pandas.DataFrame.mode — pandas 3.0.0.dev0+2097.gcdc5b7418e documentation (original) (raw)

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.

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