{
"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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"text/plain": [
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"3 19 Nov Nikolay M\n",
"4 18 Tmn Anatoliy M"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Индексация**"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 18\n",
"2 17\n",
"3 19\n",
"4 18\n",
"Name: age, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1['age']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 18\n",
"2 17\n",
"3 19\n",
"4 18\n",
"Name: age, dtype: int64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.age"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"metadata": {},
"output_type": "execute_result"
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"source": [
"df1[['name', 'city']]"
]
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{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
"df1[0: 2]"
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"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"metadata": {},
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"source": [
"df1[[0, 2]]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"source": [
"df1.ix[[0, 2]]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"metadata": {},
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}
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"source": [
"df1.loc[[0, 2]]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
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" city | \n",
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"metadata": {},
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],
"source": [
"df1.iloc[:2, :3]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
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" age | \n",
" city | \n",
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"
\n",
" \n",
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" 0 | \n",
" 20 | \n",
" Msk | \n",
" Alexander | \n",
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"metadata": {},
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}
],
"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": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
"
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" \n",
" \n",
" \n",
" count | \n",
" 5.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 18.400000 | \n",
"
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" \n",
" std | \n",
" 1.140175 | \n",
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" min | \n",
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"75% 19.000000\n",
"max 20.000000"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.describe()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"18.399999999999999"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.age.mean()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"M 3\n",
"F 2\n",
"Name: sex, dtype: int64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.sex.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Msk', 'Spb', 'Nov', 'Tmn'], dtype=object)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.city.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Группировка**"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"city\n",
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"Nov 19.0\n",
"Spb 18.0\n",
"Tmn 18.0\n",
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},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
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"source": [
