Pandas mode()

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 2, 3, 4],
        'B': [5, 5, 6, 6, 6]}

df = pd.DataFrame(data)

# calculate the mode for each column
modes = df.mode()

print(modes)

'''
Output

   A  B
0  2  6
'''

mode() Syntax

The syntax of the mode() method in Pandas is:

df.mode(axis=0, numeric_only=False, dropna=True)

mode() Arguments

The mode() method has the following arguments:

• axis (optional): specifies the axis along which to calculate the mode(s)
• numeric_only (optional): if True, only numeric data will be considered when calculating the mode
• dropna (optional): if False, NaN values will also be considered

mode() Return Value

The mode() method returns a DataFrame containing the mode(s) for each column. If there are multiple modes in a column, all of them will be included in the result.

Example 1: Mode for Each Column

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 2, 3, 4],
        'B': [5, 5, 6, 6, 6]}

df = pd.DataFrame(data)

# calculate the mode for each column
modes = df.mode()

print(modes)

Output

   A  B
0  2  6

In this example, we calculated modes for each column. The modes for A and B columns are 2 and 6 respectively because they are the values with highest frequency.

Example 2: Mode for Each Row

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [1, 3, 5, 7, 9],
        'C': [1, 2, 5, 4, 9]}

df = pd.DataFrame(data)

# calculate the mode for each row
modes = df.mode(axis=1)

print(modes)

Output

   0
0  1
1  2
2  5
3  4
4  9

In this example, we calculated modes for each row using the axis=1 argument.