{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Семинар 2. Краткий обзор Numpy. Pandas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy\n", "\n", "numpy - одно из фундаментальных расширений языка Python для выполнения научных вычислений. Кроме всего прочего предоставляет:\n", "\n", " Мощный объект для работы с данными - N-мерный массив\n", " Высокоуровневые математические функции\n", " Инструменты для интеграции программного кода на C/C++ и Fortran\n", " Реализации функций линейной алгебры, преобразования Фурье, генерации случайных чисел\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Основные структуры в pandas:\n", "\n", "Series – проиндексированный вектор значений. Имя элемента соответствует индексу, а значение – значению записи.\n", "\n", "DataFrame — проиндексированный многомерный массив значений, соответственно каждый столбец DataFrame является структурой Series." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Немного о Series" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2\n", "1 3\n", "2 6\n", "3 9\n", "4 m\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series([2, 3, 6, 9, 'm'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial\n", "a 0.443897\n", "b -1.067706\n", "c -0.881259\n", "d 1.648285\n", "e -2.120162\n", "dtype: float64 \n", "\n", "Modified\n", "a 0.443897\n", "b -1.067706\n", "c -0.881259\n", "d 2.000000\n", "e -2.120162\n", "dtype: float64\n" ] } ], "source": [ "s1 = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n", "print 'Initial\\n', s1, '\\n'\n", "s1['d'] = 2\n", "print 'Modified\\n', s1" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "a 0\n", "b 0\n", "c 0\n", "d 0\n", "e 0\n", "dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(0, index=['a', 'b', 'c', 'd', 'e'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Поведение Series немного похоже на поведение ndarray**" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.36872459490328752" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1[0]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "c 0.471481\n", "d 2.000000\n", "dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1[s1 > 0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "e -1.024658\n", "c 0.471481\n", "b -0.201952\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1[[4, 2, 1]]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a 0.691616\n", "b 0.817134\n", "c 1.602365\n", "d 7.389056\n", "e 0.358919\n", "dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(s1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Арифметика**" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a -0.737449\n", "b -0.403905\n", "c 0.942961\n", "d 4.000000\n", "e -2.049315\n", "dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 + s1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a NaN\n", "b -0.403905\n", "c 0.942961\n", "d 4.000000\n", "e NaN\n", "dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1[1:] + s1[:-1]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3, 7, 13, 17])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar1 = np.array([1, 2, 5, 8, 9])\n", "ar1[1:] + ar1[:-1]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'grades'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = pd.Series([2, 5, 4, 3, 2], name='grades')\n", "s2.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrame - наиболее часто используемый объект в pandas. Для инициализации можно использовать\n", "\n", " Словарь, в котором значениями являются 1D ndarray, списки, словари или Series\n", " 2-D numpy.ndarray\n", " Другой DataFrame\n", " И т. д.\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df1 = pd.DataFrame({'age': [20, 18, 17, 19, 18],\n", " 'city': ['Msk', 'Spb', 'Msk', 'Nov', 'Tmn'],\n", " 'name': ['Alexander', 'Maria', 'Daria', 'Nikolay', 'Anatoliy'],\n", " 'sex': ['M', 'F', 'F', 'M', 'M']})" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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agecityname
