8  可视化

点击下载本章节代码

8.1 Matplotlib数据可视化

Matplotlib模块是Python最基础的可视化库，可以绘制多种形式的图形，包括线图、直方图、饼状图、散点图、误差线图等等。在我们的工作和学习中，将数据图形化展示是一项非常重要的任务，可以让数据更加直观。

Matplotlib 最重要的特性之一就是具有良好的操作系统兼容性和图形显示底层接口兼容性。

%pip install matplotlib pandas numpy seaborn plotly --upgrade

import matplotlib.pyplot as plt
from matplotlib import animation
import pandas as pd
import numpy as np
import math

t = np.linspace(0, math.pi, 1000)
x = np.sin(t)
y = np.cos(t) + np.power(x, 2.0/3)
plt.plot(x, y, color='red', linewidth=2, label='h')
plt.plot(-x, y, color='red', linewidth=2, label='-h')
plt.xlabel('t')
plt.ylabel('h')
plt.ylim(-2, 2)
plt.xlim(-2, 2)

# plt.show()

设置绘图样式

使用 plt.style 来选择图形的绘图风格。

NoteMatplotlib 样式说明

从 Matplotlib 3.6 版本开始，seaborn 样式已被移除。可以使用以下替代方案：

• seaborn-v0_8 - 使用旧版 seaborn 样式
• default - Matplotlib 默认样式
• ggplot - ggplot2 风格
• bmh - Bayesian Methods for Hackers 样式

# 查看所有可用样式
print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

# 使用 seaborn-v0_8 样式（兼容旧代码）
plt.style.use('seaborn-v0_8')

关于plt.show()

如果你在一个脚本文件中使用 Matplotlib，那么显示图形的时候必须使用 plt.show()，而在notebook中可以不要，特别注意在notebook中画图：

• %matplotlib notebook 会在 Notebook 中启动交互式图形。

• %matplotlib inline 会在 Notebook 中启动静态图形。

import numpy as np
x = np.linspace(0, 10, 100)
fig = plt.figure()
plt.plot(x, np.sin(x), '-')
plt.plot(x, np.cos(x), '--')

fig.savefig('my_figure.png')

from IPython.display import Image
Image('my_figure.png')

fig.savefig('my_figure.pdf')

fig.canvas.get_supported_filetypes()

{'eps': 'Encapsulated Postscript',
 'jpg': 'Joint Photographic Experts Group',
 'jpeg': 'Joint Photographic Experts Group',
 'pdf': 'Portable Document Format',
 'pgf': 'PGF code for LaTeX',
 'png': 'Portable Network Graphics',
 'ps': 'Postscript',
 'raw': 'Raw RGBA bitmap',
 'rgba': 'Raw RGBA bitmap',
 'svg': 'Scalable Vector Graphics',
 'svgz': 'Scalable Vector Graphics',
 'tif': 'Tagged Image File Format',
 'tiff': 'Tagged Image File Format',
 'webp': 'WebP Image Format'}

plt.figure(figsize= (8,6)) # 创建图形
# 创建两个子图中的第一个，设置坐标轴
plt.subplot(2, 1, 1) # (行、列、子图编号)
plt.plot(x, np.sin(x))
# 创建两个子图中的第二个，设置坐标轴
plt.subplot(2, 1, 2)
plt.plot(x, np.cos(x));

fig = plt.figure()
ax = plt.axes()
x = np.linspace(0, 10, 1000)
ax.plot(x, np.sin(x))
ax.plot(x, np.cos(x))

plt.plot(x, x + 0, linestyle='solid')
plt.plot(x, x + 1, linestyle='dashed')
plt.plot(x, x + 2, linestyle='dashdot')
plt.plot(x, x + 3, linestyle='dotted');
# 你可以用下面的简写形式
plt.plot(x, x + 4, linestyle='-') # 实线
plt.plot(x, x + 5, linestyle='--') # 虚线
plt.plot(x, x + 6, linestyle='-.') # 点划线
plt.plot(x, x + 7, linestyle=':'); # 实点线

import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(1000)
plt.hist(data)

(array([  9.,  26.,  93., 174., 257., 249., 122.,  46.,  16.,   8.]),
 array([-3.18364243, -2.51917038, -1.85469833, -1.19022628, -0.52575423,
         0.13871782,  0.80318987,  1.46766192,  2.13213397,  2.79660602,
         3.46107807]),
 <BarContainer object of 10 artists>)

