Creating statistical plots easily with seaborn

seaborn is a library that builds on top of Matplotlib and Pandas to provide easy-to-use statistical plotting routines. In this recipe, we give a few examples, adapted from the official documentation, of the types of statistical plot that can be created with seaborn.

How to do it...

1. Let's import NumPy, Matplotlib, and seaborn:

   >>> import numpy as np
       from scipy import stats
       import matplotlib.pyplot as plt
       import seaborn as sns
       %matplotlib inline

2. seaborn comes with built-in datasets, which are useful when making demos. The tips dataset contains bills and tips for taxi journeys:

   >>> tips = sns.load_dataset('tips')
       tips

   

3. seaborn implements easy-to-use functions to visualize the distribution of datasets. Here, we plot the histogram, Kernel Density Estimation (KDE), and a gamma distribution fit for our dataset:

   >>> # We create two subplots sharing the same y axis.
       f, (ax1, ax2) = plt.subplots(1, 2,
        ...