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"metadata": { + "id": "aurav5W7X32x", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4izYwo5jYUp-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "np.random.seed(10)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZcHnID6LYab0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "N=30\n", + "x = np.random.rand(N)\n", + "y = np.random.rand(N)\n", + "colors= np.random.rand(N)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "JHKDJeSKY5qE", + "colab_type": "code", + "colab": {} + }, + "source": [ + "area =(30 * np.random.rand(N))**2" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vrCBb7UxZUNB", + "colab_type": 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eG2LiyjSp+TT5bAFf0EckHqJ3dze2r6nTgbhNU/82lTJRgY+gfUfQzlXQWVAJ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\nO4IiwAOJDjpyYd5JTVD0iiR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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/2Assignment.ipynb b/2Assignment.ipynb new file mode 100644 index 00000000..862ce52a --- /dev/null +++ b/2Assignment.ipynb @@ -0,0 +1,5974 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "2Assignment.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mBC5p-dbGO4w", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "206f449b-58b3-4d68-fd72-f27ccee030fb" + }, + "source": [ + "!curl \"https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv\"" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + ",survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone\n", + "0,0,3,male,22.0,1,0,7.25,S,Third,man,True,,Southampton,no,False\n", + "1,1,1,female,38.0,1,0,71.2833,C,First,woman,False,C,Cherbourg,yes,False\n", + "2,1,3,female,26.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True\n", + "3,1,1,female,35.0,1,0,53.1,S,First,woman,False,C,Southampton,yes,False\n", + "4,0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "5,0,3,male,,0,0,8.4583,Q,Third,man,True,,Queenstown,no,True\n", + "6,0,1,male,54.0,0,0,51.8625,S,First,man,True,E,Southampton,no,True\n", + "7,0,3,male,2.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n", + "8,1,3,female,27.0,0,2,11.1333,S,Third,woman,False,,Southampton,yes,False\n", + "9,1,2,female,14.0,1,0,30.0708,C,Second,child,False,,Cherbourg,yes,False\n", + "10,1,3,female,4.0,1,1,16.7,S,Third,child,False,G,Southampton,yes,False\n", + "11,1,1,female,58.0,0,0,26.55,S,First,woman,False,C,Southampton,yes,True\n", + "12,0,3,male,20.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "13,0,3,male,39.0,1,5,31.275,S,Third,man,True,,Southampton,no,False\n", + "14,0,3,female,14.0,0,0,7.8542,S,Third,child,False,,Southampton,no,True\n", + "15,1,2,female,55.0,0,0,16.0,S,Second,woman,False,,Southampton,yes,True\n", + "16,0,3,male,2.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n", + "17,1,2,male,,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n", + "18,0,3,female,31.0,1,0,18.0,S,Third,woman,False,,Southampton,no,False\n", + "19,1,3,female,,0,0,7.225,C,Third,woman,False,,Cherbourg,yes,True\n", + "20,0,2,male,35.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n", + "21,1,2,male,34.0,0,0,13.0,S,Second,man,True,D,Southampton,yes,True\n", + "22,1,3,female,15.0,0,0,8.0292,Q,Third,child,False,,Queenstown,yes,True\n", + "23,1,1,male,28.0,0,0,35.5,S,First,man,True,A,Southampton,yes,True\n", + "24,0,3,female,8.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n", + "25,1,3,female,38.0,1,5,31.3875,S,Third,woman,False,,Southampton,yes,False\n", + "26,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "27,0,1,male,19.0,3,2,263.0,S,First,man,True,C,Southampton,no,False\n", + "28,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n", + "29,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "30,0,1,male,40.0,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n", + "31,1,1,female,,1,0,146.5208,C,First,woman,False,B,Cherbourg,yes,False\n", + "32,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "33,0,2,male,66.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "34,0,1,male,28.0,1,0,82.1708,C,First,man,True,,Cherbourg,no,False\n", + "35,0,1,male,42.0,1,0,52.0,S,First,man,True,,Southampton,no,False\n", + "36,1,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True\n", + "37,0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "38,0,3,female,18.0,2,0,18.0,S,Third,woman,False,,Southampton,no,False\n", + "39,1,3,female,14.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False\n", + "40,0,3,female,40.0,1,0,9.475,S,Third,woman,False,,Southampton,no,False\n", + "41,0,2,female,27.0,1,0,21.0,S,Second,woman,False,,Southampton,no,False\n", + "42,0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n", + "43,1,2,female,3.0,1,2,41.5792,C,Second,child,False,,Cherbourg,yes,False\n", + "44,1,3,female,19.0,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n", + "45,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "46,0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False\n", + "47,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + 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"61,1,1,female,38.0,0,0,80.0,,First,woman,False,B,,yes,True\n", + "62,0,1,male,45.0,1,0,83.475,S,First,man,True,C,Southampton,no,False\n", + "63,0,3,male,4.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n", + "64,0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n", + "65,1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False\n", + "66,1,2,female,29.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True\n", + "67,0,3,male,19.0,0,0,8.1583,S,Third,man,True,,Southampton,no,True\n", + "68,1,3,female,17.0,4,2,7.925,S,Third,woman,False,,Southampton,yes,False\n", + "69,0,3,male,26.0,2,0,8.6625,S,Third,man,True,,Southampton,no,False\n", + "70,0,2,male,32.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "71,0,3,female,16.0,5,2,46.9,S,Third,woman,False,,Southampton,no,False\n", + "72,0,2,male,21.0,0,0,73.5,S,Second,man,True,,Southampton,no,True\n", + "73,0,3,male,26.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False\n", + "74,1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n", + "75,0,3,male,25.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n", + "76,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "77,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "78,1,2,male,0.83,0,2,29.0,S,Second,child,False,,Southampton,yes,False\n", + "79,1,3,female,30.0,0,0,12.475,S,Third,woman,False,,Southampton,yes,True\n", + "80,0,3,male,22.0,0,0,9.0,S,Third,man,True,,Southampton,no,True\n", + "81,1,3,male,29.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True\n", + "82,1,3,female,,0,0,7.7875,Q,Third,woman,False,,Queenstown,yes,True\n", + "83,0,1,male,28.0,0,0,47.1,S,First,man,True,,Southampton,no,True\n", + "84,1,2,female,17.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n", + "85,1,3,female,33.0,3,0,15.85,S,Third,woman,False,,Southampton,yes,False\n", + "86,0,3,male,16.0,1,3,34.375,S,Third,man,True,,Southampton,no,False\n", + "87,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "88,1,1,female,23.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False\n", + "89,0,3,male,24.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "90,0,3,male,29.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "91,0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n", + "92,0,1,male,46.0,1,0,61.175,S,First,man,True,E,Southampton,no,False\n", + "93,0,3,male,26.0,1,2,20.575,S,Third,man,True,,Southampton,no,False\n", + "94,0,3,male,59.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "95,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "96,0,1,male,71.0,0,0,34.6542,C,First,man,True,A,Cherbourg,no,True\n", + "97,1,1,male,23.0,0,1,63.3583,C,First,man,True,D,Cherbourg,yes,False\n", + "98,1,2,female,34.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False\n", + "99,0,2,male,34.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n", + "100,0,3,female,28.0,0,0,7.8958,S,Third,woman,False,,Southampton,no,True\n", + "101,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "102,0,1,male,21.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False\n", + "103,0,3,male,33.0,0,0,8.6542,S,Third,man,True,,Southampton,no,True\n", + "104,0,3,male,37.0,2,0,7.925,S,Third,man,True,,Southampton,no,False\n", + "105,0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "106,1,3,female,21.0,0,0,7.65,S,Third,woman,False,,Southampton,yes,True\n", + "107,1,3,male,,0,0,7.775,S,Third,man,True,,Southampton,yes,True\n", + "108,0,3,male,38.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "109,1,3,female,,1,0,24.15,Q,Third,woman,False,,Queenstown,yes,False\n", + "110,0,1,male,47.0,0,0,52.0,S,First,man,True,C,Southampton,no,True\n", + "111,0,3,female,14.5,1,0,14.4542,C,Third,child,False,,Cherbourg,no,False\n", + "112,0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "113,0,3,female,20.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False\n", + "114,0,3,female,17.0,0,0,14.4583,C,Third,woman,False,,Cherbourg,no,True\n", + "115,0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n", + "116,0,3,male,70.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "117,0,2,male,29.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n", + "118,0,1,male,24.0,0,1,247.5208,C,First,man,True,B,Cherbourg,no,False\n", + "119,0,3,female,2.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n", + "120,0,2,male,21.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n", + "121,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "122,0,2,male,32.5,1,0,30.0708,C,Second,man,True,,Cherbourg,no,False\n", + "123,1,2,female,32.5,0,0,13.0,S,Second,woman,False,E,Southampton,yes,True\n", + "124,0,1,male,54.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False\n", + "125,1,3,male,12.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False\n", + "126,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "127,1,3,male,24.0,0,0,7.1417,S,Third,man,True,,Southampton,yes,True\n", + "128,1,3,female,,1,1,22.3583,C,Third,woman,False,F,Cherbourg,yes,False\n", + "129,0,3,male,45.0,0,0,6.975,S,Third,man,True,,Southampton,no,True\n", + "130,0,3,male,33.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n", + "131,0,3,male,20.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n", + "132,0,3,female,47.0,1,0,14.5,S,Third,woman,False,,Southampton,no,False\n", + "133,1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "134,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "135,0,2,male,23.0,0,0,15.0458,C,Second,man,True,,Cherbourg,no,True\n", + "136,1,1,female,19.0,0,2,26.2833,S,First,woman,False,D,Southampton,yes,False\n", + "137,0,1,male,37.0,1,0,53.1,S,First,man,True,C,Southampton,no,False\n", + "138,0,3,male,16.0,0,0,9.2167,S,Third,man,True,,Southampton,no,True\n", + "139,0,1,male,24.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True\n", + "140,0,3,female,,0,2,15.2458,C,Third,woman,False,,Cherbourg,no,False\n", + "141,1,3,female,22.0,0,0,7.75,S,Third,woman,False,,Southampton,yes,True\n", + "142,1,3,female,24.0,1,0,15.85,S,Third,woman,False,,Southampton,yes,False\n", + "143,0,3,male,19.0,0,0,6.75,Q,Third,man,True,,Queenstown,no,True\n", + "144,0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True\n", + "145,0,2,male,19.0,1,1,36.75,S,Second,man,True,,Southampton,no,False\n", + "146,1,3,male,27.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True\n", + "147,0,3,female,9.0,2,2,34.375,S,Third,child,False,,Southampton,no,False\n", + "148,0,2,male,36.5,0,2,26.0,S,Second,man,True,F,Southampton,no,False\n", + "149,0,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "150,0,2,male,51.0,0,0,12.525,S,Second,man,True,,Southampton,no,True\n", + "151,1,1,female,22.0,1,0,66.6,S,First,woman,False,C,Southampton,yes,False\n", + "152,0,3,male,55.5,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "153,0,3,male,40.5,0,2,14.5,S,Third,man,True,,Southampton,no,False\n", + "154,0,3,male,,0,0,7.3125,S,Third,man,True,,Southampton,no,True\n", + "155,0,1,male,51.0,0,1,61.3792,C,First,man,True,,Cherbourg,no,False\n", + "156,1,3,female,16.0,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True\n", + "157,0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "158,0,3,male,,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "159,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n", + "160,0,3,male,44.0,0,1,16.1,S,Third,man,True,,Southampton,no,False\n", + "161,1,2,female,40.0,0,0,15.75,S,Second,woman,False,,Southampton,yes,True\n", + "162,0,3,male,26.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "163,0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "164,0,3,male,1.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n", + "165,1,3,male,9.0,0,2,20.525,S,Third,child,False,,Southampton,yes,False\n", + "166,1,1,female,,0,1,55.0,S,First,woman,False,E,Southampton,yes,False\n", + "167,0,3,female,45.0,1,4,27.9,S,Third,woman,False,,Southampton,no,False\n", + "168,0,1,male,,0,0,25.925,S,First,man,True,,Southampton,no,True\n", + "169,0,3,male,28.0,0,0,56.4958,S,Third,man,True,,Southampton,no,True\n", + "170,0,1,male,61.0,0,0,33.5,S,First,man,True,B,Southampton,no,True\n", + "171,0,3,male,4.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n", + "172,1,3,female,1.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False\n", + "173,0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n", + "174,0,1,male,56.0,0,0,30.6958,C,First,man,True,A,Cherbourg,no,True\n", + "175,0,3,male,18.0,1,1,7.8542,S,Third,man,True,,Southampton,no,False\n", + "176,0,3,male,,3,1,25.4667,S,Third,man,True,,Southampton,no,False\n", + "177,0,1,female,50.0,0,0,28.7125,C,First,woman,False,C,Cherbourg,no,True\n", + "178,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "179,0,3,male,36.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n", + "180,0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False\n", + "181,0,2,male,,0,0,15.05,C,Second,man,True,,Cherbourg,no,True\n", + "182,0,3,male,9.0,4,2,31.3875,S,Third,child,False,,Southampton,no,False\n", + "183,1,2,male,1.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False\n", + "184,1,3,female,4.0,0,2,22.025,S,Third,child,False,,Southampton,yes,False\n", + "185,0,1,male,,0,0,50.0,S,First,man,True,A,Southampton,no,True\n", + "186,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n", + "187,1,1,male,45.0,0,0,26.55,S,First,man,True,,Southampton,yes,True\n", + "188,0,3,male,40.0,1,1,15.5,Q,Third,man,True,,Queenstown,no,False\n", + "189,0,3,male,36.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "190,1,2,female,32.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "191,0,2,male,19.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "192,1,3,female,19.0,1,0,7.8542,S,Third,woman,False,,Southampton,yes,False\n", + "193,1,2,male,3.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False\n", + "194,1,1,female,44.0,0,0,27.7208,C,First,woman,False,B,Cherbourg,yes,True\n", + "195,1,1,female,58.0,0,0,146.5208,C,First,woman,False,B,Cherbourg,yes,True\n", + "196,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "197,0,3,male,42.0,0,1,8.4042,S,Third,man,True,,Southampton,no,False\n", + "198,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "199,0,2,female,24.