If you’ve used Pandas, you’ve probably seen:

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

It means Pandas isn’t sure if you’re editing the original DataFrame or a temporary view. Sometimes the change sticks, sometimes it doesn’t — which is a recipe for silent bugs.

To fix it:

1. Find the warnings

Run scripts/tests and force warnings to be raised as errors:

# If using pytest, run your tests
pytest -W error::pandas.errors.SettingWithCopyWarning
# Otherwise, run your script and make all warnings raise errors:
python -W always your_script.py 2>&1 | grep SettingWithCopy

If you’re in a notebook environment, we can force all these warning to be errors, to find where they are and address them. At the top of the notebook:

import warnings
import pandas as pd
warnings.simplefilter("error", category=pd.errors.SettingWithCopyWarning)

2. Inspect each case

Look for slices like df[df["age"] > 30] or df.loc[df["y"] == "a"] followed by mutations.

Decide whether you want a copy or intend to mutate the original.

3. Be explicit

• If you want a copy:

  df2 = df[df["age"] > 30].copy()

• If you want to modify the original:

  df.loc[df["age"] > 30, "category"] = "senior"

Always make your intent clear with .copy() or .loc. The warning goes away, and your code becomes predictable.

Long term fix: Polars instead of Pandas

polars sidesteps this entire issue. Every operation is explicit: you either create a new DataFrame or you don’t. No ambiguous views, no surprise warnings, and you also get fast performance out of the box.

Here’s the same operation in Polars:

import polars as pl

df = pl.DataFrame({"age": [25, 35, 50]})
df2 = df.filter(pl.col("age") > 30).with_columns(
    pl.lit("senior").alias("category")
)
print(df2)

No warnings, no ambiguity — just the damn result you asked for.