Python 动态交互式数据可视化实战：Matplotlib、Plotly 与 Bokeh 详解

Python 动态交互式数据可视化实战：Matplotlib、Plotly 与 Bokeh 详解 | 极客日志

pip install matplotlib seaborn plotly bokeh nbinteract imageio ipywidgets pandas numpy scikit-learn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import seaborn as sns
import os
from sklearn.decomposition import PCA
import imageio

# 加载数据集
df = sns.load_dataset('iris')

# 设置 DPI
my_dpi = 96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

# 处理物种列，使其可用于着色
df['species'] = pd.Categorical(df['species'])
my_color = df['species'].cat.codes
df_processed = df.drop('species', axis=1)

# 执行 PCA
pca = PCA(n_components=3)
pca.fit(df_processed)

# 存储结果
result = pd.DataFrame(pca.transform(df_processed), 
                      columns=['PC1', 'PC2', 'PC3'], 
                      index=df_processed.index)

# 生成不同角度的散点图
for angle in range(70, 210, 2):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(result['PC1'], result['PC2'], result['PC3'], 
               c=my_color, cmap="Set2_r", s=60)
    
    # 绘制坐标轴
    xAxisLine = ((min(result['PC1']), max(result['PC1'])), (0, 0), (0, 0))
    ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')
    yAxisLine = ((0, 0), (min(result['PC2']), max(result['PC2'])), (0, 0))
    ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')
    zAxisLine = ((0, 0), (0, 0), (min(result['PC3']), max(result['PC3'])))
    ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')

    ax.view_init(30, angle)
    ax.set_xlabel("PC1")
    ax.set_ylabel("PC2")
    ax.set_zlabel("PC3")
    ax.set_title("PCA Iris Dataset")
    filename = f'./PCA/PCA_angle{angle}.png'
    plt.savefig(filename, dpi=96)
    plt.close(fig)  # 关闭图形以释放内存

# 生成 GIF 函数
def make_gif(input_folder, save_filepath):
    episode_frames = []
    time_per_step = 0.25
    for root, _, files in os.walk(input_folder):
        file_paths = [os.path.join(root, file) for file in files]
        # 按修改时间排序
        file_paths = sorted(file_paths, key=lambda x: os.path.getmtime(x))
        episode_frames = [
            imageio.imread(file_path) 
            for file_path in file_paths if file_path.endswith('.png')
        ]
        episode_frames = np.array(episode_frames)
        imageio.mimsave(save_filepath, episode_frames, duration=time_per_step)

make_gif('./PCA/', './PCA/PCA.gif')

from celluloid import Camera
import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(2)
camera = Camera(fig)
t = np.linspace(0, 5 * np.pi, 128, endpoint=False)
t2 = np.linspace(0, 1 * np.pi, 128, endpoint=False)

for i in t2:
    axes[0].plot(t, np.cos(t/2 + i), color='b')
    axes[1].plot(t2, np.sin(t2 - i), color='b')
    camera.snap()

animation = camera.animate()
animation.save('celluloid_example.gif', writer='imagemagick')

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import datetime

init_notebook_mode(connected=True)

# 读取并预处理数据
df = pd.read_csv("TSLA.csv")
df['date'] = df['date'].astype('str')
df['high'] = df['high'].astype('float64')
df['low'] = df['low'].astype('float64')

# 日期格式转换
date2 = []
for i in df['date']:
    new_date = datetime.datetime.strptime(i, "%d/%m/%Y").strftime("%Y-%m-%d")
    date2.append(new_date)
df['date'] = date2
df.fillna(0)

# 定义轨迹
trace_high = go.Scatter(
    x=df.date,
    y=df['high'],
    name="TSLA High",
    line=dict(color='#17BECF'),
    opacity=0.8
)
trace_low = go.Scatter(
    x=df.date,
    y=df['low'],
    name="TSLA Low",
    line=dict(color='#7F7F7F'),
    opacity=0.8
)
data = [trace_high, trace_low]