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True\n", + "200,0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n", + "201,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n", + "202,0,3,male,34.0,0,0,6.4958,S,Third,man,True,,Southampton,no,True\n", + "203,0,3,male,45.5,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "204,1,3,male,18.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n", + "205,0,3,female,2.0,0,1,10.4625,S,Third,child,False,G,Southampton,no,False\n", + "206,0,3,male,32.0,1,0,15.85,S,Third,man,True,,Southampton,no,False\n", + "207,1,3,male,26.0,0,0,18.7875,C,Third,man,True,,Cherbourg,yes,True\n", + "208,1,3,female,16.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "209,1,1,male,40.0,0,0,31.0,C,First,man,True,A,Cherbourg,yes,True\n", + "210,0,3,male,24.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n", + "211,1,2,female,35.0,0,0,21.0,S,Second,woman,False,,Southampton,yes,True\n", + "212,0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "213,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "214,0,3,male,,1,0,7.75,Q,Third,man,True,,Queenstown,no,False\n", + "215,1,1,female,31.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False\n", + "216,1,3,female,27.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True\n", + "217,0,2,male,42.0,1,0,27.0,S,Second,man,True,,Southampton,no,False\n", + "218,1,1,female,32.0,0,0,76.2917,C,First,woman,False,D,Cherbourg,yes,True\n", + "219,0,2,male,30.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "220,1,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n", + "221,0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "222,0,3,male,51.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "223,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "224,1,1,male,38.0,1,0,90.0,S,First,man,True,C,Southampton,yes,False\n", + "225,0,3,male,22.0,0,0,9.35,S,Third,man,True,,Southampton,no,True\n", + "226,1,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True\n", + "227,0,3,male,20.5,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "228,0,2,male,18.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "229,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n", + "230,1,1,female,35.0,1,0,83.475,S,First,woman,False,C,Southampton,yes,False\n", + "231,0,3,male,29.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "232,0,2,male,59.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n", + "233,1,3,female,5.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False\n", + "234,0,2,male,24.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "235,0,3,female,,0,0,7.55,S,Third,woman,False,,Southampton,no,True\n", + "236,0,2,male,44.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n", + "237,1,2,female,8.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False\n", + "238,0,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "239,0,2,male,33.0,0,0,12.275,S,Second,man,True,,Southampton,no,True\n", + "240,0,3,female,,1,0,14.4542,C,Third,woman,False,,Cherbourg,no,False\n", + "241,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n", + "242,0,2,male,29.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "243,0,3,male,22.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n", + "244,0,3,male,30.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "245,0,1,male,44.0,2,0,90.0,Q,First,man,True,C,Queenstown,no,False\n", + "246,0,3,female,25.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True\n", + "247,1,2,female,24.0,0,2,14.5,S,Second,woman,False,,Southampton,yes,False\n", + "248,1,1,male,37.0,1,1,52.5542,S,First,man,True,D,Southampton,yes,False\n", + "249,0,2,male,54.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n", + "250,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "251,0,3,female,29.0,1,1,10.4625,S,Third,woman,False,G,Southampton,no,False\n", + "252,0,1,male,62.0,0,0,26.55,S,First,man,True,C,Southampton,no,True\n", + "253,0,3,male,30.0,1,0,16.1,S,Third,man,True,,Southampton,no,False\n", + "254,0,3,female,41.0,0,2,20.2125,S,Third,woman,False,,Southampton,no,False\n", + "255,1,3,female,29.0,0,2,15.2458,C,Third,woman,False,,Cherbourg,yes,False\n", + "256,1,1,female,,0,0,79.2,C,First,woman,False,,Cherbourg,yes,True\n", + "257,1,1,female,30.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n", + "258,1,1,female,35.0,0,0,512.3292,C,First,woman,False,,Cherbourg,yes,True\n", + "259,1,2,female,50.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "260,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "261,1,3,male,3.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False\n", + "262,0,1,male,52.0,1,1,79.65,S,First,man,True,E,Southampton,no,False\n", + "263,0,1,male,40.0,0,0,0.0,S,First,man,True,B,Southampton,no,True\n", + "264,0,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True\n", + "265,0,2,male,36.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "266,0,3,male,16.0,4,1,39.6875,S,Third,man,True,,Southampton,no,False\n", + "267,1,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,yes,False\n", + "268,1,1,female,58.0,0,1,153.4625,S,First,woman,False,C,Southampton,yes,False\n", + "269,1,1,female,35.0,0,0,135.6333,S,First,woman,False,C,Southampton,yes,True\n", + "270,0,1,male,,0,0,31.0,S,First,man,True,,Southampton,no,True\n", + "271,1,3,male,25.0,0,0,0.0,S,Third,man,True,,Southampton,yes,True\n", + "272,1,2,female,41.0,0,1,19.5,S,Second,woman,False,,Southampton,yes,False\n", + "273,0,1,male,37.0,0,1,29.7,C,First,man,True,C,Cherbourg,no,False\n", + "274,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "275,1,1,female,63.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False\n", + "276,0,3,female,45.0,0,0,7.75,S,Third,woman,False,,Southampton,no,True\n", + "277,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "278,0,3,male,7.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n", + "279,1,3,female,35.0,1,1,20.25,S,Third,woman,False,,Southampton,yes,False\n", + "280,0,3,male,65.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "281,0,3,male,28.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n", + "282,0,3,male,16.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n", + "283,1,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n", + "284,0,1,male,,0,0,26.0,S,First,man,True,A,Southampton,no,True\n", + "285,0,3,male,33.0,0,0,8.6625,C,Third,man,True,,Cherbourg,no,True\n", + "286,1,3,male,30.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True\n", + "287,0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "288,1,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n", + "289,1,3,female,22.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "290,1,1,female,26.0,0,0,78.85,S,First,woman,False,,Southampton,yes,True\n", + "291,1,1,female,19.0,1,0,91.0792,C,First,woman,False,B,Cherbourg,yes,False\n", + "292,0,2,male,36.0,0,0,12.875,C,Second,man,True,D,Cherbourg,no,True\n", + "293,0,3,female,24.0,0,0,8.85,S,Third,woman,False,,Southampton,no,True\n", + "294,0,3,male,24.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "295,0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n", + "296,0,3,male,23.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n", + "297,0,1,female,2.0,1,2,151.55,S,First,child,False,C,Southampton,no,False\n", + "298,1,1,male,,0,0,30.5,S,First,man,True,C,Southampton,yes,True\n", + "299,1,1,female,50.0,0,1,247.5208,C,First,woman,False,B,Cherbourg,yes,False\n", + "300,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "301,1,3,male,,2,0,23.25,Q,Third,man,True,,Queenstown,yes,False\n", + "302,0,3,male,19.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n", + "303,1,2,female,,0,0,12.35,Q,Second,woman,False,E,Queenstown,yes,True\n", + "304,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "305,1,1,male,0.92,1,2,151.55,S,First,child,False,C,Southampton,yes,False\n", + "306,1,1,female,,0,0,110.8833,C,First,woman,False,,Cherbourg,yes,True\n", + "307,1,1,female,17.0,1,0,108.9,C,First,woman,False,C,Cherbourg,yes,False\n", + "308,0,2,male,30.0,1,0,24.0,C,Second,man,True,,Cherbourg,no,False\n", + "309,1,1,female,30.0,0,0,56.9292,C,First,woman,False,E,Cherbourg,yes,True\n", + "310,1,1,female,24.0,0,0,83.1583,C,First,woman,False,C,Cherbourg,yes,True\n", + "311,1,1,female,18.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False\n", + "312,0,2,female,26.0,1,1,26.0,S,Second,woman,False,,Southampton,no,False\n", + "313,0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "314,0,2,male,43.0,1,1,26.25,S,Second,man,True,,Southampton,no,False\n", + "315,1,3,female,26.0,0,0,7.8542,S,Third,woman,False,,Southampton,yes,True\n", + "316,1,2,female,24.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "317,0,2,male,54.0,0,0,14.0,S,Second,man,True,,Southampton,no,True\n", + "318,1,1,female,31.0,0,2,164.8667,S,First,woman,False,C,Southampton,yes,False\n", + "319,1,1,female,40.0,1,1,134.5,C,First,woman,False,E,Cherbourg,yes,False\n", + "320,0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "321,0,3,male,27.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "322,1,2,female,30.0,0,0,12.35,Q,Second,woman,False,,Queenstown,yes,True\n", + "323,1,2,female,22.0,1,1,29.0,S,Second,woman,False,,Southampton,yes,False\n", + "324,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n", + "325,1,1,female,36.0,0,0,135.6333,C,First,woman,False,C,Cherbourg,yes,True\n", + "326,0,3,male,61.0,0,0,6.2375,S,Third,man,True,,Southampton,no,True\n", + "327,1,2,female,36.0,0,0,13.0,S,Second,woman,False,D,Southampton,yes,True\n", + "328,1,3,female,31.0,1,1,20.525,S,Third,woman,False,,Southampton,yes,False\n", + "329,1,1,female,16.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False\n", + "330,1,3,female,,2,0,23.25,Q,Third,woman,False,,Queenstown,yes,False\n", + "331,0,1,male,45.5,0,0,28.5,S,First,man,True,C,Southampton,no,True\n", + "332,0,1,male,38.0,0,1,153.4625,S,First,man,True,C,Southampton,no,False\n", + "333,0,3,male,16.0,2,0,18.0,S,Third,man,True,,Southampton,no,False\n", + "334,1,1,female,,1,0,133.65,S,First,woman,False,,Southampton,yes,False\n", + "335,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "336,0,1,male,29.0,1,0,66.6,S,First,man,True,C,Southampton,no,False\n", + "337,1,1,female,41.0,0,0,134.5,C,First,woman,False,E,Cherbourg,yes,True\n", + "338,1,3,male,45.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n", + "339,0,1,male,45.0,0,0,35.5,S,First,man,True,,Southampton,no,True\n", + "340,1,2,male,2.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False\n", + "341,1,1,female,24.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False\n", + "342,0,2,male,28.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "343,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "344,0,2,male,36.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "345,1,2,female,24.0,0,0,13.0,S,Second,woman,False,F,Southampton,yes,True\n", + "346,1,2,female,40.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "347,1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False\n", + "348,1,3,male,3.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False\n", + "349,0,3,male,42.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "350,0,3,male,23.0,0,0,9.225,S,Third,man,True,,Southampton,no,True\n", + "351,0,1,male,,0,0,35.0,S,First,man,True,C,Southampton,no,True\n", + "352,0,3,male,15.0,1,1,7.2292,C,Third,child,False,,Cherbourg,no,False\n", + "353,0,3,male,25.0,1,0,17.8,S,Third,man,True,,Southampton,no,False\n", + "354,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "355,0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n", + "356,1,1,female,22.0,0,1,55.0,S,First,woman,False,E,Southampton,yes,False\n", + "357,0,2,female,38.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True\n", + "358,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n", + "359,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n", + "360,0,3,male,40.0,1,4,27.9,S,Third,man,True,,Southampton,no,False\n", + "361,0,2,male,29.0,1,0,27.7208,C,Second,man,True,,Cherbourg,no,False\n", + "362,0,3,female,45.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False\n", + "363,0,3,male,35.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n", + "364,0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False\n", + "365,0,3,male,30.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "366,1,1,female,60.0,1,0,75.25,C,First,woman,False,D,Cherbourg,yes,False\n", + "367,1,3,female,,0,0,7.2292,C,Third,woman,False,,Cherbourg,yes,True\n", + "368,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "369,1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True\n", + "370,1,1,male,25.0,1,0,55.4417,C,First,man,True,E,Cherbourg,yes,False\n", + "371,0,3,male,18.0,1,0,6.4958,S,Third,man,True,,Southampton,no,False\n", + "372,0,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "373,0,1,male,22.0,0,0,135.6333,C,First,man,True,,Cherbourg,no,True\n", + "374,0,3,female,3.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n", + "375,1,1,female,,1,0,82.1708,C,First,woman,False,,Cherbourg,yes,False\n", + "376,1,3,female,22.0,0,0,7.25,S,Third,woman,False,,Southampton,yes,True\n", + "377,0,1,male,27.0,0,2,211.5,C,First,man,True,C,Cherbourg,no,False\n", + "378,0,3,male,20.0,0,0,4.0125,C,Third,man,True,,Cherbourg,no,True\n", + "379,0,3,male,19.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "380,1,1,female,42.0,0,0,227.525,C,First,woman,False,,Cherbourg,yes,True\n", + "381,1,3,female,1.0,0,2,15.7417,C,Third,child,False,,Cherbourg,yes,False\n", + "382,0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n", + "383,1,1,female,35.0,1,0,52.0,S,First,woman,False,,Southampton,yes,False\n", + "384,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "385,0,2,male,18.0,0,0,73.5,S,Second,man,True,,Southampton,no,True\n", + "386,0,3,male,1.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n", + "387,1,2,female,36.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "388,0,3,male,,0,0,7.7292,Q,Third,man,True,,Queenstown,no,True\n", + "389,1,2,female,17.0,0,0,12.0,C,Second,woman,False,,Cherbourg,yes,True\n", + "390,1,1,male,36.0,1,2,120.0,S,First,man,True,B,Southampton,yes,False\n", + "391,1,3,male,21.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True\n", + "392,0,3,male,28.0,2,0,7.925,S,Third,man,True,,Southampton,no,False\n", + "393,1,1,female,23.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False\n", + "394,1,3,female,24.0,0,2,16.7,S,Third,woman,False,G,Southampton,yes,False\n", + "395,0,3,male,22.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n", + "396,0,3,female,31.0,0,0,7.8542,S,Third,woman,False,,Southampton,no,True\n", + "397,0,2,male,46.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n", + "398,0,2,male,23.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "399,1,2,female,28.0,0,0,12.65,S,Second,woman,False,,Southampton,yes,True\n", + "400,1,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n", + "401,0,3,male,26.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "402,0,3,female,21.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False\n", + "403,0,3,male,28.0,1,0,15.85,S,Third,man,True,,Southampton,no,False\n", + "404,0,3,female,20.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True\n", + "405,0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n", + "406,0,3,male,51.0,0,0,7.75,S,Third,man,True,,Southampton,no,True\n", + "407,1,2,male,3.0,1,1,18.75,S,Second,child,False,,Southampton,yes,False\n", + "408,0,3,male,21.