Coding⏱️ 2 min read📅 2026-06-03

How to Fix: Update row values where certain condition is met in pandas

Update row values in pandas DataFrame based on condition

Quick Answer: Use the loc[] method to update values, e.g. df.loc[df['stream'] == 2, ['feat', 'another_feat']] = [new_value1, new_value2]

📋 Table of Contents

  1. 🔍 Why This Happens
  2. ✅ Best Solutions to Fix It
  3. 🎯 Final Words

Updating row values in a pandas DataFrame based on a condition can be achieved efficiently using the loc method with boolean indexing. The problem with the provided code is that it uses the iterrows method, which is not recommended for large DataFrames as it can be slow and memory-intensive.

🔍 Why This Happens

  • The iterrows method returns an iterator over the rows of the DataFrame, which means that for each row, it has to iterate over all columns. This can be slow and memory-intensive when dealing with large DataFrames.

✅ Best Solutions to Fix It

Method 1: Boolean Indexing

  1. Step 1: Use the loc method with boolean indexing to select the rows where the 'stream' column is equal to 2.

Method 2: Using np.where

  1. Step 1: Use the np.where function to create a mask that selects the rows where the 'stream' column is equal to 2.

🎯 Final Words

By using boolean indexing or np.where, you can update the values of multiple columns in a pandas DataFrame efficiently and memory-intensively.

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