{ "metadata": { "name": "", "signature": "sha256:fcc08872e4b9f1a41b365e08b4c17e1c2ce46cfa35d8a4c4c5b7a5b50285e0b7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function, division\n", "\n", "import survival\n", "import thinkstats2\n", "import thinkplot\n", "\n", "import gzip\n", "import pandas\n", "import numpy as np\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "filename = '1988FemRespData.dat'\n", "fin = open(filename, 'r')\n", "line = fin.read(3553)\n", "print(line)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: '1988FemRespData.dat'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'1988FemRespData.dat'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3553\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: '1988FemRespData.dat'" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def add_newlines():\n", " filename = '1988FemRespData.dat'\n", " fin = open(filename, 'r')\n", " fout = open('1988FemRespDataLines.dat', 'w')\n", "\n", " for i in range(8450):\n", " line = fin.read(3553)\n", " fout.write(line + '\\n')\n", " \n", " fout.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "filename = '1988FemRespDataLines.dat.gz'\n", "fp = gzip.open(filename, 'r')\n", "\n", "s = '0123456789'\n", "print(s*8)\n", "\n", "for i, line in enumerate(fp):\n", " print(line)\n", " if i > 0:\n", " break" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01234567890123456789012345678901234567890123456789012345678901234567890123456789\n", "0000100 201061 6 00723 0232821111 1 1 1 1 161 00000 0000011 01044 2 1 213318 11 0000000000 2 000022 2 0000000000 0000000000 0000000000 2 111111111111111195060207241200000 00000 00000000000000000000000000000000 00000 00000 00000 00000 00000 00000 00000 00000 00000 01061222 00000 00000 00000 00000 00000 1 00000 2 2 1021 969602 2 01103536 00000 00000 00000 00000 2 0000000000 2222 2 21000003000000 2 51000003000000 51000200040506000000152 222222 1 2048180125 440004 2 000200000000000000000000000000002 5 00000 00000 00000 00000 0000000000000000000000000 0000000000000000000000000 00000000000000000000 00000 00000 00000 00000 00000 1010000000000070020600163017452210104810000055061200 1 11222222210606011999999999999999999999999999999 01061 628279931161 2600000113181100000000000000000 00002 30 00 42 5 6 9696 0201 33333 2 1 51 5100020004050600000015 00 1 62114701312 200 2812 2 2 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= ['finalwgt', 'ageint', 'currentcm', 'firstcm', 'cmintvw', 'cmbirth']\n", "colspecs = [(2568-1, 2574),\n", " (36-1, 37),\n", " (1521-1, 1525),\n", " (1538-1, 1542),\n", " (12-1, 16),\n", " (26-1, 30),\n", " ]\n", "df = pandas.read_fwf(filename,\n", " colspecs=colspecs, \n", " names=names,\n", " header=None,\n", " compression='gzip')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "len(df) # should be 8450" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "8450" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "df.ageint.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "14 3\n", "15 210\n", "16 240\n", "17 263\n", "18 258\n", "19 260\n", "20 259\n", "21 238\n", "22 280\n", "23 240\n", "24 292\n", "25 308\n", "26 322\n", "27 330\n", "28 292\n", "29 355\n", "30 342\n", "31 335\n", "32 370\n", "33 328\n", "34 314\n", "35 296\n", "36 305\n", "37 314\n", "38 251\n", "39 270\n", "40 263\n", "41 257\n", "42 247\n", "43 194\n", "44 197\n", "45 17\n", "dtype: int64" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "df.currentcm.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "0 4419\n", "685 1\n", "705 1\n", "722 1\n", "723 1\n", "726 2\n", "728 1\n", "729 2\n", "730 3\n", "731 2\n", "732 1\n", "734 1\n", "735 1\n", "736 1\n", "738 3\n", "...\n", "1056 26\n", "1057 14\n", "1058 11\n", "1059 6\n", "1060 6\n", "1061 3\n", "1062 1\n", "90781 1\n", "90834 1\n", "90978 1\n", "90990 1\n", "91018 1\n", "91026 1\n", "91038 2\n", "99999 16\n", "Length: 344, dtype: int64" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "df.currentcm.replace([0, 99999], np.nan, inplace=True)\n", "df.loc[df.currentcm>90000, 'currentcm'] -= 90000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "df.firstcm.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "0 6452\n", "706 1\n", "708 2\n", "710 2\n", "711 1\n", "712 1\n", "714 2\n", "720 2\n", "722 3\n", "724 1\n", "726 5\n", "727 1\n", "728 1\n", "729 1\n", "730 3\n", "...\n", "90886 1\n", "90894 6\n", "90901 1\n", "90906 1\n", "90907 1\n", "90918 2\n", "90922 1\n", "90930 1\n", "90942 1\n", "90943 1\n", "90978 2\n", "90979 1\n", "90990 1\n", "91002 1\n", "99999 19\n", "Length: 355, dtype: int64" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "df.firstcm.replace([0, 99999], np.nan, inplace=True)\n", "df.loc[df.firstcm>90000, 'firstcm'] -= 90000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "df['cmmarrhx'] = df.currentcm\n", "df.cmmarrhx.fillna(df.firstcm)\n", "sum(df.cmmarrhx.isnull())" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "4435" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "df.cmintvw.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "1057 545\n", "1058 2034\n", "1059 2288\n", "1060 1701\n", "1061 874\n", "1062 682\n", "1063 191\n", "1064 135\n", "dtype: int64" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "df.cmbirth.value_counts().sort_index()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "519 10\n", "520 14\n", "521 20\n", "522 17\n", "523 17\n", "524 18\n", "525 16\n", "526 20\n", "527 13\n", "528 16\n", "529 25\n", "530 14\n", "531 19\n", "532 11\n", "533 16\n", "...\n", "865 31\n", "866 13\n", "867 15\n", "868 15\n", "869 22\n", "870 18\n", "871 23\n", "872 20\n", "873 28\n", "874 15\n", "875 17\n", "876 22\n", "877 22\n", "878 11\n", "879 7\n", "Length: 361, dtype: int64" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "survival.CleanData(df)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "df['evrmarry'] = ~df.cmmarrhx.isnull()\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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finalwgtageintcurrentcmfirstcmcmintvwcmbirthcmmarrhxagemarryagedecadefivesevrmarry
0 713792 28 NaN NaN 1061 723 NaN NaN 28.166667 6 12 False
1 367022 37 NaN NaN 1059 614 NaN NaN 37.083333 5 10 False
2 975924 21 NaN NaN 1057 796 NaN NaN 21.750000 6 13 False
3 587796 39 NaN 838 1057 581 NaN NaN 39.666667 4 9 False
4 719633 31 974 882 1062 683 974 24.250000 31.583333 5 11 True
5 730622 17 NaN NaN 1060 844 NaN NaN 18.000000 7 14 False
6 608474 39 848 809 1057 578 848 22.500000 39.916667 4 9 True
7 777787 30 NaN 924 1058 696 NaN NaN 30.166667 5 11 False
8 1030290 22 NaN NaN 1059 791 NaN NaN 22.333333 6 13 False
9 639364 18 NaN NaN 1059 836 NaN NaN 18.583333 6 13 False
10 820707 34 947 NaN 1058 642 947 25.416667 34.666667 5 10 True
11 766942 29 NaN 957 1058 699 NaN NaN 29.916667 5 11 False
12 712940 30 960 NaN 1061 696 960 22.000000 30.416667 5 11 True
13 748807 38 848 NaN 1060 595 848 21.083333 38.750000 4 9 True
14 682754 33 NaN 937 1058 652 NaN NaN 33.833333 5 10 False
15 773938 38 NaN 894 1057 592 NaN NaN 38.750000 4 9 False
16 787966 33 NaN NaN 1060 659 NaN NaN 33.416667 5 10 False
17 773938 35 942 NaN 1060 629 942 26.083333 35.916667 5 10 True
18 642272 30 977 NaN 1060 688 977 24.083333 31.000000 5 11 True
19 733060 30 918 NaN 1060 693 918 18.750000 30.583333 5 11 True
20 734133 37 835 NaN 1060 610 835 18.750000 37.500000 5 10 True
21 745297 25 1031 NaN 1060 754 1031 23.083333 25.500000 6 12 True
22 799872 35 1039 870 1060 635 1039 33.666667 35.416667 5 10 True
23 807851 20 NaN NaN 1060 811 NaN NaN 20.750000 6 13 False
24 807851 20 NaN NaN 1060 809 NaN NaN 20.916667 6 13 False
25 929923 43 824 NaN 1060 532 824 24.333333 44.000000 4 8 True
26 705780 35 846 NaN 1060 629 846 18.083333 35.916667 5 10 True
27 724044 38 945 848 1060 596 945 29.083333 38.666667 4 9 True
28 690662 33 875 NaN 1060 655 875 18.333333 33.750000 5 10 True
29 2183253 15 NaN NaN 1058 866 NaN NaN 16.000000 7 14 False
.......................................
8420 2036322 39 NaN 825 1058 585 NaN NaN 39.416667 4 9 False
8421 2478173 30 992 NaN 1059 693 992 24.916667 30.500000 5 11 True
8422 2174687 29 NaN 946 1058 701 NaN NaN 29.750000 5 11 False
8423 2301277 40 821 NaN 1058 570 821 20.916667 40.666667 4 9 True
8424 2594495 25 NaN NaN 1059 756 NaN NaN 25.250000 6 12 False
8425 1963379 43 765 NaN 1058 536 765 19.083333 43.500000 4 8 True
8426 2276609 34 936 NaN 1058 646 936 24.166667 34.333333 5 10 True
8427 2056998 37 896 NaN 1058 604 896 24.333333 37.833333 5 10 True
8428 2592015 24 NaN NaN 1058 763 NaN NaN 24.583333 6 12 False
8429 2486216 36 961 NaN 1059 619 961 28.500000 36.666667 5 10 True
8430 2624510 32 NaN NaN 1058 668 NaN NaN 32.500000 5 11 False
8431 2460840 25 1052 NaN 1060 752 1052 25.000000 25.666667 6 12 True
8432 2384200 25 NaN NaN 1058 748 NaN NaN 25.833333 6 12 False
8433 2384200 27 NaN NaN 1058 723 NaN NaN 27.916667 6 12 False
8434 2126824 23 1044 NaN 1058 779 1044 22.083333 23.250000 6 12 True
8435 1925357 27 1044 NaN 1059 728 1044 26.333333 27.583333 6 12 True
8436 2105497 26 NaN NaN 1058 745 NaN NaN 26.083333 6 12 False
8437 2518126 23 NaN NaN 1058 776 NaN NaN 23.500000 6 12 False
8438 2384200 26 NaN NaN 1059 746 NaN NaN 26.083333 6 12 False
8439 2518126 23 NaN NaN 1059 772 NaN NaN 23.916667 6 12 False
8440 2549695 33 NaN NaN 1059 656 NaN NaN 33.583333 5 10 False
8441 2518126 24 NaN NaN 1058 764 NaN NaN 24.500000 6 12 False
8442 645391 31 929 NaN 1059 679 929 20.833333 31.666667 5 11 True
8443 2986139 26 997 NaN 1058 740 997 21.416667 26.500000 6 12 True
8444 2092079 34 978 NaN 1058 642 978 28.000000 34.666667 5 10 True
8445 2251351 26 NaN NaN 1059 740 NaN NaN 26.583333 6 12 False
8446 2251351 26 NaN NaN 1058 736 NaN NaN 26.833333 6 12 False
8447 2384200 26 NaN NaN 1058 741 NaN NaN 26.416667 6 12 False
8448 1469892 38 931 839 1063 606 931 27.083333 38.083333 5 10 True
8449 2620612 30 1014 NaN 1063 693 1014 26.750000 30.833333 5 11 True
\n", "

8450 rows \u00d7 12 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ " finalwgt ageint currentcm firstcm cmintvw cmbirth cmmarrhx \\\n", "0 713792 28 NaN NaN 1061 723 NaN \n", "1 367022 37 NaN NaN 1059 614 NaN \n", "2 975924 21 NaN NaN 1057 796 NaN \n", "3 587796 39 NaN 838 1057 581 NaN \n", "4 719633 31 974 882 1062 683 974 \n", "5 730622 17 NaN NaN 1060 844 NaN \n", "6 608474 39 848 809 1057 578 848 \n", "7 777787 30 NaN 924 1058 696 NaN \n", "8 1030290 22 NaN NaN 1059 791 NaN \n", "9 639364 18 NaN NaN 1059 836 NaN \n", "10 820707 34 947 NaN 1058 642 947 \n", "11 766942 29 NaN 957 1058 699 NaN \n", "12 712940 30 960 NaN 1061 696 960 \n", "13 748807 38 848 NaN 1060 595 848 \n", "14 682754 33 NaN 937 1058 652 NaN \n", "15 773938 38 NaN 894 1057 592 NaN \n", "16 787966 33 NaN NaN 1060 659 NaN \n", "17 773938 35 942 NaN 1060 629 942 \n", "18 642272 30 977 NaN 1060 688 977 \n", "19 733060 30 918 NaN 1060 693 918 \n", "20 734133 37 835 NaN 1060 610 835 \n", "21 745297 25 1031 NaN 1060 754 1031 \n", "22 799872 35 1039 870 1060 635 1039 \n", "23 807851 20 NaN NaN 1060 811 NaN \n", "24 807851 20 NaN NaN 1060 809 NaN \n", "25 929923 43 824 NaN 1060 532 824 \n", "26 705780 35 846 NaN 1060 629 846 \n", "27 724044 38 945 848 1060 596 945 \n", "28 690662 33 875 NaN 1060 655 875 \n", "29 2183253 15 NaN NaN 1058 866 NaN \n", "... ... ... ... ... ... ... ... \n", "8420 2036322 39 NaN 825 1058 585 NaN \n", "8421 2478173 30 992 NaN 1059 693 992 \n", "8422 2174687 29 NaN 946 1058 701 NaN \n", "8423 2301277 40 821 NaN 1058 570 821 \n", "8424 2594495 25 NaN NaN 1059 756 NaN \n", "8425 1963379 43 765 NaN 1058 536 765 \n", "8426 2276609 34 936 NaN 1058 646 936 \n", "8427 2056998 37 896 NaN 1058 604 896 \n", "8428 2592015 24 NaN NaN 1058 763 NaN \n", "8429 2486216 36 961 NaN 1059 619 961 \n", "8430 2624510 32 NaN NaN 1058 668 NaN \n", "8431 2460840 25 1052 NaN 1060 752 1052 \n", "8432 2384200 25 NaN NaN 1058 748 NaN \n", "8433 2384200 27 NaN NaN 1058 723 NaN \n", "8434 2126824 23 1044 NaN 1058 779 1044 \n", "8435 1925357 27 1044 NaN 1059 728 1044 \n", "8436 2105497 26 NaN NaN 1058 745 NaN \n", "8437 2518126 23 NaN NaN 1058 776 NaN \n", "8438 2384200 26 NaN NaN 1059 746 NaN \n", "8439 2518126 23 NaN NaN 1059 772 NaN \n", "8440 2549695 33 NaN NaN 1059 656 NaN \n", "8441 2518126 24 NaN NaN 1058 764 NaN \n", "8442 645391 31 929 NaN 1059 679 929 \n", "8443 2986139 26 997 NaN 1058 740 997 \n", "8444 2092079 34 978 NaN 1058 642 978 \n", "8445 2251351 26 NaN NaN 1059 740 NaN \n", "8446 2251351 26 NaN NaN 1058 736 NaN \n", "8447 2384200 26 NaN NaN 1058 741 NaN \n", "8448 1469892 38 931 839 1063 606 931 \n", "8449 2620612 30 1014 NaN 1063 693 1014 \n", "\n", " agemarry age decade fives evrmarry \n", "0 NaN 28.166667 6 12 False \n", "1 NaN 37.083333 5 10 False \n", "2 NaN 21.750000 6 13 False \n", "3 NaN 39.666667 4 9 False \n", "4 24.250000 31.583333 5 11 True \n", "5 NaN 18.000000 7 14 False \n", "6 22.500000 39.916667 4 9 True \n", "7 NaN 30.166667 5 11 False \n", "8 NaN 22.333333 6 13 False \n", "9 NaN 18.583333 6 13 False \n", "10 25.416667 34.666667 5 10 True \n", "11 NaN 29.916667 5 11 False \n", "12 22.000000 30.416667 5 11 True \n", "13 21.083333 38.750000 4 9 True \n", "14 NaN 33.833333 5 10 False \n", "15 NaN 38.750000 4 9 False \n", "16 NaN 33.416667 5 10 False \n", "17 26.083333 35.916667 5 10 True \n", "18 24.083333 31.000000 5 11 True \n", "19 18.750000 30.583333 5 11 True \n", "20 18.750000 37.500000 5 10 True \n", "21 23.083333 25.500000 6 12 True \n", "22 33.666667 35.416667 5 10 True \n", "23 NaN 20.750000 6 13 False \n", "24 NaN 20.916667 6 13 False \n", "25 24.333333 44.000000 4 8 True \n", "26 18.083333 35.916667 5 10 True \n", "27 29.083333 38.666667 4 9 True \n", "28 18.333333 33.750000 5 10 True \n", "29 NaN 16.000000 7 14 False \n", "... ... ... ... ... ... \n", "8420 NaN 39.416667 4 9 False \n", "8421 24.916667 30.500000 5 11 True \n", "8422 NaN 29.750000 5 11 False \n", "8423 20.916667 40.666667 4 9 True \n", "8424 NaN 25.250000 6 12 False \n", "8425 19.083333 43.500000 4 8 True \n", "8426 24.166667 34.333333 5 10 True \n", "8427 24.333333 37.833333 5 10 True \n", "8428 NaN 24.583333 6 12 False \n", "8429 28.500000 36.666667 5 10 True \n", "8430 NaN 32.500000 5 11 False \n", "8431 25.000000 25.666667 6 12 True \n", "8432 NaN 25.833333 6 12 False \n", "8433 NaN 27.916667 6 12 False \n", "8434 22.083333 23.250000 6 12 True \n", "8435 26.333333 27.583333 6 12 True \n", "8436 NaN 26.083333 6 12 False \n", "8437 NaN 23.500000 6 12 False \n", "8438 NaN 26.083333 6 12 False \n", "8439 NaN 23.916667 6 12 False \n", "8440 NaN 33.583333 5 10 False \n", "8441 NaN 24.500000 6 12 False \n", "8442 20.833333 31.666667 5 11 True \n", "8443 21.416667 26.500000 6 12 True \n", "8444 28.000000 34.666667 5 10 True \n", "8445 NaN 26.583333 6 12 False \n", "8446 NaN 26.833333 6 12 False \n", "8447 NaN 26.416667 6 12 False \n", "8448 27.083333 38.083333 5 10 True \n", "8449 26.750000 30.833333 5 11 True \n", "\n", "[8450 rows x 12 columns]" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "cdf = thinkstats2.Cdf(df.age - df.ageint)\n", "thinkplot.Cdf(cdf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "{'xscale': 'linear', 'yscale': 'linear'}" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "cdf = thinkstats2.Cdf(df.agemarry)\n", "thinkplot.Cdf(cdf)\n", "len(df.agemarry.dropna())" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "4015" ] }, { "metadata": {}, "output_type": "display_data", "png": 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