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  3. Manipulating Time Series Data in Python
  • 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.

  • 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.

  • 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.

  • 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.

Initializing

Exercise

Compare annual stock price trends

In the video, you have seen how to select sub-periods from a time series.

You'll use this to compare the performance for three years of Yahoo stock prices.

Instructions

100 XP

We have already imported pandas as pd and matplotlib.pyplot as plt and we have already loaded the 'yahoo.csv' file in a variable yahoo with DateTimeIndex and a single column price.

  • Create an empty pd.DataFrame() called prices.
  • Iterate over a list containing the three years, 2013, 2014, and 2015, as string, and in each loop:
    • Use the iteration variable to select the data for this year and the column price.
    • Use .reset_index() with drop=True to remove the DatetimeIndex.
    • Rename the column price column to the appropriate year.
    • Use pd.concat() to combine the yearly data with the data in prices along axis=1.
  • Plot prices.