In [47]:

# 1st Project Titanic DataSet

In [48]:

#All good data analysis projects begin with trying to answer questions. Now that we know what column category data we have
#let's think of some questions or insights we would like to obtain from the data.
#So here's a list of questions we'll try to answer using our new data analysis skills!

#First some basic questions:

# 1.) Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)
# 2.) What deck were the passengers on and how does that relate to their class?
# 3.) Where did the passengers come from?
# 4.) Who was alone and who was with family?

# Then we'll dig deeper, with a broader question:

#5.) What factors helped someone survive the sinking?

#So let's start with the first question: Who were the passengers on the titanic?

In [1]:

import pandas as pd
from pandas import Series,DataFrame

In [50]:

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

In [51]:

titanic_df 
# Survived 1=alive 0=dead
#Pclass the class of passenger 1st class or 3r or 2nd
#SibSp did they have siblings onboard 1=they have, 2=do not have
#Parch If they do not have sibling onboared then they have Parent or child onboard
#Embarked S,C,Q stands for city  C = Cherbourg, Q = Queenstown, S = Southampton

Out[51]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

In [52]:

titanic_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

In [2]:

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

In [54]:

sns.catplot(x='Sex',data=titanic_df,kind='count')

Out[54]:

<seaborn.axisgrid.FacetGrid at 0x1adde638730>

In [55]:

# Now let's seperate the genders by classes, remember we can use the 'hue' arguement here!
sns.catplot(x='Sex',data=titanic_df,hue='Pclass',kind='count')

Out[55]:

<seaborn.axisgrid.FacetGrid at 0x1adde6f6b50>

In [56]:

# Opposite of that
sns.catplot(x='Pclass',data=titanic_df,kind='count',hue='Sex')
# Wow, quite a few more males in the 3rd class than females, an interesting

Out[56]:

<seaborn.axisgrid.FacetGrid at 0x1adde680b50>

In [57]:

#However, it might be useful to know the split between males,females,and children. How can we go about this?

In [58]:

# We'll treat anyone as under 16 as a child, and then use the apply technique with a function to create a new column

# First let's make a function to sort through the sex 
def male_female_child(passenger):
    #Take the age and sex 
    age,sex=passenger
    # Compare the age, otherwise leave the sex
    if age<16:
        return 'Child'
    else:
        return sex

In [59]:

# We'll define a new column called 'person', remember to specify axis=1 for columns and not index
titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)

In [60]:

titanic_df[0:10]

Out[60]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	person
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	male
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	female
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	female
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	female
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	male
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q	male
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S	male
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S	Child
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S	female
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C	Child

In [61]:

#Excellent! Now we have seperated the passengers between female,male,and child. 
#This will be important later on beacuse of the famous "Women and children first policy"!

In [62]:

sns.catplot(x='Pclass',data=titanic_df,kind='count',hue='person')

#Interesting, quite a bit of children in 3rd class and not so many in 1st!
#How about we create a distribution of the ages to get a more precise picture of the who the passengers were.

Out[62]:

<seaborn.axisgrid.FacetGrid at 0x1adde7e2dc0>

In [63]:

# Quick way to create a histogram using pandas
titanic_df["Age"].hist(bins=70)

Out[63]:

<AxesSubplot:>

In [64]:

# We could also get a quick overall comparison of male,female,child
titanic_df['person'].value_counts()

Out[64]:

male      537
female    271
Child      83
Name: person, dtype: int64

In [65]:

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

# Set the figure equal to a facetgrid with the pandas dataframe as its data source, set the hue,
# and change the aspect ratio.
fig =sns.FacetGrid(titanic_df,hue='Sex',aspect=4)

# Next use map to plot all the possible kdeplots for the 'Age' column by the hue choice
fig.map(sns.kdeplot,'Age',shade=True)

# Set the x max limit by the oldest passenger
oldest=titanic_df['Age'].max()

#Since we know no one can be negative years old set the x lower limit at 0
fig.set(xlim=(0,oldest))

