Rolling average air quality since 2010 for new york city

The last video was about rolling window functions. To practice this new tool, you'll start with air quality trends for New York City since 2010. In particular, you'll be using the daily Ozone concentration levels provided by the Environmental Protection Agency to calculate & plot the 90 and 360 day rolling average.

This exercise is part of the course

Manipulating Time Series Data in Python

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Exercise instructions

We have already imported pandas as pd and matplotlib.pyplot as plt.

Use pd.read_csv() to import 'ozone.csv', creating a DateTimeIndex from the 'date' column using parse_dates and index_col, and assign the result to data.
Add the columns '90D' and '360D' containing the 90 and 360 rolling calendar day .mean() for the column 'Ozone'.
Plot data starting 2010, setting 'New York City' as title.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import and inspect ozone data here
data = ____
print(____)

# Calculate 90d and 360d rolling mean for the last price
data['90D'] = ____
data['360D'] = ____

# Plot data

Edit and Run Code