How to draw 2D Heatmap using Matplotlib in python?
Last Updated : 27 May, 2025
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. 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.
Let’s see an example: We will use the tips dataset which is an inbuilt dataset. This dataset contains information about restaurant tips, total bill amount, tip amount, customer details like sex and day of the week etc. Also, we will be using Seaborn and Matplotlib libraries for this.
Python import seaborn as sns import matplotlib.pyplot as plt df = sns.load_dataset("tips") heatmap_data = df.pivot_table(index="day", columns="sex", values="tip", aggfunc="mean") sns.heatmap(heatmap_data, annot=True, cmap="YlGnBu") plt.show() Output
HeatmapSyntax
seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, annot=None, fmt='.2g', linewidths=0, linecolor='white', cbar=True, square=False, **kwargs)
Parameters:
data: Rectangular dataset (2D array, DataFrame, or similar) used to draw the heatmap.vmin, vmax: Values to anchor the colormap, otherwise inferred from the data.cmap: Colormap used to map data values to colors (e.g., "coolwarm", "YlGnBu").annot: If True, write the data value in each cell; can also be a DataFrame of strings.fmt: String formatting code to use when adding annotations.linewidths: Width of the lines that will divide each cell.linecolor: Color of the lines dividing the cells.cbar: Boolean value to display the color bar.square: If True, set the Axes aspect to “equal” so each cell will be square-shaped.
Returns: It returns a matplotlib Axes object with the heatmap drawn onto it.
Example 1- 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.
Python # Program to plot 2-D Heat map # using matplotlib.pyplot.imshow() method import numpy as np import matplotlib.pyplot as plt data = np.random.random((12, 12)) plt.imshow(data, cmap='autumn') plt.title("Heatmap with different color") plt.show() Output:
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heatmap with camp parameterExample 2- 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.
Python data = np.random.random((12, 12)) plt.imshow(data, cmap='autumn', interpolation='nearest') # Add colorbar plt.colorbar() plt.title("Heatmap with color bar") plt.show() Output:
.png)
Heatmap with colorbar scaleExample 3 - 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.
Python import matplotlib.colors as colors # Generate random data data = np.random.randint(0, 100, size=(8, 8)) # Create a custom color map # with blue and green colors colors_list = ['#0099ff', '#33cc33'] cmap = colors.ListedColormap(colors_list) # Plot the heatmap with custom colors and annotations plt.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 colorbar cbar = plt.colorbar(ticks=[0, 50, 100]) cbar.ax.set_yticklabels(['Low', 'Medium', 'High']) # Set plot title and axis labels plt.title("Customized heatmap with annotations") plt.xlabel("X-axis") plt.ylabel("Y-axis") # Display the plot plt.show() Output:
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advance customized heatmap using matplotlib libraryExample 4- 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.
Python import pandas as pd import matplotlib.pyplot as plt from matplotlib import colors df = pd.read_csv("gold_price_data.csv") # Calculate correlation between columns corr_matrix = df.corr() # Create a custom color # map with blue and green colors colors_list = ['#FF5733', '#FFC300'] cmap = colors.ListedColormap(colors_list) # Plot the heatmap with custom colors and annotations plt.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 colorbar cbar = plt.colorbar(ticks=[0, 0.5, 1]) cbar.ax.set_yticklabels(['Low', 'Medium', 'High']) # Set plot title and axis labels plt.title("Correlation Matrix Of The Dataset") plt.xlabel("Features") plt.ylabel("Features") # Set tick labels plt.xticks(range(len(corr_matrix.columns)),\ corr_matrix.columns, rotation=90) plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns) # Display the plot plt.show() Output:
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Correlation Matrix of the DatasetExample 5- 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.
Python # importing the modules import numpy as np import seaborn as sns import matplotlib.pyplot as plt # generating 2-D 10x10 matrix of random numbers # from 1 to 100 data = np.random.randint(low=1, high=100, size=(10, 10)) # plotting the heatmap hm = sns.heatmap(data=data, annot=True) # displaying the plotted heatmap plt.show()
Output:
Heatmap using seabornUse 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.