cudf.DataFrame.mode#
- DataFrame.mode(axis=0, numeric_only=False, dropna=True)[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_onlybool, default False
If True, only apply to numeric columns.
- dropnabool, default True
Don’t consider counts of NA/NaN/NaT.
- Returns:
- DataFrame
The modes of each column or row.
See also
cudf.Series.mode
Return the highest frequency value in a Series.
cudf.Series.value_counts
Return the counts of values in a Series.
Examples
>>> import cudf >>> df = cudf.DataFrame({ ... "species": ["bird", "mammal", "arthropod", "bird"], ... "legs": [2, 4, 8, 2], ... "wings": [2.0, None, 0.0, None] ... }) >>> df species legs wings 0 bird 2 2.0 1 mammal 4 <NA> 2 arthropod 8 0.0 3 bird 2 <NA>
By default, missing values are not considered, and the mode of wings are both 0 and 2. The second row of species and legs contains
NA
, because they have only one mode, but the DataFrame has two rows.>>> df.mode() species legs wings 0 bird 2 0.0 1 <NA> <NA> 2.0
Setting
dropna=False
,NA
values are considered and they can be the mode (like for wings).>>> df.mode(dropna=False) species legs wings 0 bird 2 <NA>
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 1 <NA> 2.0