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"input": [
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],
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},
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"cell_type": "code",
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"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 "
],
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"cell_type": "heading",
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"source": [
"Pandas Series Objects : glorified numpy array"
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"input": [
"a = pd.Series(np.random.randn(5), name = 'random numbers')\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": [
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"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
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{
"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
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{
"cell_type": "code",
"collapsed": false,
"input": [
"a[3]"
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"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
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"text": [
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}
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"collapsed": false,
"input": [
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],
"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
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{
"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": [
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"input": [
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"students.columns"
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"collapsed": false,
"input": [
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"students.name"
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"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"0 Peter\n",
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"input": [
"#create a boolean array which is only true when her name is Mary\n",
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"students[students.name == 'Mary']"
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\n",
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" | \n",
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" Test.Id | \n",
" Test.Name | \n",
" Grade | \n",
" Students.Tested | \n",
" Mean.Scale.Score | \n",
" Count.Test.Grade | \n",
" Pct.Test.Grade | \n",
" Population | \n",
" per.capita.income | \n",
" median.household.income | \n",
" median.family.income | \n",
" Spend | \n",
" ADAttend | \n",
" Spend.Per.ADA | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" Alameda | \n",
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" 2 | \n",
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" \n",
" 1 | \n",
" 1 | \n",
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" 8 | \n",
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" 16802 | \n",
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" 464515 | \n",
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" 16198 | \n",
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" 7 | \n",
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" 16126 | \n",
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" 441572 | \n",
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" 1 | \n",
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" 1 | \n",
