When working with Python, it is common to encounter various challenges and questions. One such question that beginners often face is related to plotting scatter plots with a normal distribution. This article aims to provide different solutions to this problem, each with its own approach and advantages.

Solution 1: Using Matplotlib

One way to solve this problem is by utilizing the Matplotlib library, which is a popular data visualization tool in Python. Here is a sample code that demonstrates how to plot a scatter plot with a normal distribution using Matplotlib:

import matplotlib.pyplot as plt
import numpy as np

# Generate random data
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)

# Plot scatter plot
plt.scatter(x, y)

# Show the plot
plt.show()

This code snippet first imports the necessary libraries, Matplotlib and NumPy. It then generates random data using the NumPy function `np.random.normal()`, which creates an array of random numbers following a normal distribution. Finally, it plots the scatter plot using `plt.scatter()` and displays it using `plt.show()`.

Solution 2: Using Seaborn

Another approach to solving this problem is by utilizing the Seaborn library, which is built on top of Matplotlib and provides additional functionality for statistical data visualization. Here is a sample code that demonstrates how to plot a scatter plot with a normal distribution using Seaborn:

import seaborn as sns
import numpy as np

# Generate random data
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)

# Plot scatter plot
sns.scatterplot(x, y)

# Show the plot
plt.show()

This code snippet first imports the necessary libraries, Seaborn and NumPy. It then generates random data using the NumPy function `np.random.normal()`. Instead of using Matplotlib’s `plt.scatter()`, it uses Seaborn’s `sns.scatterplot()` to plot the scatter plot. Finally, it displays the plot using `plt.show()`.

Solution 3: Using Plotly

A third option to solve this problem is by utilizing the Plotly library, which provides interactive and web-based data visualization capabilities. Here is a sample code that demonstrates how to plot a scatter plot with a normal distribution using Plotly:

import plotly.express as px
import numpy as np

# Generate random data
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)

# Create scatter plot figure
fig = px.scatter(x=x, y=y)

# Show the plot
fig.show()

This code snippet first imports the necessary libraries, Plotly and NumPy. It then generates random data using the NumPy function `np.random.normal()`. Instead of using Matplotlib or Seaborn, it uses Plotly’s `px.scatter()` to create the scatter plot figure. Finally, it displays the plot using `fig.show()`.

After considering these three solutions, it is important to note that the best option depends on the specific requirements and preferences of the user. Matplotlib is a versatile library with extensive customization options, making it suitable for various scenarios. Seaborn, on the other hand, provides a higher-level interface and is particularly useful for statistical data visualization. Plotly offers interactive and web-based capabilities, making it ideal for creating dynamic visualizations. Therefore, the choice between these options should be based on the specific needs of the project.