---

8.2 Pandas plot

df = pd.DataFrame(np.random.randn(1000, 4),
                  index=pd.date_range('1/1/2000', periods=1000),
                  columns=list('ABCD'))

df = df.cumsum()
df.head()

	A	B	C	D
2000-01-01	-1.148129	0.919846	0.233806	0.540728
2000-01-02	-0.567908	0.484401	-0.363631	0.858367
2000-01-03	-1.167174	1.308936	-1.344971	0.673636
2000-01-04	-1.150034	-0.318474	-0.568011	0.752190
2000-01-05	0.322542	-0.712590	-0.737587	0.828602

df.A.hist()

df["gp"] =  df.A // 15

df.gp.drop_duplicates()

2000-01-01   -1.0
2000-01-05    0.0
2000-07-02    1.0
2000-08-10    2.0
Name: gp, dtype: float64

df.head()

	A	B	C	D	gp
2000-01-01	-1.148129	0.919846	0.233806	0.540728	-1.0
2000-01-02	-0.567908	0.484401	-0.363631	0.858367	-1.0
2000-01-03	-1.167174	1.308936	-1.344971	0.673636	-1.0
2000-01-04	-1.150034	-0.318474	-0.568011	0.752190	-1.0
2000-01-05	0.322542	-0.712590	-0.737587	0.828602	0.0

df.A.hist(by= df.gp, figsize=(10, 6))

array([[<Axes: title={'center': '-1.0'}>,
        <Axes: title={'center': '0.0'}>],
       [<Axes: title={'center': '1.0'}>, <Axes: title={'center': '2.0'}>]],
      dtype=object)

df.A.plot(kind='box')

df.plot(kind='box')

df.plot(figsize= (10,5))

df = abs(df)

del df["gp"]

df.plot.area()

df.iloc[5].plot(kind='bar', figsize= (5,5))

df.plot.scatter(x = "A", y = "B", figsize= (6,6))

df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df2.plot.bar()

df2.plot.bar(stacked=True)

df2.plot.barh(stacked=True)

df2 = df2.div(df2.sum("columns"), axis = "rows")

df2.plot.barh(stacked=True)

df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.head()

	a	b	c	d
0	0.063116	0.455439	0.068626	0.991551
1	0.086392	0.590133	0.494357	0.592609
2	0.747400	0.149594	0.604976	0.344975
3	0.803134	0.331363	0.168731	0.512835
4	0.249216	0.822752	0.270448	0.135452

df.plot.scatter(x='a', y='b')

df.plot.scatter(x='a', y='b', c='c', s=100)

df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])

df['b'] = df['b'] + np.arange(1000)

df.plot.scatter(x='a', y='b')

df.plot.hexbin(x='a', y='b', gridsize=25)

import seaborn as sns

import seaborn as sns
sns.jointplot(x="a", y="b", data=df, kind="kde")

---

8.3 Plotly 交互式可视化

Important关于 cufflinks

cufflinks 库已停止维护，且与新版本的 plotly/numpy 存在兼容性问题。

推荐使用 Plotly Express，它是现代化的、官方支持的交互式可视化库。

安装 Plotly

%pip install plotly kaleido --upgrade

Plotly Express 基础示例

Plotly Express 提供了简洁的 API 来创建交互式图表。

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

# 创建示例数据
df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'category': np.random.choice(['A', 'B', 'C'], 100)
})

df.head()

	x	y	category
0	1.912363	0.058237	C
1	1.061946	1.374703	A
2	-0.846091	0.394709	A
3	0.351086	-0.668664	C
4	0.453259	-0.699143	C

# 散点图
fig = px.scatter(df, x='x', y='y', color='category',
                 title='交互式散点图',
                 labels={'x': 'X轴', 'y': 'Y轴'})
fig.show()

# 创建时间序列数据
ts_df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=100),
    'value': np.cumsum(np.random.randn(100))
})

# 线图
fig = px.line(ts_df, x='date', y='value',
              title='时间序列图',
              labels={'date': '日期', 'value': '数值'})
fig.show()

# 直方图
fig = px.histogram(df, x='x', color='category',
                   title='分组直方图',
                   nbins=20)
fig.show()

