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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 monthly average stock prices for Facebook and Google

Now, you'll apply your new resampling skills to daily stock price series for Facebook and Google for the 2015-2016 period to compare the trend of the monthly averages.

Instructions

100 XP

We have again imported pandas as pd and matplotlib.pyplot as plt for you.

  • Use pd.read_csv() to import 'stocks.csv' and set a DateTimeIndex based on the 'date' column using parse_dates and index_col, assign the result to stocks and inspect using .info().
  • Create monthly_average by applying .resample() with monthly frequency to data, using .mean() to aggregate. Plot the result using subplots.