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{
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"execution_count": 75,
"metadata": {
"collapsed": false
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"outputs": [
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{
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"metadata": {
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"outputs": [],
"source": [
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"metadata": {
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"source": [
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"metadata": {
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{
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"source": [
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"execution_count": 3,
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"collapsed": false
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"source": [
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" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 861 | \n",
" 862 | \n",
" 0 | \n",
" 2 | \n",
" Giles, Mr. Frederick Edward | \n",
" male | \n",
" 21 | \n",
" 1 | \n",
" 0 | \n",
" 28134 | \n",
" 11.5000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 862 | \n",
" 863 | \n",
" 1 | \n",
" 1 | \n",
" Swift, Mrs. Frederick Joel (Margaret Welles Ba... | \n",
" female | \n",
" 48 | \n",
" 0 | \n",
" 0 | \n",
" 17466 | \n",
" 25.9292 | \n",
" D17 | \n",
" S | \n",
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\n",
" \n",
" 863 | \n",
" 864 | \n",
" 0 | \n",
" 3 | \n",
" Sage, Miss. Dorothy Edith \"Dolly\" | \n",
" female | \n",
" NaN | \n",
" 8 | \n",
" 2 | \n",
" CA. 2343 | \n",
" 69.5500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 864 | \n",
" 865 | \n",
" 0 | \n",
" 2 | \n",
" Gill, Mr. John William | \n",
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" 24 | \n",
" 0 | \n",
" 0 | \n",
" 233866 | \n",
" 13.0000 | \n",
" NaN | \n",
" S | \n",
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\n",
" \n",
" 865 | \n",
" 866 | \n",
" 1 | \n",
" 2 | \n",
" Bystrom, Mrs. (Karolina) | \n",
" female | \n",
" 42 | \n",
" 0 | \n",
" 0 | \n",
" 236852 | \n",
" 13.0000 | \n",
" NaN | \n",
" S | \n",
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\n",
" \n",
" 866 | \n",
" 867 | \n",
" 1 | \n",
" 2 | \n",
" Duran y More, Miss. Asuncion | \n",
" female | \n",
" 27 | \n",
" 1 | \n",
" 0 | \n",
" SC/PARIS 2149 | \n",
" 13.8583 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 867 | \n",
" 868 | \n",
" 0 | \n",
" 1 | \n",
" Roebling, Mr. Washington Augustus II | \n",
" male | \n",
" 31 | \n",
" 0 | \n",
" 0 | \n",
" PC 17590 | \n",
" 50.4958 | \n",
" A24 | \n",
" S | \n",
"
\n",
" \n",
" 868 | \n",
" 869 | \n",
" 0 | \n",
" 3 | \n",
" van Melkebeke, Mr. Philemon | \n",
" male | \n",
" NaN | \n",
" 0 | \n",
" 0 | \n",
" 345777 | \n",
" 9.5000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 869 | \n",
" 870 | \n",
" 1 | \n",
" 3 | \n",
" Johnson, Master. Harold Theodor | \n",
" male | \n",
" 4 | \n",
" 1 | \n",
" 1 | \n",
" 347742 | \n",
" 11.1333 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 870 | \n",
" 871 | \n",
" 0 | \n",
" 3 | \n",
" Balkic, Mr. Cerin | \n",
" male | \n",
" 26 | \n",
" 0 | \n",
" 0 | \n",
" 349248 | \n",
" 7.8958 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 871 | \n",
" 872 | \n",
" 1 | \n",
" 1 | \n",
" Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | \n",
" female | \n",
" 47 | \n",
" 1 | \n",
" 1 | \n",
" 11751 | \n",
