Add column to dataframe with constant value

python

dataframe

pandas

best-practices

byNikita Barsukov·Oct 27, 2024

To add a constant column in a pandas DataFrame, use ['new_column_name'] = constant_value. For example, adding ConstantColumn with value 100, you do:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
df['ConstantColumn'] = 100  #Bam! New 100s for everyone!

df now includes a fancy new ConstantColumn filled to the brim with 100.

Method breakdown: Adding your column with style

While tossing a constant column into your DataFrame with df['new_column_name'] = value goes off without a hitch, it's important to know of the other methods that exist, each with its own special charm:

Inserting with precision using `df.insert()`

For those who like to keep things organized, df.insert() gives you control over the position of your new constant companion:

df.insert(0, 'Name', 'abc')  # When "first impressions" really matter.

Position matters, and insert() understands that.

Chain 'em up with `df.assign()`

For those who prefer to work in sequences of operations, df.assign() gets down to business:

df = df.assign(Name='abc')  # Addition without the surgery.

With assign(), your original DataFrame remains untouched, while you get a 'copy-paste' job with the newly added column in tow.

Giving more with less

Adding multiple columns with constant values or achieving this programmatically, a simple loop does the trick:

new_columns = {'Name': 'abc', 'Age': 30}  
for column, value in new_columns.items():
    df[column] = value  # Looping, the Pythonista's "Ctrl+C, Ctrl+V" approach.

This dynamic addition of multiple columns makes repetitive tasks feel like a breeze.

Choosing the right tool for the job

Every method has a specific application with its own advantages:

Go the Direct assignment route for quick and easy addition.
Use df.insert() for a cultured approach where position matters.
Choose df.assign() for a functional addition in a sequence of operations without disturbing the original DataFrame.

Remember, understanding your methods equals cleaner, more efficient code.