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" 1 | \n",
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\n",
" \n",
" 7 | \n",
" 1 | \n",
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" 7 | \n",
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" 5 | \n",
" 15077 | \n",
" 378.4 | \n",
" 429498 | \n",
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" 9360 | \n",
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\n",
" \n",
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" 1 | \n",
" Alameda | \n",
" 32 | \n",
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" 5 | \n",
" 15107 | \n",
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\n",
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" 1 | \n",
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" 7 | \n",
" CST English-Language Arts | \n",
" 6 | \n",
" 15277 | \n",
" 374.9 | \n",
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\n",
" \n",
" 10 | \n",
" 1 | \n",
" Alameda | \n",
" 8 | \n",
" CST Mathematics | \n",
" 6 | \n",
" 15332 | \n",
" 380.3 | \n",
" 436563 | \n",
" 0.035120 | \n",
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\n",
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" 1 | \n",
" Alameda | \n",
" 7 | \n",
" CST English-Language Arts | \n",
" 7 | \n",
" 14551 | \n",
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\n",
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" 12 | \n",
" 1 | \n",
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" 9 | \n",
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" 7 | \n",
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\n",
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" 1 | \n",
" Alameda | \n",
" 8 | \n",
" CST Mathematics | \n",
" 7 | \n",
" 12326 | \n",
" 370.2 | \n",
" 393811 | \n",
" 0.031299 | \n",
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\n",
" \n",
" 14 | \n",
" 1 | \n",
" Alameda | \n",
" 9 | \n",
" CST Algebra I | \n",
" 8 | \n",
" 11006 | \n",
" 354.2 | \n",
" 276039 | \n",
" 0.039871 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
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" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 15 | \n",
" 1 | \n",
" Alameda | \n",
" 13 | \n",
" CST Algebra II | \n",
" 8 | \n",
" 31 | \n",
" 458.9 | \n",
" 680 | \n",
" 0.045588 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 16 | \n",
" 1 | \n",
" Alameda | \n",
" 7 | \n",
" CST English-Language Arts | \n",
" 8 | \n",
" 14549 | \n",
" 373.9 | \n",
" 435491 | \n",
" 0.033408 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 17 | \n",
" 1 | \n",
" Alameda | \n",
" 29 | \n",
" CST History - Social Science Grade 8 | \n",
" 8 | \n",
" 15421 | \n",
" 363.3 | \n",
" 459125 | \n",
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" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 18 | \n",
" 1 | \n",
" Alameda | \n",
" 28 | \n",
" CST General Mathematics | \n",
" 8 | \n",
" 2143 | \n",
" 308.4 | \n",
" 145549 | \n",
" 0.014724 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 19 | \n",
" 1 | \n",
" Alameda | \n",
" 11 | \n",
" CST Geometry | \n",
" 8 | \n",
" 1732 | \n",
" 428.0 | \n",
" 29035 | \n",
" 0.059652 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 20 | \n",
" 1 | \n",
" Alameda | \n",
" 32 | \n",
" CST Science - Grade 5, Grade 8, and Grade 10 L... | \n",
" 8 | \n",
" 14552 | \n",
" 404.5 | \n",
" 436071 | \n",
" 0.033371 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 21 | \n",
" 1 | \n",
" Alameda | \n",
" 10 | \n",
" CST Integrated Math 1 | \n",
" 9 | \n",
" 121 | \n",
" 298.8 | \n",
" 980 | \n",
" 0.123469 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 22 | \n",
" 1 | \n",
" Alameda | \n",
" 20 | \n",
" CST Biology | \n",
" 9 | \n",
" 9473 | \n",
" 377.0 | \n",
" 228962 | \n",
" 0.041374 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 23 | \n",
" 1 | \n",
" Alameda | \n",
" 21 | \n",
" CST Chemistry | \n",
" 9 | \n",
" 186 | \n",
" 364.3 | \n",
" 5365 | \n",
" 0.034669 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 24 | \n",
" 1 | \n",
" Alameda | \n",
" 24 | \n",
" CST Integrated/Coordinated Science 1 | \n",
" 9 | \n",
" 905 | \n",
" 321.4 | \n",
" 33944 | \n",
" 0.026662 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 25 | \n",
" 1 | \n",
" Alameda | \n",
" 22 | \n",
" CST Earth Science | \n",
" 9 | \n",
" 1651 | \n",
" 328.8 | \n",
" 133961 | \n",
" 0.012324 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 26 | \n",
" 1 | \n",
" Alameda | \n",
" 23 | \n",
" CST Physics | \n",
" 9 | \n",
" 754 | \n",
" 312.8 | \n",
" 14112 | \n",
" 0.053430 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 27 | \n",
" 1 | \n",
" Alameda | \n",
" 25 | \n",
" CST Integrated/Coordinated Science 2 | \n",
" 9 | \n",
" 124 | \n",
" 323.0 | \n",
" 1351 | \n",
" 0.091784 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 28 | \n",
" 1 | \n",
" Alameda | \n",
" 18 | \n",
" CST World History | \n",
" 9 | \n",
" 597 | \n",
" 320.1 | \n",
" 35087 | \n",
" 0.017015 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" 29 | \n",
" 1 | \n",
" Alameda | \n",
" 11 | \n",
" CST Geometry | \n",
" 9 | \n",
" 6613 | \n",
" 353.4 | \n",
" 144063 | \n",
" 0.045904 | \n",
" 1494876 | \n",
" 34937 | \n",
" 70821 | \n",
" 87012 | \n",
" 1814932885 | \n",
" 193906 | \n",
" 9360 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 3700 | \n",
" 58 | \n",
" Yuba | \n",
" 22 | \n",
" CST Earth Science | \n",
" 10 | \n",
" 290 | \n",
" 327.0 | \n",
" 30649 | \n",
" 0.009462 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3701 | \n",
" 58 | \n",
" Yuba | \n",
" 15 | \n",
" CST Summative High School Mathematics | \n",
" 10 | \n",
" 19 | \n",
" 353.3 | \n",
" 24757 | \n",
" 0.000767 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3702 | \n",
" 58 | \n",
" Yuba | \n",
" 21 | \n",
" CST Chemistry | \n",
" 10 | \n",
" 166 | \n",
" 358.8 | \n",
" 136209 | \n",
" 0.001219 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3703 | \n",
" 58 | \n",
" Yuba | \n",
" 13 | \n",
" CST Algebra II | \n",
" 10 | \n",
" 201 | \n",
" 335.9 | \n",
" 131448 | \n",
" 0.001529 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3704 | \n",
" 58 | \n",
" Yuba | \n",
" 9 | \n",
" CST Algebra I | \n",
" 10 | \n",
" 259 | \n",
" 285.9 | \n",
" 104567 | \n",
" 0.002477 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3705 | \n",
" 58 | \n",
" Yuba | \n",
" 7 | \n",
" CST English-Language Arts | \n",
" 10 | \n",
" 931 | \n",
" 332.0 | \n",
" 455362 | \n",
" 0.002045 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3706 | \n",
" 58 | \n",
" Yuba | \n",
" 20 | \n",
" CST Biology | \n",
" 10 | \n",
" 238 | \n",
" 343.3 | \n",
" 228138 | \n",
" 0.001043 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3707 | \n",
" 58 | \n",
" Yuba | \n",
" 11 | \n",
" CST Geometry | \n",
" 10 | \n",
" 274 | \n",
" 296.1 | \n",
" 156167 | \n",
" 0.001755 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3708 | \n",
" 58 | \n",
" Yuba | \n",
" 11 | \n",
" CST Geometry | \n",