" 52.5542 | \n",
" D35 | \n",
" S | \n",
"
\n",
" \n",
" 872 | \n",
" 873 | \n",
" 0 | \n",
" 1 | \n",
" Carlsson, Mr. Frans Olof | \n",
" male | \n",
" 33 | \n",
" 0 | \n",
" 0 | \n",
" 695 | \n",
" 5.0000 | \n",
" B51 B53 B55 | \n",
" S | \n",
"
\n",
" \n",
" 873 | \n",
" 874 | \n",
" 0 | \n",
" 3 | \n",
" Vander Cruyssen, Mr. Victor | \n",
" male | \n",
" 47 | \n",
" 0 | \n",
" 0 | \n",
" 345765 | \n",
" 9.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 874 | \n",
" 875 | \n",
" 1 | \n",
" 2 | \n",
" Abelson, Mrs. Samuel (Hannah Wizosky) | \n",
" female | \n",
" 28 | \n",
" 1 | \n",
" 0 | \n",
" P/PP 3381 | \n",
" 24.0000 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 875 | \n",
" 876 | \n",
" 1 | \n",
" 3 | \n",
" Najib, Miss. Adele Kiamie \"Jane\" | \n",
" female | \n",
" 15 | \n",
" 0 | \n",
" 0 | \n",
" 2667 | \n",
" 7.2250 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 876 | \n",
" 877 | \n",
" 0 | \n",
" 3 | \n",
" Gustafsson, Mr. Alfred Ossian | \n",
" male | \n",
" 20 | \n",
" 0 | \n",
" 0 | \n",
" 7534 | \n",
" 9.8458 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 877 | \n",
" 878 | \n",
" 0 | \n",
" 3 | \n",
" Petroff, Mr. Nedelio | \n",
" male | \n",
" 19 | \n",
" 0 | \n",
" 0 | \n",
" 349212 | \n",
" 7.8958 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 878 | \n",
" 879 | \n",
" 0 | \n",
" 3 | \n",
" Laleff, Mr. Kristo | \n",
" male | \n",
" NaN | \n",
" 0 | \n",
" 0 | \n",
" 349217 | \n",
" 7.8958 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 879 | \n",
" 880 | \n",
" 1 | \n",
" 1 | \n",
" Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | \n",
" female | \n",
" 56 | \n",
" 0 | \n",
" 1 | \n",
" 11767 | \n",
" 83.1583 | \n",
" C50 | \n",
" C | \n",
"
\n",
" \n",
" 880 | \n",
" 881 | \n",
" 1 | \n",
" 2 | \n",
" Shelley, Mrs. William (Imanita Parrish Hall) | \n",
" female | \n",
" 25 | \n",
" 0 | \n",
" 1 | \n",
" 230433 | \n",
" 26.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 881 | \n",
" 882 | \n",
" 0 | \n",
" 3 | \n",
" Markun, Mr. Johann | \n",
" male | \n",
" 33 | \n",
" 0 | \n",
" 0 | \n",
" 349257 | \n",
" 7.8958 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 882 | \n",
" 883 | \n",
" 0 | \n",
" 3 | \n",
" Dahlberg, Miss. Gerda Ulrika | \n",
" female | \n",
" 22 | \n",
" 0 | \n",
" 0 | \n",
" 7552 | \n",
" 10.5167 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 883 | \n",
" 884 | \n",
" 0 | \n",
" 2 | \n",
" Banfield, Mr. Frederick James | \n",
" male | \n",
" 28 | \n",
" 0 | \n",
" 0 | \n",
" C.A./SOTON 34068 | \n",
" 10.5000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 884 | \n",
" 885 | \n",
" 0 | \n",
" 3 | \n",
" Sutehall, Mr. Henry Jr | \n",
" male | \n",
" 25 | \n",
" 0 | \n",
" 0 | \n",
" SOTON/OQ 392076 | \n",
" 7.0500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 885 | \n",
" 886 | \n",
" 0 | \n",
" 3 | \n",
" Rice, Mrs. William (Margaret Norton) | \n",
" female | \n",
" 39 | \n",
" 0 | \n",
" 5 | \n",
" 382652 | \n",
" 29.1250 | \n",
" NaN | \n",
" Q | \n",
"
\n",
" \n",
" 886 | \n",
" 887 | \n",
" 0 | \n",
" 2 | \n",
" Montvila, Rev. Juozas | \n",
" male | \n",
" 27 | \n",
" 0 | \n",
" 0 | \n",
" 211536 | \n",
" 13.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 887 | \n",
" 888 | \n",
" 1 | \n",
" 1 | \n",
" Graham, Miss. Margaret Edith | \n",
" female | \n",
" 19 | \n",
" 0 | \n",
" 0 | \n",
" 112053 | \n",
" 30.0000 | \n",
" B42 | \n",
" S | \n",
"
\n",
" \n",
" 888 | \n",
" 889 | \n",
" 0 | \n",
" 3 | \n",
" Johnston, Miss. Catherine Helen \"Carrie\" | \n",
" female | \n",
