Examples
In [1]:
import numpy as np
import pandas as pd
In [2]:
df = pd.DataFrame([('bird', 2, 2),
('mammal', 4, np.nan),
('arthropod', 8, 0),
('bird', 2, np.nan)],
index=('eagle', 'tiger', 'spider', 'duck'),
columns=('species', 'legs', 'wings'))
df
Out[2]:
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 NaN, because they have only one mode, but
the DataFrame has two rows.
In [3]:
df.mode()
Out[3]:
Setting dropna=False NaN values are considered and they can be the mode (like for wings).
In [4]:
df.mode(dropna=False)
Out[4]:
Setting numeric_only=True, only the mode of numeric columns is computed,
and columns of other types are ignored.
In [5]:
df.mode(numeric_only=True)
Out[5]:
To compute the mode over columns and not rows, use the axis parameter:
In [6]:
df.mode(axis='columns', numeric_only=True)
Out[6]: