Python数据可视化库:Matplotlib、Seaborn、Plotly、Bokeh等对比与选择

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.figure(figsize=(10, 6))
plt.plot(x, y, label='sin(x)')
plt.title('Sine Wave')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.legend()
plt.grid(True)
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# 生成数据
np.random.seed(42)
data = pd.DataFrame({
    'x': np.random.normal(0, 1, 1000),
    'y': np.random.normal(0, 1, 1000),
    'category': np.random.choice(['A', 'B', 'C'], 1000)
})

# 创建散点图
plt.figure(figsize=(10, 6))
sns.scatterplot(x='x', y='y', hue='category', data=data)
plt.title('Scatter Plot with Categories')
plt.show()

# 创建直方图
plt.figure(figsize=(10, 6))
sns.histplot(data['x'], kde=True)
plt.title('Histogram with KDE')
plt.show()

import plotly.express as px
import pandas as pd
import numpy as np

# 生成数据
np.random.seed(42)
data = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y': np.sin(x),
    'z': np.cos(x)
})

# 创建交互式线图
fig = px.line(data, x='x', y=['y', 'z'], title='Interactive Line Plot')
fig.show()

# 创建散点图
fig = px.scatter(data, x='x', y='y', size='z', color='z', title='Interactive Scatter Plot')
fig.show()

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

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

# 创建图表
p = figure(title='Sine Wave', x_axis_label='x', y_axis_label='sin(x)', plot_width=800, plot_height=400)
p.line(x, y, line_width=2, color='blue')

# 显示图表
output_notebook()
show(p)

import altair as alt
import pandas as pd
import numpy as np

# 生成数据
np.random.seed(42)
data = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y': np.sin(x),
    'category': np.random.choice(['A', 'B'], 100)
})

# 创建图表
chart = alt.Chart(data).mark_line().encode(
    x='x',
    y='y',
    color='category'
).properties(
    title='Line Chart with Categories',
    width=800,
    height=400
)

chart.show()

from pyecharts.charts import Line
from pyecharts import options as opts
import numpy as np

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

# 创建图表
line = Line()
line.add_xaxis(x)
line.add_yaxis("sin(x)", y)
line.set_global_opts(title_opts=opts.TitleOpts(title="Sine Wave"))

# 渲染图表
line.render("sine_wave.html")

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# 加载数据集
iris = sns.load_dataset('iris')

# 查看基本信息
print(iris.head())
print(iris.describe())

# 绘制配对图
plt.figure(figsize=(12, 10))
sns.pairplot(iris, hue='species')
plt.title('Pairplot of Iris Dataset')
plt.show()

# 绘制箱线图
plt.figure(figsize=(12, 6))
sns.boxplot(data=iris, orient='h')
plt.title('Boxplot of Iris Features')
plt.show()

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd
import numpy as np

# 生成数据
np.random.seed(42)
data = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y': np.sin(np.linspace(0, 10, 100)),
    'z': np.cos(np.linspace(0, 10, 100)),
    'category': np.random.choice(['A', 'B', 'C'], 100)
})

# 创建 Dash 应用
app = dash.Dash(__name__)

# 布局
app.layout = html.Div([
    html.H1('Interactive Dashboard'),
    
    html.Div([
        dcc.Graph(
            id='line-chart',
            figure=px.line(data, x='x', y=['y', 'z'], title='Line Chart')
        )
    ]),
    
    html.Div([
        dcc.Graph(
            id='scatter-chart',
            figure=px.scatter(data, x='x', y='y', color='category', title='Scatter Plot')
        )
    ]),
    
    html.Div([
        dcc.Graph(
            id='histogram',
            figure=px.histogram(data, x='y', color='category', title='Histogram')
        )
    ])
])

if __name__ == '__main__':
    app.run_server(debug=True)

import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl

# 设置全局样式
mpl.rcParams['font.family'] = 'Times New Roman'
mpl.rcParams['font.size'] = 12
mpl.rcParams['figure.figsize'] = (8, 6)
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['axes.titlesize'] = 14
mpl.rcParams['axes.labelsize'] = 12
mpl.rcParams['xtick.labelsize'] = 10
mpl.rcParams['ytick.labelsize'] = 10
mpl.rcParams['legend.fontsize'] = 10

# 生成数据
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) + np.cos(x)

# 创建图表
fig, ax = plt.subplots()

ax.plot(x, y1, label='sin(x)', color='blue')
ax.plot(x, y2, label='cos(x)', color='red')
ax.plot(x, y3, label='sin(x) + cos(x)', color='green')

ax.set_title('Trigonometric Functions')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(loc='upper right')
ax.grid(True, linestyle='--', alpha=0.7)

# 保存图表
plt.tight_layout()
plt.savefig('trigonometric_functions.png', dpi=300, bbox_inches='tight')
plt.show()