NumPy - Matplotlib

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NumPy and Matplotlib

NumPy is a Python library for numerical computing, providing support for arrays, mathematical functions, and efficient operations on large datasets.

Matplotlib is a Python library for creating static, interactive, and animated visualizations like plots and charts.

They are often used together, as NumPy generates and processes data arrays, while Matplotlib visualizes them. For example, you can use NumPy to create data points and Matplotlib to plot them as graphs.

What is Matplotlib?

Matplotlib is a Python library used to create high-quality plots and charts. It is highly customizable and can produce various types of plots, such as line plots, scatter plots, bar plots, and histograms.

Matplotlib works seamlessly with NumPy, making it easy to visualize numerical data arrays or perform operations before plotting the results.

Setting Up Matplotlib

Before starting with Matplotlib, ensure you have the library installed. You can install it using pip as shown below −

# Install Matplotlib
!pip install matplotlib

Once installed, you can import it alongside NumPy to begin creating visualizations as shown below −

import numpy as np
import matplotlib.pyplot as plt

Now that the libraries are ready, let us dive into various types of visualizations you can create using Matplotlib and NumPy.

Line Plot

A line plot is one of the simplest and most commonly used visualizations. It is used to show trends or relationships between data points.

Example

In the following example, we are creating a line plot using Matplotlib and NumPy −

import numpy as np
import matplotlib.pyplot as plt

# Generate data using NumPy
# 100 evenly spaced points between 0 and 10
x = np.linspace(0, 10, 100)  
# Compute sine values for x
y = np.sin(x)  

# Create a line plot
plt.plot(x, y, label='sin(x)', color='blue', linestyle='--')
plt.title('Line Plot of sin(x)')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.grid(True)
plt.show()

We can see a line plot displaying a sine wave with labeled axes, a legend, and a grid for better visualization −

Scatter Plot

Scatter plots are used to display relationships between two variables by showing individual data points. They are useful for identifying patterns, correlations, or outliers in data.

Example

Following is an example of creating a scatter plot showing random points with a transparent green color −

import numpy as np
import matplotlib.pyplot as plt

# Generate random data
x = np.random.rand(50)
y = np.random.rand(50)

# Create a scatter plot
plt.scatter(x, y, color='green', alpha=0.7)
plt.title('Scatter Plot of Random Points')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

We get the output as shown below −

Bar Plot

Bar plots are used to compare different categories or groups. They display data using rectangular bars, where the height represents the value.

Example

Following is an example of creating a bar plot showing the values for each category with orange-colored bars −

import numpy as np
import matplotlib.pyplot as plt

# Data for bar plot
categories = ['A', 'B', 'C', 'D']
values = [10, 20, 15, 25]

# Create a bar plot
plt.bar(categories, values, color='orange')
plt.title('Bar Plot Example')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.show()

The output obtained is as shown below −

Histogram

Histograms are used to visualize the frequency distribution of a dataset. They divide data into intervals (bins) and show the count of data points in each interval.

Example

Following is an example of creating a histogram displaying the frequency distribution of the randomly generated data −

import numpy as np
import matplotlib.pyplot as plt

# Generate random data
# 1000 samples from a normal distribution
data = np.random.randn(1000)  

# Create a histogram
plt.hist(data, bins=30, color='purple', alpha=0.8)
plt.title('Histogram of Random Data')
plt.xlabel('Bins')
plt.ylabel('Frequency')
plt.show()

After executing the above code, we get the following output −

Pie Chart

Pie charts are used to represent data as slices of a circle, showing proportions or percentages. While not always the best for precise comparisons, they can be visually appealing for specific use cases.

Example

Following is an example of creating a pie chart showing the proportion of each programming language in percentages −

import numpy as np
import matplotlib.pyplot as plt

# Data for pie chart
labels = ['Python', 'Java', 'C++', 'Ruby']
sizes = [50, 30, 15, 5]

# Create a pie chart
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Pie Chart Example')
plt.show()

The result produced is as follows −

Customizing Matplotlib Visualizations

Matplotlib offers extensive options to customize your visualizations. You can adjust colors, line styles, markers, fonts, and more.

Example

Following is an example of creating a customized plot with different line styles, colors, and markers for sine and cosine waves −

import numpy as np
import matplotlib.pyplot as plt

# Customized line plot
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label='sin(x)', color='red', linewidth=2, marker='o')
plt.plot(x, y2, label='cos(x)', color='blue', linestyle='dotted')
plt.title('Customized Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.grid(True, linestyle='--', color='gray', alpha=0.6)
plt.show()

We get the output as shown below −

Combining NumPy and Matplotlib

Using NumPy and Matplotlib together can enhance your analysis and visualization workflow. NumPy can be used to preprocess and manipulate data, while Matplotlib can be used to visualize the results.

For instance, you can generate data transformations or statistical analyses using NumPy and then visualize them with Matplotlib.

Example

Following is an example of creating a plot showing a parabolic curve representing the quadratic function −

import numpy as np
import matplotlib.pyplot as plt

# Generate and visualize a quadratic function
x = np.linspace(-10, 10, 200)
y = x**2 - 5*x + 6

plt.plot(x, y, label='y = x^2 - 5x + 6', color='magenta')
plt.title('Quadratic Function Visualization')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.show()

The output obtained is as shown below −