Skip to main content

Creating Basic Plots with Matplotlib

Matplotlib is the foundation of data visualization in Python, and it offers a variety of plot types to help you understand your data. In this article, we'll explore how to create some of the most common and useful plots: line plots, bar plots, histograms, and scatter plots.

1. Line Plots​

Line plots are one of the most common types of plots. They are useful for visualizing data points in a sequence, often over time.

1.1 Creating a Simple Line Plot​

You can create a line plot using the plt.plot() function.

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 35]

# Creating a line plot
plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Basic Line Plot
Figure 1: Basic Line Plot.

1.2 Customizing Line Plots​

You can customize line plots by changing the color, line style, and adding markers.

# Customizing a line plot
plt.plot(x, y, color='red', linestyle='--', marker='o')
plt.title('Customized Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Customized Line Plot
Figure 2: Customized Line Plot.

2. Bar Plots​

Bar plots are ideal for comparing different categories. They can be vertical or horizontal.

2.1 Creating a Vertical Bar Plot​

A vertical bar plot can be created using the plt.bar() function.

# Sample data
categories = ['A', 'B', 'C', 'D']
values = [5, 7, 8, 6]

# Creating a vertical bar plot
plt.bar(categories, values)
plt.title('Vertical Bar Plot')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.show()

Vertical Bar Plot
Figure 3: Vertical Bar Plot.

2.2 Creating a Horizontal Bar Plot​

To create a horizontal bar plot, use the plt.barh() function.

# Creating a horizontal bar plot
plt.barh(categories, values, color='purple')
plt.title('Horizontal Bar Plot')
plt.xlabel('Values')
plt.ylabel('Categories')
plt.show()

Horizontal Bar Plot
Figure 4: Horizontal Bar Plot.

3. Histograms​

Histograms are used to represent the distribution of a dataset. They show how many data points fall within a certain range of values.

3.1 Creating a Simple Histogram​

You can create a histogram using the plt.hist() function.

# Sample data
data = [1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5]

# Creating a histogram
plt.hist(data, bins=5, color='green')
plt.title('Simple Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

Simple Histogram
Figure 5: Simple Histogram

3.2 Customizing Histograms​

You can adjust the number of bins and the color of the histogram for better visualization.

# Creating a customized histogram
plt.hist(data, bins=4, color='blue', edgecolor='black')
plt.title('Customized Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

Custom Histogram
Figure 6: Custom Histogram

4. Scatter Plots​

Scatter plots are useful for visualizing the relationship between two numerical variables.

4.1 Creating a Simple Scatter Plot​

A scatter plot can be created using the plt.scatter() function.

# Sample data
x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]

# Creating a scatter plot
plt.scatter(x, y)
plt.title('Simple Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Simple Scatter Plot
Figure 7: Simple Scatter Plot

4.2 Customizing Scatter Plots​

You can customize scatter plots by changing the marker style, size, and color.

# Creating a customized scatter plot
plt.scatter(x, y, color='red', marker='x', s=100)
plt.title('Customized Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Customized Scatter Plot
Figure 8: Customized Scatter Plot

5. Conclusion​

Creating basic plots in Matplotlib is straightforward and allows you to visualize your data quickly. By mastering line plots, bar plots, histograms, and scatter plots, you'll be well-equipped to explore your data and communicate your findings effectively. In the next article, we'll delve into customizing plots in Matplotlib to make them more informative and visually appealing.