Pandas dataframe.mode()



Python | Pandas dataframe.mode()

Pandas dataframe.mode() function gets the mode(s) of each element along the axis selected. Adds a row for each mode per label, fills in gaps with nan. Note that there could be multiple values returned for the selected axis (when more than one item share the maximum frequency), which is the reason why a dataframe is returned.

 

Syntax: DataFrame.mode(axis=0, numeric_only=False)
Parameters :
axis : get mode of each column1, get mode of each row
numeric_only : if True, only apply to numeric columns

Returns : modes : DataFrame (sorted)

Example #1: Use mode() function to find the mode over the index axis.

# importing pandas as pd
import pandas as pd
 
# Creating the dataframe 
df=pd.DataFrame({"A":[14,4,5,4,1],
                 "B":[5,2,54,3,2],
                 "C":[20,20,7,3,8],
                 "D":[14,3,6,2,6]})
 
# Print the dataframe
df

Lets use the dataframe.mode() function to find the mode of dataframe

# find mode of dataframe 
df.mode()

Output :

Example #2: Use mode() function to find the mode over the column axis

# importing pandas as pd
import pandas as pd
 
# Creating the dataframe 
df=pd.DataFrame({"A":[14,4,5,4,1],
                 "B":[5,2,54,3,2],
                 "C":[20,20,7,3,8],
                 "D":[14,3,6,2,6]})
 
# Print the dataframe
df

Lets use the dataframe.mode() function to find the mode

# axis = 1 indicates over the column axis
df.mode(axis = 1)

Output :

In the 0th and 3rd row, 14 and 3 is the mode, as they have the maximum occurrence (i.e. 2). In rest of the column all element are mode because they have the same frequency of occurrence.

Last Updated on October 18, 2021 by admin

Leave a Reply

Your email address will not be published. Required fields are marked *

Recommended Blogs