pandas.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 NaN/NaT.
- Returns
- DataFrame
The modes of each column or row.
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