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Course

Manipulating Time Series Data in Python

IntermediateSkill Level

4.7+

Updated 05/2022

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

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PythonData Manipulation4 hr16 videos55 Exercises4,700 XP71,050Statement of Accomplishment

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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.

Prerequisites

Data Manipulation with pandas

1

Working with Time Series in Pandas

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.

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

2

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.

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

3

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.

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

4

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.

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!

Manipulating Time Series Data in Python

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FAQs

What prior knowledge do I need for this course?

You should be comfortable with pandas and intermediate Python. Experience with DataFrames and basic data manipulation is essential before starting.

What time series operations does this course cover?

You will learn to work with DateTimeIndex, resample time series to different frequencies, and calculate rolling and cumulative values.

What is the final project in this course?

You will build a value-weighted stock index from actual stock data, applying all the time series manipulation techniques you learned throughout the course.

What jobs use time series manipulation skills?

Financial analysts, quantitative researchers, and data scientists working with sequential data regularly use these skills to analyze trends and build forecasts.

How long does this course take to complete?

The course has 4 chapters and 55 exercises. Most learners finish it in about 5 hours.

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