores.type.value_counts(ascending=False)).reset_index()\n", + "newframe1.rename(columns={'index': 'insp_type','type':'count'}, inplace=True)\n", + "newframe1.head()" ] }, { @@ -103,10 +774,34 @@ "collapsed": false }, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 4 Create a plot that shows number of inspections per month\n", "# Bonus for creating a heatmap\n", - "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap" + "# http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html?highlight=heatmap\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "restaurant_scores.plot(x='date_y', y='business_id', kind='scatter', alpha=0.3)" ] }, { @@ -535,9 +1230,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb b/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb new file mode 100644 index 0000000..2c0764f --- /dev/null +++ b/labs/.ipynb_checkpoints/06_titanic_lab_solutions-checkpoint.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "titanic = pd.read_csv('../data/titanic.csv')\n", + "# 1. Read `titanic.csv` into a DataFrame.\n", + "titanic.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.616162\n", + "1 0.383838\n", + "Name: Survived, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2. What is the null accuracy rate for predicting survival? (This means the probability of choosing the largest unique category, either survived or not)\n", + "titanic['Survived'].value_counts() / titanic.shape[0]\n", + "# 0.616162" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 3. Can you think of some variables that are in the dataset that might contribute to predicting survival of the crash?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 4. Define Pclass and Parch as the features, and Survived as the response.\n", + "feature_cols = ['Pclass', 'Parch']\n", + "X = titanic[feature_cols]\n", + "y = titanic.Survived" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "466 0\n", + "707 1\n", + "613 0\n", + "739 0\n", + "512 1\n", + "Name: Survived, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 5. Split the data into training and testing sets. (Hint: use the train test split modules from sklearn)\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.cross_validation import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", + "train_X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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01
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1Parch0.243198
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" + ], + "text/plain": [ + " 0 1\n", + "0 Pclass -0.815161\n", + "1 Parch 0.243198" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 6. Fit a logistic regression model and examine the coefficients to confirm that they make intuitive sense.\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X_train, y_train)\n", + "pd.DataFrame(zip(X.columns, logreg.coef_[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.69506726457399104" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 7. Make predictions on the testing set and calculate the accuracy.\n", + "logreg.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb index 2dc8b4a..0ad6fa6 100644 --- a/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb +++ b/labs/.ipynb_checkpoints/Titanic_Eugene-checkpoint.ipynb @@ -157,19 +157,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n", - " RuntimeWarning)\n" - ] - }, { "data": { "text/html": [ @@ -294,7 +286,7 @@ "max 6.000000 512.329200 " ] }, - "execution_count": 3, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -318,7 +310,7 @@ "Name: Survived, dtype: float64" ] }, - "execution_count": 5, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -329,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -352,7 +344,7 @@ "dtype: int64" ] }, - "execution_count": 6, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -363,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -492,7 +484,7 @@ "max 4.000000 512.329200 " ] }, - "execution_count": 8, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -503,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -511,18 +503,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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AEhERERFpgGQqRSpTxKP5PnWlACQiIiIiUkeVSoVQOEbJ5tSStwZQABIRERERqZNCocB0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\nlVtdC7vGZTIZkpkMZRx4vY1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h/nu20t+1+vOWvD4/RR0NEhFpG9FYnNmihd+v5VrFUpmX\nTsU4eCx02TL0xXqCHvbvHmSfMUjAp4Gl0jgrDUDbgBeucPkRoDpnm8ua4fV6WT/spVgsEk+kyc9V\n8Pg66hKErt/Qxa9/8EaeODrF44cnKC34NOrURIr/+eWXeMvN87ODXM7VtdW8cDQoHM0Q7MjT29O9\n2vJFRKTJVCoVpkJRLLsHj6e938SHkzkOjYZ4/kSEfHHpgaXGph4OjAyyY1O3BpZKU1hpADoD3H7+\n/wu9h/MNEUQWc7vdDA/1Uy6XiSVS5PIl3N7aByGnw87b9m3kTbds5P/71jFOjCcvXlcqWzx2eH52\n0P33bGXHxtWHFo+/g2yhSH46wrCGp4qIrBmFQoHpSAKXN9C2s2hK5QqjZxIcHA1xeiq95HYB3/zA\n0v27B+nWwFJpMisNQH8EfNYwjHXMzwB6m2EY/4b5pgi/Xa3iZG1yOBwM9vdSLpeJxpLkiuW6HBEa\n6PHzkffu4sipGN986gzp7KVeHbF0nge/dZwbr+vjvju30LnK2UFOtxvLcjE2GWawrwu/r75zkkRE\npLpS6TSJdPt2eUvOFOYHlh4PM3OVgaXb13dyYGSIka09bRsSpfmtdA7Qg4ZhuIDfBXzAXwAR4HdN\n0/x8FeuTNczhcDA02EepVCIaS5Kfs/DWeC21zWZj7/Y+dm7s5nuHx3nq5enLZge99GoMcyzJO27f\nxB0jQ9jtKw9l8+2yOwknZvHP5hhQgwQRkZZjWRbhSJxCmbbr8laxLE6dS3FwNMTxsQRLdLDG63Zw\n684B9o8MMditD/yk+a20DXbANM2/BP7SMIx+wG6aZri6pUm7cDqdDA/1UygUiCXSFMs2vL7ado3z\nuB28986t3LJjgK89cZrx8MzF6wpzZb7x1PzsoB+/ZxsbB1f3B8/r9TNXLjM+FWaorxuPR0sBRERa\nwYUlb05PB26Po9Hl1M1Mbo7DZphDx8IkMoUlt9s40DE/sPS6PtzO9nl8pPWtdAnctGEY/wT8tWma\n/1LNgqR9eTwe1g8PkM/niSXTdWmfvb6/g48+cAPPHQ/z3UNj5AqXTuKcjM7yuYdfZv/IEO+8fRO+\nVUz1djgcOBxBpiNpgh0uNUgQEWly8USS9GyxbZa8WZbFmenM+YGlccqVKx/ucTns3HR9H/tHhtg4\n0F5HxGTtWOk7uo8DPwc8ahjGBPAF4AumaaoBgqya1+tlw7D3UvvsGgchu83G/t1DjGzt5TsHz/L8\niejF6yy4+Mfgvju2cNP1q5sd5PF3kC3OMTsZYt1gH06nhr6JiDQTy7KYDkUp42qLJW/5YokXTkY5\nNBoilFh6YOlAt/f8wNKBVX0gKNIMVnoO0EPAQ4ZhDAH/1/n/ftcwjCeBB03TfLCKNUqbutA++2IQ\nKttrujQu4HPxwXuvZ58xyNeeOE14wR+Cmdwc//gvp3jODHP/PdtWtcbZ6XJhOZ2cm47R0+mjq7M9\nPl0UEWl2lUqFiekIdpcfp2NtL+majM7y1NFpjpyKUiwtPbB0ZOv8wNJt6zSwVNaOVUV40zRDwJ8Y\nhvFnwC8DnwL+ClAAkqq5EITmzxFK1TwIbVvXySc+sJcnz88OWjjJ+rXJNJ/58ku86ab1/NgtG1Y8\nO2i+QUKQdLZAZjbE8ICOBomINFKpVOLcdBSPb+2+0Z8rVXjxVJxnj4c5Pbl0C+vugJv9u4fYZwwQ\nXGVXVJFmtKp3XIZh3MP8UrifOn9bX0LhR2pk/hyhwboEIafDzltu3sCN1/Xx9SfPcHzs0uygcsXi\n+y9McORUlPvv3oqxuWfF9+NyewAP56bjBP0u+npXflsiIrIy+Xye6WhqzZ7vE03lODQa5vCJCLlC\n6Yrb2IAdm7q5Y2SInZu6V9UFVaTZrbQL3KeAnwE2AT8Afgv4smmaSy8eFamSy4NQbbvG9QS9/Py7\nDI6dTfD1J8+Qmi1evC6RKfCF75jcsK2X9925ha5VDHrz+gPk5uYYm5hmeKAXt1ufuInI/8/enUfH\ncV8Hvv/W0tV7N9AAugGQAAluTUIiKVFctHqV5V1SvCWTmZyMJ/FM4nHGSea8d+bMvLzMmTPnzXtz\nJrGzOHH22JNZYsmLJEd2ZFtetHKRKG4giztBEktjaTS60XtVvT8aAAGKENGNBojlfs7BkdBVXfUr\nAujuW/f3u1cshUxmguGxCTy+4J0eSl1Zts3pK2Mc6hnk/PXUnPv5PTp7t0fZtz1KJORZwhEKcefU\nmgH6DJVMz9dM07xSx/EIMW9TVeOmM0KLVCxBUSpzoDevC/PiG9d45UQ/M4vjnLo0yrlrYzx6XwcP\n3N2KVuNdM93lApeLvsQYIb8hleKEEGKRjaVSjGVKq6rYQSpT4PCZSsPSmQ2/b9bVFmT/jhh3dUXQ\nNWlYKtaWWosgbK73QISo1VRGaKp8dtlWcS9CIOR2aXz4/g3cu62FZ166xJXB9PS2Ysnm+devcPTc\nEHA4RoQAACAASURBVE883EVnrPY7iR5fYLpSXFT6BgkhxKIYGh4lV3IWve/cUrAdhwvXJxuWXkky\nRwVr3C6N++ItPHpgA35DxbLm2FGIVW7eAVA8Hn8R+IRpmmOT/z8n0zTft+CRCVGlqfLZE9kso2Np\nUI3JNTb11Rrx8bnHu3nTHOL7B3vJzphP3T+S5avPnGLf9igf3N+Jz1NbknUqGzQwNI7Xo9HS1Lhq\nF+UKIcRSsiyL/sQwqB4Mt+tOD2dBsvkSb5hDHDqdYGQ8P+d+7U0+DnTH2LWlGZ9HJxz2kUpll3Ck\nQiwv1Xw6uwJMdYnspdIiRYhlx+/z4ff5SKfTjI6nUXUPmlbfNTWqorB3e5TujY18/2AvR8yhWdsP\nn0lw6nKld9C9W5trDl7cPj8ly6K3L0FTOEAg4K/H8IUQYk3K5fMMDo+t6EpvjuNwNZHhYM8gJy6O\nUJ4ji6NrCrs2N3OgO8r6lsCKvV4hFsO8AyDTND8749svmKaZWYTxCFE3wWCQYDDIWCpFJpfBWkCR\ngrn4PC4+8e7N3BeP8p2XLs5qIpfNl3n6Jxc4YiZ44uEuYo21TbPQNA3NG2QknWMimyfaEpE3MiGE\nqNJYKkUqXVyxld4KRYu3zg9z6PQg/SNzZ2+awx7274ixZ1tLzbMQhFjtav3LGIjH498E/tY0zR/X\nc0BC1FtDOExTRMGyi4xkM6guL6pa3wWfG1qDfOGTO3n1xAA/euParKZyl/vT/NHTJ3hkdxvv3bMO\nQ6+tuZ7b7aVsWfT2DdImleKEEGJebNtmIDGChY7bt/Ky6AOjWQ72DPLWuWEKJeuW+6iKQvfGRvZ3\nx9jcHpKbZELcRq0B0Oep9P95IR6PXwe+RqUi3MW6jUyIOlIUhZbmCKriIjE0SiZbwvD46xoIaarK\nI7vb2bm5ie++epmey8npbbbj8NO3+jh2fpiPP9TFjg219fupZINC9CXGCAcMGhukUpwQQswlm8uR\nGEnh9gZxraCgoFS2OXVplIM9g7MK7tws7DfYtyPK3niUkF9uigkxX7VWgfs68PV4PB4DfnHy6/+K\nx+OvAH9jmqY0QxXLkqqqNDdFiNg2o8mxRQmEGgJu/tljcc5cSfLcq5dJpgvT28YyRf77P5rs2NDI\nxx/aSEON0/I8vgAT+SIT/YO0RZvRtNqySkIIsVqNpVKkMqUVNeVtZDzP4dODHDGHyOZv3bAUYOv6\nMAe6Y8Q7G2tuvSDEWragyaGmaQ4CX4rH438MfA74L8BfUukRJMSytRSB0PYNjWxaF+Inb17npeP9\nWDPqkp6+kuT89RTv37Oeh3a1otVwXt0wcBwXV/uHaQr7CAZXVxM/IYSoheM4DAwOU1Z03CugxLVl\nO5i9SQ72DHLu2twNS31unfviLezvjtEkDUuFWJAFBUDxePxhKlPhPj15rKeQ4EesIDMDoeHRMbLZ\nMm6vv27zpw1d47H9ndyztYVnXr7Ipf4bUxlKZZvvH+qt9A56pIuNrdXfpVQUBY8vSHIiz/hEglhz\nBF2XRa9CiLWpXC7TNziMZvhxLfPM+PhEkSNmgsOnE6QminPu1xkLcKA7xt1dTbh0aVgqRD3U9Ekp\nHo//F+AXgA7gp8BvAU+bppl7xycKsUypqkq0OYJlWQwNJ8mXqWtzvGijl1/9WDdvnRvm+devMDFj\nasNgMsefP9vDfdta+ND9nfg91felMIzK3cBrA6MEfTrRlqa6jV0IIVaC1Pg4Y+k8bu/ynfLmOA4X\n+sY52DPI6ctJbOfWJawNl8q9W1vYvyNKW9PKK9wgxHJX663iz1DJ9HzNNM0rdRyPEHeUpmm0xpop\nFAoMJ1OUbRW3pz6BkKIo3LuthXhnIy8c7uXw6cSsZlpvnB2i50qSDx3o5L54C2oNWSiPL0CuXObK\n9UFcLgCZGy6EWN0sy2JwaARLceH2Bu70cG4pmy/z5tkhDp0eZDg1d8PS1kilYek9W5pxG8s7gyXE\nSlZrAHQCeEqCH7Faud1u1rVGyeZyjI6lcVQXLld9+gj5PDpPPrKJPdtaeOblS7P6OeQKZb79s4u8\nYSZ48pFNtEaqD750XUfTggyl8kyk0jSGg7jd9e+BJIQQd1o6k2FkbAK3N7Dsqrw5jsO1oQkO9gxy\n/MLwOzYsvburiQPdMTpj0rBUiKVQawD0HmDuLlxCrBI+rxef10s6nSGZTqOqbvQ69d/pjAX5/M/t\n5PVTA/zgyFWKpRu9g3oHM/zxN4/z4M423n/fetyu6u8Eut0e8m6b/qEUIb9BpFFKZgshVgfHcUgM\njZIvO3h8y6sATLFkcezCCAd7Bukbnphzv0jIzYEdMfbEW2qa+iyEqF2tAdDfAv81Ho//J+C8aZqF\n2+wvxIoWDAYIBgOMp8dJjqfRdA+6a+FvWJqq8NDONu7e1MQ/vHaZkxdHp7fZDrx8vJ8TF0b46IMb\nuWtjY013Bj2+ANlCkayUzBZCrAKFQoHB4TE0w4fbs3xezwZHs7x2cpCj54bIF2/dsFRRYMeGRg50\nx9i8LlzTVGchxMLVGgB9FNgMfAogHo/P2mia5vJ5RRKijkLBEKFgiLFUirF0GpfbV5eAIuw3+MVH\nt3H26hjPvnKJ0fEb9xRSE0X+5w/OEu9o4OMPbSRSQ/lTKZkthFgNKq+9hWWT9SlbNicuJjliDnHu\n6tic+4V8LvZuj7Jve5Rwjf3fhBD1U2sA9J/rOgohVpiGcJhwKMTwSJKJbK5upbO3dTTwxU/t5qdv\nXeenb/XN6h1kXh3jwlPHeN+e9Ty8qw1dq64c6syS2emJBFEpmS2EWCFs22YgMYKNC4/vzhc6SKbz\nHDqd4Ig5xESuNOd+W9aF2d8dY8eGhpr6vQkhFkdNn35M0/xavQcixEqjKAotzREidS6d7dJVHt3b\nwT1bmnnmlUtcuD4+va1sObxw+CpHzw3x+MNdbG4PV318w/BUFucOjMraICHEsjcxkWV4LI3hCaDf\nwSljtu1w9uoYB3sGOXt1jFuXNACvW+O+bVH2d0dpDnuXdIxCiPmptQ/Q//1O203T/E+1DUeIlefm\n0tmWo2G4F/6m19zg5V98ZAfHL4zw/GtXSM+4yzg0luevvnuae7Y08+H7Own6qivMUMkGBcgWS0z0\nDdIWbZJskBBiWamUtx6l7Ki4vXduyls6W+QNs1LCeizzzg1L9++IsXOTNCwVYrmr9RPPZ29xnBhQ\nAl5Z0IiEWKGmSmdPZLOMjqVBNXAZC5vrrSgKu7c0E+9s4IXDVzl4anDWXce3zg9zpjfJB/d3sm9H\ntOoFtbrLhaPrXBsYoTHkJRxavg0EhRBrR3JsjNFUHsPjx7gDWR/HcbjUX2lYeurSOzQs1VXu2dbM\nBw5sJOTRsOYodS2EWF5qnQLXdfNj8Xg8BPwV8OpCByXESub3+fD7fKTTaUbH02gu74KzKx5D5/GH\nuiq9g166xPUZpVXzRYtnXr403Tuovbm6ruFTa4NS2TzZ7BCxaBOqzFUXQtwBuXyeVO84mYJyR5qa\n5gpljp4b4mBPgqGx3Jz7RRu9HOiOce/WZvxeF+Gwj1RKuoMIsVLUbc6LaZrj8Xj8d4EXgC/V67hC\nrFTBYJBAIMBYKkUqU5+KcetbAvz6k3dz8PQgLxy6SqF0o9TqtaEJvvLtE9x/VysfOtBBtauDDMOD\nbdtc7R+iuSGI37/w9UxCCDEftm2TGB6l7ChEY83oxeySZlOuDWUqDUvPj1Cy7Fvuo6kKd3VFONAd\nY2NrUBqWCnGHOY6DVczV9MGq3pP+w4CsqBZikqIoNDY00BB2GBlNMpHLYXgWVjFOVRUeuKuVu7oi\nPP/aFY5fGJne5jjw2skBTl4c4ec/EGdLW3V3UFW1Mtd+KJVlIpenpam23kNCCDEfjuOQHEuRzhZx\nuX01NX2uVbFscfz8CAdPD3J9aO6GpY1BN/t3RLkvHiXglYalQiw2x3Eol8vYVhnbtsGxUVUFVVXQ\nFAVFUdBUBUNXOXfwqcvwH6s+Rz2LIISAnwderOWYQqxmiqLQ3BSh0bIYGkmSLzp4fNVNVbtZyGfw\nC+/fyt54lGdfucRwKj+9LZ0t8ZfPnGRbR5iPP9hFU7i63kEej4+SZXG1L0FLUxivp/reQ0II8U7G\n0+OMjedQXd4lne6WGMtxqGeQN8++c8PSeEcjB7qjbO1okIalQtSBbdtY5TKWXa50e2cysFEmv6aC\nHFUh4HNhGB40TZtzGYGuq4xcO5WqZSz1KoIAUAR+BPz7Go8pxKqnaRqt0fpWjNuyPsy/+dQufnas\nj58cvU55xrSRs1dT/MHTx3j3Pet49z3tVfUO0jQNzRtkcCSDz52VbJAQoi4ymQlGxzMoqoGxRNXd\nLNum53KSgz2DXOwbn3O/gLfSsHT/jigN0rBUiHkpl8vYtoVtlXEcG4XKlNGpoGYqY+PWVVxeF4ar\nsiSgHo3ka7XgIgjxeLwFeBcwYJqmVIATYh6mK8ZNZBlJpVFVN7pRXSnrmXRN5X171rN7SzPPvXKJ\ns1dv3BApWw4/euMab50f5omHutiyvrrVQR5vJRvU25egJRLC55W+FkKI6uXyeUaS49iKjuFZmsBn\nLFPg8OkER84kZrUSuNmm9hAHumN0b2yUhqVCMHsamuM4OLb1tmloqloJdHxeHV1z43IF0HV9Rdws\nrSoAisfjvwN8EbjfNM3z8Xj8AeB7QHBy+4vA46Zpzl06RQgxze/34ff7pgsl6MbCCiU0hTz88oe2\n03MlyXdfvUIqU5jeNpLK89fPn2bX5iY+8sAGQlX0DprKBiWSE/gmcpINEkLMm+M4DI0kyRZtPJ7F\nn+pmOw7nr6U42DPImd4kc1SwxmNo7NnWwv7uGNEGubEj1gbLsrCsSsYG2wac6WloiktDsRQ0J4+q\nMK9paCvVvK8mHo//S+A/UKnwlph8+G+ALPAgkAK+Cfw74HfrO0whVreGcJhwKMTIaJJMNofbW3uh\nBEVR2LW5ib13tfHNH53llRP9sz4AHL8wgtk7xmP7OjjQHUNV538eWRskhKhGNpdjaHQc3fDh8Szu\ndJdMrsQbZoJDpxMk04U591vf4udAd4ydm5sw9Ds3BUeIenEcZzqwcWwbx7FurKmB6cyNqih4DA3D\n5UbXdXRdn9X2QtdVGhv9JN0TlMu3roa4WlQTzv0q8G9N0/wKQDwe3wtsA/6DaZo9k4/9Z+D3kABI\niKrNKpQwnCRfWlihBK9b5+MPbeSeLc088/IlriYy09sKJYvnXr3Mm2eHeOKRLta3zP+urKwNEkLc\nTrlcZnhkjHwZPIu4zsdxHK4MpjnYM8jJi6NY9q3TPS5NZfeWJvZ3x6p6vRPiTpouGmCVK2Veb1U0\nQFHQNAXDq+Ny+dB1HU3T5H35NqoJgHZQ6fEz5X2AAzw/47FTwIY6jEuINUvTNFpjMwoloGMYtWda\n2pv9/Ksn7uLw6QT/eKh3VtWj68MT/Om3T3KgO8YH9nXgdc//JUHWBgkhbjYd+JRs3F4/HtfifAjL\nF8scPTfMoZ5BBpNzz7pvafBMNixtqer1TYjFtBKLBqw21bwaKFQCninvAkZN0zw247EQlSlxQogF\nmiqUkMlMMJJKo7m8Nc/BVRVleoHv9w/2cvTc8PQ2B3i9Z5CTl0b5yP0b2L2lad53jmauDfKms7Q0\nN85Kpwsh1gZrusT/4gY+fcMTHOwZ5Nj5YYpzTNFRFYW7uho50B2jqy0kd8LFkiqXy5TLRTQcPC6L\ncj6PbTuzSjx7PRoufWUVDVhtqvk0dQJ4CDgfj8cbgPcC37lpn09P7ieEqJNAwD+rUILh8dccZAR9\nBp9+7xbui0d55uVLDI3duHOayZX4xo/Pc8RM8MTDXbRUsSjY4/Fh2TZX+4doCgcIBBbW40gIsTLY\nts3w6BjZfLkS+Pjq/0GuVLY5cXGEgz2Ds6by3qwhYLBve4y921sIVlHkRYhqTFVHs6wS2HalEpqi\noGnqdEU0jzuE1+smEgmQTK7+9TQrUTUB0B8DX43H4/dQKXrgBv4AIB6PtwP/FPg/gF+p9yCFWOsU\nRaGxoYFwqPJhYyJbwuMN1HzXaFN7iN/45E5ePt7Pj9+8Tsm68eJ8sW+cP3z6OO/a3c577l2HS59f\nsKWqKm5vkNF0jvFMllhLRNL1QqxCjuOQyWTITOQplB0Mjw+Pr/4FUYZTOQ71JHjj7BC5QvmW+yjA\n1o4GDnTHiHc0VFXURYgpjuPgOA62bePYNrZj4zg22A6OY6NrCpqqoqoKLk0h4DNwu724XK4534cl\nq7O8zTsAMk3zf8TjcTfw64AN/LxpmocmN/974HPA/2ea5t/Vf5hCCKgEGdHmyE0LjH01HUvXVN5z\n7zp2bW7iu69e5kzv2PQ2y3b48dHrHDs/zOMPd7Gto2HexzXcXhzH4Wr/CCG/QWNDWN4IhFgFLMti\neGSMXLGMprtxGX48dU60WJbNUTPBjw73cv7a3A3e/R6dvduj7NseJRKSapRitvJk4YC5KqIpk0UE\nUEBVKsGKqlWyOKrqQtc0VFVFm/yvvIetPlUtKDBN86+Bv77Fpv8C/K5pmiN1GZUQ4h3puj6rUELZ\n0XC7aytCEAl5+KUPxjl9Jclzr1wmNVGc3jaaLvC33zvD3V0RPvrgRsL++X3aURQFjy9AtlQm05eg\nWYokCLFi3Qh8rEWb5paaKHL49CBHzATjE3M3LN3YFuTAjhh3dUXQNVlvuBZNT0Erl8CxURSmm3NO\nrbHxeXUMqYgm3kFdSqKYpnm9HseZy2Tm6U+AT1ApsvB7pmn+/m2es5HKeqSPmqb5s8UcnxB3ysxC\nCaOpNKruQXe5qj6Ooih0b4yweV2YF9+4xisn+plZTfbkpVHOXhvj0fs6eODuVrR5TjPRdR30IEPJ\nCYxUhqhMixNixXhbYYM6Bz6243Dh+mTD0itJ5qhgjdulce+2Zg7siBGL1JbxFivHdKPOyQppqqJg\nuDScEihWDkOBgM/AMDy4XC4pvCNqslJqQv43YA/wHmAj8PV4PH7ZNM1vvcNz/hSQV0qxJgQCfgIB\nP2OpFGPpdM39g9wujQ/fv4F7t7XwzEuXuDKYnt5WLNk8//oVjp4b4omHu+iMzb+3h9vjm5wWN0xD\n0ENDOFzT+IQQi69cLjM8Wsn4eLyBugc+2XyJN8whDp1OMDKen3O/9iYfB7pj7NrSjNslN05Wqqle\nNrZtTa61sabLPk9NRZsq+6yqTDfqnFkhbbpBpxQUEHWy7AOgeDzuo1JY4YOTJbePxePx/wp8Abhl\nABSPx/8pIJ3OxJrTEA4TDoVIjafIZdM4Tm13xlojPj73eDdvmkN872DvrAXI/SNZ/uyZU+zdHuWD\n+zvxeeb3MlKZFhckky+RnhikORLG65G5+0IsB7ZtM55OM5ErUiqD2+vDW8fAx3EcriYyHOwZ5MTF\nEcrWrdM9uqawr7uVPVubaG/yy9SlZci2bSzLwrGtStGAyYBmZoNORakEM6pyo5eNS/dO97KRn6u4\n05Z9AATspjLO12Y89jKVwgtvE4/Hm4D/F3iMSmNWIdYURVFobooQCnkwz10jn7NqygipisLe7VF2\nTPYOesMcmt7mAIfPJOi5PFrJGG1tnvcbmu5ygcvF4GgGj5ahpblRpsUJcQdUqrlNkJnIUbQcNJcH\n3fCj1bGwQaFk8da5YQ6dHqR/ZO42gU1hDwd2xNi3o4W2WJhUKos1R5AkFsfMqWc4DopyozmnMlko\nYKo5p+7WcbncssZGrFgrIQBqA4ZN05xZA3MQ8MTj8aZbFF74feBvTdM8HY/Hl2yQQiw3mqbRFmtm\nYiJXKZRgq7g91c8K9XtcfPLdm7kv3sJ3XrpEYkbX9Yl8mad/coE3zASPP9xFrHH+x/fItDghlly5\nXCadyZDPlylaNppmoBt+3HU+z8BoloM9g7x1bphCybrlPqoCOzZGONAdY3N7pWGppskH6XpxHAfL\nsrDtSmCjOA7g4NI1Sm4bq5jHsZ3pwGZq6plhBCWoEaveSgiAfEDhpsemvp/1mh2Pxx+l0qPocws5\nobZGKstMXedauN61dK0w+3r9fi9+v5dsLsfI6DiOauAyqr/Fu3ldmN/89C5ePt7PD45cozRjHval\n/jR/9M0TvGt3G++/bz3GvOfrK+jBENlSidzgELGWRowaxraWfr5r6VpheV7nchzTrWiaim3bZCYy\njKfzlMoWNgouw4Puddf9A0CpbHPy4givnxrk8kB6zv1CfoMD3TH274gSuqmy5Ozf75Wx1uNOjdmy\nLEqlItg2msqsdTSVamgqulfDpRvouo6u65OlnVVCIS/j4zksayX+G68MMubFt5BxroQAKA9vuzk1\n9f10Pj0ej3uArwK/bppmkQUIhdZWud61dL1r6Vph9vU2NvpZ197M+Hia4dE0mrtSIrRaj79nKw/f\n28Hf//Asx87dmBZn2w4/OdrH8Quj/MJjcXZtaa762OmJDAHFItbSVNPdx7X0811L17rcLOd/e8dx\nyOfzpNITFCYsEkkb3eWhobm2wijzMZTM8tJbfbx6vI9Mbu4S1t1dEd5173p2bmlCu03lrkBg5a0P\nrPeYS6USVrmEbVtok6WeVVVFmyz17Ha78XkjGIaxZl4vZcxLYyWOuVqK4yzvObbxePwB4KeAxzRN\ne/Kx9wDfNU0zMGO/dwE/BiaoNIcG8AM54GumaX5+nqd0VtJdkYVYiXeBarWWrhVuf72O45AcSzGe\nKeDy+GouI9pzeZRnX75MMn1zkhbu6mrk4w910RisbnKNZVlYxRwNIS/hUGhez1lLP9+1dK0wfb3L\naS7OsnyPmMhmGUtNUCzbqLoLl8tA1zUCAQ+ZTL7u47VshzNXkrx+aoCzV+duWOrz6OzbHuVAd4ym\n8O0DBE1TF23Mi6XWMTuOQ6lUwrEtHMuaDHAUdK3y5XG7cbuN6cxNPce70l5DZMxLY6WNeSHvDysh\nA/QWUALuB16dfOwR4PBN+x0Ett702HkqFeR+WM0JLcteU2UW19L1rqVrhXe+3lAwRMBvMzwyxkSh\njMdXfeHEeEcjX/xUiB8fvc5Lx/qxZ9xQOXUpybmrKd5/33oe3Nl62zu+N6hohp/R8QLJ1ADRpoZ5\nT4tbSz/ftXSty81y+bcvl8tkc1lSmTw2lWbIU7NPbZvpDzCWZdetoMD4RJEjZoLDpxOzmibfrDMW\n4EB3jLu7mnDp6uQ45jOG+o958d16zJZlVTI4lgXY0wUFprM4moLPZ2C4vLhcrjmzOLZdqbxWb8vl\n97gaMualsRLHXK1lHwCZppmLx+NfB74aj8f/BbAe+LfALwPE4/EYkDJNMw9cnPncySIIfaZpDi/t\nqIVYGVRVJdoSoVgsMjw6RqmGQgmGS+OD+zu5Z0szz7xyicv9M3oHlW2+d7CXN88O8eQjm9jQOv/e\nQS7DDbjpS4wR9OlEGhtlUa5Ys4rFIuPpDIWiheU42LaDg4ruMnC5F7frg+M4XOgb52DPIKcvJ2fd\n6JjJcKncu7WF/TuitDUt3pS75cCyLMrlEo5to6kOXsOmXJgqKlC5M+0xNNwBL4ZhSKVLIZaZZR8A\nTfpt4E+AF4EU8DumaT4zua0f+OfA12/xvJVy+0iIO8owDNpbo5VCCclxUI3JAGT+YhEfn/tYN0fP\nDfP861fI5m8UbhxM5vizZ0+xN97Chw504vO45n1cjy9Arlzmal+C5kgIn3f1z00Wa5tt2+TzeXL5\nPKWyQ7FUxqZyc0JzKyzVR+lcocybZ4c42DPIcGruhqWtkUrD0nu2NOM2Vv4Hfdu2KU9mbhzbQtMq\nmZupxp1TFdPcgUrmxuMxpEmnECvMigiATNPMAZ+d/Lp525zzakzTXPmvxEIsIZ/Xi8/rJZ1OkxxP\no7q8VRVKUBSFPdta2N7ZyD8e6uXwmcSs7UfMIXouJ/nQgU72xFtQ59s7SNdBDzKUnMCVyhBtbqyp\ngIMQy43jOGRzObLZPGXLpmTZ2DaT63jcKLqCsYS/6o7jcG1ogoM9gxy/MPyODUvv7mriQHeMzlhg\nxWVnHcehXCphWWVwLDRNRZ+snOZ2qbh9bgzDkNcZIVYp+csWQrxNMBgkEAgwmhwjnc3h9lb3Acfn\n0fm5d23ivngLz7x8aVYDxGyhzLd+dpE3zCGeeKSL1sj8p9y5J3sHXRsYlWlxYkWyLItCocBENk+x\nbFG2HFTNhcvwoKhgzD85WlfFksWxCyMc7Bmkb3hizv0iITcHdsTYE2/BX0Um904pFYtvC3JUVcGl\nqQSDBh53QIIcIdYg+asXQtySoig0RRppCFsMDSfJl8HjrW59UGcsyOd/bievnRzgh29cpVi6MT3k\nymCaP/7mcR7a2cb77luPe569gxRFweMLkLcsevsSNIa8RBobqhqXEEuhXC6Ty+XJ5YvT2R3HuZHd\n0Yylm842l8FklkM9CY6eGyJfvHXDUkWBHRsaKw1L14XnnbldKo7jUC6XscolcGxUFXRVRddVGvwG\nXq8EOUKI2eQVQQjxjjRNozXWTKFQYGg0ha3oGMb8+11oqsLDu9rYuSnCP7x2hZOXRqe32Q68dLyf\n4xdG+NiDG+neOP+MjqZpaN4gqYkCE9lBPJ72qq9NiHoplUrk8wVyhSLl8lSwo6C5DFwuD6r29oZ2\nd0rZsjl1aZSDpwdnFS25WcjnYu/2KPu2RwkH7vzoLcuiXCpi22U0VUFXVVRVwdAVAj4Dw/Dgcrnq\nWjJaCLE6SQAkhJgXt9vN+rYomcwEI6k0WpXrg8IBN7/4gW2YvUmee+UyozN6B6UmivyPH5wl3tnA\nxx/cSCQ0/wDLZbjRNA/9Q+PkszkiDWGpuCQWzXg6w/BoknzewrYdLMeplJtWVDR9Mtgxlk+wM1My\nnefQ6QRHzCEm3qFh6ZZ1YfZ3x9ixoaGK8vX1UyqVyGezlQagk00/NbVSVc0bCtTc+FMIIaZIACSE\nqEog4Mfv95EcS5HOZnC5q2ukGu9sZFN7mJ8cvc7PjvVh2TcWWZu9Y1y8fpz37lnHw7va0LX5t8eF\nugAAIABJREFUH9ft9ZErOFwbGCHglfVBYnEMDmco2gbooFL5Ws4rYWzb4fTlJK+dHODs1bE5S6N6\n3Rr3bYuyvztKc3hpKy0WiwXscglNU9DdGo1+DwEjguPI368QYnFIACSEqJqiKEQaG2gI2wyPjpHN\nlnF7/fMOOFy6ygf2dXDP1maeefkSF/vGp7eVLJsXDl/l6Llhnni4i03toarG5fZWymb39iVobgji\n91e3bkmId6JqGorlsNy7LKSzRd48O8ThM0OMjs9dwrojWmlYunPTjYali8lxHAqFHIpj49Iq63Qi\nQTc+bxh1ct1OOCwlpYUQi0sCICFEzVRVJdocoVwukxhOUrAVPFU0Um1p8PIrH93BsQsjPP/aFTIz\npuUMjeX4y+/2cO/WZj58/wYC3vnfZ9d1HV0PMjKeI5WekLLZYk1wHIdL/ZWGpT2Xk7OyqzO5dJV7\ntjRzoDtGe/PiNyy1LItSIYdLr/TPaYwE8HjmP81VCCHqTT4RCCEWTNd12ltbyOXzjCTHcdRKlav5\nUBSFe7Y0E+9o4IXDVznUMzjr3vrRc8Oc6U3y2L5O9u2IVlWBynB7pWy2WPVyhTJHzw1xsCfB0Fhu\nzv2ijV4OdMe4d2sznkVsLlTpsVOkXC7i0hR8HoPWSJOszRNCLBsSAAkh6sbr8bC+zUM6nWY0NY5m\n+OadefG6dZ54uIv7trXwnZcvzepFkitYPPPyJd48O8QTD3dVddd6qmy2TIsTq831oQwHTyc4dn6Y\n0hzTxTRVYeemCPt2xNjYGlyUGwAzp7UZuoquqYRDHjyesFRkE0IsSxIACSHqbiGNVNdHA3z+ybs5\n2DPIC4evUijd6E1yNZHhK98+wYN3tfLo3g7cxvzvKE9NixtOZUmlJ4i1ROSOtFhximWL4+dHOHh6\nkOtDczcsbQy6OdAd4337N+CUy1hWfdcsOY5DIZ/DpTl4DI2m5hCGYdT1HEIIsVgkABJCLIpZjVRH\nkhSKDm7f/DI3qqrwwN2t3LUpwvOvXeH4hZHpbY4Dr5wc4MTFET764Ebu7ooA87+r7fb4cByHq/0j\nhPwGjQ1hmRYnlr3EWI5DPYO8efadG5bGOxo50B1l6/oGXC6VkN8glSrXbRylUgm7XMBr6KyLhnG5\nlnMNPCGEuDUJgIQQi0rTNFqjzRSLRYZHxyjZKu55FkoI+Qx+4f1buS/ewrMvX2ZkRjWr8WyJ//XD\nc2zrCPPkI5sIh+c/rW1qWly2VCbTl6CpMYjfJ9PixPJi2TY9l5Mc7BmcVSnxZgFvpWHp/h1RGhah\nYanjOBRyWVw6hP1ugoEWuWkghFjRJAASQiwJwzBob42SzeUYHh1HraKR6tb1DfybT+3iZ8f6+Olb\n1ynPmM5z9mqK3//7t/jwg13cv6MFpYpskK7roAcZHssyPj5BVKbFiWVgLFPg8OkER84kSL9Dw9JN\n7SEOdMfo3ti4KA1LS6USVimPz+1iXaxBsj1CiFVDAiAhxJLyeb10tHsYTSZJZ/N4fIF5Pc+lq7z/\nvvXs3tLEc69c5ty11PS2suXw3EsXee14H48/3MWWdeGqxjQ1Le7awAhBn0yLE0vPdhzOX0txsGeQ\nM71JnDmW7HgMjT3bWtjfHSPaUP+GpTPX9lSyPVH5WxBCrDoSAAkhllxlfVCEYKDI4HASRXWjz3MB\ndXPYyz//8HZOXBzlH167TDp74w75cCrPX//DaXZtbuKjD2wg6Jv/ouypJqrZUpl0X4JIyEcwGKz2\n0oSoSiZX4g0zwaHTCZLpwpz7rW/xVxqWbm7C0OufpZxa2+N2qbS3SEEDIcTqJgGQEOKOMQyDjvYY\nY6kUY+n0vKvFKYrCrs1NbOsI84Mj13j91MCsO+bHL4xg9o7x2P4ODuyIoarVTYvT9SDJiTypTIKW\nSBi3u/7rKsTa5TgOVwbTHOwZ5OTF0bkblmoqu7c0sb87xvqW+WVKq1UqFnCsIqGAh3BI1vYIIdYG\nCYCEEHdcQzhMMBBgcGiEEjqGMb8u8R5D5+MPbmTf9haefeUKl/tvLBQvlCyee+XydO+gaj9ATo2h\nfziN15WhpblRepqIBckXyxw9N8yhnkEGk3M3LG1p8Ew2LG3B616ct+lSqRL4NAZ9BIONi3IOIYRY\nriQAEkIsC5qm0d4aJZ1OM5KafzYIYF1LgP/zl/byg9cv8b3Xe2eVCb4+NMGffvskB7pjPLa/A49R\n3cuex+vDsm16+4ZoCsu0OFG9vuEJDvYMcuz8MMU5GpaqisJdXY0c6I7R1RZatExMpaJbhkjYSygY\nW5RzCCHEcicBkBBiWQkGg/h8PhJDoxTR5p0NUlWF++9qZXtnI997vZe3zg9Pb3OA13sGOXVplI88\nsIFdm5uq+oCpqioeX5CxbEGmxYl5KZVtTlwc4WDPIFcTmTn3awgY7NseY+/2lqrWrNWiXCyCU6Cj\nrVmqHQoh1jQJgIQQy46mabS1tkxngwyPf97Tz4I+g8+8bwv3bW/h2ZcvMTR2o3dQOlfi7188zxEz\nwRMPddFcZRUtl8sNuOkfTuPR07Q0N8oHSTHLcCrHoZ4Eb5wdIle4dQNSBdja0cCB7hjxjoaq1qjV\nwrIsCrks4YCbhrBkfYQQQgIgIcSyFQwG8fv9JIZHyRfBM88GqgCb28P8xid38dKxfn589Nqs3kEX\nro/zB08f5933tPPue9bh0qtb2+Px3iib7XPrNEUaZH3QGmbZDqevJDnUM8j566k59/N7dPZuj7Jv\ne5RIaH6ZzYWwbZt8No1HLxNta5bfUSGEmCQBkBBiWVNVldZoM9lcjqHRcXTDN++si66pvHfPOnZv\naeLZVy5z9urY9DbLdnjxzeu8dX6YJx7uYuv6hqrGNVU2u2hZ9PYNEfK7pX/QGpOaKHL49CBHziQY\nz87dsHRja5AD3THu6oqga0sThOSzGUIBF10d60mlcpTnWHskhBBrkQRAQogVwef10tnuYWQ0SSab\nm3cDVYBIyMMvfyjOqUujfPe1K4xPFKe3jY4X+Jvnz7BzU4SPPrCRkL+6dRiapqH5gpX+QdcHiYT9\nUihhFbMdh7NXx3jt5ABnriSZo4I1bpfGvVub2d8dozUy/8zlQpWLRRy7QFtLA36/V7I+QghxCxIA\nCSFWDEVRaG6KECwUSIyMVdVAVVEU7t7UxNb1DfzojWu8erJ/1ofXExdHOXs1xaN713P/Xa1oVa7L\nqPQPCkn/oFXu//naUYZT+Tm3tzf5ONAdY9eWZtyupV0fls9maAjKOh8hhLgdCYCEECuO2+2ebqCa\nymTw+uefDXIbGh95YAP3bmvmmZcv0Tt4o0JXoWTxD69d4ejZIZ54pIuOaPWZnJn9gwxtnOZIGGOe\nQZpY/m4V/OhapTHvgcmGpUs9DdK2bUr5DO3RiPyuCSHEPEgAJIRYsRrCYQL+MsPJMQqF6u62tzX5\n+ZeP38Ub5hDfP9g7q2JX30iWr37nFPt2RPng/s6amlF6vJVpT32JFB6XQlMkjMvlqvo4YvlqDnvY\nvyPGnm0t+Dx35u20XCyiUaJzXUzWnwkhxDxJACSEWNF0XWd9WxRNszk/mkA3/PP+IKgqCvu2R9mx\noZF/PNjLG2eHprc5wKHTCU5dTvKRA53cs7W5pg+YHp8fx3G4PjiGoUNTY0imxq1gqgLdXRH274ix\nuX3xGpbejmVZlApZQn43kcaWOzIGIYRYqSQAEkKsCqFQkA3r4HrfEAVbwV1FyeyA18Un37OZPfEW\nnnn5EolkbnrbRK7EUz+5wBFziCce7iLaWF3vIKisP/L4/MDU1LgUTY2yRmgl+o+/she3y4VlzVH9\nYJGVy2XKxRxBn0H7uqhkfYQQogZSHkYIsWqoqkpbawtNIS+F7DiWZVX1/K62EL/xyZ18aH/n23oD\nXeof54++eZx/PNRLsVzdcWfyeH2oRoCBoXEGEyPYtpQnXkmqrRJYL5Zlkc+m8Rs2G9ZFaYo0SvAj\nhBA1kgBICLHqBAJ+OtfFcCkl8tmJqp6rqSrvuqed3/z0bnZsaJy1zbIdfvpWH3/w1HHO9CYXNEa3\nz4+luuntGyI5Nnb7J4g1yXEc8tkMbrXEhnVRGhsaJPARQogFkgBICLEqKYpCtDlCW0uIYm6ccrF4\n+yfN0Bh080sfjPNLj22jITD7rn8yXeDr3zf5uxdMxjKFmseoqioeX5CJgkLv9UEmJrI1H0usPqVS\nAaeUpaOtieamiAQ+QghRJ7IGSAixqrndbjrXtTKWSjGWTuP2VlemeMfGCJvXhXnxzWu8fHwA27mx\n9qPncpLz11K8/771PLizFa3GppO6ywUuF8OpLKl0huZIg5QzXuNu9PSRAgdCCFFvkgESQqwJDeEw\nHW3NKOUc+Xx1mRbDpfGhAxv4wid3srF1dm+gYtnmewd7+cq3TnJlIL2gMbo9lfVB/YkUA4PDlMvl\n2z9JrCrlUolSPk17tIGGcPhOD0cIIVYlCYCEEGuGpmm0xpqJNvop5NJVF0lojfj43Me7+eS7N72t\n78vAaJY/e/YU3/rpBbL50oLG6fb5cXQv1wZGSQyNVj1OsfJMrfXxGQ4d7THJAAohxCKSAEgIseb4\nvF4626M1FUlQFIX74lF++zO72bs9+rbtR8whfv/vj3HkTGLWdLlqVUpnBygrBtcGRhgeGZWKcatU\nsZjHKU2wvjVCpLHhTg9HCCFWPQmAhBBr0tuKJJSqy9r4PC4+8a5N/NoTd9Eamd1zKFso862fXeQv\nnuthYHRhhQ1UVcXtDVCwXVztH2ZkNImzgMBKLB+VrE+asE+nvTWKrsuyXCGEWAoSAAkh1rSpIgk+\nozIFqVqdsSD/+hM7+cj9GzBu6h10ZSDNH3/zBN97/QrF0sKmsWmahtsbIFfWuNQ7wPCoZIRWsnKp\nRLmQYX1rE+FQ6E4PRwgh1hQJgIQQAog0NrC+NUK5kK66ZLamKjy8q43f+sxu7uqKzNpmOw4vHe/n\ny08do+fy6ILHqWkabl+QfFmnt2+IkdFRyQitMIXsBF6XRUd7TLI+QghxB0gAJIQQk3RdZ31bjIBX\nIZ9NVx1YhANu/ukHtvHLH4rTGHTP2jaWKfJ3L5zl6983SaZr7x00RdM0PL4gecvFlesJRpNjEggt\nc7ZtU8iOE20O0hSJ3P4JQgghFoUEQEIIcZOpktlOaYJiMV/18+OdjXzx07t4z73r0NTZPYfO9Cb5\n8jeO8dO3rlO2Fj6FbSoQypZUevsSjKfHF3xMUX+lUgGlnKOjPYrX47nTwxFCiDVNAiAhhLgFTdNo\nb43S6HfVlA0ydI3H9nXwG5/aRVfb7DUeJcvmHw9d5Y+/dYJL/fUJWHRdx+0NkpqwuNafIJevPnAT\ni6OQnSDoUWlrbUGtsVmuEEKI+pFXYiGEeAfBYJCOtmYoZSkWclU/P9rg5Vc/toNPv3czfq9r1rZE\nMsdfPNfD0z85Tya3sN5BU1yGG90dYHA0Q9/AEKUqq9uJ+pmq8tbSFJCmpkIIsYzI6kshhLgNTdNo\na20hk5lgeGwcwxOo6k6+oijcu7WF7Z2NvHD4Kod6BpmZT3rz7DCnryT54P5O9m6PoirKnMeaL4/H\nh+M4XB9M4jFUWpoa0TRtwccV82NZFnYpS0dbs/y7CyHEMiMZICGEmKdAwM+GdTE0u0A+X31/H69b\n54mHu/i1J++mvdk/a1uuYPGdly7xZ8+con+kuuasc5lqpupoXq72j0jFuCVSKubRnSLr26IS/Agh\nxDIkAZAQQlRBURRi0SZikQDFXJpyuVz1MTqiAT7/5N187MGNuF2zPyBfTWT4yrdO8A+vXaZQXFjv\noJlj9vgC5C0XV/sSpNPpuhxXvF0+myHk04lFm1DqkMkTQghRfxIACSFEDbweT6Wil16uqYGqqio8\neHcrv/WZ3ezc1DRrm+3AKycG+NJTxzh5caRuWRtN0zC8QcayZa71J8hLoYS6sW2bfHactpawNDYV\nQohlTgIgIYSokaIoNEUitLWEKeXTlGsoOBDyG/yTR7fy2Y9sJxKa3TtofKLI//zhOb72fZPR8foF\nKy5XpVDCwGShhFqyWOKGYjGPUs6xYV0Mt9t9+ycIIYS4oyQAEkKIBXK73XS0x/AZTk3ZIICt6xv4\n4qd28749b+8ddPbqGF9+6hgvvnmtLr2Dpng8PhSXj2sDowwmRrDt+h17rchnM4R9Om2tLTLlTQgh\nVggJgIQQok4ijQ2sb41QLqQpFQtVP9+lqzy6t4MvfnoXW9bNLptcthx+eOQaX/7GMc5cHq3XkKfX\nB1mqm96+ISmUME/lcplibpz2aINMeRNCiBVGAiAhhKgjXddZ3xYj6FVraqAK0Bz28tmPbOcX3r+F\noG9276ChsTxf/t9H+d8/PEc6W6zXsFFVFY8vSK6s09uXIDVenwatq1EhO4FXL9O5rhXDMO70cIQQ\nQlRJ+gAJIcQiaAiHCfj9JIZHKSsuXK7q1oYoisKuzc1s62jgB0eu8fqpAWbGUkfPVXoHPbavg/07\nYqhqfaZf6bqOrgdJZ4uMZwZpagzh83rrcuzVwCnnaY81oKry9imEECuVZICEEGKR6LpOe2uUkFer\nORvkMXQ+/uBGPv9zO1nfMrt3UL5o8ewrl/nqMye5PlTb2qO56IaByxNkKDlB30CCYrF+2aaVbMum\nDsn6CCHECicBkBBCLLJwKERHWzOUshQLuZqOsa7Zz689cTdPPtKF1z07+3BtaII/+c5JnnvlMvli\nfSu6uT0+VCNAfyIlhRKEEEKsChIACSHEEtA0jbbWFiJBD/nseE2BhKoqPHB3K//xc/dz79bmWdsc\nB147NcCXvnGM4xeG617IwO3zTxdKGBoelUBICCHEiiUBkBBCLKFAwE9nexTVzlPIZ2s6Rjjg5hce\n3cqvfHQHzWHPrG3pbIn//aPz/M3zZxhO1ZZtmstUoYQShgRCQgghViwJgIQQYompqkprtJnmsI9C\nLl1zELF5XZh/86ldPLp3Pbo2uwjC+esp/vDp4/zwyFVK5foGKTcHQlI6WwghxEoiAZAQQtwhfr+P\njrYWlHKOYjFf0zF0TeV9e9bzm5/ezbaOhlnbypbDi29e5w+fPs65a2P1GPIsU4FQ3nJx5XqCkdGk\nBEJCCCGWPQmAhBDiDlJVlbbWFsI+nXy29kpukZCHX/5QnF98dCsh/+wqZSPjef7m+TP8rx+eY3yi\n/tXcNE2b7CGkSQ8hIYQQy540MhBCiGUgHArh9XjoHxpFN/xomlb1MRRF4e5NTWxd38AP37jKaycH\nsGckZE5cHOHs1TE+sG8993e31q130JSZPYRSmUGawkH8fl9dzyGEEEIslGSAhBBimTAMg872GLpT\npFBjuWwAt6Hx0Qc28q8/sZOOaGDWtkLJ4ruvXuFPvnOSq4n69g6aohsGhifIyHiO6wMJCoXCopxH\nCCGEqIUEQEIIsYwoikIs2kRzyFtz89QpbU1+/tUTd/Fzj3Thdc/OKPUNT/DV75zkmZcvkSvUt3fQ\nFMPtRTMC9A+nGRgcplxenPMIIYQQ1ZAASAghliG/30dne0uleWqNBRIAVEVh344Yv/WZe9iz7abe\nQcDBnkF+/xvHOHpuaNEKGHi8Phzdy7WBUYZHpGKcEEKIO2tFrAGKx+Nu4E+ATwBZ4PdM0/z9Ofb9\nKPCfgS3ABeB3TNN8bqnGKoQQ9TJVICGdTjOSSuP2BoDa1u0EvC4+9Z4t3BeP8szLl0gkb0yxm8iV\neOrHF3jDHOLxh7uINnjrdAU3KIqCxxegYFn09iVoDHkJBUN1P48QQghxOyslA/TfgD3Ae4DPA78b\nj8c/cfNO8Xh8F/BN4C+B3cCfA0/H4/GdSzdUIYSor2AwSEdbM05pglKptKBjdbWF+MIndvLB/R24\ntNlvARf7xvmjp4/zwqFeimVrQeeZi6ZpuL1Bxidseq8Pkk6nF+U8QgghxFyWfQYoHo/7gF8BPmia\n5jHgWDwe/6/AF4Bv3bT7PwF+ZJrmVya//5N4PP448BngxFKNWQgh6k3TNNpbo2Qm0uSzGRyn9gpu\nuqby7nvWsWtzM9999TKnrySnt1m2w0/e6uPYhREef2gj8c7Gegz/7WMwDMBgbKJAMj1IJBQgEPAv\nyrmEEEKImVZCBmg3lUDttRmPvQwcuMW+fwv8u1s8Hq7/sIQQYuk1hMNsWNeMXcxSLi6sp09j0M0v\nfTDOP3tsG+Gbegcl0wW+9n2T//HCWVKZxavi5jLcGJ4go5mCVIwTQgixJFZCANQGDJumObN80CDg\nicfjTTN3NCumMz3xePwu4P3AD5dkpEIIsQR0XWd9e5SAV1lQ89Qp3Rsj/NZndvOu3W2oyuzM0qnL\no3zpG8d4+Xg/lr14xQsMw1OpGDc0zkBiGMtanCl4QgghxEoIgHzAzbcEp753z/WkeDzeTGU90Eum\naT67SGMTQog7piEcpj3aQCmfXnCJacOl8aEDG/jCJ3eyoTU4a1uxbPP861f4yrdOcGVgcdfseHx+\nbNXDtYEREsOj2La9qOcTQgix9iz7NUBAnrcHOlPfZ2/1hHg8HgN+QKXK66erPaGmrYS4cOGmrnMt\nXO9aulaQ613Nbr5WXffQ1dnG8OgomWwOt9e3oOOva/Hza0/exRvmEM+/doVs/kZgNTCa5c+ePcW+\nHVE+fH8nfo9rQeeai6ZpuFxBbNvm2sAoux76aMv1Mz8bWpST1WCl/J6txL8LGfPiW2njBRnzUllp\nY17IOJXl3o8hHo8/APwU8JimaU8+9h7gu6ZpBm6x/zrgRcAC3mua5mCVp1ze/yBCCDGHXC5Pf2IU\n3e1H07TbP+E2MrkS3/7xeV453ve2bQGvi0+8dwsP7GxDUWovyDAfj33mN7ee+vFfnl/Uk8yfvEcI\nIcTyUdMb0ErIAL0FlID7gVcnH3sEOHzzjpMV474/uf97TdOs6Y7h+HgOy1r90y40TSUU8q6J611L\n1wpyvavZ7a61IRgiMTRCruTg9iwsGwTw+EMb2LWpkW//7BIDozeS7plcia8/f5qXjl7jyXdtojWy\n8HPdynK8E7lSfs9W4t+FjHnxrbTxgox5qay0MU+NtxbLPgAyTTMXj8e/Dnw1Ho//C2A98G+BX4bp\n6W4p0zTzwH8Auqj0C1IntwHkTNMcn+85LcumXF7+P/h6WUvXu5auFeR6V7N3utamSISJiSxDyRSG\nJ4CqLiyI6IgG+defuJtXTw7woyPXKM4476X+NH/wjeM8vKuV9+1Zj+FaeOZptuX381xpv2crbbwg\nY14KK228IGNeKitxzNVafrfWbu23gTeoTG37I+B3TNN8ZnJbP5U+PwCfALzAQaBvxteXl3S0Qghx\nh/n9Pjrbo6hWnmIht+DjaarKI7va+c3P7KZ74+zeQLbj8LNj/Xz5qWOcvjy64HMJIYQQi2nZZ4Cg\nkgUCPjv5dfM2dcb/71jKcQkhxHKmqiqtsWYymQmGx9K4vYEFr9dpCLj5Z4/FOdOb5LlXLpNM3yjS\nOZYp8t9fOMuODY18/KGNNATmLNQphBBC3DErIgASQghRu0DAj9frYXBolBIahuFZ8DG3dzayqT3E\nT968zks39Qg6fSXJ+esp3r9nPQ/takVb4BQ8IYQQop7kXUkIIdYATdNob20h7NPJZ9PUowKooWs8\ntr+T3/jkLrraQrO2lco23z/Uyx998wSX+ue9BFMIIYRYdBIACSHEGhIOhehoa8YqZigXi3U5ZrTR\ny69+bAeffs9m/J7ZEwsSyRx/8VwPT//kAplcqS7nE0IIIRZCAiAhhFhjNE1jfVsMnxvy2Uxdjqko\nCvdua+G3f/4e9u+Ivq0xw5tnh/jSN45x+EwCe5n3nxNCCLG6SQAkhBBrVKSxgbaWMIXcOOVyuS7H\n9Lp1nnxkE7/25F20N83uDZQrlPn2zy7y58+eon9koi7nE0IIIaolAZAQQqxhbrebzvYYhlomn8/e\n/gnz1BEN8us/t5OPPbgB9029gXoHM3zlWyd4/rUrFIpW3c4phBBCzIcEQEIIscYpikK0OUK00U8+\nO45t16cBnqYqPHh3G7/1md3s3BSZtc124OUT/XzpqWOcvDhSl6IMQgghxHxIACSEEAIAn9dLZ3sU\npZyjWMzX7bghv8E/eXQb//zD24mEZvcGGp8o8j9/eI6vf99kdLx+5xRCCCHmIgGQEEKIaaqq0tba\nQqPfVbdy2VO2dTTwxU/t5n171qGps8skmFfH+PJTx/jxm9cpW/XJQAkhhBC3IgGQEEKItwkGg6xv\nbaJcyFAu1a98tUtXeXRvB1/81C62rAvP2la2HH5w5Cp/+PRxLvSl6nZOIYQQYiYJgIQQQtySrut0\ntMfwGTaFbH2rtjU3ePnsR7bz8+/bQtDrmrVtOJXnr757mm+8eJ50tj69ioQQQogp+u13EUIIsZZF\nGhvxefMMjqTQDR+apt3+SfOgKAq7tzQT72zghcNXOdgzyMwZd2+dH+ZMb7Iu5xJCCCGmSAZICCHE\nbXk8Hjrbo2hOgUIdy2UDeAydxx/q4vNP3s26Fv+sbXkpky2EEKLOJAASQggxL4qi0Bptpun/b+/O\nw+Sq63yPvzsLSZolJCxJ2MwIzlcBh+WqwDAOijODjFfwojOg3CsKDgrjgw7OFVBARGYcNp0LCogj\n8cEF13FAFmcBRVknjAvD4ldFeIIQoiGQIJ1Alr5/nNNYNL1UJV1dp+q8X8+TJ12nf1X9/fWv6vz6\nU+ecX201a8IXSADYcbstOOHwPTnswIUv+OwgSZImigFIktSSLbbYnF122A7WDkzoctkAU6b0sf8e\n8zn5yL3Ya7dtJvSxJUkCA5AkaSO0c7lsgC37N+PIg1/Cqf973wl9XEmSDECSpI02tFz2+md/y7pn\nJ37Ftjlbzhi/kSRJLTAASZI2ybRp09hpwTz6Z8Cagd92uhxJksZkAJIkTYi5c7ZmwXazeWb1Ktav\nd/U2SVI1GYAkSRNmxowZ7LLDPKb3rWXNBC+XLUnSRDAASZImVF9fH9tvO5ftZvezZmAIHRdRAAAY\nHElEQVQVGzZs6HRJkiQ9xwAkSWqLzTfvZ5cdtqdv3eoJXy5bkqSNZQCSJLVNu5fLliSpVQYgSVLb\nPW+57LVrO12OJKnGDECSpEnx3HLZmw26XLYkqWMMQJKkSeVy2ZKkTjIASZImnctlS5I6xQAkSeqI\n5y+X7QIJkqTJYQCSJHVUsVz2dmx49mnWPftsp8uRJPU4A5AkqeOmTJnCjgu2p38GPDPwdKfLkST1\nMAOQJKky5s7Zmnnbbskzq59iw4YNnS5HktSDDECSpEqZOXMmOy/Yjr51qz0lTpI04QxAkqTKmTJl\nCgvmb8eW/VNY9etfdrocSVIPMQBJkipr69mz+dV9332g03VIknqHAUiSVGmDro8tSZpABiBJkiRJ\ntWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJ\nkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQb\nBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtWEAkiRJ\nklQbBiBJkiRJtWEAkiRJklQbBiBJkiRJtTGt0wU0IyJmAJcARwADwIWZ+YlR2u4DXAq8HLgHOCEz\nfzhZtUqSJEmqrm45AnQBsC/wGuBE4CMRccTwRhHRD1wH3Fy2vx24LiJmTV6pkiRJkqqq8gGoDDXH\nASdl5k8y82rgPOC9IzQ/ChjIzFOy8H7gKeAvJq9iSZIkSVVV+QAE7EVxqt7tDdtuAfYboe1+5fca\n3Qoc0J7SJEmSJHWTbrgGaAGwPDPXNWxbBsyMiG0y8/Fhbe8Zdv9lwB5trrHrHPsPN71g2xWnHtyB\nSjTRHNve5diqWd34XPnbi25ixcDvbs/thwtOqnbNZy+6jYeWrXnu9sJ5MznznX/YwYrGNrBmLdfd\nuYTHHh9g/jb9vGG/XeifOb3TZY1p+ZOrufTqe1m+cjXbzp7FCYfvwbZbV/vKhm6see269fzoF8tZ\nObCW2f3T2XPhHKZPm9rpstqmG44A9QPPDNs2dHtGk22Ht6u1kSbGsbarezi2vcuxVbO68bkyPPwA\nrBgotlfV8PAD8NCyNZy96LYOVTS2gTVrOWvRYm6862Hue3AFN971MGctWszAmrWdLm1Uy59czamf\nuZ0Hl67iqYG1PLh0Fad+5naWP7m606WNqhtrXrtuPYtu+CnfvvVBbrv7Ub5964MsuuGnrF23vtOl\ntU03HAFawwsDzNDtgSbbDm83pqlTuyEXtse0ab3Z96ExdWx7U93Htw5jWyVVrKlZVX2uDA8/jdur\nWvPw8NO4vYo1X3fnElYNPPu8basGnuW6O5fw1te9pENVje3Sq+9lw+Dzt20YLLZ/9LhXdaaocXRj\nzT/6xXIeWzFAH30A9NHHYysGuOehJ3jlS7fvcHWj25R9cTcEoEeAbSNiSmZuKLfNB1Zn5pMjtJ0/\nbNt8YGkrP3Crrap9mLKd5szZvNMltJVj29vqOr51GNsq6ebnWTc+V6x5Yjz2+O/+wAWe+/qxFQOV\nrBdg+cqRj5osX7namifQyoG1TGsIE1On9gF9rBxYW9maN1U3BKAfA2uB/YGh48qvBhaP0PYO4JRh\n2w4EzmnlB65atZr16zeM37AHPfHE050uoS2mTp3CVlvNcmx7VN3Htw5jWyXd/DzrxueKNU+M+dv0\nc++DxWXTffQxSHGYYv7c/krWC7Dt7Fk8NfDCU/S2nT3LmifQ7P7prFu/gT76mDq1j/XrBxlkkNn9\n0ytbM2za/FD5AJSZqyPiSuCyiDgW2An4AHAMQETMA1Zm5hrgG8DHI+KTwOXAeyiuC/paKz9z/foN\nrFvXnZPbpur1fju2va2u41vHPndSNz/Pqlr33P6RT4Ob21/dmhfOmzniaXAL582sZM1v2G8XFt+3\n7HmnwW3Vvxlv2G+XStYLcMLhe3DqZ25/3illU/qK7dY8cfZcOIfF9y/jsRUDUIbj+XP72XPhnMrW\nvKn6BgcHx2/VYeUHmV4CvBlYCZyXmReX39sAvCMzryxvvwL4DPBS4G7g3Zl5dws/bvCJJ57u2QEf\n0o0rBG2KadOmMGfO5ji2vaku41vjse0bv+Wk6Yo5ohufK64C137PrQK3YoD5c10Frl26sea169Zz\nz0NPdNUqcJsyP3RFAJpkXTG5TYS6/NEI9eor2N9eVqe+ggFoU3Tjc8Wa26/b6gVrnizdVvOmzA/V\nW6ZEkiRJktrEACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJ\nkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrD\nACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJ\nkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxA\nkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSp\nNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJ\nkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrD\nACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJkmrDACRJkiSpNgxAkiRJ\nkmrDACRJkiSpNgxAkiRJkmpjWqcLaEZE/ANwLEVg+1xmnjJG2/2BC4E/AH4FXJCZn5uUQiVJkiRV\nWuWPAEXEB4CjgMOBNwNHR8TJo7SdB1wP3ATsDZwFXBwRh05OtZIkSZKqrBuOAJ0EnJ6ZtwNExCnA\nx4BPjND2TcDSzDyjvP1ARLwWeBtww2QUK0mSJKm6Kn0EKCIWADsDP2jYfAvwovJoz3A3AO8cYfvs\nNpQnSZIkqctU/QjQAmAQeLRh2zKgD9ip/Po5mbkEWDJ0OyK2pzh97sy2VypJkiSp8joegCJiJrDj\nKN/eAiAzn23Y9kz5/4wmHvebFOHp8lZqmjq10gfGJsxQP+vQ3zr1FexvL6tTX6Ga/axiTSPpxueK\nNbdft9UL1jxZuq3mTamzb3BwcAJLaV1EHAR8l+JIz3CnAOcCs4ZCUBlsBoB9M/PHozzm5sA1wO7A\ngZn5y3bULkmSJKm7dPwIUGbezCjXIpXXAJ0LzOd3p7bNpwhLS0e5z5bAd4AXA681/EiSJEkaUulj\nXJm5FHgY+KOGza8GlmTmsuHtI6IP+BawEPjjzPzpZNQpSZIkqTt0/AhQEy4Fzo2IRygWP/g4cP7Q\nNyNiW2B1Zj4NvAt4DfBGYFXDSnHPZuYTk1q1JEmSpMrphgB0PrAd8M/AOuCfMvP/NXx/MbAIOBs4\ngiIkXTvsMW4GDm5/qZIkSZKqrOOLIEiSJEnSZKn0NUCSJEmSNJEMQJIkSZJqwwAkSZIkqTYMQJIk\nSZJqoxtWgZtQETEDuIRixbgB4MLM/MQoba+mWFJ7kGJ1uUHgjZl5/SSVO2HKft8F/HVmfn+UNvtQ\nLDv+cuAe4ITM/OHkVTkxmuxr149tROwAXAS8luK5/DXgtMx8doS2XT+2Lfa3q8c3InYFPg0cCDwO\nfCozLxilbS+MbSv9rczYRsS/Al/KzCsn+2ePp5W5rmqa2YdXQSv7pKpo5bVWNRFxHbAsM4/tdC3j\niYg3Uaxe3Lif+mZm/mVHCxtFRGwGfBJ4K/AMcEVmfrizVY0uIo6hWAG68ffbB2zIzKayTR2PAF0A\n7EvxeUEnAh+JiCNGafsy4G3AAmB++f+/T0KNE6qcTK4Cdh+jTT9wHcWS4fsCtwPXRcSsSSlygjTT\n11IvjO03gZkUE9lRFH8Ufmx4o14ZW5rsb6lrx7f8QOfrgGXA3sB7gNMj4qgR2nb92LbS31LHxzYi\n+iLiYuBPJvPntqiVua4yWtiHV0Er+6SO24jXWmWUNR7a6TpasDtwDcU+amg/9a6OVjS2i4DXAX9K\nsX/9q4j4q86WNKav8Lvf63zgRcAvgH9s9gFqdQSo/GPhOOCQzPwJ8JOIOA94L0VSb2y7GfB7wF2Z\n+etJL3aCRMTLgC830fQoYCAzTylvvz8i/hz4C6By726OpNm+9sLYRkQArwLmZebyctuZFJ+bdcqw\n5r0wtk33twfGdx7wI+DE8gOeH4iIG4E/otjpN+r6saWF/lZhbMt3/b9Y1vFkJ2oYTytzXZW0MF91\nXIv74KpoZd9SGRExBzgP+M9O19KClwH3ZOZvOl3IeMrf77HAwZn5X+W2C4D9gM92srbRZOYzwHNz\nQEScVn552sj3eKFaBSBgL4o+396w7RbgQyO0DWAD8MtJqKudDgJuBE6nOEQ/mv0ofheNbgUOoHv+\nkGq2r70wto8Brx+aeEt9wOwR2vbC2LbS364e38x8jOI0BAAi4kDgjynerR2u68e2xf5WYWz3BZYA\nbwH+q4N1jKWVua5Kmt2HV0Er+6RKaPG1ViUXUOzPdux0IS3YnS4564AiAD+Zmc/NJZl5XgfraUkZ\n4D4IHJuZa5u9X90C0AJgeWaua9i2DJgZEdtk5uMN218GrAK+GBGvAR4GPpKZ35m0aidAZl429HXx\nhtWoFlBcP9BoGbBHG8pqixb62vVjm5kradi5lqc2vBf4jxGa98LYttLfrh/fIRHxELAzcC0jv3Pf\n9WPbqIn+dnxsM/Pasr7x9jOd1MpcVxkt7MM7rsV9UuU08VqrhIg4GHg1xTWOl43TvEoCeH1EfBiY\nCnwdOLOVP9An0YuBhyLi/1C8SbIZxfU1f5eZgx2trDknAo9k5rdauVPdrgHqp7i4q9HQ7RnDtr8U\nmAXcABwCXA98OyL2bWuFnTPa72b476UX9OLYnk9xTvdIFy324tiO1d9eGt8jKK4r2IeRz23utbEd\nr79tH9uImBkRu47yr3+ifk6btTLXaWKMtU+qovFeax1XXg92GcUpe8Ofz5UVEbtQ7KdWU5yO/AHg\naIrT+KpoC+D3geOBd1DUexLw/g7W1IrjKK5hakndAtAaXrjzH7r9vMPtmXk2sGNmfiEz/zszP0ox\n6R7f/jI7YrTfTdVPQ2hZr41tRJxLsbM6OjPvH6FJT43teP3tpfHNzB+Wq5v9DXB8RAw/at9TYzte\nfydpbPcDfg78bIR/VV70oFHTc502XRP74MppYt9SBWcBizOzK46qDcnMJcA2mXlcZt6dmVdThInj\nyyOFVbMO2BJ4a2bemZn/Avwd8O7OljW+iHglxamRX231vlV8wrfTI8C2ETElMzeU2+YDqzPzBRez\nloe4G91Pd6xMszEeofhdNJoPLO1ALW3XK2NbrkT1boqJ919GadYzY9tkf7t6fCNie+CActIcch/F\naQlbASsatnf92LbY37aPbWbeTPe/OdjSXKeN1+w+qQpafa1VwJHAvIh4qrw9AyAi3pKZW3WurPGN\n8Dq7n2LFwLkUy49XyVJgTWb+qmFbUpwiWXWHAN8fYV4YV7fv5Fv1Y2AtsH/DtlcDi4c3jIhFEfG5\nYZv3Bn7avvI66g7gD4dtO7Dc3lN6ZWwj4iMU73wfmZlfH6NpT4xts/3tgfH9PeCfI2JBw7ZXAL/J\nzOF/oPTC2Dbd3x4Y28nS9FynjdfCPrgqWtm3VMFBFNf+7FX+uwa4uvy6siLizyJieUTMbNi8D/B4\nRa+/u4Pi+sDdGrbtDjzUmXJash/Fwj8tq9URoMxcHRFXApdFxLHAThTnOh4DEBHzgJWZuYbihXZV\nRHwPuI3i/M0DgSqvi96SYf39BvDxiPgkcDnFqjD9FB/s1vV6bWzL5WJPB/4euK3sHwCZuazXxrbF\n/nb7+C6m+BDIKyLiZIo/Ws4DzoGefN220t9uH9tJMd5cp0033j6pY4WNbczXWtVk5sONt8sjQYOZ\n+WCHSmrWbRSnmv5TRJwN7Erxez63o1WNIjN/FsWHzH4+Ik6kWETlFODszlbWlD2BL2zMHet2BAjg\nZIqlS28CLgbOaDgcvBT4S4ByNYkTKXZw/01xseAh5bmd3Wr4ah6N/X0K+J8US2LeRfH5Bodm5upJ\nrXDijNXXXhjbwyhev6cDj5b/lpb/Q++NbSv97erxLU9ZOhx4mmIivRz4x8z8VNmkp8a2xf5WbWyr\nvELSWHNdN6jy7xbG3ydVThOvNU2AzPwtxalZ21GEzs8Cl2XmhR0tbGxHU3yQ6A+AzwMXZeanO1pR\nc7YHntiYO/YNDlZ9HyNJkiRJE6OOR4AkSZIk1ZQBSJIkSVJtGIAkSZIk1YYBSJIkSVJtGIAkSZIk\n1YYBSJIkSVJtGIAkSZIk1YYBSJIkSVJtGIAkSZIk1ca0ThcgCSLiIWCXhk2DwG+BHwFnZOYPxrn/\nQcB3gYWZuaRNZUqSOmhT54pN+LmLgBdl5sHteHxpsnkESKqGQeB8YH75bwfgAGAl8J2I2KnJx5Ak\n9a6JmCuk2vMIkFQdT2fmrxtuL4uI9wCPAP8LuLgzZUmSKsS5QtpEBiCp2taX/6+JiGnAmcDbge2A\n+4DTMvM/ht8pIrameJfwUGB74AngauCkzFxTtvlb4D3ATsCjwBWZeU75vVkUk+gbgK2B+4GPZea3\n2tRPSdLGG5ornomInSn2/68F5gDLgC9l5qkAEXEMcDpwHfAO4KbMPCIidgMuBA4C1gH/BrwvM39T\nPvb0iDivvE8/8O/A8Q3fl7qGp8BJFRUROwKfoji/+wbgIuB44G+APYF/Ba6JiJeMcPfPA3sBbwJ2\nA95PEZyOLx/7jcBp5e3dgFOAD0fE28r7n1P+jNcDLy1//lciovHcc0lShw2bK64HrgG2BF4H/D5F\nGPpgRBzWcLddgQXA3hT7/tnA94HpwGvK++4KfLXhPgdSvCF2IPDnFKfend+ufknt5BEgqTo+FBH/\nt/x6GrAZxZGXtwBPAscCf91wFOb0iADYaoTH+jfg5sy8t7y9JCJOAl5e3n4xsAZYkpm/Ar4eEY8A\nSxq+/xTwUGaujIgzgO9RHEmSJHXOWHPFcuBK4GuZ+UjZ5qKIOI1i/39NuW0QODszHwKIiHcDWwBH\nZeaqcttxwFsjYnp5n0cz8/jy659HxFeAP2lTH6W2MgBJ1XEZxVEeKE5nWJGZTwFExP+geGfuzsY7\nZObp5fcPGvZYlwKHRcQ7gZcAewALKSZJgC8C7wR+FhH3UZzK8I0yDAGcSzFR/iYi7qQIVF8eqkeS\n1DGjzhUAEfFp4C0RsR/FEf4/oDgVeuqwx/lFw9d7Aj8bCj8AmXkP8OHyMQEeGHb/J4BZm9oZqRMM\nQFJ1rMjMX47yvbVAXzMPEhF9FOd27w58GfgK8EPgs0NtMvNxYO+IOAD4M+AQ4H0RcWZmnpOZd5Tn\nkf8pxTt8bwfOiIhDMvO7G9c9SdIEGHWuiIh+4AfADODrwCLgP4FbhrfNzGcabq5t4ueuH2FbU/OS\nVDUGIKk7/JxignolcM/Qxoi4A7gK+HFD270prt15VWbeVbabTvFO4APl7bcBW2fmJcDtwEcj4nLg\nKOCciDgLuCUzrwWujYiTgXuBN1N83pAkqXoOoZgD5mXmcoCImAvMY+ywch/wrojYsuHMg30prv/c\np70lS5PPACR1gcxcHREXU4ST5RRh5F0Up7ZdT/FZEEOT22MUYenIsu22wIcoJsAZZZuZwAURsYri\n3cKdKVb++V75/RcDR0fE8RShaX+KD9+7tY3dlCRtmqHTmN8eEd+g2G//PcXfezNGvRd8iWJluC+U\n13xuBlwC/CQzHy1PgZN6hqvASdXQzIeYnkpxceulwN0UgeXQzPx542Nk5lLgGOAwinf1vkYxKX4S\neEXZ5gqKJbXPoLgu6KsU7/S9r3ysE4EbgS8ACXwU+GBmXrUpnZQkbZIx54rMXAycDJxEsW+/guKN\nrasoziAY7X6rKY4eTQduo3hj7R7gyIkoWqqavsFBPzxekiRJUj14BEiSJElSbRiAJEmSJNWGAUiS\nJElSbRiAJEmSJNWGAUiSJElSbRiAJEmSJNWGAUiSJElSbRiAJEmSJNWGAUiSJElSbRiAJEmSJNWG\nAUiSJElSbRiAJEmSJNXG/wfgf+XzPFWa4QAAAABJRU5ErkJggg==\n", 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EREREpE4ymSzheErNDhpIAUhEREREpA5i8VlSuZKaHTSYApCIiIiISA1ZlkUwFGEOJx6v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lwHV0Uz65bKiIgFwAXAf7a7liY8CbgtM//Q7kLGU/5+TwCOyMz/KpddCBwKfLydtY0mMzcCj8wB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\nJEmSpNowAEmSJEmqDQOQJEmSpNr4/zbtvRJhDx6SAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -535,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -552,7 +544,7 @@ " [0, 3]], dtype=int64)" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -565,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1466,7 +1458,7 @@ " [0]], dtype=int64)" ] }, - "execution_count": 16, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1478,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -1491,7 +1483,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -1508,7 +1500,7 @@ " [0, 2]], dtype=int64)" ] }, - "execution_count": 19, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1519,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 54, "metadata": { "collapsed": false }, @@ -1538,7 +1530,7 @@ "0.63228699551569512" ] }, - "execution_count": 20, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1551,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -1566,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -1577,7 +1569,7 @@ "array([ 0.61452514, 0.68156425, 0.70786517, 0.75280899, 0.70621469])" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1586,6 +1578,106 @@ "scores" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linreg = LinearRegression()\n", + "linreg.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.820560041245\n", + "[ 0.05306367 -0.19793906]\n" + ] + } + ], + "source": [ + "print linreg.intercept_\n", + "print linreg.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -132,7 +132,7 @@ "4 0 373450 8.0500 NaN S " ] }, - "execution_count": 3, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -429,10 +429,11 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/labs/Titanic_Eugene.ipynb b/labs/Titanic_Eugene.ipynb index 2dc8b4a..0ad6fa6 100644 --- a/labs/Titanic_Eugene.ipynb +++ b/labs/Titanic_Eugene.ipynb @@ -157,19 +157,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Eugene\\Anaconda2\\envs\\bersonenv\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n", - " RuntimeWarning)\n" - ] - }, { "data": { "text/html": [ @@ -294,7 +286,7 @@ "max 6.000000 512.329200 " ] }, - "execution_count": 3, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -318,7 +310,7 @@ "Name: Survived, dtype: float64" ] }, - "execution_count": 5, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -329,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -352,7 +344,7 @@ "dtype: int64" ] }, - "execution_count": 6, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -363,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -492,7 +484,7 @@ "max 4.000000 512.329200 " ] }, - "execution_count": 8, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -503,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -511,18 +503,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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AEhERERFpgGQqRSpTxKP5PnWlACQiIiIiUkeVSoVQOEbJ5tSStwZQABIRERERqZNCocB0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\nlVtdC7vGZTIZkpkMZRx4vY1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h/nu20t+1+vOWvD4/RR0NEhFpG9FYnNmihd+v5VrFUpmX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\nAUiSJElSbRiAJEmSJNXG/wfgf+XzPFWa4QAAAABJRU5ErkJggg==\n", 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EREREpE4ymSzheErNDhpIAUhEREREpA5i8VlSuZKaHTSYApCIiIiISA1ZlkUwFGEOJx6v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lwHV0Uz65bKiIgFwAXAf7a7liY8CbgtM//Q7kLGU/5+TwCOyMz/KpddCBwKfLydtY0mMzcCj8wB\nEXFG+eMZIz/i0WoVgICDKPp8Q8OyHwFvH6FtAFuB305BXZPpcOA7wJkUm+hHcyjF76LRj4Fn0Tl/\nSE20r90wtvcDLxiaeEs9wLwR2nbD2DbT344e38y8n2I3BAAi4jDgLyi+rR2u48e2yf5WYWwPAZYD\nxwL/1cY6xtLMXFclE12HV0Ez66RKaPKzViUXUqzP9mh3IU3Ynw7Z64AiAD+YmY/MJZl5QRvraUoZ\n4N4GnJCZAxN9XN0C0DJgVWZubli2EpgdEbtm5gMNy58ErAM+HRHPAe4B3pmZ35iyalsgMy8d+rn4\nwmpUyyiOH2i0EjhgEsqaFE30tePHNjPX0rByLXdteBPw7RGad8PYNtPfjh/fIRFxN7AXcDUjf3Pf\n8WPbaAL9bfvYZubVZX3jrWfaqZm5rjKaWIe3XZPrpMqZwGetEiLiCODZFMc4XjpO8yoJ4AUR8Q6g\nF/gScHYzf6BPoccBd0fE/6H4kmQmxfE1/5iZg22tbGJOBu7NzK8286C6HQPUR3FwV6Oh27OGLd8P\nmANcCxwJfB34WkQcMqkVts9ov5vhv5du0I1j+16KfbpHOmixG8d2rP520/geQ3FcwVMZed/mbhvb\n8fo76WMbEbMj4vGj/Otr1etMsmbmOrXGWOukKhrvs9Z25fFgl1Lssjf8/VxZEbE3xXpqPcXuyG8F\nXkmxG18V7Qw8ETgJeA1FvacAb2ljTc04keIYpqbULQBt4NEr/6Hb22xuz8xzgT0y81OZ+d+Z+S6K\nSfekyS+zLUb73VR9N4SmddvYRsT5FCurV2bmHSM06aqxHa+/3TS+mfnT8uxmfw+cFBHDt9p31diO\n198pGttDgV8DvxrhX5VPetBownOddtwE1sGVM4F1SxWcA9yUmR2xVW1IZi4Hds3MEzPz1sy8kiJM\nnFRuKayazcAuwMsz88bM/HfgH4HXt7es8UXE0yl2jfxCs4+t4ht+Mt0LLIqIaZm5tVy2FFifmY86\nmLXcxN3oDjrjzDTb416K30WjpcCKNtQy6bplbMszUb2eYuL991Gadc3YTrC/HT2+EbEYeFY5aQ65\nnWK3hLnA6oblHT+2TfZ30sc2M79P53852NRcp+030XVSFTT7WauA44AlEfFQeXsWQEQcm5lz21fW\n+Eb4nN1BccbAhRSnH6+SFcCGzPxdw7Kk2EWy6o4EfjDCvDCuTl/JN+vnwADwzIZlzwZuGt4wIi6P\niE8MW3ww8MvJK6+tfgL82bBlh5XLu0q3jG1EvJPim+/jMvNLYzTtirGdaH+7YHwfC/xbRCxrWPY0\n4A+ZOfwPlG4Y2wn3twvGdqpMeK7T9mtiHVwVzaxbquBwimN/Dir/XQVcWf5cWRHxVxGxKiJmNyx+\nKvBARY+/+wnF8YH7NizbH7i7PeU05VCKE/80rVZbgDJzfURcAVwaEScAe1Ls6/hqgIhYAqzNzA0U\nH7TPRcT3gOsp9t88DKjyedGbMqy/XwbeExEfAD5GcVaYPooLu3W8bhvb8nSxZwL/BFxf9g+AzFzZ\nbWPbZH87fXxvorgI5GURcSrFHy0XAOdBV35um+lvp4/tlBhvrtOOG2+d1LbCxjbmZ61qMvOextvl\nlqDBzLyrTSVN1PUUu5r+a0ScCzye4vd8flurGkVm/iqKi8x+MiJOpjiJymnAue2tbEIOBD61PQ+s\n2xYggFMpTl16HXAxcFbD5uAVwN8AlGeTOJliBfffFAcLHlnu29mphp/No7G/DwH/k+KUmDdTXN/g\nqMxcP6UVts5Yfe2GsT2a4vN7JnBf+W9F+T9039g209+OHt9yl6WXAA9TTKQfAz6YmR8qm3TV2DbZ\n36qNbZXPkDTWXNcJqvy7hfHXSZUzgc+aWiAz/0ixa9ZuFKHz48Clmfm+thY2tldSXEj0h8AngYsy\n88NtrWhiFgNrtueBPYODVV/HSJIkSVJr1HELkCRJkqSaMgBJkiRJqg0DkCRJkqTaMABJkiRJqg0D\nkCRJkqTaMABJkiRJqg0DkCRJkqTaMABJkiRJqg0DkCRJkqTamN7uAiRBRNwN7N2waBD4I/Az4KzM\n/OE4jz8c+C6wT2Yun6QyJUlttKNzxQ687uXAYzLziMl4fmmquQVIqoZB4L3A0vLf7sCzgLXANyJi\nzwk+hySpe7VirpBqzy1AUnU8nJm/b7i9MiLeANwL/C/g4vaUJUmqEOcKaQcZgKRq21L+vyEipgNn\nA68CdgNuB87IzG8Pf1BEzKf4lvAoYDGwBrgSOCUzN5Rt/gF4A7AncB9wWWaeV943h2ISfREwH7gD\neHdmfnWS+ilJ2n5Dc8XGiNiLYv3/XGABsBL4TGaeDhARrwbOBK4BXgNcl5nHRMS+wPuAw4HNwDeB\nN2fmH8rnnhERF5SP6QO+BZzUcL/UMdwFTqqoiNgD+BDF/t3XAhcBJwF/DxwI/AdwVUQ8YYSHfxI4\nCHgpsC/wForgdFL53C8Gzihv7wucBrwjIl5RPv688jVeAOxXvv7nI6Jx33NJUpsNmyu+DlwF7AI8\nD3giRRh6W0Qc3fCwxwPLgIMp1v3zgB8AM4DnlI99PPCFhsccRvGF2GHACyl2vXvvZPVLmkxuAZKq\n4+0R8X/Ln6cDMym2vBwLPAicAPxdw1aYMyMCYO4Iz/VN4PuZ+Yvy9vKIOAV4cnn7ccAGYHlm/g74\nUkTcCyxvuP8h4O7MXBsRZwHfo9iSJElqn7HmilXAFcAXM/Pess1FEXEGxfr/qnLZIHBuZt4NEBGv\nB3YGjs/MdeWyE4GXR8SM8jH3ZeZJ5c+/jojPA8+fpD5Kk8oAJFXHpRRbeaDYnWF1Zj4EEBH/g+Kb\nuRsbH5CZZ5b3Hz7suS4Bjo6I1wJPAA4A9qGYJAE+DbwW+FVE3E6xK8OXyzAEcD7FRPmHiLiRIlB9\ndqgeSVLbjDpXAETEh4FjI+JQii38T6HYFbp32PPc2fDzgcCvhsIPQGbeBryjfE6A3wx7/Bpgzo52\nRmoHA5BUHasz87ej3DcA9EzkSSKih2Lf7v2BzwKfB34KfHyoTWY+ABwcEc8C/go4EnhzRJydmedl\n5k/K/cj/kuIbvlcBZ0XEkZn53e3rniSpBUadKyKiD/ghMAv4EnA58J/Aj4a3zcyNDTcHJvC6W0ZY\nNqF5SaoaA5DUGX5NMUE9HbhtaGFE/AT4HPDzhrYHUxy784zMvLlsN4Pim8DflLdfAczPzI8ANwDv\nioiPAccD50XEOcCPMvNq4OqIOBX4BfAyiusNSZKq50iKOWBJZq4CiIiFwBLGDiu3A6+LiF0a9jw4\nhOL4z6dObsnS1DMASR0gM9dHxMUU4WQVRRh5HcWubV+nuBbE0OR2P0VYOq5suwh4O8UEOKtsMxu4\nMCLWUXxbuBfFmX++V97/OOCVEXESRWh6JsXF9348id2UJO2Yod2YXxURX6ZYb/8Txd97s0Z9FHyG\n4sxwnyqP+ZwJfAS4JTPvK3eBk7qGZ4GTqmEiFzE9neLg1kuAWykCy1GZ+evG58jMFcCrgaMpvtX7\nIsWk+AHgaWWbyyhOqX0WxXFBX6D4pu/N5XOdDHwH+BSQwLuAt2Xm53akk5KkHTLmXJGZNwGnAqdQ\nrNsvo/hi63MUexCM9rj1FFuPZgDXU3yxdhtwXCuKlqqmZ3DQi8dLkiRJqge3AEmSJEmqDQOQJEmS\npNowAEmSJEmqDQOQJEmSpNowAEmSJEmqDQOQJEmSpNowAEmSJEmqDQOQJEmSpNowAEmSJEmqDQOQ\nJEmSpNowAEmSJEmqDQOQJEmSpNr4/zbtvRJhDx6SAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -535,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -552,7 +544,7 @@ " [0, 3]], dtype=int64)" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -565,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1466,7 +1458,7 @@ " [0]], dtype=int64)" ] }, - "execution_count": 16, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1478,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -1491,7 +1483,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -1508,7 +1500,7 @@ " [0, 2]], dtype=int64)" ] }, - "execution_count": 19, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1519,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 54, "metadata": { "collapsed": false }, @@ -1538,7 +1530,7 @@ "0.63228699551569512" ] }, - "execution_count": 20, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1551,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -1566,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -1577,7 +1569,7 @@ "array([ 0.61452514, 0.68156425, 0.70786517, 0.75280899, 0.70621469])" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1586,6 +1578,106 @@ "scores" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linreg = LinearRegression()\n", + "linreg.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.820560041245\n", + "[ 0.05306367 -0.19793906]\n" + ] + } + ], + "source": [ + "print linreg.intercept_\n", + "print linreg.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data.Parch,data.Survived)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "feature_cols = ['al']\n", + "X = glass[feature_cols]\n", + "y = glass.assorted\n", + "linreg.fit(X, y)\n", + "assorted_pred = linreg.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -1597,6 +1689,7 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [bersonenv]", "language": "python", diff --git a/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb new file mode 100644 index 0000000..1c6efb2 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/06_logistic_regression-checkpoint.ipynb @@ -0,0 +1,3581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agenda\n", + "\n", + "1. Refresh your memory on how to do linear regression in scikit-learn\n", + "2. Attempt to use linear regression for classification\n", + "3. Show you why logistic regression is a better alternative for classification\n", + "4. Brief overview of probability, odds, e, log, and log-odds\n", + "5. Explain the form of logistic regression\n", + "6. Explain how to interpret logistic regression coefficients\n", + "7. Compare logistic regression with other models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Predicting a Continuous Response" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# glass identification dataset\n", + "import pandas as pd\n", + "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data'\n", + "col_names = ['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type']\n", + "glass = pd.read_csv(url, names=col_names, index_col='id')\n", + "glass['assorted'] = glass.glass_type.map({1:0, 2:0, 3:0, 4:0, 5:1, 6:1, 7:1})" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ri na mg al si k ca ba fe glass_type \\\n", + "id \n", + "1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0.0 0.0 1 \n", + "2 1.51761 13.89 3.60 1.36 72.73 0.48 7.83 0.0 0.0 1 \n", + "3 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0.0 0.0 1 \n", + "4 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0.0 0.0 1 \n", + "5 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0.0 0.0 1 \n", + "\n", + " assorted \n", + "id \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glass.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretend that we want to predict **ri**, and our only feature is **al**. How would we do it using machine learning? We would frame it as a regression problem, and use a linear regression model with **al** as the only feature and **ri** as the response.\n", + "\n", + "How would we **visualize** this model? Create a scatter plot with **al** on the x-axis and **ri** on the y-axis, and draw the line of best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x='al', y='ri', data=glass, ci=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we had an **al** value of 2, what would we predict for **ri**? Roughly 1.517.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exercise: Draw the scatter plot using Pandas.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# scatter plot using Pandas\n", + "glass.plot(kind='scatter', x='al', y='ri')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit a linear regression model to predict ri from al\n", + "from sklearn.linear_model import LinearRegression\n", + "linreg = LinearRegression()\n", + "feature_cols = ['al']\n", + "X = glass[feature_cols]\n", + "y = glass.ri\n", + "linreg.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.52194533024\n", + "[-0.00247761]\n" + ] + } + ], + "source": [ + "# look at the coefficients to get the equation for the line, but then how do you plot the line?\n", + "print linreg.intercept_\n", + "print linreg.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.51946772]\n", + "[ 1.51699012]\n", + "[ 1.51451251]\n" + ] + } + ], + "source": [ + "# you could make predictions for arbitrary points, and then plot a line connecting them\n", + "print linreg.predict(1)\n", + "print linreg.predict(2)\n", + "print linreg.predict(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# or you could make predictions for all values of X, and then plot those predictions connected by a line\n", + "ri_pred = linreg.predict(X)\n", + "\n", + "# draw regression line with matplotlib and pandas\n", + "plt.scatter(glass.al, glass.ri)\n", + "plt.plot(glass.al, ri_pred, color='red')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Refresher: interpreting linear regression coefficients" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear regression equation: $y = \\beta_0 + \\beta_1x$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.51699012])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute prediction for al=2 using the predict method\n", + "linreg.predict(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featurecoef
