How To Use Map In Python Pandas

Introduction

Python Pandas is a powerful library for data manipulation and analysis. It provides various functions and methods to perform various operations on data. One of the most commonly used functions is the 'map' function. In this article, we will discuss how to use the 'map' function in Python Pandas.

What is the 'map' function?

The 'map' function is used to apply a function to each element of a Pandas Series or DataFrame. It takes a function as input and applies it to each element of the Series or DataFrame. The output of the function is then returned as a new Series or DataFrame.

Example:

Suppose we have a DataFrame with two columns 'Name' and 'Age'. We want to add a new column 'Gender' based on the value of 'Name' column. We can use the 'map' function to achieve this.

 import pandas as pd df = pd.DataFrame({'Name': ['John', 'Mary', 'Peter'], 'Age': [25, 30, 35]}) def get_gender(name): if name.startswith('J'): return 'Male' else: return 'Female' df['Gender'] = df['Name'].map(get_gender) print(df) 

The output of the above code will be:

 Name Age Gender 0 John 25 Male 1 Mary 30 Female 2 Peter 35 Female 

Using Lambda Function with 'map'

We can also use lambda function with 'map' function. Lambda function is an anonymous function which takes any number of arguments but can have only one expression. It is useful when we need a simple function for a short period of time.

Example:

Suppose we have a Series 'scores' containing the scores of students in a test. We want to convert the scores into grades based on the following criteria:

• 90-100: A+
• 80-89: A
• 70-79: B
• 60-69: C
• 50-59: D
• <50: F

We can use lambda function with 'map' function to achieve this.

 import pandas as pd scores = pd.Series([85, 92, 78, 65, 43, 91, 67, 73, 55, 82]) grades = scores.map(lambda x: 'A+' if x >= 90 else 'A' if x >= 80 else 'B' if x >= 70 else 'C' if x >= 60 else 'D' if x >= 50 else 'F') print(grades) 

The output of the above code will be:

 0 A 1 A+ 2 B 3 C 4 F 5 A+ 6 C 7 B 8 D 9 A dtype: object 

Question and Answer

Q. What is the use of 'map' function in Python Pandas?

The 'map' function in Python Pandas is used to apply a function to each element of a Series or DataFrame and return the output as a new Series or DataFrame.

Q. Can we use lambda function with 'map' function?

Yes, we can use lambda function with 'map' function. Lambda function is an anonymous function which is useful when we need a simple function for a short period of time.

Q. Give an example of using 'map' function with DataFrame.

Suppose we have a DataFrame with two columns 'Name' and 'Age'. We want to add a new column 'Gender' based on the value of 'Name' column. We can use the 'map' function to achieve this.

 import pandas as pd df = pd.DataFrame({'Name': ['John', 'Mary', 'Peter'], 'Age': [25, 30, 35]}) def get_gender(name): if name.startswith('J'): return 'Male' else: return 'Female' df['Gender'] = df['Name'].map(get_gender) print(df)