Write a Pandas Snippet for a Specific Data Transformation

Pandas has five ways to do everything and four of them are slow, deprecated, or trigger a SettingWithCopyWarning you will ignore until it corrupts a result. This prompt gets you a vectorized, commented snippet for one specific transformation instead of a vague tutorial. The key is pasting your actual column names and dtypes: the model writes very different code for a string column that needs parsing versus an already-typed datetime, and it stops guessing whether a column is numeric. Asking it to preserve the original DataFrame and handle NaN explicitly prevents the two bugs that waste the most debugging time. The two-sentence rationale at the end is not filler; it tells you whether the approach assumes sorted data or unique keys, which is exactly where transformations break on the full dataset after working on your sample. Use it for groupby aggregations, reshaping, window calculations, date handling, and messy joins where you know the output you want but not the cleanest call.

What you can expect back

```python
import pandas as pd

# Work on a copy so the original stays intact
result = sales_df.copy()

# Sort so the per-user diff is chronological
result = result.sort_values(["user_id", "month"])

# Month-over-month change within each user; first month has no prior -> NaN -> 0
result["mrr_delta"] = (
    result.groupby("user_id")["mrr"].diff().fillna(0.0)
)

result = result[["user_id", "month", "mrr", "mrr_delta"]]
```

Why: groupby().diff() is vectorized in C and far faster than apply or a Python loop over users. Assumptions: there is at most one row per user per month, and month is a proper datetime so sorting orders correctly; if months can be missing, decide whether gaps should be reindexed before diffing.

Illustrative example — your results will vary by tool and inputs.

Get sharper results

• 01Always paste dtypes, not just names; the model chooses .dt accessors versus string parsing based on them.
• 02If you get a SettingWithCopyWarning, paste it back and ask for a .loc or .assign rewrite.
• 03For big frames, ask whether the operation can run in a single groupby pass instead of multiple merges.
• 04Request a tiny synthetic DataFrame in the answer so you can run and verify the snippet immediately.

Adapt it for your case