0al-0.002478
\n", + "
" + ], + "text/plain": [ + " feature coef\n", + "0 al -0.002478" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine coefficient for al\n", + "pd.DataFrame(zip(feature_cols, linreg.coef_), columns=['feature', 'coef'])" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Note that we can't use a cross_val_score if we want to investigate variable relationships" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation:** A 1 unit increase in 'al' is associated with a 0.0025 unit decrease in 'ri'." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.51451251])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute prediction for al=3 using the predict method\n", + "linreg.predict(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: Predicting a Categorical Response" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's change our task, so that we're predicting **assorted** using **al**. Let's visualize the relationship to figure out how to do this:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(glass.al, glass.assorted)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's draw a **regression line**, like we did before:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# fit a linear regression model and store the predictions\n", + "feature_cols = ['al']\n", + "X = glass[feature_cols]\n", + "y = glass.assorted\n", + "linreg.fit(X, y)\n", + "assorted_pred = linreg.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot that includes the regression line\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, assorted_pred, color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If **al=3**, what class do we predict for assorted? **1**\n", + "\n", + "If **al=1.5**, what class do we predict for assorted? **0**\n", + "\n", + "So, we predict the 0 class for **lower** values of al, and the 1 class for **higher** values of al. What's our cutoff value? Around **al=2**, because that's where the linear regression line crosses the midpoint between predicting class 0 and class 1.\n", + "\n", + "So, we'll say that if **assorted_pred >= 0.5**, we predict a class of **1**, else we predict a class of **0**." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# understanding np.where\n", + "import numpy as np\n", + "nums = np.array([5, 15, 8])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['small', 'big', 'small'], \n", + " dtype='|S5')" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# np.where returns the first value if the condition is True, and the second value if the condition is False\n", + "np.where(nums > 10, 'big', 'small')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.06545853, 0.19576455, 0.28597641, 0.16068216, 0.13562331,\n", + " 0.32607057, 0.08550561, 0.04039968, 0.20077632, 0.19576455])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine the predictions\n", + "assorted_pred[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n", + " 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n", + " 0, 1, 1, 1, 1, 0, 1])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# transform predictions to 1 or 0\n", + "assorted_pred_class = np.where(assorted_pred >= 0.5, 1, 0)\n", + "assorted_pred_class" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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yt9lsMtA1W/G+LPVTgClFmJdAUGC0v/6M+YN3CZYrj33m/Ex/+iwYFs3P4b/v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\nk7eEXkSWhfNjwjcUJHfcW/lK1Ocj6blnIykgAY7+8gfG334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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the class predictions\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, assorted_pred_class, color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What went wrong? This is a line plot, and it connects points in the order they are found. Let's sort the DataFrame by \"al\" to fix this:" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" + ] + } + ], + "source": [ + "# add predicted class to DataFrame\n", + "glass['assorted_pred_class'] = assorted_pred_class\n", + "\n", + "# sort DataFrame by al\n", + "glass.sort('al', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the class predictions again\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, glass.assorted_pred_class, color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3: Using Logistic Regression Instead\n", + "\n", + "Logistic regression can do what we just did, but better.." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# fit a linear regression model and store the class predictions\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg = LogisticRegression()\n", + "feature_cols = ['al']\n", + "X = glass[feature_cols]\n", + "y = glass.assorted\n", + "logreg.fit(X, y)\n", + "assorted_pred_class = logreg.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print the class predictions\n", + "assorted_pred_class" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the class predictions\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, assorted_pred_class, color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What if we wanted the **predicted probabilities** instead of just the **class predictions**, to understand how confident we are in a given prediction?" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# store the predicted probabilites of class 1\n", + "assorted_pred_prob = logreg.predict_proba(X)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the predicted probabilities\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, assorted_pred_prob, color='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.89253652 0.10746348]]\n", + "[[ 0.52645662 0.47354338]]\n", + "[[ 0.12953623 0.87046377]]\n" + ] + } + ], + "source": [ + "# examine some example predictions\n", + "print logreg.predict_proba(1)\n", + "print logreg.predict_proba(2)\n", + "print logreg.predict_proba(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is this? The first column indicates the predicted probability of **class 0**, and the second column indicates the predicted probability of **class 1**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 4: Probability, odds, e, log, log-odds\n", + "\n", + "$$probability = \\frac {one\\ outcome} {all\\ outcomes}$$\n", + "\n", + "$$odds = \\frac {one\\ outcome} {all\\ other\\ outcomes}$$\n", + "\n", + "Examples:\n", + "\n", + "- Dice roll of 1: probability = 1/6, odds = 1/5\n", + "- Even dice roll: probability = 3/6, odds = 3/3 = 1\n", + "- Dice roll less than 5: probability = 4/6, odds = 4/2 = 2\n", + "\n", + "$$odds = \\frac {probability} {1 - probability}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " probability odds\n", + "0 0.10 0.111111\n", + "1 0.20 0.250000\n", + "2 0.25 0.333333\n", + "3 0.50 1.000000\n", + "4 0.60 1.500000\n", + "5 0.80 4.000000\n", + "6 0.90 9.000000" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a table of probability versus odds\n", + "table = pd.DataFrame({'probability':[0.1, 0.2, 0.25, 0.5, 0.6, 0.8, 0.9]})\n", + "table['odds'] = table.probability/(1 - table.probability)\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is **e**? It is the base rate of growth shared by all continually growing processes:" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.7182818284590451" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# exponential function: e^1\n", + "e = np.exp(1)\n", + "e" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is a **(natural) log**? It gives you the time needed to reach a certain level of growth:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# time needed to grow 1 unit to 2.718 units\n", + "np.log(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also the **inverse** of the exponential function:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5.0" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(np.exp(5))" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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probabilityoddslogodds
00.100.111111-2.197225
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" + ], + "text/plain": [ + " probability odds logodds\n", + "0 0.10 0.111111 -2.197225\n", + "1 0.20 0.250000 -1.386294\n", + "2 0.25 0.333333 -1.098612\n", + "3 0.50 1.000000 0.000000\n", + "4 0.60 1.500000 0.405465\n", + "5 0.80 4.000000 1.386294\n", + "6 0.90 9.000000 2.197225" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add log-odds to the table\n", + "table['logodds'] = np.log(table.odds)\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 5: What is Logistic Regression?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Linear regression:** continuous response is modeled as a linear combination of the features:\n", + "\n", + "$$y = \\beta_0 + \\beta_1x$$\n", + "\n", + "**Logistic regression:** log-odds of a categorical response being \"true\" (1) is modeled as a linear combination of the features:\n", + "\n", + "$$\\log \\left({p\\over 1-p}\\right) = \\beta_0 + \\beta_1x$$\n", + "\n", + "This is called the **logit function**.\n", + "\n", + "Probability is sometimes written as pi:\n", + "\n", + "$$\\log \\left({\\pi\\over 1-\\pi}\\right) = \\beta_0 + \\beta_1x$$\n", + "\n", + "The equation can be rearranged into the **logistic function**:\n", + "\n", + "$$\\pi = \\frac{e^{\\beta_0 + \\beta_1x}} {1 + e^{\\beta_0 + \\beta_1x}}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In other words:\n", + "\n", + "- Logistic regression outputs the **probabilities of a specific class**\n", + "- Those probabilities can be converted into **class predictions**\n", + "\n", + "The **logistic function** has some nice properties:\n", + "\n", + "- Takes on an \"s\" shape\n", + "- Output is bounded by 0 and 1\n", + "\n", + "Notes:\n", + "\n", + "- **Multinomial logistic regression** is used when there are more than 2 classes.\n", + "- Coefficients are estimated using **maximum likelihood estimation**, meaning that we choose parameters that maximize the likelihood of the observed data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 6: Interpreting Logistic Regression Coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the predicted probabilities again\n", + "plt.scatter(glass.al, glass.assorted)\n", + "plt.plot(glass.al, assorted_pred_prob, color='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10592543]])" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute predicted log-odds for al=2 using the equation\n", + "logodds = logreg.intercept_ + logreg.coef_ * 2\n", + "logodds" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.89949172]])" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert log-odds to odds\n", + "odds = np.exp(logodds)\n", + "odds" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.47354338]])" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert odds to probability\n", + "prob = odds/(1 + odds)\n", + "prob" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.47354338])" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute predicted probability for al=2 using the predict_proba method\n", + "logreg.predict_proba(2)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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featurecoef
0al[2.01099096417]
\n", + "
" + ], + "text/plain": [ + " feature coef\n", + "0 al [2.01099096417]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine the coefficient for al\n", + "pd.DataFrame(zip(feature_cols, logreg.coef_), columns=['feature', 'coef'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation:** A 1 unit increase in 'al' is associated with a 2.0109 unit increase in the log-odds of 'assorted'." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.87045351351387434" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# increasing al by 1 (so that al=3) increases the log-odds by 2.0109\n", + "\n", + "# the -0.10592543 is the logodds we calculated a few cells ago for al=2\n", + "# I am stepping through the equation by one \"unit\" of al\n", + "\n", + "logodds = -0.10592543 + 2.0109\n", + "odds = np.exp(logodds)\n", + "prob = odds/(1 + odds)\n", + "prob" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.87046377])" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute predicted probability for al=3 using the predict_proba method\n", + "logreg.predict_proba(3)[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Bottom line:** Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability)." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-4.12790736])" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine the intercept\n", + "logreg.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation:** For an 'al' value of 0, the log-odds of 'assorted' is -4.127" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.01586095])" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert log-odds to probability\n", + "# Probability of assorted is low if al = 0\n", + "logodds = logreg.intercept_\n", + "odds = np.exp(logodds)\n", + "prob = odds/(1 + odds)\n", + "prob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That makes sense from the plot above, because the probability of assorted=1 should be very low for such a low 'al' value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](images/logistic_betas.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changing the $\\beta_0$ value shifts the curve **horizontally**, whereas changing the $\\beta_1$ value changes the **slope** of the curve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 7: Comparing Logistic Regression with Other Models\n", + "\n", + "Advantages of logistic regression:\n", + "\n", + "- Highly interpretable (if you remember how)\n", + "- Model training and prediction are fast\n", + "- No tuning is required (excluding regularization)\n", + "- Features don't need scaling\n", + "- Can perform well with a small number of observations\n", + "- Outputs well-calibrated predicted probabilities\n", + "\n", + "Disadvantages of logistic regression:\n", + "\n", + "- Presumes a linear relationship between the features and the log-odds of the response\n", + "- Performance is (generally) not competitive with the best supervised learning methods\n", + "- Sensitive to irrelevant features\n", + "- Can't automatically learn feature interactions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: Confusion Matrix\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[160 3]\n", + " [ 31 20]]\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "preds = logreg.predict(X)\n", + "print metrics.confusion_matrix(y, preds)\n", + "# Note that we can't make this martix using cross_val_score so a train_test_split has to do!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Top Left: True Negatives
\n", + "##Top Right False Negatives
\n", + "##Bottom Left: False Negatives
\n", + "##Bottom Right: True Positives
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise** Calculate:\n", + "Accuracy\n", + "Sensitivity\n", + "Specificity\n", + "Precision by hand\n", + "\n", + "\n", + "\n", + "












