1. Lernen
    2. /
  2. Kurse
    3. /
  3. Manipulating Time Series Data in Python
  • 1

    Working with Time Series in Pandas

    Kostenlos

    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

Übung

Plotting multi-period returns

The last time series method you have learned about in the video was .pct_change(). Let's use this function to calculate returns for various calendar day periods, and plot the result to compare the different patterns.

We'll be using Google stock prices from 2014-2016.

Anweisungen

100 XP

We have already imported pandas as pd, and matplotlib.pyplot as plt. We have also loaded 'GOOG' stock prices for the years 2014-2016, set the frequency to calendar daily, and assigned the result to google.

  • Create the columns 'daily_return', 'monthly_return', and 'annual_return' that contain the pct_change() of 'Close' for 1, 30 and 360 calendar days, respectively, and multiply each by 100.
  • Plot the result using subplots=True.