Original examples and case studies of Python drawing charts

Original examples and case analysis of Python charting

Introduction:
Python is a widely used programming language with powerful data processing and visualization capabilities . Charts are one of the most common visualization tools in fields such as data analysis, scientific research, and business decision-making. This article will introduce how to use Python to draw charts through specific examples and case analysis, and attach detailed code examples.

1. Line chart example
The line chart is a commonly used visual expression, suitable for showing the trend of data changes over time or other variables.

Example 1:
Suppose a company's sales were recorded in the past year, and we use a line chart to show the changes in sales over time.

import matplotlib.pyplot as plt

# 销售额数据
sales = [100, 150, 120, 180, 200, 250, 300, 280, 350, 400, 380, 450]

# 月份数据
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

# 绘制折线图
plt.plot(months, sales)
plt.title('Sales Trend')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.show()

Run the above code to generate a line chart showing the sales trend over time.

Example 2:
Based on Example 1, we also recorded the sales of different product lines and need to show the trends of each product line.

import matplotlib.pyplot as plt

# 产品销售额数据
product_a = [100, 150, 120, 180, 200, 250, 300, 280, 350, 400, 380, 450]
product_b = [80, 120, 90, 150, 170, 200, 230, 210, 260, 300, 280, 330]
product_c = [70, 90, 80, 120, 150, 180, 200, 190, 220, 270, 250, 300]

# 月份数据
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

# 绘制折线图
plt.plot(months, product_a, label='Product A')
plt.plot(months, product_b, label='Product B')
plt.plot(months, product_c, label='Product C')
plt.title('Sales Trend by Product')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.legend()
plt.show()

Run the above code to generate a line chart showing the sales trends of each product line over time. Through the legend, you can clearly see the sales of each of the three products.

2. Histogram Example
Histogram is a commonly used visual expression, suitable for comparing values ​​between different categories or variables.

Example 3:
Assume that a company's annual sales are recorded, and we use a bar chart to display the annual sales.

import matplotlib.pyplot as plt

# 销售额数据
sales = [1000, 1200, 1500, 1800, 2000]

# 年份数据
years = ['2014', '2015', '2016', '2017', '2018']

# 绘制柱状图
plt.bar(years, sales)
plt.title('Annual Sales')
plt.xlabel('Year')
plt.ylabel('Sales ($)')
plt.show()

Run the above code to generate a histogram showing annual sales.

Example 4:
Based on Example 3, we also recorded the sales of different product lines, and we need to show the annual sales of each product line.

import matplotlib.pyplot as plt
import numpy as np

# 产品销售额数据
product_a = [1000, 1200, 1500, 1800, 2000]
product_b = [800, 900, 1200, 1500, 1700]
product_c = [600, 800, 1000, 1200, 1400]

# 年份数据
years = ['2014', '2015', '2016', '2017', '2018']

# 绘制柱状图
x = np.arange(len(years))
width = 0.2

plt.bar(x - width, product_a, width, label='Product A')
plt.bar(x, product_b, width, label='Product B')
plt.bar(x + width, product_c, width, label='Product C')

plt.title('Annual Sales by Product')
plt.xlabel('Year')
plt.ylabel('Sales ($)')
plt.xticks(x, years)
plt.legend()
plt.show()

Run the above code to generate a histogram showing the annual sales of each product line. Through different colors of columns and legends, you can clearly compare the sales of each product in each year.

Conclusion:
Charts are an important part of data visualization and can help us better understand and analyze data. Python provides a rich and powerful drawing library. This article introduces the method of using Python to draw line charts and column charts through examples and case analysis, and provides specific code examples. I hope readers can better use Python for data visualization through the guidance of this article.

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