diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..d436fbc --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# List Comprehensions Lab\n", + "\n", + "Complete the following set of exercises to solidify your knowledge of list comprehensions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (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[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Use a list comprehension to create and print a list of consecutive integers starting with 1 and ending with 50." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Use a list comprehension to create and print a list of even numbers starting with 2 and ending with 200." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 Numpy array below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n", + " [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n", + " [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n", + " [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n", + " [0.17003563, 0.46843998, 0.92796258, 0.69814654],\n", + " [0.41290051, 0.19561071, 0.16284783, 0.97016248],\n", + " [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n", + " [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n", + " [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n", + " [0.80930099, 0.50962849, 0.94555126, 0.33364763]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Add a condition to the list comprehension above so that only values greater than or equal to 0.5 are printed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 Numpy array below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.array([[[0.55867166, 0.06210792, 0.08147297],\n", + " [0.82579068, 0.91512478, 0.06833034]],\n", + "\n", + " [[0.05440634, 0.65857693, 0.30296619],\n", + " [0.06769833, 0.96031863, 0.51293743]],\n", + "\n", + " [[0.09143215, 0.71893382, 0.45850679],\n", + " [0.58256464, 0.59005654, 0.56266457]],\n", + "\n", + " [[0.71600294, 0.87392666, 0.11434044],\n", + " [0.8694668 , 0.65669313, 0.10708681]],\n", + "\n", + " [[0.07529684, 0.46470767, 0.47984544],\n", + " [0.65368638, 0.14901286, 0.23760688]]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Add a condition to the list comprehension above so that the last value in each subarray is printed, but only if it is less than or equal to 0.5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Use a list comprehension to select and print the names of all CSV files in the */data* directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. Use a list comprehension and the Pandas `read_csv` and `concat` methods to read all CSV files in the */data* directory and combine them into a single data frame. Display the top 10 rows of the resulting data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8. Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9. Use a list comprehension to add a new column (20) to the data frame whose values are the values in column 19 minus 0.1. Display the top 10 rows of the resulting data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10. Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index c5931c4..60c6301 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -29,10 +29,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n" + ] + } + ], + "source": [ + "list = [i for i in range(1,51)]\n", + "print(list)" + ] }, { "cell_type": "markdown", @@ -43,10 +54,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n" + ] + } + ], + "source": [ + "list = [i for i in range(2,201,2)]\n", + "print(list)" + ] }, { "cell_type": "markdown", @@ -57,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -75,10 +97,186 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117 0.48006452 0.7876326 0.77109654]\n", + "----\n", + "Esto es una lista con listas: 0.84062117\n", + "\n", + "\n", + "Esto es una lista con listas: 0.48006452\n", + "\n", + "\n", + "Esto es una lista con listas: 0.7876326\n", + "\n", + "\n", + "Esto es una lista con listas: 0.77109654\n", + "\n", + "\n", + "[0.44409793 0.09014516 0.81835917 0.87645456]\n", + "----\n", + "Esto es una lista con listas: 0.44409793\n", + "\n", + "\n", + "Esto es una lista con listas: 0.09014516\n", + "\n", + "\n", + "Esto es una lista con listas: 0.81835917\n", + "\n", + "\n", + "Esto es una lista con listas: 0.87645456\n", + "\n", + "\n", + "[0.7066597 0.09610873 0.41247947 0.57433389]\n", + "----\n", + "Esto es una lista con listas: 0.7066597\n", + "\n", + "\n", + "Esto es una lista con listas: 0.09610873\n", + "\n", + "\n", + "Esto es una lista con listas: 0.41247947\n", + "\n", + "\n", + "Esto es una lista con listas: 0.57433389\n", + "\n", + "\n", + "[0.29960807 0.42315023 0.34452557 0.4751035 ]\n", + "----\n", + "Esto es una lista con listas: 0.29960807\n", + "\n", + "\n", + "Esto es una lista con listas: 0.42315023\n", + "\n", + "\n", + "Esto es una lista con listas: 0.34452557\n", + "\n", + "\n", + "Esto es una lista con listas: 0.4751035\n", + "\n", + "\n", + "[0.17003563 0.46843998 0.92796258 0.69814654]\n", + "----\n", + "Esto es una lista con listas: 0.17003563\n", + "\n", + "\n", + "Esto es una lista con listas: 0.46843998\n", + "\n", + "\n", + "Esto es una lista con listas: 0.92796258\n", + "\n", + "\n", + "Esto es una lista con listas: 0.69814654\n", + "\n", + "\n", + "[0.41290051 