Skip to content

Instantly share code, notes, and snippets.

@TarekDib03

TarekDib03/gist:62c22d427d6ae08efec2

Created August 22, 2015 12:22
Show Gist options
  • Save TarekDib03/62c22d427d6ae08efec2 to your computer and use it in GitHub Desktop.
Save TarekDib03/62c22d427d6ae08efec2 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
gistfile1.txt
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns \n",
"%matplotlib inline\n",
"\n",
"# Set default matplot figure size\n",
"pylab.rcParams['figure.figsize'] = (10.0, 8.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading Data Set using Pandas"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic_df = pd.read_csv('train.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"\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",
"\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 "
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check the first 5 rows of the data frame\n",
"titanic_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
" u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
" dtype='object')"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Column names\n",
"titanic_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 73.1+ KB\n"
]
}
],
"source": [
"# Information about the data set\n",
"titanic_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Pclass\n",
"1 216\n",
"2 184\n",
"3 491\n",
"Name: Pclass, dtype: int64"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Number of passengers in each class\n",
"titanic_df.groupby('Pclass')['Pclass'].count()"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0xa59e34ec>"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAEZCAYAAAAUgWt1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njuUmSSqO5SZJKs7/AZ/28OiroaXtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa59ae14c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Instead of a group by, use seaborn to plot the count of passengers in each class\n",
"fg = sns.factorplot('Pclass', data=titanic_df, kind='count', aspect=1.5)\n",
"fg.set_xlabels('Class')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Sex\n",
"female 314\n",
"male 577\n",
"Name: Sex, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.groupby('Sex')['Sex'].count()"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0xa5b785ec>"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAEZCAYAAAAUgWt1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa5b780ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Instead of a group by, use seaborn to plot the number of males and females\n",
"sns.factorplot('Sex', data=titanic_df, kind='count', aspect=1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are almost two times males as much as there were females. "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Sex Pclass\n",
"female 1 94\n",
" 2 76\n",
" 3 144\n",
"male 1 122\n",
" 2 108\n",
" 3 347\n",
"Name: Sex, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Number of men and women in each of the passenger class\n",
"titanic_df.groupby(['Sex', 'Pclass'])['Sex'].count()"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0xa560c10c>"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAEZCAYAAAB7BkqpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa5592c8c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Again use saeborn to group by Sex and class\n",
"g = sns.factorplot('Pclass', data=titanic_df, hue='Sex', kind='count', aspect=1.75)\n",
"g.set_xlabels('Class')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As shown in the figure above, there are more than two times males than females in class 3. However, in classes 1\n",
"and 2, the ratio of male to female is almost 1."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Pclass</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>female</th>\n",
" <td>91</td>\n",
" <td>70</td>\n",
" <td>72</td>\n",
" <td>233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>45</td>\n",
" <td>17</td>\n",
" <td>47</td>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>136</td>\n",
" <td>87</td>\n",
" <td>119</td>\n",
" <td>342</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Pclass 1 2 3 All\n",
"Sex \n",
"female 91 70 72 233\n",
"male 45 17 47 109\n",
"All 136 87 119 342"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Number of passengers who survived in each class grouped by sex. Also total was found for each class grouped by sex.\n",
"titanic_df.pivot_table('Survived', 'Sex', 'Pclass', aggfunc=np.sum, margins=True)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"not_survived = titanic_df[titanic_df['Survived']==0]"
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9e2f9bac>"
]
},
"execution_count": 357,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAScAAAEZCAYAAADVKuOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw0lSkQwnSUUynCQV6f8BGDNlP93ojFgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9e2f95ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Factor plot of those who survived vs. who didn't\n",
"sns.factorplot('Survived', data=titanic_df, kind='count')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"549"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Total number of passengers who didn't survive \n",
"len(not_survived)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Pclass</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>female</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>77</td>\n",
" <td>91</td>\n",
" <td>300</td>\n",
" <td>468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>80</td>\n",
" <td>97</td>\n",
" <td>372</td>\n",
" <td>549</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Pclass 1 2 3 All\n",
"Sex \n",
"female 3 6 72 81\n",
"male 77 91 300 468\n",
"All 80 97 372 549"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Number of passengers who did not survive in each class grouped by sex.\n",
"not_survived.pivot_table('Survived', 'Sex', 'Pclass', aggfunc=len, margins=True)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Passengers who survived and who didn't survive grouped by class and sex\n",
"table = pd.crosstab(index=[titanic_df.Survived,titanic_df.Pclass], columns=[titanic_df.Sex,titanic_df.Embarked])"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <th colspan=\"9\" halign=\"left\">female</th>\n",
" <th colspan=\"9\" halign=\"left\">male</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Embarked</th>\n",
" <th colspan=\"3\" halign=\"left\">C</th>\n",
" <th colspan=\"3\" halign=\"left\">Q</th>\n",
" <th colspan=\"3\" halign=\"left\">S</th>\n",
" <th colspan=\"3\" halign=\"left\">C</th>\n",
" <th colspan=\"3\" halign=\"left\">Q</th>\n",
" <th colspan=\"3\" halign=\"left\">S</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Survived</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>55</td>\n",
" <td>25</td>\n",
" <td>8</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>51</td>\n",
" <td>82</td>\n",
" <td>231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>42</td>\n",
" <td>7</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>46</td>\n",
" <td>61</td>\n",
" <td>33</td>\n",
" <td>17</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>28</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Sex female male \\\n",
"Embarked C Q S C Q S \n",
"Pclass 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 \n",
"Survived \n",
"0 1 0 8 0 0 9 2 6 55 25 8 33 1 1 36 51 82 \n",
"1 42 7 15 1 2 24 46 61 33 17 2 10 0 0 3 28 15 \n",
"\n",
"Sex \n",
"Embarked \n",
"Pclass 3 \n",
"Survived \n",
"0 231 \n",
"1 34 "
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.unstack()"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(MultiIndex(levels=[[u'Female', u'Male'], [u'Cherbourg', u'Queenstown', u'Southampton']],\n",
" labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n",
" names=[u'Sex', u'Embarked']),\n",
" MultiIndex(levels=[[0, 1], [1, 2, 3]],\n",
" labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],\n",
" names=[u'Survived', u'Pclass']))"
]
},
"execution_count": 225,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table.columns, table.index"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th colspan=\"3\" halign=\"left\">Female</th>\n",
" <th colspan=\"3\" halign=\"left\">Male</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>Embarked</th>\n",
