# Project 02 — Python Data Explorer

## Dataset inspection

- Number of rows:
- Number of columns:
- Numerical columns:
- Categorical columns:
- What each column represents:

## Data-quality check

- Missing values found:
- Duplicate rows found:
- Cleaning actions taken:
- Why these actions were appropriate:

## Selected summaries

- Average monthly spending:
- Average number of purchases:
- Largest membership group:
- Highest-spending membership group:

## Three important findings

1.
2.
3.

## Responsible data check

- [ ] Personal data is not unnecessarily exposed
- [ ] Customer IDs are used instead of full names
- [ ] Missing values are handled transparently
- [ ] Rows are not deleted without checking the reason
- [ ] Findings are based on the data and not assumptions

## Completion checklist

- [ ] Python and pandas are installed
- [ ] The dataset loads successfully
- [ ] I inspected the rows and columns
- [ ] I checked data types
- [ ] I identified missing values
- [ ] I removed duplicate rows
- [ ] I calculated summary statistics
- [ ] I grouped or filtered the data
- [ ] I created one chart
- [ ] I wrote three findings
- [ ] I completed the README