Update a dataframe in pandas while iterating row by row

python

pandas

dataframe

vectorization

byNikita Barsukov·Mar 9, 2025

The Quickest and Efficient way to update specific cells in a DataFrame during iteration uses df.at and df.iat. Here is an illustrative snippet:

import pandas as pd

# Assuming 'df' is your DataFrame and 'your_function' that transforms value
# Here "idx" is staying home for a quicker update!
for idx in df.index:
    df.at[idx, 'column_to_change'] = your_function(df.loc[idx, 'relevant_column'])

For the uncompromising need of speed, always favor vectorization over iteration. However, when there's no other way out, df.at or df.loc are your go-to partners to keep 'df' updated like live news.

Pragmatic patterns for iteration

Loop literacy and performance

While loops might be as tempting as mom's homemade cookies, they aren't the most efficient for updating dataframes. If you’re cornered into iterating:

Grab df.iterrows() for sequential row access, but don't trust it for updating.
Summon df.itertuples() for a speedier iteration compared to df.iterrows().
Always seek vectorization, it’s the Usain Bolt of DataFrame operations!

Threading the needle: Safe updates

Beware of the snake in the grass: SettingWithCopyWarning! Make sure you're giving a makeover to the original DataFrame and not a mere reflection. Stick to .loc on the right occasions and you're safe. Shun df.ix[] and .set_value(), they're old news!

Working with other columns and rows

When updates call for data from other rows or columns:

Employ DataFrame functions like shift() or cumsum() that respect the DataFrame's personal space.
Effortlessly find column indices using df.columns.get_loc() for precise column targeting.
Invoke df.apply() with lambda functions to conduct row-wise or column-wise operations conditionally.

Coding conditional checks while hula hooping with loops

If you need to include conditional checks in the loop:

Use if-else blocks to pick the value to assign.
For a lookup from another DataFrame while iterating, use df.loc or df.merge.

Imagine you have to change 'status' based on 'age'. It's easy, just like changing TV channels:

# Who doesn't love a good old 'if-else' check!
for i in df.index:
    if df.at[i, 'age'] > 18:
        df.at[i, 'status'] = 'adult'
    else:
        df.at[i, 'status'] = 'minor'

Yes, it works, but remember, vectorization is the cool kid on the block:

# Lambdas: they are like ninjas, small but powerful!
df['status'] = df['age'].apply(lambda x: 'adult' if x > 18 else 'minor')

Powerful maneuvers and coding kung fu

No vectorization, no problem!

Sometimes, vectorization isn't the answer. In those lonely times:

Use df.at or df.loc for accurate and in-situ updates.
When you need to perform operations row-wise, use .apply().

Bulletproof updates minus warnings

Dodging SettingWithCopyWarning like Neo in Matrix:

Operate on the actual DataFrame. No doppelgangers!
Understanding pandas’ indexing helps you distinguish a genuine DataFrame from a con.

Updating using external DataFrame or Series

When updating requires data from other Pandas' entities:

Pre-merge DataFrames using pd.merge() or .join().
Use .map() with a Series for quick-draw column updates based on another key-value structure.

explain-codes / Python / Update a dataframe in pandas while iterating row by row

Linked

Create a Pandas Dataframe by appending one row at a time



Pandas convert dataframe to array of tuples



Split a Pandas column of lists into multiple columns



How to iterate over columns of a pandas dataframe



How can I get a value from a cell of a dataframe?



Get a list from Pandas DataFrame column headers



Right way to reverse a pandas DataFrame?



Pragmatic patterns for iteration Coding conditional checks while hula hooping with loops Powerful maneuvers and coding kung fu