" <th>Cherbourg</th>\n",
" <th>Queenstown</th>\n",
" <th>Southampton</th>\n",
" <th>Cherbourg</th>\n",
" <th>Queenstown</th>\n",
" <th>Southampton</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>33</td>\n",
" <td>36</td>\n",
" <td>231</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">1</th>\n",
" <th>1</th>\n",
" <td>42</td>\n",
" <td>1</td>\n",
" <td>46</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>61</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15</td>\n",
" <td>24</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Sex Female Male \\\n",
"Embarked Cherbourg Queenstown Southampton Cherbourg Queenstown \n",
"Survived Pclass \n",
"0 1 1 0 2 25 1 \n",
" 2 0 0 6 8 1 \n",
" 3 8 9 55 33 36 \n",
"1 1 42 1 46 17 0 \n",
" 2 7 2 61 2 0 \n",
" 3 15 24 33 10 3 \n",
"\n",
"Sex \n",
"Embarked Southampton \n",
"Survived Pclass \n",
"0 1 51 \n",
" 2 82 \n",
" 3 231 \n",
"1 1 28 \n",
" 2 15 \n",
" 3 34 "
]
},
"execution_count": 224,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Change name of columns\n",
"table.columns.set_levels(['Female', 'Male'], level=0, inplace=True)\n",
"table.columns.set_levels(['Cherbourg','Queenstown','Southampton'], level=1, inplace=True)\n",
"table"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average and median age of passengers are 30 and 28 years old, respectively\n"
]
}
],
"source": [
"print('Average and median age of passengers are %0.f and %0.f years old, respectively'%(titanic_df.Age.mean(), \n",
" titanic_df.Age.median()))"
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 714.000000\n",
"mean 29.699118\n",
"std 14.526497\n",
"min 0.420000\n",
"25% 20.125000\n",
"50% 28.000000\n",
"75% 38.000000\n",
"max 80.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 246,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.Age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Drop missing values for the records in which age passenger is missing\n",
"age = titanic_df['Age'].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 347,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x9ff81f2c>"
]
},
"execution_count": 347,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAH4CAYAAAAGgepgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa26113ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Distribution of age, with an overlay of a density plot\n",
"age_dist = sns.distplot(age)\n",
"age_dist.set_title(\"Distribution of Passengers' Ages\")"
]
},
{
"cell_type": "code",
"execution_count": 348,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x9fe3140c>"
]
},
"execution_count": 348,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAHhCAYAAABKqzsLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9ffcb62c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Another way to plot a histogram of ages is shown below\n",
"titanic_df['Age'].hist(bins=50)"
]
},
{
"cell_type": "code",
"execution_count": 327,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(dtype('int64'), dtype('int64'), 204)"
]
},
"execution_count": 327,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df['Parch'].dtype, titanic_df['SibSp'].dtype, len(titanic_df.Cabin.dropna())"
]
},
{
"cell_type": "code",
"execution_count": 331,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a function to define those who are children (less than 16)\n",
"def male_female_child(passenger):\n",
" age, sex = passenger\n",
" \n",
" if age < 16:\n",
" return 'child'\n",
" else:\n",
" return sex"
]
},
{
"cell_type": "code",
"execution_count": 332,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic_df['person'] = titanic_df[['Age', 'Sex']].apply(male_female_child, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 338,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>child</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>child</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"\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",
"\n",
" Parch Ticket Fare Cabin Embarked person \n",
"0 0 A/5 21171 7.2500 NaN S male \n",
"1 0 PC 17599 71.2833 C85 C female \n",
"2 0 STON/O2. 3101282 7.9250 NaN S female \n",
"3 0 113803 53.1000 C123 S female \n",
"4 0 373450 8.0500 NaN S male \n",
"5 0 330877 8.4583 NaN Q male \n",
"6 0 17463 51.8625 E46 S male \n",
"7 1 349909 21.0750 NaN S child \n",
"8 2 347742 11.1333 NaN S female \n",
"9 0 237736 30.0708 NaN C child "
]
},
"execution_count": 338,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lets have a look at the first 10 rows of the data frame\n",
"titanic_df[:10]"
]
},
{
"cell_type": "code",
"execution_count": 454,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9a2dbd8c>"
]
},
"execution_count": 454,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAosAAAEZCAYAAAD/rG/CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x998a44cc>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Lets do a factorplot of passengers splitted into sex, children and class\n",
"sns.factorplot('Pclass', data=titanic_df, kind='count', hue='person', order=[1,2,3], \n",
" hue_order=['child','female','male'], aspect=2)"
]
},
{
"cell_type": "code",
"execution_count": 354,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"male 537\n",
"female 271\n",
"child 83\n",
"dtype: int64"
]
},
"execution_count": 354,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count number of men, women and children\n",
"titanic_df['person'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 353,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9eb75ecc>"
]
},
"execution_count": 353,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9AAAAFiCAYAAAAeMzC/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9eb75d2c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Do the same as above, but split the passengers into either survived or not\n",
"sns.factorplot('Pclass', data=titanic_df, kind='count', hue='person', col='Survived', order=[1,2,3], \n",
" hue_order=['child','female','male'], aspect=1.25, size=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are much more children in third class than there are in first and second class. However, one may expect that\n",
"there woould be more children in 1st and 2nd class than there are in 3rd class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### kde plot, Distribution of Passengers' Ages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grouped by Gender"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9abecacc>"
]
},
"execution_count": 364,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA5wAAADaCAYAAADKW009AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9abeca4c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = sns.FacetGrid(titanic_df, hue='Sex', aspect=4)\n",
"fig.map(sns.kdeplot, 'Age', shade=True)\n",
"oldest = titanic_df['Age'].max()\n",
"fig.set(xlim=(0,oldest))\n",
"fig.set(title='Distribution of Age Grouped by Gender')\n",
"fig.add_legend()"
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9abd4bac>"
]
},
"execution_count": 366,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA50AAADRCAYAAABPXtbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9a7bc10c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = sns.FacetGrid(titanic_df, hue='person', aspect=4)\n",
"fig.map(sns.kdeplot, 'Age', shade=True)\n",
"oldest = titanic_df['Age'].max()\n",
"fig.set(xlim=(0,oldest))\n",
"fig.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grouped by Class"
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9a76c18c>"
]
},
"execution_count": 367,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAADaCAYAAAAR4OcfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nolCSJEmSWuw/W9HpqzTUexQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9a76418c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = sns.FacetGrid(titanic_df, hue='Pclass', aspect=4)\n",
"fig.map(sns.kdeplot, 'Age', shade=True)\n",
"oldest = titanic_df['Age'].max()\n",
"fig.set(xlim=(0,oldest))\n",
"fig.set(title='Distribution of Age Grouped by Class')\n",
"fig.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the plot above, class 1 has a normal distribution. However, classes 2 and 3 have a skewed distribution towards\n",
"20 and 30-year old passengers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What cabins did the Passengers stay in?"
