Introduction to common libraries and extensions for Python drawing charts

Python is a powerful and easy-to-use programming language that makes data visualization easier thanks to its rich charting library. In this article, we will introduce several commonly used Python chart drawing libraries and some of their extensions, and also provide some specific code examples.

1. Matplotlib
   Matplotlib is one of the most classic and widely used charting libraries in Python. It offers a wide variety of chart types, including line charts, scatter charts, bar charts, pie charts, and more. The following is a simple example showing how to use Matplotlib to draw a simple line chart:

import matplotlib.pyplot as plt

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

plt.plot(x, y)
plt.xlabel('x轴')
plt.ylabel('y轴')
plt.title('简单折线图')
plt.show()

2. Seaborn
   Seaborn is a statistical graphics library built on top of Matplotlib. Some advanced chart types and more beautiful default styles are provided. The following is an example code for a scatter plot with trend lines and confidence intervals drawn using Seaborn:

import seaborn as sns

tips = sns.load_dataset('tips')

sns.regplot(x='total_bill', y='tip', data=tips)
plt.xlabel('总账单')
plt.ylabel('小费')
plt.title('账单金额和小费之间的关系')
plt.show()

3. Plotly
   Plotly is an interactive chart drawing library that can For creating beautiful and interactive charts. It supports drawing various types of charts, including scatter plots, bar charts, area charts, etc. The following is an example code for drawing a histogram using Plotly:

import plotly.express as px

df = px.data.tips()

fig = px.bar(df, x='day', y='total_bill', color='sex', barmode='group')
fig.show()

4. ggplot
   ggplot is a Python implementation based on the famous ggplot2 package in R language, which provides a simple And flexible way to draw various types of charts. The following is a sample code for a scatter plot drawn using ggplot:

from ggplot import *

df = mpg

ggplot(aes(x='displ', y='hwy', color='class'), data=df) +
    geom_point() +
    xlab('发动机排量') +
    ylab('高速公路里程') +
    ggtitle('散点图') +
    theme_bw()

The above is just a brief introduction to several common Python chart drawing libraries. In fact, there are many other libraries, such as Bokeh, Altair, Pygal, etc. Depending on different needs, you can choose different libraries to draw charts.

To sum up, the Python chart drawing library provides rich functions and flexible options, allowing us to better understand and display data through visualization. By using these libraries, we can easily create various types of charts and we can also customize and adjust them according to our needs. I hope this article can help readers have a preliminary understanding of the Python chart drawing library and deepen their impression through the code examples provided.

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