0.19561071 0.16284783 0.97016248]\n", + "----\n", + "Esto es una lista con listas: 0.41290051\n", + "\n", + "\n", + "Esto es una lista con listas: 0.19561071\n", + "\n", + "\n", + "Esto es una lista con listas: 0.16284783\n", + "\n", + "\n", + "Esto es una lista con listas: 0.97016248\n", + "\n", + "\n", + "[0.71725408 0.87702738 0.31244595 0.76615487]\n", + "----\n", + "Esto es una lista con listas: 0.71725408\n", + "\n", + "\n", + "Esto es una lista con listas: 0.87702738\n", + "\n", + "\n", + "Esto es una lista con listas: 0.31244595\n", + "\n", + "\n", + "Esto es una lista con listas: 0.76615487\n", + "\n", + "\n", + "[0.20754036 0.57871812 0.07214068 0.40356048]\n", + "----\n", + "Esto es una lista con listas: 0.20754036\n", + "\n", + "\n", + "Esto es una lista con listas: 0.57871812\n", + "\n", + "\n", + "Esto es una lista con listas: 0.07214068\n", + "\n", + "\n", + "Esto es una lista con listas: 0.40356048\n", + "\n", + "\n", + "[0.12149553 0.53222417 0.9976855 0.12536346]\n", + "----\n", + "Esto es una lista con listas: 0.12149553\n", + "\n", + "\n", + "Esto es una lista con listas: 0.53222417\n", + "\n", + "\n", + "Esto es una lista con listas: 0.9976855\n", + "\n", + "\n", + "Esto es una lista con listas: 0.12536346\n", + "\n", + "\n", + "[0.80930099 0.50962849 0.94555126 0.33364763]\n", + "----\n", + "Esto es una lista con listas: 0.80930099\n", + "\n", + "\n", + "Esto es una lista con listas: 0.50962849\n", + "\n", + "\n", + "Esto es una lista con listas: 0.94555126\n", + "\n", + "\n", + "Esto es una lista con listas: 0.33364763\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for primer_nivel in a:\n", + " print(primer_nivel)\n", + " print('----')\n", + " for segundo_nivel in primer_nivel:\n", + " print('Esto es una lista con listas: ', segundo_nivel)\n", + " print('\\n')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n" + ] + } + ], + "source": [ + "x = [ segundo_nivel \n", + " for primer_nivel\n", + " in a for segundo_nivel in primer_nivel]\n", + "\n", + "print(x)" + ] }, { "cell_type": "markdown", @@ -89,10 +287,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n" + ] + } + ], + "source": [ + "x = [ segundo_nivel \n", + " \n", + " for primer_nivel in a \n", + " for segundo_nivel in primer_nivel\n", + " if segundo_nivel >= 0.5]\n", + "\n", + "print(x)" + ] }, { "cell_type": "markdown", @@ -103,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -125,10 +339,189 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.55867166 0.06210792 0.08147297]\n", + " [0.82579068 0.91512478 0.06833034]]\n", + "----\n", + "Esto es una lista con listas: [0.55867166 0.06210792 0.08147297]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.55867166\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.06210792\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.08147297\n", + "==============================\n", + "Esto es una lista con listas: [0.82579068 0.91512478 0.06833034]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.82579068\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.91512478\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.06833034\n", + "==============================\n", + "[[0.05440634 0.65857693 0.30296619]\n", + " [0.06769833 0.96031863 0.51293743]]\n", + "----\n", + "Esto es una lista con listas: [0.05440634 0.65857693 0.30296619]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.05440634\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.65857693\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.30296619\n", + "==============================\n", + "Esto es una lista con listas: [0.06769833 0.96031863 0.51293743]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.06769833\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.96031863\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.51293743\n", + "==============================\n", + "[[0.09143215 0.71893382 0.45850679]\n", + " [0.58256464 0.59005654 0.56266457]]\n", + "----\n", + "Esto es una lista con listas: [0.09143215 0.71893382 0.45850679]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.09143215\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.71893382\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.45850679\n", + "==============================\n", + "Esto es una lista con listas: [0.58256464 0.59005654 0.56266457]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.58256464\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.59005654\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.56266457\n", + "==============================\n", + "[[0.71600294 0.87392666 0.11434044]\n", + " [0.8694668 0.65669313 0.10708681]]\n", + "----\n", + "Esto es una lista con listas: [0.71600294 0.87392666 0.11434044]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.71600294\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.87392666\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.11434044\n", + "==============================\n", + "Esto es una lista con listas: [0.8694668 0.65669313 0.10708681]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.8694668\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.65669313\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.10708681\n", + "==============================\n", + "[[0.07529684 0.46470767 