
Historical financial data is monitored and recorded by computer and saved for us to pore over later but there are many ways the data can be faulty.
In this lesson: how missing data can occur and what we can do about it.
Time: 00:00:27

Lesson outline
Pandas makes it very convenient to compute various statistics on a dataframe:
- Global statistics: mean, median, std, sum, etc. [more]
- Rolling statistics: rolling_mean, rolling_std, etc. [more]
You will use these functions to analyze stock movement over time.
Specifically, you will compute:
- Bollinger Bands: A way of quantifying how far stock price has deviated from some norm.
- Daily returns: Day-to-day change in stock price.