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Lohith
Lohith

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Creating Line Plots with Object-Oriented API and Subplot Function in Python

#numpy #beginners #matplotlib #python

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 matplotlib.pyplot as plt 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()
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Figure 1

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 numpy as np 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()
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Figure 2

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:

  1. 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.

  2. Example:
    Let's create a simple figure with two subplots side by side:

import matplotlib.pyplot as plt 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 1x2 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()
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Figure 3

In this example:

  • We create a 1x2 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! 📊🔍

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