From bd2bd4544ade2fbe36f220836381b7f9e66c8e6d Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Tue, 6 Nov 2018 07:13:44 -0600 Subject: [PATCH 1/6] 0154590 From 88ffcd65ea7d5885e56a84ce6734205afea0a9df Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Tue, 4 Dec 2018 10:01:00 +0800 Subject: [PATCH 2/6] =?UTF-8?q?0154590=E5=88=98=E5=A9=B7=E5=A9=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../0154590\357\274\2106\357\274\211.ipynb" | 785 ++++++++++++++++++ 1 file changed, 785 insertions(+) create mode 100644 "exer6/0154590\357\274\2106\357\274\211.ipynb" diff --git "a/exer6/0154590\357\274\2106\357\274\211.ipynb" "b/exer6/0154590\357\274\2106\357\274\211.ipynb" new file mode 100644 index 0000000..bffe960 --- /dev/null +++ "b/exer6/0154590\357\274\2106\357\274\211.ipynb" @@ -0,0 +1,785 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 第六次练习" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ 请务必交到exer6文件夹下，**谢绝交到master下**\n", + "+ 请不要改动任何文件，拜托\n", + "+ 请在12月4日前提交。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "请写一下姓名和学号：\n", + "+ 姓名 刘婷婷\n", + "+ 学号 0154590" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 请建立一个数据框，列出你在双十一购买的至少五样物品。\n", + "用它们的名称作为index的label，在列里列出这些物品（数据可以是虚拟的）的：\n", + "+ 价格\n", + "+ 购买的数量\n", + "+ 收到的时间（类似1113即可，表示11月13日收到）\n", + "+ 你的评价（从1到5，1表示很不好，5表示很好）" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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priceamounttimecomment
肖四肖八100411134
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" + ], + "text/plain": [ + " price amount time comment\n", + "肖四肖八 100 4 1113 4\n", + "运动鞋 200 3 1114 4\n", + "护肤品 300 2 1115 4\n", + "羽绒服 400 1 1116 5\n", + "猫粮 64 2 1115 4" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from pandas import Series, DataFrame\n", + "goodsInfo = {\"price\": [100, 200, 300, 400, 64],\n", + " \"amount\": [4, 3, 2, 1, 2],\n", + " \"time\": [1113, 1114, 1115, 1116, 1115],\n", + " \"comment\": [4, 4, 4, 5, 4]\n", + " }\n", + "frame = pd.DataFrame(goodsInfo)\n", + "frame = DataFrame(goodsInfo, columns=[\"price\", \"amount\", \"time\", \"comment\"], index=[\"肖四肖八\", \"运动鞋\", \"护肤品\", \"羽绒服\", \"猫粮\"])\n", + "frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 用index的label读取某一个物品数据" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price 100\n", + "amount 4\n", + "time 1113\n", + "comment 4\n", + "Name: 肖四肖八, dtype: int64\n" + ] + } + ], + "source": [ + "print(frame.loc[\"肖四肖八\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 选择价格>200的物品" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " amount comment price time\n", + "肖四肖八 4 4 100 1113" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "studyGoods = {\"price\": frame[\"price\"][0:1],\n", + " \"amount\": frame[\"amount\"][0:1],\n", + " \"time\": frame[\"time\"][0:1],\n", + " \"comment\": frame[\"comment\"][0:1]\n", + " }\n", + "DataFrame(studyGoods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 读取评价那列的数据" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "肖四肖八 4\n", + "运动鞋 4\n", + "护肤品 4\n", + "羽绒服 5\n", + "猫粮 4\n", + "Name: comment, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame['comment']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 根据label对数据框重新排序，按照对你的迫切程度" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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priceamounttimecomment
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" + ], + "text/plain": [ + " price amount time comment\n", + "肖四肖八 100 4 1113 4\n", + "运动鞋 200 3 1114 4\n", + "护肤品 300 2 1115 4\n", + "猫粮 64 2 1115 4\n", + "羽绒服 400 1 1116 5" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame.sort_values(by='time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 得到各样物品评价的排名" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "肖四肖八 1.0\n", + "运动鞋 2.0\n", + "护肤品 3.0\n", + "羽绒服 5.0\n", + "猫粮 4.0\n", + "Name: comment, dtype: float64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame['comment'].rank(method='first') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 计算一下这些物品的总花费、最大值、最小值和平均值" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1064" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.DataFrame(frame)\n", + "df['price'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "400" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['price'].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['price'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "212.8" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['price'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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": "3.6.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 68f9e5bdce686d74c37c3ee5ef96a6a34c08e521 Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Sun, 30 Dec 2018 18:49:23 +0800 Subject: [PATCH 3/6] =?UTF-8?q?0154590=E5=88=98=E5=A9=B7=E5=A9=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- exer8/0154590.ipynb | 5831 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5831 insertions(+) create mode 100644 exer8/0154590.ipynb diff --git a/exer8/0154590.ipynb b/exer8/0154590.ipynb new file mode 100644 index 0000000..08bb9c3 --- /dev/null +++ b/exer8/0154590.ipynb @@ -0,0 +1,5831 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 课程论文" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 请务必交到exer8文件夹下，**谢绝交到master下**\n", + "+ 请不要改动任何文件，拜托\n", + "+ 请于12月30日前先在github上提交\n", + "+ 请在元旦后提交纸质版，将本页面文件先打印为pdf格式，再去打印店付印\n", + "+ 请将论文模板和本页面文件一起装订，前者放上面，本页面文件放下面\n", + "+ 纸质版提交时间和地点请留意微信群通知" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "请写一下姓名和学号：\n", + "+ 姓名 刘婷婷\n", + "+ 学号 0154590" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "plt.rcParams['font.sans-serif']=['SimHei'] \n", + "plt.rcParams['axes.unicode_minus']=False " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 样本均值分布的统计试验" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ 请将CEPS.csv数据读入python\n", + "+ 请从中随机抽取1000个数据\n", + "+ 请根据问卷从数据中挑选两个连续型变量（likert量表可以近似看作连续变量）\n", + "+ 计算这两个连续变量的均值\n", + "+ 重复随机抽取—计算均值这个过程30次，得到两个变量30个样本均值\n", + "+ 绘制这30个样本均值的直方图\n", + "+ 计算均值的均值和标准误" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\11651\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (20,22,23,25,28,29,39,49,74,124,125,126,127,128,129,130,131,138,140,141,147,160,161,162,165,170,174,175,176,177,179,180,181,182,183,184,188,191,194,195,196,199,221,222,223,224,251,252,254,289,290,294,295,296) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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idsclsidsschidsctyidsframesubsamplesweightfallgrade9stcog...steco_3cstonlystsibstsibrankstmedustfedustprhedustfdrunkstprfightstprrel
61666167164431133185.6909941010...2223626112
712713236233288.219666017...2211666112
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12500125012937619114257.6210941010...2222222112
178031780440910527112539.1025391111...31555211
1518115182352912333298.0102840016...21112
177041770540710527112768.7126461012...21444221
4205420610827722466.666138117...3213323122
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10181019328233254.069702019...21636112
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181711817241510727112832.268311107...2211366112
665766581734512111852.2965091119...21377112
14467144683358722112676.579590005...2213666111
19410194114381122833398.827271117...1211636112
85268527216561433451.0437930116...2211666112
60086009160421133188.6860051014...2211233112
1559315594363942433272.9494320010...21333112
861862277233236.002518013...2211233112
682468251774612111899.096802119...21333111
503504185233222.034149007...21388112
1603616037373962433386.8999940116...11323112
10273102742516517112258.348389115...2211455112
1594915950371962433290.1849370015...2211366112
..................................................................
