Manipulating Time Series Data in Python
In this course you'll learn the basics of working with time series data.
- 4 hours
- 16 Videos
- 55 Exercises
- 14,488 Participants
- 4,700 XP
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Course Description
In this course you'll learn the basics of manipulating time series data. Time series data are data that are indexed by a sequence of dates or times. You'll learn how to use methods built into Pandas to work with this index. You'll also learn how resample time series to change the frequency. This course will also show you how to calculate rolling and cumulative values for times series. Finally, you'll use all your new skills to build a value-weighted stock index from actual stock data.
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1
Working with Time Series in Pandas
FreeThis 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.
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Window Functions: Rolling & Expanding Metrics
This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.
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Basic Time Series Metrics & Resampling
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.
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Putting it all together: Building a value-weighted index
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.
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1
Working with Time Series in Pandas
FreeThis 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
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
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
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.
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Stefan Jansen
Founder & Lead Data Scientist at Applied Artificial Intelligence
Stefan is Lead Data Scientist at Applied AI where he advises Fortune 500 companies and startups on translating business goals into a data & AI strategy, building data science teams, and developing machine learning solutions. Prior to his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. Stefan holds Master degrees from Harvard and Free University Berlin, and a CFA Charter. He is the author for ‘Machine Learning for Algorithmic Trading’ and teaches data science at General Assembly.
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Collaborators
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Lore Dirick
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Nick Solomon