\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "#### Accuracy = (157 + 28) / 214 == .8644\n", + "#### Sensitivity (Recall) = 28 / (23 + 28) == .5490\n", + "#### Specificity = 157 / (157 + 6) == .9631\n", + "#### Precision = 28 / (28 + 6) == .823" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.98 0.90 163\n", + " 1 0.87 0.39 0.54 51\n", + "\n", + "avg / total 0.85 0.84 0.82 214\n", + "\n" + ] + } + ], + "source": [ + "print metrics.classification_report(y, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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E28AqrEjFKt2GcfenJg2Lz723vl7aeMzvw+ebSGPjbTQ2/gJRs1rbVZWUBELn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\neTRMAf4DUYw0C6hzOp1HXC5X00h0+lJjuNKftnO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# MORE DATA\n", + "\n", + "# Logistic Regression is a high bias low variance model that is also non-parametric\n", + "\n", + "from sklearn.datasets import make_circles\n", + "from sklearn.cross_validation import cross_val_score\n", + "circles_X, circles_y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)\n", + "plt.scatter(circles_X[:,0], circles_X[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qAUGRBLu4n5cmb8BUDv34Kp2MZezx9iZw+HA+TE7G19eXCePG4e/tfd41tbQK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# It has a linear decision boundary, IE the shape is draws between classes are lines!\n", + "\n", + "from matplotlib.colors import ListedColormap\n", + "import numpy as np\n", + "\n", + "h = .02 # step size in the mesh\n", + "\n", + "# Create color maps\n", + "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", + "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", + "\n", + "# we create an instance of Neighbours Classifier and fit the data.\n", + "logreg = LogisticRegression()\n", + "logreg.fit(circles_X, circles_y)\n", + "\n", + "# Plot the decision boundary. For that, we will assign a color to each\n", + "# point in the mesh [x_min, m_max]x[y_min, y_max].\n", + "x_min, x_max = circles_X[:, 0].min() - 1, circles_X[:, 0].max() + 1\n", + "y_min, y_max = circles_X[:, 1].min() - 1, circles_X[:, 1].max() + 1\n", + "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", + " np.arange(y_min, y_max, h))\n", + "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "\n", + "# Put the result into a color plot\n", + "Z = Z.reshape(xx.shape)\n", + "plt.figure()\n", + "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", + "\n", + "# Plot also the training points\n", + "plt.scatter(circles_X[:, 0], circles_X[:, 1], c=circles_y, cmap=cmap_bold)\n", + "plt.xlim(xx.min(), xx.max())\n", + "plt.ylim(yy.min(), yy.max())\n", + "plt.title(\"Circle classification Logistic Regression\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.48899999999999999" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = LogisticRegression()\n", + "cross_val_score(logreg, circles_X, circles_y, cv=5, scoring='accuracy').mean()\n", + "# lame" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier # compare to knn\n", + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "cross_val_score(knn, circles_X, circles_y, cv=5, scoring='accuracy').mean()\n", + "# not as lame, remember?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 5., ..., 0., 0., 0.],\n", + " [ 0., 0., 0., ..., 10., 0., 0.],\n", + " [ 0., 0., 0., ..., 16., 9., 0.],\n", + " ..., \n", + " [ 0., 0., 1., ..., 6., 0., 0.],\n", + " [ 0., 0., 2., ..., 12., 0., 0.],\n", + " [ 0., 0., 10., ..., 12., 1., 0.]])" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import datasets\n", + "\n", + "# new dataset, handwritten digits!\n", + "digits = datasets.load_digits()\n", + "digits.data" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(digits.images[-5], cmap=plt.cm.gray_r, interpolation='nearest')\n", + "# the number 9\n", + "\n", + "\n", + "digits.target[-5]" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.data.shape\n", + "# 1,797 observations, 64 features (8 x 8 image)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "digits_X, digits_y = digits.data, digits.target" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.92101881133607011" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = LogisticRegression()\n", + "cross_val_score(logreg, digits_X, digits_y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9627899114966898" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compare to KNN\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "cross_val_score(knn, digits_X, digits_y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Thought Exercise, why would KNN potentially be a better model than logsitci regression\n", + "# for handwriting?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# OK so wait, when should we use Logistic Regression?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Using dataset of a 1978 survey conducted to measure likliehood of women to perform extramarital affairs\n", + "# http://statsmodels.sourceforge.net/stable/datasets/generated/fair.html\n", + "\n", + "import statsmodels.api as sm\n", + "affairs_df = sm.datasets.fair.load_pandas().data" + ] + }, + { + "cell_type": "code", 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" + ], + "text/plain": [ + " rate_marriage age yrs_married children religious educ occupation \\\n", + "0 3.0 32.0 9.0 3.0 3.0 17.0 2.0 \n", + "1 3.0 27.0 13.0 3.0 1.0 14.0 3.0 \n", + "2 4.0 22.0 2.5 0.0 1.0 16.0 3.0 \n", + "3 4.0 37.0 16.5 4.0 3.0 16.0 5.0 \n", + "4 5.0 27.0 9.0 1.0 1.0 14.0 3.0 \n", + "\n", + " occupation_husb affairs \n", + "0 5.0 0.111111 \n", + "1 4.0 3.230769 \n", + "2 5.0 1.400000 \n", + "3 5.0 0.727273 \n", + "4 4.0 4.666666 " + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "affairs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "affairs_df['affair_binary'] = (affairs_df['affairs'] > 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " rate_marriage age yrs_married children religious \\\n", + "rate_marriage 1.000000 -0.111127 -0.128978 -0.129161 0.078794 \n", + "age -0.111127 1.000000 0.894082 0.673902 0.136598 \n", + "yrs_married -0.128978 0.894082 1.000000 0.772806 0.132683 \n", + "children -0.129161 0.673902 0.772806 1.000000 0.141845 \n", + "religious 0.078794 0.136598 0.132683 0.141845 1.000000 \n", + "educ 0.079869 0.027960 -0.109058 -0.141918 0.032245 \n", + "occupation 0.039528 0.106127 0.041782 -0.015068 0.035746 \n", + "occupation_husb 0.027745 0.162567 0.128135 0.086660 0.004061 \n", + "affairs -0.178068 -0.089964 -0.087737 -0.070278 -0.125933 \n", + "affair_binary -0.331776 0.146519 0.203109 0.159833 -0.129299 \n", + "\n", + " educ occupation occupation_husb affairs \\\n", + "rate_marriage 0.079869 0.039528 0.027745 -0.178068 \n", + "age 0.027960 0.106127 0.162567 -0.089964 \n", + "yrs_married -0.109058 0.041782 0.128135 -0.087737 \n", + "children -0.141918 -0.015068 0.086660 -0.070278 \n", + "religious 0.032245 0.035746 0.004061 -0.125933 \n", + "educ 1.000000 0.382286 0.183932 -0.017740 \n", + "occupation 0.382286 1.000000 0.201156 0.004469 \n", + "occupation_husb 0.183932 0.201156 1.000000 -0.015614 \n", + "affairs -0.017740 0.004469 -0.015614 1.000000 \n", + "affair_binary -0.075280 0.028981 0.017637 0.464046 \n", + "\n", + " affair_binary \n", + "rate_marriage -0.331776 \n", + "age 0.146519 \n", + "yrs_married 0.203109 \n", + "children 0.159833 \n", + "religious -0.129299 \n", + "educ -0.075280 \n", + "occupation 0.028981 \n", + "occupation_husb 0.017637 \n", + "affairs 0.464046 \n", + "affair_binary 1.000000 " + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "affairs_df.corr()\n", + "# Obviously affairs will correlate to affair_binary but what else?\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# It seems children, yrs_married, rate_married, and age all correlate to affair_binary\n", + "# Remember correlations are NOT the single way to identify which features to use\n", + "# Correlations only give us a number determining how linearlly correlated the variables are\n", + "# We may find another variable that affects affairs by evaluating the coefficients of our LR" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "affairs_X = affairs_df.drop(['affairs', 'affair_binary'], axis=1)\n", + "affairs_y = affairs_df['affair_binary']" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.71630094 0.69749216 0.74137931 0.71226415 0.70125786 0.73113208\n", + " 0.71855346 0.70125786 0.74685535 0.75314465]\n", + "0.72196378226\n" + ] + } + ], + "source": [ + "model = LogisticRegression()\n", + "from sklearn.cross_validation import cross_val_score\n", + "# check the accuracy on the training set\n", + "scores = cross_val_score(model, affairs_X, affairs_y, cv=10)\n", + "print scores\n", + "print scores.mean()\n", + "\n", + "# Looks pretty good" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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01
0rate_marriage[-0.702300201706]
1age[-0.0546769400998]
2yrs_married[0.105079088955]
3children[-0.00117231032]
4religious[-0.367121091053]
5educ[-0.0328106363897]
6occupation[0.161411859069]
7occupation_husb[0.0145734984752]
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" + ], + "text/plain": [ + " 0 1\n", + "0 rate_marriage [-0.702300201706]\n", + "1 age [-0.0546769400998]\n", + "2 yrs_married [0.105079088955]\n", + "3 children [-0.00117231032]\n", + "4 religious [-0.367121091053]\n", + "5 educ [-0.0328106363897]\n", + "6 occupation [0.161411859069]\n", + "7 occupation_husb [0.0145734984752]" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Explore individual features that make the biggest impact\n", + "# religious, yrs_married, and occupation. But one of these variables doesn't quite make sense right?\n", + "pd.DataFrame(zip(affairs_X.columns, np.transpose(model.coef_)))" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Dummy Variables:\n", + "\n", + "# Encoding qualitiative (nominal) data using separate columns (see slides for linear regression for more)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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featurescoef
0rate_marriage[-0.697845453825]
1age[-0.0563368031972]
2yrs_married[0.103893444136]
3children[0.0181853982481]
4religious[-0.368506616998]
5educ[0.00864804494766]
6occ__2.0[0.298118794658]
7occ__3.0[0.608150180777]
8occ__4.0[0.346511273036]
9occ__5.0[0.942259498161]
10occ__6.0[0.918150144304]
11occ_husb__2.0[0.219957140288]
12occ_husb__3.0[0.32476602929]
13occ_husb__4.0[0.189354154353]
14occ_husb__5.0[0.21309298898]
15occ_husb__6.0[0.214179979671]
\n", + "
" + ], + "text/plain": [ + " features coef\n", + "0 rate_marriage [-0.697845453825]\n", + "1 age [-0.0563368031972]\n", + "2 yrs_married [0.103893444136]\n", + "3 children [0.0181853982481]\n", + "4 religious [-0.368506616998]\n", + "5 educ [0.00864804494766]\n", + "6 occ__2.0 [0.298118794658]\n", + "7 occ__3.0 [0.608150180777]\n", + "8 occ__4.0 [0.346511273036]\n", + "9 occ__5.0 [0.942259498161]\n", + "10 occ__6.0 [0.918150144304]\n", + "11 occ_husb__2.0 [0.219957140288]\n", + "12 occ_husb__3.0 [0.32476602929]\n", + "13 occ_husb__4.0 [0.189354154353]\n", + "14 occ_husb__5.0 [0.21309298898]\n", + "15 occ_husb__6.0 [0.214179979671]" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(zip(affairs_X.columns, np.transpose(model.coef_)), columns = ['features', 'coef'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# compare KNN to LR" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.68630248906529234" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "cross_val_score(knn, affairs_X, affairs_y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.72558005785768587" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = LogisticRegression()\n", + "cross_val_score(logreg, affairs_X, affairs_y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# When we are investigating individual correlations between features and categorical responses\n", + "# Logistic regression has a good shot :)\n", + "\n", + "# KNN relies on the entire n-space to make predictions while LR uses the model parameters to focus\n", + "# on one or more particular features\n", + "\n", + "# LR has concept of \"importance\" of features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Final Thought Experiment\n", + "\n", + "# Why might KNN (a kind of look alike model) not perform well here?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb new file mode 100644 index 0000000..1db6bae --- /dev/null +++ b/notebooks/.ipynb_checkpoints/07_nlp-checkpoint.ipynb @@ -0,0 +1,2836 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Natural Language Processing (NLP)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "*Adapted from [NLP Crash Course](http://files.meetup.com/7616132/DC-NLP-2013-09%20Charlie%20Greenbacker.pdf) by Charlie Greenbacker and [Introduction to NLP](http://spark-public.s3.amazonaws.com/nlp/slides/intro.pdf) by Dan Jurafsky*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is NLP?\n", + "\n", + "- Using computers to process (analyze, understand, generate) natural human languages\n", + "- Most knowledge created by humans is unstructured text, and we need a way to make sense of it\n", + "- Build probabilistic model using data about a language" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What are some of the higher level task areas?\n", + "\n", + "- **Information retrieval**: Find relevant results and similar results\n", + " - [Google](https://www.google.com/)\n", + "- **Information extraction**: Structured information from unstructured documents\n", + " - [Events from Gmail](https://support.google.com/calendar/answer/6084018?hl=en)\n", + "- **Machine translation**: One language to another\n", + " - [Google Translate](https://translate.google.com/)\n", + "- **Text simplification**: Preserve the meaning of text, but simplify the grammar and vocabulary\n", + " - [Rewordify](https://rewordify.com/)\n", + " - [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page)\n", + "- **Predictive text input**: Faster or easier typing\n", + " - [A friend's application](https://justmarkham.shinyapps.io/textprediction/)\n", + " - [A much better application](https://farsite.shinyapps.io/swiftkey-cap/)\n", + "- **Sentiment analysis**: Attitude of speaker\n", + " - [Hater News](http://haternews.herokuapp.com/)\n", + "- **Automatic summarization**: Extractive or abstractive summarization\n", + " - [autotldr](https://www.reddit.com/r/technology/comments/35brc8/21_million_people_still_use_aol_dialup/cr2zzj0)\n", + "- **Natural Language Generation**: Generate text from data\n", + " - [How a computer describes a sports match](http://www.bbc.com/news/technology-34204052)\n", + " - [Publishers withdraw more than 120 gibberish papers](http://www.nature.com/news/publishers-withdraw-more-than-120-gibberish-papers-1.14763)\n", + "- **Speech recognition and generation**: Speech-to-text, text-to-speech\n", + " - [Google's Web Speech API demo](https://www.google.com/intl/en/chrome/demos/speech.html)\n", + " - [Vocalware Text-to-Speech demo](https://www.vocalware.com/index/demo)\n", + "- **Question answering**: Determine the intent of the question, match query with knowledge base, evaluate hypotheses\n", + " - [How did supercomputer Watson beat Jeopardy champion Ken Jennings?](http://blog.ted.com/how-did-supercomputer-watson-beat-jeopardy-champion-ken-jennings-experts-discuss/)\n", + " - [IBM's Watson Trivia Challenge](http://www.nytimes.com/interactive/2010/06/16/magazine/watson-trivia-game.html)\n", + " - [The AI Behind Watson](http://www.aaai.org/Magazine/Watson/watson.php)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What are some of the lower level components?\n", + "\n", + "- **Tokenization**: breaking text into tokens (words, sentences, n-grams)\n", + "- **Stopword removal**: a/an/the\n", + "- **Stemming and lemmatization**: root word\n", + "- **TF-IDF**: word importance\n", + "- **Part-of-speech tagging**: noun/verb/adjective\n", + "- **Named entity recognition**: person/organization/location\n", + "- **Spelling correction**: \"New Yrok City\"\n", + "- **Word sense disambiguation**: \"buy a mouse\"\n", + "- **Segmentation**: \"New York City subway\"\n", + "- **Language detection**: \"translate this page\"\n", + "- **Machine learning**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why is NLP hard?\n", + "\n", + "- **Ambiguity**:\n", + " - Hospitals are Sued by 7 Foot Doctors\n", + " - Juvenile Court to Try Shooting Defendant\n", + " - Local High School Dropouts Cut in Half\n", + "- **Non-standard English**: text messages\n", + "- **Idioms**: \"throw in the towel\"\n", + "- **Newly coined words**: \"retweet\"\n", + "- **Tricky entity names**: \"Where is A Bug's Life playing?\"\n", + "- **World knowledge**: \"Mary and Sue are sisters\", \"Mary and Sue are mothers\"\n", + "\n", + "NLP requires an understanding of the **language** and the **world**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Reading in the Yelp Reviews" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- \"corpus\" = collection of documents\n", + "- \"corpora\" = plural form of corpus" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", + " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import scipy as sp\n", + "from sklearn.cross_validation import train_test_split, cross_val_score\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn import metrics\n", + "from textblob import TextBlob, Word\n", + "from nltk.stem.snowball import SnowballStemmer\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# read yelp.csv into a DataFrame\n", + "url = '../data/yelp.csv'\n", + "yelp = pd.read_csv(url, encoding='unicode-escape')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# EXERCISE create a new DataFrame called yelp_best_worst \n", + "# that only contains the 5-star and 1-star reviews\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# ANSWER\n", + "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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09yKzy9PApeiPPOUJEtnvkg2011-01-26fWKvX83p0-ka4JS3dc6E5A5My wife took me here on my birthday for breakf...reviewrLtl8ZkDX5vH5nAx9C3q5Q250
