全面解析Python中的数据可视化与交互式分析工具

import matplotlib.pyplot as plt
import numpy as np

# 生成数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图表
plt.plot(x, y, label='Sine Wave')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Sine Wave Plot')
plt.legend()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# 加载示例数据集
tips = sns.load_dataset("tips")

# 创建箱线图
sns.boxplot(x="day", y="total_bill", data=tips)
plt.title('Boxplot of Total Bill by Day')
plt.show()

import plotly.express as px

# 加载示例数据集
df = px.data.iris()

# 创建散点图
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species',
                 title='Sepal Width vs. Sepal Length')
fig.show()

from bokeh.plotting import figure, show
from bokeh.io import output_notebook
import numpy as np

# 在notebook中显示图表
output_notebook()

# 生成数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 创建图表
p = figure(title="Simple Line Plot", x_axis_label='X-axis', y_axis_label='Y-axis')
p.line(x, y, legend_label='Sine Wave', line_width=2)

# 显示图表
show(p)

import matplotlib.pyplot as plt
import numpy as np

# 创建一个2x2的子图布局
fig, axs = plt.subplots(2, 2)

# 在第一个子图中绘制正弦波
x = np.linspace(0, 10, 100)
y = np.sin(x)
axs[0, 0].plot(x, y)

# 在第二个子图中绘制余弦波
y = np.cos(x)
axs[0, 1].plot(x, y)

# 在第三个子图中绘制正切波
y = np.tan(x)
axs[1, 0].plot(x, y)

# 在第四个子图中绘制正弦和余弦波
axs[1, 1].plot(x, np.sin(x), label='Sine')
axs[1, 1].plot(x, np.cos(x), label='Cosine')
axs[1, 1].legend()

plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# 使用不同的样式主题
sns.set_style("whitegrid")

# 加载示例数据集
tips = sns.load_dataset("tips")

# 创建一个带有分类条形图的图表
sns.barplot(x="day", y="total_bill", data=tips)
plt.title('Total Bill by Day')
plt.show()

import plotly.graph_objects as go
import numpy as np

# 生成数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
x, y = np.meshgrid(x, y)
z = np.sin(np.sqrt(x**2 + y**2))

# 创建3D表面图
fig = go.Figure(data=[go.Surface(z=z)])
fig.update_layout(title='3D Surface Plot')
fig.show()

import plotly.express as px

# 加载示例数据集
df = px.data.iris()

# 创建一个散点图，添加悬停信息
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species',
                 title='Sepal Width vs. Sepal Length with Hover',
                 hover_data=['petal_width', 'petal_length'])
fig.show()