{ "metadata": { "name": "", "signature": "sha256:79d8dedf0bba6df355694887672ad9aa645f8c12b4aa5034a6927755b5001662" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Are first babies more likely to be late?\n", "\n", "Copyright 2015 Allen Downey\n", "\n", "License: [Creative Commons Attribution 4.0 International](http://creativecommons.org/licenses/by/4.0/)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function, division\n", "\n", "import marriage\n", "\n", "import thinkstats2\n", "import thinkplot\n", "\n", "import math\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def ReadFemPreg(dct_file, dat_file, usecols):\n", " \"\"\"Reads the NSFG pregnancy data.\n", "\n", " dct_file: string file name\n", " dat_file: string file name\n", "\n", " returns: DataFrame\n", " \"\"\"\n", " dct = thinkstats2.ReadStataDct(dct_file, encoding='ISO-8859-1')\n", " df = dct.ReadFixedWidth(dat_file, compression='gzip', usecols=usecols)\n", " return df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "usecols = ['outcome', 'prglngth', 'birthord', 'finalwgt']\n", "preg6 = ReadFemPreg('2002FemPreg.dct', '2002FemPreg.dat.gz', usecols)\n", "print(preg6.shape)\n", "preg6 = preg6[preg6.outcome == 1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(13593, 4)\n" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "preg6.birthord.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "1 4413\n", "2 2874\n", "3 1234\n", "4 421\n", "5 126\n", "6 50\n", "7 20\n", "8 7\n", "9 2\n", "10 1\n", "dtype: int64" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "preg6.prglngth.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "0 1\n", "4 1\n", "9 1\n", "13 1\n", "17 2\n", "18 1\n", "19 1\n", "20 1\n", "21 2\n", "22 7\n", "23 1\n", "24 13\n", "25 3\n", "26 35\n", "27 3\n", "28 32\n", "29 21\n", "30 138\n", "31 27\n", "32 115\n", "33 49\n", "34 60\n", "35 311\n", "36 321\n", "37 455\n", "38 607\n", "39 4693\n", "40 1116\n", "41 587\n", "42 328\n", "43 148\n", "44 46\n", "45 10\n", "46 1\n", "47 1\n", "48 7\n", "50 2\n", "dtype: int64" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a list of DataFrames, one for each cycle:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sum(preg6.prglngth >= 27)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "9078" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "preg6.finalwgt.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "count 9148.000000\n", "mean 8404.364417\n", "std 9142.507490\n", "min 118.656790\n", "25% 3995.837136\n", "50% 6415.602101\n", "75% 9555.442649\n", "max 261879.953864\n", "Name: finalwgt, dtype: float64" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "usecols = ['outcome', 'prglngth', 'birthord', 'wgtq1q16']\n", "preg7 = ReadFemPreg('2006_2010_FemPregSetup.dct', '2006_2010_FemPreg.dat.gz', usecols)\n", "print(preg7.shape)\n", "preg7 = preg7[preg7.outcome == 1]\n", "preg7['finalwgt'] = preg7.wgtq1q16\n", "preg7.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(20492, 4)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 78, "text": [ "(14292, 5)" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "preg7.birthord.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "1 6683\n", "2 4415\n", "3 2030\n", "4 734\n", "5 269\n", "6 102\n", "7 36\n", "8 13\n", "9 7\n", "10 2\n", "11 1\n", "dtype: int64" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "preg7.prglngth.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "0 1\n", "7 1\n", "17 1\n", "19 2\n", "20 2\n", "21 4\n", "22 16\n", "23 4\n", "24 15\n", "25 14\n", "26 65\n", "27 21\n", "28 37\n", "29 26\n", "30 184\n", "31 40\n", "32 156\n", "33 59\n", "34 155\n", "35 509\n", "36 622\n", "37 835\n", "38 1304\n", "39 6263\n", "40 2280\n", "41 907\n", "42 553\n", "43 185\n", "44 19\n", "45 3\n", "46 4\n", "48 3\n", "52 1\n", "57 1\n", "dtype: int64" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(preg7.prglngth >= 27)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "14167" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# note: min and max should be 38.700642694-49735.071265\n", "preg7.finalwgt.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "count 14292.000000\n", "mean 5306.068571\n", "std 5967.788429\n", "min 44.023984\n", "25% 1509.629839\n", "50% 3078.497086\n", "75% 6594.905896\n", "max 30226.354508\n", "Name: finalwgt, dtype: float64" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "usecols = ['outcome', 'prglngth', 'birthord', 'wgt2011_2013']\n", "preg8 = ReadFemPreg('2011_2013_FemPregSetup.dct', '2011_2013_FemPregData.dat.gz', usecols)\n", "print(preg7.shape)\n", "preg8 = preg8[preg8.outcome == 1]\n", "preg8['finalwgt'] = preg8.wgt2011_2013\n", "preg8.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(14292, 5)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 80, "text": [ "(6670, 5)" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "preg8.birthord.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "1 3141\n", "2 2076\n", "3 957\n", "4 327\n", "5 113\n", "6 34\n", "7 16\n", "8 5\n", "9 1\n", "dtype: int64" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "preg8.prglngth.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "17 1\n", "20 2\n", "21 2\n", "22 8\n", "23 3\n", "24 12\n", "25 6\n", "26 21\n", "27 7\n", "28 22\n", "29 19\n", "30 61\n", "31 24\n", "32 90\n", "33 30\n", "34 97\n", "35 219\n", "36 283\n", "37 406\n", "38 706\n", "39 2566\n", "40 1306\n", "41 426\n", "42 277\n", "43 63\n", "44 8\n", "45 2\n", "46 1\n", "47 2\n", "dtype: int64" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(preg8.prglngth >= 27)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "6615" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "preg8.finalwgt.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "count 6670.000000\n", "mean 11173.091142\n", "std 12939.068547\n", "min 1714.541000\n", "25% 3985.078597\n", "50% 6834.593716\n", "75% 12984.239442\n", "max 85207.950000\n", "Name: finalwgt, dtype: float64" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "#resp8 = marriage.ReadFemResp2013()\n", "#marriage.Validate2013(resp8)\n", "#resp8 = resp8[resp8.parity >= 1]\n", "\n", "#resp7 = marriage.ReadFemResp2010()\n", "#marriage.Validate2010(resp7)\n", "#resp7 = resp7[resp7.parity >= 1]\n", "\n", "#resp6 = marriage.ReadFemResp2002()\n", "#marriage.Validate2002(resp6)\n", "#resp6 = resp6[resp6.parity >= 1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "#resps = [resp6, resp7, resp8]\n", "pregs = [preg6, preg7, preg8]\n", "\n", "for preg in pregs:\n", " print(len(preg))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "9148\n", "14292\n", "6670\n" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "def SummarizeCycle(df):\n", " ages = df.age.min(), df.age.max()\n", " ages= np.array(ages)\n", " \n", " intvws = df.cmintvw.min(), df.cmintvw.max()\n", " intvws = np.array(intvws) / 12 + 1900\n", " \n", " births = df.cmbirth.min(), df.cmbirth.max()\n", " births = np.array(births) / 12 + 1900\n", "\n", " print('# & ', intvws.astype(int), '&', len(df), '&', births.astype(int), r'\\\\')\n", " \n", "#for resp in reversed(resps):\n", "# SummarizeCycle(resp)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "def ResampleAndSelect(resp, preg):\n", " sample = thinkstats2.ResampleRowsWeighted(resp, column='finalwgt')\n", " print('1')\n", " \n", " dfs = [preg[preg.caseid == caseid] for caseid in sample.caseid]\n", " print('2')\n", " \n", " rows = pd.concat(dfs, ignore_index=True)\n", " print('3')\n", "\n", " return rows\n", "\n", "#%time sample6 = ResampleAndSelect(resp6, preg6)\n", "#sample6.shape" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "#grouped6 = preg6.groupby('caseid')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "def ResampleAndSelect(resp, grouped):\n", " sample = thinkstats2.ResampleRowsWeighted(resp, column='finalwgt')\n", " print('1')\n", "\n", " dfs = [grouped.get_group(caseid) for caseid in sample.caseid]\n", " print('2', len(dfs))\n", "\n", " rows = pd.concat(dfs, ignore_index=True)\n", " print('3')\n", " return rows\n", "\n", "#%time sample6 = ResampleAndSelect(resp6, grouped6)\n", "#sample6.shape" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "def ResamplePreg(preg):\n", " sample = thinkstats2.ResampleRowsWeighted(preg, column='finalwgt')\n", " return sample\n", "\n", "#%time sample7 = ResamplePreg(preg7)\n", "#ample7.shape" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "def Resample(pregs):\n", " samples = [thinkstats2.ResampleRowsWeighted(preg, column='finalwgt') \n", " for preg in pregs]\n", " sample = pd.concat(samples)\n", " return sample\n", "\n", "sample = Resample(pregs)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "firsts = sample[sample.birthord == 1]\n", "firsts.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "(13864, 6)" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "others = sample[sample.birthord > 1]\n", "others.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "(16246, 6)" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(firsts.prglngth.isnull())" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "0" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(others.prglngth.isnull())" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "0" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "firsts.prglngth.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "0 7\n", "17 5\n", "20 1\n", "21 4\n", "22 14\n", "23 1\n", "24 13\n", "25 17\n", "26 51\n", "27 12\n", "28 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len(others.prglngth)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "(13864, 16246)" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "firsts.prglngth.mean(), others.prglngth.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "(38.6130986728217, 38.518342976732733)" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "diff = firsts.prglngth.mean() - others.prglngth.mean()\n", "diff, diff * 7, diff * 7 * 24" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "(0.094755696088967056, 0.66328987262276939, 15.918956942946465)" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "firsts.prglngth.std(), others.prglngth.std()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "(2.8703583448993912, 2.3963914827683821)" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "se1 = firsts.prglngth.std() / math.sqrt(len(firsts.prglngth))\n", "se1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "0.024377650370258221" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "se2 = others.prglngth.std() / math.sqrt(len(others.prglngth))\n", "se2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "0.01880115556593805" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "diff / se1, diff / se2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "(3.8869905282000872, 5.0398868174164484)" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "cdf_firsts = thinkstats2.Cdf(firsts.prglngth, label='firsts')\n", "\n", "thinkplot.PrePlot(2)\n", "thinkplot.Cdf(cdf_firsts)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc='upper left')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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GgtSCPAtQXwwEqYk5HqCBMBCkBre93T+eBajAy06lBudYgAaLZwhSg6t0ptHJk/auUo3U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\nkiRJkiRJkiSp+f0/5LZh8yAbqmEAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "cdf_others = thinkstats2.Cdf(others.prglngth, label='others')\n", "\n", "thinkplot.Cdf(cdf_others)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc='upper left')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "jitter = 0.5\n", "\n", "length_firsts = thinkstats2.Jitter(firsts.prglngth, jitter)\n", "cdf_firsts = thinkstats2.Cdf(length_firsts, label='firsts')\n", "\n", "length_others = thinkstats2.Jitter(others.prglngth, jitter)\n", "cdf_others = thinkstats2.Cdf(length_others, label='others')\n", "\n", "thinkplot.PrePlot(2)\n", "thinkplot.Cdf(cdf_firsts)\n", "thinkplot.Cdf(cdf_others)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " xlim=[30, 45],\n", " ylabel='CDF',\n", " loc='upper left')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "def RemainingDurationPmf(pmf, weeks):\n", " \"\"\"Returns PMF of remaining duration conditioned on weeks.\n", " \n", " pmf: PMF of pregnancy length\n", " weeks: current weeks of pregnancy\n", " \"\"\"\n", " new = thinkstats2.Pmf(label=pmf.label)\n", " for x, p in pmf.Items():\n", " if x >= weeks:\n", " new[x - weeks] = p\n", " new.Normalize()\n", " return new" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "pmf_firsts = thinkstats2.Pmf(length_firsts, label='firsts')\n", "pmf_others = thinkstats2.Pmf(length_others, label='others')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "def PlotRemaining(pmfs, weeks):\n", " \"\"\"Plot CDF of remaining weeks.\n", " \n", " pmfs: list of PMF\n", " weeks: current weeks of pregnancy\n", " \"\"\"\n", " cdfs = [RemainingDurationPmf(pmf, weeks).MakeCdf()\n", " for pmf in pmfs]\n", "\n", " thinkplot.PrePlot(len(cdfs))\n", " thinkplot.Cdfs(cdfs)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "loc = 'lower right'\n", "PlotRemaining([pmf_firsts, pmf_others], 30)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotRemaining([pmf_firsts, pmf_others], 32)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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S2kAN6Ih1k1EhcDDqvMq6rS+rgXUxjUhO2OE2L8/vrsXvD9LS6iW/oRMHcNXn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\nhGXAGsyOZYBbgBDdO5Z/A5R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"text": [ "" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotRemaining([pmf_firsts, pmf_others], 34)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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OwFDTK782UZ3JInJMPZEQLgC2AjuAW9u5/xrgc+AL4CNgWg/E1Oc0uL08tK4Y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\n1orCljcktF/kKS4umrOzU7l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"text": [ "" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotRemaining([pmf_firsts, pmf_others], 36)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotRemaining([pmf_firsts, pmf_others], 38)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotRemaining([pmf_firsts, pmf_others], 39)\n", "thinkplot.Config(xlabel='pregnancy length (weeks)',\n", " ylabel='CDF',\n", " loc=loc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Q9pzxF7TLjLNTbyl3XTBE4w9ERDoTVQlh40n/lJkZbn/towljc1pqeoiISAei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\n8y9a/Q1YEO4ggqwAeAOYR/T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"text": [ "" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "def PercentilesRemaining(pmfs, weeks):\n", " cdfs = [RemainingDurationPmf(pmf, weeks).MakeCdf()\n", " for pmf in pmfs]\n", " \n", " for cdf in cdfs:\n", " print([cdf.Percentile(p) * 7 for p in [25, 50, 75]])\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "PercentilesRemaining([pmf_firsts, pmf_others], 39)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2.7143548802028761, 6.4367781041855068, 13.206703169606556]\n", "[2.1736408667971858, 4.9317939465359615, 9.3739933152324184]\n" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "percentiles = [50, 75, 25]\n", "\n", "def PercentilesRemaining(pmf, ts):\n", " array = np.zeros((len(ts), len(percentiles)))\n", " \n", " for i, t in enumerate(ts):\n", " cdf = RemainingDurationPmf(pmf, t).MakeCdf()\n", " ps = [cdf.Percentile(p) * 7 for p in percentiles]\n", " #print(t, ps)\n", " array[i, :] = ps\n", " \n", " return array" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "ts = np.arange(36, 44)\n", "ps_firsts = PercentilesRemaining(pmf_firsts, ts)\n", "ps_firsts" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "array([[ 22.64578082, 29.14642029, 17.95618349],\n", " [ 16.10550278, 22.63422464, 11.78280693],\n", " [ 9.97194385, 16.53705261, 5.9922938 ],\n", " [ 6.4367781 , 13.20670317, 2.71435488],\n", " [ 6.76981776, 12.44368589, 2.86739126],\n", " [ 5.59852858, 10.38567513, 2.37625612],\n", " [ 4.38732448, 7.85479168, 2.06292504],\n", " [ 2.57863214, 6.10710894, 1.19931778]])" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "ps_others = PercentilesRemaining(pmf_others, ts)\n", "ps_others" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "array([[ 21.60271001, 26.40089751, 17.18171224],\n", " [ 15.00369008, 19.79528516, 11.06307321],\n", " [ 8.87411349, 13.48416292, 5.44552693],\n", " [ 4.93179395, 9.37399332, 2.17364087],\n", " [ 4.65239838, 9.90509012, 1.87870862],\n", " [ 5.5198786 , 9.65339663, 2.22971555],\n", " [ 4.32478024, 7.90697449, 1.72204552],\n", " [ 4.01518618, 7.66307581, 1.71902067]])" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "nrows, ncols = ps_firsts.shape\n", "\n", "thinkplot.PrePlot(3)\n", "for i in range(ncols):\n", " thinkplot.Plot(ts, ps_firsts[:, i], label=percentiles[i])\n", "\n", "thinkplot.PrePlot(3)\n", "for i in range(ncols):\n", " thinkplot.Plot(ts, ps_others[:, i], linestyle='dashed')\n", " \n", "thinkplot.Config(xlabel='t (weeks)',\n", " ylabel='remaining time (days)',\n", " legend=True,\n", " loc='upper right')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SJDFr1izS09PZvHkzdnZ29V7XmPaSF9OhfAJfrN3K6m92GAdGDevFsufvxtraPM1janMs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\nk5HqNPGMVfk5fGXTauxm0e9Us+EKh53Lq1ML4lxQn+eiPs814/5nFub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"text": [ "" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "sample = Resample(pregs)\n", "sample.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "(30110, 6)" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "firsts = sample[sample.birthord == 1] \n", "others = sample[sample.birthord > 1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "jitter = 0.5\n", "lengths = thinkstats2.Jitter(firsts.prglngth, jitter)\n", "pmf_firsts = thinkstats2.Pmf(lengths, label='firsts')\n", "\n", "lengths = thinkstats2.Jitter(others.prglngth, jitter)\n", "pmf_others = thinkstats2.Pmf(lengths, label='others')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "def SelectFirsts(preg):\n", " return preg[preg.birthord == 1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "def SelectOthers(preg):\n", " return preg[preg.birthord > 1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "def MeanRemaining(pmf, t):\n", " rem_pmf = RemainingDurationPmf(pmf, t)\n", " mean = rem_pmf.Mean()\n", " return mean\n", "\n", "MeanRemaining(pmf_firsts, 36)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "3.4346722690706315" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "def MedianRemaining(pmf, t):\n", " rem_pmf = RemainingDurationPmf(pmf, t)\n", " rem_cdf = rem_pmf.MakeCdf()\n", " median = rem_cdf.Percentile(50)\n", " return median\n", "\n", "MedianRemaining(pmf_firsts, 36)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "3.2662877239065011" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "def ProbRemainingLess(pmf, t, weeks=1):\n", " rem_pmf = RemainingDurationPmf(pmf, t)\n", " rem_cdf = rem_pmf.MakeCdf()\n", " p = rem_cdf[weeks]\n", " return p\n", "\n", "ProbRemainingLess(pmf_firsts, 36)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ "0.048448823385729728" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "def Remaining(preg, iters, ts, select_func, stat_func):\n", " \"\"\"Computes remaining lifetime versus weeks of pregnancy.\n", " \n", " preg: DataFrame of pregnancy data\n", " iters: int, how many resampling iterations to run\n", " ts: sequence of float, times to evaluate, in weeks\n", " select_func: function that selects a subset of pregnancies\n", " stat_func: function that computes summary statistic\n", " \n", " returns: array with one row for each percentile, one column\n", " for each item in ts\n", " \"\"\"\n", " # make an array to hold one row per iteration,\n", " # one col per element of ts\n", " array = np.zeros((iters, len(ts)))\n", "\n", " for i in range(iters):\n", " sample = Resample(pregs)\n", " group = select_func(sample)\n", "\n", " lengths = thinkstats2.Jitter(group.prglngth, jitter=0.5)\n", " pmf = thinkstats2.Pmf(lengths)\n", "\n", " stats = [stat_func(pmf, t) for t in ts]\n", " array[i, :] = stats\n", "\n", " array = np.sort(array, axis=0)\n", " rows = [thinkstats2.PercentileRow(array, p) for p in [10, 50, 90]]\n", " return rows" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "def PlotStats(stat_func, iters, ts):\n", " \"\"\"Plots a summary statistic versus weeks of pregnancy.\n", " \n", " stat_func: function that computes summary statistic\n", " iters: int, how many resampling iterations to run\n", " ts: sequence of float, times to evaluate, in weeks\n", " \"\"\"\n", " # list of labels and select_funcs\n", " labels = [('firsts', SelectFirsts), \n", " ('others', SelectOthers)]\n", "\n", " thinkplot.PrePlot(2)\n", " for label, select_func in labels:\n", " rows = Remaining(pmf_firsts, iters, ts, select_func, stat_func)\n", " thinkplot.FillBetween(ts, rows[0], rows[2], color='gray')\n", " thinkplot.Plot(ts, rows[1], label=label)\n", " print(label)\n", " for t, stat in zip(ts, rows[1]):\n", " print(t, stat)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "iters = 101\n", "ts = np.arange(36, 43.5, 0.5)\n", "PlotStats(MeanRemaining, iters, ts)\n", "\n", "thinkplot.Config(xlabel='week of pregnancy',\n", " ylabel='remaining time (weeks)',\n", " legend=True,\n", " loc='upper right')\n", "\n", "thinkplot.Save(root='first1', formats=['pdf', 'png'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "firsts\n", "36.0 3.39754156441\n", "36.5 2.97508980334\n", "37.0 2.55456121193\n", "37.5 2.13886585341\n", "38.0 1.74537235835\n", "38.5 1.42681323973\n", "39.0 1.26628280514\n", "39.5 1.23078085688\n", "40.0 1.17889460956\n", "40.5 1.09181678181\n", "41.0 0.980629908801\n", "41.5 0.862167177764\n", "42.0 0.751218876485\n", "42.5 0.68828895529\n", "43.0 0.629491972579\n", "others" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "36.0 3.13540208072\n", "36.5 2.70930346011\n", "37.0 2.29039746926\n", "37.5 1.88055601698\n", "38.0 1.49280931068\n", "38.5 1.17409914359\n", "39.0 1.0078923987\n", "39.5 0.985300112973\n", "40.0 0.998127077118\n", "40.5 1.01971112502\n", "41.0 1.01329044103\n", "41.5 0.95862350461\n", "42.0 0.911055084446\n", "42.5 0.911300869622\n", "43.0 0.98479776888\n", "Writing first1.pdf\n", "Writing" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " first1.png\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotStats(MedianRemaining, iters, ts)\n", "\n", "thinkplot.Config(xlabel='t (weeks)',\n", " ylabel='week of pregnancy',\n", " legend=True,\n", " loc='upper right')\n", "\n", "thinkplot.Save(root='first2', formats=['pdf', 'png'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "firsts\n", "36.0 3.241368552\n", "36.5 2.77492320164\n", "37.0 2.31279655389\n", "37.5 1.86185466683\n", "38.0 1.43469000445\n", "38.5 1.08499873418\n", "39.0 0.928918281925\n", "39.5 0.942092187176\n", "40.0 0.944034901894\n", "40.5 0.893216546978\n", "41.0 0.794109392844\n", "41.5 0.680121224128\n", "42.0 0.569214418639\n", "42.5 0.506499558605\n", "43.0 0.443257768096\n", "others" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "36.0 3.08268446578\n", "36.5 2.61050684262\n", "37.0 2.14660335686\n", "37.5 1.69113799843\n", "38.0 1.26087655638\n", "38.5 0.90146729939\n", "39.0 0.707699287694\n", "39.5 0.669730616568\n", "40.0 0.675815864934\n", "40.5 0.735595662149\n", "41.0 0.753391859416\n", "41.5 0.699055415717\n", "42.0 0.623727458023\n", "42.5 0.565583628248\n", "43.0 0.515788745635\n", "Writing first2.pdf\n", "Writing" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " first2.png\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "PlotStats(ProbRemainingLess, iters, ts)\n", "\n", "thinkplot.Config(xlabel='week of pregnancy',\n", " ylabel='prob(birth within 1 week)',\n", " legend=True,\n", " loc='upper left')\n", "\n", "thinkplot.Save(root='first3', formats=['pdf', 'png'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "firsts\n", "36.0 0.0518651832461\n", "36.5 0.0631412127784\n", "37.0 0.087215320911\n", "37.5 0.157749488115\n", "38.0 0.311731315043\n", "38.5 0.466408539138\n", "39.0 0.524979746152\n", "39.5 0.521584861029\n", "40.0 0.519954389966\n", "40.5 0.54363256785\n", "41.0 0.592375366569\n", "41.5 0.665464382326\n", "42.0 0.723489932886\n", "42.5 0.774373259053\n", "43.0 0.815533980583\n", "others" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "36.0 0.0570483391844\n", "36.5 0.0735008328706\n", "37.0 0.10406256789\n", "37.5 0.18672074508\n", "38.0 0.364137483787\n", "38.5 0.547933884298\n", "39.0 0.631398013751\n", "39.5 0.644706857261\n", "40.0 0.631614654003\n", "40.5 0.607536231884\n", "41.0 0.608486017358\n", "41.5 0.648367952522\n", "42.0 0.687192118227\n", "42.5 0.721030042918\n", "43.0 0.739837398374\n", "Writing first3.pdf\n", "Writing" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " first3.png\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }