# coding: utf-8

# In[2]:

import pandas as pd

from pandas import Series, DataFrame

titanic_df = pd.read_csv('train.csv')

titanic_df.head()

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titanic_df.info()

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# Let's import what we'll need for the analysis and visualization

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

get_ipython().magic(u'matplotlib inline')

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#plot that shows ratio of men to women on board

sns.factorplot('Sex',data=titanic_df, kind = "count")

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#plot that shows ratio of men to women on board and their respective classes

#since we are using the a count plot we must specify the order (default is descending count)

class_list = titanic_df.Pclass

pclass = list(set(class_list))

sns.factorplot('Sex', hue = 'Pclass', hue_order = pclass, data = titanic_df, kind = "count")

# In[7]:

#alternative view of previous plot; I think this one is easier to visually interpret

sns.factorplot('Pclass', hue = 'Sex', data = titanic_df, kind = "count", order = pclass)

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def male_female_child(passenger):

return sex

titanic_df['person'] = titanic_df[['Age', 'Sex']].apply(male_female_child, axis = 1)

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titanic_df.tail()

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list(titanic_df.person).count('child')

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sns.factorplot('Pclass', data = titanic_df, hue = 'person', kind = 'count', order = pclass)

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#show the distribution of ages

titanic_df['Age'].hist(bins=70)

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titanic_df['person'].value_counts()

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titanic_df['person'].value_counts(normalize = True)

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# Another way to visualize the data is to use FacetGrid to plot multiple kedplots on one plot

fig = sns.FacetGrid(titanic_df, hue="Sex",aspect=4)

fig.map(sns.kdeplot, 'Age', shade = True)

oldest = titanic_df.Age.max()

fig.set(xlim = (0,oldest))

fig.add_legend()

# In[16]:

fig = sns.FacetGrid(titanic_df, hue = "person", aspect = 4)

fig.map(sns.kdeplot, 'Age', shade = True)

fig.set(xlim = (0, oldest))

fig.add_legend()

# In[17]:

fig = sns.FacetGrid(titanic_df, hue = "Pclass", aspect = 4, hue_order = pclass)

fig.map(sns.kdeplot, 'Age', shade = True)

fig.set(xlim = (0, oldest))

fig.add_legend()

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#we will now explore the passengers class and cabins

#we first must drop NaN values

deck = titanic_df['Cabin'].dropna()

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deck.head()

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levels = []

for level in deck:

levels.append(level[0])

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cabin_df = DataFrame(levels)

cabin_df.columns = ['Cabin']

cabin = sorted(list(set(cabin_df.Cabin)))

sns.factorplot('Cabin', data= cabin_df, kind = 'count', order = cabin, palette = "winter_d")

# In[22]:

classes = titanic_df.Pclass[deck.index]

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cabin_df["Pclass"] = classes.values

class_list2 = cabin_df.Pclass

pclass2 = list(set(class_list2))

pclass2

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sns.factorplot('Cabin', hue = "Pclass", data= cabin_df, kind = 'count', order = cabin, hue_order = pclass2)

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cabin_df.Cabin = cabin_df[cabin_df.Cabin != 'T']

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sns.factorplot('Cabin', data = cabin_df, kind = 'count', palette = 'summer_d', order = cabin )

# In[48]:

# The column 'Embarked' can take on 3 possible values, C,Q, or S for Cherbourg, Queenstown, Southhampton

sns.factorplot('Embarked', data = titanic_df, hue = 'Pclass', kind = 'count', order = ['C', 'Q', 'S'] , hue_order = [1, 2, 3])

# In[51]:

#now we will find out who was traveling alone

titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch

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titanic_df.Alone = titanic_df.Alone.map(lambda x: 'With Family' if x > 0 else 'Alone')

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titanic_df.Alone.head()

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sns.factorplot('Alone', data = titanic_df, kind = 'count', palette = 'Blues')

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sns.factorplot('Alone', hue = 'Pclass', hue_order = [1, 2, 3], data = titanic_df, kind = 'count', palette = 'Blues')

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titanic_df["Survivor"] = titanic_df.Survived.map({0: "no", 1: "yes"})

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sns.factorplot('Survivor', kind = 'count', data = titanic_df)

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sns.factorplot('Survivor', hue = 'Pclass', hue_order = pclass, kind = 'count', data = titanic_df)

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sns.factorplot('Pclass','Survived', data = titanic_df, x_order = [1, 2, 3])

# In[75]:

sns.factorplot('Pclass', 'Survived', hue='person', x_order = [1, 2, 3], data=titanic_df, type = 'Count')

# In[77]:

sns.lmplot('Age','Survived',data=titanic_df)

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sns.lmplot('Age', 'Survived', hue = 'Pclass', hue_order = [1, 2, 3], data = titanic_df)

# In[81]:

generations = [10, 20, 30, 40, 60, 80]

sns.lmplot('Age', 'Survived', hue = 'Pclass', data = titanic_df, x_bins = generations, palette = 'winter')

# In[82]:

sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter',x_bins=generations)

# In[83]:

survived = titanic_df.Survived[deck.index]

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cabin_df["Survived"] = survived

cabin_df.head()

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survivor = titanic_df.Survivor[deck.index]

cabin_df['Survivor'] = survivor

cabin_df.head()

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sns.factorplot('Survivor', hue = 'Cabin', x_order = ['yes', 'no'], hue_order = ['A', 'B', 'C', 'D', 'E', 'F', 'G'], data = cabin_df, kind = 'count')

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