{ "metadata": { "name": "", "signature": "sha256:6db4df9b0a1a67c9acccb4ddeb38cdb41eb4ac9c98266339b04df08167167a6e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm #used for statistical modeling\n", "import matplotlib.pyplot as plt\n", "#to have plots show up automatically in notebook\n", "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Pandas Series Objects : glorified numpy array" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = pd.Series(np.random.randn(5), name = 'random numbers')\n", "a" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "0 -0.542996\n", "1 -1.411180\n", "2 -1.775740\n", "3 1.705594\n", "4 0.638039\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "#Series can be sliced\n", "a[-3:] #gives last three items of the series...the index has stayed with the numbers" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "2 -1.775740\n", "3 1.705594\n", "4 0.638039\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "a[3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "1.705594493602101" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "a[[1,3,2]] " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "1 -1.411180\n", "3 1.705594\n", "2 -1.775740\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "#Boolean indexing...indexes dont have to be a number\n", "a[[True,False,False,True,True]]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "0 -0.542996\n", "3 1.705594\n", "4 0.638039\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "a.index = list('abcde') #new defining of list- now can get index with labels...\n", "a" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "a -0.542996\n", "b -1.411180\n", "c -1.775740\n", "d 1.705594\n", "e 0.638039\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "#can now also slice with labels\n", "a['c']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "-1.7757400675142934" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "#concatenating the values of lists\n", "#Mixing data sets on an index\n", "a[-3:] + a[:3] #need to have a common index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "a NaN\n", "b NaN\n", "c -3.55148\n", "d NaN\n", "e NaN\n", "Name: random numbers, dtype: float64" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "#A series makes a good look up table\n", "#Making grades look up table 4->A etc\n", "gp_lut = pd.Series(data=[4,3,2,1,0],index=list('ABCDF'))\n", "gp_lut\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "A 4\n", "B 3\n", "C 2\n", "D 1\n", "F 0\n", "dtype: int64" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "my_grades = ['A','B','B','A','A']\n", "my_points = gp_lut[my_grades]\n", "my_points\n", "\n", "my_gpa = my_points.mean()\n", "my_gpa" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "3.6000000000000001" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "students = pd.DataFrame({'name' : ['Peter', 'Paul', 'Mary',\n", " 'Peter', 'Paul', 'Mary'],\n", " 'subject' : ['English', 'English', 'English',\n", " 'Math', 'Math', 'Math'],\n", " 'grade' : [85.0, 76.0, 92.0, 77.0, 68.0, 87.0]})\n", "students" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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gradenamesubject
0 85 Peter English
1 76 Paul English
2 92 Mary English
3 77 Peter Math
4 68 Paul Math
5 87 Mary Math
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ " grade name subject\n", "0 85 Peter English\n", "1 76 Paul English\n", "2 92 Mary English\n", "3 77 Peter Math\n", "4 68 Paul Math\n", "5 87 Mary Math" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# We can get the column names\n", "students.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "Index(['grade', 'name', 'subject'], dtype='object')" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# ...or as an attribute\n", "students.name" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "0 Peter\n", "1 Paul\n", "2 Mary\n", "3 Peter\n", "4 Paul\n", "5 Mary\n", "Name: name, dtype: object" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "students[['subject','grade']]" ], "language": "python", "metadata": {}, "outputs": [ { "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", "
subjectgrade
0 English 85
1 English 76
2 English 92
3 Math 77
4 Math 68
5 Math 87
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ " subject grade\n", "0 English 85\n", "1 English 76\n", "2 English 92\n", "3 Math 77\n", "4 Math 68\n", "5 Math 87" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "#create a boolean array which is only true when her name is Mary\n", "#Now we will take only Mary's grades\n", "students[students.name == 'Mary']" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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gradenamesubject
2 92 Mary English
5 87 Mary Math
