How to Add New Column to Existing DataFrame in Pandas

Hey there, data enthusiasts! Working with data in Python can be a breeze, especially when you’re using the powerful Pandas library. If you’ve ever wondered how to add a new column to an existing DataFrame, you’re in the right place! Whether you’re a beginner or just need a quick refresher, this post will walk you through several simple and fun ways to add columns to your DataFrame. Let’s dive in!

        Adding a New Column to an Existing DataFrame in Pandas

What is a DataFrame Anyway?
Before we get into adding columns, let’s quickly recap what a DataFrame is. Think of a DataFrame as a supercharged Excel spreadsheet in Python. It’s a 2-dimensional labeled data structure with rows and columns, and it’s the bread and butter of data manipulation in Pandas.

1. Adding a New Column with a Single Value
Want to add a new column where every row has the same value? Easy-peasy! Let’s see how.

import pandas as pd

# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Adding a new column with a single value
df['Country'] = 'USA'

print(df)

Output:

In this example, we added a ‘Country’ column with the value ‘USA’ for all rows. Simple, right?

2. Adding a New Column with Different Values
What if you want to add different values for each row? No problem!

# Adding a new column with a list of values
df['City'] = ['New York', 'Los Angeles', 'Chicago']

print(df)

Output:

Just make sure the list you’re adding matches the number of rows in your DataFrame, or Pandas will throw an error.

3. Adding a New Column Based on Existing Columns
Sometimes, you might want to create a new column based on existing data. Here’s how you can do that:

# Adding a new column based on existing columns
df['Age in 5 Years'] = df['Age'] + 5

print(df)

Output:

Here, we added 5 to each value in the ‘Age’ column to create a new ‘Age in 5 Years’ column. Super handy for quick calculations!

4. Adding a New Column Using a Function
Feeling fancy? You can apply a function to an existing column to create a new one.

# Adding a new column using a lambda function
df['Is Adult'] = df['Age'].apply(lambda x: 'Yes' if x >= 18 else 'No')

print(df)

# Adding a new column at a specific position
df.insert(1, 'Gender', ['Female', 'Male', 'Male'])

print(df)