method: A detailed guide (with examples)

Updated: February 18, 2024 By: Guest Contributor Post a comment


The function is an essential tool in the data manipulation toolkit offered by the pandas library in Python. It allows for mapping of each element of a series through a function or a mapping correspondence, making data transformations and applications straightforward and efficient. This tutorial will delve into the capabilities of the map() method, offering a range of examples from basic to advanced usage.

The Purpose of

Before moving on to examples, understand that the method applies a given function, dictionary, or Series to each item in the series. The operation can perform a variety of tasks, such as transforming values, substituting values based on a dictionary, or even changing data types.

Basic Usage

Let’s start with some basic examples:

import pandas as pd

# Creating a Series
data = pd.Series([1, 2, 3, 4])

# Mapping each value to its square
squared_data = x: x**2)

This will output:

0     1
1     4
2     9
3    16
dtype: int64

Using Dictionaries for Mapping

Next, let’s see how to use dictionaries for more specific mappings:

import pandas as pd

# Creating a Series
fruit_series = pd.Series(['apple', 'banana', 'cherry', 'date'])

# Mapping using a dictionary
dict_mapping = {'apple': 'green', 'banana': 'yellow', 'cherry': 'red', 'date': 'brown'}
colored_fruits =

Series to Series Mapping

Mapping can also be achieved by applying another Series, providing a powerful way to perform more complex transformations:

import pandas as pd

# Creating two Series
letters = pd.Series(['a', 'b', 'c', 'd'])
alphabets = pd.Series(['alpha', 'beta', 'gamma', 'delta'], index=['a', 'b', 'c', 'd'])

# Mapping using Series
dict_mapping = pd.Series(['alpha', 'beta', 'gamma', 'delta'], index=['a', 'b', 'c', 'd'])
alphabetic_representation =

Handling Missing Data

One of the advantages of map() is its ability to handle missing data gracefully. If no mapping is found for a certain element, pandas will automatically assign a NaN value. This behavior can be very useful in data cleaning processes:

import pandas as pd

# Creating a Series
numbers = pd.Series([1, 2, None, 4])

# Mapping with a lambda function, missing data handling
handled_missing_data = x: x**2 if pd.notnull(x) else x)

Advanced Usage

For more sophisticated tasks, map() can be used in conjunction with functions to perform complex data manipulations. Let’s explore an example where we convert a series of strings to a series of lengths:

import pandas as pd

# Creating a Series
string_series = pd.Series(['pandas', 'python', 'data'])

# Mapping to get the length of each string
length_series =

Combining map() with Other Pandas Methods

Finally, map() can be effectively combined with other pandas methods to streamline data transformations. For instance, using map() to format strings, which can then be used with the str accessor to perform string operations:

import pandas as pd

# Creating a Series
numerical_series = pd.Series([100, 200, 300, 400])

# Mapping to format string
formatted_series ='The value is {}'.format)


The method is a versatile tool that offers a wide array of data manipulation possibilities. Through the examples provided—from basic to advanced usage—it’s clear that whether you’re transforming, substituting, or even handling missing data, map() can simplify the process, making your data ready for further analysis or visualization.