Adding empty columns to your DataFrame in Python Pandas

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Pandas is a cornerstone tool for data manipulation in Python, offering extensive functionalities for data analysis. One common task in data processing is adding new columns to a DataFrame. This article provides a detailed guide on how to add an empty column to a Pandas DataFrame, using practical examples. Adding an empty column to a DataFrame is a straightforward task in Pandas. This capability is particularly useful for data preparation and manipulation, enhancing the versatility of your data analysis workflows.

Before diving into the how, let’s understand the why. Adding an empty column can be useful for:

  • Preparing a dataset for additional data.
  • Allocating space for calculated fields.
  • Structuring data for further processing.


Ensure you have the following before starting:

  • Python installed on your machine.
  • Pandas library installed (pip install pandas).

Step-by-step example

Let’s consider a simple DataFrame containing names and ages.

Data preparation

First, we create our initial DataFrame:

import pandas as pd
# Sample data
data = {
    'Name': ['Sachin', 'Manju', 'Ram', 'Raju', 'David', 'Wilson'],
    'Age': [30, 25, 40, 35, 28, 33]
# Create DataFrame
df = pd.DataFrame(data)

Adding an empty column

Now, let’s add an empty column named ‘NewColumn’:

# Add an empty column
df['NewColumn'] = None

Finally, let’s verify our updated DataFrame:

# Display the DataFrame

The output should display the original data with an additional empty column ‘NewColumn’.

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Author: user