]
},
{
"cell_type": "code",
"execution_count": 384,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 C85\n",
"3 C123\n",
"6 E46\n",
"10 G6\n",
"11 C103\n",
"Name: Cabin, dtype: object"
]
},
"execution_count": 384,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deck = titanic_df['Cabin'].dropna()\n",
"deck.head()"
]
},
{
"cell_type": "code",
"execution_count": 388,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Grab the first letter of the cabin letter\n",
"d = []\n",
"for c in deck:\n",
" d.append(c[0])"
]
},
{
"cell_type": "code",
"execution_count": 390,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['C', 'C', 'E', 'G', 'C', 'D', 'A', 'C', 'B', 'D']"
]
},
"execution_count": 390,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 398,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Counter({'C': 59, 'B': 47, 'D': 33, 'E': 32, 'A': 15, 'F': 13, 'G': 4, 'T': 1})"
]
},
"execution_count": 398,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"Counter(d)"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x99e810cc>"
]
},
"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAEZCAYAAACdL4HtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFFBJREFUeJzt3X2QXXV9x/H3upvFhiwhOywokgJG/HZqCwWpKDoEKgJR\naZDaqlWLVKrW6GBHh9aUWkct+AC0pT5UoximY20FLQ9iKG1gErU+VEvVGPsFEXd2tOLCbjBLkCWb\n7R/3rvtLSnbvLnv2bO6+XzOZ7D13zzmfe2Zv8tnf+d1zOsbHx5EkSVLDE+oOIEmStJBYjiRJkgqW\nI0mSpILlSJIkqWA5kiRJKliOJEmSCl1Vbjwi3g6cCywBPgh8GdgI7AG2Aesy02sJSJKkBaOykaOI\nOB14TmaeCpwOPBW4ElifmacBHcDaqvYvSZI0G1WeVjsL+E5E3ADcDNwEPDMztzaf3wScWeH+JUmS\nZqzK02p9wErgxTRGjW6mMVo0YQRYXuH+JUmSZqzKcnQ/8L3M3A3cFRE/B55SPN8D7JhuI7t3j413\ndXVWFFGSJC1SHft7ospy9CXgYuCqiDgSWApsjojVmbkFWANsnm4jw8O7KowoSZIWo76+nv0+11Hl\njWcj4n3AGTTmNr0d+CGwAegGtgN/NN2n1QYHd/ppNkmSNKf6+nr2O3JUaTmaC5YjzcTo6CgDA/11\nx5hzK1ceTXd3d90xJKltTFWOKr3OkTTfBgb6WXvFZXQtb5+5/rsffJAb37aeVauOqzuKJC0KliO1\nna7ly1nS21t3DEnSAcrbh0iSJBUsR5IkSQXLkSRJUsFyJEmSVLAcSZIkFSxHkiRJBcuRJElSwXIk\nSZJUsBxJkiQVLEeSJEkFy5EkSVLBciRJklSwHEmSJBUsR5IkSQXLkSRJUsFyJEmSVLAcSZIkFSxH\nkiRJBcuRJElSwXIkSZJUsBxJkiQVLEeSJEkFy5EkSVLBciRJklSwHEmSJBUsR5IkSQXLkSRJUsFy\nJEmSVLAcSZIkFbqq3kFE/BfwYPPhD4DLgY3AHmAbsC4zx6vOIUmS1IpKy1FEPBEgM88olt0ErM/M\nrRHxEWAtcEOVOSRJklpV9cjRCcDSiPjX5r7+HDgpM7c2n98EnIXlSJIkLRBVzzl6CPhAZp4NvAH4\n1D7PjwDLK84gSZLUsqpHju4Cvg+QmXdHxAPAicXzPcCOqTawYsVSuro6q0uotjI8vKzuCJXo7V1G\nX19P3TEkaVGouhxdCBwPrIuII2mUodsiYnVmbgHWAJun2sDw8K6KI6qdDA2N1B2hEkNDIwwO7qw7\nhiS1jal+4ay6HH0C+GRETMwxuhB4ANgQEd3AduD6ijNIkiS1rNJylJm7gVc/xlOnV7lfSZKk2fIi\nkJIkSQXLkSRJUsFyJEmSVLAcSZIkFSxHkiRJBcuRJElSoerrHGkejI6OMjDQX3eMObdy5dF0d3fX\nHUOStMhYjtrAwEA/L730Epb0tM/tJR7duZPr3/N+Vq06ru4okqRFxnLUJpb09NB9yCF1x5Ak6YDn\nnCNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmSCpYjSZKkguVIkiSp\nYDmSJEkqWI4kSZIKliNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmS\nCpYjSZKkQlfVO4iIw4FvAs8H9gAbm39vA9Zl5njVGSRJklpV6chRRCwBPgo8BHQAVwHrM/O05uO1\nVe5fkiRppqo+rfYB4CPA/zYfn5SZW5tfbwLOrHj/kiRJM1JZOYqI1wCDmXlbc1FH88+EEWB5VfuX\nJEmajSrnHF0IjEfEmcBvANcCfcXzPcCO6TayYsVSuro6q0nYJoaHl9UdoRK9vcvo6+uZ0ToeC0nS\n41VZOcrM1RNfR8QdwBuAD0TE6szcAqwBNk+3neHhXVVFbBtDQyN1R6jE0NAIg4M7Z7xOO5rNsZAk\n7d9Uv3BW/mm1wjjwVmBDRHQD24Hr53H/kiRJ05qXcpSZZxQPT5+PfUqSJM2GF4GUJEkqWI4kSZIK\nliNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmSCpYjSZKkguVIkiSp\nYDmSJEkqWI4kSZIKliNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmS\nCl11B5BUjdHRUQYG+uuOMedWrjya7u7uumNIamOWI6lNDQz086KPf5jO3t66o8yZsaEhbrnojaxa\ndVzdUSS1sWnLUUT8XWa+eZ9l12bmBdXFkjQXOnt76errqzuGJB1Q9luOIuLjwCrg5Ij4tX3WObTq\nYJIkSXWYauTor4CjgauBdwIdzeW7ge3VxpKkudOu86/AOVhSFfZbjjLzXuBe4PiIOARYzmRBWgYM\nVR9Pkh6/gYF+zvnktXT2HlZ3lDk1NnQ/t154gXOwpDnWypyj9cCf0ShD48VTx1YVSpLmWmfvYXQd\nfkTdMSQdAFr5tNpFwKrMHKw6jCRJUt1auQhkPzBcdRBJkqSFoJWRo+8DX4qI24FHmsvGM/Nd060Y\nEZ3ABuDpNE7JvaG5jY3AHmAbsC4zx/e3DUmSpPnUysjRj4BbgdHm4w4mJ2ZP58XAnsx8HnApcBlw\nJbA+M09rbmftjBJLkiRVaNqRo8x852w3npk3RsTnmw+PoXF67szM3Npctgk4C7hhtvuQJEmaS618\nWm3PYyz+cWYe1coOMnMsIjYC5wG/C7ygeHqExiUCJEmSFoRWRo5+ceotIpbQKDmnzmQnmfmaiDgC\n+DrwxOKpHmDHVOuuWLGUrq7Omexu0RkeXlZ3hEr09i6jr69nRut4LCZ5LCa167GA2R0PSVOb0Y1n\nM/NR4LqIuLSV74+IVwNHZeblwMPAGPCNiFidmVuANcDmqbYxPLxrJhEXpaGhkbojVGJoaITBwZ0z\nXqcdeSwmeSz2NpvjIYkpf6lo5bRaeYPZDuAZTH5qbTrXAxsjYguwBLgY+B9gQ0R007gNyfUtbkuS\nJKlyrYwcncHklbHHgfuBl7Wy8cx8eD/fe3or60uSJM23VuYcvaY5yhPN79/WPL0mSZLUdqa9zlFE\nnAzcBVwLXAP0R8Szqw4mSZJUh1ZOq10NvCwzvwbQLEZXA8+qMpgkSVIdWrlC9sETxQggM7/K3h/H\nlyRJahutlKPhiDhv4kFEvAR4oLpIkiRJ9WnltNrrgJsj4hM0Psq/B3hupakkSZJq0srI0TnALuCX\naXwE/wH8KL4kSWpTrZSj1wPPy8yHMvPbwInAm6uNJUmSVI9WylEXMFo8HqVxak2SJKnttDLn6Abg\n9oj4Zxpzjs4Hbqo0lSRJUk2mHTnKzD+lcV2jAI4F/jYzW7rxrCRJ0oGmlZEjMvM64LqKs0iSJNWu\nlTlHkiRJi4blSJIkqWA5kiRJKliOJEmSCpYjSZKkguVIkiSpYDmSJEkqWI4kSZIKliNJkqSC5UiS\nJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmSCpYjSZKkguVIkiSpYDmSJEkqdFW1\n4YhYAlwDHA0cBLwH+B6wEdgDbAPWZeZ4VRkkSZJmqsqRo1cCg5l5GnAO8CHgSmB9c1kHsLbC/UuS\nJM1YleXoOuAdxX4eBU7KzK3NZZuAMyvcvyRJ0oxVdlotMx8CiIgeGkXpUuCK4ltGgOVV7V+SJGk2\nKitHABGxEvgc8KHM/HREvL94ugfYMd02VqxYSldXZ1UR28Lw8LK6I1Sit3cZfX09M1rHYzHJYzGp\nXY8FzO54SJpalROyjwBuA96YmXc0F98ZEaszcwuwBtg83XaGh3dVFbFtDA2N1B2hEkNDIwwO7pzx\nOu3IYzHJY7G32RwPSUz5S0WVI0fraZw2e0dETMw9uhi4OiK6ge3A9RXuX5IkacaqnHN0MY0ytK/T\nq9qnJEnS4+VFICVJkgqWI0mSpILlSJIkqWA5kiRJKliOJEmSCpYjSZKkguVIkiSpYDmSJEkqWI4k\nSZIKliNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpEJX3QFmY3R0lIGB/rpjVGLlyqPp7u6uO4Yk\nSYvWAVmOBgb6efmbXkf30qV1R5lTo7t28U8f/BirVh1XdxRJkhatA7IcAXQvXcpBBx9cdwxJktRm\nnHMkSZJUsBxJkiQVLEeSJEkFy5EkSVLBciRJklQ4YD+tJkmaOa8TJ03PciRJi8jAQD9rPvxpOg/t\nqzvKnBrbMcimN77C68RpTliOJGmR6Ty0j67DnlR3DGnBcs6RJElSwXIkSZJUsBxJkiQVLEeSJEkF\ny5EkSVKh8k+rRcQpwHsz84yIeBqwEdgDbAPWZeZ41RkkSZJaVenIUURcAmwADmouugpYn5mnAR3A\n2ir3L0mSNFNVn1b7PnA+jSIEcFJmbm1+vQk4s+L9S5IkzUil5SgzPwfsLhZ1FF+PAMur3L8kSdJM\nzfcVsvcUX/cAO6ZbYcWKpXR1de61bHh42RzHWjh6e5fR19czo3Xa9Xh4LCZ5LCZ5LPY20+PhsZCm\nN9/l6M6IWJ2ZW4A1wObpVhge3vX/lg0NjVQQbWEYGhphcHDnjNdpRx6LSR6LSR6Lvc30eHgspIap\nivR8laOJT6S9FdgQEd3AduD6edq/JElSSyovR5n5Q+DU5td3A6dXvU9JkqTZ8iKQkiRJBcuRJElS\nwXIkSZJUsBxJkiQVLEeSJEkFy5EkSVLBciRJklSwHEmSJBUsR5IkSQXLkSRJUsFyJEmSVLAcSZIk\nFSxHkiRJBcuRJElSwXIkSZJUsBxJkiQVLEeSJEkFy5EkSVKhq+4AkiTVYXR0lIGB/rpjVGLlyqPp\n7u6uO8YBy3IkSVqUBgb6eeEVn6Vz+eF1R5lTYw/+lC+87XdYteq4uqMcsCxHkqRFq3P54XT1Hll3\nDC0wzjmSJEkqWI4kSZIKliNJkqSC5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqWA5kiRJKniF\nbEmSFjnvM7e3eS9HEfEE4MPA8cAjwEWZec9855AkSQ0DA/286N0309lzRN1R5tTYzvu45S/OnfF9\n5uoYOToP6M7MUyPiFODK5jJJklSTzp4j6Dr0KXXHWBDqmHP0XOBWgMz8GnByDRkkSZIeUx0jR4cA\nPysej0XEEzJzz0w2Mrpr19ymWgAez2t6dOfOOUxSv8fzenY/+OAcJqnf43k9Y0NDc5ikfo/n9YwN\n3T+HSRaG2b6msR2Dc5ykfrN9TWMP/nSOk9Rvtq9pbOd9c5ykfrN9TR3j4+NzHGVqEXEl8NXMvK75\neCAzV85rCEmSpP2o47Tal4EXAkTEs4Fv15BBkiTpMdVxWu1fgBdExJebjy+sIYMkSdJjmvfTapIk\nSQuZV8iWJEkqWI4kSZIKliNJkqSC5UiSJKngjWdbEBGXAG8Bjs3MR+rOU4eIOB34DPBdoAM4CPjj\nzPzvOnPVISKeAbwPWAosA76Qme+sNVRNHuPnYgnwNxPXMVtM9jkWEwYz8/fqSVSfiDiGxmVavlks\nvj0z311PonpFxFOB9wNPAXYBDwOXZOb2WoPNs4i4Angm8CQa/37+gAX6HrEcteZVwKeBlwPX1pyl\nLuPAv2fm7wNExAuAdwPn1ppqnkXEoTR+Fl6Smfc0b6R8XUS8PjM/WnO8OowDmzPzFQARcTCwJSLu\nysxv1Rtt3u31HhHfzcwz6g5Rt4hYCtxI4ybrX2su+03gQ8CiOj6Z+TaAiLgAiMxcX3Ok/fK02jSa\nvw3eDXwUWFdvmlp1NP9M6AXa71rz01tLowzcA9C87c0fANfUmqo+5c8EmfkQjffKS+uJU6t93yMS\nNH6B3DxRjAAy8z8tjgv7veLI0fQuAj6RmXdFxCMR8azM/HrdoWryWxFxB41TaicA59Wcpw5PBu4t\nFzQLgSbdB5xUd4iaTLxHJtySmVfUlqZev7rPsXhlZv64tjT1OQa4Z+JBRNwALKfxb8nzM/NHNeXS\nFCxHU4iIFcAaoC8i3kzjB/pNNEYKFqPbi9MnTwe+EhFHLrJ5WP3s8x9/RBwLHJWZX6wn0oJzDDBQ\nd4ia/OI9IrY7OgI03gsnTzzIzPMAIuIrQGddoTQ1T6tN7VXAxzPz7MxcA5wCnBURh9WcayFov1tZ\nt+bzwDnNCZZExBLgKuAZtaZaICLiEBqjrYtuQra0HzcCZ0bEKRMLIuJpwFE05qlpAXLkaGqvpVGQ\nAMjMhyPiszT+8X9vbanqMc7kKYMxoAf4k0U2akRm7mxOJtzQnIzdA9yUmX9fc7S67Ptz0QW8IzPv\nrjdWLcpjUVqTmT+vI1DN/I+fxmn3iDgXeG9EPJnGe2QMeEtmLtYRVljgPx/eW02SJKngaTVJkqSC\n5UiSJKlgOZIkSSpYjiRJkgqWI0mSpILlSJIkqeB1jiQtSM0LSl4OnAbsBoaBt2bmnfv5/mOAmzPz\n1x/juVuA12bmT6pLLKldOHIkacFpXmDzC8D9wAmZeSLwLmBT87Y+M5KZL7IYSWqVF4GUtOBExPOB\nj2Xmqn2WnwN8E7iMxi1bjgASOB94EnAHcCewCriLxmjRzyLih8Bq4AzgHGAF8FTgtsxcNw8vSdIB\nxJEjSQvRicDX912YmbcCvwL8PDNPBZ4G/BLwwua3HAVclpknAPcClzaXjzN5u4Ln0ChTxwPnRoT3\nxZO0F+ccSVqIxtjPL2+Z+cWIeCAi1tEoSscBB9MoP9/JzG80v/UfgI3Fqh3Nv/8jMx8CiIgfAL1z\nH1/SgcyRI0kL0TeAk/ZdGBGXR8Ra4FPACHANsJXJ4rO7+PYn7PN4QnkT2PFiXUkCLEeSFqDM/CLw\n04j4y+bkbCLibOAC4GzgM5l5LXAfjU+zddIoOScUp8n+EPi3eQ8v6YDnaTVJC9VvA38NbIuIR4FB\nYA2NU27/GBHnAz8BbgSOBW6nMTn7sog4FvgW8Pbmtsb3+SNJ++Wn1SRJkgqeVpMkSSpYjiRJkgqW\nI0mSpILlSJIkqWA5kiRJKliOJEmSCpYjSZKkwv8BVEz6Ufj8yJ4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x99e810ac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Now lets factorplot the cabins. First transfer the d list into a data frame. Then rename the column Cabin \n",
"cabin_df = DataFrame(d)\n",
"cabin_df.columns=['Cabin']\n",
"sns.factorplot('Cabin', data=cabin_df, kind='count', order=['A','B','C','D','E','F','G','T'], aspect=2, \n",
" palette='winter_d')"
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Drop the 'T' cabin\n",
"cabin_df = cabin_df[cabin_df['Cabin'] != 'T']"
]
},
{
"cell_type": "code",
"execution_count": 433,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x98c849ac>"
]
},
"execution_count": 433,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAEZCAYAAACdL4HtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\netbRLFEP9gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x98c8478c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Then replot the Cabins factorplot as above\n",
"sns.factorplot('Cabin', data=cabin_df, kind='count', order=['A','B','C','D','E','F','G'], aspect=2, \n",
" palette='Greens_d')"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 434,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Below is a link to the list of matplotlib colormaps\n",
"url = 'http://matplotlib.org/api/pyplot_summary.html?highlight=colormaps#matplotlib.pyplot.colormaps'\n",
"import webbrowser\n",
"webbrowser.open(url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Where did the passengers come from i.e. Where did the passengers land into the ship from?"
]
},
{
"cell_type": "code",
"execution_count": 476,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9866c76c>"
]
},
