paid course

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
6,774 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.

In this course you'll learn the basics of working with time series data.

Course Outline

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.

This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.

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.

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.

1
Working with Time Series in Pandas
Free

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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Stefan has applied data science to business, investment and policy decisions for over 15 years. He has built an early warning system for financial crises, and worked as advisor to Central Banks and the World Bank before moving into fintech. He is founder and lead data scientist at Applied Artificial Intelligence, and instructor at General Assembly.

Course Instructor

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Collaborator(s)

Lore Dirick
Nick Solomon

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Manipulating Time Series Data in Python

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Course Description

Course Outline

Working with Time Series in Pandas

How to use dates & times with pandas

Your first time series

Indexing & resampling time series

Create a time series of air quality data

Compare annual stock price trends

Set and change time series frequency

Lags, changes, and returns for stock price series

Shifting stock prices across time

Calculating stock price changes

Plotting multi-period returns

Window Functions: Rolling & Expanding Metrics

Rolling window functions with pandas

Rolling average air quality since 2010 for new york city

Rolling 360-day median & std. deviation for nyc ozone data since 2000

Rolling quantiles for daily air quality in nyc

Expanding window functions with pandas

Cumulative sum vs .diff()

Cumulative return on $1,000 invested in google vs apple I

Cumulative return on $1,000 invested in google vs apple II

Case study: S&P500 price simulation

Random walk I

Random walk II

Random walk III

Relationships between time series: correlation

Annual return correlations among several stocks

Basic Time Series Metrics & Resampling

Compare time series growth rates

Compare the performance of several asset classes

Comparing stock prices with a benchmark

Plot performance difference vs benchmark index

Changing the time series frequency: resampling

Convert monthly to weekly data

Create weekly from monthly unemployment data

Upsampling & interpolation with `.resample()`

Use interpolation to create weekly employment data

Interpolate debt/GDP and compare to unemployment

Downsampling & aggregation

Compare weekly, monthly and annual ozone trends for NYC & LA

Compare monthly average stock prices for Facebook and Google

Compare quarterly GDP growth rate and stock returns

Visualize monthly mean, median and standard deviation of S&P500 returns

Putting it all together: Building a value-weighted index

Select index components & import data

Explore and clean company listing information

Select and inspect index components

Import index component price information

Build a market-cap weighted index

Calculate number of shares outstanding

Create time series of market value

Calculate & plot the composite index

Evaluate index performance

Calculate the contribution of each stock to the index

Compare index performance against benchmark I

Compare index performance against benchmark II

Index correlation & exporting to excel

Visualize your index constituent correlations

Save your analysis to multiple excel worksheets

Congratulations!