Which libraries in Python can be used for data visualization?

As a powerful programming language, Python has a rich data visualization library to help users display data more intuitively and better understand and analyze data. This article will introduce several commonly used Python data visualization libraries and provide specific code examples to help readers better master the use of these libraries.

1. Matplotlib

Matplotlib is one of the most commonly used data visualization libraries in Python. It can create various types of charts, including line charts, scatter charts, histograms, etc. The following is a simple line chart example:

import matplotlib.pyplot as plt

# 创建数据
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 6]

# 绘制折线图
plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

2. Seaborn

Seaborn is a data visualization library based on Matplotlib, providing a simpler interface and more beautiful style. The following is a simple box plot example:

import seaborn as sns
import matplotlib.pyplot as plt

# 创建数据
data = [0, 1, 2, 3, 4, 5]

# 绘制箱线图
sns.boxplot(data=data)
plt.title('Box Plot')
plt.show()

3. Plotly

Plotly is an interactive data visualization library that can create line graphs, scatter plots, heat maps, etc. Various charts within. Here is a simple scatter plot example:

import plotly.express as px

# 创建数据
data = {'x': [1, 2, 3, 4, 5], 'y': [2, 3, 5, 7, 6]}

# 绘制散点图
fig = px.scatter(data, x='x', y='y')
fig.update_layout(title='Scatter Plot')
fig.show()

4. Bokeh

Bokeh is a library for creating interactive charts that can be interacted with on the web and add toolbars. The following is a simple histogram example:

from bokeh.plotting import figure, show

# 创建数据
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 6]

# 绘制柱状图
p = figure(x_axis_label='X', y_axis_label='Y')
p.vbar(x=x, top=y, width=0.5, color='blue')
show(p)

The above are several commonly used Python data visualization libraries and their code examples. Readers can choose the appropriate library to display data according to their own needs, so as to understand and analyze the data more intuitively.

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