# 箱线图
fig = px.box(df, x='category', y='y',
             title='箱线图',
             labels={'category': '类别', 'y': '数值'})
fig.show()

TipPlotly 优势

• 交互式：可以缩放、平移、悬停查看数据
• 现代化：API 简洁，文档完善
• 导出方便：支持导出为 HTML、PNG、PDF 等格式
• 样式丰富：内置多种主题和配色方案

---

8.4 Seaborn 可视化库

https://seaborn.pydata.org/index.html

Seaborn是基于matplotlib的图形可视化python包。它提供了一种高度交互式界面，便于用户能够做出各种有吸引力的统计图表。

https://seaborn.pydata.org/examples/index.html

# library
import seaborn as sns
import pandas as pd
import numpy as np

# Create a dataset (fake)
df = pd.DataFrame(np.random.random((5,5)), columns=["a","b","c","d","e"])

# Default heatmap: just a visualization of this square matrix
p1 = sns.heatmap(df)

# library
import seaborn as sns
import pandas as pd
import numpy as np

# Create a dataset (fake)
df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])

# Calculate correlation between each pair of variable
corr_matrix=df.corr()

# plot it
sns.heatmap(corr_matrix, cmap='PuOr')

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

# Load the example diamonds dataset
diamonds = sns.load_dataset("diamonds")

# Draw a scatter plot while assigning point colors and sizes to different
# variables in the dataset
f, ax = plt.subplots(figsize=(6.5, 6.5))
sns.despine(f, left=True, bottom=True)
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="ch:r=-.2,d=.3_r",
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax).get_figure().savefig("output.png", dpi=600)

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="darkgrid")

# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")

# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",
                hue="region", style="event",
                data=fmri).get_figure()

plt.savefig('saving-a-seaborn-plot-as-png-file-transparent.png',
            transparent=True, dpi = 600)

8.5 folium 地图可视化

https://python-visualization.github.io/folium/

%pip install folium --upgrade

import folium
local = [23.132, 113.345]
# local = [23.155, 113.327]
# local = [23.019, 113.411]
m = folium.Map(location=local,zoom_start=16)
m

Make this Notebook Trusted to load map: File -> Trust Notebook

local = [23.087, 113.37]
# 使用 OpenStreetMap 默认底图（推荐）
m = folium.Map(
    location=local,
    zoom_start=12
)

folium.Circle(
    radius=800,
    location=[23.133, 113.342],
    popup='暨南大学石牌校区',
    color='crimson',
    fill=False,
).add_to(m)

folium.CircleMarker(
    location=[23.155, 113.327],
    radius=15,
    popup='暨南大学华文校区',
    color='#3186cc',
    fill=True,
    fill_color='#3186cc'
).add_to(m)

folium.CircleMarker(
    location=[23.02829562968323, 113.41585435512872],
    radius=20,
    popup='暨南大学番禺校区',
    color='#3186cc',
    fill=True,
    fill_color='#3186cc'
).add_to(m)


m

Make this Notebook Trusted to load map: File -> Trust Notebook

# generated data
import numpy as np
data = (
    np.random.normal(size=(300, 3)) *
    np.array([[0.05, 0.05, 0.05]]) +
    np.array([[23.087, 113.3, 1]])
).tolist()
data

[[23.101546158896962, 113.3559836955326, 0.9894410386499295],
 [23.206066012475347, 113.28795855682371, 0.9697259884862655],
 [23.08392476288006, 113.31453781250104, 1.1457594502764785],
 [23.05744528889505, 113.26004875931372, 0.9663768346529729],
 [23.15062299486233, 113.27805517048259, 0.9699878594343987],
 [23.053550813569373, 113.32890588328601, 1.0285583972598435],
 [23.11700101151718, 113.2645146717489, 1.0123019799199646],
 [22.988245823027835, 113.36205459061783, 1.0213025595499607],
 [23.06797485277542, 113.2475510613123, 1.0678908634916637],
 [23.113647836037643, 113.26745103577356, 0.9833261378031858],
 [23.02788304183722, 113.29248277873555, 1.0485264172966904],
 [23.086198525528356, 113.31983241241606, 0.9861298841373867],
 [23.03678805466783, 113.2178792720758, 1.0300796832941865],
 [23.03190415207701, 113.31423851297714, 0.9875371525376696],
 [23.133864486563954, 113.33765968641369, 0.954020298362893],
 [23.05061989931659, 113.26879211682939, 1.0318210550935296],
 [23.080373304865525, 113.32076671521173, 0.9484319637298709],
 [23.06565902220455, 113.24147309164248, 1.0191142810226768],
 [23.09457688506146, 113.20814558271736, 1.0587279469062612],
 [23.033043142349737, 113.25947713610235, 0.971192305711924],
 [23.133188934447073, 113.20943452320223, 0.985551648762346],
 [23.061611564875616, 113.21007840055954, 0.9545026360980701],
 [22.92949340772551, 113.3917423787921, 0.9671716890919827],
 [23.10480392649642, 113.31245600651629, 0.9504886581013213],
 [23.04687459649217, 113.31398187808027, 0.9538575967131679],
 [23.084922768638346, 113.32529193747344, 0.9873995724465672],
 [23.08896541891964, 113.35785372525008, 0.9157596898432434],
 [23.106167720661947, 113.35278371290791, 1.0355012834131738],
 [23.05893028657467, 113.2929090241397, 0.9934086301799155],
 [23.100254528276455, 113.28569365438582, 0.9697582497976975],
 [23.100267991995757, 113.286648687338, 1.0056439386208098],
 [23.03259614630495, 113.24719218805389, 1.0839653809248087],
 [22.97580135199514, 113.24816441660852, 1.0551549850624784],
 [23.103506845381656, 113.22330985200823, 0.9569660339952333],
 [23.08155740623373, 113.31613205616186, 1.0326594920042422],
 [23.073514276242808, 113.32540798577915, 0.9606159335638937],
 [23.103565120240706, 113.29486106671403, 0.9517507480906046],
 [23.130036247698392, 113.30598496230941, 1.0315628910852292],
 [23.06172600519158, 113.34370785449661, 0.8643292849416976],
 [23.08350620732617, 113.3118986748248, 0.9598038207035481],
 [23.102995587263003, 113.2895455264216, 0.9318667962924058],
 [23.10204806419112, 113.27763963289941, 1.0139441449767197],
 [23.115735263735523, 113.27383146677859, 1.0348766682524373],
 [23.132802851003458, 113.32455084150797, 0.9994405837394807],
 [23.049495981452957, 113.30419036994795, 1.0122565627558844],
 [23.147660250287952, 113.25536669971527, 1.0755398448963323],
 [23.094723730180903, 113.31657460963422, 0.9582743372937494],
 [23.062005972898888, 113.25758907214419, 0.9749254976806571],
 [22.991771318835386, 113.2812616857264, 0.9328772228469442],
 [23.049192713120952, 113.34566886812418, 1.1096961100540148],
 [23.179543669195223, 113.30634395230997, 1.0076373939597834],
 [23.159172272036777, 113.38710064786338, 1.0377810437776769],
 [23.094354898266655, 113.36640501532717, 1.0164539529046976],
 [23.09323607284136, 113.24652490903779, 1.0128609793741368],
 [23.094476930990293, 113.32342885554453, 1.096327285304553],
 [23.105007514905655, 113.30529102790933, 0.9890556942384832],
 [23.091909321239278, 113.32022865360726, 0.9251134127624585],
 [23.090656888748512, 113.2022509894404, 1.0047565942782015],
 [23.10606563497312, 113.23501090279797, 1.0389226317344133],
 [23.185574921177714, 113.31506088060226, 1.015511034342598],
 [23.102342136665346, 113.30388305894634, 0.9119640157887134],
 [23.09668312329003, 113.2582863727005, 0.9852901900639525],
 [23.116175383856547, 113.3496341901885, 1.0915732474658644],
 [23.07708212017613, 113.37017657700088, 1.0225520330642228],
 [23.086635919287826, 113.25674682791953, 1.000633665169261],
 [23.06894690904764, 113.32549528878612, 1.0469078046666602],
 [23.154272229223178, 113.21089497578609, 1.0263571839012067],
 [23.073836627667013, 113.34570114123244, 1.035732742264827],
 [23.07420299554474, 113.30710595491108, 0.9879650610930449],
 [23.11858561257243, 113.32592757308089, 0.9450176998938496],
 [23.088559030678386, 113.26314798633604, 0.9849678240197471],
 [23.092829938297204, 113.3655845037067, 1.0367888707997495],
 [23.03049972223146, 113.26097158613538, 1.0245420831333782],
 [23.082442911994377, 113.42196232903193, 0.9851451567888506],
 [23.02411571633941, 113.32108872844569, 0.9510298949858036],
 [23.08991603247511, 113.2987189027804, 0.9240252083410196],
 [23.042445220352434, 113.19280546817197, 0.9949373451178886],
 [23.142057078532737, 113.27334082216913, 1.135848859000205],
 [23.044166853422606, 113.24246022491229, 0.9810834442319656],
 [23.103931467669263, 113.31492372045238, 0.9278816522198023],
 [23.103881652639437, 113.26037895188306, 1.0545027097131334],
 [23.031695645572015, 113.248340515816, 0.9969239310787659],
 [23.03082136012085, 113.29253051857123, 0.9989543654854337],
 [23.055958700010013, 113.30430725570966, 1.0957409015343604],
 [23.089140984979867, 113.33144474615686, 1.0266459650104982],
 [23.118319616195976, 113.26298829712078, 0.9689674177943207],
 [23.077475738629357, 113.28911213474665, 1.0172518729918973],
 [22.952284938177062, 113.33681720264026, 0.9170150081491624],
 [23.200036503806867, 113.30214750970147, 1.0603565271169324],
 [23.003771493334824, 113.30415844415325, 0.9512732654977779],
 [23.103204831569524, 113.35727060713299, 1.0444823453666077],
 [23.097919089350608, 113.20240969019638, 0.9619512464096537],
 [23.10843029829919, 113.38770649554057, 1.0082074407738013],
 [23.175133216252163, 113.33260435280593, 0.9943749116946058],
 [23.167183751728953, 113.26602378851273, 0.9219507863435309],
 [23.08743018875501, 113.25406109007916, 1.0151754103621693],
 [23.136870550173224, 113.32119096703018, 1.003565775868763],
 [23.12793182141984, 113.26215981628003, 0.9962469445693592],
 [23.10231837640217, 113.29553830872608, 0.9155928482775786],
 [23.175745276009383, 113.21698140066228, 0.9898214801815608],
 [23.1085401870954, 113.22508174832579, 1.0398980968170766],
 [23.066480181254942, 113.34927654908746, 1.0202787086027927],
 [23.044712979840856, 113.27304058943267, 1.0595017791206822],
 [23.065222357960188, 113.36301273166636, 1.0857198710386575],
 [23.064704484873037, 113.19498840267134, 1.0146185288968683],
 [23.08884366737496, 113.33693441670778, 1.0036040256761976],
 [23.059823900040083, 113.33577366057769, 0.9768670377023632],
 [23.04401161610398, 113.34870234800107, 1.0973053560927335],
 [23.089294568045034, 113.40648681172956, 1.0672954423208023],
 [22.945826689126843, 113.33555580590073, 1.0344023855972806],
 [23.04111115355714, 113.17695539460153, 0.9788217131971989],
 [23.06820848896712, 113.32961578569528, 1.024723822637628],
 [23.16470166843589, 113.29593443086718, 1.0266264371321348],
 [23.130655370411766, 113.29760988337169, 0.9288641272323043],
 [23.1299580459227, 113.29052350896032, 0.9338362948257889],
 [23.06675875854123, 113.29923343715164, 0.9908695977885447],
 [23.02809171977561, 113.34476257559587, 0.9474159547995182],
 [23.136822861421464, 113.31225617272405, 0.9095865953532773],
 [23.12122913804494, 113.31572937900808, 0.9928438356557356],
 [23.049598194789308, 113.3790651920976, 1.06995431249201],
 [23.048634917456727, 113.27009885590954, 1.039837070566207],
