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yangju2011/titanic_jy.ipynb

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titanic_jy.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Who survived in Titanic? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Introduction and Questions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this report, I am exploring what types of passengers are more likely to survive in Titanic.\n",
"\n",
"Questions to be investigated\n",
"1) were female passengers more likely to survive than male?\n",
"2) were passengers with higher class more likely to survive?\n",
"3) were younger passengers more likley to survive than old?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Date import and cleaning"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"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": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"titanic_df = pd.read_csv('D:/Dropbox/Data-Analysis/p2/titanic-data.csv')\n",
"\n",
"titanic_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"891"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_passengers = titanic_df['PassengerId'].count()\n",
"total_passengers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To investigate which factors are correlated with high survival rates, I first group the data by the 'Survived' column"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>447.016393</td>\n",
" <td>2.531876</td>\n",
" <td>30.626179</td>\n",
" <td>0.553734</td>\n",
" <td>0.329690</td>\n",
" <td>22.117887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>444.368421</td>\n",
" <td>1.950292</td>\n",
" <td>28.343690</td>\n",
" <td>0.473684</td>\n",
" <td>0.464912</td>\n",
" <td>48.395408</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Age SibSp Parch Fare\n",
"Survived \n",
"0 447.016393 2.531876 30.626179 0.553734 0.329690 22.117887\n",
"1 444.368421 1.950292 28.343690 0.473684 0.464912 48.395408"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survived_group = titanic_df.groupby('Survived')\n",
"survived_group.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the mean value, it seems that people who survived had a higher socio-economic status, indicated by higher Fare and high class (low Pclass value). Therefore, I will first calculate the average survival rate for people with different classes. Then I will plot the histogram of fares for people who survived and who didn't. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xb924ef0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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srfE6LScCn4uI31LOFg+mXOw31m16X6ZcFHdq1TX/N5T29vFxLm855ULaidxqu7p2sJTS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\niO6mAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb924320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"survival_per_class = titanic_df.groupby('Pclass')['Survived'].mean()\n",
"my_xticks = ['Upper','Middle','Lower']\n",
"\n",
"survival_per_class.plot.bar()\n",
"plt.suptitle('Average Survival Rate of Different Passenger Classes')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Survived\n",
"0 [[Axes(0.125,0.125;0.775x0.775)]]\n",
"1 [[Axes(0.125,0.125;0.775x0.775)]]\n",
"dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ARZm52XNt+3ZSM8+1UUTEa4G/oXoO01rgi5n52brtQMbxXJsqIxGfBQ4D3gZ8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\n/wd12m8so8onSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb969390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MRJwPHAK8AjgNODsiTujpiOaBOkB8ATigretqYD1wKHAlcFVE7FU/5/nAVcCl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbc1e278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# survived_group.hist(column = 'Fare')\n",
"survived_group.hist(column = 'Fare',bins = 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the figures above, we can see the average survival rate of upper class is the highest, lower class lowest.\n",
"The distribution of fare for passengers who didn't survive (top figure) show lower fare than passengers who survived. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I also notice that people who survived tend to have a lower average age. I will plot the age distribution for survived and nonsurvived group. I will also calculate the average survival rate for different sex.\n",
"\n",
"For this purpose, I will create a new dataframe from the original dataframe by dropping out columns, leaving the column'Age', 'Survived', 'Sex'. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Sex Age\n",
"0 0 male 22.0\n",
"1 1 female 38.0\n",
"2 1 female 26.0\n",
"3 1 female 35.0\n",
"4 0 male 35.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agesex_df = titanic_df.drop('PassengerId',axis = 1).drop('Pclass',axis = 1).drop('Name',axis = 1).drop('SibSp',axis = 1).drop('Parch',axis = 1).drop('Ticket',axis = 1).drop('Fare',axis = 1).drop('Embarked',axis = 1).drop('Cabin',axis = 1)\n",
"agesex_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"agesex_survival = agesex_df[agesex_df['Survived']!=0]\n",
"agesex_nonsurvival = agesex_df[agesex_df['Survived']==0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>714.000000</td>\n",
" <td>714.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.406162</td>\n",
" <td>29.699118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.491460</td>\n",
" <td>14.526497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>20.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>38.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>80.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Age\n",
"count 714.000000 714.000000\n",
"mean 0.406162 29.699118\n",
"std 0.491460 14.526497\n",
"min 0.000000 0.420000\n",
"25% 0.000000 20.125000\n",
"50% 0.000000 28.000000\n",
"75% 1.000000 38.000000\n",
"max 1.000000 80.000000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agesex_df.dropna(inplace=True) #count from 891 to 714, clean NaN\n",
"agesex_df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xb8144a8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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C3gjsDryIEgqelplr6/rLgCsy86Q21CpJkmbRtOcURMRC4J3A8Zn5W2Af4NpGIKiupAwl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\nYCiQJEnV/wPrC6HRX1rO6QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb8146d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"agesex_survival['Age'].hist()\n",
"plt.suptitle('Age Distribution of Survived Passengers')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xc296080>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TdqHcbXNjVw9Jj7F7QpIkNWJLgyRJasTQIEmSGjE0SJKkRgwNkiSpEUODJElqxNAgSZIa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\nBkmS1IihQZIkNWJokCRJjRgaJElSI/8fEEjumcajoKcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc40a320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"agesex_nonsurvival['Age'].hist()\n",
"plt.suptitle('Age Distribution of Non-Survived Passengers')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>Age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>female</th>\n",
" <td>0.754789</td>\n",
" <td>27.915709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>0.205298</td>\n",
" <td>30.726645</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Age\n",
"Sex \n",
"female 0.754789 27.915709\n",
"male 0.205298 30.726645"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"female_df = agesex_df[agesex_df['Sex'] == 'female']\n",
"male_df = agesex_df[agesex_df['Sex'] == 'male']\n",
"male_df.head()\n",
"agesex_df.groupby('Sex').mean()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xc61dbe0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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gBgFJkhrMICBJUoMZBCRJajCDgCRJDVb7S4ciYi7l+7oXU75K8/TMPGOCuodSvsd7V8rX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\nAUmSGswgIElSgxkEJElqsP8P38nudRd7JB8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc33b438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = female_df['Survived'].mean(),male_df['Survived'].mean()\n",
"x = [0,1]\n",
"\n",
"my_xticks = ['Female','Male']\n",
"\n",
"plt.xticks(x,my_xticks)\n",
"plt.bar(x,y)\n",
"plt.suptitle('Survival Rate for Female and Male Passengers')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far, it seems that females passengers tend to have a higher survival rate (0.75) than male(0.2). Next I'm going to use statistical analysis to test whether the survival rate between female and male passengers is significantly different. \n",
"\n",
"My hypothesis is \n",
"H0: u(f) <= u(m)\n",
"H1: female passengers are more likely to survive than male, u(f) > u(m) \n",
"\n",
"I will then perform two-independent sample t-test to assess whether this hypothesis is true of false.\n",
"Assuming both groups are normally distributed, the standard deviation of the populations should be equal, and samples have to be randomly drawn independent of each other."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.6123549634e-55\n"
]
}
],
"source": [
"from scipy.stats import ttest_ind\n",
"\n",
"t, p = ttest_ind(female_df['Survived'],male_df['Survived'])\n",
"p = p/2 #one-tail ttest\n",
"\n",
"print p "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For alpha = 0.001, 2.6123549634e-55 is much smaller than alpha, thus reject the null hypothesis, female passengers are more likely to survive. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In conclusion, I report that passengers with higher socio-economic status and higher fares were more likely to survive. The average survival rate of upper class is more than twice of lower class. \n",
"\n",
"The demographic features of survived passengers are 1) younger passengers are more likely to survive, as shown by the age distribution. 2) female passengers have higher survival rate than male.\n",
"\n",
"These findings indicate that a upper class young female (guess who is this? Rose!) was more likely to survive in Titanic."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) https://docs.scipy.org/doc/scipy/reference/stats.html\n",
"2) http://stackoverflow.com/questions/13404468/t-test-in-pandas-python\n",
"3) http://stackoverflow.com/questions/3100985/plot-with-custom-text-for-x-axis-points\n",
"4) http://matplotlib.org/users/pyplot_tutorial.html\n",
"5) http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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