60316032160421133188.6860051014...1213233112
1560156142113112523.5715330012...2211333112
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523552361413692285.7111131011...2211899112
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859485952185715113179.0556641013...2211434212
13508135093148121113597.1059571111...2213233112
1533815339356922333270.8056340010...2223488112
4480448111830822114.5746381011...1211677112
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18762187634271102833251.4793851010...21666112
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16411164123849925111664.248779107...12323112
10796107972616817112210.5063481015...2213333112
10282102832516517112258.3483891117...21344112
10341035328233265.7887270112...31333112
3386338784216113697.6870121115...21477221
14222142233308522112929.824219015...11333112
496497185233219.7800600015...1213222111
181731817441510727112816.3857421013...2213366112
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60046005160421133190.6211851016...2211366112
168291683039310126113652.990723016...2211366121
125412553693112746.2744140111...21344112
4191419210827722389.7999271111...21545112
11385113862727118112327.3583981012...2213666111
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1000 rows × 300 columns

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2676.579590 0 \n", + "19410 19411 438 112 28 3 3 398.827271 1 \n", + "8526 8527 216 56 14 3 3 451.043793 0 \n", + "6008 6009 160 42 11 3 3 188.686005 1 \n", + "15593 15594 363 94 24 3 3 272.949432 0 \n", + "861 862 27 7 2 3 3 236.002518 0 \n", + "6824 6825 177 46 12 1 1 1899.096802 1 \n", + "503 504 18 5 2 3 3 222.034149 0 \n", + "16036 16037 373 96 24 3 3 386.899994 0 \n", + "10273 10274 251 65 17 1 1 2258.348389 1 \n", + "15949 15950 371 96 24 3 3 290.184937 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6031 6032 160 42 11 3 3 188.686005 1 \n", + "1560 1561 42 11 3 1 1 2523.571533 0 \n", + "8741 8742 220 57 15 1 1 2576.434082 1 \n", + "5235 5236 141 36 9 2 2 85.711113 1 \n", + "4821 4822 129 33 9 2 2 73.548485 1 \n", + "8594 8595 218 57 15 1 1 3179.055664 1 \n", + "13508 13509 314 81 21 1 1 3597.105957 1 \n", + "15338 15339 356 92 23 3 3 270.805634 0 \n", + "4480 4481 118 30 8 2 2 114.574638 1 \n", + "3362 3363 84 21 6 1 1 3697.687012 1 \n", + "18762 18763 427 110 28 3 3 251.479385 1 \n", + "1885 1886 50 13 4 1 1 1705.000488 1 \n", + "16411 16412 384 99 25 1 1 1664.248779 1 \n", + "10796 10797 261 68 17 1 1 2210.506348 1 \n", + "10282 10283 251 65 17 1 1 2258.348389 1 \n", + "1034 1035 32 8 2 3 3 265.788727 0 \n", + "3386 3387 84 21 6 1 1 3697.687012 1 \n", + "14222 14223 330 85 22 1 1 2929.824219 0 \n", + "496 497 18 5 2 3 3 219.780060 0 \n", + "18173 18174 415 107 27 1 1 2816.385742 1 \n", + "4265 4266 110 28 7 2 2 390.305664 1 \n", + "14173 14174 329 85 22 1 1 2566.042236 0 \n", + "18698 18699 426 109 28 3 3 293.343750 1 \n", + "14611 14612 339 88 22 1 1 3060.337402 0 \n", + "13424 13425 312 81 21 1 1 4011.244141 1 \n", + "6004 6005 160 42 11 3 3 190.621185 1 \n", + "16829 16830 393 101 26 1 1 3652.990723 0 \n", + "1254 1255 36 9 3 1 1 2746.274414 0 \n", + "4191 4192 108 27 7 2 2 389.799927 1 \n", + "11385 11386 272 71 18 1 1 2327.358398 1 \n", + "\n", + " grade9 stcog ... steco_3c stonly stsib stsibrank stmedu stfedu \\\n", + "6166 0 10 ... 2 2 2 3 6 2 \n", + "712 1 7 ... 2 2 1 1 6 6 \n", + "877 1 6 ... 2 1 3 1 \n", + "17555 0 10 ... 1 2 2 3 1 3 \n", + "12094 1 9 ... 2 2 2 2 2 2 \n", + "1708 0 11 ... 2 1 3 3 \n", + "5490 1 19 ... 2 1 8 3 \n", + "16684 0 9 ... 2 1 8 8 \n", + "2384 1 11 ... 1 2 1 3 3 3 \n", + "12500 0 10 ... 2 2 2 2 2 2 \n", + "17803 1 11 ... 3 1 5 5 \n", + "15181 0 16 ... 2 1 \n", + "17704 0 12 ... 2 1 4 4 \n", + "4205 1 7 ... 