maxframe.dataframe.DataFrame.mode#

DataFrame.mode(axis=0, numeric_only=False, dropna=True, combine_size=None)#

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

>>> import maxframe.tensor as mt
>>> import maxframe.dataframe as md
>>> df = md.DataFrame([('bird', 2, 2),
...                    ('mammal', 4, mt.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, mt.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df.execute()
           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().execute()
  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).execute()
  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).execute()
   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).execute()
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN