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Manipulating Time Series Data in Python

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

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4 Hours16 Videos55 Exercises44,513 Learners4700 XPFinance Fundamentals in Python TrackTime Series with Python Track

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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.
  1. 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.

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    How to use dates & times with pandas
    50 xp
    Your first time series
    100 xp
    Indexing & resampling time series
    50 xp
    Create a time series of air quality data
    100 xp
    Compare annual stock price trends
    100 xp
    Set and change time series frequency
    100 xp
    Lags, changes, and returns for stock price series
    50 xp
    Shifting stock prices across time
    100 xp
    Calculating stock price changes
    100 xp
    Plotting multi-period returns
    100 xp
  2. 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.

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    Compare time series growth rates
    50 xp
    Compare the performance of several asset classes
    100 xp
    Comparing stock prices with a benchmark
    100 xp
    Plot performance difference vs benchmark index
    100 xp
    Changing the time series frequency: resampling
    50 xp
    Convert monthly to weekly data
    100 xp
    Create weekly from monthly unemployment data
    100 xp
    Upsampling & interpolation with .resample()
    50 xp
    Use interpolation to create weekly employment data
    100 xp
    Interpolate debt/GDP and compare to unemployment
    100 xp
    Downsampling & aggregation
    50 xp
    Compare weekly, monthly and annual ozone trends for NYC & LA
    100 xp
    Compare monthly average stock prices for Facebook and Google
    100 xp
    Compare quarterly GDP growth rate and stock returns
    100 xp
    Visualize monthly mean, median and standard deviation of S&P500 returns
    100 xp
  3. 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.

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    Rolling window functions with pandas
    50 xp
    Rolling average air quality since 2010 for new york city
    100 xp
    Rolling 360-day median & std. deviation for nyc ozone data since 2000
    100 xp
    Rolling quantiles for daily air quality in nyc
    100 xp
    Expanding window functions with pandas
    50 xp
    Cumulative sum vs .diff()
    100 xp
    Cumulative return on $1,000 invested in google vs apple I
    100 xp
    Cumulative return on $1,000 invested in google vs apple II
    100 xp
    Case study: S&P500 price simulation
    50 xp
    Random walk I
    100 xp
    Random walk II
    100 xp
    Random walk III
    100 xp
    Relationships between time series: correlation
    50 xp
    Annual return correlations among several stocks
    100 xp
  4. 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.

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    Select index components & import data
    50 xp
    Explore and clean company listing information
    100 xp
    Select and inspect index components
    100 xp
    Import index component price information
    100 xp
    Build a market-cap weighted index
    50 xp
    Calculate number of shares outstanding
    100 xp
    Create time series of market value
    100 xp
    Calculate & plot the composite index
    100 xp
    Evaluate index performance
    50 xp
    Calculate the contribution of each stock to the index
    100 xp
    Compare index performance against benchmark I
    100 xp
    Compare index performance against benchmark II
    100 xp
    Index correlation & exporting to Excel
    50 xp
    Visualize your index constituent correlations
    100 xp
    Save your analysis to multiple excel worksheets
    100 xp
    Congratulations!
    50 xp

In the following tracks

Finance Fundamentals in PythonTime Series with Python

Datasets

Air quality dataStock data

Collaborators

Lore Dirick
Nick Solomon

Prerequisites

Data Manipulation with pandas
Stefan Jansen Headshot

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Stefan is the Founder & Lead Data Scientist at Applied Artificial Intelligence. He has 15 years of experience in finance and investments, with a big focus on emerging markets.
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