3 2 1 3 3 2 \n", + "12807 1 9 ... 2 2 2 2 3 2 \n", + "1018 1 9 ... 2 1 6 3 \n", + "14406 1 3 ... 2 1 2 3 \n", + "18171 0 7 ... 2 2 1 1 3 6 \n", + "6657 1 19 ... 2 1 3 7 \n", + "14467 0 5 ... 2 2 1 3 6 6 \n", + "19410 1 7 ... 1 2 1 1 6 3 \n", + "8526 1 16 ... 2 2 1 1 6 6 \n", + "6008 0 14 ... 2 2 1 1 2 3 \n", + "15593 0 10 ... 2 1 3 3 \n", + "861 1 3 ... 2 2 1 1 2 3 \n", + "6824 1 9 ... 2 1 3 3 \n", + "503 0 7 ... 2 1 3 8 \n", + "16036 1 16 ... 1 1 3 2 \n", + "10273 1 5 ... 2 2 1 1 4 5 \n", + "15949 0 15 ... 2 2 1 1 3 6 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "6031 0 14 ... 1 2 1 3 2 3 \n", + "1560 0 12 ... 2 2 1 1 3 3 \n", + "8741 1 3 ... 1 2 1 1 2 3 \n", + "5235 0 11 ... 2 2 1 1 8 9 \n", + "4821 0 16 ... 2 1 4 4 \n", + "8594 0 13 ... 2 2 1 1 4 3 \n", + "13508 1 11 ... 2 2 1 3 2 3 \n", + "15338 0 10 ... 2 2 2 3 4 8 \n", + "4480 0 11 ... 1 2 1 1 6 7 \n", + "3362 1 11 ... 2 1 3 3 \n", + "18762 0 10 ... 2 1 6 6 \n", + "1885 0 12 ... 1 2 1 3 3 3 \n", + "16411 0 7 ... 1 2 3 2 \n", + "10796 0 15 ... 2 2 1 3 3 3 \n", + "10282 1 17 ... 2 1 3 4 \n", + "1034 1 12 ... 3 1 3 3 \n", + "3386 1 15 ... 2 1 4 7 \n", + "14222 1 5 ... 1 1 3 3 \n", + "496 0 15 ... 1 2 1 3 2 2 \n", + "18173 0 13 ... 2 2 1 3 3 6 \n", + "4265 0 13 ... 2 1 8 9 \n", + "14173 1 9 ... 2 2 6 3 \n", + "18698 1 12 ... 2 1 3 3 \n", + "14611 0 10 ... 2 2 1 3 2 3 \n", + "13424 0 11 ... 1 2 1 3 3 3 \n", + "6004 0 16 ... 2 2 1 1 3 6 \n", + "16829 1 6 ... 2 2 1 1 3 6 \n", + "1254 1 11 ... 2 1 3 4 \n", + "4191 1 11 ... 2 1 5 4 \n", + "11385 0 12 ... 2 2 1 3 6 6 \n", + "\n", + " stprhedu stfdrunk stprfight stprrel \n", + "6166 6 1 1 2 \n", + "712 6 1 1 2 \n", + "877 3 2 1 2 \n", + "17555 3 1 1 2 \n", + "12094 2 1 1 2 \n", + "1708 3 2 1 2 \n", + "5490 8 1 1 2 \n", + "16684 8 2 1 1 \n", + "2384 3 2 2 1 \n", + "12500 2 1 1 2 \n", + "17803 5 2 1 1 \n", + "15181 1 1 2 \n", + "17704 4 2 2 1 \n", + "4205 3 1 2 2 \n", + "12807 3 1 1 2 \n", + "1018 6 1 1 2 \n", + "14406 3 1 1 2 \n", + "18171 6 1 1 2 \n", + "6657 7 1 1 2 \n", + "14467 6 1 1 1 \n", + "19410 6 1 1 2 \n", + "8526 6 1 1 2 \n", + "6008 3 1 1 2 \n", + "15593 3 1 1 2 \n", + "861 3 1 1 2 \n", + "6824 3 1 1 1 \n", + "503 8 1 1 2 \n", + "16036 3 1 1 2 \n", + "10273 5 1 1 2 \n", + "15949 6 1 1 2 \n", + "... ... ... ... ... \n", + "6031 3 1 1 2 \n", + "1560 3 1 1 2 \n", + "8741 3 1 1 2 \n", + "5235 9 1 1 2 \n", + "4821 4 1 1 1 \n", + "8594 4 2 1 2 \n", + "13508 3 1 1 2 \n", + "15338 8 1 1 2 \n", + "4480 7 1 1 2 \n", + "3362 3 1 1 2 \n", + "18762 6 1 1 2 \n", + "1885 3 1 1 2 \n", + "16411 3 1 1 2 \n", + "10796 3 1 1 2 \n", + "10282 4 1 1 2 \n", + "1034 3 1 1 2 \n", + "3386 7 2 2 1 \n", + "14222 3 1 1 2 \n", + "496 2 1 1 1 \n", + "18173 6 1 1 2 \n", + "4265 9 1 1 2 \n", + "14173 6 1 1 2 \n", + "18698 3 1 1 2 \n", + "14611 3 1 1 2 \n", + "13424 3 2 1 2 \n", + "6004 6 1 1 2 \n", + "16829 6 1 2 1 \n", + "1254 4 1 1 2 \n", + "4191 5 1 1 2 \n", + "11385 6 1 1 1 \n", + "\n", + "[1000 rows x 300 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=df.sample(n=1000)#随机抽取1000个数据\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6166 4.0\n", + "712 3.0\n", + "877 3.0\n", + "17555 2.0\n", + "12094 2.0\n", + "1708 4.0\n", + "5490 4.0\n", + "16684 3.0\n", + "2384 3.0\n", + "12500 NaN\n", + "17803 3.0\n", + "15181 4.0\n", + "17704 4.0\n", + "4205 4.0\n", + "12807 2.0\n", + "1018 3.0\n", + "14406 2.0\n", + "18171 NaN\n", + "6657 4.0\n", + "14467 2.0\n", + "19410 3.0\n", + "8526 4.0\n", + "6008 3.0\n", + "15593 3.0\n", + "861 3.0\n", + "6824 3.0\n", + "503 NaN\n", + "16036 3.0\n", + "10273 3.0\n", + "15949 2.0\n", + " ... \n", + "6031 3.0\n", + "1560 2.0\n", + "8741 2.0\n", + "5235 3.0\n", + "4821 4.0\n", + "8594 3.0\n", + "13508 3.0\n", + "15338 4.0\n", + "4480 2.0\n", + "3362 4.0\n", + "18762 2.0\n", + "1885 3.0\n", + "16411 2.0\n", + "10796 4.0\n", + "10282 1.0\n", + "1034 3.0\n", + "3386 3.0\n", + "14222 3.0\n", + "496 4.0\n", + "18173 3.0\n", + "4265 4.0\n", + "14173 4.0\n", + "18698 4.0\n", + "14611 4.0\n", + "13424 2.0\n", + "6004 3.0\n", + "16829 4.0\n", + "1254 3.0\n", + "4191 4.0\n", + "11385 3.0\n", + "Name: a1206, dtype: float64\n", + "6166 4.0\n", + "712 3.0\n", + "877 4.0\n", + "17555 4.0\n", + "12094 3.0\n", + "1708 4.0\n", + "5490 4.0\n", + "16684 4.0\n", + "2384 4.0\n", + "12500 NaN\n", + "17803 4.0\n", + "15181 4.0\n", + "17704 4.0\n", + "4205 4.0\n", + "12807 3.0\n", + "1018 3.0\n", + "14406 4.0\n", + "18171 3.0\n", + "6657 4.0\n", + "14467 4.0\n", + "19410 4.0\n", + "8526 4.0\n", + "6008 3.0\n", + "15593 4.0\n", + "861 3.0\n", + "6824 4.0\n", + "503 NaN\n", + "16036 1.0\n", + "10273 4.0\n", + "15949 3.0\n", + " ... \n", + "6031 4.0\n", + "1560 4.0\n", + "8741 4.0\n", + "5235 3.0\n", + "4821 4.0\n", + "8594 4.0\n", + "13508 4.0\n", + "15338 4.0\n", + "4480 4.0\n", + "3362 4.0\n", + "18762 4.0\n", + "1885 4.0\n", + "16411 3.0\n", + "10796 4.0\n", + "10282 1.0\n", + "1034 4.0\n", + "3386 3.0\n", + "14222 3.0\n", + "496 4.0\n", + "18173 3.0\n", + "4265 4.0\n", + "14173 4.0\n", + "18698 4.0\n", + "14611 4.0\n", + "13424 2.0\n", + "6004 4.0\n", + "16829 4.0\n", + "1254 4.0\n", + "4191 4.0\n", + "11385 3.0\n", + "Name: a1207, dtype: float64\n" + ] + } + ], + "source": [ + "x1=a['a1206']\n", + "print(x1)\n", + "x2=a['a1207']\n", + "print(x2)#根据问卷从数据中挑选'a1201'和'a1202\"这两个连续型变量\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.9705882352941178" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.4444444444444446" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 2.9885057471264367, 2: 2.9310710498409334, 3: 2.9630411826821543, 4: 2.9842602308499475, 5: 2.9602094240837697, 6: 2.9989384288747347, 7: 3.018828451882845, 8: 2.9621451104100944, 9: 2.988469601677149, 10: 3.0021141649048624, 11: 2.9707724425887263, 12: 3.031479538300105, 13: 3.049060542797495, 14: 3.0240083507306887, 15: 2.99789029535865, 16: 2.9605809128630707, 17: 3.01673640167364, 18: 2.933684210526316, 19: 2.9968354430379747, 20: 2.988469601677149, 21: 2.962303664921466, 22: 3.0010493179433366, 23: 3.0063626723223753, 24: 3.0113753877973113, 25: 3.0063559322033897, 26: 2.9572649572649574, 27: 2.9735169491525424, 28: 2.9736008447729674, 29: 3.020811654526535, 30: 2.9651162790697674}\n", + "{1: 3.47052740434333, 2: 3.500527983104541, 3: 3.5015641293013555, 4: 3.495833333333333, 5: 3.4803312629399588, 6: 3.5236593059936907, 7: 3.5114345114345116, 8: 3.449064449064449, 9: 3.5346432264736296, 10: 3.492179353493222, 11: 3.51702786377709, 12: 3.497398543184183, 13: 3.463364293085655, 14: 3.5389408099688473, 15: 3.526150627615063, 16: 3.507201646090535, 17: 3.494802494802495, 18: 3.4953173777315296, 19: 3.504166666666667, 20: 3.5073221757322175, 21: 3.4869927159209158, 22: 3.498956158663883, 23: 3.5276907001044933, 24: 3.5292908530318603, 25: 3.529718456725756, 26: 3.4547368421052633, 27: 3.4812108559498958, 28: 3.473298429319372, 29: 3.489648033126294, 30: 3.444906444906445}\n" + ] + } + ], + "source": [ + "count=0\n", + "mean_1206={}\n", + "mean_1207={}\n", + "while count<30:#重复随机抽取—计算均值这个过程30次，得到两个变量30个样本均值\n", + " df_a=df.sample(n=1000)\n", + " mean_1206_a=df_a['a1206'].mean()\n", + " mean_1206[count+1]= mean_1206_a\n", + " mean_1207_a=df_a['a1207'].mean()\n", + " mean_1207[count+1]= mean_1207_a\n", + " count=count+1\n", + "print(mean_1206)\n", + "print(mean_1207)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4shLCo6Sbqs1Wuuf6UaRRCJW6A6/k5/PJN/+qFhHXkMaEM3DgwJg8ueimiWZmVouklxqJ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\n9R1LJR1JuobAzMz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ind=np.arange(len(mean_1206.values()))\n", + "width=0.5\n", + "fig, ax = plt.subplots()\n", + "rects1 = ax.bar(ind - width/2, mean_1206.values(), width, \n", + " color='Y', label='a1206均值')\n", + "ax.set_ylabel('同意程度')\n", + "ax.set_title('随机抽取a1206题样本的同意程度均值')\n", + "ax.set_xticks(ind)\n", + "ax.set_xticklabels(mean_1206.keys())\n", + "ax.legend()\n", + "ax.set_ylim(1,5)\n", + "\n", + "def autolabel(rects, xpos='center'):\n", + " \"\"\"\n", + " Attach a text label above each bar in *rects*, displaying its height.\n", + "\n", + " *xpos* indicates which side to place the text w.r.t. the center of\n", + " the bar. It can be one of the following {'center', 'right', 'left'}.\n", + " \"\"\"\n", + "\n", + " xpos = xpos.lower() # normalize the case of the parameter\n", + " ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n", + " offset = {'center': 1, 'right': 1, 'left': 1} # x_txt = x + w*off\n", + "\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()*offset[xpos], 2*height,\n", + " '{}'.format(height), ha=ha[xpos], va='bottom')\n", + "\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ind=np.arange(len(mean_1207.values()))\n", + "width=0.5\n", + "fig, ax = plt.subplots()\n", + "rects1 = ax.bar(ind - width/2, mean_1206.values(), width, \n", + " color='Y', label='a1207均值')\n", + "ax.set_ylabel('同意程度')\n", + "ax.set_title('随机抽取a1207题样本的同意程度均值')\n", + "ax.set_xticks(ind)\n", + "ax.set_xticklabels(mean_1207.keys())\n", + "ax.legend()\n", + "ax.set_ylim(1,5)\n", + "\n", + "def autolabel(rects, xpos='center'):\n", + " \"\"\"\n", + " Attach a text label above each bar in *rects*, displaying its height.\n", + "\n", + " *xpos* indicates which side to place the text w.r.t. the center of\n", + " the bar. It can be one of the following {'center', 'right', 'left'}.\n", + " \"\"\"\n", + "\n", + " xpos = xpos.lower() # normalize the case of the parameter\n", + " ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n", + " offset = {'center': 1, 'right': 1, 'left': 1} # x_txt = x + w*off\n", + "\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()*offset[xpos], 2*height,\n", + " '{}'.format(height), ha=ha[xpos], va='bottom')\n", + "\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "样本均值的均值如下：\n", + "\n", + "1206 2.988162\n", + "1207 3.497597\n", + "dtype: float64\n", + "\n", + "标准误如下：\n", + "\n", + "1206 0.028477\n", + "1207 0.025322\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "a1206=[]\n", + "for i in mean_1206.values():\n", + " a1206.append(i)\n", + " \n", + "a1207=[]\n", + "for i in mean_1207.values():\n", + " a1207.append(i)\n", + " \n", + "average={'1206':a1206,\n", + " '1207':a1207,\n", + " }\n", + "frame = pd.DataFrame(average,index=mean_1206.keys())\n", + "\n", + "#计算30个样本均值的均值和标准误\n", + "print('样本均值的均值如下：\\n')\n", + "print(frame.mean())\n", + "print('\\n标准误如下：\\n')\n", + "print(frame.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 回归分析" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ 请从CEPS.csv数据里挑选若干变量建立回归方程，要求至少三个自变量\n", + " + 如，学生的学业成绩受认知水平、家庭收入的影响\n", + " + 考虑因变量和自变量间的实质关系，变量间关系应该是有意义\n", + " + 选择自变量时，注意变量的类型，如果是分类变量，需要进行编码\n", + "+ 请报告回归方程的结果，需要包括：\n", + " + 模型拟合指标\n", + " + 模型的显著性检验结果\n", + " + 变量的系数\n", + " + 各系数的显著性检验结果\n", + " + 对模型结果的解释\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ 步骤如下：\n", + "+ 取 c25\"你对自己未来有没有信心\" 做因变量y\n", + "+ 取 c12“目前的成绩” 、b35\"父母对你未来是否有信心\"、b09“家庭经济条件”做自变量" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\11651\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (20,22,23,25,28,29,39,49,74,124,125,126,127,128,129,130,131,138,140,141,147,160,161,162,165,170,174,175,177,179,180,181,182,183,184,188,191,195,196,199,221,222,223,224,251,252,289,290,294,295,296) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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1000 rows × 300 columns

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269.611053 0 \n", + "5577 5578 148 38 10 3 2 63.820496 1 \n", + "18086 18087 413 106 27 1 1 2890.422119 1 \n", + "4486 4487 119 30 8 2 2 152.462509 1 \n", + "3643 3644 91 23 6 1 1 3200.472412 1 \n", + "9180 9181 230 60 15 1 1 3893.780273 1 \n", + "8100 8101 207 54 14 3 3 289.692108 0 \n", + "2674 2675 66 17 5 3 3 159.017761 0 \n", + "10574 10575 257 67 17 1 1 2164.127930 1 \n", + "13481 13482 313 81 21 1 1 3728.043945 1 \n", + "19243 19244 435 112 28 3 3 273.053802 1 \n", + "... ... ... ... ... ... ... ... ... \n", + "8262 8263 211 55 14 3 3 302.970917 0 \n", + "2373 2374 60 15 4 1 1 1711.570312 1 \n", + "13433 13434 312 81 21 1 1 4011.244141 1 \n", + "7547 7548 193 50 13 1 3 1174.650635 0 \n", + "13196 13197 307 79 20 3 3 328.509613 0 \n", + "528 529 19 5 2 3 3 297.335205 0 \n", + "11991 11992 284 74 19 1 1 2515.791016 1 \n", + "13117 13118 305 79 20 3 3 283.090729 0 \n", + "8978 8979 225 58 15 1 1 3365.785156 1 \n", + "1427 1428 40 10 3 1 1 2729.416748 0 \n", + "9184 9185 230 60 15 1 1 3893.780273 1 \n", + "16684 16685 391 101 26 1 1 3338.685547 0 \n", + "8048 8049 206 54 14 3 3 292.725128 0 \n", + "2455 2456 62 16 4 1 1 1762.942627 1 \n", + "14015 14016 326 84 21 1 1 1302.465454 1 \n", + "10808 10809 262 68 17 1 1 2330.469727 1 \n", + "375 376 13 4 1 3 3 239.403671 0 \n", + "19337 19338 437 112 28 3 3 445.649567 1 \n", + "6370 6371 168 44 11 3 3 93.271454 1 \n", + "16387 16388 383 99 25 1 1 1664.248779 1 \n", + "15409 15410 359 93 24 3 3 287.871735 0 \n", + "7542 7543 192 50 13 1 3 1154.048950 0 \n", + "16468 16469 385 99 25 1 1 1594.551270 1 \n", + "16660 16661 390 100 25 1 1 1558.569702 1 \n", + "845 846 26 7 2 3 3 215.709335 0 \n", + "17812 17813 409 105 27 1 1 2539.102539 1 \n", + "15838 15839 368 95 24 3 3 297.490601 0 \n", + "2204 2205 57 15 4 1 1 1616.520264 1 \n", + "16805 16806 393 101 26 1 1 3199.416504 0 \n", + "6811 6812 177 46 12 1 1 2168.327637 1 \n", + "\n", + " grade9 stcog ... steco_3c stonly stsib stsibrank stmedu stfedu \\\n", + "3424 0 13 ... 1 2 1 1 2 3 \n", + "5093 1 9 ... 2 1 8 8 \n", + "762 1 11 ... 1 1 3 1 \n", + "6755 0 6 ... 1 2 1 3 2 2 \n", + "2676 0 12 ... 2 1 7 7 \n", + "10135 0 10 ... 3 1 3 9 \n", + "10626 1 10 ... 2 1 6 6 \n", + "166 0 13 ... 2 1 8 9 \n", + "12697 0 9 ... 3 1 3 3 \n", + "16130 1 14 ... 2 2 2 2 2 2 \n", + "14656 0 1 ... 1 1 3 3 \n", + "7657 0 8 ... 2 2 2 2 3 8 \n", + "11019 1 14 ... 1 2 2 1 3 3 \n", + "3680 1 11 ... 2 1 3 3 \n", + "11828 0 13 ... 2 2 1 1 2 3 \n", + "16976 0 4 ... 1 2 4 3 1 2 \n", + "18189 0 13 ... 2 1 2 8 \n", + "11877 0 14 ... 1 2 3 3 3 2 \n", + "15556 1 15 ... 2 1 7 7 \n", + "12714 0 12 ... 3 1 6 8 \n", + "5577 0 7 ... 1 1 3 3 \n", + "18086 1 7 ... 2 2 1 3 3 3 \n", + "4486 1 18 ... 2 1 6 8 \n", + "3643 1 11 ... 2 2 1 1 2 3 \n", + "9180 0 12 ... 2 2 1 1 3 2 \n", + "8100 0 12 ... 2 2 1 3 3 6 \n", + "2674 0 8 ... 2 1 8 8 \n", + "10574 0 9 ... 2 1 7 3 \n", + "13481 0 1 ... 1 2 1 3 3 3 \n", + "19243 0 10 ... 2 1 4 4 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "8262 0 12 ... 2 2 2 3 2 6 \n", + "2373 1 10 ... 3 2 2 2 2 2 \n", + "13433 0 4 ... 1 2 1 3 2 2 \n", + "7547 1 12 ... 1 2 1 3 3 3 \n", + "13196 1 4 ... 2 1 3 2 \n", + "528 1 5 ... 2 2 1 1 3 3 \n", + "11991 0 8 ... 2 2 1 3 2 3 \n", + "13117 0 16 ... 1 2 1 1 3 6 \n", + "8978 1 7 ... 2 1 4 8 \n", + "1427 1 12 ... 2 1 9 8 \n", + "9184 0 9 ... 2 2 1 1 3 3 \n", + "16684 0 9 ... 2 1 8 8 \n", + "8048 0 8 ... 1 2 5 2 2 3 \n", + "2455 0 6 ... 2 2 1 3 4 4 \n", + "14015 1 11 ... 1 2 1 1 2 3 \n", + "10808 1 19 ... 2 1 2 3 \n", + "375 0 11 ... 2 2 1 1 6 8 \n", + "19337 1 14 ... 3 1 9 9 \n", + "6370 0 8 ... 2 2 3 2 3 2 \n", + "16387 0 3 ... 1 2 1 3 3 3 \n", + "15409 0 15 ... 2 1 4 8 \n", + "7542 1 14 ... 2 2 3 1 2 3 \n", + "16468 1 4 ... 1 2 1 3 3 3 \n", + "16660 1 9 ... 1 2 2 2 2 3 \n", + "845 0 11 ... 2 1 3 2 \n", + "17812 1 4 ... 2 1 2 3 \n", + "15838 0 11 ... 2 2 1 3 3 3 \n", + "2204 0 12 ... 1 2 1 1 4 4 \n", + "16805 1 13 ... 1 2 3 2 2 2 \n", + "6811 1 12 ... 1 2 1 1 2 3 \n", + "\n", + " stprhedu stfdrunk stprfight stprrel \n", + "3424 3 1 1 1 \n", + "5093 8 1 1 2 \n", + "762 3 1 1 2 \n", + "6755 2 1 1 2 \n", + "2676 7 1 1 1 \n", + "10135 9 1 1 2 \n", + "10626 6 1 1 2 \n", + "166 9 1 1 2 \n", + "12697 3 1 1 2 \n", + "16130 2 1 1 2 \n", + "14656 3 2 1 1 \n", + "7657 8 2 2 2 \n", + "11019 3 1 1 2 \n", + "3680 3 1 1 2 \n", + "11828 3 1 1 2 \n", + "16976 2 1 1 2 \n", + "18189 8 1 1 2 \n", + "11877 3 1 1 2 \n", + "15556 7 2 1 1 \n", + "12714 8 2 1 2 \n", + "5577 3 1 1 2 \n", + "18086 3 1 1 2 \n", + "4486 8 1 1 1 \n", + "3643 3 1 1 2 \n", + "9180 3 1 1 1 \n", + "8100 6 1 1 2 \n", + "2674 8 1 1 2 \n", + "10574 7 1 1 2 \n", + "13481 3 1 1 2 \n", + "19243 4 1 1 2 \n", + "... ... ... ... ... \n", + "8262 6 1 1 2 \n", + "2373 2 1 1 1 \n", + "13433 2 \n", + "7547 3 1 1 1 \n", + "13196 3 1 1 1 \n", + "528 3 1 1 2 \n", + "11991 3 1 1 2 \n", + "13117 6 1 1 2 \n", + "8978 8 1 2 1 \n", + "1427 9 1 1 2 \n", + "9184 3 1 1 2 \n", + "16684 8 2 1 1 \n", + "8048 3 1 1 2 \n", + "2455 4 1 1 2 \n", + "14015 3 1 1 2 \n", + "10808 3 1 1 2 \n", + "375 8 1 1 2 \n", + "19337 9 1 1 2 \n", + "6370 3 1 2 2 \n", + "16387 3 1 1 2 \n", + "15409 8 1 1 2 \n", + "7542 3 1 2 1 \n", + "16468 3 1 1 2 \n", + "16660 3 1 2 2 \n", + "845 3 1 1 2 \n", + "17812 3 1 1 1 \n", + "15838 3 1 1 1 \n", + "2204 4 1 1 2 \n", + "16805 2 1 1 2 \n", + "6811 3 1 1 2 \n", + "\n", + "[1000 rows x 300 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import csv\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from itertools import islice\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "import pandas as pd\n", + "\n", + "sentinels = {'c25': [' '], 'c12': [' '], 'b35': [' '], 'b09': [' ']}\n", + "df = pd.read_csv('CEPS.csv',encoding='gb2312', na_values=sentinels)\n", + "sdf=df.sample(n=1000)\n", + "sdf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2. 3. 2.]\n", + " [ 4. 3. 3.]\n", + " [ 2. 3. 2.]\n", + " ..., \n", + " [ 3. 3. 3.]\n", + " [ 4. 3. 2.]\n", + " [ 3. 3. 3.]]\n", + "[ 3. 3. 1. 4. 4. 3. 4. 3. 3. 2. 3. 2. 3. 4. 4. 4. 3. 3.\n", + " 4. 3. 3. 3. 4. 4. 4. 3. 4. 4. 4. 3. 4. 3. 4. 3. 2. 4.\n", + " 4. 3. 4. 3. 4. 3. 1. 4. 2. 3. 3. 3. 4. 4. 2. 3. 2. 4.\n", + " 3. 4. 4. 4. 3. 3. 3. 3. 3. 3. 3. 3. 3. 3. 2. 2. 3. 3.\n", + " 2. 3. 4. 3. 3. 4. 4. 3. 3. 3. 3. 2. 4. 3. 3. 3. 3. 3.\n", + " 2. 2. 3. 4. 3. 2. 3. 4. 3. 3. 2. 2. 4. 2. 3. 3. 3. 3.\n", + " 2. 3. 4. 4. 2. 3. 4. 3. 3. 2. 3. 3. 2. 4. 4. 3. 4. 3.\n", + " 3. 4. 1. 3. 4. 4. 3. 4. 3. 2. 4. 4. 3. 4. 3. 4. 3. 4.\n", + " 3. 3. 4. 2. 3. 3. 3. 2. 4. 4. 3. 3. 3. 3. 2. 4. 3. 2.\n", + " 4. 3. 4. 3. 3. 2. 3. 4. 2. 3. 3. 4. 2. 4. 3. 3. 3. 2.\n", + " 4. 4. 4. 4. 4. 4. 3. 3. 4. 2. 2. 4. 4. 3. 3. 3. 4. 3.\n", + " 3. 2. 3. 2. 2. 1. 4. 3. 3. 3. 3. 3. 3. 3. 4. 3. 4. 3.\n", + " 4. 3. 3. 3. 4. 4. 3. 2. 3. 4. 3. 3. 2. 3. 4. 2. 2. 4.\n", + " 2. 2. 3. 3. 3. 3. 2. 4. 3. 3. 4. 4. 4. 3. 4. 3. 4. 3.\n", + " 3. 1. 2. 4. 2. 3. 2. 3. 1. 2. 4. 3. 4. 3. 4. 3. 3. 4.\n", + " 4. 2. 3. 3. 3. 4. 4. 4. 2. 3. 3. 4. 3. 3. 3. 3. 4. 4.\n", + " 3. 3. 4. 3. 1. 3. 4. 3. 4. 4. 4. 3. 3. 3. 4. 3. 4. 4.\n", + " 3. 3. 4. 4. 3. 2. 1. 3. 3. 3. 3. 3. 3. 3. 2. 4. 4. 3.\n", + " 4. 3. 4. 4. 4. 3. 2. 3. 3. 4. 4. 1. 4. 1. 3. 4. 4. 3.\n", + " 3. 2. 3. 2. 3. 4. 4. 4. 2. 4. 3. 4. 4. 3. 4. 3. 4. 2.\n", + " 2. 2. 4. 4. 3. 3. 2. 3. 3. 4. 3. 4. 1. 4. 4. 2. 4. 4.\n", + " 4. 4. 3. 1. 2. 3. 4. 2. 3. 4. 4. 3. 4. 3. 2. 3. 2. 4.\n", + " 3. 2. 3. 3. 4. 2. 4. 2. 4. 3. 4. 3. 3. 4. 3. 4. 3. 4.