When to Use Pandas `map`, `applymap`, or `apply`?

Choosing Among map, applymap, and apply in Pandas

When working with Pandas DataFrames, it is often necessary to apply functions to the data in various ways. Three commonly used methods for vectorization are map, applymap, and apply. Each has its own unique purpose and application.

Map

map is a method specific to Series objects and applies a function to each element in the Series. It expects a function that takes a single value as input and returns a single value.

Example:

import pandas as pd

# Create a Series
series = pd.Series([1, 2, 3, 4, 5])

# Apply a function to each element
def square(x):
    return x**2

# Apply the function to the series using map
squared_series = series.map(square)

print(squared_series)

Output:

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

Applymap

applymap applies a function to each element of a DataFrame, performing the operation element-wise. Like map, it expects a function that takes a single value as input and returns a single value.

Example:

# Create a DataFrame
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})

# Apply a function to each element of the DataFrame
def format_number(x):
    return "{:.2f}".format(x)

# Apply the function to the DataFrame using applymap
formatted_df = df.applymap(format_number)

print(formatted_df)

Output:

   a  b
0  1.00  4.00
1  2.00  5.00
2  3.00  6.00

Apply

apply applies a function to each row or column of a DataFrame, depending on the axis parameter. It is more versatile than map and applymap and can handle functions that require passing multiple values as inputs.

Example:

# Apply a function to each row of the DataFrame
def get_max_min_diff(row):
    return row.max() - row.min()

max_min_diff = df.apply(get_max_min_diff, axis=1)

print(max_min_diff)

Output:

0    3.00
1    3.00
2    3.00
dtype: float64

Usage Summary

• map: Element-wise function application to Series
• applymap: Element-wise function application to DataFrame
• apply: Row/column-wise function application to DataFrame, with flexible input/output handling

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