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  3. Manipulating Time Series Data in Python

Exercise

Visualize monthly mean, median and standard deviation of S&P500 returns

You have also learned how to calculate several aggregate statistics from upsampled data.

Let's use this to explore how the monthly mean, median and standard deviation of daily S&P500 returns have trended over the last 10 years.

Instructions

100 XP

As usual, we have imported pandas as pd and matplotlib.pyplot as plt for you.

  • Use pd.read_csv() to import 'sp500.csv', set a DateTimeIndex based on the 'date' column using parse_dates and index_col, assign the results to sp500, and inspect using .info().
  • Convert sp500 to a pd.Series() using .squeeze(), and apply .pct_change() to calculate daily_returns.
  • .resample() daily_returns to month-end frequency (alias: 'M'), and apply .agg() to calculate 'mean', 'median', and 'std'. Assign the result to stats.
  • .plot() stats.