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Data Visualization Part-1

In [5]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")
import warnings 
warnings.filterwarnings('ignore') # To silent the seaborn plotting warning
In [7]:
# Althernate way to read the dataset is directly from the URL. Uncomment these if you have difficuly following above command

train_df = pd.read_csv('https://raw.githubusercontent.com/nphardly/titanic/master/data/inputs/train.csv')
test_df = pd.read_csv('https://raw.githubusercontent.com/nphardly/titanic/master/data/inputs/test.csv')


# Luckily  Titanic dataset is available with seaborn package.
# train_df = sns.load_dataset("titanic")

train_df.head()
Out[7]:
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

How many Male and Female were on the Titanic?

In [8]:
train_df[['Sex', 'Name']].groupby('Sex').count()
Out[8]:
Name
Sex
female 314
male 577

How many female pessenger were travelling in Pclass 3?

In [9]:
train_df[['Sex', 'Pclass', 'Name']].groupby(['Sex', 'Pclass']).count()
Out[9]:
Name
Sex Pclass
female 1 94
2 76
3 144
male 1 122
2 108
3 347

Univariate Data Analysis

This type of data consists of only one variable. The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes. It does not deal with causes or relationships and the main purpose of the analysis is to describe the data and find patterns that exist within it. The example of a univariate data can be height.

Plotting univariate distributions

Plotting Distribution

In [10]:
heights = [160, 150, 181, 162, 145, 167, 145, 146, 135, 150, 154]
sns.distplot(heights)
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x1086fb748>

Let's explore the Titanic Dataset

Density Graph

In [11]:
df = train_df.dropna()
sns.distplot(df['Age'], bins=10)
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x107b926a0>

Count Plot

In [12]:
sns.countplot("Sex", data=train_df)
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x11eded128>
In [13]:
sns.countplot('Pclass',data=train_df)
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x11ee3bba8>
In [14]:
ax = sns.countplot('Survived',data=train_df)
plt.xticks(np.arange(0,2,1),['{}'.format(i) for i in ['Died','Survived']])
Out[14]:
([<matplotlib.axis.XTick at 0x11ef0cac8>,
  <matplotlib.axis.XTick at 0x11ef0c3c8>],
 <a list of 2 Text xticklabel objects>)

Bivariate Data Analysis

Bivariate analysis is the simultaneous analysis of two variables (attributes). It explores the concept of relationship between two variables, whether there exists an association and the strength of this association, or whether there are differences between two variables and the significance of these differences.

How many male passenger survived in the accident?

In [15]:
train_df[['Sex', 'Survived', 'Name']].groupby(['Sex', 'Survived']).count()
Out[15]:
Name
Sex Survived
female 0 81
1 233
male 0 468
1 109
In [16]:
sns.countplot("Sex", hue='Survived', data=train_df)
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x11ee3bcc0>

Excercise

Note: sibsp is Number of Siblings/Spouses Aboard. Parch is Number of Parents/Children Aboard

  1. Plot the count distribution of Parch in the Titanic Dataset
  2. Plot the count distribution of number of Males and Females across different 'Pclass'
In [24]:
sns.countplot('Parch',data=train_df)
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x11f4e4240>
In [25]:
# Solution to Ex: 2
sns.countplot("Sex",hue='Survived', data=train_df)
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x11f5b6a20>

Visualizing statistical relationships

The dataset "tips" explores the records of people visiting restaurant

In [26]:
tips = sns.load_dataset("tips")
tips.head()
Out[26]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
In [27]:
sns.relplot(x="total_bill", y="tip", data=tips)
Out[27]:
<seaborn.axisgrid.FacetGrid at 0x11f671588>
In [28]:
sns.relplot(x="total_bill", y="tip", data=tips, hue='smoker')
Out[28]:
<seaborn.axisgrid.FacetGrid at 0x11f7bf320>

Excercise

  1. Plot the number of males and females in the 'tips' dataset.
  2. Plot the distribution of smoker in Males and Females.
  3. Plot the relation graph between tip and day.
In [29]:
# @title Please try yourself (Solution 3)
sns.countplot('sex', data=tips)
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x11f7bfe48>
In [30]:
# @title Please try yourself (Solution 4)
sns.countplot('sex', hue='smoker', data=tips)
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0x11f981438>
In [31]:
# @title Please try yourself (Solution 5)
sns.relplot('day', 'tip', data=tips, kind='line')
Out[31]:
<seaborn.axisgrid.FacetGrid at 0x11fad3320>

Plotting with categorical data

Categorical scatterplots

In [32]:
sns.catplot(x="day", y="total_bill", data=tips)
Out[32]:
<seaborn.axisgrid.FacetGrid at 0x11fac9128>
In [34]:
sns.catplot(x="day", y="total_bill", hue='sex', data=tips)
Out[34]:
<seaborn.axisgrid.FacetGrid at 0x11fa42d30>

Count of Male and Female on Friday

In [35]:
# Do it yourself please

Visualizing pairwise relationships in a dataset

In [36]:
# Titanic Dataset
train_df = pd.read_csv('https://raw.githubusercontent.com/nphardly/titanic/master/data/inputs/train.csv')

sns.pairplot(train_df, vars=["Survived", "Fare", "Pclass", "SibSp"])
Out[36]:
<seaborn.axisgrid.PairGrid at 0x11ffdd2b0>
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