 [23.033238254710025, 113.24622685241481, 0.9528717872341834],
 [23.055223681088982, 113.31703726488796, 0.9269648158630294],
 [23.111763865814094, 113.330714652435, 1.0081795149804298],
 [23.159514940356463, 113.3827896710709, 1.0125560088552084],
 [23.015485603424164, 113.37168997940708, 0.8901657271145049],
 [23.090062941586222, 113.31442240303598, 1.0437725746635351],
 [23.10047396900036, 113.26930355685936, 1.056195916419667],
 [23.086397403228723, 113.29500972551187, 0.9215703922399998],
 [23.07181575355332, 113.27191797654557, 0.9771365604813234],
 [23.082061644828187, 113.24971311589125, 0.9821751047997827],
 [23.11405362091158, 113.28946835987047, 0.9944232666304159],
 [23.174221426693236, 113.27016329800094, 0.9280187680354476],
 [23.065415541299505, 113.34002676353906, 1.0593466485224678],
 [23.063303058891776, 113.33684793920631, 0.9667912491794345],
 [23.140455345891258, 113.29750672964703, 1.0325443825426526],
 [23.156996865473428, 113.21065182613407, 0.99585894771756],
 [23.038011746216338, 113.28391566408796, 1.0228530218334027],
 [23.0589002853561, 113.41590655916437, 0.9999214835993677],
 [23.17138554578974, 113.38701118194078, 0.9850042161036289],
 [23.03373035184639, 113.28016118131181, 0.975209717968077],
 [23.113778442614173, 113.29613019680484, 0.9672118741049642],
 [23.074381748014467, 113.33745430210156, 1.019546702641773],
 [23.067943665429517, 113.25848873955499, 0.9433265035275158],
 [23.170215842016887, 113.28495632846364, 1.0096770568309708],
 [23.067720545643997, 113.32999565199998, 0.9607360926143252],
 [23.029012302732987, 113.30578275270763, 0.9672616324072332],
 [23.139783201308784, 113.25740272595408, 0.931829158073609],
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 [23.145146433696386, 113.26296146926379, 0.8997795925437833],
 [23.102690986633903, 113.20091171665314, 1.0082426316059916],
 [23.069118097622297, 113.30415096230118, 0.9864598087796809],
 [23.086958138539327, 113.32280016523009, 1.014023187927512],
 [23.162865123714795, 113.36267068030831, 0.9309042465674608],
 [23.03848251916326, 113.38267607197582, 0.9745923728996312],
 [23.047355022919856, 113.35134423231115, 1.077505487253896],
 [23.081906724336644, 113.26406931003574, 1.0249938223041481],
 [23.1019448944326, 113.25728707102219, 0.9605602425468109],
 [23.05656609272457, 113.37055769425989, 1.0513148030074184],
 [23.137824499025943, 113.37660944247588, 1.0213143234556135],
 [23.18100685010041, 113.26796434313987, 0.9676950648284746],
 [23.08486875700642, 113.26163942778828, 1.0425312384465804],
 [23.069705949158397, 113.30261580785015, 0.9676064491953289],
 [23.094955179678305, 113.3636746167014, 1.1034452276613536],
 [23.051051722427978, 113.40340414824736, 0.9605732629926806],
 [22.999719671183087, 113.25752059900128, 1.140748598965253],
 [23.060876728946333, 113.28822670935287, 0.9455912109436538],
 [23.143157080024576, 113.31652806597364, 0.9897672504095643],
 [23.09760293550695, 113.33698646088699, 0.9450511327056311],
 [23.129991297730452, 113.25342782957449, 0.9393332792480567],
 [22.982729928393535, 113.29058769396606, 0.8962222376027345],
 [23.040320067325865, 113.2388214245268, 0.9811546407868701],
 [23.10123708337022, 113.31970396964944, 0.8871928935919335],
 [23.057278485695278, 113.21240448164177, 1.107355418814855],
 [23.099095555477987, 113.31728391870408, 0.9596210597979108],
 [23.05220912306624, 113.30810341654148, 0.9381472905560007],
 [23.04573475178744, 113.40729979798776, 0.9733943001883945],
 [23.061146633710784, 113.31108527381849, 0.9829948186800115],
 [23.09851117127804, 113.30790134844794, 1.0162554723402633],
 [23.170017690282346, 113.32826990699701, 1.0345083284089052],
 [23.07146333313078, 113.32748676968639, 0.9745144000946859],
