Other tests that compare two samples are the Chi-Square test, which also was an option to test these distributions' relationships. This other test was recommended by my peer reviewers, including Praveen and Colin. Although Chi-Square would certainly work, I chose to run the KS test because it's easier to execute using Python, my statistical tool of choice.
In order to run the test, I first created a sample of the distribution I created for male riders so it would be the same size as that of the smaller, female distribution. For this, I employed the random.choice function from the numpy package, using (14) as my random seed to ensure reproducibility.
Msample = np.random.choice(dfM.tripduration, size=798450, replace=False)
After making sure I was comparing distributions with the same size, I ran the KS test with a single line of code:
scipy.stats.ks_2samp(dfF.tripduration, Msample)
The output of the test displays both the KS statistic and a p-value. The KS statistic is: 0.09456 and the p-value is: 0.0.