Viewing a matrix of scatterplots

If you don't have many variables in your dataset, it is a good idea to view all the possible scatterplots for your data. You can do this with one function call from either seaborn or pandas. These functions display a matrix of plots with kernel density estimation plots or histograms on the diagonal.

How to do it...

1. Imports the following:

   import pandas as pd
   from dautil import data
   from dautil import ts
   import matplotlib.pyplot as plt
   import seaborn as sns
   import matplotlib as mpl

2. Load the weather data with the following lines:

   df = data.Weather.load()
   df = ts.groupby_yday(df).mean()
   df.columns = [data.Weather.get_header(c) for c in df.columns]

3. Plot with the Seaborn pairplot() function, which plots histograms on the diagonal by default:

   %matplotlib inline
   
   # Seaborn plotting, issues due to NaNs
   sns.pairplot(df.fillna(0))

   The following plots are the result:

   
4. Plot similarly with the pandas scatter_matrix() function and request kernel density estimation plots on the diagonal:

   sns.set({'figure.figsize': '16, 12'})
   mpl.rcParams['axes.linewidth'] = 9
   mpl.rcParams['lines.linewidth'] = 2
   plots = pd.scatter_matrix(df, marker='o', diagonal='kde')
   plt.show()

Refer to the following plots for the end result:

The complete code is available in the scatter_matrix.ipynb file in this book's code bundle.