#Finally add a legend IT MEANS ADDING THE LABELS ON GRAPH LIKE HERE IT IS SEX,MALE AND FEMALE
fig.add_legend()

Out[65]:

<seaborn.axisgrid.FacetGrid at 0x1addd267940>

In [66]:

# We could have done the same thing for the 'person' column to include children:
fig =sns.FacetGrid(titanic_df,hue='person',aspect=4)

# Next use map to plot all the possible kdeplots for the 'Age' column by the hue choice
fig.map(sns.kdeplot,'Age',shade=True)

# Set the x max limit by the oldest passenger
oldest=titanic_df['Age'].max()

#Since we know no one can be negative years old set the x lower limit at 0
fig.set(xlim=(0,oldest))

#Finally add a legend IT MEANS ADDING THE LABELS ON GRAPH LIKE HERE IT IS SEX,MALE AND FEMALE
fig.add_legend()

Out[66]:

<seaborn.axisgrid.FacetGrid at 0x1adde9c2eb0>

In [67]:

# Let's do the same for class by changing the hue argument:

fig =sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)

# Next use map to plot all the possible kdeplots for the 'Age' column by the hue choice
fig.map(sns.kdeplot,'Age',shade=True)

# Set the x max limit by the oldest passenger
oldest=titanic_df['Age'].max()

#Since we know no one can be negative years old set the x lower limit at 0
fig.set(xlim=(0,oldest))

#Finally add a legend IT MEANS ADDING THE LABELS ON GRAPH LIKE HERE IT IS SEX,MALE AND FEMALE
fig.add_legend()

Out[67]:

<seaborn.axisgrid.FacetGrid at 0x1adde9f7790>

In [68]:

titanic_df['person'].value_counts()

Out[68]:

male      537
female    271
Child      83
Name: person, dtype: int64

In [69]:

titanic_df.head()

Out[69]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	person
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	male
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	female
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	female
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S	female
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S	male

In [70]:

# First we'll drop the NaN values and create a new object, deck
deck=titanic_df['Cabin'].dropna()

In [71]:

deck.value_counts()

Out[71]:

G6             4
C23 C25 C27    4
B96 B98        4
D              3
E101           3
              ..
B79            1
C101           1
C32            1
B94            1
B78            1
Name: Cabin, Length: 147, dtype: int64

In [72]:

# So let's grab that letter for the deck level with a simple for loop

# Set empty list
levels=[]

# Loop to grab first letter
for level in deck:
    levels.append(level[0])
    
# Reset DataFrame and use factor plot
cabin_df=DataFrame(levels)
cabin_df.columns=['cabin']

sns.catplot(x='cabin',data=cabin_df,kind='count',palette='gist_gray')

Out[72]:

<seaborn.axisgrid.FacetGrid at 0x1addea82e20>

In [74]:

# Redefine cabin_df as everything but where the row was equal to 'T'
# Removing T and G since there value is low so we won't consider them
cabin_df=cabin_df[cabin_df.cabin!='T']

cabin_df=cabin_df[cabin_df.cabin!='G']
sns.catplot(x='cabin',data=cabin_df,kind='count',palette='summer')

Out[74]:

<seaborn.axisgrid.FacetGrid at 0x1addfafcb50>

In [75]:

cabin_df.value_counts()

Out[75]:

cabin
C        59
B        47
D        33
E        32
A        15
F        13
dtype: int64

In [76]:

# Great now that we've analyzed the distribution by decks, let's go ahead and answer our third question:

# 3.) Where did the passengers come from?

In [77]:

titanic_df.head()

Out[77]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	person
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	male
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	female
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	female
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S	female
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S	male

In [78]:

sns.catplot(x='Embarked',data=titanic_df,kind='count')
# So most people came from S - Southampton city

Out[78]:

<seaborn.axisgrid.FacetGrid at 0x1addfad51f0>

In [79]:

titanic_df['Embarked'].value_counts()

Out[79]:

S    644
C    168
Q     77
Name: Embarked, dtype: int64

In [80]:

# Now to sort which class of people came from which city we can do this
sns.catplot(x='Embarked',data=titanic_df,kind='count',hue='Pclass')

# to change the order of cities in graph we can do
sns.catplot(x='Embarked',data=titanic_df,kind='count',order=['C','Q','S'],hue="Pclass")

Out[80]:

<seaborn.axisgrid.FacetGrid at 0x1addfce4fd0>

In [81]:

# An interesting find here is that in Queenstown, almost all the passengers that boarded there were 3rd class. 
# It would be intersting to look at the economics of that town in that time period for further investigation.

