What is heatmap?
A heatmap is a graphical representation of data that uses colors to depict values across a two-dimensional matrix. It is a visual summary of information, with colors ranging from cool to warm tones, allowing quick identification of patterns, trends, and anomalies. Each color's intensity represents the magnitude of the data at specific points within the matrix.
Benefits of using heatmaps
Data patterns identification: Heatmaps make it easy to identify trends, clusters, and outliers within datasets, providing valuable insights into the underlying data.
User-friendly representation: Heatmaps offer a user-friendly approach to display data, making complex information understandable.
Decision-making support: Businesses can utilize heatmaps to make informed decisions based on visualized data trends and patterns.
Time-saving analysis: Heatmaps simplify data analysis, reducing the time and effort required to interpret data.
Effective communication: Whether in business reports, scientific papers, or presentations, heatmaps enhance communication by presenting information engagingly and memorably.
3D heatmap
Creating a 3D heatmap in Python involves using various libraries. In this example, we define the mathematical function representing the 3D heatmap,
Code
You can experiment with the plot yourself by clicking the "Run" button in the code. After running the code, you will observe an interactive 3D heatmap with user-friendly features. You can click the "Reset Original View" button to restore the initial perspective and use your cursor to rotate and explore the heatmap from different angles.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def my_function(x, y):
return np.sin(x) + np.cos(y)
value_x = np.linspace(-2*np.pi, 2*np.pi, 100)
value_y = np.linspace(-2*np.pi, 2*np.pi, 100)
input1, input2 = np.meshgrid(value_x, value_y)
output = my_function(input1, input2)
view = plt.figure()
ax = view.add_subplot(111, projection='3d')
ax.plot_surface(input1, input2, output, cmap='RdYlBu')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
plt.show()Code explanation
Line 1–3: Import all the necessary libraries and modules.
Line 5–6: Defines a mathematical function that takes two input arguments
xandyand returns the sum ofsine(x)andcos(y).Line 8–9: Generates 100 equally spaced values between
to and assigns them to value_xandvalue_y.Line 10: Creates a 2D grid of
value_xandvalue_yto forminput1andinput2representing thexandycoordinates for the 3D plot.Line 11: Evaluates the
my_functionusinginput1andinput2and stores the resulting values in theoutput.Line 13–15: Creates a 3D plot surface on the subplot
plotwithin the figureview, usinginput1,input2, andoutputdata and applying theRdYlBucolormap for color mapping. Thecmapparameter sets the colormap tocoolwarm, which helps visualize the positive and negative values with contrasting colors.Line 17–19: Sets the labels for the x, y, and z axes, respectively.
Line 21: Display the plot using
plt.show().
Output
Applications
Heatmaps have diverse applications across various fields due to their ability to visually represent complex data patterns and provide valuable insights. Some of the key applications of heatmaps include:
Business analytics: Heatmaps are valuable in analyzing customer behavior, sales patterns, and product performance. They enable businesses to identify trends, customer preferences, and potential market opportunities.
Geographical analysis: Geographical heatmaps represent data on geographic maps, illustrating population densities, crime rates, or environmental factors. These visualizations are valuable in urban planning, disaster management, and resource allocation.
Scientific research: In scientific research, heatmaps visualize and analyze complex data like gene expression levels, brain activity, and climate data. They aid researchers in identifying patterns and correlations, providing insights into various scientific phenomena.
Conclusion
In conclusion, a heatmap is a data visualization technique that uses colors to represent the distribution and intensity of values within a dataset. It provides a clear and concise visual summary, allowing quick identification of patterns, trends, and anomalies, making it a powerful tool for various fields, from business analytics to scientific research.
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