xorbits.pandas.DataFrame.mode#
- DataFrame.mode(axis: Axis = 0, numeric_only: bool = False, dropna: bool = True) 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
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
andlegs
containsNaN
.>>> 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.