How Do Pandas' `map`, `applymap`, and `apply` Methods Differ?

Understanding the Differences Between Map, Applymap, and Apply Methods in Pandas

When working with vectorization in Pandas, it's crucial to understand the distinctions between the map, applymap, and apply methods. These methods provide flexible ways to apply functions element-wise or row/column-wise to DataFrames and Series.

Map:
Map is a Series method designed for element-wise operations. It takes a function and applies it to each element in a Series. Consider the following example:

import pandas as pd

series = pd.Series([1, 2, 3, 4, 5])
squared_series = series.map(lambda x: x ** 2)
print(squared_series)

Output:

0    1
1    4
2    9
3   16
4   25
dtype: int64

Applymap:
Applymap is a DataFrame method that performs element-wise operations on the entire DataFrame. It applies the specified function to each individual element within the DataFrame:

dataframe = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

formatted_dataframe = dataframe.applymap(lambda x: f'{x:.2f}')
print(formatted_dataframe)

Output:

   A   B
0  1.00  4.00
1  2.00  5.00
2  3.00  6.00

Apply:
Unlike map and applymap, apply operates on rows or columns of a DataFrame. It takes a function and applies it to each row or column, depending on the axis parameter specified:

# Apply function to each row
row_max = dataframe.apply(lambda row: row.max(), axis=1)
print(row_max)

# Apply function to each column
col_min = dataframe.apply(lambda col: col.min(), axis=0)
print(col_min)

Output:

0    3
1    5
2    6
dtype: int64

A    1
B    4
dtype: int64

Usage Considerations:

• Use map for element-wise operations on Series.
• Use applymap for element-wise operations on DataFrames.
• Use apply for row or column-wise operations on DataFrames.

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