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ " grade name subject\n", "2 92 Mary English\n", "5 87 Mary Math" ] } ], "prompt_number": 47 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Now Pandas By Example With Star Test Results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = pd.read_csv('star_2013_clean_wide.csv')\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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County.CodeCounty.NameTest.IdTest.NameGradeStudents.TestedMean.Scale.ScoreCount.Test.GradePct.Test.GradePopulationper.capita.incomemedian.household.incomemedian.family.incomeSpendADAttendSpend.Per.ADA
0 1 Alameda 7 CST English-Language Arts 2 16814 372.1 464896 0.036167 1494876 34937 70821 87012 1814932885 193906 9360
1 1 Alameda 8 CST Mathematics 2 16802 398.4 464515 0.036171 1494876 34937 70821 87012 1814932885 193906 9360
2 1 Alameda 8 CST Mathematics 3 16198 411.1 443961 0.036485 1494876 34937 70821 87012 1814932885 193906 9360
3 1 Alameda 7 CST English-Language Arts 3 16126 357.2 441572 0.036520 1494876 34937 70821 87012 1814932885 193906 9360
4 1 Alameda 7 CST English-Language Arts 4 15390 387.0 428906 0.035882 1494876 34937 70821 87012 1814932885 193906 9360
5 1 Alameda 8 CST Mathematics 4 15532 405.8 433012 0.035870 1494876 34937 70821 87012 1814932885 193906 9360
6 1 Alameda 8 CST Mathematics 5 15146 411.6 432775 0.034997 1494876 34937 70821 87012 1814932885 193906 9360
7 1 Alameda 7 CST English-Language Arts 5 15077 378.4 429498 0.035104 1494876 34937 70821 87012 1814932885 193906 9360
8 1 Alameda 32 CST Science - Grade 5, Grade 8, and Grade 10 L... 5 15107 380.6 431142 0.035040 1494876 34937 70821 87012 1814932885 193906 9360
9 1 Alameda 7 CST English-Language Arts 6 15277 374.9 434374 0.035170 1494876 34937 70821 87012 1814932885 193906 9360
10 1 Alameda 8 CST Mathematics 6 15332 380.3 436563 0.035120 1494876 34937 70821 87012 1814932885 193906 9360
11 1 Alameda 7 CST English-Language Arts 7 14551 378.4 431187 0.033746 1494876 34937 70821 87012 1814932885 193906 9360
12 1 Alameda 9 CST Algebra I 7 2239 431.6 37803 0.059228 1494876 34937 70821 87012 1814932885 193906 9360
13 1 Alameda 8 CST Mathematics 7 12326 370.2 393811 0.031299 1494876 34937 70821 87012 1814932885 193906 9360
14 1 Alameda 9 CST Algebra I 8 11006 354.2 276039 0.039871 1494876 34937 70821 87012 1814932885 193906 9360
15 1 Alameda 13 CST Algebra II 8 31 458.9 680 0.045588 1494876 34937 70821 87012 1814932885 193906 9360
16 1 Alameda 7 CST English-Language Arts 8 14549 373.9 435491 0.033408 1494876 34937 70821 87012 1814932885 193906 9360
17 1 Alameda 29 CST History - Social Science Grade 8 8 15421 363.3 459125 0.033588 1494876 34937 70821 87012 1814932885 193906 9360
18 1 Alameda 28 CST General Mathematics 8 2143 308.4 145549 0.014724 1494876 34937 70821 87012 1814932885 193906 9360
19 1 Alameda 11 CST Geometry 8 1732 428.0 29035 0.059652 1494876 34937 70821 87012 1814932885 193906 9360
20 1 Alameda 32 CST Science - Grade 5, Grade 8, and Grade 10 L... 8 14552 404.5 436071 0.033371 1494876 34937 70821 87012 1814932885 193906 9360
21 1 Alameda 10 CST Integrated Math 1 9 121 298.8 980 0.123469 1494876 34937 70821 87012 1814932885 193906 9360
22 1 Alameda 20 CST Biology 9 9473 377.0 228962 0.041374 1494876 34937 70821 87012 1814932885 193906 9360
23 1 Alameda 21 CST Chemistry 9 186 364.3 5365 0.034669 1494876 34937 70821 87012 1814932885 193906 9360
24 1 Alameda 24 CST Integrated/Coordinated Science 1 9 905 321.4 33944 0.026662 1494876 34937 70821 87012 1814932885 193906 9360
25 1 Alameda 22 CST Earth Science 9 1651 328.8 133961 0.012324 1494876 34937 70821 87012 1814932885 193906 9360
26 1 Alameda 23 CST Physics 9 754 312.8 14112 0.053430 1494876 34937 70821 87012 1814932885 193906 9360
27 1 Alameda 25 CST Integrated/Coordinated Science 2 9 124 323.0 1351 0.091784 1494876 34937 70821 87012 1814932885 193906 9360
28 1 Alameda 18 CST World History 9 597 320.1 35087 0.017015 1494876 34937 70821 87012 1814932885 193906 9360
29 1 Alameda 11 CST Geometry 9 6613 353.4 144063 0.045904 1494876 34937 70821 87012 1814932885 193906 9360
...................................................
3700 58 Yuba 22 CST Earth Science 10 290 327.0 30649 0.009462 71817 20046 46617 52775 99237214 11710 8474
3701 58 Yuba 15 CST Summative High School Mathematics 10 19 353.3 24757 0.000767 71817 20046 46617 52775 99237214 11710 8474
3702 58 Yuba 21 CST Chemistry 10 166 358.8 136209 0.001219 71817 20046 46617 52775 99237214 11710 8474
3703 58 Yuba 13 CST Algebra II 10 201 335.9 131448 0.001529 71817 20046 46617 52775 99237214 11710 8474
3704 58 Yuba 9 CST Algebra I 10 259 285.9 104567 0.002477 71817 20046 46617 52775 99237214 11710 8474
3705 58 Yuba 7 CST English-Language Arts 10 931 332.0 455362 0.002045 71817 20046 46617 52775 99237214 11710 8474
3706 58 Yuba 20 CST Biology 10 238 343.3 228138 0.001043 71817 20046 46617 52775 99237214 11710 8474
3707 58 Yuba 11 CST Geometry 10 274 296.1 156167 0.001755 71817 20046 46617 52775 99237214 11710 8474
3708 58 Yuba 11 CST Geometry 11 151 287.1 78308 0.001928 71817 20046 46617 52775 99237214 11710 8474
3709 58 Yuba 19 CST U.S. History 11 895 325.7 447386 0.002001 71817 20046 46617 52775 99237214 11710 8474
3710 58 Yuba 20 CST Biology 11 186 330.3 99776 0.001864 71817 20046 46617 52775 99237214 11710 8474