" 11 | \n",
" 151 | \n",
" 287.1 | \n",
" 78308 | \n",
" 0.001928 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3709 | \n",
" 58 | \n",
" Yuba | \n",
" 19 | \n",
" CST U.S. History | \n",
" 11 | \n",
" 895 | \n",
" 325.7 | \n",
" 447386 | \n",
" 0.002001 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3710 | \n",
" 58 | \n",
" Yuba | \n",
" 20 | \n",
" CST Biology | \n",
" 11 | \n",
" 186 | \n",
" 330.3 | \n",
" 99776 | \n",
" 0.001864 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3711 | \n",
" 58 | \n",
" Yuba | \n",
" 7 | \n",
" CST English-Language Arts | \n",
" 11 | \n",
" 899 | \n",
" 324.9 | \n",
" 440115 | \n",
" 0.002043 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3712 | \n",
" 58 | \n",
" Yuba | \n",
" 9 | \n",
" CST Algebra I | \n",
" 11 | \n",
" 187 | \n",
" 281.3 | \n",
" 49868 | \n",
" 0.003750 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3713 | \n",
" 58 | \n",
" Yuba | \n",
" 13 | \n",
" CST Algebra II | \n",
" 11 | \n",
" 199 | \n",
" 302.4 | \n",
" 122079 | \n",
" 0.001630 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3714 | \n",
" 58 | \n",
" Yuba | \n",
" 21 | \n",
" CST Chemistry | \n",
" 11 | \n",
" 121 | \n",
" 342.4 | \n",
" 133804 | \n",
" 0.000904 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3715 | \n",
" 58 | \n",
" Yuba | \n",
" 15 | \n",
" CST Summative High School Mathematics | \n",
" 11 | \n",
" 153 | \n",
" 322.6 | \n",
" 124304 | \n",
" 0.001231 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3716 | \n",
" 58 | \n",
" Yuba | \n",
" 22 | \n",
" CST Earth Science | \n",
" 11 | \n",
" 187 | \n",
" 326.8 | \n",
" 42331 | \n",
" 0.004418 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3717 | \n",
" 58 | \n",
" Yuba | \n",
" 23 | \n",
" CST Physics | \n",
" 11 | \n",
" 91 | \n",
" 363.8 | \n",
" 56726 | \n",
" 0.001604 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3718 | \n",
" 58 | \n",
" Yuba | \n",
" 18 | \n",
" CST World History | \n",
" 11 | \n",
" 40 | \n",
" 300.3 | \n",
" 16163 | \n",
" 0.002475 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3719 | \n",
" 58 | \n",
" Yuba | \n",
" 18 | \n",
" CST World History | \n",
" 13 | \n",
" 874 | \n",
" 329.1 | \n",
" 474255 | \n",
" 0.001843 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3720 | \n",
" 58 | \n",
" Yuba | \n",
" 25 | \n",
" CST Integrated/Coordinated Science 2 | \n",
" 13 | \n",
" 14 | \n",
" 302.3 | \n",
" 3742 | \n",
" 0.003741 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3721 | \n",
" 58 | \n",
" Yuba | \n",
" 20 | \n",
" CST Biology | \n",
" 13 | \n",
" 876 | \n",
" 340.6 | \n",
" 556893 | \n",
" 0.001573 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3722 | \n",
" 58 | \n",
" Yuba | \n",
" 22 | \n",
" CST Earth Science | \n",
" 13 | \n",
" 875 | \n",
" 328.9 | \n",
" 206991 | \n",
" 0.004227 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3723 | \n",
" 58 | \n",
" Yuba | \n",
" 23 | \n",
" CST Physics | \n",
" 13 | \n",
" 92 | \n",
" 363.6 | \n",
" 80833 | \n",
" 0.001138 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3724 | \n",
" 58 | \n",
" Yuba | \n",
" 28 | \n",
" CST General Mathematics | \n",
" 13 | \n",
" 585 | \n",
" 299.5 | \n",
" 190615 | \n",
" 0.003069 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3725 | \n",
" 58 | \n",
" Yuba | \n",
" 13 | \n",
" CST Algebra II | \n",
" 13 | \n",
" 431 | \n",
" 320.6 | \n",
" 286737 | \n",
" 0.001503 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3726 | \n",
" 58 | \n",
" Yuba | \n",
" 21 | \n",
" CST Chemistry | \n",
" 13 | \n",
" 287 | \n",
" 351.9 | \n",
" 275452 | \n",
" 0.001042 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3727 | \n",
" 58 | \n",
" Yuba | \n",
" 15 | \n",
" CST Summative High School Mathematics | \n",
" 13 | \n",
" 172 | \n",
" 326.0 | \n",
" 149987 | \n",
" 0.001147 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3728 | \n",
" 58 | \n",
" Yuba | \n",
" 11 | \n",
" CST Geometry | \n",
" 13 | \n",
" 695 | \n",
" 307.8 | \n",
" 407658 | \n",
" 0.001705 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
" 3729 | \n",
" 58 | \n",
" Yuba | \n",
" 9 | \n",
" CST Algebra I | \n",
" 13 | \n",
" 1495 | \n",
" 319.4 | \n",
" 711705 | \n",
" 0.002101 | \n",
" 71817 | \n",
" 20046 | \n",
" 46617 | \n",
" 52775 | \n",
" 99237214 | \n",
" 11710 | \n",
" 8474 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"
\n",
" \n",
" \n",
" Grade | \n",
" 2 | \n",
" 3 | \n",
" 4 | \n",
" 5 | \n",
" 6 | \n",
" 7 | \n",
" 8 | \n",
" 9 | \n",
" 10 | \n",
" 11 | \n",
" 13 | \n",
"
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" \n",
" Test.Name | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
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" \n",
" \n",
" \n",
" CST Algebra I | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 37803 | \n",
" 276039 | \n",
" 243349 | \n",
" 104555 | \n",
" 49846 | \n",
" 711671 | \n",
"
\n",
" \n",
" CST Algebra II | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 680 | \n",
" 32400 | \n",
" 131448 | \n",
" 122079 | \n",
" 286737 | \n",
"
\n",
" \n",
" CST Biology | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 228962 | \n",
" 228138 | \n",
" 99753 | \n",
" 556867 | \n",
"
\n",
" \n",
" CST Chemistry | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 5365 | \n",
" 136209 | \n",
" 133804 | \n",
" 275452 | \n",
"
\n",
" \n",
" CST Earth Science | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 133961 | \n",
" 30649 | \n",
" 42316 | \n",
" 206974 | \n",
"
\n",
" \n",
" CST English-Language Arts | \n",
" 464896 | \n",
" 441572 | \n",
" 428906 | \n",
" 429498 | \n",
" 434374 | \n",
" 431187 | \n",
" 435491 | \n",
" 463195 | \n",
" 455237 | \n",
" 439972 | \n",
" NaN | \n",
"
\n",
" \n",
" CST General Mathematics | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 145549 | \n",
" 45052 | \n",
" NaN | \n",
" NaN | \n",
" 190615 | \n",
"
\n",
" \n",
" CST Geometry | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 29035 | \n",
" 144063 | \n",
" 156167 | \n",
" 78308 | \n",
" 407658 | \n",
"
\n",
" \n",
" CST History - Social Science Grade 8 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 459125 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" CST Integrated Math 1 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 125 | \n",
" 980 | \n",
" 5965 | \n",
" 6318 | \n",
" 13486 | \n",
"
\n",
" \n",
" CST Integrated Math 2 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 236 | \n",
" 1432 | \n",
" 3364 | \n",
" 5102 | \n",
"
\n",
" \n",
" CST Integrated Math 3 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 61 | \n",
" 153 | \n",
" 530 | \n",
" 769 | \n",
"
\n",
" \n",
" CST Integrated/Coordinated Science 1 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 33944 | \n",
" 4401 | \n",
" 7963 | \n",
" 46386 | \n",
"
\n",
" \n",
" CST Integrated/Coordinated Science 2 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 1351 | \n",
" 1413 | \n",
" 955 | \n",
" 3742 | \n",
"
\n",
" \n",
" CST Integrated/Coordinated Science 3 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 11 | \n",
" 46 | \n",
" 874 | \n",
" 952 | \n",
"
\n",
" \n",
" CST Integrated/Coordinated Science 4 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 18 | \n",
" NaN | \n",
" NaN | \n",
" 43 | \n",
"
\n",
" \n",
" CST Mathematics | \n",
" 464515 | \n",
" 443961 | \n",
" 433012 | \n",
" 432775 | \n",
" 436563 | \n",
" 393811 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" CST Physics | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 14112 | \n",
" 9902 | \n",
" 56726 | \n",
" 80833 | \n",
"
\n",
" \n",
" CST Science - Grade 5, Grade 8, and Grade 10 Life Science | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 431142 | \n",
" NaN | \n",
" NaN | \n",
" 436071 | \n",
" NaN | \n",
" 451253 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" CST Summative High School Mathematics | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 809 | \n",
" 24757 | \n",
" 124304 | \n",
" 149987 | \n",
"
\n",
" \n",
" CST U.S. History | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 447253 | \n",
" NaN | \n",
"
\n",
" \n",
" CST World History | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 35087 | \n",
" 422893 | \n",
" 16163 | \n",
" 474237 | \n",
"
\n",
" \n",
"
\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",
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5HaMuIh1EZKqILBCRN0XkN/l8/Voy1XkxV5xEpEREKkXkmbizAIjI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\nrkJggg==\n",
"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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\n",
"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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dI+luSZek5WdJuk3SDkm3S3pOyzarJN0v6T5JS6vMZ2Zm5am0DUXS8cDxEdGQ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\nVgoXKGZmVgoXKGZmVor/D/Was7qIVy2FAAAAAElFTkSuQmCC\n",
"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",
" County.Code | \n",
" County.Name | \n",
" Test.Id | \n",
" Test.Name | \n",
" Grade | \n",
" Students.Tested | \n",
" Mean.Scale.Score | \n",
" Count.Test.Grade | \n",
" Pct.Test.Grade | \n",
" Population | \n",
" per.capita.income | \n",
" median.household.income | \n",
" median.family.income | \n",
" Spend | \n",
" ADAttend | \n",
" Spend.Per.ADA | \n",
"
\n",
" \n",
" \n",
" \n",
" 2918 | \n",
" 46 | \n",
" Sierra | \n",
" 8 | \n",
" CST Mathematics | \n",
" 7 | \n",
" 27 | \n",
" 259.5 | \n",
" 393811 | \n",
" 0.000069 | \n",
" 3277 | \n",
" 26137 | \n",
" 50308 | \n",
" 56469 | \n",
" 4739373 | \n",
" 354 | \n",
" 13391 | \n",
"
\n",
" \n",
"
\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",
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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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MkHsiO/aJiJ9QzLVyccrwTknvbM421s+JiAGKJqxvS7oJ6E/PLwc+\nIulmigL3g2PkeFLeiLiDYn76jen1GynmgDebkC8bNusQFXPB3wIcGhG/7HQes+lyDcWsAySNzAT5\nCRcmNlO4hmJmZqVwDcXMzErhAsXMzErhAsXMzErhAsXMzErhAsXMzErhAsXMzErx/wEhZRuowlE7\nnwAAAABJRU5ErkJggg==\n",
"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",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | Mean.Scale.Score | R-squared: | 0.298 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.285 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 22.93 | \n",
"
\n",
"\n",
" Date: | Sat, 28 Feb 2015 | Prob (F-statistic): | 1.35e-05 | \n",
"
\n",
"\n",
" Time: | 10:51:10 | Log-Likelihood: | -212.31 | \n",
"
\n",
"\n",
" No. Observations: | 56 | AIC: | 428.6 | \n",
"
\n",
"\n",
" Df Residuals: | 54 | BIC: | 432.7 | \n",
"
\n",
"\n",
" Df Model: | 1 | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [95.0% Conf. Int.] | \n",
"
\n",
"\n",
" const | 332.0775 | 6.059 | 54.808 | 0.000 | 319.930 344.225 | \n",
"
\n",
"\n",
" median.family.income | 0.0004 | 8.87e-05 | 4.788 | 0.000 | 0.000 0.001 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 0.749 | Durbin-Watson: | 1.990 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.688 | Jarque-Bera (JB): | 0.280 | \n",
"
\n",
"\n",
" Skew: | 0.142 | Prob(JB): | 0.869 | \n",
"
\n",
"\n",
" Kurtosis: | 3.197 | Cond. No. | 2.84e+05 | \n",
"
\n",
"
"
],
"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",
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JwKSmNLUlYr1rO5HyZPZsl/Kkf39nUH7/e+jaNdfqDMOIGU26vERkLdArLrOF\ntuTyimVeqbffhkcfrU95UlLi4iEhpDwJm1gbY8OIGbGIoYjIn4ArVfWtKIVkSlsyKLHZ5Pj66/V7\nRFavdsajrAxKS6FLl+xqyZBYGmPDiDFxiaHsD6wRkQUiMj94zItSVFsg9n7VV1+FKVOoPuIIOPFE\nZ0jGj3dJGR980AXYW2FMws5Xljqeu5dldoYlMVvJJbF/3/FDI5hOH8lkH8rEqEXElahdKlFWgvyC\n9pKS9ClPfvhDuOKKUFOeWL4yw2inRF3BK+wHWarYmK064s2tBJlpn077fXoiP9ebOnbRTw86SPXQ\nQ1VHj1ZdulR1585QXisd2ahbb3XeDaN5kIWKjZnEUE7F5fQ6Epc5eA/gU1XNSYQ2WzGU2MQ3msvO\nnYw65UwOeaEzg1nHFjozm568efJn/PavT7n0JxGTrbGzoLxhZE5cYii/AS4C1gOdgcuAO6IU1RbI\nql91+3a3W/3yy+GrX+XH617iA/biHJ7gm6zjGi5g/d77pjUmUeiMIl9ZOp1R1jVpKT74033QCKbT\nRzLa2Kiq64E9VHWnqt4HnBWtrNzTmg/FyspKrrrq59EW0Nq61dVWHz4cevRwAfXCQnjmGTY88hA3\n5/+FNbwA3N9s7a0Npifq1hcXz6O4eJ7FTwyjvdCUTwxYgnN1PQDcCIwGXoraF9eInlZ4EZtHS+Ib\nkfr2P/1U9ZFHVIcMUS0oUO3XT/VXv1J9/fX4azcMI6cQkxhKIS79fCdgFLA3cIeqbojGxDVO3Peh\nhB4/+OgjNxOZM8e5tU4+2e0ROf986N49LNmAx3EjwzCaJBYxFFXdCAjQXVUnquroXBkTv6hu+a3v\nvQf33us2GB58MPzxj3Deea6OyIIFbqlvSMbEF/+v6QwPHzSC6fSRTHJ5DQRuwrm9CkXkeOA6VR0Y\ntTgfqd9bMgx4PfO9JW+/DXPnuj0iL7zgUp5ccokrSJWllCdR7osxDKPtk4nL60Vc2d9Fqnp80Pay\nqh6dBX3p9MTa5QXNWM4aw5Qn6bTb8lzD8J+45PJapqoni8iKJIOyUlWPjVJYI3pib1Aa5dVX63er\nv/46m044gTvf/YSarvtzxf9eHrsPa8uZZRhtg1jEUIDVIvI9oKOIHC4itwN/iVJUW2CXX1UVVq6E\niRNdIaqiInjzTbjxRhbMmEHPJTVMWjGCJ54axKBBQ6NbZtyUzgaIS84sX/zUPuj0QSOYTh/JxKD8\nBDgK2IqukVaXAAAUb0lEQVQriPUx8NMoRXmPKqxbB2PHQs+eMHAgfPIJ/O53sGkT/OY3cOaZ3Pyr\ne2LxYZ1Lwk4iaRhGDol6XXLYD7K4D6VZ7NihumSJ6pVXqh58sOoRR6iOH6/6wguqdXVpb8lGzqvW\n0pK9KZnugbF9L4aRPcjlPhQRmQ8obslwGjuUm1VesYqhbN8Oixe7eMijj8JXvuKC6mVl0KtXk3mz\nfIlPNCco35zfyfa9GEb2yEYMpbGZwL+AFcDVQP/gURQ8+kdt6RrR1Toz3Vq2bFGdP1912DDV/fZT\n7dtXdepU1fXrd7ts0aJFGXWX7tt8FBmIGyJTnZnSnFlXc64NW2cYpHuf4qgzFR80qprOsCELM5TG\n9qH0AIqBIcHjcWCWqq6OxrRFS6uWvn72GVRUuJnIk0/Csce6srjXX+82HraC0tLS3bS0p1oiPu97\naeh9ysvLy7Eyw8ghmVgd3KbGYcC/gR9HbeWa0NJsy9wiX/2HH6o++KDqoEGqe++tWlysetddqps3\nN/v1m4MPcZXGaO5YZ3M2Fia+v09G+4Mcz1AQkc7AucCFQCFwGzA3MusWEbsvfYXaWtf2hW/9//43\nzJvnZiJLl0L//m4m8oc/QNeu2ReeJcLcuJjINFzfX+Ozq9QZmmEYHtOQpcFlF34R+AVwTNSWLdMH\nLZihNPpt8q23VH/7W9Uzz3QzkQsuUJ01S/Xjj5v9Osm01K+a7ZVPU6dODfX1oppxxM1P3dD7FDed\n6fBBo6rpDBuyMENp7IO7DvikgcfHTXbsinEtA2qANcDkoH0isAkX8F8BnJ10zzhcIa91QEkD/TZ7\nIFP/+XvmddO1//3fqqedprrvvqoXX6w6Z47qZ581u++GaM0fWTbdQCec0C80103YxjB5HKZOndri\nfqLCgvLRYjrDJacGJZTOoUvwsyPwHPAtYAIwOs21vQLjsyfOvbYB6JDmuhYNZkVFhf7y6JP0lS8X\n6NZ99lG97DLVJ55Q3bq1Rf21FcKMBYTZl+1RMYxwyYZBaTLbcGtQ1c+Dw064WvQfBOfp1kKfj1tF\nth3YKCIbgL44Q9RqSktLKc3Lc59z/fpBx0h/dW+I60qrjONeMcMSaRrtmYxKALcUEekgIjW4Al2L\ntH7J8U9E5CURuUdECoK2A3GusASbgINCFVRUBGeckRVj0lh+nzilG8nLywutXG8UteTrWRtSP9FR\nWVnJwIFDqKoaSFXVwJzkZssEX3JPmU7/iHqGUgf0FpF9gEoRKQLuBK4PLrkBmAZc1lAX6RqHDRtG\nYWEhAAUFBfTu3ZuioiKg/s3N9XmC1OdvvPFGrr12Ctu23QrA4sVDuOGGsVx99dU50VtTU0Pv3r13\n7U6vrq6murq6Rf2VlpYyceJoHn74Hrp23Z/ycrcvoyX91c+cnCHp1Oluystnxeb9TXc+bdrdbNt2\nFnAoUERtLVxzzS/Jy8uLhb7EeU1NTaz0+H4e1/Gsrq5m+vTpALs+L6OmyfT1ob2QyLVArarenNRW\nCMxX1WNEZCyAqk4JnqsAJqjqspR+NFuao8DSjWSOb+4je2+NOJON1CuRzVBEpBuwQ1U/FJF83K77\n60Sku6puDi4bBKwKjucBM0XkFpyr63BgeVT62hK+ffBmim97VOIajzKMrBFVtB84BrePpQZYCfxv\n0H5/cP4S8ChwQNI943Gru9YBpQ3024p1DtmjoaWEUSytbU1/vix59EXn1KlTY7/z35exNJ3hgs+r\nvFR1FdAnTfv3G7lnEjApKk1xoLk7yZvC19VQbZW+ffvuiocZRnsjazGUsIhDDCVOLibz2xuGkQle\nx1DaKnHLBmx+e8Mw4kKk+1DaIpnWWE8s34uahAutpftIsqWztZjO8PBBI5hOH7EZShvAt9VQhmG0\nTSyG0kx8KdtrGIaRTDZiKGZQWkCcgvKGYRiZkA2DYjGUFlBaWsqCBbNZsGB2g8bEF7+q6QwXH3T6\noBFMp4+YQTEMwzBCwVxehmEY7QBzeRmGYRjeYAYlInzxq5rOcPFBpw8awXT6iBkUwzAMIxQshmIY\nhtEOsBiKYRiG4Q1mUCLCF7+q6QwXH3T6oBFMp4+YQckilZWVlJSUUVJSRmVlZa7lGIZhhIrFULKE\n5QAzDCOXWAwly0Q5g8g07b1hGIavmEEJSMwgqqoGUlU1kEGDhrbKqPjiVzWd4eKDTh80gun0ETMo\nAVHPIMrLR5CfPwaYAcwIKiuOyOhei70YhuEDFkMJyEZt9pakvbfYi2EYYWD1UNIQlUGJ6wd3Ngyd\nYRhtHwvKZ5HW1mZPxRe/qukMFx90+qARTKePRFZTXkQ6A4uBPKAT8Jiqjkt6vhy4Ceimqu8HbeOA\n4cBO4ApVXRCVvnTEsTZ7efkIli4dSm2tO3exlxk51WQVKw3DSEekLi8R6aKqn4tIR2ApcJWqLhWR\ng4HfA0cAJ6jq+yLSC5gJnAQcBCwEeqpqXUqfXu5DaQ1x+gCPq2vQMIzGaTMxFBHpgputDFXVNSLy\nJ+AG4DHqDco4oE5Vpwb3VAATVfW5lL7anUGJExbTMQw/8T6GIiIdRKQGeAdYFBiT84FNqroy5fID\ngU1J55twMxUv8cWvajrDxQedPmgE0+kjkcVQAAJ3VW8R2QeoFJFzgHFASdJljVnMtFORYcOGUVhY\nCEBBQQG9e/emqKgIqH9zc32eIC56Gjqvqalp1vUDBpzM4sWj2LbN/X6dOo1iwICxkf++Ufefq/HM\nxXlNTU2s9Ph+HtfxrK6uZvr06QC7Pi+jJmvLhkXkWpyB+AnwedD8VeBN4GTgUgBVnRJcXwFMUNVl\nKf2YyyvHxCmmYxhGZngdQxGRbsAOVf1QRPKBSuA6VX0q6ZrX+GJQvi/1QflvpFoP3wyKffgahhEH\nfI+h9ACeDmIoy4D5ycYkYJdlUNU1wMPAGuBJYKRXliOF6urq0PODRUGqSymumM7w8EEjmE4fiSyG\noqqrgD5NXPO1lPNJwKSoNGWb3fODQW2ta7NZimEYbRFLvRIhtsTWMIy44LvLq93T0gzDll3YMAwf\nMYMSEdXV1S3KD5btuIsv/t/m6MylQfZhPH3QCKbTRyLdh2I0Pz+YxV1aR2pqmKVLh1pqGMPIEhZD\niRkWd2kdNn6GkZ5sxFBshhIz4phd2DAMIxMshhIRLfWrhl2XpSl88f9mqrM1pZbDwIfx9EEjmE4f\nsRlKDIljXRZfSBjk+uwEFj8xjGxhMRTDMIx2gO1DMQzDMLzBDEpE+OJXNZ3h4oNOHzSC6fQRMyiG\nYRhGKFgMxTAMox1gMRTDMAzDG8ygRIQvflXTGS4+6PRBI5hOHzGDYhiGYYSCxVAMwzDaARZDMQzD\nMLzBDEpE+OJXNZ3h4oNOHzSC6fQRMyiGYRhGKFgMxTAMox1gMRTDMAzDGyIzKCLSWUSWiUiNiKwR\nkclB+w0i8lLQ/pSIHJx0zzgRWS8i60SkJCpt2cAXv6rpDBcfdPqgEUynj0RmUFR1C3CGqvYGjgXO\nEJFvATeq6nFB+6PABAAR6QV8F+gFnAXcISLezqBqampyLSEjTGe4+KDTB41gOn0k0g9sVf08OOwE\n7AG8r6qfJF3yJeDfwfH5wCx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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",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | Mean.Scale.Score | R-squared: | 0.359 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.335 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 14.82 | \n",
"
\n",
"\n",
" Date: | Sat, 28 Feb 2015 | Prob (F-statistic): | 7.71e-06 | \n",
"
\n",
"\n",
" Time: | 10:55:14 | Log-Likelihood: | -209.78 | \n",
"
\n",
"\n",
" No. Observations: | 56 | AIC: | 425.6 | \n",
"
\n",
"\n",
" Df Residuals: | 53 | BIC: | 431.6 | \n",
"
\n",
"\n",
" Df Model: | 2 | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [95.0% Conf. Int.] | \n",
"
\n",
"\n",
" const | 308.8693 | 11.900 | 25.955 | 0.000 | 285.000 332.738 | \n",
"
\n",
"\n",
" median.family.income | 0.0004 | 8.56e-05 | 4.935 | 0.000 | 0.000 0.001 | \n",
"
\n",
"\n",
" Spend.Per.ADA | 0.0025 | 0.001 | 2.239 | 0.029 | 0.000 0.005 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 0.769 | Durbin-Watson: | 1.849 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.681 | Jarque-Bera (JB): | 0.865 | \n",
"
\n",
"\n",
" Skew: | -0.189 | Prob(JB): | 0.649 | \n",
"
\n",
"\n",
" Kurtosis: | 2.523 | Cond. No. | 5.82e+05 | \n",
"
\n",
"
"
],
"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": {}
}
]
}