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pandas.DataFrame.mode

DataFrame.mode(axis=0, numeric_only=False, dropna=True)[source]

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. If you want to impute missing values with the mode in a dataframe df, you can just do this: df.fillna(df.mode().iloc[0])

Parameters:

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

  • 0 or ‘index’ : get mode of each column
  • 1 or ‘columns’ : get mode of each row

numeric_only : boolean, default False

if True, only apply to numeric columns

dropna : boolean, default True

Don’t consider counts of NaN/NaT.

New in version 0.24.0.

Returns:
modes : DataFrame (sorted)

Examples

>>> df = pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3]})
>>> df.mode()
   A
0  1
1  2
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