Manipulating Time Series Data in Python

Working with Time Series in Pandas
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This chapter lays the foundations to leverage the powerful time series functionality made available by how Pandas represents dates, in particular by the DateTimeIndex. You will learn how to create and manipulate date information and time series, and how to do calculations with time-aware DataFrames to shift your data in time or create period-specific returns.

Basic Time Series Metrics & Resampling
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This chapter dives deeper into the essential time series functionality made available through the pandas DataTimeIndex. It introduces resampling and how to compare different time series by normalizing their start points.

Window Functions: Rolling & Expanding Metrics
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This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.


Putting it all together: Building a value-weighted index
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This chapter combines the previous concepts by teaching you how to create a value-weighted index. This index uses market-cap data contained in the stock exchange listings to calculate weights and 2016 stock price information. Index performance is then compared against benchmarks to evaluate the performance of the index you created.