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" ], "text/plain": [ " age city name\n", "0 20 Msk Alexander\n", "1 18 Spb Maria" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.iloc[:2, :3]" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age city name sex\n", "0 20 Msk Alexander M\n", "2 17 Msk Daria F" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1[df1.city == 'Msk']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Что можно узнать о данных?**" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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age
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" ], "text/plain": [ " age city name sex\n", "2 17 Msk Daria F\n", "3 19 Nov Nikolay M\n", "1 18 Spb Maria F\n", "4 18 Tmn Anatoliy M" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.ix[df1.groupby('city').age.idxmin()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Операции с таблицами**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Вставка столбцов" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "country = pd.Series('RU', index=range(5))\n", "df1.insert(2,'country',country)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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agecitycountrynamesex
020MskRUAlexanderM
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020NaNNaNKaterinaF
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" ], "text/plain": [ " age city country name sex\n", "0 20 Msk RU Alexander M\n", "1 18 Spb RU Maria F\n", "2 17 Msk RU Daria F\n", "3 19 Nov RU Nikolay M\n", "4 18 Tmn RU Anatoliy M\n", "0 20 NaN NaN Katerina F\n", "1 21 NaN NaN Boris M" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.DataFrame({'age': [20, 21],\n", " 'name': ['Katerina', 'Boris'],\n", " 'sex': ['F', 'M']})\n", "df4 = df1.append(df2)\n", "df4" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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agecitycountrynamesex
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" ], "text/plain": [ " age city country name sex\n", "0 20 Msk RU Alexander M\n", "1 18 Spb RU Maria F\n", "2 17 Msk RU Daria F\n", "3 19 Nov RU Nikolay M\n", "4 18 Tmn RU Anatoliy M" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df3 = pd.DataFrame({'city': ['Msk', 'Spb', 'Nov', 'Tmn'],\n", " 'population': [15.6, 5.2, 0.22, 0.7]})" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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agecitycountrynamesexpopulation
020MskRUAlexanderM15.60
117MskRUDariaF15.60
218SpbRUMariaF5.20
319NovRUNikolayM0.22
418TmnRUAnatoliyM0.70
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" ], "text/plain": [ " age city country name sex population\n", "0 20 Msk RU Alexander M 15.60\n", "1 17 Msk RU Daria F 15.60\n", "2 18 Spb RU Maria F 5.20\n", "3 19 Nov RU Nikolay M 0.22\n", "4 18 Tmn RU Anatoliy M 0.70" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.merge(df3, on='city')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Работа с данными**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train = pd.read_csv('../Data/titanic.csv')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
4503Allen, Mr. William Henrymale35003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale23134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female270234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female141023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale411PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale580011378326.5500C103S
121303Saundercock, Mr. William Henrymale2000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale391534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female550024870616.0000NaNS
161703Rice, Master. Eugenemale24138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female311034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale350023986526.0000NaNS
212212Beesley, Mr. Lawrencemale340024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale280011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale83134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female381534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale193219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
86186202Giles, Mr. Frederick Edwardmale21102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48001746625.9292D17S
