In this article, we will explain about plotting heatmaps using the matplotlib library. A heatmap is a great tool for visualizing data across the surface. It highlights data that have a higher or lower concentration in the data distribution.
Heatmap
A 2-D Heatmap is a data visualization tool that helps to represent the magnitude of the matrix in form of a colored table. In Python, we can plot 2-D Heatmaps using the Matplotlib and Seaborn packages. There are different methods to plot 2-D Heatmaps, some of which are discussed below.
Use Cases For Heatmaps
As we know the Heatmap is just a colored representation of a matrix. However, heatmap has a very large use case. We can use heatmaps for the following purpose.
- It is used to see the correlation between columns of a dataset where we can use a darker color for columns having a high correlation.
- We can also use heatmaps for plotting various time series and finance-related data where the Y-axis will be the month and X-axis will be the year and the element of the heatmap will be our data.
Basic Heatmap Using Python Matplotlib Library
Create a 12×12 Heatmap with Random data using Matplotlib
Here we will plot the heatmap using matplotlib.pyplot.imshow() function.
Syntax: matplotlib.pyplot.imshow(X, cmap=None, alpha=None)
- X :- this is input data matrix which is to be displayed
- cmap :- Colormap we use t dispay the heatmap
- alpha :- it specifies the opacity or transpiracy of the heatmap
Python3
# Program to plot 2-D Heat map# using matplotlib.pyplot.imshow() methodimport numpy as npimport matplotlib.pyplot as pltdata = np.random.random(( 12 , 12 ))plt.imshow( data )plt.title( "2-D Heat Map" )plt.show() |
Output:
Hetamap of random 12×12 matrix
Choosing Different Colormaps in Heatmap Using Matplotlib
We can choose different colors for Heatmap using the cmap parameter. cmap can help us in making our heatmap more informative.
Python3
# Program to plot 2-D Heat map# using matplotlib.pyplot.imshow() methodimport numpy as npimport matplotlib.pyplot as pltdata = np.random.random((12, 12))plt.imshow(data, cmap='autumn')plt.title("Heatmap with different color")plt.show() |
Output:
heatmap with camp parameter
Adding Colorbar to Heatmap Using Matplotlib
we can add a colorbar to the heatmap using plt.colorbar(). colorbar shows the weight of color relatively between a certain range.
Python3
data = np.random.random((12, 12))plt.imshow(data, cmap='autumn', interpolation='nearest')# Add colorbarplt.colorbar()plt.title("Heatmap with color bar")plt.show() |
Output:
Heatmap with colorbar scale
Customized Heatmap Using Matplotlib Library
we can customize this heatmap using different functions and parameters to make it more informative and beautiful. we will use plt.annotate() to annotate values in the heatmap. Also, we will use colors library to customize the color of the heatmap.
Python3
import matplotlib.colors as colors# Generate random datadata = np.random.randint(0, 100, size=(8, 8))# Create a custom color map# with blue and green colorscolors_list = ['#0099ff', '#33cc33']cmap = colors.ListedColormap(colors_list)# Plot the heatmap with custom colors and annotationsplt.imshow(data, cmap=cmap, vmin=0,\ vmax=100, extent=[0, 8, 0, 8])for i in range(8): for j in range(8): plt.annotate(str(data[i][j]), xy=(j+0.5, i+0.5), ha='center', va='center', color='white')# Add colorbarcbar = plt.colorbar(ticks=[0, 50, 100])cbar.ax.set_yticklabels(['Low', 'Medium', 'High'])# Set plot title and axis labelsplt.title("Customized heatmap with annotations")plt.xlabel("X-axis")plt.ylabel("Y-axis")# Display the plotplt.show() |
Output:
advance customized heatmap using matplotlib library
Plotting Correlation Matrix of a Dataset Using Heatmap
Next, we will use a heatmap to plot the correlation between columns of the dataset. We will use correlation to find the relation between columns of the dataset. We can download the dataset from here.
Python3
import pandas as pdimport matplotlib.pyplot as pltfrom matplotlib import colorsdf = pd.read_csv("gold_price_data.csv")# Calculate correlation between columnscorr_matrix = df.corr()# Create a custom color# map with blue and green colorscolors_list = ['#FF5733', '#FFC300']cmap = colors.ListedColormap(colors_list)# Plot the heatmap with custom colors and annotationsplt.imshow(corr_matrix, cmap=cmap, vmin=0\ , vmax=1, extent=[0, 5, 0, 5])for i in range(5): for j in range(5): plt.annotate(str(round(corr_matrix.values[i][j], 2)),\ xy=(j+0.25, i+0.7), ha='center', va='center', color='white')# Add colorbarcbar = plt.colorbar(ticks=[0, 0.5, 1])cbar.ax.set_yticklabels(['Low', 'Medium', 'High'])# Set plot title and axis labelsplt.title("Correlation Matrix Of The Dataset")plt.xlabel("Features")plt.ylabel("Features")# Set tick labelsplt.xticks(range(len(corr_matrix.columns)),\ corr_matrix.columns, rotation=90)plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns)# Display the plotplt.show() |
output:
Correlation Matrix of the Dataset
Heatmap Using Seaborn Library
We can also use the Seaborn library to plot heatmaps even plotting heatmaps using Seaborn is comparatively easier than the matplotlib library. To plot the heatmap using Seaborn we will use sns.heatmap() function from the Seaborn library.
Python3
# importing the modulesimport numpy as npimport seaborn as snsimport matplotlib.pyplot as plt # generating 2-D 10x10 matrix of random numbers# from 1 to 100data = np.random.randint(low=1, high=100, size=(10, 10)) # plotting the heatmaphm = sns.heatmap(data=data, annot=True) # displaying the plotted heatmapplt.show() |
Output:
Heatmap using seaborn