"execution_count": 476,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAEZCAYAAAD8N6OvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiQV7P8DOrRkRlRorK8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9866ce2c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Embarked', data=titanic_df, kind='count', hue='Pclass', hue_order=range(1,4), aspect=2,\n",
" order = ['C','Q','S'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the figure above, one may conclude that almost all of the passengers who boarded from Queenstown were in third \n",
"class. On the other hand, many who boarded from Cherbourg were in first class. The biggest portion of passengers \n",
"who boarded the ship came from Southampton, in which 353 passengers were in third class, 164 in second class and \n",
"127 passengers were in first class. In such cases, one may need to look at the economic situation at these different towns at that period of time to understand why most passengers who boarded from Queenstown were in third class for example."
]
},
{
"cell_type": "code",
"execution_count": 453,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"S 644\n",
"C 168\n",
"Q 77\n",
"dtype: int64"
]
},
"execution_count": 453,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.Embarked.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 470,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# For tabulated values, use crosstab pandas method instead of the factorplot in seaborn\n",
"port = pd.crosstab(index=[titanic_df.Pclass], columns=[titanic_df.Embarked])\n",
"port.columns = [['Cherbourg','Queenstown','Southampton']]"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cherbourg</th>\n",
" <th>Queenstown</th>\n",
" <th>Southampton</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>85</td>\n",
" <td>2</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>66</td>\n",
" <td>72</td>\n",
" <td>353</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cherbourg Queenstown Southampton\n",
"Pclass \n",
"1 85 2 127\n",
"2 17 3 164\n",
"3 66 72 353"
]
},
"execution_count": 466,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port"
]
},
{
"cell_type": "code",
"execution_count": 471,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([1, 2, 3], dtype='int64', name=u'Pclass')"
]
},
"execution_count": 471,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port.index"
]
},
{
"cell_type": "code",
"execution_count": 472,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Cherbourg', u'Queenstown', u'Southampton'], dtype='object')"
]
},
"execution_count": 472,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port.columns"
]
},
{
"cell_type": "code",
"execution_count": 473,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"port.index=[['First','Second','Third']]"
]
},
{
"cell_type": "code",
"execution_count": 474,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cherbourg</th>\n",
" <th>Queenstown</th>\n",
" <th>Southampton</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>First</th>\n",
" <td>85</td>\n",
" <td>2</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Second</th>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Third</th>\n",
" <td>66</td>\n",
" <td>72</td>\n",
" <td>353</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cherbourg Queenstown Southampton\n",
"First 85 2 127\n",
"Second 17 3 164\n",
"Third 66 72 353"
]
},
"execution_count": 474,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Who was alone and who was with parents or siblings?"