3711 58 Yuba 7 CST English-Language Arts 11 899 324.9 440115 0.002043 71817 20046 46617 52775 99237214 11710 8474
3712 58 Yuba 9 CST Algebra I 11 187 281.3 49868 0.003750 71817 20046 46617 52775 99237214 11710 8474
3713 58 Yuba 13 CST Algebra II 11 199 302.4 122079 0.001630 71817 20046 46617 52775 99237214 11710 8474
3714 58 Yuba 21 CST Chemistry 11 121 342.4 133804 0.000904 71817 20046 46617 52775 99237214 11710 8474
3715 58 Yuba 15 CST Summative High School Mathematics 11 153 322.6 124304 0.001231 71817 20046 46617 52775 99237214 11710 8474
3716 58 Yuba 22 CST Earth Science 11 187 326.8 42331 0.004418 71817 20046 46617 52775 99237214 11710 8474
3717 58 Yuba 23 CST Physics 11 91 363.8 56726 0.001604 71817 20046 46617 52775 99237214 11710 8474
3718 58 Yuba 18 CST World History 11 40 300.3 16163 0.002475 71817 20046 46617 52775 99237214 11710 8474
3719 58 Yuba 18 CST World History 13 874 329.1 474255 0.001843 71817 20046 46617 52775 99237214 11710 8474
3720 58 Yuba 25 CST Integrated/Coordinated Science 2 13 14 302.3 3742 0.003741 71817 20046 46617 52775 99237214 11710 8474
3721 58 Yuba 20 CST Biology 13 876 340.6 556893 0.001573 71817 20046 46617 52775 99237214 11710 8474
3722 58 Yuba 22 CST Earth Science 13 875 328.9 206991 0.004227 71817 20046 46617 52775 99237214 11710 8474
3723 58 Yuba 23 CST Physics 13 92 363.6 80833 0.001138 71817 20046 46617 52775 99237214 11710 8474
3724 58 Yuba 28 CST General Mathematics 13 585 299.5 190615 0.003069 71817 20046 46617 52775 99237214 11710 8474
3725 58 Yuba 13 CST Algebra II 13 431 320.6 286737 0.001503 71817 20046 46617 52775 99237214 11710 8474
3726 58 Yuba 21 CST Chemistry 13 287 351.9 275452 0.001042 71817 20046 46617 52775 99237214 11710 8474
3727 58 Yuba 15 CST Summative High School Mathematics 13 172 326.0 149987 0.001147 71817 20046 46617 52775 99237214 11710 8474
3728 58 Yuba 11 CST Geometry 13 695 307.8 407658 0.001705 71817 20046 46617 52775 99237214 11710 8474
3729 58 Yuba 9 CST Algebra I 13 1495 319.4 711705 0.002101 71817 20046 46617 52775 99237214 11710 8474
\n", "

3730 rows \u00d7 16 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ " County.Code County.Name Test.Id \\\n", "0 1 Alameda 7 \n", "1 1 Alameda 8 \n", "2 1 Alameda 8 \n", "3 1 Alameda 7 \n", "4 1 Alameda 7 \n", "5 1 Alameda 8 \n", "6 1 Alameda 8 \n", "7 1 Alameda 7 \n", "8 1 Alameda 32 \n", "9 1 Alameda 7 \n", "10 1 Alameda 8 \n", "11 1 Alameda 7 \n", "12 1 Alameda 9 \n", "13 1 Alameda 8 \n", "14 1 Alameda 9 \n", "15 1 Alameda 13 \n", "16 1 Alameda 7 \n", "17 1 Alameda 29 \n", "18 1 Alameda 28 \n", "19 1 Alameda 11 \n", "20 1 Alameda 32 \n", "21 1 Alameda 10 \n", "22 1 Alameda 20 \n", "23 1 Alameda 21 \n", "24 1 Alameda 24 \n", "25 1 Alameda 22 \n", "26 1 Alameda 23 \n", "27 1 Alameda 25 \n", "28 1 Alameda 18 \n", "29 1 Alameda 11 \n", "... ... ... ... \n", "3700 58 Yuba 22 \n", "3701 58 Yuba 15 \n", "3702 58 Yuba 21 \n", "3703 58 Yuba 13 \n", "3704 58 Yuba 9 \n", "3705 58 Yuba 7 \n", "3706 58 Yuba 20 \n", "3707 58 Yuba 11 \n", "3708 58 Yuba 11 \n", "3709 58 Yuba 19 \n", "3710 58 Yuba 20 \n", "3711 58 Yuba 7 \n", "3712 58 Yuba 9 \n", "3713 58 Yuba 13 \n", "3714 58 Yuba 21 \n", "3715 58 Yuba 15 \n", "3716 58 Yuba 22 \n", "3717 58 Yuba 23 \n", "3718 58 Yuba 18 \n", "3719 58 Yuba 18 \n", "3720 58 Yuba 25 \n", "3721 58 Yuba 20 \n", "3722 58 Yuba 22 \n", "3723 58 Yuba 23 \n", "3724 58 Yuba 28 \n", "3725 58 Yuba 13 \n", "3726 58 Yuba 21 \n", "3727 58 Yuba 15 \n", "3728 58 Yuba 11 \n", "3729 58 Yuba 9 \n", "\n", " Test.Name Grade \\\n", "0 CST English-Language Arts 2 \n", "1 CST Mathematics 2 \n", "2 CST Mathematics 3 \n", "3 CST English-Language Arts 3 \n", "4 CST English-Language Arts 4 \n", "5 CST Mathematics 4 \n", "6 CST Mathematics 5 \n", "7 CST English-Language Arts 5 \n", "8 CST Science - Grade 5, Grade 8, and Grade 10 L... 5 \n", "9 CST English-Language Arts 6 \n", "10 CST Mathematics 6 \n", "11 CST English-Language Arts 7 \n", "12 CST Algebra I 7 \n", "13 CST Mathematics 7 \n", "14 CST Algebra I 8 \n", "15 CST Algebra II 8 \n", "16 CST English-Language Arts 8 \n", "17 CST History - Social Science Grade 8 8 \n", "18 CST General Mathematics 8 \n", "19 CST Geometry 8 \n", "20 CST Science - Grade 5, Grade 8, and Grade 10 L... 8 \n", "21 CST Integrated Math 1 9 \n", "22 CST Biology 9 \n", "23 CST Chemistry 9 \n", "24 CST Integrated/Coordinated Science 1 9 \n", "25 CST Earth Science 9 \n", "26 CST Physics 9 \n", "27 CST Integrated/Coordinated Science 2 9 \n", "28 CST World History 9 \n", "29 CST Geometry 9 \n", "... ... ... \n", "3700 CST Earth Science 10 \n", "3701 CST Summative High School Mathematics 10 \n", "3702 CST Chemistry 10 \n", "3703 CST Algebra II 10 \n", "3704 CST Algebra I 10 \n", "3705 CST English-Language Arts 10 \n", "3706 CST Biology 10 \n", "3707 CST Geometry 10 \n", "3708 CST Geometry 11 \n", "3709 CST U.S. History 11 \n", "3710 CST Biology 11 \n", "3711 CST English-Language Arts 11 \n", "3712 CST Algebra I 11 \n", "3713 CST Algebra II 11 \n", "3714 CST Chemistry 11 \n", "3715 CST Summative High School Mathematics 11 \n", "3716 CST Earth Science 11 \n", "3717 CST