Weekly average temperature and visibility

Is there a correlation between temperature and visibility? Let's find out.

In this exercise, your job is to plot the weekly average temperature and visibility as subplots. To do this, you need to first select the appropriate columns and then resample by week, aggregating the mean.

In addition to creating the subplots, you will compute the Pearson correlation coefficient using .corr(). The Pearson correlation coefficient, known also as Pearson's r, ranges from -1 (indicating total negative linear correlation) to 1 (indicating total positive linear correlation). A value close to 1 here would indicate that there is a strong correlation between temperature and visibility.

The DataFrame df_clean has been pre-loaded for you.

Import matplotlib.pyplot as plt.
Select the 'visibility' and 'dry_bulb_faren' columns and resample them by week, aggregating the mean. Assign the result to weekly_mean.
Print the output of weekly_mean.corr().
Plot the weekly_mean dataframe with .plot(), specifying subplots=True.

Exercise

Weekly average temperature and visibility

Instructions