1ZRJwVLyzEJq1VAihDhYiow2011-07-27IjZ33sJrzXqU-0X6U8NwyA5I have no idea why some people give bad review...review0a2KyEL0d3Yb1V6aivbIuQ000
3_1QQZuf4zZOyFCvXc0o6Vg2010-05-27G-WvGaISbqqaMHlNnByodA5Rosie, Dakota, and I LOVE Chaparral Dog Park!!...reviewuZetl9T0NcROGOyFfughhg120
46ozycU1RpktNG2-1BroVtw2012-01-051uJFq2r5QfJG_6ExMRCaGw5General Manager Scott Petello is a good egg!!!...reviewvYmM4KTsC8ZfQBg-j5MWkw000
6zp713qNhx8d9KCJJnrw1xA2010-02-12riFQ3vxNpP4rWLk_CSri2A5Drop what you're doing and drive here. After I...reviewwFweIWhv2fREZV_dYkz_1g774
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\n", + "
" + ], + "text/plain": [ + " cab call me please tonight you\n", + "0 1 1 0 1 0 0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# transforming a new sentence, what do you notice?\n", + "pd.DataFrame(vect.transform(['please call yourself a cab']).toarray(), columns=vect.get_feature_names())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# use CountVectorizer to create document-term matrices from X_train and X_test\n", + "vect = CountVectorizer()\n", + "X_train_dtm = vect.fit_transform(X_train)\n", + "X_test_dtm = vect.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3064, 16825)\n", + "(1022, 16825)\n" + ] + } + ], + "source": [ + "# rows are documents, columns are terms (phrases) (aka \"tokens\" or \"features\")\n", + "print X_train_dtm.shape\n", + "print X_test_dtm.shape\n", + "# Why do they have the same number of features" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'00', u'000', u'00a', u'00am', u'00pm', u'01', u'02', u'03', u'03342', u'04', u'05', u'06', u'07', u'09', u'0buxoc0crqjpvkezo3bqog', u'0l', u'10', u'100', u'1000', u'1000x', u'1001', u'100th', u'101', u'102', u'105', u'1070', u'108', u'10am', u'10ish', u'10min', u'10mins', u'10minutes', u'10pm', u'10th', u'10x', u'11', u'110', u'1100', u'111', u'111th', u'112', u'115th', u'118', u'11a', u'11am', u'11p', u'11pm', u'12', u'120', u'128i']\n" + ] + } + ], + "source": [ + "# first 50 features\n", + "print vect.get_feature_names()[:50]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'yyyyy', u'z11', u'za', u'zabba', u'zach', u'zam', u'zanella', u'zankou', u'zappos', u'zatsiki', u'zen', u'zero', u'zest', u'zexperience', u'zha', u'zhou', u'zia', u'zihuatenejo', u'zilch', u'zin', u'zinburger', u'zinburgergeist', u'zinc', u'zinfandel', u'zing', u'zip', u'zipcar', u'zipper', u'zippers', u'zipps', u'ziti', u'zoe', u'zombi', u'zombies', u'zone', u'zones', u'zoning', u'zoo', u'zoyo', u'zucca', u'zucchini', u'zuchinni', u'zumba', u'zupa', u'zuzu', u'zwiebel', u'zzed', u'\\xe9clairs', u'\\xe9cole', u'\\xe9m']\n" + ] + } + ], + "source": [ + "# last 50 features\n", + "print vect.get_feature_names()[-50:]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n", + " dtype=, encoding=u'utf-8', input=u'content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 1), preprocessor=None, stop_words=None,\n", + " strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " tokenizer=None, vocabulary=None)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show vectorizer options\n", + "vect" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[CountVectorizer documentation](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **lowercase:** boolean, True by default\n", + "- Convert all characters to lowercase before tokenizing." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# EXERCISE: create a coun vectorizer that doesn't lowercase the words\n", + "# fit transform X_train and see how many features there are\n", + "# Hint there should be over 20k\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3064, 20838)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ANSWER\n", + "vect = CountVectorizer(lowercase=False)\n", + "X_train_dtm = vect.fit_transform(X_train)\n", + "X_train_dtm.shape # has more features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **ngram_range:** tuple (min_n, max_n)\n", + "- The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3064, 169847)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# include 1-grams and 2-grams\n", + "vect = CountVectorizer(ngram_range=(1, 2))\n", + "X_train_dtm = vect.fit_transform(X_train)\n", + "X_train_dtm.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'zone out', u'zone when', u'zones', u'zones dolls', u'zoning', u'zoning issues', u'zoo', u'zoo and', u'zoo is', u'zoo not', u'zoo the', u'zoo ve', u'zoyo', u'zoyo for', u'zucca', u'zucca appetizer', u'zucchini', u'zucchini and', u'zucchini bread', u'zucchini broccoli', u'zucchini carrots', u'zucchini fries', u'zucchini pieces', u'zucchini strips', u'zucchini veal', u'zucchini very', u'zucchini with', u'zuchinni', u'zuchinni again', u'zuchinni the', u'zumba', u'zumba class', u'zumba or', u'zumba yogalates', u'zupa', u'zupa flavors', u'zuzu', u'zuzu in', u'zuzu is', u'zuzu the', u'zwiebel', u'zwiebel kr\\xe4uter', u'zzed', u'zzed in', u'\\xe9clairs', u'\\xe9clairs napoleons', u'\\xe9cole', u'\\xe9cole len\\xf4tre', u'\\xe9m', u'\\xe9m all']\n" + ] + } + ], + "source": [ + "# last 50 features\n", + "print vect.get_feature_names()[-50:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Predicting the star rating:**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.925636007828\n" + ] + } + ], + "source": [ + "# use default options for CountVectorizer\n", + "vect = CountVectorizer()\n", + "\n", + "# create document-term matrices :: only single words\n", + "X_train_dtm = vect.fit_transform(X_train)\n", + "X_test_dtm = vect.transform(X_test)\n", + "\n", + "# use logistic regression with document feature matrix, NOT the text column\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X_train_dtm, y_train)\n", + "y_pred_class = logreg.predict(X_test_dtm)\n", + "\n", + "# calculate accuracy\n", + "print metrics.accuracy_score(y_test, y_pred_class)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.81996086105675148" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate null accuracy, which is the accuracy of our null model (just guessing the most common thing)\n", + "y_test_binary = np.where(y_test==5, 1, 0)\n", + "max(y_test_binary.mean(), 1 - y_test_binary.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# EXERCISE define a function, tokenize_test, that does five things:\n", + "\n", + "# Has a single input, vect, that is a countvectorizer\n", + "# instantiates a logistic regression\n", + "# fit_transforms X using the vectorizer\n", + "# print the number of features (phrases)\n", + "# prints the output of a 5 fold cross validation using accuracy as our metric\n", + "\n", + "\n", + "# eg.\n", + "\n", + "# vect = CountVectorizer(ngram_range=(1, 2))\n", + "# tokenize_test(vect)\n", + "\n", + "# Should output:\n", + "# Features: 209471\n", + "# Accuracy: 0.933431652596\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ANSWER\n", + "def tokenize_test(vect):\n", + " logreg = LogisticRegression()\n", + " X_dtm = vect.fit_transform(X)\n", + " print 'Features: ', X_dtm.shape[1]\n", + " print 'Accuracy: ', cross_val_score(logreg, X_dtm, y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 209471\n", + "Accuracy: 0.933431652596\n" + ] + } + ], + "source": [ + "# include 1-grams and 2-grams\n", + "vect = CountVectorizer(ngram_range=(1, 2))\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3: Stopword Removal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **What:** Remove common words that will likely appear in any text\n", + "- **Why:** They don't tell you much about your text" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n", + " dtype=, encoding=u'utf-8', input=u'content',\n", + " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", + " ngram_range=(1, 2), preprocessor=None, stop_words=None,\n", + " strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n", + " tokenizer=None, vocabulary=None)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show vectorizer options\n", + "vect" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **stop_words:** string {'english'}, list, or None (default)\n", + "- If 'english', a built-in stop word list for English is used.\n", + "- If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens.\n", + "- If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 191339\n", + "Accuracy: 0.926581494928\n" + ] + } + ], + "source": [ + "# remove English stop words\n", + "vect = CountVectorizer(stop_words='english', ngram_range=(1, 2))\n", + "tokenize_test(vect)\n", + "# made predictions worse! Why?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "frozenset(['all', 'six', 'less', 'being', 'indeed', 'over', 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', 'fify', 'where', 'mill', 'only', 'find', 'before', 'one', 'whose', 'system', 'how', 'somewhere', 'with', 'thick', 'show', 'had', 'enough', 'should', 'to', 'must', 'whom', 'seeming', 'under', 'ours', 'has', 'might', 'thereafter', 'latterly', 'do', 'them', 'his', 'around', 'than', 'get', 'very', 'de', 'none', 'cannot', 'every', 'whether', 'they', 'front', 'during', 'thus', 'now', 'him', 'nor', 'name', 'several', 'hereafter', 'always', 'who', 'cry', 'whither', 'this', 'someone', 'either', 'each', 'become', 'thereupon', 'sometime', 'side', 'two', 'therein', 'twelve', 'because', 'often', 'ten', 'our', 'eg', 'some', 'back', 'up', 'go', 'namely', 'towards', 'are', 'further', 'beyond', 'ourselves', 'yet', 'out', 'even', 'will', 'what', 'still', 'for', 'bottom', 'mine', 'since', 'please', 'forty', 'per', 'its', 'everything', 'behind', 'un', 'above', 'between', 'it', 'neither', 'seemed', 'ever', 'across', 'she', 'somehow', 'be', 'we', 'full', 'never', 'sixty', 'however', 'here', 'otherwise', 'were', 'whereupon', 'nowhere', 'although', 'found', 'alone', 're', 'along', 'fifteen', 'by', 'both', 'about', 'last', 'would', 'anything', 'via', 'many', 'could', 'thence', 'put', 'against', 'keep', 'etc', 'amount', 'became', 'ltd', 'hence', 'onto', 'or', 'con', 'among', 'already', 'co', 'afterwards', 'formerly', 'within', 'seems', 'into', 'others', 'while', 'whatever', 'except', 'down', 'hers', 'everyone', 'done', 'least', 'another', 'whoever', 'moreover', 'couldnt', 'throughout', 'anyhow', 'yourself', 'three', 'from', 'her', 'few', 'together', 'top', 'there', 'due', 'been', 'next', 'anyone', 'eleven', 'much', 'call', 'therefore', 'interest', 'then', 'thru', 'themselves', 'hundred', 'was', 'sincere', 'empty', 'more', 'himself', 'elsewhere', 'mostly', 'on', 'fire', 'am', 'becoming', 'hereby', 'amongst', 'else', 'part', 'everywhere', 'too', 'herself', 'former', 'those', 'he', 'me', 'myself', 'made', 'twenty', 'these', 'bill', 'cant', 'us', 'until', 'besides', 'nevertheless', 'below', 'anywhere', 'nine', 'can', 'of', 'your', 'toward', 'my', 'something', 'and', 'whereafter', 'whenever', 'give', 'almost', 'wherever', 'is', 'describe', 'beforehand', 'herein', 'an', 'as', 'itself', 'at', 'have', 'in', 'seem', 'whence', 'ie', 'any', 'fill', 'again', 'hasnt', 'inc', 'thereby', 'thin', 'no', 'perhaps', 'latter', 'meanwhile', 'when', 'detail', 'same', 'wherein', 'beside', 'also', 'that', 'other', 'take', 'which', 'becomes', 'you', 'if', 'nobody', 'see', 'though', 'may', 'after', 'upon', 'most', 'hereupon', 'eight', 'but', 'serious', 'nothing', 'such', 'why', 'a', 'off', 'whereby', 'third', 'i', 'whole', 'noone', 'sometimes', 'well', 'amoungst', 'yours', 'their', 'rather', 'without', 'so', 'five', 'the', 'first', 'whereas', 'once'])\n" + ] + } + ], + "source": [ + "# set of stop words\n", + "print vect.get_stop_words()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 4: Other CountVectorizer Options" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **max_features:** int or None, default=None\n", + "- If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 100\n", + "Accuracy: 0.888893346555\n" + ] + } + ], + "source": [ + "# remove English stop words and only keep 100 features, MUCH FASTER\n", + "vect = CountVectorizer(stop_words='english', max_features=100)\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'amazing', u'area', u'asked', u'atmosphere', u'awesome', u'bad', u'bar', u'best', u'better', u'big', u'came', u'cheese', u'chicken', u'coffee', u'come', u'day', u'definitely', u'delicious', u'did', u'didn', u'dinner', u'don', u'eat', u'excellent', u'experience', u'favorite', u'feel', u'food', u'free', u'fresh', u'friendly', u'friends', u'going', u'good', u'got', u'great', u'happy', u'home', u'hot', u'hour', u'just', u'know', u'like', u'little', u'll', u'location', u'long', u'looking', u'lot', u'love', u'lunch', u'make', u'meal', u'menu', u'minutes', u'need', u'new', u'nice', u'night', u'order', u'ordered', u'people', u'perfect', u'phoenix', u'pizza', u'place', u'pretty', u'price', u'prices', u'really', u'recommend', u'restaurant', u'right', u'said', u'salad', u'sauce', u'say', u'service', u'staff', u'store', u'sure', u'table', u'thing', u'things', u'think', u'time', u'times', u'told', u'took', u'tried', u'try', u've', u'wait', u'want', u'way', u'went', u'wine', u'work', u'worth', u'years']\n" + ] + } + ], + "source": [ + "# all 100 features\n", + "print vect.get_feature_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 100000\n", + "Accuracy: 0.93612563031\n" + ] + } + ], + "source": [ + "# include 1-grams and 2-grams, and limit the number of features\n", + "vect = CountVectorizer(ngram_range=(1, 2), max_features=100000)\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **min_df:** float in range [0.0, 1.0] or int, default=1\n", + "- When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 32700\n", + "Accuracy: 0.93783741955\n" + ] + } + ], + "source": [ + "# include 1-grams and 2-grams, and only include terms that appear at least 3 times\n", + "vect = CountVectorizer(ngram_range=(1, 2), min_df=3)\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 5: Introduction to TextBlob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TextBlob: \"Simplified Text Processing\"" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My wife took me here on my birthday for breakfast and it was excellent. The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning. It looked like the place fills up pretty quickly so the earlier you get here the better.\r\n", + "\r\n", + "Do yourself a favor and get their Bloody Mary. It was phenomenal and simply the best I've ever had. I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it. It was amazing.\r\n", + "\r\n", + "While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. It was the best \"toast\" I've ever had.\r\n", + "\r\n", + "Anyway, I can't wait to go back!\n" + ] + } + ], + "source": [ + "# print the first review\n", + "print yelp_best_worst.text[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# save it as a TextBlob object\n", + "review = TextBlob(yelp_best_worst.text[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WordList([u'My', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'was', u'excellent', u'The', u'weather', u'was', u'perfect', u'which', u'made', u'sitting', u'outside', u'overlooking', u'their', u'grounds', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'was', u'excellent', u'and', u'our', u'food', u'arrived', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'looked', u'like', u'the', u'place', u'fills', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'was', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'had', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredients', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'was', u'amazing', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'looks', u'excellent', u'I', u'had', u'the', u'white', u'truffle', u'scrambled', u'eggs', u'vegetable', u'skillet', u'and', u'it', u'was', u'tasty', u'and', u'delicious', u'It', u'came', u'with', u'2', u'pieces', u'of', u'their', u'griddled', u'bread', u'with', u'was', u'amazing', u'and', u'it', u'absolutely', u'made', u'the', u'meal', u'complete', u'It', u'was', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'had', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back'])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# list the words\n", + "review.words" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Sentence(\"My wife took me here on my birthday for breakfast and it was excellent.\"),\n", + " Sentence(\"The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure.\"),\n", + " Sentence(\"Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning.\"),\n", + " Sentence(\"It looked like the place fills up pretty quickly so the earlier you get here the better.\"),\n", + " Sentence(\"Do yourself a favor and get their Bloody Mary.\"),\n", + " Sentence(\"It was phenomenal and simply the best I've ever had.\"),\n", + " Sentence(\"I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it.\"),\n", + " Sentence(\"It was amazing.\"),\n", + " Sentence(\"While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious.\"),\n", + " Sentence(\"It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete.\"),\n", + " Sentence(\"It was the best \"toast\" I've ever had.\"),\n", + " Sentence(\"Anyway, I can't wait to go back!\")]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# list the sentences\n", + "review.sentences" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TextBlob(\"my wife took me here on my birthday for breakfast and it was excellent. the weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. our waitress was excellent and our food arrived quickly on the semi-busy saturday morning. it looked like the place fills up pretty quickly so the earlier you get here the better.\n", + "\n", + "do yourself a favor and get their bloody mary. it was phenomenal and simply the best i've ever had. i'm pretty sure they only use ingredients from their garden and blend them fresh when you order it. it was amazing.\n", + "\n", + "while everything on the menu looks excellent, i had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. it came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. it was the best \"toast\" i've ever had.\n", + "\n", + "anyway, i can't wait to go back!\")" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# some string methods are available\n", + "review.lower()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 6: Stemming and Lemmatization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Stemming:**\n", + "\n", + "- **What:** Reduce a word to its base/stem/root form\n", + "- **Why:** Often makes sense to treat related words the same way\n", + "- **Notes:**\n", + " - Uses a \"simple\" and fast rule-based approach\n", + " - Stemmed words are usually not shown to users (used for analysis/indexing)\n", + " - Some search engines treat words with the same stem as synonyms" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'my', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'was', u'excel', u'the', u'weather', u'was', u'perfect', u'which', u'made', u'sit', u'outsid', u'overlook', u'their', u'ground', u'an', u'absolut', u'pleasur', u'our', u'waitress', u'was', u'excel', u'and', u'our', u'food', u'arriv', u'quick', u'on', u'the', u'semi-busi', u'saturday', u'morn', u'it', u'look', u'like', u'the', u'place', u'fill', u'up', u'pretti', u'quick', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'bloodi', u'mari', u'it', u'was', u'phenomen', u'and', u'simpli', u'the', u'best', u'i', u've', u'ever', u'had', u'i', u\"'m\", u'pretti', u'sure', u'they', u'onli', u'use', u'ingredi', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'it', u'was', u'amaz', u'while', u'everyth', u'on', u'the', u'menu', u'look', u'excel', u'i', u'had', u'the', u'white', u'truffl', u'scrambl', u'egg', u'veget', u'skillet', u'and', u'it', u'was', u'tasti', u'and', u'delici', u'it', u'came', u'with', u'2', u'piec', u'of', u'their', u'griddl', u'bread', u'with', u'was', u'amaz', u'and', u'it', u'absolut', u'made', u'the', u'meal', u'complet', u'it', u'was', u'the', u'best', u'toast', u'i', u've', u'ever', u'had', u'anyway', u'i', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n" + ] + } + ], + "source": [ + "# initialize stemmer\n", + "stemmer = SnowballStemmer('english')\n", + "\n", + "# stem each word\n", + "print [stemmer.stem(word) for word in review.words]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Lemmatization**\n", + "\n", + "- **What:** Derive the canonical form ('lemma') of a word\n", + "- **Why:** Can be better than stemming\n", + "- **Notes:** Uses a dictionary-based approach (slower than stemming)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "word = Word('wolves')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "u'wolv'" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stemmer.stem(word)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'My', u'wife', u'took', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'wa', u'excellent', u'The', u'weather', u'wa', u'perfect', u'which', u'made', u'sitting', u'outside', u'overlooking', u'their', u'ground', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'wa', u'excellent', u'and', u'our', u'food', u'arrived', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'looked', u'like', u'the', u'place', u'fill', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'wa', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'had', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredient', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'wa', u'amazing', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'look', u'excellent', u'I', u'had', u'the', u'white', u'truffle', u'scrambled', u'egg', u'vegetable', u'skillet', u'and', u'it', u'wa', u'tasty', u'and', u'delicious', u'It', u'came', u'with', u'2', u'piece', u'of', u'their', u'griddled', u'bread', u'with', u'wa', u'amazing', u'and', u'it', u'absolutely', u'made', u'the', u'meal', u'complete', u'It', u'wa', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'had', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n" + ] + } + ], + "source": [ + "# assume every word is a noun\n", + "print [word.lemmatize() for word in review.words]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[u'My', u'wife', u'take', u'me', u'here', u'on', u'my', u'birthday', u'for', u'breakfast', u'and', u'it', u'be', u'excellent', u'The', u'weather', u'be', u'perfect', u'which', u'make', u'sit', u'outside', u'overlook', u'their', u'ground', u'an', u'absolute', u'pleasure', u'Our', u'waitress', u'be', u'excellent', u'and', u'our', u'food', u'arrive', u'quickly', u'on', u'the', u'semi-busy', u'Saturday', u'morning', u'It', u'look', u'like', u'the', u'place', u'fill', u'up', u'pretty', u'quickly', u'so', u'the', u'earlier', u'you', u'get', u'here', u'the', u'better', u'Do', u'yourself', u'a', u'favor', u'and', u'get', u'their', u'Bloody', u'Mary', u'It', u'be', u'phenomenal', u'and', u'simply', u'the', u'best', u'I', u\"'ve\", u'ever', u'have', u'I', u\"'m\", u'pretty', u'sure', u'they', u'only', u'use', u'ingredients', u'from', u'their', u'garden', u'and', u'blend', u'them', u'fresh', u'when', u'you', u'order', u'it', u'It', u'be', u'amaze', u'While', u'EVERYTHING', u'on', u'the', u'menu', u'look', u'excellent', u'I', u'have', u'the', u'white', u'truffle', u'scramble', u'egg', u'vegetable', u'skillet', u'and', u'it', u'be', u'tasty', u'and', u'delicious', u'It', u'come', u'with', u'2', u'piece', u'of', u'their', u'griddle', u'bread', u'with', u'be', u'amaze', u'and', u'it', u'absolutely', u'make', u'the', u'meal', u'complete', u'It', u'be', u'the', u'best', u'toast', u'I', u\"'ve\", u'ever', u'have', u'Anyway', u'I', u'ca', u\"n't\", u'wait', u'to', u'go', u'back']\n" + ] + } + ], + "source": [ + "# assume every word is a verb\n", + "print [word.lemmatize(pos='v') for word in review.words]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# define a function that accepts text and returns a list of lemmas\n", + "def word_tokenize(text, how='lemma'):\n", + " words = TextBlob(text).words\n", + " if how == 'lemma':\n", + " return [word.lemmatize() for word in words]\n", + " elif how == 'stem':\n", + " return [stemmer.stem(word) for word in words]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 23856\n", + "Accuracy: 0.932208859444\n" + ] + } + ], + "source": [ + "# use word_tokenize LEMMA as the feature extraction function (WARNING: SLOW!)\n", + "# this will lemmatize each word\n", + "vect = CountVectorizer(analyzer=word_tokenize(x, how='lemma'))\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 15237\n", + "Accuracy: 0.938818413169\n" + ] + } + ], + "source": [ + "# use word_tokenize STEM as the feature extraction function (WARNING: SLOW!)\n", + "# this will lemmatize each word\n", + "vect = CountVectorizer(analyzer=lambda x:word_tokenize(x, how='stem'))\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 7: Term Frequency-Inverse Document Frequency (TF-IDF)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **What:** Computes \"relative frequency\" that a word appears in a document compared to its frequency across all documents\n", + "- **Why:** More useful than \"term frequency\" for identifying \"important\" words in each document (high frequency in that document, low frequency in other documents)\n", + "- **Notes:** Used for search engine scoring, text summarization, document clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# example documents\n", + "simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cab call me please tonight you\n", + "0 0.000000 0.385372 0.000000 0.000000 0.652491 0.652491\n", + "1 0.720333 0.425441 0.547832 0.000000 0.000000 0.000000\n", + "2 0.000000 0.266075 0.342620 0.901008 0.000000 0.000000" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TfidfVectorizer\n", + "vect = TfidfVectorizer()\n", + "pd.DataFrame(vect.fit_transform(simple_train).toarray(), columns=vect.get_feature_names())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**More details:** [TF-IDF is about what matters](http://planspace.org/20150524-tfidf_is_about_what_matters/)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 28881)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a document-term matrix using TF-IDF\n", + "vect = TfidfVectorizer(stop_words='english')\n", + "dtm = vect.fit_transform(yelp.text)\n", + "features = vect.get_feature_names()\n", + "dtm.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 18884\n", + "Accuracy: 0.873473450843\n" + ] + } + ], + "source": [ + "vect = TfidfVectorizer(stop_words='english')\n", + "tokenize_test(vect)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 8: Using TF-IDF to Summarize a Yelp Review\n", + "\n", + "Reddit's autotldr uses the [SMMRY](http://smmry.com/about) algorithm, which is based on TF-IDF!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def summarize():\n", + " \n", + " # choose a random review that is at least 300 characters\n", + " review_length = 0\n", + " while review_length < 300:\n", + " review_id = np.random.randint(0, len(yelp))\n", + " review_text = yelp.text[review_id]\n", + " review_length = len(review_text)\n", + " \n", + " # create a dictionary of words and their TF-IDF scores\n", + " word_scores = {}\n", + " for word in TextBlob(review_text).words:\n", + " word = word.lower()\n", + " if word in features:\n", + " word_scores[word] = dtm[review_id, features.index(word)]\n", + " \n", + " # print words with the top 5 TF-IDF scores\n", + " print 'TOP SCORING WORDS:'\n", + " top_scores = sorted(word_scores.items(), key=lambda x: x[1], reverse=True)[:5]\n", + " for word, score in top_scores:\n", + " print word\n", + " \n", + " # print 5 random words\n", + " print '\\n' + 'RANDOM WORDS:'\n", + " random_words = np.random.choice(word_scores.keys(), size=5, replace=False)\n", + " for word in random_words:\n", + " print word\n", + " \n", + " # print the review\n", + " print '\\n' + review_text" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOP SCORING WORDS:\n", + "mussels\n", + "rushed\n", + "seared\n", + "dish\n", + "table\n", + "\n", + "RANDOM WORDS:\n", + "men\n", + "seared\n", + "sushi\n", + "pm\n", + "thrilled\n", + "\n", + "I stopped in with a friend on a Saturday night after a friend had recommended it to me and was totally thrilled we did. We sat at a table for sushi and waited no longer than 10 minutes for a table - amazing for 8 pm on a Saturday in old towne.\r\n", + "\r\n", + "Unfortunately, they had sold out of the oysters, but the other dishes we'd ordered were fabulous - starting with a seared tuna dish, then softshell crab roll, rainbow roll, and the green mussels. The seared tuna was cooked and cut perfectly - just the right temperature with a peppery \"rub,\" the rolls were a decent size with thick cuts of fish and plenty of flavorful crab, and the mussels were a tad overcooked for my preference but the creamy texture and buttery flavor still made it a winner. \r\n", + "\r\n", + "The thing I appreciated most was actually the service - we weren't rushed or hassled, and the food came out a dish at a time, giving us a chance to eat and enjoy before getting our next dish. At other sushi joints, I am constantly feeling rushed while trying to fit all of the dishes on the table (and I'm not what you'd call a slow eater!). This was a happy medium for me. The server confirmed that we were ready for the bill instead of just dropping it off without asking - it's the little details that make a big difference to me.\r\n", + "\r\n", + "My only other con was creepy old men leering at us from the bar...but that's what you get in Scottsdale.\n" + ] + } + ], + "source": [ + "summarize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 9: Sentiment Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My wife took me here on my birthday for breakfast and it was excellent. The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning. It looked like the place fills up pretty quickly so the earlier you get here the better.\r\n", + "\r\n", + "Do yourself a favor and get their Bloody Mary. It was phenomenal and simply the best I've ever had. I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it. It was amazing.\r\n", + "\r\n", + "While EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. It was the best \"toast\" I've ever had.\r\n", + "\r\n", + "Anyway, I can't wait to go back!\n" + ] + } + ], + "source": [ + "print review" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.40246913580246907" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# polarity ranges from -1 (most negative) to 1 (most positive)\n", + "review.sentiment.polarity" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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business_iddatereview_idstarstexttypeuser_idcoolusefulfunnylength
09yKzy9PApeiPPOUJEtnvkg2011-01-26fWKvX83p0-ka4JS3dc6E5A5My wife took me here on my birthday for breakf...reviewrLtl8ZkDX5vH5nAx9C3q5Q250895
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" + ], + "text/plain": [ + " business_id date review_id stars \\\n", + "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", + "\n", + " text type \\\n", + "0 My wife took me here on my birthday for breakf... review \n", + "\n", + " user_id cool useful funny length \n", + "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 895 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# understanding the apply method\n", + "yelp['length'] = yelp.text.apply(len)\n", + "yelp.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7OXRoBVNTUzYfkqRWNL3y8b+Aq4D/CQTwa8DnI+K1KaW9EXEVsAZ4D/A94D8AuyJiUUrp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\nY9YCX4qI30wpPdi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show a histogram of yelp review lengths\n", + "\n", + "yelp['length'].hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# define a function that accepts text and returns the polarity\n", + "def detect_sentiment(text):\n", + " return TextBlob(text).sentiment.polarity" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create a new DataFrame column for sentiment (WARNING: SLOW!)\n", + "yelp['sentiment'] = yelp.text.apply(detect_sentiment)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CQ4OKDcmeP/0JJkxI/nXOPjvZ51+zxn/BoWLDg8LLKBs2bNBlFBGRjOlq0Vi8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\n0T77hPE1HMa7zLiNGzcydux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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# EXERCISE: make a box plot of sentiment grouped by stars and a histogram of yelp sentiment\n", + "# You should have five boxplots in the same graph for sentiment for the first graph\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# ANSWER\n", + "\n", + "\n", + "yelp.boxplot(column='sentiment', by='stars')" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yelp['sentiment'].hist() # Mostly positive!" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "254 Our server Gary was awesome. Food was amazing....\n", + "347 3 syllables for this place. \\r\\nA-MAZ-ING!\\r\\n...\n", + "420 LOVE the food!!!!\n", + "459 Love it!!! Wish we still lived in Arizona as C...\n", + "679 Excellent burger\n", + "Name: text, dtype: object" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# EXERCISE Show the reviews with most positive sentiment (a score of 1)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# Answer \n", + "\n", + "yelp[yelp.sentiment == 1].text.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "773 This was absolutely horrible. I got the suprem...\n", + "1517 Nasty workers and over priced trash\n", + "3266 Absolutely awful... these guys have NO idea wh...\n", + "4766 Very bad food!\n", + "5812 I wouldn't send my worst enemy to this place.\n", + "Name: text, dtype: object" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reviews with most negative sentiment\n", + "yelp[yelp.sentiment == -1].text.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# widen the column display\n", + "pd.set_option('max_colwidth', 500)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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business_iddatereview_idstarstexttypeuser_idcoolusefulfunnylengthsentiment
