Data visualization example in Python: Scatter plot

With the rapid development of data science and machine learning, more and more programmers and data analysts are beginning to use Python to analyze and visualize data. Python developers have developed API interfaces for many data visualization tools to meet the needs of data visualization and interactive interfaces. This article will introduce an example of data visualization in Python-scatter plot.

1. Introduction to Scatter Chart

Scatter chart is a commonly used data visualization display method, used to show the relationship between two variables. The main purpose of a scatter plot is to discover relationships between variables, or relationships between multiple groups with different orders. Scatter plots can show trend lines or regression lines. If you have multiple variables in your dataset, you can use color or size as additional dimensions.

2. Scatter plots in Python

Python provides many libraries for data visualization, such as Matplotlib, Seaborn, Plotly, etc. These libraries provide various types of visualization charts, including scatter plots.

We will use the Matplotlib library to implement the scatter plot. Matplotlib is a Python library for data visualization. It can create various types of graphs such as line graphs, scatter plots, bar graphs, contour graphs, etc.

3. Example Demonstration

Before implementing the scatter plot, you need to install the Matplotlib library. If you have already installed this library, you can start implementing the scatter plot directly.

1. Import the Matplotlib library

Import the Matplotlib library and give it an alias plt.

import matplotlib.pyplot as plt

2. Create data

Normally, we need to have some data to create a scatter plot. To do this, we create two arrays to store the data for the x and y axes.

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [5, 6, 3, 4, 3, 1, 2, 4, 8, 9]

3. Draw a scatter plot

To draw a scatter plot, we can use the plt.scatter() function. This function accepts x-axis and y-axis data as parameters and can specify other properties such as color, size, etc.

plt.scatter(x, y)
plt.show()

4. Add titles and labels

To add titles and labels, we can use the plt.title(), plt.xlabel() and plt.ylabel() functions.

plt.scatter(x, y)
plt.title('Scatter Plot Example')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.show()

5. Modify the attributes of the scatter plot

To modify the various attributes of the scatter plot, we can use the various parameters provided by the plt.scatter() function.

plt.scatter(x, y, c='red', marker='x', s=200, alpha=0.5)
plt.title('Scatter Plot Example')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.show()

We mentioned some parameters above, the meanings of these parameters are as follows:

• c, specify the color.
• marker, specifies the shape of the scatter points.
• s, specifies the size of the scatter points.
• alpha, specifies the transparency of scatter points.

4. Summary

Through the scatter plot example in this article, we learned how to use the Matplotlib library to create a scatter plot. We created a simple scatter plot using the plt.scatter() function and then added a title and labels. Finally, we changed the properties of the scatter plot and made it more visual.

Python has a wide range of applications, and the continuous growth and development of various libraries and frameworks can help data scientists and engineers easily process and interpret data to support better decision-making.

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