Prerequisites: Introduction to Seaborn
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated to the data structures from pandas.
Factor Plot
Factor Plot is used to draw a different types of categorical plot. The default plot that is shown is a point plot, but we can plot other seaborn categorical plots by using of kind parameter, like box plots, violin plots, bar plots, or strip plots.
Note: For viewing the Pokemon Dataset file, Click Here
Dataset Snippet :
Code 1: Point plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Stage v / s Attack point plot sns.factorplot(x ='Stage', y ='Attack', data = df) sns.factorplot(x ='Stage', y ='Defense', data = df) # Show the plots plt.show() |
Output:
Code 2: Violin plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Type 1 v / s Attack violin plot sns.factorplot(x ='Type 1', y ='Attack', kind = 'violin', data = df) # show the plots plt.show() |
Output:
Code 3: Bar plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Type 1 v / s Defense bar plot # with Stage column is used for # colour encoding i.e # on the basis of Stages different # colours is decided, here in this # dataset, 3 Stage is mention so # 3 different colours is used. sns.factorplot(x ='Type 1', y ='Defense', kind = 'bar', hue = 'Stage', data = df) # show the plots plt.show() |
Output:
Code 4: Box plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Stage v / s Defense box plot sns.factorplot(x ='Stage', y ='Defense', kind = 'box', data = df) # show the plots plt.show() |
Output:
Code 5: Strip plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Stage v / s Defense strip plot sns.factorplot(x ='Stage', y ='Defense', kind = 'strip', data = df) # show the plots plt.show() |
Output:
Code 6: Count plot using factorplot() method of seaborn.
# importing required library import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # read a csv file df = pd.read_csv('Pokemon.csv') # Stage v / s count - count plot sns.factorplot(x ='Stage', kind = 'count', data = df) # show the plots plt.show() |
Output:
