Quick Start: Drawing Statistical Charts with Python
Title: Quick Start: Using Python to Draw Statistical Charts, with Specific Code Examples
Article:
Drawing Statistical Charts is Data Analysis and Data Visualization one of the important links. As a powerful and easy-to-learn programming language, Python provides a variety of drawing libraries, such as Matplotlib and Seaborn. This article will introduce how to use Python to draw various common statistical charts through specific code examples.
- Line chart
A line chart is one of the most common statistical charts used to show trends over time, categories, or other variables. The following is a sample code for drawing a line chart using the Matplotlib library:
import matplotlib.pyplot as plt # 准备数据 x = [1, 2, 3, 4, 5] y = [10, 15, 7, 12, 9] # 绘制折线图 plt.plot(x, y) # 添加标题和标签 plt.title("折线图示例") plt.xlabel("X轴") plt.ylabel("Y轴") # 显示图表 plt.show()
- Bar chart
Bar charts are often used to compare the size of data between different categories. The following is a sample code for using the Matplotlib library to draw a histogram:
import matplotlib.pyplot as plt # 准备数据 x = ["A", "B", "C", "D", "E"] y = [10, 15, 7, 12, 9] # 绘制柱状图 plt.bar(x, y) # 添加标题和标签 plt.title("柱状图示例") plt.xlabel("类别") plt.ylabel("数值") # 显示图表 plt.show()
- pie chart
Pie charts are often used to represent data proportions and proportional relationships. The following is a sample code for drawing a pie chart using the Matplotlib library:
import matplotlib.pyplot as plt # 准备数据 labels = ["A", "B", "C", "D"] sizes = [30, 20, 25, 15] # 绘制饼图 plt.pie(sizes, labels=labels, autopct='%1.1f%%') # 添加标题 plt.title("饼图示例") # 显示图表 plt.show()
- Scatter plot
A scatter plot is used to represent the relationship between two variables. The following is a sample code for drawing a scatter plot using the Seaborn library:
import seaborn as sns # 准备数据 x = [1, 2, 3, 4, 5] y = [10, 15, 7, 12, 9] # 绘制散点图 sns.scatterplot(x, y) # 添加标题和标签 plt.title("散点图示例") plt.xlabel("X轴") plt.ylabel("Y轴") # 显示图表 plt.show()
The above sample code only covers common statistical chart types, and only a small part of its functions. Python's drawing library provides more options and functions, which can be further studied and practiced according to specific needs.
Summary:
This article introduces how to use Python to draw statistical charts through specific code examples. By learning these basic skills and methods, you can freely draw various types of statistical charts according to your own needs to better display and analyze data. I hope this article will help you learn statistical charts!
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