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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing the Titanic Disaster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction:\n",
"\n",
"This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic). \n",
"To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n",
"\n",
"\n",
"### Step 1. Import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3. Assign it to a variable titanic "
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>22.0</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>38.0</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>26.0</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>35.0</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>35.0</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 22.0 1 \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",
"4 Allen, Mr. William Henry male 35.0 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": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4. Set PassengerId as the index "
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>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",
" <tr>\n",
" <th>PassengerId</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>1</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</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>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</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>3</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</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>4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</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>5</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</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": [
" Survived Pclass \\\n",
"PassengerId \n",
"1 0 3 \n",
"2 1 1 \n",
"3 1 3 \n",
"4 1 1 \n",
"5 0 3 \n",
"\n",
" Name Sex Age \\\n",
"PassengerId \n",
"1 Braund, Mr. Owen Harris male 22.0 \n",
"2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n",
"3 Heikkinen, Miss. Laina female 26.0 \n",
"4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
"5 Allen, Mr. William Henry male 35.0 \n",
"\n",
" SibSp Parch Ticket Fare Cabin Embarked \n",
"PassengerId \n",
"1 1 0 A/5 21171 7.2500 NaN S \n",
"2 1 0 PC 17599 71.2833 C85 C \n",
"3 0 0 STON/O2. 3101282 7.9250 NaN S \n",
"4 1 0 113803 53.1000 C123 S \n",
"5 0 0 373450 8.0500 NaN S "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5. Create a pie chart presenting the male/female proportion"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Gliu3hKKi+vzww0ssWPAIixaNZfPmVixbdvmvHlNSUp2SkioAFBdXBYowpuSX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\nWlJBiYhIWvo/rI6mZkORh2MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117e78690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-5, 85)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\n+rWhg8zCobPbiHQMEEsMYTb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gHtiSnKuBOPrgKD0jGccJFz/tiqwPMhVdgcyy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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a678c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7. How many people survived?"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"342"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8. Create a histogram with the Fare payed"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c10e290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BONUS: Create your own question and answer it."
]
},
{
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|