Download: https://osdyn.ifremer.fr/pyweb/notebooks/utils/plot/heatmap.ipynb

Visualize correlation from heatmap

import numpy as np
import os
import pandas as pd
import matplotlib.pylab as plt

from osdyn.utils.plot.heatmap import heatmap

def corrplot(
    data,
    size_scale=500,
    marker="s",
    size_range=[0, 1],
    color_range=[-1, 1],
):
    """
    Dedicated to correlation plots but can be used for other purposes.

    Parameters
    ----------
    data : pandas.Dataframe
        data containing the x axes, y axes, values.
    size_scale : int, optional
        multiplicator of data value. The default is 500.
    marker : str, optional
        "s" "c" "t"... The default is "s".
    size_range : list of 2 int or float, optional
        Values to anchor the sizes. The default is [0, 1].
    color_range : list of 2 int or float, optional
        Values to anchor the color. The default is [-1, 1].

    Returns
    -------
    Call heatmap

    """
    corr = pd.melt(data.reset_index(), id_vars="index").replace(np.nan, 0)
    corr.columns = ["x", "y", "value"]
    heatmap(
        corr["x"],
        corr["y"],
        color=corr["value"],
        color_range=color_range,
        palette=plt.get_cmap("seismic_r", lut=256),
        size=corr["value"].abs(),
        size_range=size_range,
        marker=marker,
        x_ticklabel=data.columns,
        y_ticklabel=data.columns[::-1],
        size_scale=size_scale,
    )

# Load the Automobile dataset.
# This gets a cleaned version of UCI dataset
# found at http://archive.ics.uci.edu/ml/datasets/automobile
#data = pd.read_csv("https://raw.githubusercontent.com/drazenz/heatmap/master/autos.clean.csv")
data = pd.read_csv(os.path.join(os.environ["INPUT_DATA_PATH"],"autos.clean.csv"))

data.corr()

	symboling	normalized-losses	wheel-base	length	width	height	curb-weight	engine-size	bore	stroke	compression-ratio	horsepower	peak-rpm	city-mpg	highway-mpg	price
symboling	1.000000	0.466264	-0.535987	-0.365404	-0.242423	-0.550160	-0.233118	-0.110581	-0.140019	-0.008153	-0.182196	0.075819	0.279740	-0.035527	0.036233	-0.082391
normalized-losses	0.466264	1.000000	-0.056661	0.019424	0.086802	-0.373737	0.099404	0.112360	-0.029862	0.055045	-0.114713	0.217299	0.239543	-0.225016	-0.181877	0.133999
wheel-base	-0.535987	-0.056661	1.000000	0.876024	0.814507	0.590742	0.782097	0.572027	0.493244	0.158018	0.250313	0.371147	-0.360305	-0.470606	-0.543304	0.584642
length	-0.365404	0.019424	0.876024	1.000000	0.857170	0.492063	0.880665	0.685025	0.608971	0.123952	0.159733	0.579821	-0.285970	-0.665192	-0.698142	0.690628
width	-0.242423	0.086802	0.814507	0.857170	1.000000	0.306002	0.866201	0.729436	0.544885	0.188822	0.189867	0.615077	-0.245800	-0.633531	-0.680635	0.751265
height	-0.550160	-0.373737	0.590742	0.492063	0.306002	1.000000	0.307581	0.074694	0.180449	-0.060663	0.259737	-0.087027	-0.309974	-0.049800	-0.104812	0.135486
curb-weight	-0.233118	0.099404	0.782097	0.880665	0.866201	0.307581	1.000000	0.849072	0.644060	0.167438	0.156433	0.757976	-0.279361	-0.749543	-0.794889	0.834415
engine-size	-0.110581	0.112360	0.572027	0.685025	0.729436	0.074694	0.849072	1.000000	0.572609	0.205928	0.028889	0.822676	-0.256733	-0.650546	-0.679571	0.872335
bore	-0.140019	-0.029862	0.493244	0.608971	0.544885	0.180449	0.644060	0.572609	1.000000	-0.055390	0.001263	0.566936	-0.267392	-0.582027	-0.591309	0.543155
stroke	-0.008153	0.055045	0.158018	0.123952	0.188822	-0.060663	0.167438	0.205928	-0.055390	1.000000	0.187871	0.098267	-0.063561	-0.033956	-0.034636	0.082269
compression-ratio	-0.182196	-0.114713	0.250313	0.159733	0.189867	0.259737	0.156433	0.028889	0.001263	0.187871	1.000000	-0.214514	-0.435780	0.331425	0.268465	0.071107
horsepower	0.075819	0.217299	0.371147	0.579821	0.615077	-0.087027	0.757976	0.822676	0.566936	0.098267	-0.214514	1.000000	0.107885	-0.822214	-0.804575	0.809575
peak-rpm	0.279740	0.239543	-0.360305	-0.285970	-0.245800	-0.309974	-0.279361	-0.256733	-0.267392	-0.063561	-0.435780	0.107885	1.000000	-0.115413	-0.058598	-0.101616
city-mpg	-0.035527	-0.225016	-0.470606	-0.665192	-0.633531	-0.049800	-0.749543	-0.650546	-0.582027	-0.033956	0.331425	-0.822214	-0.115413	1.000000	0.972044	-0.686571
highway-mpg	0.036233	-0.181877	-0.543304	-0.698142	-0.680635	-0.104812	-0.794889	-0.679571	-0.591309	-0.034636	0.268465	-0.804575	-0.058598	0.972044	1.000000	-0.704692
price	-0.082391	0.133999	0.584642	0.690628	0.751265	0.135486	0.834415	0.872335	0.543155	0.082269	0.071107	0.809575	-0.101616	-0.686571	-0.704692	1.000000

plt.figure(figsize=(8, 8))
corrplot(data.corr(), size_scale=300)