]
},
{
"cell_type": "code",
"execution_count": 481,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SibSp Parch\n",
"0 1 0\n",
"1 1 0\n",
"2 0 0\n",
"3 1 0\n",
"4 0 0"
]
},
"execution_count": 481,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df[['SibSp','Parch']].head()"
]
},
{
"cell_type": "code",
"execution_count": 552,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"# Alone dataframe i.e. the passenger has no siblings or parents\n",
"alone_df = titanic_df[(titanic_df['SibSp'] == 0) & (titanic_df['Parch']==0)]\n",
"# Add Alone column\n",
"alone_df['Alone'] = 'Alone'\n",
"# Not alone data frame i.e. the passenger has either a sibling or a parent.\n",
"not_alone_df = titanic_df[(titanic_df['SibSp'] != 0) | (titanic_df['Parch']!=0)]\n",
"not_alone_df['Alone'] = 'With family'\n",
"\n",
"# Merge the above dataframes\n",
"comb = [alone_df, not_alone_df]\n",
"# Merge and sort by index\n",
"titanic_df = pd.concat(comb).sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 519,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[537, 354]"
]
},
"execution_count": 519,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[len(alone_df), len(not_alone_df)]"
]
},
{
"cell_type": "code",
"execution_count": 520,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" <th>Alone</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113783</td>\n",
" <td>26.5500</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name Sex Age \\\n",
"2 3 1 3 Heikkinen, Miss. Laina female 26 \n",
"4 5 0 3 Allen, Mr. William Henry male 35 \n",
"5 6 0 3 Moran, Mr. James male NaN \n",
"6 7 0 1 McCarthy, Mr. Timothy J male 54 \n",
"11 12 1 1 Bonnell, Miss. Elizabeth female 58 \n",
"\n",
" SibSp Parch Ticket Fare Cabin Embarked person Alone \n",
"2 0 0 STON/O2. 3101282 7.9250 NaN S female Alone \n",
"4 0 0 373450 8.0500 NaN S male Alone \n",
"5 0 0 330877 8.4583 NaN Q male Alone \n",
"6 0 0 17463 51.8625 E46 S male Alone \n",
"11 0 0 113783 26.5500 C103 S female Alone "
]
},
"execution_count": 520,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the first five records of the alone data frame\n",
"alone_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 521,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" <th>Alone</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>child</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"3 4 1 1 \n",
"7 8 0 3 \n",
"8 9 1 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",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"7 Palsson, Master. Gosta Leonard male 2 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked person Alone \n",
"0 0 A/5 21171 7.2500 NaN S male With family \n",
"1 0 PC 17599 71.2833 C85 C female With family \n",
"3 0 113803 53.1000 C123 S female With family \n",
"7 1 349909 21.0750 NaN S child With family \n",
"8 2 347742 11.1333 NaN S female With family "
]
},
"execution_count": 521,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the first five rows of the not alone data frame\n",
"not_alone_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 553,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" <th>Alone</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"\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",
"\n",
" Parch Ticket Fare Cabin Embarked person Alone \n",
"0 0 A/5 21171 7.2500 NaN S male With family \n",
"1 0 PC 17599 71.2833 C85 C female With family \n",
"2 0 STON/O2. 3101282 7.9250 NaN S female Alone \n",
"3 0 113803 53.1000 C123 S female With family \n",
"4 0 373450 8.0500 NaN S male Alone "
]
},
"execution_count": 553,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 539,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\" Another way to perform the above\\ntitanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch\\n\\ntitanic_df['Alone'].loc[titanic_df['Alone']>0] = 'With family'\\ntitanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Alone'\""
]
},
"execution_count": 539,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\" Another way to perform the above\n",
"titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch\n",
"\n",
"titanic_df['Alone'].loc[titanic_df['Alone']>0] = 'With family'\n",
"titanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Alone'\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 551,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9548b6cc>"
]
},
"execution_count": 551,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA5QAAAEaCAYAAABuExjJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAVal/g6FUwnGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x959f0d6c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fg=sns.factorplot('Alone', data=titanic_df, kind='count', hue='Pclass', col='person', hue_order=range(1,4),\n",
" palette='Blues')\n",
"fg.set_xlabels('Status')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the figure above, it is clear that most children traveled with family in third class. For men, most traveled alone in third class. On the other hand, the number of female passengers who traveled either with family or alone among the second and third class is comparable. However, more women traveled with family than alone in first class. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Factors Affecting the Surviving"
]
},
{
"cell_type": "code",
"execution_count": 554,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''Now lets look at the factors that help someone survived the sinking. We start this analysis by adding a new\n",
"cloumn to the titanic data frame. Use the Survived column to map to the new column with factors 0:no and 1:yes\n",
"using the map method'''\n",
"titanic_df['Survivor'] = titanic_df.Survived.map({0:'no', 1:'yes'})"
]
},
{
"cell_type": "code",
"execution_count": 555,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" <th>Alone</th>\n",
" <th>Survivor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>With family</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" <td>yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>Alone</td>\n",
" <td>yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" <td>yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"\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",
"\n",
" Parch Ticket Fare Cabin Embarked person Alone \\\n",
"0 0 A/5 21171 7.2500 NaN S male With family \n",
"1 0 PC 17599 71.2833 C85 C female With family \n",
"2 0 STON/O2. 3101282 7.9250 NaN S female Alone \n",
"3 0 113803 53.1000 C123 S female With family \n",
"4 0 373450 8.0500 NaN S male Alone \n",
"\n",
" Survivor \n",
"0 no \n",
"1 yes \n",
"2 yes \n",
"3 yes \n",
"4 no "
]
},
"execution_count": 555,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Class Factor"
]
},
{
"cell_type": "code",
"execution_count": 570,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x92e0334c>"
]
},
"execution_count": 570,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVUAAAEZCAYAAAAnlrY5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x92e03c2c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Survived vs. class Grouped by gender\n",
"sns.factorplot('Pclass','Survived', hue='person', data=titanic_df, order=range(1,4), \n",
" hue_order = ['child','female','male'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the figure above, being a male or a third class reduce the chance for one to survive. "
]
},
{
"cell_type": "code",
"execution_count": 566,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x932f65cc>"
]
},
"execution_count": 566,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA5QAAAEaCAYAAABuExjJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x932f672c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Survivor', data=titanic_df, hue='Pclass', kind='count', palette='Pastel2', hue_order=range(1,4),\n",
" col='person')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Age Factor"
]
},
{
"cell_type": "code",
"execution_count": 571,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x92e2904c>"
]
},
"execution_count": 571,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFhCAYAAACh/xvXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nByCzZ9rD9/5FAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x92e290ac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Linear plot of age vs. survived\n",
"sns.lmplot('Age', 'Survived', data=titanic_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There seems to be a general linear trend between age and the survived field. The plot shows that the older the passenger is, the less chance he/she would survive."
]
},
{
"cell_type": "code",
"execution_count": 573,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x92d152cc>"
]
},
"execution_count": 573,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAFhCAYAAACveqf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x92d0a1ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Survived vs. Age grouped by Sex\n",
"sns.lmplot('Age', 'Survived', data=titanic_df, hue='Sex')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Older women have higher rate of survival than older men as shown in the figure above. Also, older women has higher\n",
"rate of srvival than younger women; an opposite trend to the one for the male passengers."
]
},
{
"cell_type": "code",
"execution_count": 576,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9265a06c>"
]
},
"execution_count": 576,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAFhCAYAAABu/e1RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngkIIIUSg/w9hxOX69RLWhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x925efe8c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Survived vs. Age gruped by class\n",
"sns.lmplot('Age', 'Survived', hue='Pclass', data=titanic_df, palette='winter', hue_order=range(1,4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In all three classes, the chance to survive reduced as the passengers got older."
]
},
{
"cell_type": "code",
"execution_count": 578,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9231fcac>"
]
},
"execution_count": 578,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAFhCAYAAABu/e1RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkkAhhBCiKgkUQgghqvr/AQlmuipFknBwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x923508ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a generation bin\n",
"generations = [10,20,40,60,80]\n",
"sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,x_bins=generations, hue_order=[1,2,3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Deck Factor"
]
},
{
"cell_type": "code",
"execution_count": 580,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
" u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked',\n",
" u'person', u'Alone', u'Survivor'],\n",
" dtype='object')"
]
},
"execution_count": 580,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 606,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic_DF = titanic_df.dropna(subset=['Cabin'])"
]
},
{
"cell_type": "code",
"execution_count": 596,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['C', 'C', 'E', 'G', 'C', 'D', 'A', 'C', 'B', 'D']"
]
},
"execution_count": 596,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 607,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(204, 204)"
]
},
"execution_count": 607,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(titanic_DF), len(d)"
]
},
{
"cell_type": "code",
"execution_count": 608,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tarek/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"titanic_DF['Deck'] = d"
]
},
{
"cell_type": "code",
"execution_count": 613,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic_DF = titanic_DF[titanic_DF.Deck != 'T']"
]
},
{
"cell_type": "code",
"execution_count": 614,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>person</th>\n",
" <th>Alone</th>\n",
" <th>Survivor</th>\n",
" <th>Deck</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" <td>yes</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>With family</td>\n",
" <td>yes</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>male</td>\n",
" <td>Alone</td>\n",
" <td>no</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>female</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>PP 9549</td>\n",
" <td>16.7000</td>\n",
" <td>G6</td>\n",
" <td>S</td>\n",
" <td>child</td>\n",
" <td>With family</td>\n",
" <td>yes</td>\n",
" <td>G</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>female</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113783</td>\n",
" <td>26.5500</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" <td>female</td>\n",
" <td>Alone</td>\n",
" <td>yes</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"1 2 1 1 \n",
"3 4 1 1 \n",
"6 7 0 1 \n",
"10 11 1 3 \n",
"11 12 1 1 \n",
"\n",
" Name Sex Age SibSp \\\n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"6 McCarthy, Mr. Timothy J male 54 0 \n",
"10 Sandstrom, Miss. Marguerite Rut female 4 1 \n",
"11 Bonnell, Miss. Elizabeth female 58 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked person Alone Survivor Deck \n",
"1 0 PC 17599 71.2833 C85 C female With family yes C \n",
"3 0 113803 53.1000 C123 S female With family yes C \n",
"6 0 17463 51.8625 E46 S male Alone no E \n",
"10 1 PP 9549 16.7000 G6 S child With family yes G \n",
"11 0 113783 26.5500 C103 S female Alone yes C "
]
},
"execution_count": 614,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_DF.head()"
]
},
{
"cell_type": "code",
"execution_count": 616,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x9270680c>"
]
},
"execution_count": 616,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARkAAAEZCAYAAACjEFEXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPKWQERFPKWRExFMKGRHxlEJGRDz1/4h/KEwIfjO+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x926df44c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Deck', 'Survived', data=titanic_DF, order=['A','B','C','D','E','F','G'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There does not seem to be any relation between deck and the survival rate as shown in the above figure!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Family Status Factor"
]
},
{
"cell_type": "code",
"execution_count": 621,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x91c1cbac>"
]
},
"execution_count": 621,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARkAAAEZCAYAAACjEFEXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x91c1cb4c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Alone', 'Survived', data=titanic_df, palette='winter') #hue='person', \n",
" #hue_order=['child', 'female', 'male'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There seems that the survival rate diminishes significantly for those who were alone. However, lets check if a\n",
"gender or age play a factor. From the figure below, one may conclude that the survival rate for women and children\n",
"are much higher than that of men, as was concluded previously and as anticipated. However, the survival rate is not\n",
"significant for either gender or for children who were with family versus who were alone. Moreover, the survival \n",
"rate for women and children increases for those who were alone. For men, the survival rate diminishes slightly \n",
"for those who were alone versus for those who were with family."
]
},
{
"cell_type": "code",
"execution_count": 622,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x91bf57cc>"
]
},
"execution_count": 622,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVYAAAEZCAYAAADMoQ06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x91bf588c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('Alone', 'Survived', data=titanic_df, palette='winter', hue='person', \n",
" hue_order=['child', 'female', 'male'])"
]
},
{
"cell_type": "code",
"execution_count": 626,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x915553ac>"
]
},
"execution_count": 626,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA50AAAEaCAYAAACSBjMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJqs3fcOmw81LFl5IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x915553ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Lets split it by class now!\n",
"sns.factorplot('Alone', 'Survived', data=titanic_df, palette='summer', hue='person', \n",
" hue_order=['child', 'female', 'male'], col='Pclass', col_order=[1,2,3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predictive Modeling"
]
},
{
"cell_type": "code",
"execution_count": 627,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sklearn"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment