When working with pandas, you might see the dreaded SettingWithCopyWarning:

"A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer, col_indexer] = value instead.".

Although this message is alarming, it’s a warning rather than an error. It alerts you that pandas cannot be sure whether you are modifying the original DataFrame or a temporary view. Ignoring it can lead to subtle bugs, so understanding the cause is important.

What triggers SettingWithCopyWarning?

SettingWithCopyWarning happens mainly due to chained assignment, where you index a DataFrame twice before assigning. For example:

# df is a slice of another DataFrame – could be a view or a copy
no_property_type = df[df["propertyType"] == "Not Specified"]

# Pandas doesn’t know whether this assignment updates df or only the slice
no_property_type["propertyType"] = "House"

Pandas can’t determine whether the slice is a view or a copy. To be safe, it warns you that your assignment may not affect the original data. Even assignments with .apply() like df['col'] = df[col].apply(...) can trigger the warning.

How to fix the warning

1. Use .copy() to ensure you’re working on a separate DataFrame

If you intend to work on a separate copy and leave the original data unchanged, explicitly call .copy():

# take a deep copy before modifying
clean_df = df_test.copy()

clean_df['col'] = clean_df['col'].apply(
    lambda x: np.nan if get_absolute_median_z_score(x, median, median_dev) >= 2 else x
)

This makes it clear that clean_df is an independent DataFrame, so your modifications won’t affect the original.

2. Use .loc for assignments to a view

If you want to update the original DataFrame, avoid chained indexing and use .loc[row_indexer, col_indexer]:

# update the original DataFrame safely
mask = df['species'] == 'beaver'

# Using .loc ensures pandas knows you intend to modify df directly
# row_indexer is mask, col_indexer is the column name
 df.loc[mask, 'n'] = df.loc[mask, 'n'].apply(lambda x: min(x, 3))

.loc makes your intention explicit and suppresses the warning. You can chain other operations inside .loc too. For example, to apply a function on the column only for certain rows:

 df.loc[df['col'] > 0, 'col'] = df.loc[df['col'] > 0, 'col'].apply(np.sqrt)

3. Understand when a view vs. a copy is returned

Pandas sometimes returns a view (sharing memory with the original DataFrame) and sometimes a copy (new memory). Using .loc and .copy() tells pandas explicitly what you want. Ignoring the warning can result in your code not updating the data you expect.

Conclusion

SettingWithCopyWarning warns you about potential bugs when working with pandas DataFrames. It’s not an error, but you should never ignore it. Avoid chained assignments by using .loc for in‑place updates or .copy() when you need a separate copy. Following these practices will eliminate the warning and make your code more reliable.