Value	Description
`True`	Only numeric rows/columns will be considered (e.g. `float`, `int`, `boolean`).
`False`	Attempt computation with all types (e.g. strings and dates), and throw an error whenever the mode cannot be computed.

By default, numeric_only=False.

3. dropnalink | boolean | optional

Whether or not to ignore NaN. By default, dropna=True.

Return Value

A DataFrame holding the mode of each row or column of the source DataFrame.

Examples

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,3,3], "B":[3,4,5], "C":[2,2,2]})
df
                
            
               A  B  C
0  2  3  2
1  3  4  2
2  3  5  2

Column-wise mode

To compute the mode column-wise:


        
        
            
                
                
                    df.mode()   # or axis=0
                
            
               A    B  C
0  3.0  3  2.0
1  NaN  4  NaN
2  NaN  5  NaN

Here, note the following:

the mode of the first column is 3.
the mode of the second column is 3, 4 and 5 since each of these values occur exactly once.
since mode(~) returns all the modes, we end up with 3 rows here, and NaN is used to fill the empty entries.
the return type is DataFrame.

Row-wise mode

To compute the mode row-wise, set axis=1:


        
        
            
                
                
                    df.mode(axis=1)
                
            
               0    1    2
0  2.0  NaN  NaN
1  2.0  3.0  4.0
2  2.0  3.0  5.0

Specifying numeric_only

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[4,4], "B":[2,True]})
df
                
            
               A  B
0  4  2
1  4  True

Here, column B contains mixed types.

False

By default, numeric_only=False, which means that rows/columns with mixed types will also be considered:


        
        
            
                
                
                    df.mode()   # numeric_only=False
                
            
               A    B
0  4.0  True
1  NaN  2

True

By setting numeric_only=True, only numeric rows/columns will be considered:


        
        
            
                
                
                    df.mode(numeric_only=True)
                
            
               A
0  4

Notice how column B was ignored since it contained mixed types.

Specifying dropna

Consider the following DataFrame with some missing values:


        
        
            
                
                
                    df = pd.DataFrame({"A":[3,3,pd.np.nan],"B":[5,pd.np.nan,pd.np.nan]})
df
                
            
               A    B
0  3.0  5.0
1  3.0  NaN
2  NaN  NaN

By default, dropna=True, which means that all NaN are ignored:


        
        
            
                
                
                    df.mode()   # dropna=True
                
            
               A    B
0  3.0  5.0

To consider missing values:


        
        
            
                
                
                    df.mode(dropna=False)
                
            
               A    B
0  3.0  NaN

Here, note the following:

even though column A contained a NaN, the right mode (3) is returned.
the mode for column B is NaN since it occurred twice, whereas the value 5 only occurred once.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.mode.html

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