Physics 11 \n", "3718 CST World History 11 \n", "3719 CST World History 13 \n", "3720 CST Integrated/Coordinated Science 2 13 \n", "3721 CST Biology 13 \n", "3722 CST Earth Science 13 \n", "3723 CST Physics 13 \n", "3724 CST General Mathematics 13 \n", "3725 CST Algebra II 13 \n", "3726 CST Chemistry 13 \n", "3727 CST Summative High School Mathematics 13 \n", "3728 CST Geometry 13 \n", "3729 CST Algebra I 13 \n", "\n", " Students.Tested Mean.Scale.Score Count.Test.Grade Pct.Test.Grade \\\n", "0 16814 372.1 464896 0.036167 \n", "1 16802 398.4 464515 0.036171 \n", "2 16198 411.1 443961 0.036485 \n", "3 16126 357.2 441572 0.036520 \n", "4 15390 387.0 428906 0.035882 \n", "5 15532 405.8 433012 0.035870 \n", "6 15146 411.6 432775 0.034997 \n", "7 15077 378.4 429498 0.035104 \n", "8 15107 380.6 431142 0.035040 \n", "9 15277 374.9 434374 0.035170 \n", "10 15332 380.3 436563 0.035120 \n", "11 14551 378.4 431187 0.033746 \n", "12 2239 431.6 37803 0.059228 \n", "13 12326 370.2 393811 0.031299 \n", "14 11006 354.2 276039 0.039871 \n", "15 31 458.9 680 0.045588 \n", "16 14549 373.9 435491 0.033408 \n", "17 15421 363.3 459125 0.033588 \n", "18 2143 308.4 145549 0.014724 \n", "19 1732 428.0 29035 0.059652 \n", "20 14552 404.5 436071 0.033371 \n", "21 121 298.8 980 0.123469 \n", "22 9473 377.0 228962 0.041374 \n", "23 186 364.3 5365 0.034669 \n", "24 905 321.4 33944 0.026662 \n", "25 1651 328.8 133961 0.012324 \n", "26 754 312.8 14112 0.053430 \n", "27 124 323.0 1351 0.091784 \n", "28 597 320.1 35087 0.017015 \n", "29 6613 353.4 144063 0.045904 \n", "... ... ... ... ... \n", "3700 290 327.0 30649 0.009462 \n", "3701 19 353.3 24757 0.000767 \n", "3702 166 358.8 136209 0.001219 \n", "3703 201 335.9 131448 0.001529 \n", "3704 259 285.9 104567 0.002477 \n", "3705 931 332.0 455362 0.002045 \n", "3706 238 343.3 228138 0.001043 \n", "3707 274 296.1 156167 0.001755 \n", "3708 151 287.1 78308 0.001928 \n", "3709 895 325.7 447386 0.002001 \n", "3710 186 330.3 99776 0.001864 \n", "3711 899 324.9 440115 0.002043 \n", "3712 187 281.3 49868 0.003750 \n", "3713 199 302.4 122079 0.001630 \n", "3714 121 342.4 133804 0.000904 \n", "3715 153 322.6 124304 0.001231 \n", "3716 187 326.8 42331 0.004418 \n", "3717 91 363.8 56726 0.001604 \n", "3718 40 300.3 16163 0.002475 \n", "3719 874 329.1 474255 0.001843 \n", "3720 14 302.3 3742 0.003741 \n", "3721 876 340.6 556893 0.001573 \n", "3722 875 328.9 206991 0.004227 \n", "3723 92 363.6 80833 0.001138 \n", "3724 585 299.5 190615 0.003069 \n", "3725 431 320.6 286737 0.001503 \n", "3726 287 351.9 275452 0.001042 \n", "3727 172 326.0 149987 0.001147 \n", "3728 695 307.8 407658 0.001705 \n", "3729 1495 319.4 711705 0.002101 \n", "\n", " Population per.capita.income median.household.income \\\n", "0 1494876 34937 70821 \n", "1 1494876 34937 70821 \n", "2 1494876 34937 70821 \n", "3 1494876 34937 70821 \n", "4 1494876 34937 70821 \n", "5 1494876 34937 70821 \n", "6 1494876 34937 70821 \n", "7 1494876 34937 70821 \n", "8 1494876 34937 70821 \n", "9 1494876 34937 70821 \n", "10 1494876 34937 70821 \n", "11 1494876 34937 70821 \n", "12 1494876 34937 70821 \n", "13 1494876 34937 70821 \n", "14 1494876 34937 70821 \n", "15 1494876 34937 70821 \n", "16 1494876 34937 70821 \n", "17 1494876 34937 70821 \n", "18 1494876 34937 70821 \n", "19 1494876 34937 70821 \n", "20 1494876 34937 70821 \n", "21 1494876 34937 70821 \n", "22 1494876 34937 70821 \n", "23 1494876 34937 70821 \n", "24 1494876 34937 70821 \n", "25 1494876 34937 70821 \n", "26 1494876 34937 70821 \n", "27 1494876 34937 70821 \n", "28 1494876 34937 70821 \n", "29 1494876 34937 70821 \n", "... ... ... ... \n", "3700 71817 20046 46617 \n", "3701 71817 20046 46617 \n", "3702 71817 20046 46617 \n", "3703 71817 20046 46617 \n", "3704 71817 20046 46617 \n", "3705 71817 20046 46617 \n", "3706 71817 20046 46617 \n", "3707 71817 20046 46617 \n", "3708 71817 20046 46617 \n", "3709 71817 20046 46617 \n", "3710 71817 20046 46617 \n", "3711 71817 20046 46617 \n", "3712 71817 20046 46617 \n", "3713 71817 20046 46617 \n", "3714 71817 20046 46617 \n", "3715 71817 20046 46617 \n", "3716 71817 20046 46617 \n", "3717 71817 20046 46617 \n", "3718 71817 20046 46617 \n", "3719 71817 20046 46617 \n", "3720 71817 20046 46617 \n", "3721 71817 20046 46617 \n", "3722 71817 20046 46617 \n", "3723 71817 20046 46617 \n", "3724 71817 20046 46617 \n", "3725 71817 20046 46617 \n", "3726 71817 20046 46617 \n", "3727 71817 20046 46617 \n", "3728 71817 20046 46617 \n", "3729 71817 20046 46617 \n", "\n", " median.family.income Spend ADAttend Spend.Per.ADA \n", "0 87012 1814932885 193906 9360 \n", "1 87012 1814932885 193906 9360 \n", "2 87012 1814932885 193906 9360 \n", "3 87012 1814932885 193906 9360 \n", "4 87012 1814932885 193906 9360 \n", "5 87012 1814932885 193906 9360 \n", "6 87012 1814932885 193906 9360 \n", "7 87012 1814932885 193906 9360 \n", "8 87012 1814932885 193906 9360 \n", "9 87012 1814932885 193906 9360 \n", "10 87012 1814932885 193906 9360 \n", "11 87012 1814932885 193906 9360 \n", "12 87012 1814932885 193906 9360 \n", "13 87012 1814932885 