\n", + " 3. 3. 3. 3. 4. 2. 4. 2. 4. 3. 3. 2. 2. 4. 4. 3. 4. 2.\n", + " 3. 4. 4. 2. 3. 3. 3. 4. 3. 4. 4. 4. 3. 2. 3. 3. 2. 3.\n", + " 4. 4. 3. 3. 4. 4. 3. 4. 3. 4. 3. 3. 4. 4. 2. 4. 3. 2.\n", + " 3. 2. 3. 3. 4. 3. 3. 2. 2. 3. 3. 4. 4. 3. 4. 3. 4. 4.\n", + " 4. 3. 3. 2. 4. 3. 2. 3. 3. 3. 4. 3. 4. 4. 3. 4. 2. 3.\n", + " 4. 4. 3. 3. 2. 4. 2. 4. 3. 3. 3. 3. 4. 3. 3. 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2.\n", + " 4. 4. 4. 2. 3. 3. 4. 2. 3. 4. 4. 4. 3. 2. 3. 3. 2. 2.\n", + " 3. 4. 3. 4. 2. 3. 4. 3. 4. 2. 3. 4. 3. 4. 3. 2. 4. 3.\n", + " 4. 3. 3. 3. 3. 3. 4. 4. 3. 2. 2. 3. 3. 4. 4. 4. 4. 3.\n", + " 3. 4. 3. 3. 4. 3. 2. 4. 2. 2. 4. 3. 2. 4. 3. 2. 3. 4.\n", + " 4. 3. 4. 4. 3. 3. 3. 3. 3. 2. 3. 3. 4. 3. 2. 2. 3. 4.\n", + " 4. 3. 4. 4. 4. 4. 3. 4. 3. 4. 4. 3. 4. 3. 3. 3. 2. 3.\n", + " 3. 4. 2. 3. 3. 4. 2. 3. 4. 4. 3. 3. 3. 3. 3. 4. 2. 4.\n", + " 4. 3. 2. 3. 3. 4. 4. 3. 3. 4. 3. 2. 4. 3. 2. 3. 4. 3.\n", + " 2.]\n", + "[[ 1. 2. 3. 2.]\n", + " [ 1. 4. 3. 3.]\n", + " [ 1. 2. 3. 2.]\n", + " ..., \n", + " [ 1. 3. 3. 3.]\n", + " [ 1. 4. 3. 2.]\n", + " [ 1. 3. 3. 3.]]\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.965\n", + "Model: OLS Adj. R-squared: 0.965\n", + "Method: Least Squares F-statistic: 8853.\n", + "Date: Sun, 30 Dec 2018 Prob (F-statistic): 0.00\n", + "Time: 18:17:21 Log-Likelihood: -871.94\n", + "No. Observations: 955 AIC: 1750.\n", + "Df Residuals: 952 BIC: 1764.\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "x1 0.1102 0.018 6.014 0.000 0.074 0.146\n", + "x2 0.6613 0.026 25.196 0.000 0.610 0.713\n", + "x3 0.2460 0.026 9.586 0.000 0.196 0.296\n", + "==============================================================================\n", + "Omnibus: 14.613 Durbin-Watson: 1.956\n", + "Prob(Omnibus): 0.001 Jarque-Bera (JB): 20.188\n", + "Skew: 0.160 Prob(JB): 4.13e-05\n", + "Kurtosis: 3.636 Cond. No. 9.44\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "data1= pd.DataFrame({\n", + " 'y': sdf.c25,\n", + " 'x1': sdf.c12,\n", + " 'x2': sdf.b35,\n", + " 'x3': sdf.b09\n", + "})\n", + "#构建自变量\n", + "data1=data1.dropna(axis=0,how='any')#删除缺失值\n", + "model_x = ['x1','x2','x3']\n", + "X = data1.loc[ :,model_x].values\n", + "#构建因变量\n", + "y = data1['y'].values\n", + "#加入截距项\n", + "X_model = sm.add_constant(X)\n", + "print(X)\n", + "print(y)\n", + "print(X_model)\n", + "model = sm.OLS(y, X) \n", + "#拟合ols回归\n", + "results = model.fit()\n", + "results.params\n", + "\n", + "#输出结果\n", + "print(results.summary())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "+ 由回归结果可知，调整的判定系数为0.965，即多元回归模型的拟合程度较好\n", + " + F检验统计量的值为8853，显著大于F检验的临界值，即模型的线性关系显著\n", + " + 三个自变量的系数分别为 0.1102、0.6613、0.2460\n", + " + 由P值可知，三个自变量的P值结果都小于0.05，即三个自变量的系数都是显著的\n", + " + 从线性关系检验和回归系数显著性检验都能看出，模型的拟合程度较好，且各个自变量对因变量的影响都是显著的\n", + "+ 综上可知，对自己未来有没有信心，受到目前的成绩、父母对自己未来的信心和家庭经济条件的影响\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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": "3.6.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 43e577d5372c32f7aac1c0d729b4d131ab189acf Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Sun, 30 Dec 2018 23:13:06 +0800 Subject: [PATCH 4/6] =?UTF-8?q?0154590=E5=88=98=E5=A9=B7=E5=A9=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit From feb5e1fe7fb42e4957a318088d1ae7d17fe7e5f1 Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Sun, 30 Dec 2018 23:18:09 +0800 Subject: [PATCH 5/6] =?UTF-8?q?0154590=E5=88=98=E5=A9=B7=E5=A9=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit From 24fedd76e1b5a570a834f3bd58085f38e6c3abca Mon Sep 17 00:00:00 2001 From: OnismONE <43160224+OnismONE@users.noreply.github.com> Date: Sun, 30 Dec 2018 23:35:26 +0800 Subject: [PATCH 6/6] =?UTF-8?q?0154590=E5=88=98=E5=A9=B7=E5=A9=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit