1. In the world of Analytics, the best way to get insights is by visualizing the data. We have already used Matplotlib, a 2D plotting library that allows us to plot different graphs and charts.
2. Another complimentary package that is based on data visualization library is Seaborn, which provide a higher level interface to draw statistical graphics.

Seaborn

• Is a python data visualization library for statistical plotting

• Is based on matplotlib (built on top of matplotlib)

• Is designed to work with NumPy and pandas data structures

• Provides a high-level interface for drawing attractive and informative statistical graphics.

• Comes equipped with preset styles and color palettes so you can create complex, aesthetically pleasing charts with a few lines of code.

Seaborn vs Matplotlib

Seaborn is built on top of Python’s core visualization library matplotlib, but it’s meant to serve as a complement, not a replacement.

• In most cases, we’ll still use matplotlib for simple plotting

• On Seaborn’s official website, they state: “If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too.

• Seaborn helps resolve the two major problems faced by Matplotlib, the problems are −

 *      • Default Matplotlib parameters

 *      • Working with data frames

In [ ]:

# Let's see the difference between codes of matplotlib and Seaborn 

In [ ]:

# Matplotlib 
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

x = np.linspace(0, 10, 1000)
plt.plot(x, np.sin(x), x, np.cos(x));
plt.show()

In [ ]:

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

sns.set()

x = np.linspace(0, 10, 1000)
# print(x)
plt.plot(x, np.sin(x), x, np.cos(x));
plt.show()

Data visualization using Seaborn

1. Visualizing statistical relationships
2. Visualizing categorical data

Visualizing statistical relationships (This can be also defined as relationship between variables)

The process of understanding relationships between variables of a dataset and how these relationships, in turn, depend on other variables is known as statistical analysis

relplot()

• This is a figure-level-function that makes use of two other axes functions for Visualizing Statistical Relationships which are –

* scatterplot()

* lineplot()

• By default it plots scatterplot()

In [ ]:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
sns.set()

In [ ]:

df = sns.load_dataset('tips')
df.head()

Out[ ]:

	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 [ ]:

df.tail()

Out[ ]:

	total_bill	tip	sex	smoker	day	time	size
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

In [ ]:

sns.relplot(x = 'total_bill', y = 'tip', data = df, kind = 'scatter')
plt.show() #that how there is direct relation between the food ordered and tip given. 

In [ ]:

# We can also change kind to line. 
sns.relplot(x = 'total_bill', y = 'tip', data = df, kind = 'line')
plt.show() #there is direct relation between the food ordered and tip given. 

In [ ]:

# Parameters -
# • x, y
# • data
# • hue: It separtes the colour of dots with their types. 
# • size
# • col: It can help to have different sex graphs. 
# • style: They are used for showing differnt style of points.

In [ ]:

sns.relplot(x = 'total_bill', y = 'tip', data = df, hue = 'time')
plt.show() # By using hue we can see different time of lunch and dinner. 

In [ ]:

sns.relplot(x = 'total_bill', y = 'tip', data = df, hue = 'time', style = 'sex')
plt.show() # By style we can see circle are male and x are female. 

In [ ]:

sns.relplot(x = 'total_bill', y = 'tip', data = df, hue = 'time', col='sex')
plt.show() # col generated two differenet graphs when sex is male or female. 

Let’s do the same with lines.

In [ ]:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
sns.set()

In [ ]:

print(sns.get_dataset_names())

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'exercise', 'flights', 'fmri', 'gammas', 'geyser', 'iris', 'mpg', 'penguins', 'planets', 'tips', 'titanic']

In [ ]:

df = sns.load_dataset('flights')
df.head()

Out[ ]:

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

In [ ]:

df.tail()

Out[ ]:

	year	month	passengers
139	1960	Aug	606
140	1960	Sep	508
141	1960	Oct	461
142	1960	Nov	390
143	1960	Dec	432

In [ ]:

sns.relplot(x = 'year', y = 'passengers', data = df, kind = 'line')
plt.show() # So the dark blue line gives us exact average and rest of the shade tells us the diversity at that point. 

In [ ]:

sns.lineplot(x = 'year', y = 'passengers', data = df)
plt.show()

In [ ]:

sns.relplot(x = 'year', y = 'passengers', data = df, kind = 'line', hue = 'month')
plt.show()

In [ ]:

sns.relplot(x = 'year', y = 'passengers', data = df, kind = 'line', 
            col = 'month')
plt.show()