xorbits.pandas.DataFrame.mode#

DataFrame.mode(axis: Union[int, Literal['index', 'columns', 'rows']] = 0, numeric_only: bool = False, dropna: bool = True) → pandas.core.frame.DataFrame[source]#

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

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:

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

    • 1 or ‘columns’ : get mode of each row.

  • numeric_only (bool, default False) – If True, only apply to numeric columns.

  • dropna (bool, default True) – Don’t consider counts of NaN/NaT.

Returns

The modes of each column or row.

Return type

DataFrame

See also

Series.mode

Return the highest frequency value in a Series.

Series.value_counts

Return the counts of values in a Series.

Examples

>>> 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.

>>> 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).

>>> 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.

>>> 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:

>>> 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

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.