Pandas SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame

Updated: February 21, 2024 By: Guest Contributor Post a comment

The Problem

The SettingWithCopyWarning in Pandas can be a confusing and irritating issue for many users. It’s a warning that alerts the user when they attempt to set a value on a DataFrame slice, suggesting that the operation might not work as expected. This tutorial will delve into why this warning occurs and outline multiple solutions to deal with it effectively.

Why the Warning Occurs?

Before jumping into solutions, it’s important to understand why this warning occurs. At its core, the SettingWithCopyWarning is designed to prevent situations where changes intended for the original DataFrame accidentally only modify a temporary object. This often happens when slicing DataFrames without explicitly creating a new DataFrame.

Solution 1: Use .copy()

Making a copy of the slice ensures any modifications are made on a distinct object, eliminating ambiguity about where changes are applied.

  • Step 1: Slice the DataFrame to select your subset.
  • Step 2: Append .copy() to the slicing operation to explicitly create a copy.
  • Step 3: Perform your intended modifications on this copy.


subset = df[df['column_name'] > 10].copy()
subset['new_column'] = subset['column_name'] + 1

Notes: This approach ensures changes are isolated to the copy, reducing the risk of unintended side effects. However, it does increase memory usage as a separate copy of the data is maintained.

Solution 2: Use .loc[] for Modifications

The .loc[] method allows for more explicit and safer data selection and modification within the original DataFrame, addressing the root cause of the warning.

  • Step 1: Use .loc[] with conditions or indices to pinpoint your subset.
  • Step 2: Apply your modifications directly using the same .loc[] notation.


df.loc[df['column_name'] > 10, 'new_column'] = df['column_name'] + 1

Notes: This method maintains data integrity and avoids the necessity for a separate copy, but requires that the conditions used for selection be carefully constructed to avoid unintended modifications to other parts of the DataFrame.

Solution 3: Use query() Method

For users comfortable with SQL-like syntax, the query() method can be a concise and readable way to select and modify DataFrames.

  • Step 1: Use the query() method to select a subset.
  • Step 2: Follow with a .copy() or .loc[] method for modifications, depending on preference and context.


subset = df.query('column_name > 10').copy()
subset['new_column'] = subset['column_name'] + 1
# Or using loc[] directly
# df.loc[df.query('column_name > 10').index, 'new_column'] = df['column_name'] + 1

Notes: While this method offers a clean syntax, it may lead to readability issues for those less familiar with query languages. Additionally, performance could be a concern with very large DataFrames.

Other Noteworthy Recommendations

Aside from these solutions, understanding Pandas configurations and settings can also help in managing this warning. Ensuring you’re modifying the original DataFrame when intended, and being explicit about your operations, will often prevent confusion and errors. Regularly using methods like .equals() to check DataFrame equality can also be beneficial in debugging.