In Python data analysis, how do I monitor health?

In Python data analysis, monitoring the health of your data and processes is crucial to ensure the integrity and performance of your analysis. Here are some methods you can use:

1. **Data Validation**: Regularly check if the incoming data conforms to expected formats and value ranges.

2. **Statistical Monitoring**: Use statistical tests and visualizations to detect anomalies in your datasets that could indicate bigger issues.

3. **Logging**: Implement logging of your data processing steps to catch errors and monitor performance over time.

4. **Performance Metrics**: Monitor metrics such as execution time and memory usage to ensure your code runs efficiently.