390106JT5p8e8Chtd0CZpcARw2009-08-06KowGVoP_gygzdSu6Mt3zKQ5RIP AZ Coffee Connection. :( I stopped by two days ago unaware that they had closed. I am severely bummed. This place is irreplaceable! Damn you, Starbucks and McDonalds!reviewjKeaOrPyJ-dI9SNeVqrbww100175-0.302083
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" + ], + "text/plain": [ + " business_id date review_id stars \\\n", + "390 106JT5p8e8Chtd0CZpcARw 2009-08-06 KowGVoP_gygzdSu6Mt3zKQ 5 \n", + "\n", + " text \\\n", + "390 RIP AZ Coffee Connection. :( I stopped by two days ago unaware that they had closed. I am severely bummed. This place is irreplaceable! Damn you, Starbucks and McDonalds! \n", + "\n", + " type user_id cool useful funny length sentiment \n", + "390 review jKeaOrPyJ-dI9SNeVqrbww 1 0 0 175 -0.302083 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Where sentiment can go wrong\n", + "\n", + "# negative sentiment in a 5-star review\n", + "yelp[(yelp.stars == 5) & (yelp.sentiment < -0.3)].head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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business_iddatereview_idstarstexttypeuser_idcoolusefulfunnylengthsentiment
178153YGfwmbW73JhFiemNeyzQ2012-06-22Gi-4O3EhE175vujbFGDIew1If you like the stuck up Scottsdale vibe this is a good place for you. The food isn't impressive. Nice outdoor seating.reviewHqgx3IdJAAaoQjvrUnbNvw0121190.766667
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" + ], + "text/plain": [ + " business_id date review_id stars \\\n", + "1781 53YGfwmbW73JhFiemNeyzQ 2012-06-22 Gi-4O3EhE175vujbFGDIew 1 \n", + "\n", + " text \\\n", + "1781 If you like the stuck up Scottsdale vibe this is a good place for you. The food isn't impressive. Nice outdoor seating. \n", + "\n", + " type user_id cool useful funny length sentiment \n", + "1781 review Hqgx3IdJAAaoQjvrUnbNvw 0 1 2 119 0.766667 " + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# positive sentiment in a 1-star review\n", + "yelp[(yelp.stars == 1) & (yelp.sentiment > 0.5)].head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reset the column display width\n", + "pd.reset_option('max_colwidth')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: Adding Features to a Document-Term Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# create a DataFrame that only contains the 5-star and 1-star reviews\n", + "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]\n", + "\n", + "# define X and y\n", + "feature_cols = ['text', 'sentiment', 'cool', 'useful', 'funny']\n", + "X = yelp_best_worst[feature_cols]\n", + "y = yelp_best_worst.stars\n", + "\n", + "# split into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3064, 16825)\n", + "(1022, 16825)\n" + ] + } + ], + "source": [ + "# use CountVectorizer with text column only\n", + "vect = CountVectorizer()\n", + "X_train_dtm = vect.fit_transform(X_train.text)\n", + "X_test_dtm = vect.transform(X_test.text)\n", + "print X_train_dtm.shape\n", + "print X_test_dtm.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sentiment cool useful funny\n", + "6841 0.197778 0 0 1\n", + "1728 0.412500 0 0 0\n", + "3853 0.398148 0 0 0\n", + "671 0.129219 0 0 0\n", + "4920 0.473611 0 0 0" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the other four feature columns that I want to use to predict stars, not just text\n", + "X_train.drop('text', axis=1).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3064, 4)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cast other feature columns to float and convert to a sparse matrix\n", + "# Why a sparse matrix, because the other matrix is sparse and the data types must match up\n", + "extra = sp.sparse.csr_matrix(X_train.drop('text', axis=1).astype(float))\n", + "extra.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3064, 16829)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# combine sparse matrices\n", + "X_train_dtm_extra = sp.sparse.hstack((X_train_dtm, extra))\n", + "X_train_dtm_extra.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1022, 16829)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# repeat for testing set\n", + "extra = sp.sparse.csr_matrix(X_test.drop('text', axis=1).astype(float))\n", + "X_test_dtm_extra = sp.sparse.hstack((X_test_dtm, extra))\n", + "X_test_dtm_extra.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.925636007828\n" + ] + } + ], + "source": [ + "# use logistic regression with text column only\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X_train_dtm, y_train)\n", + "y_pred_class = logreg.predict(X_test_dtm)\n", + "print metrics.accuracy_score(y_test, y_pred_class)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.937377690802\n" + ] + } + ], + "source": [ + "# use logistic regression with all features\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X_train_dtm_extra, y_train)\n", + "y_pred_class = logreg.predict(X_test_dtm_extra)\n", + "print metrics.accuracy_score(y_test, y_pred_class)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: Fun TextBlob Features" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TextBlob(\"15 minutes late\")" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# spelling correction\n", + "TextBlob('15 minuets late').correct()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('part', 0.9929478138222849), (u'parrot', 0.007052186177715092)]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# spellcheck\n", + "Word('parot').spellcheck()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'tip laterally',\n", + " u'enclose with a bank',\n", + " u'do business with a bank or keep an account at a bank',\n", + " u'act as the banker in a game or in gambling',\n", + " u'be in the banking business',\n", + " u'put into a bank account',\n", + " u'cover with ashes so to control the rate of burning',\n", + " u'have confidence or faith in']" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# definitions\n", + "Word('bank').define('v')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "- NLP is a gigantic field\n", + "- Understanding the basics broadens the types of data you can work with\n", + "- Simple techniques go a long way\n", + "- Use scikit-learn for NLP whenever possible" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..286dcb3 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/06_logistic_regression.ipynb b/notebooks/06_logistic_regression.ipynb index 3e08915..bc28e36 100644 --- a/notebooks/06_logistic_regression.ipynb +++ b/notebooks/06_logistic_regression.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -180,7 +180,7 @@ "5 0 " ] }, - "execution_count": 47, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -221,18 +221,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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bvfeTN84yMBK6qkdyKj+mlWkqm8yFknCeTfdBnUt9Nxsf/NREfqlrkI6+Ua9+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\n/5rGmBrghDFmOzAK7Ae+O81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Ny32pIiKrnkJaCpKvl0wn3L+/Jfut7lmLiBSXCsekIIVUdhdyz1pERAqnkJaCFNJLPtwa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\nWw03ePanvGrU2Fh/3k+q1agWaoDaqMMaJkdf/wC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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -261,18 +261,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 51, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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wWYPKE49+58WlloZkTYPPIhOXkoZkTYPPIhOXkoZkTSuaRSYujWlkwUt9+cU0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\nwnqfAAAAAElFTkSuQmCC\n", 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TKAXFZ+Ia4R1pCX0/k3nPHd3vR0RSlWpQMsz9m7Z4kxOH7EXhpBqJvninJq7lnlt+wK3r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\nIiIiIo7z/wGdl3N/Y9pF6wAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -312,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -323,7 +323,7 @@ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, - "execution_count": 53, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -394,18 +394,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 56, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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YM+dNdu7sTPfu+5g8+fspucGoC6FtZUo3mYjET8lEmnj22cVcc80m6uvnAT727fNz7733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\nth4gmixZVZ9+P+3Wr6Pgow8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -823,16 +823,20 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" + "ename": "NameError", + "evalue": "name 'assorted_pred_class' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[1;31m# add predicted class to DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mglass\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'assorted_pred_class'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0massorted_pred_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[1;31m# sort DataFrame by al\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mglass\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'al'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'assorted_pred_class' is not defined" ] } ], @@ -3559,9 +3563,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [sfdat26-env]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "Python [sfdat26-env]" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/07_nlp.ipynb b/notebooks/07_nlp.ipynb index e1a9c7b..c1bd7cd 100644 --- a/notebooks/07_nlp.ipynb +++ b/notebooks/07_nlp.ipynb @@ -116,17 +116,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", - " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + "ename": "ImportError", + "evalue": "No module named textblob", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[1;32mfrom\u001b[0m \u001b[0mtextblob\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWord\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msnowball\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSnowballStemmer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: No module named textblob" ] } ], @@ -145,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -159,16 +162,25 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": { - "collapsed": true + "collapsed": false + }, + "outputs": [], + "source": [ + "import nltk" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false }, "outputs": [], "source": [ "# EXERCISE create a new DataFrame called yelp_best_worst \n", - "# that only contains the 5-star and 1-star reviews\n", - "\n", - "\n", + "# that only contains thimport nltke 5-star and 1-star reviews\n", "\n", "\n", "\n", @@ -183,13 +195,37 @@ "\n", "\n", "\n", - "# ANSWER\n", - "yelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]" + " \n", + "# ANSWERyelp_best_worst = yelp[(yelp.stars==5) | (yelp.stars==1)]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'business_id', u'date', u'review_id', u'stars', u'text', u'type',\n", + " u'user_id', u'cool', u'useful', u'funny'],\n", + " dtype='object')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yelp.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 37, "metadata": { "collapsed": false }, @@ -307,7 +343,7 @@ "6 wFweIWhv2fREZV_dYkz_1g 7 7 4 " ] }, - "execution_count": 6, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -345,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -361,7 +397,7 @@ "Name: text, dtype: object" ] }, - "execution_count": 9, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -388,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -400,7 +436,40 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import nltk" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['how', 'now', 'brown', 'cow']" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nltk.word_tokenize(\"how now brown cow\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": { "collapsed": false }, @@ -460,7 +529,7 @@ "2 0 1 1 2 0 0" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -474,7 +543,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "collapsed": false }, @@ -514,7 +601,7 @@ "0 1 1 0 1 0 0" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -526,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -540,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -563,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -583,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -603,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -619,7 +706,7 @@ " tokenizer=None, vocabulary=None)" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -646,30 +733,36 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 62, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3064, 20838)\n", + "(1022, 20838)\n" + ] + } + ], "source": [ - "# EXERCISE: create a coun vectorizer that doesn't lowercase the words\n", + "# EXERCISE: create a count vectorizer that doesn't lowercase the words\n", "# fit transform X_train and see how many features there are\n", "# Hint there should be over 20k\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "# use CountVectorizer to create document-term matrices from X_train and X_test\n", + "vect = CountVectorizer(lowercase=False)\n", + "X_train_dtm = vect.fit_transform(X_train)\n", + "X_test_dtm = vect.transform(X_test)\n", + "print X_train_dtm.shape\n", + "print X_test_dtm.shape" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "metadata": { "collapsed": false }, @@ -680,7 +773,7 @@ "(3064, 20838)" ] }, - "execution_count": 19, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -702,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -713,7 +806,7 @@ "(3064, 169847)" ] }, - "execution_count": 20, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -727,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -754,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -786,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -797,7 +890,7 @@ "0.81996086105675148" ] }, - "execution_count": 23, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -810,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -887,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -903,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -940,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -956,7 +1049,7 @@ " tokenizer=None, vocabulary=None)" ] }, - "execution_count": 27, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2814,9 +2907,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:sfdat28]", + "display_name": "Python [bersonenv]", "language": "python", - "name": "conda-env-sfdat28-py" + "name": "Python [bersonenv]" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb new file mode 100644 index 0000000..d619220 --- /dev/null +++ b/notebooks/Untitled.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import nltk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n" + ] + } + ], + "source": [ + "nltk.download()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [bersonenv]", + "language": "python", + "name": "Python [bersonenv]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 072b4adcfe4e4aef0fdcd4a7f39f283794e98c5b Mon Sep 17 00:00:00 2001 From: bersonperson Date: Sun, 6 Nov 2016 09:59:31 -0800 Subject: [PATCH 04/12] changed folder name --- .../.ipynb_checkpoints/Misc-checkpoint.ipynb | 418 +++++++++++++++++- 1 file changed, 416 insertions(+), 2 deletions(-) diff --git a/Untitled Folder/.ipynb_checkpoints/Misc-checkpoint.ipynb b/Untitled Folder/.ipynb_checkpoints/Misc-checkpoint.ipynb index 286dcb3..0428e8e 100644 --- a/Untitled Folder/.ipynb_checkpoints/Misc-checkpoint.ipynb +++ b/Untitled Folder/.ipynb_checkpoints/Misc-checkpoint.ipynb @@ -1,6 +1,420 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Skipping line 104: expected 1 fields, saw 3\n", + "Skipping line 114: expected 1 fields, saw 3\n", + "Skipping line 116: expected 1 fields, saw 3\n", + "Skipping line 118: expected 1 fields, saw 3\n", + "Skipping line 120: expected 1 fields, saw 3\n", + "Skipping line 125: expected 1 fields, saw 3\n", + "Skipping line 126: expected 1 fields, saw 3\n", + "Skipping line 377: expected 1 fields, saw 2\n", + "Skipping line 839: expected 1 fields, saw 3\n", + "Skipping line 840: expected 1 fields, saw 3\n", + "Skipping line 841: expected 1 fields, saw 3\n", + "Skipping line 842: expected 1 fields, saw 3\n", + "Skipping line 843: expected 1 fields, saw 3\n", + "Skipping line 844: expected 1 fields, saw 3\n", + "Skipping line 852: expected 1 fields, saw 2\n", + "Skipping line 853: expected 1 fields, saw 3\n", + "Skipping line 854: expected 1 fields, saw 3\n", + "Skipping line 855: expected 1 fields, saw 3\n", + "Skipping line 856: expected 1 fields, saw 3\n", + "Skipping line 857: expected 1 fields, saw 3\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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