" NaN | \n",
" 1 | \n",
" 2 | \n",
" W./C. 6607 | \n",
" 23.4500 | \n",
" NaN | \n",
" S | \n",
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\n",
" \n",
" 889 | \n",
" 890 | \n",
" 1 | \n",
" 1 | \n",
" Behr, Mr. Karl Howell | \n",
" male | \n",
" 26 | \n",
" 0 | \n",
" 0 | \n",
" 111369 | \n",
" 30.0000 | \n",
" C148 | \n",
" C | \n",
"
\n",
" \n",
" 890 | \n",
" 891 | \n",
" 0 | \n",
" 3 | \n",
" Dooley, Mr. Patrick | \n",
" male | \n",
" 32 | \n",
" 0 | \n",
" 0 | \n",
" 370376 | \n",
" 7.7500 | \n",
" NaN | \n",
" Q | \n",
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" \n",
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891 rows × 12 columns
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"
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" 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",
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"23 24 1 1 \n",
"24 25 0 3 \n",
"25 26 1 3 \n",
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"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": {
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" | \n",
" PassengerId | \n",
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" Sex | \n",
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" SibSp | \n",
" Parch | \n",
" Ticket | \n",
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" 113803 | \n",
" 53.1000 | \n",
" C123 | \n",
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" 8 | \n",
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" 347742 | \n",
" 11.1333 | \n",
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" S | \n",
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" 9 | \n",
" 10 | \n",
" 1 | \n",
" 2 | \n",
" Nasser, Mrs. Nicholas (Adele Achem) | \n",
" female | \n",
" 14.0 | \n",
" 1 | \n",
" 0 | \n",
" 237736 | \n",
" 30.0708 | \n",
" NaN | \n",
" C | \n",
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" \n",
" 10 | \n",
" 11 | \n",
" 1 | \n",
" 3 | \n",
" Sandstrom, Miss. Marguerite Rut | \n",
" female | \n",
" 4.0 | \n",
" 1 | \n",
" 1 | \n",
" PP 9549 | \n",
" 16.7000 | \n",
" G6 | \n",
" S | \n",
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\n",
" \n",
" 22 | \n",
" 23 | \n",
" 1 | \n",
" 3 | \n",
" McGowan, Miss. Anna \"Annie\" | \n",
" female | \n",
" 15.0 | \n",
" 0 | \n",
" 0 | \n",
" 330923 | \n",
" 8.0292 | \n",
" NaN | \n",
" Q | \n",
"
\n",
" \n",
" 25 | \n",
" 26 | \n",
" 1 | \n",
" 3 | \n",
" Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... | \n",
" female | \n",
" 38.0 | \n",
" 1 | \n",
" 5 | \n",
" 347077 | \n",
" 31.3875 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 39 | \n",
" 40 | \n",
" 1 | \n",
" 3 | \n",
" Nicola-Yarred, Miss. Jamila | \n",
" female | \n",
" 14.0 | \n",
" 1 | \n",
" 0 | \n",
" 2651 | \n",
" 11.2417 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 43 | \n",
" 44 | \n",
" 1 | \n",
" 2 | \n",
" Laroche, Miss. Simonne Marie Anne Andree | \n",
" female | \n",
" 3.0 | \n",
" 1 | \n",
" 2 | \n",
" SC/Paris 2123 | \n",
" 41.5792 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 44 | \n",
" 45 | \n",
" 1 | \n",
" 3 | \n",
" Devaney, Miss. Margaret Delia | \n",
" female | \n",
" 19.0 | \n",
" 0 | \n",
" 0 | \n",
" 330958 | \n",
" 7.8792 | \n",
" NaN | \n",
" Q | \n",
"
\n",
" \n",
" 53 | \n",
" 54 | \n",
" 1 | \n",
" 2 | \n",
" Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... | \n",
" female | \n",
" 29.0 | \n",
" 1 | \n",
" 0 | \n",
" 2926 | \n",
" 26.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 56 | \n",
" 57 | \n",
" 1 | \n",
" 2 | \n",
