Implementation of pandas DataFrame map method

Pandas2.2 DataFrame

Function application, GroupBy & window

()

()Methods are used to apply a function to each element of a DataFrame. It is the easiest element-by-element operation method and is often used for data conversion or cleaning.

Method signature

(func, na_action=None)

Parameter description

Return value

• Returns a new DataFrame with the same shape as the original DataFrame, and each element isfuncThe results after application.

Example

Example 1: Simple mapping (such as converting each element into a string)

import pandas as pd

df = ({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Convert each element to a stringresult = (str)
print(result)

Output:

   A  B
0  1  4
1  2  5
2  3  6

Example 2: Custom function mapping (such as adding 10)

# Add 10 to each elementresult = (lambda x: x + 10)
print(result)

Output:

    A   B
0  11  14
1  12  15
2  13  16

Example 3: Use na_action='ignore' to ignore the NaN value

import numpy as np

df_with_nan = ({
    'A': [1, , 3],
    'B': [, 5, 6]
})

# Add 1 to non-NaN elements onlyresult = df_with_nan.map(lambda x: x + 1, na_action='ignore')
print(result)

Output:

     A    B
0  2.0  NaN
1  NaN  6.0
2  4.0  7.0

Summarize

• map()Is an ideal tool for one-to-one transformation of each element in a DataFrame.
• Support skipNaNValues ​​are mapped.
• Commonly used in format conversion, numerical conversion and other scenarios.

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