Visualizing Data with Titanic DataSet

Before we start getting our hands dirty with the python and csv files, we first understand what is present in the dataset.

The dataset consists of the following columns:

1. PassengerId
2. Survived
3. Pclass
4. Name
5. Sex
6. Age
7. SibSp
8. Parch
9. Ticket
10. Fare
11. Cabin
12. Embarked

Procedure followed throughout the project

1. Importing Packages
2. Data Inspection
   • Dataset at a glance
   • Columns at a glance
   • To check whether data contains any null values
3. Data Cleaning
   • Dropping data column
   • Populating the missing ages
   • Missing embarkation ports
4. Data Visualization
   • Scatter plot of Fare versus Age
   • Bar Graph to represent the Seat Class taken by passengers
   • Histogram to represent Age
   • Bar plot of the Survival Rate of People
   • Heat Map of Survial vs PClass
   • Cool way to represent Age, Survival and Pclass
   • Violin plot of Gender versus Age
   • Bar plot of Embarkment vs Count keeping Pclass into consideration
5. Sugestion

1. Importing Python Packages

Importing the required python packages for the project

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

2. Data inspection

We perform the initial inspection by importing the required .csv file into the Jupyter notebook, looking into the file as we are not expected to remember all the details of the present in the file

2.1 Dataset at a glance

#reading and refering the data using the variable `data`
data = pd.read_csv("Titanic-DataSet.csv")
data

	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

2.2 Columns at a glance

variable.dtypes prints the columns of the dataset along with the datatype in a tabular manner

data.dtypes

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object

2.3 To check whether data contains any null values

This step is crucial during the inspection stage, as this gives the analyst a rough idea of the number of data cells that needs to be filled with calculated values

data.isnull().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

Conclusion

As we can see from the given table, columns like Age, Cabin and Embarked consists of null value which shall be either filled or dropped during the Data Cleaning Process.

3. Data Cleaning

One has to clean the data i.e., remove any null values and ambiguities (if any), and try to achieve uniformity in the dataset.

3.1 Dropping data column

Out of the 891 data entries, 687 data cells are empty in the dataset, so we can safely drop the column as it doesn't prove handy for us.

# By using the parameter inplace, we are indicating that this change shall be made permanent in our dataset. 
# Which by default is false 
data.drop('Cabin', axis=1, inplace=True)

We now print the columns of the dataset to check if 'cabin' is dropped or not.

for i in data.columns:
    print(i)

PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Embarked

3.2 Populating the missing ages

Here I plan to clean the data by filling in the mean value of the age in cell which have a NULL value

data['Age'].fillna(data['Age'].mean(), inplace=True)
data.isnull().sum()

PassengerId    0
Survived       0
Pclass         0
Name           0
Sex            0
Age            0
SibSp          0
Parch          0
Ticket         0
Fare           0
Embarked       2
dtype: int64

3.3 Missing embarkation ports

We notice that the Embarked column contains some missing value. So we first find out the null entries

# data['Embarked'].isnull().sum()    { to check number of data entries with null value, turns out to be 2 }
missingports = data[data['Embarked'].isnull()]
missingports

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Embarked
61	62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	NaN
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	NaN

We notice that both passengers were travelling with the same ticket number. And we would be filling the Embarked column with the mode of the column. For that, we will have to find the mode.

table = pd.crosstab(data['Embarked'],data['Sex'])
print(table)

Sex       female  male
Embarked              
C             73    95
Q             36    41
S            203   441

From the above table we conclude that most women we travelling to Southampton. Hence we will be filling the NaN with S in both cases. And then recheck the null status of the Embarked column using isnull().sum()

data['Embarked'].fillna('S',inplace=True)

data['Embarked'].isnull().sum()

0

4. Data Visualization

This section of the project consists of basic visualization of the dataset. The purpose of this section is for the better understanding of the dataset in a more clearer and visual manner.

4.1 Scatter plot of Fare versus Age

f, ax = plt.subplots(1,1, figsize=(10, 5))
sns.scatterplot(x="Fare", y="Age", data=data, ax=ax); 

4.2 Bar Graph to represent the Seat Class taken by passengers

ax = sns.countplot(x="Pclass", data=data)
ax.set_title('data');

4.3 Histogram to represent Age

sns.histplot(data['Age'], bins=25, 
             color = 'navy', alpha = 1, 
             binwidth=10, shrink=.95)

<AxesSubplot:xlabel='Age', ylabel='Count'>

4.4 Bar plot of the Survival Rate of People

sns.catplot(x ="Sex", hue ="Survived",
kind ="count", data = data)

<seaborn.axisgrid.FacetGrid at 0x208f688d070>

4.5 Heat Map of Survial vs PClass

# Group the dataset by Pclass and Survived and then unstack them
group = data.groupby(['Pclass', 'Survived'])
pclass_survived = group.size().unstack()

# Heatmap - Color encoded 2D representation of data.
sns.heatmap(pclass_survived, annot = True, fmt ="d")

<AxesSubplot:xlabel='Survived', ylabel='Pclass'>

4.6 Cool way to represent Age, Survival and Pclass

fig = plt.figure(figsize=(12, 8))
gs = fig.add_gridspec(3,1)
gs.update(hspace= -0.55)

axes = list()
colors = ["#022133", "#5c693b", "#51371c"]

for idx, cls, c in zip(range(3), sorted(data['Pclass'].unique()), colors):
    axes.append(fig.add_subplot(gs[idx, 0]))
    
    # you can also draw density plot with matplotlib + scipy.
    sns.kdeplot(x='Age', data=data[data['Pclass']==cls], 
                fill=True, ax=axes[idx], cut=0, bw_method=0.25, 
                lw=1.4, edgecolor='lightgray', hue='Survived', 
                multiple="stack", palette='PuBu', alpha=0.7
               ) 
    
    axes[idx].set_ylim(0, 0.04)
    axes[idx].set_xlim(0, 85)
    
    axes[idx].set_yticks([])
    if idx != 2 : axes[idx].set_xticks([])
    axes[idx].set_ylabel('')
    axes[idx].set_xlabel('')
    
    spines = ["top","right","left","bottom"]
    for s in spines:
        axes[idx].spines[s].set_visible(False)
        
    axes[idx].patch.set_alpha(0)
    axes[idx].text(-0.2,0,f'Pclass {cls}',fontweight="light", fontfamily='serif', fontsize=11,ha="right")
    if idx != 1 : axes[idx].get_legend().remove()
        
fig.text(0.13,0.81,"Age distribution by Pclass", fontfamily='serif', fontsize=16)

plt.show() 

4.7 Violin plot of Gender versus Age

# Violinplot Displays distribution of data across all levels of a category.
sns.violinplot(x ="Sex", y ="Age", hue ="Survived", data = data, split = True)

<AxesSubplot:xlabel='Sex', ylabel='Age'>

This graph summarises age range of people who were saved. The survival rate is –

• Good for children.
• High for women in the age range 20-50.
• Less for men as the age increases.

4.8 Count plot of Embarkment vs Count keeping Pclass into consideration

sns.catplot(x ='Embarked', hue ='Survived',
            kind ='count', col ='Pclass', 
            data = data)

<seaborn.axisgrid.FacetGrid at 0x208f6bfc160>

Suggestion

As with most datasets the more information we have the better it can be analysed. I believe that we could add the following variables:

• life boat number - the reason why there were so many fatalities on board the Titanic was due to there not been enough lifeboats. I believe that if we knew the lifeboat number and the capacity of a lifeboat we could determine if it was possible for more people to survive

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