In [82]:

# Now let's take a look at the 4th question:

# 4.) Who was alone and who was with family?

# Let's start by adding a new column to define alone
# We'll add the parent/child column with the sibsp column
titanic_df['Alone']=titanic_df.Parch + titanic_df.SibSp

titanic_df['Alone']

Out[82]:

0      1
1      1
2      0
3      1
4      0
      ..
886    0
887    0
888    3
889    0
890    0
Name: Alone, Length: 891, dtype: int64

In [83]:

# NOTE :- Now we know that if the Alone column is anything but 0, then the passenger had family aboard and wasn't alone.
# So let's change the column now so that if the value is greater than 0,
# we know the passenger was with his/her family, otherwise they were alone.

In [84]:

titanic_df['Alone'].loc[titanic_df['Alone']>0] = "With Family" 
titanic_df['Alone'].loc[titanic_df['Alone'] ==0] = "Alone"
# USE loc to go throw each value in that perticular column

C:\Users\HP\anaconda3\lib\site-packages\pandas\core\indexing.py:1637: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_block(indexer, value, name)

In [85]:

titanic_df.head(10) # DO NOT RUN THE ABOVE CODE MULTIPLE TIMES

Out[85]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	person	Alone
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	male	With Family
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	female	With Family
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	female	Alone
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	female	With Family
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	male	Alone
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q	male	Alone
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S	male	Alone
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S	Child	With Family
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S	female	With Family
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C	Child	With Family

In [86]:

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

Out[86]:

<seaborn.axisgrid.FacetGrid at 0x1addfcb6a30>

In [87]:

titanic_df['Alone'].value_counts()

Out[87]:

Alone          537
With Family    354
Name: Alone, dtype: int64

In [88]:

sns.catplot(x='Alone',data=titanic_df,palette='Blues',kind='count',hue='Sex')

Out[88]:

<seaborn.axisgrid.FacetGrid at 0x1addfdec8e0>

In [89]:

# Great work! Now that we've throughly analyzed the data let's go ahead and take a look at the most interesting (and open-ended) question: 
# What factors helped someone survive the sinking?

In [90]:

# Let's start by creating a new column for legibility purposes through mapping (Lec 36)
titanic_df['Survivor']= titanic_df.Survived.map({0:'no',1:'yes'})

In [91]:

titanic_df.head(10)

Out[91]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	person	Alone	Survivor
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	male	With Family	no
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	female	With Family	yes
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	female	Alone	yes
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	female	With Family	yes
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	male	Alone	no
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q	male	Alone	no
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S	male	Alone	no
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S	Child	With Family	no
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S	female	With Family	yes
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C	Child	With Family	yes

In [92]:

# Let's just get a quick overall view of survied vs died. 
sns.catplot(x='Survivor',data=titanic_df,palette='Set1',kind='count')
# seems like mostly people die

Out[92]:

<seaborn.axisgrid.FacetGrid at 0x1addfe4d310>

In [93]:

# Let's see if the class of the passengers had an effect on their survival rate, since the movie Titanic popularized the notion that 
# the 3rd class passengers did not do as well as their 1st and 2nd class counterparts.

sns.catplot(x='Pclass',y='Survived',data=titanic_df,kind='point')

Out[93]:

<seaborn.axisgrid.FacetGrid at 0x1addfe706d0>

In [94]:

# Look like survival rates for the 3rd class are substantially lower! But maybe this effect is being 
# caused by the large amount of men in the 3rd class in combination with the women and children first policy.
# Let's use 'hue' to get a clearer picture on this.