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "409,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n", + "410,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "411,0,3,male,,0,0,6.8583,Q,Third,man,True,,Queenstown,no,True\n", + "412,1,1,female,33.0,1,0,90.0,Q,First,woman,False,C,Queenstown,yes,False\n", + "413,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "414,1,3,male,44.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n", + "415,0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True\n", + "416,1,2,female,34.0,1,1,32.5,S,Second,woman,False,,Southampton,yes,False\n", + "417,1,2,female,18.0,0,2,13.0,S,Second,woman,False,,Southampton,yes,False\n", + "418,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "419,0,3,female,10.0,0,2,24.15,S,Third,child,False,,Southampton,no,False\n", + "420,0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n", + "421,0,3,male,21.0,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True\n", + "422,0,3,male,29.0,0,0,7.875,S,Third,man,True,,Southampton,no,True\n", + "423,0,3,female,28.0,1,1,14.4,S,Third,woman,False,,Southampton,no,False\n", + "424,0,3,male,18.0,1,1,20.2125,S,Third,man,True,,Southampton,no,False\n", + "425,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "426,1,2,female,28.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "427,1,2,female,19.0,0,0,26.0,S,Second,woman,False,,Southampton,yes,True\n", + "428,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "429,1,3,male,32.0,0,0,8.05,S,Third,man,True,E,Southampton,yes,True\n", + "430,1,1,male,28.0,0,0,26.55,S,First,man,True,C,Southampton,yes,True\n", + "431,1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False\n", + "432,1,2,female,42.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "433,0,3,male,17.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n", + "434,0,1,male,50.0,1,0,55.9,S,First,man,True,E,Southampton,no,False\n", + "435,1,1,female,14.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False\n", + "436,0,3,female,21.0,2,2,34.375,S,Third,woman,False,,Southampton,no,False\n", + "437,1,2,female,24.0,2,3,18.75,S,Second,woman,False,,Southampton,yes,False\n", + "438,0,1,male,64.0,1,4,263.0,S,First,man,True,C,Southampton,no,False\n", + "439,0,2,male,31.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "440,1,2,female,45.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False\n", + "441,0,3,male,20.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n", + "442,0,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,no,False\n", + "443,1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "444,1,3,male,,0,0,8.1125,S,Third,man,True,,Southampton,yes,True\n", + "445,1,1,male,4.0,0,2,81.8583,S,First,child,False,A,Southampton,yes,False\n", + "446,1,2,female,13.0,0,1,19.5,S,Second,child,False,,Southampton,yes,False\n", + "447,1,1,male,34.0,0,0,26.55,S,First,man,True,,Southampton,yes,True\n", + "448,1,3,female,5.0,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n", + "449,1,1,male,52.0,0,0,30.5,S,First,man,True,C,Southampton,yes,True\n", + "450,0,2,male,36.0,1,2,27.75,S,Second,man,True,,Southampton,no,False\n", + "451,0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False\n", + "452,0,1,male,30.0,0,0,27.75,C,First,man,True,C,Cherbourg,no,True\n", + "453,1,1,male,49.0,1,0,89.1042,C,First,man,True,C,Cherbourg,yes,False\n", + "454,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "455,1,3,male,29.0,0,0,7.8958,C,Third,man,True,,Cherbourg,yes,True\n", + "456,0,1,male,65.0,0,0,26.55,S,First,man,True,E,Southampton,no,True\n", + "457,1,1,female,,1,0,51.8625,S,First,woman,False,D,Southampton,yes,False\n", + "458,1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n", + "459,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "460,1,1,male,48.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True\n", + "461,0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "462,0,1,male,47.0,0,0,38.5,S,First,man,True,E,Southampton,no,True\n", + "463,0,2,male,48.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "464,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "465,0,3,male,38.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n", + "466,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "467,0,1,male,56.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n", + "468,0,3,male,,0,0,7.725,Q,Third,man,True,,Queenstown,no,True\n", + "469,1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n", + "470,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "471,0,3,male,38.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "472,1,2,female,33.0,1,2,27.75,S,Second,woman,False,,Southampton,yes,False\n", + "473,1,2,female,23.0,0,0,13.7917,C,Second,woman,False,D,Cherbourg,yes,True\n", + "474,0,3,female,22.0,0,0,9.8375,S,Third,woman,False,,Southampton,no,True\n", + "475,0,1,male,,0,0,52.0,S,First,man,True,A,Southampton,no,True\n", + "476,0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n", + "477,0,3,male,29.0,1,0,7.0458,S,Third,man,True,,Southampton,no,False\n", + "478,0,3,male,22.0,0,0,7.5208,S,Third,man,True,,Southampton,no,True\n", + "479,1,3,female,2.0,0,1,12.2875,S,Third,child,False,,Southampton,yes,False\n", + "480,0,3,male,9.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n", + "481,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "482,0,3,male,50.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "483,1,3,female,63.0,0,0,9.5875,S,Third,woman,False,,Southampton,yes,True\n", + "484,1,1,male,25.0,1,0,91.0792,C,First,man,True,B,Cherbourg,yes,False\n", + "485,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n", + "486,1,1,female,35.0,1,0,90.0,S,First,woman,False,C,Southampton,yes,False\n", + "487,0,1,male,58.0,0,0,29.7,C,First,man,True,B,Cherbourg,no,True\n", + "488,0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "489,1,3,male,9.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False\n", + "490,0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False\n", + "491,0,3,male,21.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "492,0,1,male,55.0,0,0,30.5,S,First,man,True,C,Southampton,no,True\n", + "493,0,1,male,71.0,0,0,49.5042,C,First,man,True,,Cherbourg,no,True\n", + "494,0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "495,0,3,male,,0,0,14.4583,C,Third,man,True,,Cherbourg,no,True\n", + "496,1,1,female,54.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False\n", + "497,0,3,male,,0,0,15.1,S,Third,man,True,,Southampton,no,True\n", + "498,0,1,female,25.0,1,2,151.55,S,First,woman,False,C,Southampton,no,False\n", + "499,0,3,male,24.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n", + "500,0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "501,0,3,female,21.0,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True\n", + "502,0,3,female,,0,0,7.6292,Q,Third,woman,False,,Queenstown,no,True\n", + "503,0,3,female,37.0,0,0,9.5875,S,Third,woman,False,,Southampton,no,True\n", + "504,1,1,female,16.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n", + "505,0,1,male,18.0,1,0,108.9,C,First,man,True,C,Cherbourg,no,False\n", + "506,1,2,female,33.0,0,2,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "507,1,1,male,,0,0,26.55,S,First,man,True,,Southampton,yes,True\n", + "508,0,3,male,28.0,0,0,22.525,S,Third,man,True,,Southampton,no,True\n", + "509,1,3,male,26.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n", + "510,1,3,male,29.0,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True\n", + "511,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "512,1,1,male,36.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n", + "513,1,1,female,54.0,1,0,59.4,C,First,woman,False,,Cherbourg,yes,False\n", + "514,0,3,male,24.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True\n", + "515,0,1,male,47.0,0,0,34.0208,S,First,man,True,D,Southampton,no,True\n", + "516,1,2,female,34.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True\n", + "517,0,3,male,,0,0,24.15,Q,Third,man,True,,Queenstown,no,True\n", + "518,1,2,female,36.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "519,0,3,male,32.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "520,1,1,female,30.0,0,0,93.5,S,First,woman,False,B,Southampton,yes,True\n", + "521,0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "522,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "523,1,1,female,44.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False\n", + "524,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n", + "525,0,3,male,40.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "526,1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n", + "527,0,1,male,,0,0,221.7792,S,First,man,True,C,Southampton,no,True\n", + "528,0,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n", + "529,0,2,male,23.0,2,1,11.5,S,Second,man,True,,Southampton,no,False\n", + "530,1,2,female,2.0,1,1,26.0,S,Second,child,False,,Southampton,yes,False\n", + "531,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n", + "532,0,3,male,17.0,1,1,7.2292,C,Third,man,True,,Cherbourg,no,False\n", + "533,1,3,female,,0,2,22.3583,C,Third,woman,False,,Cherbourg,yes,False\n", + "534,0,3,female,30.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True\n", + "535,1,2,female,7.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False\n", + "536,0,1,male,45.0,0,0,26.55,S,First,man,True,B,Southampton,no,True\n", + "537,1,1,female,30.0,0,0,106.425,C,First,woman,False,,Cherbourg,yes,True\n", + "538,0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True\n", + "539,1,1,female,22.0,0,2,49.5,C,First,woman,False,B,Cherbourg,yes,False\n", + "540,1,1,female,36.0,0,2,71.0,S,First,woman,False,B,Southampton,yes,False\n", + "541,0,3,female,9.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n", + "542,0,3,female,11.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n", + "543,1,2,male,32.0,1,0,26.0,S,Second,man,True,,Southampton,yes,False\n", + "544,0,1,male,50.0,1,0,106.425,C,First,man,True,C,Cherbourg,no,False\n", + "545,0,1,male,64.0,0,0,26.0,S,First,man,True,,Southampton,no,True\n", + "546,1,2,female,19.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n", + "547,1,2,male,,0,0,13.8625,C,Second,man,True,,Cherbourg,yes,True\n", + "548,0,3,male,33.0,1,1,20.525,S,Third,man,True,,Southampton,no,False\n", + "549,1,2,male,8.0,1,1,36.75,S,Second,child,False,,Southampton,yes,False\n", + "550,1,1,male,17.0,0,2,110.8833,C,First,man,True,C,Cherbourg,yes,False\n", + "551,0,2,male,27.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n", + "552,0,3,male,,0,0,7.8292,Q,Third,man,True,,Queenstown,no,True\n", + "553,1,3,male,22.0,0,0,7.225,C,Third,man,True,,Cherbourg,yes,True\n", + "554,1,3,female,22.0,0,0,7.775,S,Third,woman,False,,Southampton,yes,True\n", + "555,0,1,male,62.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n", + "556,1,1,female,48.0,1,0,39.6,C,First,woman,False,A,Cherbourg,yes,False\n", + "557,0,1,male,,0,0,227.525,C,First,man,True,,Cherbourg,no,True\n", + "558,1,1,female,39.0,1,1,79.65,S,First,woman,False,E,Southampton,yes,False\n", + "559,1,3,female,36.0,1,0,17.4,S,Third,woman,False,,Southampton,yes,False\n", + "560,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "561,0,3,male,40.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "562,0,2,male,28.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n", + "563,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "564,0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True\n", + "565,0,3,male,24.0,2,0,24.15,S,Third,man,True,,Southampton,no,False\n", + "566,0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "567,0,3,female,29.0,0,4,21.075,S,Third,woman,False,,Southampton,no,False\n", + "568,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n", + "569,1,3,male,32.0,0,0,7.8542,S,Third,man,True,,Southampton,yes,True\n", + "570,1,2,male,62.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True\n", + "571,1,1,female,53.0,2,0,51.4792,S,First,woman,False,C,Southampton,yes,False\n", + "572,1,1,male,36.0,0,0,26.3875,S,First,man,True,E,Southampton,yes,True\n", + "573,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n", + "574,0,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "575,0,3,male,19.0,0,0,14.5,S,Third,man,True,,Southampton,no,True\n", + "576,1,2,female,34.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "577,1,1,female,39.0,1,0,55.9,S,First,woman,False,E,Southampton,yes,False\n", + "578,0,3,female,,1,0,14.4583,C,Third,woman,False,,Cherbourg,no,False\n", + "579,1,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n", + "580,1,2,female,25.0,1,1,30.0,S,Second,woman,False,,Southampton,yes,False\n", + "581,1,1,female,39.0,1,1,110.8833,C,First,woman,False,C,Cherbourg,yes,False\n", + "582,0,2,male,54.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n", + "583,0,1,male,36.0,0,0,40.125,C,First,man,True,A,Cherbourg,no,True\n", + "584,0,3,male,,0,0,8.7125,C,Third,man,True,,Cherbourg,no,True\n", + "585,1,1,female,18.0,0,2,79.65,S,First,woman,False,E,Southampton,yes,False\n", + "586,0,2,male,47.0,0,0,15.0,S,Second,man,True,,Southampton,no,True\n", + "587,1,1,male,60.0,1,1,79.2,C,First,man,True,B,Cherbourg,yes,False\n", + "588,0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "589,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "590,0,3,male,35.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n", + "591,1,1,female,52.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False\n", + "592,0,3,male,47.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n", + "593,0,3,female,,0,2,7.75,Q,Third,woman,False,,Queenstown,no,False\n", + "594,0,2,male,37.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n", + "595,0,3,male,36.0,1,1,24.15,S,Third,man,True,,Southampton,no,False\n", + "596,1,2,female,,0,0,33.0,S,Second,woman,False,,Southampton,yes,True\n", + "597,0,3,male,49.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n", + "598,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "599,1,1,male,49.0,1,0,56.9292,C,First,man,True,A,Cherbourg,yes,False\n", + "600,1,2,female,24.0,2,1,27.0,S,Second,woman,False,,Southampton,yes,False\n", + "601,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "602,0,1,male,,0,0,42.4,S,First,man,True,,Southampton,no,True\n", + "603,0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "604,1,1,male,35.0,0,0,26.55,C,First,man,True,,Cherbourg,yes,True\n", + "605,0,3,male,36.0,1,0,15.55,S,Third,man,True,,Southampton,no,False\n", + "606,0,3,male,30.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "607,1,1,male,27.0,0,0,30.5,S,First,man,True,,Southampton,yes,True\n", + "608,1,2,female,22.0,1,2,41.5792,C,Second,woman,False,,Cherbourg,yes,False\n", + "609,1,1,female,40.0,0,0,153.4625,S,First,woman,False,C,Southampton,yes,True\n", + "610,0,3,female,39.0,1,5,31.275,S,Third,woman,False,,Southampton,no,False\n", + "611,0,3,male,,0,0,7.05,S,Third,man,True,,Southampton,no,True\n", + "612,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n", + "613,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "614,0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "615,1,2,female,24.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False\n", + "616,0,3,male,34.0,1,1,14.4,S,Third,man,True,,Southampton,no,False\n", + "617,0,3,female,26.0,1,0,16.1,S,Third,woman,False,,Southampton,no,False\n", + "618,1,2,female,4.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False\n", + "619,0,2,male,26.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "620,0,3,male,27.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False\n", + "621,1,1,male,42.0,1,0,52.5542,S,First,man,True,D,Southampton,yes,False\n", + "622,1,3,male,20.0,1,1,15.7417,C,Third,man,True,,Cherbourg,yes,False\n", + "623,0,3,male,21.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n", + "624,0,3,male,21.0,0,0,16.1,S,Third,man,True,,Southampton,no,True\n", + "625,0,1,male,61.0,0,0,32.3208,S,First,man,True,D,Southampton,no,True\n", + "626,0,2,male,57.0,0,0,12.35,Q,Second,man,True,,Queenstown,no,True\n", + "627,1,1,female,21.0,0,0,77.9583,S,First,woman,False,D,Southampton,yes,True\n", + "628,0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "629,0,3,male,,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True\n", + "630,1,1,male,80.0,0,0,30.0,S,First,man,True,A,Southampton,yes,True\n", + "631,0,3,male,51.0,0,0,7.0542,S,Third,man,True,,Southampton,no,True\n", + "632,1,1,male,32.0,0,0,30.5,C,First,man,True,B,Cherbourg,yes,True\n", + "633,0,1,male,,0,0,0.0,S,First,man,True,,Southampton,no,True\n", + "634,0,3,female,9.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n", + "635,1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "636,0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n", + "637,0,2,male,31.0,1,1,26.25,S,Second,man,True,,Southampton,no,False\n", + "638,0,3,female,41.0,0,5,39.6875,S,Third,woman,False,,Southampton,no,False\n", + "639,0,3,male,,1,0,16.1,S,Third,man,True,,Southampton,no,False\n", + "640,0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n", + "641,1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True\n", + "642,0,3,female,2.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n", + "643,1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n", + "644,1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n", + "645,1,1,male,48.0,1,0,76.7292,C,First,man,True,D,Cherbourg,yes,False\n", + "646,0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "647,1,1,male,56.0,0,0,35.5,C,First,man,True,A,Cherbourg,yes,True\n", + "648,0,3,male,,0,0,7.55,S,Third,man,True,,Southampton,no,True\n", + "649,1,3,female,23.0,0,0,7.55,S,Third,woman,False,,Southampton,yes,True\n", + "650,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "651,1,2,female,18.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False\n", + "652,0,3,male,21.0,0,0,8.4333,S,Third,man,True,,Southampton,no,True\n", + "653,1,3,female,,0,0,7.8292,Q,Third,woman,False,,Queenstown,yes,True\n", + "654,0,3,female,18.0,0,0,6.75,Q,Third,woman,False,,Queenstown,no,True\n", + "655,0,2,male,24.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n", + "656,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "657,0,3,female,32.0,1,1,15.5,Q,Third,woman,False,,Queenstown,no,False\n", + "658,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "659,0,1,male,58.0,0,2,113.275,C,First,man,True,D,Cherbourg,no,False\n", + "660,1,1,male,50.0,2,0,133.65,S,First,man,True,,Southampton,yes,False\n", + "661,0,3,male,40.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "662,0,1,male,47.0,0,0,25.5875,S,First,man,True,E,Southampton,no,True\n", + "663,0,3,male,36.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True\n", + "664,1,3,male,20.0,1,0,7.925,S,Third,man,True,,Southampton,yes,False\n", + "665,0,2,male,32.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n", + "666,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "667,0,3,male,,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "668,0,3,male,43.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "669,1,1,female,,1,0,52.0,S,First,woman,False,C,Southampton,yes,False\n", + "670,1,2,female,40.0,1,1,39.0,S,Second,woman,False,,Southampton,yes,False\n", + "671,0,1,male,31.0,1,0,52.0,S,First,man,True,B,Southampton,no,False\n", + "672,0,2,male,70.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n", + "673,1,2,male,31.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n", + "674,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "675,0,3,male,18.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "676,0,3,male,24.5,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "677,1,3,female,18.0,0,0,9.8417,S,Third,woman,False,,Southampton,yes,True\n", + "678,0,3,female,43.0,1,6,46.9,S,Third,woman,False,,Southampton,no,False\n", + "679,1,1,male,36.0,0,1,512.3292,C,First,man,True,B,Cherbourg,yes,False\n", + "680,0,3,female,,0,0,8.1375,Q,Third,woman,False,,Queenstown,no,True\n", + "681,1,1,male,27.0,0,0,76.7292,C,First,man,True,D,Cherbourg,yes,True\n", + "682,0,3,male,20.0,0,0,9.225,S,Third,man,True,,Southampton,no,True\n", + "683,0,3,male,14.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n", + "684,0,2,male,60.0,1,1,39.0,S,Second,man,True,,Southampton,no,False\n", + "685,0,2,male,25.0,1,2,41.5792,C,Second,man,True,,Cherbourg,no,False\n", + "686,0,3,male,14.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n", + "687,0,3,male,19.0,0,0,10.1708,S,Third,man,True,,Southampton,no,True\n", + "688,0,3,male,18.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n", + "689,1,1,female,15.0,0,1,211.3375,S,First,child,False,B,Southampton,yes,False\n", + "690,1,1,male,31.0,1,0,57.0,S,First,man,True,B,Southampton,yes,False\n", + "691,1,3,female,4.0,0,1,13.4167,C,Third,child,False,,Cherbourg,yes,False\n", + "692,1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n", + "693,0,3,male,25.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n", + "694,0,1,male,60.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n", + "695,0,2,male,52.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n", + "696,0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "697,1,3,female,,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True\n", + "698,0,1,male,49.0,1,1,110.8833,C,First,man,True,C,Cherbourg,no,False\n", + "699,0,3,male,42.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n", + "700,1,1,female,18.0,1,0,227.525,C,First,woman,False,C,Cherbourg,yes,False\n", + "701,1,1,male,35.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n", + "702,0,3,female,18.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False\n", + "703,0,3,male,25.0,0,0,7.7417,Q,Third,man,True,,Queenstown,no,True\n", + "704,0,3,male,26.0,1,0,7.8542,S,Third,man,True,,Southampton,no,False\n", + "705,0,2,male,39.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n", + "706,1,2,female,45.0,0,0,13.5,S,Second,woman,False,,Southampton,yes,True\n", + "707,1,1,male,42.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n", + "708,1,1,female,22.0,0,0,151.55,S,First,woman,False,,Southampton,yes,True\n", + "709,1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False\n", + "710,1,1,female,24.0,0,0,49.5042,C,First,woman,False,C,Cherbourg,yes,True\n", + "711,0,1,male,,0,0,26.55,S,First,man,True,C,Southampton,no,True\n", + "712,1,1,male,48.0,1,0,52.0,S,First,man,True,C,Southampton,yes,False\n", + "713,0,3,male,29.0,0,0,9.4833,S,Third,man,True,,Southampton,no,True\n", + "714,0,2,male,52.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "715,0,3,male,19.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n", + "716,1,1,female,38.0,0,0,227.525,C,First,woman,False,C,Cherbourg,yes,True\n", + "717,1,2,female,27.0,0,0,10.5,S,Second,woman,False,E,Southampton,yes,True\n", + "718,0,3,male,,0,0,15.5,Q,Third,man,True,,Queenstown,no,True\n", + "719,0,3,male,33.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n", + "720,1,2,female,6.0,0,1,33.0,S,Second,child,False,,Southampton,yes,False\n", + "721,0,3,male,17.0,1,0,7.0542,S,Third,man,True,,Southampton,no,False\n", + "722,0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "723,0,2,male,50.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "724,1,1,male,27.0,1,0,53.1,S,First,man,True,E,Southampton,yes,False\n", + "725,0,3,male,20.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n", + "726,1,2,female,30.0,3,0,21.0,S,Second,woman,False,,Southampton,yes,False\n", + "727,1,3,female,,0,0,7.7375,Q,Third,woman,False,,Queenstown,yes,True\n", + "728,0,2,male,25.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n", + "729,0,3,female,25.0,1,0,7.925,S,Third,woman,False,,Southampton,no,False\n", + "730,1,1,female,29.0,0,0,211.3375,S,First,woman,False,B,Southampton,yes,True\n", + "731,0,3,male,11.0,0,0,18.7875,C,Third,child,False,,Cherbourg,no,True\n", + "732,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n", + "733,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "734,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n", + "735,0,3,male,28.5,0,0,16.1,S,Third,man,True,,Southampton,no,True\n", + "736,0,3,female,48.0,1,3,34.375,S,Third,woman,False,,Southampton,no,False\n", + "737,1,1,male,35.0,0,0,512.3292,C,First,man,True,B,Cherbourg,yes,True\n", + "738,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "739,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "740,1,1,male,,0,0,30.0,S,First,man,True,D,Southampton,yes,True\n", + "741,0,1,male,36.0,1,0,78.85,S,First,man,True,C,Southampton,no,False\n", + "742,1,1,female,21.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False\n", + "743,0,3,male,24.0,1,0,16.1,S,Third,man,True,,Southampton,no,False\n", + "744,1,3,male,31.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n", + "745,0,1,male,70.0,1,1,71.0,S,First,man,True,B,Southampton,no,False\n", + "746,0,3,male,16.0,1,1,20.25,S,Third,man,True,,Southampton,no,False\n", + "747,1,2,female,30.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n", + "748,0,1,male,19.0,1,0,53.1,S,First,man,True,D,Southampton,no,False\n", + "749,0,3,male,31.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n", + "750,1,2,female,4.0,1,1,23.0,S,Second,child,False,,Southampton,yes,False\n", + "751,1,3,male,6.0,0,1,12.475,S,Third,child,False,E,Southampton,yes,False\n", + "752,0,3,male,33.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n", + "753,0,3,male,23.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n", + "754,1,2,female,48.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False\n", + "755,1,2,male,0.67,1,1,14.5,S,Second,child,False,,Southampton,yes,False\n", + "756,0,3,male,28.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n", + "757,0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True\n", + "758,0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n", + "759,1,1,female,33.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n", + "760,0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True\n", + "761,0,3,male,41.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n", + 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"B7W0f-1-HAoG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "titanic_data_url = \"https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gxn7a60uHO82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "b0571608-9fad-447c-f1b6-79001293ef20" + }, + "source": [ + "import pandas as pd\n", + "\n", + "titanic_data = pd.read_csv(titanic_data_url)\n", + "titanic_data.head()\n", + "\n" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Unnamed: 0survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
0003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
1111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
2213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
3311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
4403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
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" + ], + "text/plain": [ + " Unnamed: 0 survived pclass sex ... deck embark_town alive alone\n", + "0 0 0 3 male ... NaN Southampton no False\n", + "1 1 1 1 female ... C Cherbourg yes False\n", + "2 2 1 3 female ... NaN Southampton yes True\n", + "3 3 1 1 female ... C Southampton yes False\n", + "4 4 0 3 male ... NaN Southampton no True\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jQp3bqQAIC_t", + "colab_type": "code", + "colab": { + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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+ "ok": true, + "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "status": 200, + "status_text": "" + } + }, + "base_uri": "https://localhost:8080/", + "height": 40 + }, + "outputId": "cb5a15a4-6b30-40aa-b432-3eea7c8cf3fa" + }, + "source": [ + "from google.colab import files\n", + "upload= files.upload()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "r5YB7xxmJ8rA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "dataset_url = \"https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5LySpasOI4YU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "6d19e2de-7243-48a1-bf44-04bbd9cf5c5e" + }, + "source": [ + "df = pd.read_csv(dataset_url)\n", + "df.head()" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Unnamed: 0survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
0003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
1111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
2213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
3311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
4403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
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" + ], + "text/plain": [ + " Unnamed: 0 survived pclass sex ... deck embark_town alive alone\n", + "0 0 0 3 male ... NaN Southampton no False\n", + "1 1 1 1 female ... C Cherbourg yes False\n", + "2 2 1 3 female ... NaN Southampton yes True\n", + "3 3 1 1 female ... C Southampton yes False\n", + "4 4 0 3 male ... NaN Southampton no True\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "h1Ib_wanKQtG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8e87700e-71c6-4a4e-d2cc-e45ccfb3e63a" + }, + "source": [ + "df.shape" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(891, 16)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mG96DBExKbP2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "5c4570b6-3c04-4e7b-f167-86e135ffcdd8" + }, + "source": [ + "!wget https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2019-09-11 02:49:44-- https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 60473 (59K) [text/plain]\n", + "Saving to: ‘titanic.csv’\n", + "\n", + "\rtitanic.csv 0%[ ] 0 --.-KB/s \rtitanic.csv 100%[===================>] 59.06K --.-KB/s in 0.01s \n", + "\n", + "2019-09-11 02:49:44 (4.12 MB/s) - ‘titanic.csv’ saved [60473/60473]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pmOTu9pgKmY_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "dae0b5b3-6ea7-418f-dfce-865ea4047e02" + }, + "source": [ + "!curl https://raw.githubusercontent.com/ryanleeallred/datasets/master/titanic.csv -o titanic.data.2" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 60473 100 60473 0 0 968k 0 --:--:-- --:--:-- --:--:-- 968k\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Naps19dfK1YH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "outputId": "df9a190b-0a09-44b8-ef95-852fb525225e" + }, + "source": [ + "df.head(15)" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Unnamed: 0survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
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...................................................
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314 rows × 16 columns

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" + ], + "text/plain": [ + " Unnamed: 0 survived pclass sex ... deck embark_town alive alone\n", + "1 1 1 1 female ... C Cherbourg yes False\n", + "2 2 1 3 female ... unknown Southampton yes True\n", + "3 3 1 1 female ... C Southampton yes False\n", + "8 8 1 3 female ... unknown Southampton yes False\n", + "9 9 1 2 female ... unknown Cherbourg yes False\n", + "10 10 1 3 female ... G Southampton yes False\n", + "11 11 1 1 female ... C Southampton yes True\n", + "14 14 0 3 female ... unknown Southampton no True\n", + "15 15 1 2 female ... unknown Southampton yes True\n", + "18 18 0 3 female ... unknown Southampton no False\n", + "19 19 1 3 female ... unknown Cherbourg yes True\n", + "22 22 1 3 female ... unknown Queenstown yes True\n", + "24 24 0 3 female ... unknown Southampton no False\n", + "25 25 1 3 female ... unknown Southampton yes False\n", + "28 28 1 3 female ... unknown Queenstown yes True\n", + "31 31 1 1 female ... B Cherbourg yes False\n", + "32 32 1 3 female ... unknown Queenstown yes True\n", + "38 38 0 3 female ... unknown Southampton no False\n", + "39 39 1 3 female ... unknown Cherbourg yes False\n", + "40 40 0 3 female ... unknown Southampton no False\n", + "41 41 0 2 female ... unknown Southampton no False\n", + "43 43 1 2 female ... unknown Cherbourg yes False\n", + "44 44 1 3 female ... unknown Queenstown yes True\n", + "47 47 1 3 female ... unknown Queenstown yes True\n", + "49 49 0 3 female ... unknown Southampton no False\n", + "52 52 1 1 female ... D Cherbourg yes False\n", + "53 53 1 2 female ... unknown Southampton yes False\n", + "56 56 1 2 female ... unknown Southampton yes True\n", + "58 58 1 2 female ... unknown Southampton yes False\n", + "61 61 1 1 female ... B unknown yes True\n", + ".. ... ... ... ... ... ... ... ... ...\n", + "807 807 0 3 female ... unknown Southampton no True\n", + "809 809 1 1 female ... E Southampton yes False\n", + "813 813 0 3 female ... unknown Southampton no False\n", + "816 816 0 3 female ... unknown Southampton no True\n", + "820 820 1 1 female ... B Southampton yes False\n", + "823 823 1 3 female ... E Southampton yes False\n", + "829 829 1 1 female ... B unknown yes True\n", + "830 830 1 3 female ... unknown Cherbourg yes False\n", + "835 835 1 1 female ... E Cherbourg yes False\n", + "842 842 1 1 female ... unknown Cherbourg yes True\n", + "849 849 1 1 female ... C Cherbourg yes False\n", + "852 852 0 3 female ... unknown Cherbourg no False\n", + "853 853 1 1 female ... D Southampton yes False\n", + "854 854 0 2 female ... unknown Southampton no False\n", + "855 855 1 3 female ... unknown Southampton yes False\n", + "856 856 1 1 female ... unknown Southampton yes False\n", + "858 858 1 3 female ... unknown Cherbourg yes False\n", + "862 862 1 1 female ... D Southampton yes True\n", + "863 863 0 3 female ... unknown Southampton no False\n", + "865 865 1 2 female ... unknown Southampton yes True\n", + "866 866 1 2 female ... unknown Cherbourg yes False\n", + "871 871 1 1 female ... D Southampton yes False\n", + "874 874 1 2 female ... unknown Cherbourg yes False\n", + "875 875 1 3 female ... unknown Cherbourg yes True\n", + "879 879 1 1 female ... C Cherbourg yes False\n", + "880 880 1 2 female ... unknown Southampton yes False\n", + "882 882 0 3 female ... unknown Southampton no True\n", + "885 885 0 3 female ... unknown Queenstown no False\n", + "887 887 1 1 female ... B Southampton yes True\n", + "888 888 0 3 female ... unknown Southampton no False\n", + "\n", + "[314 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6DmgXls5NL_H", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "outputId": "aa28fc8c-feb9-481e-ff02-c3f76aa41680" + }, + "source": [ + "df[df['alive'] == 'yes'] & df[df['sex'] == 'female']" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 1788\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1789\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1790\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'alive'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'yes'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'female'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[1;32m 2021\u001b[0m \u001b[0;31m# Another DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2022\u001b[0m \u001b[0mpass_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_series_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2024\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2025\u001b[0m \u001b[0;31m# For these values of `axis`, we end up dispatching to Series op,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_combine_frame\u001b[0;34m(self, other, func, fill_value, level)\u001b[0m\n\u001b[1;32m 5086\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_series_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5087\u001b[0m \u001b[0;31m# iterate over columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5088\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_to_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_arith_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5089\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5090\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mdispatch_to_series\u001b[0;34m(left, right, func, str_rep, axis)\u001b[0m\n\u001b[1;32m 1155\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mreturn\u001b[0m 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\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mcolumn_op\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcolumn_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m return {i: func(a.iloc[:, i], b.iloc[:, i])\n\u001b[0;32m-> 1135\u001b[0;31m for i in range(len(a.columns))}\n\u001b[0m\u001b[1;32m 1136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcolumn_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m return {i: func(a.iloc[:, i], b.iloc[:, i])\n\u001b[0;32m-> 1135\u001b[0;31m for i in range(len(a.columns))}\n\u001b[0m\u001b[1;32m 1136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_arith_op\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m 5082\u001b[0m \u001b[0;31m# left._binop(right, func, fill_value=fill_value)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5083\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5084\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5085\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5086\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_series_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1848\u001b[0m filler = (fill_int if is_self_int_dtype and is_other_int_dtype\n\u001b[1;32m 1849\u001b[0m else fill_bool)\n\u001b[0;32m-> 1850\u001b[0;31m \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0movalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1851\u001b[0m unfilled = self._constructor(res_values,\n\u001b[1;32m 1852\u001b[0m index=self.index, name=res_name)\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 1795\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1796\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1797\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvec_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1798\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1799\u001b[0m \u001b[0;31m# let null fall thru\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.vec_binop\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.vec_binop\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for &: 'float' and 'bool'" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZbMc2EQFV6cb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1c0bb740-6d71-4521-fca4-1fd88c3135e0" + }, + "source": [ + "df[df['alive'] == 'yes'].shape" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(342, 16)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TLAecEOrWN7M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6642d1bd-c991-4777-bc43-2e68834e5806" + }, + "source": [ + "len(df[df['alive'] == 'yes'])" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "342" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 43 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b9BsCk3fWpR8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "3c49b2da-e480-4aaa-d3d0-d33e89884f6d" + }, + "source": [ + "df['alive'] .value_counts()" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 549\n", + "yes 342\n", + "Name: alive, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J5337oq4Wz1h", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gBcuDM3KW4aI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.describe(percentiles=[.2, .4, .75])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "kxsJipZnXOeK", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.describe(exclude=\"number\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TX60_FKkXXwr", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.describe(exclude=\"number\").T" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "j2QzFEw8Xdlj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "0219df76-0446-45f8-9120-eec4262bdc9d" + }, + "source": [ + "df['fare'].value_counts()" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "8.0500 43\n", + "13.0000 42\n", + "7.8958 38\n", + "7.7500 34\n", + "26.0000 31\n", + "10.5000 24\n", + "7.9250 18\n", + "7.7750 16\n", + "26.5500 15\n", + "0.0000 15\n", + "7.2292 15\n", + "7.8542 13\n", + "8.6625 13\n", + "7.2500 13\n", + "7.2250 12\n", + "16.1000 9\n", + "9.5000 9\n", + "24.1500 8\n", + "15.5000 8\n", + "56.4958 7\n", + "52.0000 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"b471642f-fe6a-4c45-f81b-b134ccf00a15" + }, + "source": [ + "df['fare'].hist()\n", + "\n" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "height": 1000 + }, + "outputId": "89909e77-1880-4f95-b3dc-d1e855dd362e" + }, + "source": [ + "import random\n", + "dir(random)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['BPF',\n", + " 'LOG4',\n", + " 'NV_MAGICCONST',\n", + " 'RECIP_BPF',\n", + " 'Random',\n", + " 'SG_MAGICCONST',\n", + " 'SystemRandom',\n", + " 'TWOPI',\n", + " '_BuiltinMethodType',\n", + " '_MethodType',\n", + " '_Sequence',\n", + " '_Set',\n", + " '__all__',\n", + " '__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " '_acos',\n", + " '_bisect',\n", + " '_ceil',\n", + " '_cos',\n", + " '_e',\n", + " '_exp',\n", + " '_inst',\n", + " '_itertools',\n", + " '_log',\n", + " '_pi',\n", + " '_random',\n", + " '_sha512',\n", + " '_sin',\n", + " '_sqrt',\n", + " '_test',\n", + " '_test_generator',\n", + " '_urandom',\n", + " '_warn',\n", + " 'betavariate',\n", + " 'choice',\n", + " 'choices',\n", + " 'expovariate',\n", + " 'gammavariate',\n", + " 'gauss',\n", + " 'getrandbits',\n", + " 'getstate',\n", + " 'lognormvariate',\n", + " 'normalvariate',\n", + " 'paretovariate',\n", + " 'randint',\n", + " 'random',\n", + " 'randrange',\n", + " 'sample',\n", + " 'seed',\n", + " 'setstate',\n", + " 'shuffle',\n", + " 'triangular',\n", + " 'uniform',\n", + " 'vonmisesvariate',\n", + " 'weibullvariate']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-3k3H3q1LLWV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from collections import namedtuple\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VMIsAwpEMfX-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ceaf42a3-1cd0-4d2e-d9db-12c18a6e408a" + }, + "source": [ + "user = namedtuple('user', ['age','excercise_time','weight'])\n", + "example_user =user(True, 150, True)\n", + "print(example_user)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "user(age=True, excercise_time=150, weight=True)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MdHJqQ5Jmi8R", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e8425ece-57ae-4256-bbf8-0f7d80b1edce" + }, + "source": [ + "random.uniform(5,300)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "290.278106151131" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WNqxu6aZm-BD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "00d0e935-c248-4c24-951e-4edb05ea2a2b" + }, + "source": [ + "random.random()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8310663135402135" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HFWVXPpYn98S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "afc5a57a-d591-40d7-beb6-44fbfd42fad3" + }, + "source": [ + "excercise_time = random.uniform(5,300)\n", + "excercise_time" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "74.93535783211424" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ok18D0uvqdOL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7e2aeb94-0942-444e-fe2c-aefb173f7bc5" + }, + "source": [ + "random.random() < 0.1 + (excercise_time / 1500)" + ], + "execution_count": 126, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 126 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4zzuxY2yq7VC", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + " \n", + "users = []\n", + " \n", + " \n", + "excercise_time = random.uniform(5,300)\n", + "age = random.random() < 0.1 + (excercise_time / 150)\n", + "users.append(user(age,excercise_time,True))\n", + "\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WZ0hSB5q1Vl2", + "colab_type": "code", + "colab": {} + }, + "source": [ + "excercise_time = random.uniform(10,600)\n", + "age = random.random() < 0.3 + (excercise_time / 1500)\n", + "users.append(user(age,excercise_time,False))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tHm5-tmksonp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ad259712-d089-474c-c7b7-2ba0b8d36fbe" + }, + "source": [ + "random.shuffle(users)\n", + "print(users[:10])" + ], + "execution_count": 129, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[user(age=True, excercise_time=32.64009575916977, weight=True), user(age=False, excercise_time=365.19105013948837, weight=False)]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BASDoNMKr1Az", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ef98951e-7732-4b8c-eeaa-cea91f1c6c2f" + }, + "source": [ + "random.shuffle(users)\n", + "print(users[:10])" + ], + "execution_count": 130, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[user(age=False, excercise_time=365.19105013948837, weight=False), user(age=True, excercise_time=32.64009575916977, weight=True)]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ii6mXeKlQIC_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "outputId": "2413519f-0857-4b2a-d776-1ad823da85bd" + }, + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "user_data = pd.DataFrame(users)\n", + "user_data.head()" + ], + "execution_count": 131, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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02012-01-01 00:00:00-1.8-3.98648.0101.24Fog
12012-01-01 01:00:00-1.8-3.78748.0101.24Fog
22012-01-01 02:00:00-1.8-3.48974.0101.26Freezing Drizzle,Fog
32012-01-01 03:00:00-1.5-3.28864.0101.27Freezing Drizzle,Fog
42012-01-01 04:00:00-1.5-3.38874.8101.23Fog
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Temp (C)Dew Point Temp (C)Rel Hum (%)Wind Spd (km/h)Visibility (km)Stn Press (kPa)Weather
Date/Time
2012-01-01 00:00:00-1.8-3.98648.0101.24Fog
2012-01-01 01:00:00-1.8-3.78748.0101.24Fog
2012-01-01 02:00:00-1.8-3.48974.0101.26Freezing Drizzle,Fog
2012-01-01 03:00:00-1.5-3.28864.0101.27Freezing Drizzle,Fog
2012-01-01 04:00:00-1.5-3.38874.8101.23Fog
2012-01-01 05:00:00-1.4-3.38796.4101.27Fog
2012-01-01 06:00:00-1.5-3.18976.4101.29Fog
2012-01-01 07:00:00-1.4-3.68578.0101.26Fog
2012-01-01 08:00:00-1.4-3.68598.0101.23Fog
2012-01-01 09:00:00-1.3-3.188154.0101.20Fog
2012-01-01 10:00:00-1.0-2.39191.2101.15Fog
2012-01-01 11:00:00-0.5-2.18974.0100.98Fog
2012-01-01 12:00:00-0.2-2.08894.8100.79Fog
2012-01-01 13:00:000.2-1.787134.8100.58Fog
2012-01-01 14:00:000.8-1.187204.8100.31Fog
2012-01-01 15:00:001.8-0.485226.4100.07Fog
2012-01-01 16:00:002.6-0.2821312.999.93Mostly Cloudy
2012-01-01 17:00:003.00.0811316.199.81Cloudy
2012-01-01 18:00:003.81.0821512.999.74Rain
2012-01-01 19:00:003.11.3881512.999.68Rain
2012-01-01 20:00:003.21.3871925.099.50Cloudy
2012-01-01 21:00:004.01.7852025.099.39Cloudy
2012-01-01 22:00:004.41.9842419.399.32Rain Showers
2012-01-01 23:00:005.32.0793025.099.31Cloudy
2012-01-02 00:00:005.21.5773525.099.26Rain Showers
2012-01-02 01:00:004.60.0723925.099.26Cloudy
2012-01-02 02:00:003.9-0.9713225.099.26Mostly Cloudy
2012-01-02 03:00:003.7-1.5693325.099.30Mostly Cloudy
2012-01-02 04:00:002.9-2.3693225.099.26Mostly Cloudy
2012-01-02 05:00:002.6-2.3703225.099.21Mostly Cloudy
........................
2012-12-30 18:00:00-12.6-16.0762425.0101.36Mainly Clear
2012-12-30 19:00:00-13.4-16.5772625.0101.47Mainly Clear
2012-12-30 20:00:00-13.8-16.5802425.0101.52Clear
2012-12-30 21:00:00-13.8-16.5802025.0101.50Mainly Clear
2012-12-30 22:00:00-13.7-16.3811925.0101.54Mainly Clear
2012-12-30 23:00:00-12.1-15.1782825.0101.52Mostly Cloudy
2012-12-31 00:00:00-11.1-14.4772625.0101.51Cloudy
2012-12-31 01:00:00-10.7-14.0771525.0101.50Cloudy
2012-12-31 02:00:00-10.1-13.477925.0101.45Cloudy
2012-12-31 03:00:00-11.8-14.481625.0101.42Mostly Cloudy
2012-12-31 04:00:00-10.5-12.8831125.0101.34Cloudy
2012-12-31 05:00:00-10.2-12.484625.0101.28Cloudy
2012-12-31 06:00:00-9.7-11.785425.0101.23Cloudy
2012-12-31 07:00:00-9.3-11.385019.3101.19Snow Showers
2012-12-31 08:00:00-8.6-10.38743.2101.14Snow Showers
2012-12-31 09:00:00-8.1-9.68942.4101.09Snow
2012-12-31 10:00:00-7.4-8.98946.4101.05Snow,Fog
2012-12-31 11:00:00-6.7-7.99199.7100.93Snow
2012-12-31 12:00:00-5.8-7.588412.9100.78Snow
2012-12-31 13:00:00-4.6-6.686412.9100.63Snow
2012-12-31 14:00:00-3.4-5.784611.3100.57Snow
2012-12-31 15:00:00-2.3-4.68499.7100.47Snow
2012-12-31 16:00:00-1.4-4.0821312.9100.40Snow
2012-12-31 17:00:00-1.1-3.385199.7100.30Snow
2012-12-31 18:00:00-1.3-3.188179.7100.19Snow
2012-12-31 19:00:000.1-2.781309.7100.13Snow
2012-12-31 20:00:000.2-2.483249.7100.03Snow
2012-12-31 21:00:00-0.5-1.593284.899.95Snow
2012-12-31 22:00:00-0.2-1.889289.799.91Snow
2012-12-31 23:00:000.0-2.1863011.399.89Snow
\n", + "

8784 rows × 7 columns

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" + ], + "text/plain": [ + " Temp (C) ... Weather\n", + "Date/Time ... \n", + "2012-01-01 00:00:00 -1.8 ... Fog\n", + "2012-01-01 01:00:00 -1.8 ... Fog\n", + "2012-01-01 02:00:00 -1.8 ... Freezing Drizzle,Fog\n", + "2012-01-01 03:00:00 -1.5 ... Freezing Drizzle,Fog\n", + "2012-01-01 04:00:00 -1.5 ... Fog\n", + "2012-01-01 05:00:00 -1.4 ... Fog\n", + "2012-01-01 06:00:00 -1.5 ... Fog\n", + "2012-01-01 07:00:00 -1.4 ... Fog\n", + "2012-01-01 08:00:00 -1.4 ... Fog\n", + "2012-01-01 09:00:00 -1.3 ... Fog\n", + "2012-01-01 10:00:00 -1.0 ... Fog\n", + "2012-01-01 11:00:00 -0.5 ... Fog\n", + "2012-01-01 12:00:00 -0.2 ... Fog\n", + "2012-01-01 13:00:00 0.2 ... Fog\n", + "2012-01-01 14:00:00 0.8 ... Fog\n", + "2012-01-01 15:00:00 1.8 ... Fog\n", + "2012-01-01 16:00:00 2.6 ... Mostly Cloudy\n", + "2012-01-01 17:00:00 3.0 ... Cloudy\n", + "2012-01-01 18:00:00 3.8 ... Rain\n", + "2012-01-01 19:00:00 3.1 ... Rain\n", + "2012-01-01 20:00:00 3.2 ... Cloudy\n", + "2012-01-01 21:00:00 4.0 ... Cloudy\n", + "2012-01-01 22:00:00 4.4 ... Rain Showers\n", + "2012-01-01 23:00:00 5.3 ... Cloudy\n", + "2012-01-02 00:00:00 5.2 ... Rain Showers\n", + "2012-01-02 01:00:00 4.6 ... Cloudy\n", + "2012-01-02 02:00:00 3.9 ... Mostly Cloudy\n", + "2012-01-02 03:00:00 3.7 ... Mostly Cloudy\n", + "2012-01-02 04:00:00 2.9 ... Mostly Cloudy\n", + "2012-01-02 05:00:00 2.6 ... Mostly Cloudy\n", + "... ... ... ...\n", + "2012-12-30 18:00:00 -12.6 ... Mainly Clear\n", + "2012-12-30 19:00:00 -13.4 ... Mainly Clear\n", + "2012-12-30 20:00:00 -13.8 ... Clear\n", + "2012-12-30 21:00:00 -13.8 ... Mainly Clear\n", + "2012-12-30 22:00:00 -13.7 ... Mainly Clear\n", + "2012-12-30 23:00:00 -12.1 ... Mostly Cloudy\n", + "2012-12-31 00:00:00 -11.1 ... Cloudy\n", + "2012-12-31 01:00:00 -10.7 ... Cloudy\n", + "2012-12-31 02:00:00 -10.1 ... Cloudy\n", + "2012-12-31 03:00:00 -11.8 ... Mostly Cloudy\n", + "2012-12-31 04:00:00 -10.5 ... Cloudy\n", + "2012-12-31 05:00:00 -10.2 ... Cloudy\n", + "2012-12-31 06:00:00 -9.7 ... Cloudy\n", + "2012-12-31 07:00:00 -9.3 ... Snow Showers\n", + "2012-12-31 08:00:00 -8.6 ... Snow Showers\n", + "2012-12-31 09:00:00 -8.1 ... Snow\n", + "2012-12-31 10:00:00 -7.4 ... Snow,Fog\n", + "2012-12-31 11:00:00 -6.7 ... Snow\n", + "2012-12-31 12:00:00 -5.8 ... Snow\n", + "2012-12-31 13:00:00 -4.6 ... Snow\n", + "2012-12-31 14:00:00 -3.4 ... Snow\n", + "2012-12-31 15:00:00 -2.3 ... Snow\n", + "2012-12-31 16:00:00 -1.4 ... Snow\n", + "2012-12-31 17:00:00 -1.1 ... Snow\n", + "2012-12-31 18:00:00 -1.3 ... Snow\n", + "2012-12-31 19:00:00 0.1 ... Snow\n", + "2012-12-31 20:00:00 0.2 ... Snow\n", + "2012-12-31 21:00:00 -0.5 ... Snow\n", + "2012-12-31 22:00:00 -0.2 ... Snow\n", + "2012-12-31 23:00:00 0.0 ... Snow\n", + "\n", + "[8784 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Asd0Wq-2Y5Hy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "288c7aa6-7137-4688-fd1b-7fd14ad0840f" + }, + "source": [ + "type(df_weather['Date/Time'])" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bs14Y_dJY-r7", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_list = [1,2,3]\n", + "x_arr = np.array([1,2,3])\n", + "x_series = pd.Series([1,2,3])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ltLodLa1ZADd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "3d8ebf9a-dd3d-4d22-d7fb-94300d1e9bcb" + }, + "source": [ + "for i in x_series:\n", + " print(i)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9L-b7f8nZEaK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c554e4a5-9aca-4a8f-f8dc-e1871edc4db5" + }, + "source": [ + "df_weather['Weather'].str.contains('Snow')" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 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"height": 1000 + }, + "outputId": "f1e32cc4-1bec-4207-8a00-0414ada72949" + }, + "source": [ + "df_weather['Weather'].str.contains('Rain')" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 True\n", + "19 True\n", + "20 False\n", + "21 False\n", + "22 True\n", + "23 False\n", + "24 True\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "8754 False\n", + "8755 False\n", + "8756 False\n", + "8757 False\n", + "8758 False\n", + "8759 False\n", + "8760 False\n", + "8761 False\n", + "8762 False\n", + "8763 False\n", + "8764 False\n", + "8765 False\n", + "8766 False\n", + "8767 False\n", + "8768 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Date/TimeTemp (C)Dew Point Temp (C)Rel Hum (%)Wind Spd (km/h)Visibility (km)Stn Press (kPa)Weatheris_percipitation
02012-01-01 00:00:00-1.8-3.98648.0101.24FogFalse
12012-01-01 01:00:00-1.8-3.78748.0101.24FogFalse
22012-01-01 02:00:00-1.8-3.48974.0101.26Freezing Drizzle,FogFalse
32012-01-01 03:00:00-1.5-3.28864.0101.27Freezing Drizzle,FogFalse
42012-01-01 04:00:00-1.5-3.38874.8101.23FogFalse
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" + ], + "text/plain": [ + " Date/Time Temp (C) ... Weather is_percipitation\n", + "0 2012-01-01 00:00:00 -1.8 ... Fog False\n", + "1 2012-01-01 01:00:00 -1.8 ... Fog False\n", + "2 2012-01-01 02:00:00 -1.8 ... Freezing Drizzle,Fog False\n", + "3 2012-01-01 03:00:00 -1.5 ... Freezing Drizzle,Fog False\n", + "4 2012-01-01 04:00:00 -1.5 ... Fog False\n", + "\n", + "[5 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nYvwcXm1ZUSr", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_weather['Visibility (mi)'] = df_weather['Visibility (km)']*0.621" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sK76_8rEZZM2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "3f51d16c-9198-42e5-cc72-b1f2a16c70e6" + }, + "source": [ + "df_weather.head()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Date/TimeTemp (C)Dew Point Temp (C)Rel Hum (%)Wind Spd (km/h)Visibility (km)Stn Press (kPa)Weatheris_percipitationVisibility (mi)
02012-01-01 00:00:00-1.8-3.98648.0101.24FogFalse4.9680
12012-01-01 01:00:00-1.8-3.78748.0101.24FogFalse4.9680
22012-01-01 02:00:00-1.8-3.48974.0101.26Freezing Drizzle,FogFalse2.4840
32012-01-01 03:00:00-1.5-3.28864.0101.27Freezing Drizzle,FogFalse2.4840
42012-01-01 04:00:00-1.5-3.38874.8101.23FogFalse2.9808
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" + ], + "text/plain": [ + " Date/Time Temp (C) ... is_percipitation Visibility (mi)\n", + "0 2012-01-01 00:00:00 -1.8 ... False 4.9680\n", + "1 2012-01-01 01:00:00 -1.8 ... False 4.9680\n", + "2 2012-01-01 02:00:00 -1.8 ... False 2.4840\n", + "3 2012-01-01 03:00:00 -1.5 ... False 2.4840\n", + "4 2012-01-01 04:00:00 -1.5 ... False 2.9808\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E4URvp2nZawN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "945ac930-8516-465d-e8df-b843a4e43ecb" + }, + "source": [ + "!wget https://resources.lendingclub.com/LoanStats_2018Q4.csv.zip" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2019-09-13 02:15:48-- https://resources.lendingclub.com/LoanStats_2018Q4.csv.zip\n", + "Resolving resources.lendingclub.com (resources.lendingclub.com)... 64.48.1.20\n", + "Connecting to resources.lendingclub.com (resources.lendingclub.com)|64.48.1.20|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified [application/zip]\n", + "Saving to: ‘LoanStats_2018Q4.csv.zip’\n", + "\n", + "LoanStats_2018Q4.cs [ <=>] 21.58M 886KB/s in 25s \n", + "\n", + "2019-09-13 02:16:14 (879 KB/s) - ‘LoanStats_2018Q4.csv.zip’ saved [22631049]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wFF4JISbZ5gl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "a7ff5e49-cb9b-413d-b522-5153f405f776" + }, + "source": [ + "!unzip LoanStats_2018Q4.csv.zip" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Archive: LoanStats_2018Q4.csv.zip\n", + " inflating: LoanStats_2018Q4.csv \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nqsFBA-saCw7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 207 + }, + "outputId": "aa135fda-43bf-4275-dd00-32ee2357f45c" + }, + "source": [ + "!head LoanStats_2018Q4.csv" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)\n", + "\"id\",\"member_id\",\"loan_amnt\",\"funded_amnt\",\"funded_amnt_inv\",\"term\",\"int_rate\",\"installment\",\"grade\",\"sub_grade\",\"emp_title\",\"emp_length\",\"home_ownership\",\"annual_inc\",\"verification_status\",\"issue_d\",\"loan_status\",\"pymnt_plan\",\"url\",\"desc\",\"purpose\",\"title\",\"zip_code\",\"addr_state\",\"dti\",\"delinq_2yrs\",\"earliest_cr_line\",\"inq_last_6mths\",\"mths_since_last_delinq\",\"mths_since_last_record\",\"open_acc\",\"pub_rec\",\"revol_bal\",\"revol_util\",\"total_acc\",\"initial_list_status\",\"out_prncp\",\"out_prncp_inv\",\"total_pymnt\",\"total_pymnt_inv\",\"total_rec_prncp\",\"total_rec_int\",\"total_rec_late_fee\",\"recoveries\",\"collection_recovery_fee\",\"last_pymnt_d\",\"last_pymnt_amnt\",\"next_pymnt_d\",\"last_credit_pull_d\",\"collections_12_mths_ex_med\",\"mths_since_last_major_derog\",\"policy_code\",\"application_type\",\"annual_inc_joint\",\"dti_joint\",\"verification_status_joint\",\"acc_now_delinq\",\"tot_coll_amt\",\"tot_cur_bal\",\"open_acc_6m\",\"open_act_il\",\"open_il_12m\",\"open_il_24m\",\"mths_since_rcnt_il\",\"total_bal_il\",\"il_util\",\"open_rv_12m\",\"open_rv_24m\",\"max_bal_bc\",\"all_util\",\"total_rev_hi_lim\",\"inq_fi\",\"total_cu_tl\",\"inq_last_12m\",\"acc_open_past_24mths\",\"avg_cur_bal\",\"bc_open_to_buy\",\"bc_util\",\"chargeoff_within_12_mths\",\"delinq_amnt\",\"mo_sin_old_il_acct\",\"mo_sin_old_rev_tl_op\",\"mo_sin_rcnt_rev_tl_op\",\"mo_sin_rcnt_tl\",\"mort_acc\",\"mths_since_recent_bc\",\"mths_since_recent_bc_dlq\",\"mths_since_recent_inq\",\"mths_since_recent_revol_delinq\",\"num_accts_ever_120_pd\",\"num_actv_bc_tl\",\"num_actv_rev_tl\",\"num_bc_sats\",\"num_bc_tl\",\"num_il_tl\",\"num_op_rev_tl\",\"num_rev_accts\",\"num_rev_tl_bal_gt_0\",\"num_sats\",\"num_tl_120dpd_2m\",\"num_tl_30dpd\",\"num_tl_90g_dpd_24m\",\"num_tl_op_past_12m\",\"pct_tl_nvr_dlq\",\"percent_bc_gt_75\",\"pub_rec_bankruptcies\",\"tax_liens\",\"tot_hi_cred_lim\",\"total_bal_ex_mort\",\"total_bc_limit\",\"total_il_high_credit_limit\",\"revol_bal_joint\",\"sec_app_earliest_cr_line\",\"sec_app_inq_last_6mths\",\"sec_app_mort_acc\",\"sec_app_open_acc\",\"sec_app_revol_util\",\"sec_app_open_act_il\",\"sec_app_num_rev_accts\",\"sec_app_chargeoff_within_12_mths\",\"sec_app_collections_12_mths_ex_med\",\"sec_app_mths_since_last_major_derog\",\"hardship_flag\",\"hardship_type\",\"hardship_reason\",\"hardship_status\",\"deferral_term\",\"hardship_amount\",\"hardship_start_date\",\"hardship_end_date\",\"payment_plan_start_date\",\"hardship_length\",\"hardship_dpd\",\"hardship_loan_status\",\"orig_projected_additional_accrued_interest\",\"hardship_payoff_balance_amount\",\"hardship_last_payment_amount\",\"debt_settlement_flag\",\"debt_settlement_flag_date\",\"settlement_status\",\"settlement_date\",\"settlement_amount\",\"settlement_percentage\",\"settlement_term\"\n", + "\"\",\"\",\"10000\",\"10000\",\"10000\",\" 36 months\",\" 10.33%\",\"324.23\",\"B\",\"B1\",\"\",\"< 1 year\",\"MORTGAGE\",\"280000\",\"Not Verified\",\"Dec-2018\",\"Current\",\"n\",\"\",\"\",\"debt_consolidation\",\"Debt 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"code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 + }, + "outputId": "98472e46-3bad-4da2-a1db-79067f0fa41c" + }, + "source": [ + "df = pd.read_csv('LoanStats_2018Q4.csv')\n", + "df.head()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,1,2,3,4,7,13,18,19,24,25,27,28,29,30,31,32,34,36,37,38,39,40,41,42,43,44,46,49,50,51,53,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,123,124,125,126,127,128,129,130,131,132,133,134,135,136,138,139,140,141,142,143) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)
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\n", + "
" + ], + "text/plain": [ + " Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)\n", + "id member_id loan_amnt funded_amnt funded_amnt_inv term int_rate installment grade sub_grade emp_title emp_length home_ownership annual_inc verification_status issue_d loan_status pymnt_plan url desc purpose title zip_code addr_state dti delinq_2yrs earliest_cr_line inq_last_6mths mths_since_last_delinq mths_since_last_record open_acc pub_rec revol_bal revol_util total_acc initial_list_status out_prncp out_prncp_inv total_pymnt total_pymnt_inv total_rec_prncp total_rec_int total_rec_late_fee recoveries collection_recovery_fee last_pymnt_d last_pymnt_amnt next_pymnt_d last_credit_pull_d collections_12_mths_ex_med mths_since_last_major_derog policy_code application_type annual_inc_joint dti_joint verification_status_joint acc_now_delinq tot_coll_amt tot_cur_bal open_acc_6m open_act_il open_il_12m open_il_24m mths_since_rcnt_il total_bal_il il_util open_rv_12m open_rv_24m 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{ + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "k-mJqiO9aLRM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 372 + }, + "outputId": "acfaf61e-219d-4973-c745-d6ad3d291b69" + }, + "source": [ + "df = pd.read_csv('LoanStats_2018Q4.csv', header=1)\n", + "df.head()" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,123,124,125,128,129,130,133,138,139,140) have mixed types. 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3NaNNaN230002300023000.060 months20.89%620.81DD4Operator5 yearsRENT68107.0Source VerifiedDec-2018CurrentnNaNNaNdebt_consolidationDebt consolidation672xxKS0.520Feb-19970NaNNaN5097613%10w21353.1621353.164307.454307.45...0.000750097633000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
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"128408 15.02%\n", + "128409 13.56%\n", + "128410 11.06%\n", + "128411 16.91%\n", + "Name: int_rate, Length: 128412, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 43 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u3pHnh2_bpj8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "46ee1c59-faff-435f-fd4d-712b081f60c4" + }, + "source": [ + "a = '16.91%'\n", + "a[:-1]" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'16.91'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5vxnQrutbq9r", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "48a8133a-68b8-4bd0-8d37-be7be7ae77fc" + }, + "source": [ + "a.strip('%')" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'16.91'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 45 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mhk5MmCdbvax", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "dad8be77-b756-48a3-c1f9-d3ed49e72b29" + }, + "source": [ + "b = '%16%.91%'\n", + "b.strip('%')" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'16%.91'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 46 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uCQ2JFgxbx1-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6d864d33-e602-4d19-b656-5ca9cc183fc6" + }, + "source": [ + "b[:-1]" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'%16%.91'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 47 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8CoK7tJbb1Hd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "987a6ed2-1b80-4525-9a90-e16f32b0335f" + }, + "source": [ + "b.replace('%', '')" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'16.91'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Al9AETy8b3yu", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "914d032e-da45-44ef-f41a-ded90a80aa04" + }, + "source": [ + "float(b.replace(\"%\", ''))" + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "16.91" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 49 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1-jolqRLb6-i", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def remove_percent_to_float(x):\n", + " return float(x[:-1])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7V7Kb29sb_QG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "remove_percent_to_float_v2 = lambda x: float(x[:-1])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sJqLX-9gcCLa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "cc76959c-e7ed-470c-fe29-cfe7851103ab" + }, + "source": [ + "remove_percent_to_float('16.91%')" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "16.91" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 53 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VyjrBmF7cKSR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1a4f3b96-8af4-4da7-e246-c57daba6a27f" + }, + "source": [ + "remove_percent_to_float_v2('16.91%')" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "16.91" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 54 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xwodc7rNcLmQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9ecceee1-6319-4ebe-fd00-5cecb85cff93" + }, + "source": [ + "a = ['16.91%', '12.23%', '15.75%']\n", + "[remove_percent_to_float(i) for i in a]" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[16.91, 12.23, 15.75]" + ] + }, + "metadata": { + "tags": [] + }, + 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0100001000010000.036 months10.33324.23BB1NaN< 1 yearMORTGAGE280000.0Not VerifiedDec-2018Currentndebt_consolidationDebt consolidation974xxOR6.152Jan-1996018.0...367828613642090054912NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
1400040004000.036 months23.40155.68EE1Security3 yearsRENT90000.0Source VerifiedDec-2018Currentndebt_consolidationDebt consolidation070xxNJ26.330Sep-2006459.0...98655669262190071555NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
2500050005000.036 months17.97180.69DD1Administrative6 yearsMORTGAGE59280.0Source VerifiedDec-2018Late (31-120 days)ndebt_consolidationDebt consolidation490xxMI10.510Apr-20110NaN...136927117491380010000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
3230002300023000.060 months20.89620.81DD4Operator5 yearsRENT68107.0Source VerifiedDec-2018Currentndebt_consolidationDebt consolidation672xxKS0.520Feb-19970NaN...750097633000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
4800080008000.036 months23.40311.35EE1Manager10+ yearsOWN43000.0Source VerifiedDec-2018Currentndebt_consolidationDebt consolidation357xxAL33.240Jan-19950NaN...19974431078230032206NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaN
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title zip_code addr_state dti \\\n", + "0 debt_consolidation Debt consolidation 974xx OR 6.15 \n", + "1 debt_consolidation Debt consolidation 070xx NJ 26.33 \n", + "2 debt_consolidation Debt consolidation 490xx MI 10.51 \n", + "3 debt_consolidation Debt consolidation 672xx KS 0.52 \n", + "4 debt_consolidation Debt consolidation 357xx AL 33.24 \n", + "\n", + " delinq_2yrs earliest_cr_line inq_last_6mths mths_since_last_delinq ... \\\n", + "0 2 Jan-1996 0 18.0 ... \n", + "1 0 Sep-2006 4 59.0 ... \n", + "2 0 Apr-2011 0 NaN ... \n", + "3 0 Feb-1997 0 NaN ... \n", + "4 0 Jan-1995 0 NaN ... \n", + "\n", + " tot_hi_cred_lim total_bal_ex_mort total_bc_limit \\\n", + "0 367828 61364 20900 \n", + "1 98655 66926 21900 \n", + "2 136927 11749 13800 \n", + "3 7500 976 3300 \n", + "4 199744 31078 2300 \n", + "\n", + " total_il_high_credit_limit revol_bal_joint sec_app_earliest_cr_line \\\n", + "0 54912 NaN NaN \n", + "1 71555 NaN NaN \n", + "2 10000 NaN NaN \n", + "3 0 NaN NaN \n", + "4 32206 NaN NaN 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'unknown'" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "JLx-PXT5clIl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df['emp_title'] = df['emp_title'].apply(clean_title)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yKzZ9exXcng4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "outputId": "b64ae218-c63c-472f-8c93-50ff5328ab0c" + }, + "source": [ + "df['emp_title'].value_counts(dropna=False).head(20)" + ], + "execution_count": 64, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "unknown 20947\n", + "teacher 2557\n", + "manager 2395\n", + "registered nurse 1418\n", + "driver 1258\n", + "supervisor 1160\n", + "truck driver 920\n", + "rn 834\n", + "office manager 805\n", + "sales 803\n", + "general manager 791\n", + "project manager 720\n", + "owner 625\n", + "director 523\n", + "operations manager 518\n", + "sales manager 500\n", + "police officer 440\n", + "nurse 425\n", + "technician 420\n", + "engineer 412\n", + "Name: emp_title, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 64 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KmZ9irWZcqaY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1e1e3d91-4376-4735-ed12-6a5d40a6f724" + }, + "source": [ + "type(np.NaN)" + ], + "execution_count": 67, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "float" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 67 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "B7M1GNuzfoZ9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FYaWT5tgctgG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 129 + }, + "outputId": "1522b3c9-b12f-407e-e980-c0043b630117" + }, + "source": [ + "dd.emp_title_manager = df['emp_title'].str." + ], + "execution_count": 68, + "outputs": [ + { + "output_type": "error", + "ename": "SyntaxError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m dd.emp_title_manager = df['emp_title'].str.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qf35tppOfx-e", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Copy_of_DS_Unit_1_Sprint_Challenge_1_0919.ipynb b/Copy_of_DS_Unit_1_Sprint_Challenge_1_0919.ipynb new file mode 100644 index 00000000..0d8750e3 --- /dev/null +++ b/Copy_of_DS_Unit_1_Sprint_Challenge_1_0919.ipynb @@ -0,0 +1,2563 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of DS_Unit_1_Sprint_Challenge_1_0919.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NooAiTdnafkz", + "colab_type": "text" + }, + "source": [ + "# Data Science Unit 1 Sprint Challenge 1\n", + "\n", + "## Loading, cleaning, visualizing, and analyzing data\n", + "\n", + "In this sprint challenge you will look at a dataset of the survival of patients who underwent surgery for breast cancer.\n", + "\n", + "http://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival\n", + "\n", + "Data Set Information:\n", + "The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.\n", + "\n", + "Attribute Information:\n", + "1. Age of patient at time of operation (numerical)\n", + "2. Patient's year of operation (year - 1900, numerical)\n", + "3. Number of positive axillary nodes detected (numerical)\n", + "4. Survival status (class attribute)\n", + "-- 1 = the patient survived 5 years or longer\n", + "-- 2 = the patient died within 5 year\n", + "\n", + "Sprint challenges are evaluated based on satisfactory completion of each part. It is suggested you work through it in order, getting each aspect reasonably working, before trying to deeply explore, iterate, or refine any given step. Once you get to the end, if you want to go back and improve things, go for it!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DUjOBLFAr3A5", + "colab_type": "text" + }, + "source": [ + "## Part 0 - Revert your version of Pandas right from the start\n", + "I don't want any of you to get stuck because of Pandas bugs, so right from the get-go revert back to version `0.23.4`\n", + "- Run the cell below\n", + "- Then restart your runtime. Go to `Runtime` -> `Restart runtime...` in the top menu (or click the \"RESTART RUNTIME\" button that shows up in the output of the cell below).\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PWq6GbkjsRYQ", + "colab_type": "code", + "outputId": "41f3b032-8b20-4bfa-98da-e54bc5ec6d29", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + } + }, + "source": [ + "!pip install pandas==0.23.4" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas==0.23.4 in /usr/local/lib/python3.6/dist-packages (0.23.4)\n", + "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from pandas==0.23.4) (1.16.5)\n", + "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas==0.23.4) (2.5.3)\n", + "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas==0.23.4) (2018.9)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.5.0->pandas==0.23.4) (1.12.0)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5wch6ksCbJtZ", + "colab_type": "text" + }, + "source": [ + "## Part 1 - Load and validate the data\n", + "\n", + "- Load the data as a `pandas` data frame.\n", + "- Validate that it has the appropriate number of observations (you can check the raw file, and also read the dataset description from UCI).\n", + "- Validate that you have no missing values.\n", + "- Add informative names to the features.\n", + "- The survival variable is encoded as 1 for surviving >5 years and 2 for not - change this to be 0 for not surviving and 1 for surviving >5 years (0/1 is a more traditional encoding of binary variables)\n", + "\n", + "At the end, print the first five rows of the dataset to demonstrate the above." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "287TpoGKFRVK", + "colab_type": "code", + "colab": {} + }, + "source": [ + "haberman_data_url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5CkQQTsH-QZ-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "\n", + "haberman_data = pd.read_csv(haberman_data_url)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "UxoAK3tI-Qdy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "outputId": "73a9b743-8041-42a4-ca63-924b3a9657be" + }, + "source": [ + "haberman_data.head(20)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age year num-pos-nodes survived\n", + "0 30 64 1 1\n", + "1 30 62 3 1\n", + "2 30 65 0 1\n", + "3 31 59 2 1\n", + "4 31 65 4 1\n", + "5 33 58 10 1\n", + "6 33 60 0 1\n", + "7 34 59 0 0\n", + "8 34 66 9 0\n", + "9 34 58 30 1\n", + "10 34 60 1 1\n", + "11 34 61 10 1\n", + "12 34 67 7 1\n", + "13 34 60 0 1\n", + "14 35 64 13 1\n", + "15 35 63 0 1\n", + "16 36 60 1 1\n", + "17 36 69 0 1\n", + "18 37 60 0 1\n", + "19 37 63 0 1" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7DMQqHKsC4gV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "3fb72017-65f4-4a52-c131-519edffae2c6" + }, + "source": [ + "df.year.unique()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([64, 62, 65, 59, 58, 60, 66, 61, 67, 63, 69, 68])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G7rLytbrO38L", + "colab_type": "text" + }, + "source": [ + "## Part 2 - Examine the distribution and relationships of the features\n", + "\n", + "Explore the data - create at least *2* tables (can be summary statistics or crosstabulations) and *2* plots illustrating the nature of the data.\n", + "\n", + "This is open-ended, so to remind - first *complete* this task as a baseline, then go on to the remaining sections, and *then* as time allows revisit and explore further.\n", + "\n", + "Hint - you may need to bin some variables depending on your chosen tables/plots." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IAkllgCIFVj0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vy1lUxNbA9R3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "outputId": "f265d6de-579a-495a-91a1-b8fff6d5a517" + }, + "source": [ + "df['survived'].hist()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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(41.6, 52.0]\n", + "age ... \n", + "(29.947, 40.6] 38 ... 0\n", + "(40.6, 51.2] 85 ... 1\n", + "(51.2, 61.8] 83 ... 1\n", + "(61.8, 72.4] 51 ... 0\n", + "(72.4, 83.0] 9 ... 0\n", + "\n", + "[5 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LPgSrFbHIEFa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "42e4c643-3c8a-43fa-a66a-d7ec0290740d" + }, + "source": [ + "ct3.plot(kind=\"bar\",stacked=True)" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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(67.167, 69.0]\n", + "age ... \n", + "(29.947, 40.6] 11 ... 2\n", + "(40.6, 51.2] 21 ... 5\n", + "(51.2, 61.8] 16 ... 11\n", + "(61.8, 72.4] 14 ... 5\n", + "(72.4, 83.0] 1 ... 1\n", + "\n", + "[5 rows x 6 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 51 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NliBPZ74NbXa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "258ec42a-8882-4685-a6ac-eb8a40d848ad" + }, + "source": [ + "ct4.plot(kind=\"bar\",stacked=True)" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 52 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + "age (29.947, 40.6] (40.6, 51.2] ... (61.8, 72.4] (72.4, 83.0]\n", + "year ... \n", + "58 6 10 ... 11 1\n", + "59 5 11 ... 3 0\n", + "60 9 6 ... 2 0\n", + "61 1 10 ... 7 0\n", + "62 2 7 ... 3 2\n", + "63 5 12 ... 5 1\n", + "64 3 13 ... 4 0\n", + "65 3 9 ... 4 3\n", + "66 4 8 ... 9 0\n", + "67 3 8 ... 6 1\n", + "68 0 2 ... 5 1\n", + "69 2 3 ... 0 0\n", + "\n", + "[12 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 57 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jLrCXEirOD-L", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303 + }, + "outputId": "784ebbf7-95fc-4929-f9e8-5529b10c2d70" + }, + "source": [ + "ct5.plot(kind=\"bar\",stacked=True)" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 58 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " age year num-pos-nodes survived\n", + "0 30 64 1 1\n", + "1 30 62 3 1\n", + "2 30 65 0 1\n", + "3 31 59 2 1\n", + "4 31 65 4 1" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZM8JckA2bgnp", + "colab_type": "text" + }, + "source": [ + "## Part 4 - Analysis and Interpretation\n", + "\n", + "Now that you've looked at the data, answer the following questions:\n", + "\n", + "- What is at least one feature that looks to have a positive relationship with survival? (As that feature goes up in value rate of survival increases)\n", + "- What is at least one feature that looks to have a negative relationship with survival? (As that feature goes down in value rate of survival increases)\n", + "- How are those two features related with each other, and what might that mean?\n", + "\n", + "Answer with text, but feel free to intersperse example code/results or refer to it from earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6dKITTOVtHWo", + "colab_type": "text" + }, + "source": [ + "Your Text Answer Here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nB4jP6dfMedB", + "colab_type": "text" + }, + "source": [ + "What is at least one feature that looks to have a positive relationship with survival?\n", + "\n", + "not found yet" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "90OSBGhOjk1b", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EitUf98zQ10s", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G-hInHT6Lzr_", + "colab_type": "text" + }, + "source": [ + "What is at least one feature that looks to have a negative relationship with survival?\n", + "\n", + "Age: we find that the survival rate is higher if the patient is younger.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QOT3dr5RQfbI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file