Introduction to Data Visualization with SeaBorn

seaborn

Seaborn is a python data visualization library based on Matplotlib. We can draw attractive and informative statistical graphs with high level interface using SeaBorn. We know that, we need a visualization technique to get a good understanding on our data set. SeaBorn is built to provide high level visualization for our datasets.

We can choose several themes with different color patterns to make our data set more informative. This is highly integrated with PyData. However,in this article we only see basic operations in seaborn. Let’s see what those operations are.

Import

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb

Import Dataset from Github

database=sb.load_dataset('diamonds')
print(database)
carat        cut color clarity  depth  table  price     x     y     z
0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43
1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31
2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31
3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63
4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75
...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...
53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50
53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61
53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56
53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74
53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64

[53940 rows x 10 columns]

DistPlot

sb.distplot(database['carat'])
plt.ylabel('Density')
plt.show()

Change Dataset

database=sb.load_dataset('tips')
print(database)
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
..          ...   ...     ...    ...   ...     ...   ...
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

[244 rows x 7 columns]

Joint Plot

sb.jointplot(x='total_bill',y='tip',data=database)
plt.show()

Change Dataset

database=sb.load_dataset('flights')
print(database)
year      month  passengers
0    1949    January         112
1    1949   February         118
2    1949      March         132
3    1949      April         129
4    1949        May         121
..    ...        ...         ...
139  1960     August         606
140  1960  September         508
141  1960    October         461
142  1960   November         390
143  1960   December         432

[144 rows x 3 columns]

Cat Plot

sb.catplot(x='year',y='passengers',data=database)
plt.show()

Cat Plot : Violin

sb.catplot(x='year',y='passengers',data=database,kind='violin')
plt.show()

Cat Plot : Box

sb.catplot(x='year',y='passengers',data=database,kind='box')
plt.show()

Change Dataset

database=sb.load_dataset('tips')
print(database)
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
..          ...   ...     ...    ...   ...     ...   ...
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

[244 rows x 7 columns]
graph=sb.FacetGrid(database,col='sex',hue='smoker')
graph.map(plt.scatter,'total_bill','tip')
plt.show()

Please refer documentation for more details.