Multiple graphs

Multiple graphs

Multiple graphs in Python is important for comprehensive data analysis. By plotting different datasets or variables together, you can compare them easily, visualize relationships, and communicate findings effectively. Multiple graphs enhance the clarity of your analysis, enabling you to uncover insights that might be hidden in a single graph. They help you tell a compelling story about your data, guiding your audience through key findings and conclusions succinctly.

 

Here is the example of Multiple graphs

 

# Create figure and axes
fig, ax = plt.subplots(2, 2)

# Plot graphs
ax[0, 0].plot(np.linspace(0, 100, 20), np.linspace(0, 100, 20)**2, marker='o', markersize=2, markeredgecolor='y')
ax[0, 1].hist(np.random.randn(500), bins=30, color='purple', alpha=0.5)
ax[1, 0].bar([1, 3, 5, 7], np.array([1, 2, 3, 4]), color='r')
ax[1, 1].boxplot(np.random.randn(500, 5))

# Add spacing between graphs
fig.subplots_adjust(hspace=0.5, wspace=0.3)

# Add titles to each graph
ax[0, 0].set_title("Line Plot", fontsize=9)
ax[0, 1].set_title("Histogram", fontsize=9)
ax[1, 0].set_title("Bar Plot", fontsize=9)
ax[1, 1].set_title("Box Plot", fontsize=9)

plt.show()

 

 Result