Get started quickly: a basic tutorial on drawing charts in Python

Quick Start: Basic Tutorial on Drawing Charts in Python

Introduction:
In the world of data visualization, drawing charts is an important skill. Python is a powerful programming language that provides many libraries and tools to make charting easy and fun. This article will introduce you to basic Python chart drawing skills and provide specific code examples. Let’s get started quickly!

1. Preparation
Before using Python to draw charts, we need to install the matplotlib library. This is a widely used charting library that provides a rich set of visualization functions and tools. You can use the following command to install matplotlib:

pip install matplotlib

2. Draw a line chart
A line chart is a commonly used chart type that can show data trends over time. Here is a simple example that shows the number of user visits per day for a week:

import matplotlib.pyplot as plt # 数据 days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] visits = [100, 120, 90, 80, 95, 130, 110] # 绘制折线图 plt.plot(days, visits) # 设置标题和轴标签 plt.title("Daily Visits") plt.xlabel("Day") plt.ylabel("Visits") # 显示图表 plt.show()

Run the above code, and you will get a line chart showing the number of user visits per day.

3. Draw a bar chart
Bar charts can be used to compare data between different categories or groups. The following example shows the average price of houses in three cities:

import matplotlib.pyplot as plt # 数据 cities = ['New York', 'London', 'Tokyo'] prices = [3400, 2500, 3800] # 绘制条形图 plt.bar(cities, prices) # 设置标题和轴标签 plt.title("Average House Prices") plt.xlabel("City") plt.ylabel("Price") # 显示图表 plt.show()

4. Draw a scatter plot
A scatter plot can show the relationship between two variables. The following example shows the relationship between students' math scores and physics scores:

import matplotlib.pyplot as plt # 数据 math_scores = [85, 90, 92, 88, 79, 95, 87, 92, 78, 82] physics_scores = [79, 82, 78, 85, 88, 90, 92, 85, 89, 92] # 绘制散点图 plt.scatter(math_scores, physics_scores) # 设置标题和轴标签 plt.title("Math vs. Physics Scores") plt.xlabel("Math Score") plt.ylabel("Physics Score") # 显示图表 plt.show()

5. Draw a pie chart
A pie chart can show the proportion of different categories. The following example shows the use of three modes of transportation:

import matplotlib.pyplot as plt # 数据 labels = ['Car', 'Bus', 'Bike'] usage = [70, 15, 15] # 绘制饼图 plt.pie(usage, labels=labels, autopct='%1.1f%%') # 设置标题 plt.title("Transportation Usage") # 显示图表 plt.show()

Conclusion:
This article introduces the basic skills of drawing charts in Python and provides specific code examples. By learning these basics, you can start your own data visualization journey. I hope this article was helpful to you, and I wish you have fun in the world of Python charting!

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