reportpy

Get a plain-language overview of a pandas DataFrame in one line.

Inspired by R's report package. It reads your data and tells you, in plain sentences, what is in each column, what looks wrong, and what to clean first.

Install

pip install reportpy

Use

import pandas as pd
import reportpy

df = pd.read_csv("flights.csv")
df.diagnose()

You can also import the function directly:

from reportpy import diagnose

diagnose(df, date_cols=["Date"], dayfirst=True)
diagnose(df, brief=True)
text = diagnose(df, return_text=True)

What you get

• A quick summary at the top: rows, columns, and what needs attention
• For each column: type, missing values, spread, and a plain-language reading
• Date coverage: range, gaps, busiest and quietest periods
• Data-quality checks: placeholder codes like 9999, numbers stored as text, messy labels, duplicates, impossible date orders
• How columns relate to each other
• A short cleaning checklist

Example output

At a glance:
  - 8,000 rows and 10 columns (2 numeric, 6 categorical, 2 date).
  - Needs attention: 1 column carries no information; 1 column has extreme values or heavy skew.

- ticket_price [numeric]: 8,000 valid values, 0.00% missing
      In plain terms: A typical value is about 17,286. Half of the rows fall between 10,578 and
        22,446; the full range runs from 7,482 to 55,986.
      Takeaway: right-skewed with a long tail of large values; lean on median and percentiles,
        and consider a log transform.

License

MIT