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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

Use interpolation to create weekly employment data

You have recently used the civilian US unemployment rate, and converted it from monthly to weekly frequency using simple forward or backfill methods.

Compare your previous approach to the new .interpolate() method that you learned about in this video.

Instructions

100 XP

We have imported pandas as pd and matplotlib.pyplot as plt for you. We have also loaded the monthly unemployment rate from 2010 to 2016 into a variable monthly.

  • Inspect monthly using .info().
  • Create a pd.date_range() with weekly dates, using the .min() and .max() of the index of monthly as start and end, respectively, and assign the result to weekly_dates.
  • Apply .reindex() using weekly_dates to monthly and assign the output to weekly.
  • Create new columns 'ffill' and 'interpolated' by applying .ffill() and .interpolate() to weekly.UNRATE.
  • Show a plot of weekly.