" Rugg, Miss. Emily | \n",
" female | \n",
" 21.0 | \n",
" 0 | \n",
" 0 | \n",
" C.A. 31026 | \n",
" 10.5000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 58 | \n",
" 59 | \n",
" 1 | \n",
" 2 | \n",
" West, Miss. Constance Mirium | \n",
" female | \n",
" 5.0 | \n",
" 1 | \n",
" 2 | \n",
" C.A. 34651 | \n",
" 27.7500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 61 | \n",
" 62 | \n",
" 1 | \n",
" 1 | \n",
" Icard, Miss. Amelie | \n",
" female | \n",
" 38.0 | \n",
" 0 | \n",
" 0 | \n",
" 113572 | \n",
" 80.0000 | \n",
" B28 | \n",
" NaN | \n",
"
\n",
" \n",
" 66 | \n",
" 67 | \n",
" 1 | \n",
" 2 | \n",
" Nye, Mrs. (Elizabeth Ramell) | \n",
" female | \n",
" 29.0 | \n",
" 0 | \n",
" 0 | \n",
" C.A. 29395 | \n",
" 10.5000 | \n",
" F33 | \n",
" S | \n",
"
\n",
" \n",
" 68 | \n",
" 69 | \n",
" 1 | \n",
" 3 | \n",
" Andersson, Miss. Erna Alexandra | \n",
" female | \n",
" 17.0 | \n",
" 4 | \n",
" 2 | \n",
" 3101281 | \n",
" 7.9250 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 79 | \n",
" 80 | \n",
" 1 | \n",
" 3 | \n",
" Dowdell, Miss. Elizabeth | \n",
" female | \n",
" 30.0 | \n",
" 0 | \n",
" 0 | \n",
" 364516 | \n",
" 12.4750 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 84 | \n",
" 85 | \n",
" 1 | \n",
" 2 | \n",
" Ilett, Miss. Bertha | \n",
" female | \n",
" 17.0 | \n",
" 0 | \n",
" 0 | \n",
" SO/C 14885 | \n",
" 10.5000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 85 | \n",
" 86 | \n",
" 1 | \n",
" 3 | \n",
" Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu... | \n",
" female | \n",
" 33.0 | \n",
" 3 | \n",
" 0 | \n",
" 3101278 | \n",
" 15.8500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 88 | \n",
" 89 | \n",
" 1 | \n",
" 1 | \n",
" Fortune, Miss. Mabel Helen | \n",
" female | \n",
" 23.0 | \n",
" 3 | \n",
" 2 | \n",
" 19950 | \n",
" 263.0000 | \n",
" C23 C25 C27 | \n",
" S | \n",
"
\n",
" \n",
" 98 | \n",
" 99 | \n",
" 1 | \n",
" 2 | \n",
" Doling, Mrs. John T (Ada Julia Bone) | \n",
" female | \n",
" 34.0 | \n",
" 0 | \n",
" 1 | \n",
" 231919 | \n",
" 23.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 106 | \n",
" 107 | \n",
" 1 | \n",
" 3 | \n",
" Salkjelsvik, Miss. Anna Kristine | \n",
" female | \n",
" 21.0 | \n",
" 0 | \n",
" 0 | \n",
" 343120 | \n",
" 7.6500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 123 | \n",
" 124 | \n",
" 1 | \n",
" 2 | \n",
" Webber, Miss. Susan | \n",
" female | \n",
" 32.5 | \n",
" 0 | \n",
" 0 | \n",
" 27267 | \n",
" 13.0000 | \n",
" E101 | \n",
" S | \n",
"
\n",
" \n",
" 133 | \n",
" 134 | \n",
" 1 | \n",
" 2 | \n",
" Weisz, Mrs. Leopold (Mathilde Francoise Pede) | \n",
" female | \n",
" 29.0 | \n",
" 1 | \n",
" 0 | \n",
" 228414 | \n",
" 26.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 136 | \n",
" 137 | \n",
" 1 | \n",
" 1 | \n",
" Newsom, Miss. Helen Monypeny | \n",
" female | \n",
" 19.0 | \n",
" 0 | \n",
" 2 | \n",
" 11752 | \n",
" 26.2833 | \n",
" D47 | \n",
" S | \n",
"
\n",
" \n",
" 141 | \n",
" 142 | \n",
" 1 | \n",
" 3 | \n",
" Nysten, Miss. Anna Sofia | \n",
" female | \n",
" 22.0 | \n",
" 0 | \n",
" 0 | \n",
" 347081 | \n",
" 7.7500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 142 | \n",
" 143 | \n",
" 1 | \n",
" 3 | \n",
" Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ... | \n",
" female | \n",
" 24.0 | \n",
" 1 | \n",
" 0 | \n",
" STON/O2. 3101279 | \n",
" 15.8500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 151 | \n",
" 152 | \n",
" 1 | \n",
" 1 | \n",
" Pears, Mrs. Thomas (Edith Wearne) | \n",
" female | \n",
" 22.0 | \n",
" 1 | \n",
" 0 | \n",
" 113776 | \n",
" 66.6000 | \n",
" C2 | \n",
" S | \n",
"
\n",
" \n",
" 156 | \n",
" 157 | \n",
" 1 | \n",
" 3 | \n",
" Gilnagh, Miss. Katherine \"Katie\" | \n",
" female | \n",
" 16.0 | \n",
" 0 | \n",
" 0 | \n",
" 35851 | \n",
" 7.7333 | \n",
" NaN | \n",
" Q | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 710 | \n",
" 711 | \n",
" 1 | \n",
" 1 | \n",
" Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\") | \n",
" female | \n",
" 24.0 | \n",
" 0 | \n",
" 0 | \n",
" PC 17482 | \n",
" 49.5042 | \n",
" C90 | \n",
" C | \n",
"
\n",
" \n",
" 716 | \n",
" 717 | \n",
" 1 | \n",
" 1 | \n",
" Endres, Miss. Caroline Louise | \n",
" female | \n",
" 38.0 | \n",
" 0 | \n",
" 0 | \n",
" PC 17757 | \n",
" 227.5250 | \n",
" C45 | \n",
" C | \n",
"
\n",
" \n",
" 717 | \n",
" 718 | \n",
" 1 | \n",
" 2 | \n",
" Troutt, Miss. Edwina Celia \"Winnie\" | \n",
" female | \n",
" 27.0 | \n",
" 0 | \n",
" 0 | \n",
" 34218 | \n",
" 10.5000 | \n",
" E101 | \n",
" S | \n",
"
\n",
" \n",
" 720 | \n",
" 721 | \n",
" 1 | \n",
" 2 | \n",
" Harper, Miss. Annie Jessie \"Nina\" | \n",
" female | \n",
" 6.0 | \n",
" 0 | \n",
" 1 | \n",
" 248727 | \n",
" 33.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 726 | \n",
" 727 | \n",
" 1 | \n",
" 2 | \n",
" Renouf, Mrs. Peter Henry (Lillian Jefferys) | \n",
" female | \n",
" 30.0 | \n",
" 3 | \n",
" 0 | \n",
" 31027 | \n",
" 21.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 730 | \n",
" 731 | \n",
" 1 | \n",
" 1 | \n",
" Allen, Miss. Elisabeth Walton | \n",
" female | \n",
" 29.0 | \n",
" 0 | \n",
" 0 | \n",
" 24160 | \n",
" 211.3375 | \n",
" B5 | \n",
" S | \n",
"
\n",
" \n",
" 742 | \n",
" 743 | \n",
" 1 | \n",
" 1 | \n",
" Ryerson, Miss. Susan Parker \"Suzette\" | \n",
" female | \n",
" 21.0 | \n",
" 2 | \n",
" 2 | \n",
" PC 17608 | \n",
" 262.3750 | \n",
" B57 B59 B63 B66 | \n",
" C | \n",
"
\n",
" \n",
" 747 | \n",
" 748 | \n",
" 1 | \n",
" 2 | \n",
" Sinkkonen, Miss. Anna | \n",
" female | \n",
" 30.0 | \n",
" 0 | \n",
" 0 | \n",
" 250648 | \n",
" 13.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 750 | \n",
" 751 | \n",
" 1 | \n",
" 2 | \n",
" Wells, Miss. Joan | \n",
" female | \n",
" 4.0 | \n",
" 1 | \n",
" 1 | \n",
" 29103 | \n",
" 23.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 759 | \n",
" 760 | \n",
" 1 | \n",
" 1 | \n",
" Rothes, the Countess. of (Lucy Noel Martha Dye... | \n",
" female | \n",
" 33.0 | \n",
" 0 | \n",
" 0 | \n",
" 110152 | \n",
" 86.5000 | \n",
" B77 | \n",
" S | \n",
"
\n",
" \n",
" 763 | \n",
" 764 | \n",
" 1 | \n",
" 1 | \n",
" Carter, Mrs. William Ernest (Lucile Polk) | \n",
" female | \n",
" 36.0 | \n",
" 1 | \n",
" 2 | \n",
" 113760 | \n",
" 120.0000 | \n",
" B96 B98 | \n",
" S | \n",
"
\n",
" \n",
" 777 | \n",
" 778 | \n",
" 1 | \n",
" 3 | \n",
" Emanuel, Miss. Virginia Ethel | \n",
" female | \n",
" 5.0 | \n",
" 0 | \n",
" 0 | \n",
" 364516 | \n",
" 12.4750 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 780 | \n",
" 781 | \n",
" 1 | \n",
" 3 | \n",
" Ayoub, Miss. Banoura | \n",
" female | \n",
" 13.0 | \n",
" 0 | \n",
" 0 | \n",
" 2687 | \n",
" 7.2292 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 781 | \n",
" 782 | \n",
" 1 | \n",
" 1 | \n",
" Dick, Mrs. Albert Adrian (Vera Gillespie) | \n",
" female | \n",
" 17.0 | \n",
" 1 | \n",
" 0 | \n",
" 17474 | \n",
" 57.0000 | \n",
" B20 | \n",
" S | \n",
"
\n",
" \n",
" 786 | \n",
" 787 | \n",
" 1 | \n",
" 3 | \n",
" Sjoblom, Miss. Anna Sofia | \n",
" female | \n",
" 18.0 | \n",
" 0 | \n",
" 0 | \n",
" 3101265 | \n",
" 7.4958 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 797 | \n",