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86486502Gill, Mr. John Williammale240023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female420023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale2710SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale3100PC 1759050.4958A24S
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
86987013Johnson, Master. Harold Theodormale41134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female2810P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female150026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale200075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19003492127.8958NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female250123043326.0000NaNS
88188203Markun, Mr. Johannmale33003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale2200755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale2800C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale2500SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female390538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale270021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale190011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale260011136930.0000C148C
89089103Dooley, Mr. Patrickmale32003703767.7500NaNQ
\n", "

891 rows × 12 columns

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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "10 11 1 3 \n", "11 12 1 1 \n", "12 13 0 3 \n", "13 14 0 3 \n", "14 15 0 3 \n", "15 16 1 2 \n", "16 17 0 3 \n", "17 18 1 2 \n", "18 19 0 3 \n", "19 20 1 3 \n", "20 21 0 2 \n", "21 22 1 2 \n", "22 23 1 3 \n", "23 24 1 1 \n", "24 25 0 3 \n", "25 26 1 3 \n", "26 27 0 3 \n", "27 28 0 1 \n", "28 29 1 3 \n", "29 30 0 3 \n", ".. ... ... ... \n", "861 862 0 2 \n", "862 863 1 1 \n", "863 864 0 3 \n", "864 865 0 2 \n", "865 866 1 2 \n", "866 867 1 2 \n", "867 868 0 1 \n", "868 869 0 3 \n", "869 870 1 3 \n", "870 871 0 3 \n", "871 872 1 1 \n", "872 873 0 1 \n", "873 874 0 3 \n", "874 875 1 2 \n", "875 876 1 3 \n", "876 877 0 3 \n", "877 878 0 3 \n", "878 879 0 3 \n", "879 880 1 1 \n", "880 881 1 2 \n", "881 882 0 3 \n", "882 883 0 3 \n", "883 884 0 2 \n", "884 885 0 3 \n", "885 886 0 3 \n", "886 887 0 2 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", "2 Heikkinen, Miss. Laina female 26 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", "4 Allen, Mr. William Henry male 35 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54 0 \n", "7 Palsson, Master. Gosta Leonard male 2 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 \n", "10 Sandstrom, Miss. Marguerite Rut female 4 1 \n", "11 Bonnell, Miss. Elizabeth female 58 0 \n", "12 Saundercock, Mr. William Henry male 20 0 \n", "13 Andersson, Mr. Anders Johan male 39 1 \n", "14 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 \n", "15 Hewlett, Mrs. (Mary D Kingcome) female 55 0 \n", "16 Rice, Master. Eugene male 2 4 \n", "17 Williams, Mr. Charles Eugene male NaN 0 \n", "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31 1 \n", "19 Masselmani, Mrs. Fatima female NaN 0 \n", "20 Fynney, Mr. Joseph J male 35 0 \n", "21 Beesley, Mr. Lawrence male 34 0 \n", "22 McGowan, Miss. Anna \"Annie\" female 15 0 \n", "23 Sloper, Mr. William Thompson male 28 0 \n", "24 Palsson, Miss. Torborg Danira female 8 3 \n", "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38 1 \n", "26 Emir, Mr. Farred Chehab male NaN 0 \n", "27 Fortune, Mr. Charles Alexander male 19 3 \n", "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", "29 Todoroff, Mr. Lalio male NaN 0 \n", ".. ... ... ... ... \n", "861 Giles, Mr. Frederick Edward male 21 1 \n", "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48 0 \n", "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", "864 Gill, Mr. John William male 24 0 \n", "865 Bystrom, Mrs. (Karolina) female 42 0 \n", "866 Duran y More, Miss. Asuncion female 27 1 \n", "867 Roebling, Mr. Washington Augustus II male 31 0 \n", "868 van Melkebeke, Mr. Philemon male NaN 0 \n", "869 Johnson, Master. Harold Theodor male 4 1 \n", "870 Balkic, Mr. Cerin male 26 0 \n", "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 \n", "872 Carlsson, Mr. Frans Olof male 33 0 \n", "873 Vander Cruyssen, Mr. Victor male 47 0 \n", "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 \n", "875 Najib, Miss. Adele Kiamie \"Jane\" female 15 0 \n", "876 Gustafsson, Mr. Alfred Ossian male 20 0 \n", "877 Petroff, Mr. Nedelio male 19 0 \n", "878 Laleff, Mr. Kristo male NaN 0 \n", "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 \n", "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 \n", "881 Markun, Mr. Johann male 33 0 \n", "882 Dahlberg, Miss. Gerda Ulrika female 22 0 \n", "883 Banfield, Mr. Frederick James male 28 0 \n", "884 Sutehall, Mr. Henry Jr male 25 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39 0 \n", "886 Montvila, Rev. Juozas male 27 0 \n", "887 Graham, Miss. Margaret Edith female 19 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26 0 \n", "890 Dooley, Mr. Patrick male 32 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", "6 0 17463 51.8625 E46 S \n", "7 1 349909 21.0750 NaN S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C \n", "10 1 PP 9549 16.7000 G6 S \n", "11 0 113783 26.5500 C103 S \n", "12 0 A/5. 2151 8.0500 NaN S \n", "13 5 347082 31.2750 NaN S \n", "14 0 350406 7.8542 NaN S \n", "15 0 248706 16.0000 NaN S \n", "16 1 382652 29.1250 NaN Q \n", "17 0 244373 13.0000 NaN S \n", "18 0 345763 18.0000 NaN S \n", "19 0 2649 7.2250 NaN C \n", "20 0 239865 26.0000 NaN S \n", "21 0 248698 13.0000 D56 S \n", "22 0 330923 8.0292 NaN Q \n", "23 0 113788 35.5000 A6 S \n", "24 1 349909 21.0750 NaN S \n", "25 5 347077 31.3875 NaN S \n", "26 0 2631 7.2250 NaN C \n", "27 2 19950 263.0000 C23 C25 C27 S \n", "28 0 330959 7.8792 NaN Q \n", "29 0 349216 7.8958 NaN S \n", ".. ... ... ... ... ... \n", "861 0 28134 11.5000 NaN S \n", "862 0 17466 25.9292 D17 S \n", "863 2 CA. 2343 69.5500 NaN S \n", "864 0 233866 13.0000 NaN S \n", "865 0 236852 13.0000 NaN S \n", "866 0 SC/PARIS 2149 13.8583 NaN C \n", "867 0 PC 17590 50.4958 A24 S \n", "868 0 345777 9.5000 NaN S \n", "869 1 347742 11.1333 NaN S \n", "870 0 349248 7.8958 NaN S \n", "871 1 11751 52.5542 D35 S \n", "872 0 695 5.0000 B51 B53 B55 S \n", "873 0 345765 9.0000 NaN S \n", "874 0 P/PP 3381 24.0000 NaN C \n", "875 0 2667 7.2250 NaN C \n", "876 0 7534 9.8458 NaN S \n", "877 0 349212 7.8958 NaN S \n", "878 0 349217 7.8958 NaN S \n", "879 1 11767 83.1583 C50 C \n", "880 1 230433 26.0000 NaN S \n", "881 0 349257 7.8958 NaN S \n", "882 0 7552 10.5167 NaN S \n", "883 0 C.A./SOTON 34068 10.5000 NaN S \n", "884 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 5 382652 29.1250 NaN Q \n", "886 0 211536 13.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
222313McGowan, Miss. Anna \"Annie\"female15.0003309238.0292NaNQ
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875NaNS
394013Nicola-Yarred, Miss. Jamilafemale14.010265111.2417NaNC
434412Laroche, Miss. Simonne Marie Anne Andreefemale3.012SC/Paris 212341.5792NaNC
444513Devaney, Miss. Margaret Deliafemale19.0003309587.8792NaNQ
535412Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...female29.010292626.0000NaNS
565712Rugg, Miss. Emilyfemale21.000C.A. 3102610.5000NaNS
585912West, Miss. Constance Miriumfemale5.012C.A. 3465127.7500NaNS
616211Icard, Miss. Ameliefemale38.00011357280.0000B28NaN
666712Nye, Mrs. (Elizabeth Ramell)female29.000C.A. 2939510.5000F33S
686913Andersson, Miss. Erna Alexandrafemale17.04231012817.9250NaNS
798013Dowdell, Miss. Elizabethfemale30.00036451612.4750NaNS
848512Ilett, Miss. Berthafemale17.000SO/C 1488510.5000NaNS
858613Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...female33.030310127815.8500NaNS
888911Fortune, Miss. Mabel Helenfemale23.03219950263.0000C23 C25 C27S
989912Doling, Mrs. John T (Ada Julia Bone)female34.00123191923.0000NaNS
10610713Salkjelsvik, Miss. Anna Kristinefemale21.0003431207.6500NaNS
12312412Webber, Miss. Susanfemale32.5002726713.0000E101S
13313412Weisz, Mrs. Leopold (Mathilde Francoise Pede)female29.01022841426.0000NaNS
13613711Newsom, Miss. Helen Monypenyfemale19.0021175226.2833D47S
14114213Nysten, Miss. Anna Sofiafemale22.0003470817.7500NaNS
14214313Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...female24.010STON/O2. 310127915.8500NaNS
15115211Pears, Mrs. Thomas (Edith Wearne)female22.01011377666.6000C2S
15615713Gilnagh, Miss. Katherine \"Katie\"female16.000358517.7333NaNQ
.......................................