193906 9360 \n", "14 87012 1814932885 193906 9360 \n", "15 87012 1814932885 193906 9360 \n", "16 87012 1814932885 193906 9360 \n", "17 87012 1814932885 193906 9360 \n", "18 87012 1814932885 193906 9360 \n", "19 87012 1814932885 193906 9360 \n", "20 87012 1814932885 193906 9360 \n", "21 87012 1814932885 193906 9360 \n", "22 87012 1814932885 193906 9360 \n", "23 87012 1814932885 193906 9360 \n", "24 87012 1814932885 193906 9360 \n", "25 87012 1814932885 193906 9360 \n", "26 87012 1814932885 193906 9360 \n", "27 87012 1814932885 193906 9360 \n", "28 87012 1814932885 193906 9360 \n", "29 87012 1814932885 193906 9360 \n", "... ... ... ... ... \n", "3700 52775 99237214 11710 8474 \n", "3701 52775 99237214 11710 8474 \n", "3702 52775 99237214 11710 8474 \n", "3703 52775 99237214 11710 8474 \n", "3704 52775 99237214 11710 8474 \n", "3705 52775 99237214 11710 8474 \n", "3706 52775 99237214 11710 8474 \n", "3707 52775 99237214 11710 8474 \n", "3708 52775 99237214 11710 8474 \n", "3709 52775 99237214 11710 8474 \n", "3710 52775 99237214 11710 8474 \n", "3711 52775 99237214 11710 8474 \n", "3712 52775 99237214 11710 8474 \n", "3713 52775 99237214 11710 8474 \n", "3714 52775 99237214 11710 8474 \n", "3715 52775 99237214 11710 8474 \n", "3716 52775 99237214 11710 8474 \n", "3717 52775 99237214 11710 8474 \n", "3718 52775 99237214 11710 8474 \n", "3719 52775 99237214 11710 8474 \n", "3720 52775 99237214 11710 8474 \n", "3721 52775 99237214 11710 8474 \n", "3722 52775 99237214 11710 8474 \n", "3723 52775 99237214 11710 8474 \n", "3724 52775 99237214 11710 8474 \n", "3725 52775 99237214 11710 8474 \n", "3726 52775 99237214 11710 8474 \n", "3727 52775 99237214 11710 8474 \n", "3728 52775 99237214 11710 8474 \n", "3729 52775 99237214 11710 8474 \n", "\n", "[3730 rows x 16 columns]" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "data.columns #A look at the available columns " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "Index(['County.Code', 'County.Name', 'Test.Id', 'Test.Name', 'Grade', 'Students.Tested', 'Mean.Scale.Score', 'Count.Test.Grade', 'Pct.Test.Grade', 'Population', 'per.capita.income', 'median.household.income', 'median.family.income', 'Spend', 'ADAttend', 'Spend.Per.ADA'], dtype='object')" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "#What are the unique test names?\n", "data['Test.Name'].unique()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "array(['CST English-Language Arts', 'CST Mathematics',\n", " 'CST Science - Grade 5, Grade 8, and Grade 10 Life Science',\n", " 'CST Algebra I', 'CST Algebra II',\n", " 'CST History - Social Science Grade 8', 'CST General Mathematics',\n", " 'CST Geometry', 'CST Integrated Math 1', 'CST Biology',\n", " 'CST Chemistry', 'CST Integrated/Coordinated Science 1',\n", " 'CST Earth Science', 'CST Physics',\n", " 'CST Integrated/Coordinated Science 2', 'CST World History',\n", " 'CST Summative High School Mathematics', 'CST Integrated Math 2',\n", " 'CST U.S. History', 'CST Integrated/Coordinated Science 4',\n", " 'CST Integrated Math 3', 'CST Integrated/Coordinated Science 3'], dtype=object)" ] } ], "prompt_number": 50 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "How many students took each test by grade?\n", " This introduces us to pivot tables..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.pivot_table(values='Students.Tested',index='Test.Name',columns='Grade',aggfunc='sum')\n", "#What is 13? CST Tests?" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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Grade23456789101113
Test.Name
CST Algebra I NaN NaN NaN NaN NaN 37803 276039 243349 104555 49846 711671
CST Algebra II NaN NaN NaN NaN NaN NaN 680 32400 131448 122079 286737
CST Biology NaN NaN NaN NaN NaN NaN NaN 228962 228138 99753 556867
CST Chemistry NaN NaN NaN NaN NaN NaN NaN 5365 136209 133804 275452
CST Earth Science NaN NaN NaN NaN NaN NaN NaN 133961 30649 42316 206974
CST English-Language Arts 464896 441572 428906 429498 434374 431187 435491 463195 455237 439972 NaN
CST General Mathematics NaN NaN NaN NaN NaN NaN 145549 45052 NaN NaN 190615
CST Geometry NaN NaN NaN NaN NaN NaN 29035 144063 156167 78308 407658
CST History - Social Science Grade 8 NaN NaN NaN NaN NaN NaN 459125 NaN NaN NaN NaN
CST Integrated Math 1 NaN NaN NaN NaN NaN NaN 125 980 5965 6318 13486
CST Integrated Math 2 NaN NaN NaN NaN NaN NaN NaN 236 1432 3364 5102
CST Integrated Math 3 NaN NaN NaN NaN NaN NaN NaN 61 153 530 769
CST Integrated/Coordinated Science 1 NaN NaN NaN NaN NaN NaN NaN 33944 4401 7963 46386
CST Integrated/Coordinated Science 2 NaN NaN NaN NaN NaN NaN NaN 1351 1413 955 3742
CST Integrated/Coordinated Science 3 NaN NaN NaN NaN NaN NaN NaN 11 46 874 952
CST Integrated/Coordinated Science 4 NaN NaN NaN NaN NaN NaN NaN 18 NaN NaN 43
CST Mathematics 464515 443961 433012 432775 436563 393811 NaN NaN NaN NaN NaN
CST Physics NaN NaN NaN NaN NaN NaN NaN 14112 9902 56726 80833
CST Science - Grade 5, Grade 8, and Grade 10 Life Science NaN NaN NaN 431142 NaN NaN 436071 NaN 451253 NaN NaN