" 798 | \n",
" 1 | \n",
" 3 | \n",
" Osman, Mrs. Mara | \n",
" female | \n",
" 31.0 | \n",
" 0 | \n",
" 0 | \n",
" 349244 | \n",
" 8.6833 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 801 | \n",
" 802 | \n",
" 1 | \n",
" 2 | \n",
" Collyer, Mrs. Harvey (Charlotte Annie Tate) | \n",
" female | \n",
" 31.0 | \n",
" 1 | \n",
" 1 | \n",
" C.A. 31921 | \n",
" 26.2500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 809 | \n",
" 810 | \n",
" 1 | \n",
" 1 | \n",
" Chambers, Mrs. Norman Campbell (Bertha Griggs) | \n",
" female | \n",
" 33.0 | \n",
" 1 | \n",
" 0 | \n",
" 113806 | \n",
" 53.1000 | \n",
" E8 | \n",
" S | \n",
"
\n",
" \n",
" 823 | \n",
" 824 | \n",
" 1 | \n",
" 3 | \n",
" Moor, Mrs. (Beila) | \n",
" female | \n",
" 27.0 | \n",
" 0 | \n",
" 1 | \n",
" 392096 | \n",
" 12.4750 | \n",
" E121 | \n",
" S | \n",
"
\n",
" \n",
" 830 | \n",
" 831 | \n",
" 1 | \n",
" 3 | \n",
" Yasbeck, Mrs. Antoni (Selini Alexander) | \n",
" female | \n",
" 15.0 | \n",
" 1 | \n",
" 0 | \n",
" 2659 | \n",
" 14.4542 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 835 | \n",
" 836 | \n",
" 1 | \n",
" 1 | \n",
" Compton, Miss. Sara Rebecca | \n",
" female | \n",
" 39.0 | \n",
" 1 | \n",
" 1 | \n",
" PC 17756 | \n",
" 83.1583 | \n",
" E49 | \n",
" C | \n",
"
\n",
" \n",
" 842 | \n",
" 843 | \n",
" 1 | \n",
" 1 | \n",
" Serepeca, Miss. Augusta | \n",
" female | \n",
" 30.0 | \n",
" 0 | \n",
" 0 | \n",
" 113798 | \n",
" 31.0000 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 853 | \n",
" 854 | \n",
" 1 | \n",
" 1 | \n",
" Lines, Miss. Mary Conover | \n",
" female | \n",
" 16.0 | \n",
" 0 | \n",
" 1 | \n",
" PC 17592 | \n",
" 39.4000 | \n",
" D28 | \n",
" S | \n",
"
\n",
" \n",
" 855 | \n",
" 856 | \n",
" 1 | \n",
" 3 | \n",
" Aks, Mrs. Sam (Leah Rosen) | \n",
" female | \n",
" 18.0 | \n",
" 0 | \n",
" 1 | \n",
" 392091 | \n",
" 9.3500 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 858 | \n",
" 859 | \n",
" 1 | \n",
" 3 | \n",
" Baclini, Mrs. Solomon (Latifa Qurban) | \n",
" female | \n",
" 24.0 | \n",
" 0 | \n",
" 3 | \n",
" 2666 | \n",
" 19.2583 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 866 | \n",
" 867 | \n",
" 1 | \n",
" 2 | \n",
" Duran y More, Miss. Asuncion | \n",
" female | \n",
" 27.0 | \n",
" 1 | \n",
" 0 | \n",
" SC/PARIS 2149 | \n",
" 13.8583 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 874 | \n",
" 875 | \n",
" 1 | \n",
" 2 | \n",
" Abelson, Mrs. Samuel (Hannah Wizosky) | \n",
" female | \n",
" 28.0 | \n",
" 1 | \n",
" 0 | \n",
" P/PP 3381 | \n",
" 24.0000 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 875 | \n",
" 876 | \n",
" 1 | \n",
" 3 | \n",
" Najib, Miss. Adele Kiamie \"Jane\" | \n",
" female | \n",
" 15.0 | \n",
" 0 | \n",
" 0 | \n",
" 2667 | \n",
" 7.2250 | \n",
" NaN | \n",
" C | \n",
"
\n",
" \n",
" 880 | \n",
" 881 | \n",
" 1 | \n",
" 2 | \n",
" Shelley, Mrs. William (Imanita Parrish Hall) | \n",
" female | \n",
" 25.0 | \n",
" 0 | \n",
" 1 | \n",
" 230433 | \n",
" 26.0000 | \n",
" NaN | \n",
" S | \n",
"
\n",
" \n",
" 887 | \n",
" 888 | \n",
" 1 | \n",
" 1 | \n",
" Graham, Miss. Margaret Edith | \n",
" female | \n",
" 19.0 | \n",
" 0 | \n",
" 0 | \n",
" 112053 | \n",
" 30.0000 | \n",
" B42 | \n",
" S | \n",
"
\n",
" \n",
"
\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": {
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Lj5csDHnnw7a3kQQAAO1C4Q1fOY47fXluTjp50n0dG/O4873KLXGvZofj4+4zwAcH3dd0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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
}