mode(self, axis: 'Axis' = 0, numeric_only: 'bool' = False, dropna: 'bool' = True) -> 'DataFrame'

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters

axis : {0 or 'index', 1 or 'columns'}, default 0

The axis to iterate over while searching for the mode:

numeric_only : bool, default False

If True, only apply to numeric columns.

dropna : bool, default True

Don't consider counts of NaN/NaT.

Returns

DataFrame

The modes of each column or row.

Get the mode(s) of each element along the selected axis.

See Also

Series.mode

Return the highest frequency value in a Series.

Series.value_counts

Return the counts of values in a Series.

Examples

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([('bird', 2, 2),
...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
... df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN .

This example is valid syntax, but we were not able to check execution

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

This example is valid syntax, but we were not able to check execution

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True , only the mode of numeric columns is computed, and columns of other types are ignored.

This example is valid syntax, but we were not able to check execution

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

This example is valid syntax, but we were not able to check execution

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN

See :

Back References

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pandas.core.ops.flex_arith_method_FRAME.<locals>.f

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File: /pandas/core/frame.py#10316
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