71071111Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")female24.000PC 1748249.5042C90C
71671711Endres, Miss. Caroline Louisefemale38.000PC 17757227.5250C45C
71771812Troutt, Miss. Edwina Celia \"Winnie\"female27.0003421810.5000E101S
72072112Harper, Miss. Annie Jessie \"Nina\"female6.00124872733.0000NaNS
72672712Renouf, Mrs. Peter Henry (Lillian Jefferys)female30.0303102721.0000NaNS
73073111Allen, Miss. Elisabeth Waltonfemale29.00024160211.3375B5S
74274311Ryerson, Miss. Susan Parker \"Suzette\"female21.022PC 17608262.3750B57 B59 B63 B66C
74774812Sinkkonen, Miss. Annafemale30.00025064813.0000NaNS
75075112Wells, Miss. Joanfemale4.0112910323.0000NaNS
75976011Rothes, the Countess. of (Lucy Noel Martha Dye...female33.00011015286.5000B77S
76376411Carter, Mrs. William Ernest (Lucile Polk)female36.012113760120.0000B96 B98S
77777813Emanuel, Miss. Virginia Ethelfemale5.00036451612.4750NaNS
78078113Ayoub, Miss. Banourafemale13.00026877.2292NaNC
78178211Dick, Mrs. Albert Adrian (Vera Gillespie)female17.0101747457.0000B20S
78678713Sjoblom, Miss. Anna Sofiafemale18.00031012657.4958NaNS
79779813Osman, Mrs. Marafemale31.0003492448.6833NaNS
80180212Collyer, Mrs. Harvey (Charlotte Annie Tate)female31.011C.A. 3192126.2500NaNS
80981011Chambers, Mrs. Norman Campbell (Bertha Griggs)female33.01011380653.1000E8S
82382413Moor, Mrs. (Beila)female27.00139209612.4750E121S
83083113Yasbeck, Mrs. Antoni (Selini Alexander)female15.010265914.4542NaNC
83583611Compton, Miss. Sara Rebeccafemale39.011PC 1775683.1583E49C
84284311Serepeca, Miss. Augustafemale30.00011379831.0000NaNC
85385411Lines, Miss. Mary Conoverfemale16.001PC 1759239.4000D28S
85585613Aks, Mrs. Sam (Leah Rosen)female18.0013920919.3500NaNS
85885913Baclini, Mrs. Solomon (Latifa Qurban)female24.003266619.2583NaNC
86686712Duran y More, Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female15.00026677.2250NaNC
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
\n", "

155 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "8 9 1 3 \n", "9 10 1 2 \n", "10 11 1 3 \n", "22 23 1 3 \n", "25 26 1 3 \n", "39 40 1 3 \n", "43 44 1 2 \n", "44 45 1 3 \n", "53 54 1 2 \n", "56 57 1 2 \n", "58 59 1 2 \n", "61 62 1 1 \n", "66 67 1 2 \n", "68 69 1 3 \n", "79 80 1 3 \n", "84 85 1 2 \n", "85 86 1 3 \n", "88 89 1 1 \n", "98 99 1 2 \n", "106 107 1 3 \n", "123 124 1 2 \n", "133 134 1 2 \n", "136 137 1 1 \n", "141 142 1 3 \n", "142 143 1 3 \n", "151 152 1 1 \n", "156 157 1 3 \n", ".. ... ... ... \n", "710 711 1 1 \n", "716 717 1 1 \n", "717 718 1 2 \n", "720 721 1 2 \n", "726 727 1 2 \n", "730 731 1 1 \n", "742 743 1 1 \n", "747 748 1 2 \n", "750 751 1 2 \n", "759 760 1 1 \n", "763 764 1 1 \n", "777 778 1 3 \n", "780 781 1 3 \n", "781 782 1 1 \n", "786 787 1 3 \n", "797 798 1 3 \n", "801 802 1 2 \n", "809 810 1 1 \n", "823 824 1 3 \n", "830 831 1 3 \n", "835 836 1 1 \n", "842 843 1 1 \n", "853 854 1 1 \n", "855 856 1 3 \n", "858 859 1 3 \n", "866 867 1 2 \n", "874 875 1 2 \n", "875 876 1 3 \n", "880 881 1 2 \n", "887 888 1 1 \n", "\n", " Name Sex Age SibSp \\\n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", "22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n", "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", "39 Nicola-Yarred, Miss. Jamila female 14.0 1 \n", "43 Laroche, Miss. Simonne Marie Anne Andree female 3.0 1 \n", "44 Devaney, Miss. Margaret Delia female 19.0 0 \n", "53 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... female 29.0 1 \n", "56 Rugg, Miss. Emily female 21.0 0 \n", "58 West, Miss. Constance Mirium female 5.0 1 \n", "61 Icard, Miss. Amelie female 38.0 0 \n", "66 Nye, Mrs. (Elizabeth Ramell) female 29.0 0 \n", "68 Andersson, Miss. Erna Alexandra female 17.0 4 \n", "79 Dowdell, Miss. Elizabeth female 30.0 0 \n", "84 Ilett, Miss. Bertha female 17.0 0 \n", "85 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu... female 33.0 3 \n", "88 Fortune, Miss. Mabel Helen female 23.0 3 \n", "98 Doling, Mrs. John T (Ada Julia Bone) female 34.0 0 \n", "106 Salkjelsvik, Miss. Anna Kristine female 21.0 0 \n", "123 Webber, Miss. Susan female 32.5 0 \n", "133 Weisz, Mrs. Leopold (Mathilde Francoise Pede) female 29.0 1 \n", "136 Newsom, Miss. Helen Monypeny female 19.0 0 \n", "141 Nysten, Miss. Anna Sofia female 22.0 0 \n", "142 Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ... female 24.0 1 \n", "151 Pears, Mrs. Thomas (Edith Wearne) female 22.0 1 \n", "156 Gilnagh, Miss. Katherine \"Katie\" female 16.0 0 \n", ".. ... ... ... ... \n", "710 Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\") female 24.0 0 \n", "716 Endres, Miss. Caroline Louise female 38.0 0 \n", "717 Troutt, Miss. Edwina Celia \"Winnie\" female 27.0 0 \n", "720 Harper, Miss. Annie Jessie \"Nina\" female 6.0 0 \n", "726 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30.0 3 \n", "730 Allen, Miss. Elisabeth Walton female 29.0 0 \n", "742 Ryerson, Miss. Susan Parker \"Suzette\" female 21.0 2 \n", "747 Sinkkonen, Miss. Anna female 30.0 0 \n", "750 Wells, Miss. Joan female 4.0 1 \n", "759 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 \n", "763 Carter, Mrs. William Ernest (Lucile Polk) female 36.0 1 \n", "777 Emanuel, Miss. Virginia Ethel female 5.0 0 \n", "780 Ayoub, Miss. Banoura female 13.0 0 \n", "781 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.0 1 \n", "786 Sjoblom, Miss. Anna Sofia female 18.0 0 \n", "797 Osman, Mrs. Mara female 31.0 0 \n", "801 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31.0 1 \n", "809 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.0 1 \n", "823 Moor, Mrs. (Beila) female 27.0 0 \n", "830 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0 1 \n", "835 Compton, Miss. Sara Rebecca female 39.0 1 \n", "842 Serepeca, Miss. Augusta female 30.0 0 \n", "853 Lines, Miss. Mary Conover female 16.0 0 \n", "855 Aks, Mrs. Sam (Leah Rosen) female 18.0 0 \n", "858 Baclini, Mrs. Solomon (Latifa Qurban) female 24.0 0 \n", "866 Duran y More, Miss. Asuncion female 27.0 1 \n", "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n", "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n", "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C \n", "10 1 PP 9549 16.7000 G6 S \n", "22 0 330923 8.0292 NaN Q \n", "25 5 347077 31.3875 NaN S \n", "39 0 2651 11.2417 NaN C \n", "43 2 SC/Paris 2123 41.5792 NaN C \n", "44 0 330958 7.8792 NaN Q \n", "53 0 2926 26.0000 NaN S \n", "56 0 C.A. 31026 10.5000 NaN S \n", "58 2 C.A. 34651 27.7500 NaN S \n", "61 0 113572 80.0000 B28 NaN \n", "66 0 C.A. 29395 10.5000 F33 S \n", "68 2 3101281 7.9250 NaN S \n", "79 0 364516 12.4750 NaN S \n", "84 0 SO/C 14885 10.5000 NaN S \n", "85 0 3101278 15.8500 NaN S \n", "88 2 19950 263.0000 C23 C25 C27 S \n", "98 1 231919 23.0000 NaN S \n", "106 0 343120 7.6500 NaN S \n", "123 0 27267 13.0000 E101 S \n", "133 0 228414 26.0000 NaN S \n", "136 2 11752 26.2833 D47 S \n", "141 0 347081 7.7500 NaN S \n", "142 0 STON/O2. 3101279 15.8500 NaN S \n", "151 0 113776 66.6000 C2 S \n", "156 0 35851 7.7333 NaN Q \n", ".. ... ... ... ... ... \n", "710 0 PC 17482 49.5042 C90 C \n", "716 0 PC 17757 227.5250 C45 C \n", "717 0 34218 10.5000 E101 S \n", "720 1 248727 33.0000 NaN S \n", "726 0 31027 21.0000 NaN S \n", "730 0 24160 211.3375 B5 S \n", "742 2 PC 17608 262.3750 B57 B59 B63 B66 C \n", "747 0 250648 13.0000 NaN S \n", "750 1 29103 23.0000 NaN S \n", "759 0 110152 86.5000 B77 S \n", "763 2 113760 120.0000 B96 B98 S \n", "777 0 364516 12.4750 NaN S \n", "780 0 2687 7.2292 NaN C \n", "781 0 17474 57.0000 B20 S \n", "786 0 3101265 7.4958 NaN S \n", "797 0 349244 8.6833 NaN S \n", "801 1 C.A. 31921 26.2500 NaN S \n", "809 0 113806 53.1000 E8 S \n", "823 1 392096 12.4750 E121 S \n", "830 0 2659 14.4542 NaN C \n", "835 1 PC 17756 83.1583 E49 C \n", "842 0 113798 31.0000 NaN C \n", "853 1 PC 17592 39.4000 D28 S \n", "855 1 392091 9.3500 NaN S \n", "858 3 2666 19.2583 NaN C \n", "866 0 SC/PARIS 2149 13.8583 NaN C \n", "874 0 P/PP 3381 24.0000 NaN C \n", "875 0 2667 7.2250 NaN C \n", "880 1 230433 26.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "\n", "[155 rows x 12 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train[(train.Sex == 'female') & (train.Survived==1)& (train.Age<40)]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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l+49cRvgN7UcwEwC6Uj2F959K+i/GmOu93jTG3CzpFZL+xI+BoTNV6jpZakFR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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 9))\n", "plt.scatter(train.Fare[train.Survived == 1], train.Age[train.Survived == 1], color='r')\n", "plt.scatter(train.Fare[train.Survived == 0], train.Age[train.Survived == 0], color='b')\n", "plt.ylabel('Age', fontsize=20)\n", "plt.xlabel('Fare', fontsize=20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }