Plotting different types of plots using Factor plot in seaborn

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 :
pokemon data set pic



Code 1 : Point plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
point plot

Code 2 : Violin plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
violin plot

Code 3 : Bar plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
bar plot

Code 4 : Box plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
box plot

Code 5 : Strip plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
strip plot

Code 6 : Count plot using factorplot() method of seaborn.

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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()

chevron_right


Output:
Count plot




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.


Article Tags :

Be the First to upvote.


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.