Pandas code from a plain-English data task

Describe the transformation or analysis you need and get clean, commented pandas code that handles messy data and explains each step.

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Prompt template
Write Python pandas code for the following task.

Task: {{task}}

My DataFrame (name, columns with types, and a few sample rows):
{{dataframe}}

Output I want: {{output}}

Guidelines:
- Start with two or three lines on your approach and any assumptions about the data. If an assumption could change the result, say how, and ask me at the end.
- Write idiomatic, readable code using method chaining where it stays clear. No loops over rows unless unavoidable. No deprecated functions.
- Handle the real-world issues likely in this data: missing values, wrong dtypes, duplicate rows, inconsistent text casing, and time zones. Do not silently drop rows; if rows are removed, print how many.
- Add short comments explaining why, not what.
- Include a tiny runnable example with 4-6 made-up rows so I can check the logic before I run it on the full data.
- End with "How to check it": 2-3 assertions or quick checks (row counts, totals, null counts) I can add.

Format: Approach, Code (one block), Example output, How to check it, Questions.
Example output
**Approach**: Parse dates, aggregate per customer, compute recency from the latest order date in the data (not today's date) so results are reproducible. Missing amounts are treated as 0 spend but still count as orders; tell me if that is wrong. ```python import pandas as pd df = df_orders.copy() df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce") bad = df["order_date"].isna().sum() print(f"Unparseable dates dropped: {bad}") df = df.dropna(subset=["order_date"]).drop_duplicates("order_id") as_of = df["order_date"].max() customers = ( df.groupby("customer_id") .agg(total_spend=("amount", "sum"), orders=("order_id", "count"), last_order=("order_date", "max")) .assign(days_since_last=lambda d: (as_of - d["last_order"]).dt.days) .assign(at_risk=lambda d: d["days_since_last"] > 90) .sort_values("total_spend", ascending=False) .reset_index() ) ``` **Example output**: customer 7: spend 240.0, orders 3, days_since_last 12, at_risk False. **How to check it** - assert customers["orders"].sum() == len(df) - assert abs(customers["total_spend"].sum() - df["amount"].sum()) < 1e-6 **Questions**: Should recency be measured against today instead of the last date in the data?

Why it works

Asking for a tiny runnable example and assertions makes the output verifiable rather than just plausible. Pasting 3-5 real rows greatly reduces dtype and format mistakes.

Licensed CC0 (public domain) Version 1 · history