0.47984544]\n", + " [0.65368638 0.14901286 0.23760688]]\n", + "----\n", + "Esto es una lista con listas: [0.07529684 0.46470767 0.47984544]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.07529684\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.46470767\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.47984544\n", + "==============================\n", + "Esto es una lista con listas: [0.65368638 0.14901286 0.23760688]\n", + "\n", + "\n", + "==============================\n", + "Edto es in elemento: 0.65368638\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.14901286\n", + "==============================\n", + "==============================\n", + "Edto es in elemento: 0.23760688\n", + "==============================\n" + ] + } + ], + "source": [ + "for primer_nivel in b:\n", + " print(primer_nivel)\n", + " print('----')\n", + " for segundo_nivel in primer_nivel:\n", + " print('Esto es una lista con listas: ', segundo_nivel)\n", + " print('\\n')\n", + " for tercer_nivel in segundo_nivel:\n", + " print('==='*10)\n", + " print('Edto es in elemento: ', tercer_nivel)\n", + " print('==='*10)\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.55867166, 0.82579068, 0.91512478, 0.65857693, 0.96031863, 0.51293743, 0.71893382, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.8694668, 0.65669313, 0.65368638]\n" + ] + } + ], + "source": [ + "x = [ tercer_nivel \n", + " for primer_nivel in b \n", + " for segundo_nivel in primer_nivel\n", + " for tercer_nivel in segundo_nivel\n", + " if tercer_nivel >= 0.5\n", + " ]\n", + "\n", + "print(x)" + ] }, { "cell_type": "markdown", @@ -139,10 +532,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08147297, 0.06833034, 0.30296619, 0.45850679, 0.11434044, 0.10708681, 0.47984544, 0.23760688]\n" + ] + } + ], + "source": [ + "x = [ tercer_nivel \n", + " \n", + " for primer_nivel in b\n", + " for segundo_nivel in primer_nivel\n", + " for tercer_nivel in segundo_nivel\n", + " if (tercer_nivel <= 0.5) & (tercer_nivel == segundo_nivel[-1])\n", + " ]\n", + "\n", + "print(x)" + ] }, { "cell_type": "markdown", @@ -153,10 +564,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "['sample_file_1.csv',\n", + " 'sample_file_0.csv',\n", + " 'sample_file_2.csv',\n", + " 'sample_file_3.csv',\n", + " 'sample_file_7.csv',\n", + " 'sample_file_6.csv',\n", + " 'sample_file_4.csv',\n", + " 'sample_file_5.csv',\n", + " 'sample_file_8.csv',\n", + " 'sample_file_9.csv']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = [file for file in os.listdir('../data')\n", + " if file.endswith('.csv')\n", + " ]\n", + "\n", + "files" + ] }, { "cell_type": "markdown", @@ -167,10 +604,600 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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"execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'loc'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m data_medians = [col_number for col_number in data\n\u001b[0m\u001b[1;32m 2\u001b[0m if data.loc[:,col_number].median(axis=0)<0.48]\n\u001b[1;32m 3\u001b[0m \u001b[0mdata_medians\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1\u001b[0m data_medians = [col_number for col_number in data\n\u001b[0;32m----> 2\u001b[0;31m if data.loc[:,col_number].median(axis=0)<0.48]\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata_medians\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'loc'" + ] + } + ], + "source": [ + "data_medians = [col_number for col_number in data\n", + " if data.loc[:,col_number].median(axis=0)<0.48]\n", + "data_medians" + ] }, { "cell_type": "markdown", @@ -195,10 +1239,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'iloc'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m19\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'20'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'iloc'" + ] + } + ], + "source": [ + "values = [value-0.1 for value in data.iloc[:,19]]\n", + "data['20'] = values\n", + "data.head(20)" + ] }, { "cell_type": "markdown", @@ -207,6 +1267,32 @@ "### 10. Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75." ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'loc'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m values = [row\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol_number\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcol_number\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;36m0.7\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.75\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m ]\n\u001b[1;32m 5\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1\u001b[0m values = [row\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol_number\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcol_number\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;36m0.7\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.75\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m ]\n\u001b[1;32m 5\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'loc'" + ] + } + ], + "source": [ + "values = [row\n", + " for col_number in data \n", + " for row in data.loc[:,col_number] if 0.7 < row < 0.75\n", + " ]\n", + "values" + ] + }, { "cell_type": "code", "execution_count": null, @@ -231,7 +1317,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.9.5" } }, "nbformat": 4,