CST Summative High School Mathematics NaN NaN NaN NaN NaN NaN NaN 809 24757 124304 149987
CST U.S. History NaN NaN NaN NaN NaN NaN NaN NaN NaN 447253 NaN
CST World History NaN NaN NaN NaN NaN NaN NaN 35087 422893 16163 474237
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "Grade 2 3 \\\n", "Test.Name \n", "CST Algebra I NaN NaN \n", "CST Algebra II NaN NaN \n", "CST Biology NaN NaN \n", "CST Chemistry NaN NaN \n", "CST Earth Science NaN NaN \n", "CST English-Language Arts 464896 441572 \n", "CST General Mathematics NaN NaN \n", "CST Geometry NaN NaN \n", "CST History - Social Science Grade 8 NaN NaN \n", "CST Integrated Math 1 NaN NaN \n", "CST Integrated Math 2 NaN NaN \n", "CST Integrated Math 3 NaN NaN \n", "CST Integrated/Coordinated Science 1 NaN NaN \n", "CST Integrated/Coordinated Science 2 NaN NaN \n", "CST Integrated/Coordinated Science 3 NaN NaN \n", "CST Integrated/Coordinated Science 4 NaN NaN \n", "CST Mathematics 464515 443961 \n", "CST Physics NaN NaN \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science NaN NaN \n", "CST Summative High School Mathematics NaN NaN \n", "CST U.S. History NaN NaN \n", "CST World History NaN NaN \n", "\n", "Grade 4 5 \\\n", "Test.Name \n", "CST Algebra I NaN NaN \n", "CST Algebra II NaN NaN \n", "CST Biology NaN NaN \n", "CST Chemistry NaN NaN \n", "CST Earth Science NaN NaN \n", "CST English-Language Arts 428906 429498 \n", "CST General Mathematics NaN NaN \n", "CST Geometry NaN NaN \n", "CST History - Social Science Grade 8 NaN NaN \n", "CST Integrated Math 1 NaN NaN \n", "CST Integrated Math 2 NaN NaN \n", "CST Integrated Math 3 NaN NaN \n", "CST Integrated/Coordinated Science 1 NaN NaN \n", "CST Integrated/Coordinated Science 2 NaN NaN \n", "CST Integrated/Coordinated Science 3 NaN NaN \n", "CST Integrated/Coordinated Science 4 NaN NaN \n", "CST Mathematics 433012 432775 \n", "CST Physics NaN NaN \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science NaN 431142 \n", "CST Summative High School Mathematics NaN NaN \n", "CST U.S. History NaN NaN \n", "CST World History NaN NaN \n", "\n", "Grade 6 7 \\\n", "Test.Name \n", "CST Algebra I NaN 37803 \n", "CST Algebra II NaN NaN \n", "CST Biology NaN NaN \n", "CST Chemistry NaN NaN \n", "CST Earth Science NaN NaN \n", "CST English-Language Arts 434374 431187 \n", "CST General Mathematics NaN NaN \n", "CST Geometry NaN NaN \n", "CST History - Social Science Grade 8 NaN NaN \n", "CST Integrated Math 1 NaN NaN \n", "CST Integrated Math 2 NaN NaN \n", "CST Integrated Math 3 NaN NaN \n", "CST Integrated/Coordinated Science 1 NaN NaN \n", "CST Integrated/Coordinated Science 2 NaN NaN \n", "CST Integrated/Coordinated Science 3 NaN NaN \n", "CST Integrated/Coordinated Science 4 NaN NaN \n", "CST Mathematics 436563 393811 \n", "CST Physics NaN NaN \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science NaN NaN \n", "CST Summative High School Mathematics NaN NaN \n", "CST U.S. History NaN NaN \n", "CST World History NaN NaN \n", "\n", "Grade 8 9 \\\n", "Test.Name \n", "CST Algebra I 276039 243349 \n", "CST Algebra II 680 32400 \n", "CST Biology NaN 228962 \n", "CST Chemistry NaN 5365 \n", "CST Earth Science NaN 133961 \n", "CST English-Language Arts 435491 463195 \n", "CST General Mathematics 145549 45052 \n", "CST Geometry 29035 144063 \n", "CST History - Social Science Grade 8 459125 NaN \n", "CST Integrated Math 1 125 980 \n", "CST Integrated Math 2 NaN 236 \n", "CST Integrated Math 3 NaN 61 \n", "CST Integrated/Coordinated Science 1 NaN 33944 \n", "CST Integrated/Coordinated Science 2 NaN 1351 \n", "CST Integrated/Coordinated Science 3 NaN 11 \n", "CST Integrated/Coordinated Science 4 NaN 18 \n", "CST Mathematics NaN NaN \n", "CST Physics NaN 14112 \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science 436071 NaN \n", "CST Summative High School Mathematics NaN 809 \n", "CST U.S. History NaN NaN \n", "CST World History NaN 35087 \n", "\n", "Grade 10 11 \\\n", "Test.Name \n", "CST Algebra I 104555 49846 \n", "CST Algebra II 131448 122079 \n", "CST Biology 228138 99753 \n", "CST Chemistry 136209 133804 \n", "CST Earth Science 30649 42316 \n", "CST English-Language Arts 455237 439972 \n", "CST General Mathematics NaN NaN \n", "CST Geometry 156167 78308 \n", "CST History - Social Science Grade 8 NaN NaN \n", "CST Integrated Math 1 5965 6318 \n", "CST Integrated Math 2 1432 3364 \n", "CST Integrated Math 3 153 530 \n", "CST Integrated/Coordinated Science 1 4401 7963 \n", "CST Integrated/Coordinated Science 2 1413 955 \n", "CST Integrated/Coordinated Science 3 46 874 \n", "CST Integrated/Coordinated Science 4 NaN NaN \n", "CST Mathematics NaN NaN \n", "CST Physics 9902 56726 \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science 451253 NaN \n", "CST Summative High School Mathematics 24757 124304 \n", "CST U.S. History NaN 447253 \n", "CST World History 422893 16163 \n", "\n", "Grade 13 \n", "Test.Name \n", "CST Algebra I 711671 \n", "CST Algebra II 286737 \n", "CST Biology 556867 \n", "CST Chemistry 275452 \n", "CST Earth Science 206974 \n", "CST English-Language Arts NaN \n", "CST General Mathematics 190615 \n", "CST Geometry 407658 \n", "CST History - Social Science Grade 8 NaN \n", "CST Integrated Math 1 13486 \n", "CST Integrated Math 2 5102 \n", "CST Integrated Math 3 769 \n", "CST Integrated/Coordinated Science 1 46386 \n", "CST Integrated/Coordinated Science 2 3742 \n", "CST Integrated/Coordinated Science 3 952 \n", "CST Integrated/Coordinated Science 4 43 \n", "CST Mathematics NaN \n", "CST Physics 80833 \n", "CST Science - Grade 5, Grade 8, and Grade 10 Life Science NaN \n", "CST Summative High School Mathematics 149987 \n", "CST U.S. History NaN \n", "CST World History 474237 " ] } ], "prompt_number": 52 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Lets see how english grades vary by grade" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#First keep english only\n", "english = data[data['Test.Name']=='CST English-Language Arts']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "#computes the mean of the bodies\n", "english_grade = english.groupby('Grade')['Mean.Scale.Score'].aggregate(np.mean) \n", "english_grade" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "Grade\n", "2 355.771930\n", "3 343.361404\n", "4 370.684211\n", "5 363.074138\n", "6 361.592982\n", "7 366.333333\n", "8 362.998276\n", "9 360.000000\n", "10 344.621053\n", "11 340.910526\n", "Name: Mean.Scale.Score, dtype: float64" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "english_grade.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 56 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Now we want average across counties, not students\n", "because it's not fair to average counties " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We add a new calculated column to our dataframe\n", "english['Weighted.Score'] = english['Mean.Scale.Score'] * english['Pct.Test.Grade']\n", "# Then group by grade and plot the aggregated sum of this column\n", "english.groupby('Grade')['Weighted.Score'].aggregate(np.sum).plot()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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M2P7mVne7b1/YtCnC7bfD0KGJt0/2djQaTbh9585lfPIJbNgQIRKp3eslczsm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\nK1mNMcZkX07HqItIaxGZIiK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"text": [ "" ] } ], "prompt_number": 59 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "So This data tells us that juniors are the dumbest, and if your a good parent you should\n", "take your student out after 4th grade" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Effect of Income for 7th grade test scores?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Keep the relevant rows only\n", "math_7th = data[(data['Test.Name'] == 'CST Mathematics') &\n", " (data['Grade'] == 7)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "math_7th.plot(x='median.family.income', y='Mean.Scale.Score', kind='scatter')\n", "#We use scatter because if we did line plot, there is no garuntee that the results are in gradual \n", "#order, so then we would just have connected lines everywhere\n", "#But cant we sort the data first? Would that solve the problem?" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's peek at the offending row\n", "math_7th[math_7th['Mean.Scale.Score'] < 300]" ], "language": "python", "metadata": {}, "outputs": [ { "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", "
County.CodeCounty.NameTest.IdTest.NameGradeStudents.TestedMean.Scale.ScoreCount.Test.GradePct.Test.GradePopulationper.capita.incomemedian.household.incomemedian.family.incomeSpendADAttendSpend.Per.ADA
2918 46 Sierra 8 CST Mathematics 7 27 259.5 393811 0.000069 3277 26137 50308 56469 4739373 354 13391
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ " County.Code County.Name Test.Id Test.Name Grade \\\n", "2918 46 Sierra 8 CST Mathematics 7 \n", "\n", " Students.Tested Mean.Scale.Score Count.Test.Grade Pct.Test.Grade \\\n", "2918 27 259.5 393811 0.000069 \n", "\n", " Population per.capita.income median.household.income \\\n", "2918 3277 26137 50308 \n", "\n", " median.family.income Spend ADAttend Spend.Per.ADA \n", "2918 56469 4739373 354 13391 " ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "math_7th.plot(x='Students.Tested', y='Mean.Scale.Score',\n", " kind='scatter', logx=True)\n", "#Try running the same without logx--the data gets squished." ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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V9gW2VGy2hSS52BRcH5txLDKORcaxyE/VXmfzklZBfQk4OyJ+I+mrwFkRca2k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\n", "text": [ "" ] } ], "prompt_number": 66 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Clearly Sierra is an outlier...so we should take it out" ] }, { "cell_type": "code", "collapsed": false, "input": [ "math_7th = math_7th[math_7th['County.Name'] != 'Sierra']\n", "math_7th.plot(x='median.family.income', y='Mean.Scale.Score', kind='scatter')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 67, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 67 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Cryptic Magic Starts Here (So does all the math):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's fit a linear model to our data\n", "y = math_7th['Mean.Scale.Score'] # response\n", "X = math_7th['median.family.income'] # predictor\n", "X = sm.add_constant(X) # Add a constant term to the predictor\n", "# The actual fitting happens here\n", "est = sm.OLS(y, X) #fit least squares model\n", "est = est.fit()\n", "est.summary()" ], "language": "python", "metadata": {}, "outputs": [ { "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", "
OLS Regression Results
Dep. Variable: Mean.Scale.Score R-squared: 0.298
Model: OLS Adj. R-squared: 0.285
Method: Least Squares F-statistic: 22.93
Date: Sat, 28 Feb 2015 Prob (F-statistic): 1.35e-05
Time: 10:51:10 Log-Likelihood: -212.31
No. Observations: 56 AIC: 428.6
Df Residuals: 54 BIC: 432.7
Df Model: 1
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
const 332.0775 6.059 54.808 0.000 319.930 344.225
median.family.income 0.0004 8.87e-05 4.788 0.000 0.000 0.001
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 0.749 Durbin-Watson: 1.990
Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.280
Skew: 0.142 Prob(JB): 0.869
Kurtosis: 3.197 Cond. No. 2.84e+05
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 68, "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Mean.Scale.Score R-squared: 0.298\n", "Model: OLS Adj. R-squared: 0.285\n", "Method: Least Squares F-statistic: 22.93\n", "Date: Sat, 28 Feb 2015 Prob (F-statistic): 1.35e-05\n", "Time: 10:51:10 Log-Likelihood: -212.31\n", "No. Observations: 56 AIC: 428.6\n", "Df Residuals: 54 BIC: 432.7\n", "Df Model: 1 \n", "========================================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "----------------------------------------------------------------------------------------\n", "const 332.0775 6.059 54.808 0.000 319.930 344.225\n", "median.family.income 0.0004 8.87e-05 4.788 0.000 0.000 0.001\n", "==============================================================================\n", "Omnibus: 0.749 Durbin-Watson: 1.990\n", "Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.280\n", "Skew: 0.142 Prob(JB): 0.869\n", "Kurtosis: 3.197 Cond. No. 2.84e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 2.84e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's plot the regression line on top of the data\n", "x_ = np.array([X.min(), X.max()])\n", "y_ = est.predict(x_)\n", "math_7th.plot(x='median.family.income', y='Mean.Scale.Score', kind='scatter')\n", "plt.plot(x_[:, 1], y_, 'r-')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 69 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Whats the effect of spending on 7th grade math scores?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's fit a linear model to our data\n", "y = math_7th['Mean.Scale.Score'] # response\n", "X = math_7th[['median.family.income', 'Spend.Per.ADA']] # predictor\n", "X = sm.add_constant(X) # Add a constant term to the predictor\n", "# The actual fitting happens here\n", "est = sm.OLS(y, X)\n", "est = est.fit()\n", "est.summary()" ], "language": "python", "metadata": {}, "outputs": [ { "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", "
OLS Regression Results
Dep. Variable: Mean.Scale.Score R-squared: 0.359
Model: OLS Adj. R-squared: 0.335
Method: Least Squares F-statistic: 14.82
Date: Sat, 28 Feb 2015 Prob (F-statistic): 7.71e-06
Time: 10:55:14 Log-Likelihood: -209.78
No. Observations: 56 AIC: 425.6
Df Residuals: 53 BIC: 431.6
Df Model: 2
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
const 308.8693 11.900 25.955 0.000 285.000 332.738
median.family.income 0.0004 8.56e-05 4.935 0.000 0.000 0.001
Spend.Per.ADA 0.0025 0.001 2.239 0.029 0.000 0.005
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 0.769 Durbin-Watson: 1.849
Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.865
Skew: -0.189 Prob(JB): 0.649
Kurtosis: 2.523 Cond. No. 5.82e+05
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Mean.Scale.Score R-squared: 0.359\n", "Model: OLS Adj. R-squared: 0.335\n", "Method: Least Squares F-statistic: 14.82\n", "Date: Sat, 28 Feb 2015 Prob (F-statistic): 7.71e-06\n", "Time: 10:55:14 Log-Likelihood: -209.78\n", "No. Observations: 56 AIC: 425.6\n", "Df Residuals: 53 BIC: 431.6\n", "Df Model: 2 \n", "========================================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "----------------------------------------------------------------------------------------\n", "const 308.8693 11.900 25.955 0.000 285.000 332.738\n", "median.family.income 0.0004 8.56e-05 4.935 0.000 0.000 0.001\n", "Spend.Per.ADA 0.0025 0.001 2.239 0.029 0.000 0.005\n", "==============================================================================\n", "Omnibus: 0.769 Durbin-Watson: 1.849\n", "Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.865\n", "Skew: -0.189 Prob(JB): 0.649\n", "Kurtosis: 2.523 Cond. No. 5.82e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 5.82e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }