Case study: heat map

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

DATA_URL = 'https://raw.githubusercontent.com/health-data-science-OR/' \
            + 'hpdm139-datasets/main/wisconsin.zip'

def load_wisconsin(path):
    '''
    Load and clean the wisconsin breast cancer 
    dataset.
    '''
    recoded_label = {'diagnosis': {'M':'1',
                                   'B':'0'}}
    
    to_drop = ['Unnamed: 0', 'id']
    df = (pd.read_csv(path)
            .drop(to_drop, axis=1) 
            .replace(recoded_label)
            .astype({'diagnosis': np.byte})
         )
    return df[df.columns[1:]], df['diagnosis']

X, y = load_wisconsin(DATA_URL)
X.head().head(2)

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	17.99	10.38	122.8	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.9	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902

2 rows × 30 columns

Visualising Correlations with a heatmap

def correlation_heat_map(correlation_matrix, cmap='inferno_r', figsize=(8,8)):
    '''
    Correlation heat map
    
    Params:
    -------
    correlation_matrix: pd.DataFrame,
        n x n matrix with feature names
        
    cmap: str, optional (default='inferno_r')
        The colour mapping for the heatmap
        
    figsize: tuple (int, int)
        Size of figure.
        
    Returns:
    -------
    tuple (figure, image)
    '''
    fig = plt.figure(figsize=figsize)
    ax = fig.add_subplot()
    im = ax.imshow(correlation_matrix, cmap=cmap)

    # Make sure there are enough visible tick marks
    ax.set_xticks(ticks=np.arange(len(correlation_matrix.columns)))
    ax.set_yticks(ticks=np.arange(len(correlation_matrix.columns)))

    # add the feature names and rotate x axis 90 degrees.
    ax.set_xticklabels(list(correlation_matrix.columns), rotation=90)
    ax.set_yticklabels(list(correlation_matrix.index))

    # add colour coding legend
    cbar = fig.colorbar(im, ax=ax)
    cbar.ax.set_ylabel('Correlation', rotation=-90, va="bottom");
    
    return fig, im

correlation_matrix = X.corr()
fig, im = correlation_heat_map(correlation_matrix = correlation_matrix)