Python | Pandas dataframe.mode()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

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.

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# 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

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

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# find mode of dataframe 
df.mode()

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Output :

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

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# 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

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

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

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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.



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