Simple Line Plot using Matplotlib
A simple line plot in Matplotlib is a basic visualization that represents the relationship between two variables (usually denoted as X and Y) using a continuous line. It’s commonly used to display trends, patterns, or changes over time.
Here’s how you can create a simple line plot using Matplotlib in Python:
import numpy as np
# Define data values
x_values = np.array([1, 2, 3, 4]) # X-axis points
y_values = x_values * 2 # Y-axis points (twice the corresponding x-values)
# Create the line plot
plt.plot(x_values, y_values)
# Add labels and title
plt.xlabel(“X-axis“)
plt.ylabel(“Y-axis“)
plt.title(“Simple Line Plot“)
# Display the plot
plt.show()
In this example:
We use NumPy to define the x-values (evenly spaced points from 1 to 4).
The y-values are calculated as twice the corresponding x-values.
The plt.plot() function creates the line plot.
We set labels for the axes and a title for the plot.
If you’d like to see more examples or explore different line plot styles, let me know! 🚀
Object-Oriented API
Let’s delve into the object-oriented API in Matplotlib.
Object-Oriented Interface (OO):
The object-oriented API gives you more control and customization over your plots.
It involves working directly with Matplotlib objects, such as Figure and Axes.
You create a Figure and one or more Axes explicitly, then use methods on these objects to add data, configure limits, set labels, etc.
This approach is more flexible and powerful, especially for complex visualizations.
Now, let’s create a simple example using the object-oriented interface. We’ll plot the distance traveled by an object under free-fall with respect to time.
import matplotlib.pyplot as plt
# Generate data points
time = np.arange(0., 10., 0.2)
g = 9.8 # Acceleration due to gravity (m/s^2)
velocity = g * time
distance = 0.5 * g * np.power(time, 2)
# Create a Figure and Axes
fig, ax = plt.subplots(figsize=(9, 7), dpi=100)
# Plot distance vs. time
ax.plot(time, distance, ‘bo-‘, label=“Distance“)
ax.set_xlabel(“Time“)
ax.set_ylabel(“Distance“)
ax.grid(True)
ax.legend()
# Show the plot
plt.show()
In this example:
We create a Figure using plt.subplots() and obtain an Axes object (ax).
The ax.plot() method is used to plot the distance data.
We customize the plot by setting labels, grid, and adding a legend.
Feel free to explore more features of the object-oriented API for richer and more complex visualizations! 🚀
The Subplot() function
The plt.subplot() function in Matplotlib allows you to create multiple subplots within a single figure. You can arrange these subplots in a grid, specifying the number of rows and columns. Here’s how it works:
Creating Subplots:
The plt.subplot() function takes three integer arguments: nrows, ncols, and index.
nrows represents the number of rows in the grid.
ncols represents the number of columns in the grid.
index specifies the position of the subplot within the grid (starting from 1).
The function returns an Axes object representing the subplot.
Example:
Let’s create a simple figure with two subplots side by side:
import numpy as np
# Create some sample data
x = np.array([0, 1, 2, 3])
y1 = np.array([3, 8, 1, 10])
y2 = np.array([10, 20, 30, 40])
# Create a 1×2 grid of subplots
plt.subplot(1, 2, 1) # First subplot
plt.plot(x, y1, label=“Plot 1“)
plt.xlabel(“X-axis“)
plt.ylabel(“Y-axis“)
plt.title(“Subplot 1“)
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2) # Second subplot
plt.plot(x, y2, label=“Plot 2“, color=“orange“)
plt.xlabel(“X-axis“)
plt.ylabel(“Y-axis“)
plt.title(“Subplot 2“)
plt.grid(True)
plt.legend()
plt.tight_layout() # Adjust spacing between subplots
plt.show()
In this example:
We create a 1×2 grid of subplots using plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2).
Each subplot contains a simple line plot with different data (y1 and y2).
We customize the labels, titles, and grid for each subplot.
Feel free to explore more complex arrangements by adjusting the nrows and ncols parameters! 📊🔍
