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Exercise

Rolling quantiles for daily air quality in nyc

You learned in the last video how to calculate rolling quantiles to describe changes in the dispersion of a time series over time in a way that is less sensitive to outliers than using the mean and standard deviation.

Let's calculate rolling quantiles - at 10%, 50% (median) and 90% - of the distribution of daily average ozone concentration in NYC using a 360-day rolling window.

Instructions
100 XP

We have already imported pandas as pd and matplotlib.pyplot as plt. We have also loaded the ozone data from 2000-2017 into the variable data.

  • Apply .resample() with daily frequency 'D' to data and apply .interpolate() to fill missing values, and reassign to data.
  • Inspect the result using .info().
  • Create a .rolling() window using 360 periods, select the column 'Ozone', and assign the result to rolling.
  • Insert three new columns, 'q10', 'q50' and 'q90' into data, calculating the respective quantiles from rolling.
  • Plot data.
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