# 布局配置
layout = dict(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label='1m', step='month', stepmode='backward'),
                dict(count=6, label='6m', step='month', stepmode='backward'),
                dict(step='all')
            ])
        ),
        rangeslider=dict(visible=True),
        type='date'
    )
)
fig = dict(data=data, layout=layout)
iplot(fig, filename="Time Series with Rangeslider")
plotly.offline.plot(fig, filename='Plotly_Stock.html')

import pandas as pd
import numpy as np
from bokeh.plotting import figure
from bokeh.io import output_file, show, output_notebook
from bokeh.models import CustomJS
from bokeh.models.widgets import CheckboxGroup
from bokeh.layouts import row
from bokeh.palettes import Viridis4
from bokeh.models.annotations import Title, Legend

output_notebook()

# 读取数据
df = pd.read_csv("TSLA.csv")
df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')

p = figure(x_axis_type='datetime', plot_width=800)
aline = p.line(df['date'], df['high'], line_width=2, color=Viridis4[0])
bline = p.line(df['date'], df['low'], line_width=2, color=Viridis4[1])
cline = p.line(df['date'], df['open'], line_width=2, color=Viridis4[2])
dline = p.line(df['date'], df['close'], line_width=2, color=Viridis4[3])

p.yaxis.axis_label = 'Price'
p.xaxis.axis_label = 'Time Span'

legend = Legend(items=[
    ("High Price", [aline]),
    ("Low Price", [bline]),
    ("Open Price", [cline]),
    ("Close Price", [dline])
], location=(0, 450))

t = Title()
t.text = 'Tesla Stock Market Analysis'
p.title = t
p.add_layout(legend, 'left')

checkboxes = CheckboxGroup(labels=list(['High Price', 'Low Price', 'Open Price', 'Close Price']), active=[0, 1, 2, 3])

callback = CustomJS(code="""
aline.visible = false;
bline.visible = false;
cline.visible = false;
dline.visible = false;
// cb_obj is injected in thanks to the callback
if (cb_obj.active.includes(0)){aline.visible = true;}
if (cb_obj.active.includes(1)){bline.visible = true;}
if (cb_obj.active.includes(2)){cline.visible = true;}
if (cb_obj.active.includes(3)){dline.visible = true;}""",
args={'aline': aline, 'bline': bline, 'cline': cline, 'dline': dline})

checkboxes.js_on_click(callback)
output_file('Tesla_Stock_Widget.html')
show(row(p, checkboxes))

import seaborn as sns
sns.set(style="ticks")
df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")

from ipywidgets import interact
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import nbinteract as nbi

def cars(name, number):
    return '{} has {} cars!'.format(name, number)

interact(cars, name='Nemo', number={'Zero': 0, 'Two': 2, 'Three': 3, 'Ten': 10});

库名	特点	适用场景
Matplotlib	基础、灵活、静态为主	学术论文、静态报告、自定义绘图
Plotly	交互性强、Web 友好	网页展示、交互式报表、金融数据
Bokeh	仪表板、组件丰富	实时数据监控、Web 应用后台
Seaborn	统计美观、基于 Matplotlib	统计分析、探索性数据分析 (EDA)
nbinteract	Notebook 原生	教学演示、快速原型验证

Python 动态交互式数据可视化实战：Matplotlib、Plotly 与 Bokeh 详解

Python 动态交互式数据可视化实战

前言

环境准备

Matplotlib：基础绘图与动画

1. 3D PCA 方差 GIF 图

2. Celluloid 简化动画

Plotly：交互式 Web 图表

1. 特斯拉股票时间序列

Bokeh：仪表板与组件

1. 交互式复选框控制

Seaborn：统计图表

Pair Plot 示例

nbinteract：Jupyter Notebook 交互

其他常用库

总结与对比

更多推荐文章

相关免费在线工具

Python 动态交互式数据可视化实战：Matplotlib、Plotly 与 Bokeh 详解

Python 动态交互式数据可视化实战

前言

环境准备

Matplotlib：基础绘图与动画

1. 3D PCA 方差 GIF 图

2. Celluloid 简化动画

Plotly：交互式 Web 图表

1. 特斯拉股票时间序列

Bokeh：仪表板与组件

1. 交互式复选框控制

Seaborn：统计图表

Pair Plot 示例

nbinteract：Jupyter Notebook 交互

其他常用库

总结与对比

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更多推荐文章

相关免费在线工具