In [95]:

# Let's use a factor plot again, but now considering class and gender
sns.catplot(x='Pclass',y='Survived',data=titanic_df,kind='point',hue='person')

Out[95]:

<seaborn.axisgrid.FacetGrid at 0x1addfdf5cd0>

In [96]:

# From this data it looks like being a male or being in 3rd class were both not favourable for survival.
# Even regardless of class the result of 
# being a male in any class dramatically decreases your chances of survival.

In [97]:

#But what about age? Did being younger or older have an effect on survival rate?

# Let's use a linear plot on age versus survival
sns.lmplot(x='Age',y='Survived',data=titanic_df)

Out[97]:

<seaborn.axisgrid.FacetGrid at 0x1addea82a90>

In [98]:

# Looks like there is a general trend that the older the passenger was, the less likely they survived. 
# Let's go ahead and use hue to take a look at the effect of class and age.

In [99]:

# Let's use a linear plot on age versus survival using hue for class seperation
sns.lmplot(x='Age',y='Survived',data=titanic_df,hue='Pclass',palette='winter')

Out[99]:

<seaborn.axisgrid.FacetGrid at 0x1addfd2e670>

In [100]:

# We can also use the x_bin argument to clean up this figure and grab the data and bin it by age with a std attached!

# Let's use a linear plot on age versus survival using hue for class seperation
generations=[10,20,40,60,80]
sns.lmplot(x='Age',y='Survived',data=titanic_df,hue='Pclass',palette='winter',x_bins=generations)

Out[100]:

<seaborn.axisgrid.FacetGrid at 0x1addfc56eb0>

In [101]:

#Interesting find on the older 1st class passengers! What about if we relate gender and age with the survival set?
sns.lmplot(x='Age',y='Survived',data=titanic_df,hue='Sex',palette='winter',x_bins=generations)

Out[101]:

<seaborn.axisgrid.FacetGrid at 0x1addfafc8e0>

In [102]:

# Awesome! we've gotten some really great insights on how gender,age, and class all related to a passengers chance of survival. Now you take control: Answer the following questions using pandas and seaborn:

# 1.) Did the deck have an effect on the passengers survival rate? Did this answer match up with your intuition?
# 2.) Did having a family member increase the odds of surviving the crash?

In [113]:

#1 Did the deck have an effect on the passengers survival rate? Did this answer match up with your intuition?

In [103]:

deck=pd.concat([titanic_df,cabin_df],axis=1)

In [112]:

sns.catplot(x='cabin',y='Survived',data=deck,kind='bar',order=['A','B','C','D','E','F','T'])
            

Out[112]:

<seaborn.axisgrid.FacetGrid at 0x1ade2157070>

ans- The first class or the upper deck people have higher chance of servival then the lower deck

In [130]:

sns.catplot(x='cabin',y='Survived',data=deck,kind='bar',order=['A','B','C','D','E','F','T'],hue='Sex',palette='seismic')

Out[130]:

<seaborn.axisgrid.FacetGrid at 0x1ade383b340>

In [129]:

sns.catplot(x='Survivor',hue='Sex',kind='count',data=titanic_df,palette='seismic')
# THE FEMALE SURVIVE MORE THEN MALES

Out[129]:

<seaborn.axisgrid.FacetGrid at 0x1ade3b94430>

In [124]:

titanic_df.head()

Out[124]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	person	Alone	Survivor
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	male	With Family	no
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	female	With Family	yes
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	female	Alone	yes
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S	female	With Family	yes
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S	male	Alone	no

In [128]:

#2.) Did having a family member increase the odds of surviving the crash?
sns.catplot(x='Survivor',hue='Alone',kind='count',data=titanic_df,hue_order=['Alone','With Family'],palette='seismic')

Out[128]:

<seaborn.axisgrid.FacetGrid at 0x1ade3804940>

In [131]:

# Ans-
Those who survived and having a family has a greater chance of surviving

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