Heatmaps

Heatmaps are a type of visualization that display correlations between different features of a dataset. Correlations can be positive or negative, and strong or weak.

The features are set as rows and columns, and the cells are color-coded based on their correlation value. Features with a high positive number are strongly positively correlated.

Exercise 10.05: Checking for Correlations between Features

In this exercise, you will plot a heatmap to observe whether there are any correlations between features of the new_air DataFrame:

1. Import numpy as np:

   import numpy as np

2. Create a variable called corr that will store the correlations between the features of new_air. Calculate these correlations by applying the .corr() function on new_air:

   corr = new_air.corr()

3. Mask the zero values using the zeros_like() function, with corr as the correlations to check, and set dtype as np.bool:

   mask = np.zeros_like(corr, dtype=np.bool)
   mask[np.triu_indices_from(mask)]...