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Plotting different types of plots using Factor plot in seaborn

  • Last Updated : 10 Jul, 2020

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.

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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 :
pokemon data set pic

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:
point plot

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:
violin plot

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:
bar plot

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:
box plot

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:
strip plot

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:
Count plot




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