 [23.104647004323915, 113.34822637709222, 1.0705690175611116],
 [23.11341387636721, 113.21919049891045, 1.0210252443867878],
 [23.047677626049417, 113.32557356687758, 0.9966251510825506],
 [23.057187771631714, 113.2927998114717, 0.9436919906172835],
 [23.14200334833561, 113.27865345712694, 0.9563501656180777],
 [23.030684371370953, 113.27724068377748, 1.0016042886318273],
 [23.107204063174848, 113.3441113671808, 0.9798827824266236],
 [23.061240695729083, 113.26547770816381, 0.9104500446504628],
 [23.11406332938526, 113.34433537304587, 1.0066496259339799],
 [23.0771963391058, 113.26019265161098, 0.9888784371231408],
 [23.077298451018603, 113.28224950682745, 0.9889312516533824],
 [23.1678222307225, 113.2408723050003, 1.0807849616426077],
 [23.026728267722447, 113.22988606591832, 0.9726622404871234],
 [23.150472403014202, 113.32160562093566, 0.9668452753603307],
 [23.10804509368375, 113.35100338998645, 1.0143406805371933],
 [23.011618659589534, 113.27685996012794, 1.0381139248485423],
 [23.089911317481743, 113.25536623988076, 0.97591495322806],
 [23.074803700299842, 113.21018729287981, 1.0812702432178511],
 [23.0602257785443, 113.35655311401517, 1.003301754421832],
 [23.109792776732405, 113.34223141500799, 0.9714383257695531],
 [23.109974548641983, 113.29663226861786, 1.0988404154512421],
 [23.076653178993055, 113.2903620248449, 1.0747781827801373],
 [23.066006819026672, 113.39154904272304, 0.8866985950738957],
 [23.06187011789522, 113.329274847491, 0.9531444209855836],
 [23.09215722733291, 113.33500394073502, 0.9979606393446608],
 [23.108783716927473, 113.41311896034186, 1.0783989692033518],
 [23.051587608702615, 113.27271074466547, 1.0126534377897292],
 [23.08112625981011, 113.34069103959165, 1.0003259426212479],
 [23.09298487152137, 113.24428845541146, 1.0114063444382395],
 [23.096636180424216, 113.32259280126202, 0.9559099236091522],
 [23.0709517069991, 113.2105009352563, 1.056331817902246],
 [23.12751518041026, 113.40032450273308, 1.1028222249971276],
 [23.201596091357615, 113.23470170525503, 0.9631116984534311],
 [22.983921730495997, 113.30921051331782, 0.9542044693906839],
 [23.13908838982864, 113.27736158913784, 1.0293151459148855],
 [23.091292604822932, 113.27072968232939, 0.9467253935742427],
 [23.11243915411646, 113.31074953589068, 1.063458515813779],
 [23.18282414038781, 113.2594498807081, 1.094745961417506],
 [23.124390198834625, 113.21274418888906, 1.0051802690222171],
 [23.20289473916005, 113.33532217174167, 0.9562736851382847],
 [23.1338188440322, 113.21589166408948, 0.9992927625602154],
 [23.124782037583262, 113.31429206976628, 0.9798818993761326],
 [23.063475022354456, 113.23145341339422, 1.0037239663998783],
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 [23.164798012245267, 113.27927654826411, 0.9519859276080208],
 [23.05682563583087, 113.30348737007574, 0.876547437225224],
 [23.22814127031294, 113.29614688200238, 1.0002345771830492],
 [23.112337645383587, 113.26396100167692, 1.0075606958772216],
 [23.133193003264992, 113.25938134990102, 1.0001811516254056],
 [22.96985992733565, 113.24540021833023, 0.951494630982537],
 [23.0599286111793, 113.36433002022495, 1.0633314275589105],
 [23.022383968315925, 113.38705523706578, 0.9361622235332209],
 [23.135965935419875, 113.35963882798059, 1.0356457318609125],
 [23.119490421252756, 113.23357045072092, 0.9501147001693675],
 [23.1121715603478, 113.379355886597, 0.9450458197698964],
 [23.08659916923502, 113.25445740530346, 0.9656327236391073],
 [23.187975592516526, 113.34894333608283, 1.0225955580246116],
 [23.174693976281468, 113.3146175424754, 1.028027624165148],
 [23.033120849321246, 113.34638920258789, 1.064646065061616],
 [23.06364842128837, 113.2564567834396, 1.048962215241505],
 [23.1165423647422, 113.22475265125878, 0.971290594252817],
 [23.132360226911942, 113.31003300379429, 0.9053035246691227],
 [22.971457470232167, 113.27045265694564, 1.0192232335525084],
 [23.09583254755467, 113.35974778015907, 0.9802258814445237],
 [23.081395347061516, 113.28785519320817, 1.0251467613861796],
 [23.146684915488716, 113.23140841140338, 0.922242725025256],
 [23.03449389997694, 113.33237660619729, 0.9747125337496771],
 [23.09517536813799, 113.34188223683267, 0.8794253875133888],
 [23.091801954347964, 113.27297205884416, 0.951116021291397],
 [23.081736673630974, 113.22836764928176, 1.050614746862662],
 [23.11737790384634, 113.32435149374318, 0.9579633801677969],
 [23.115768801617623, 113.28584560714927, 0.962981547252387],
 [23.044850353496773, 113.35725144638734, 0.9929068777302035],
 [23.09387782241977, 113.2558852069788, 1.0610866248514026],
 [23.090925471899908, 113.40708706190583, 1.0213397343133817],
 [23.055196968384735, 113.35077244301102, 1.0082878095134529],
 [23.09765606569975, 113.25954840216448, 0.9712221999872299],
 [23.124012119137152, 113.30807697246041, 0.9749031453223709],
 [23.009727529268947, 113.19040387274985, 1.0379639114632377],
 [23.10001395284033, 113.28747637330294, 0.9570058783914028],
 [23.014572534806238, 113.39872222828662, 0.9224812967866087],
 [23.07164522022995, 113.29889814680485, 0.9327168732976062],
 [23.01018365235604, 113.29741155022909, 0.9881200106951283],
 [23.137736776915073, 113.28645903280649, 0.9747978631538995],
 [23.110167732851284, 113.21622307964186, 0.9626972725343087],
 [23.07988530984633, 113.32042015008516, 0.9968664674689669],
 [23.13218200197872, 113.23997081058624, 1.1112442673324139],
 [23.037958935150062, 113.34953782521065, 1.0568375040575133],
 [23.00258933385775, 113.29521946145935, 0.9923533050846497],
 [22.99500366218763, 113.24697403651301, 1.0747148274953087],
 [23.101269523033718, 113.26627385906585, 1.1585747242869329],
 [23.056907707790835, 113.32364639473913, 0.9840376338944937],
 [23.09766136616909, 113.29146312158494, 0.977209916201389],
 [23.10566443354946, 113.31365735821936, 0.9632676123505158],
 [23.06488068135747, 113.28208969919801, 0.9538044549560156],
 [23.09563083615826, 113.29870977158149, 0.9074605325437103],
 [23.096463726724775, 113.24795742271954, 0.9884978400779303],
 [23.084297301650086, 113.24860556357082, 1.0525404605482656],
 [23.11105550299495, 113.24446903885507, 1.0221646946782512],
 [23.071566092065293, 113.22453258501857, 1.0503125037667875],
 [23.06960951703729, 113.23378700517061, 1.044900348882142],
 [23.130719218203776, 113.31953370185602, 0.9996287949576063],
 [23.03112919824916, 113.3405694784075, 0.8656620974664794],
 [23.117407666993948, 113.21479000784922, 0.82408691259813],
 [23.02401494112507, 113.3543676556085, 1.0490127724904623],
 [23.0523612476613, 113.36598442520975, 0.9512744778441958],
 [23.11562477051131, 113.27900507513928, 0.9836612199847468],
 [23.065294517554626, 113.33172458280367, 0.9539224247205167],
 [23.106441639260403, 113.2406889320042, 1.0337442356174757],
 [23.01002305085585, 113.19105233814925, 0.9812985669879906],
 [23.16699080983821, 113.26956759674246, 1.0410340776043785],
 [23.12737946419699, 113.26680070685237, 0.9806937951959852],
 [23.053700797281152, 113.3364897616824, 1.0324335040210815],
 [23.0300604809055, 113.27740444123657, 0.963145386349546],
 [23.160867148166215, 113.35988825149546, 0.9986512849632265],
 [23.087078292019775, 113.27332694280972, 1.016882367943754],
 [22.98442906835672, 113.31708083462408, 0.9413037776585411],
 [23.107705509824882, 113.31158926726981, 0.9361983698889469],
 [22.997215755947845, 113.28806263832456, 1.0174447450127926]]

# HeatMap（热力图）
from folium.plugins import HeatMap

# 创建地图（使用默认 OpenStreetMap 底图）
m = folium.Map([23.087, 113.3], zoom_start=12)

# 添加热力图层
HeatMap(data).add_to(m)

# 保存为 HTML 文件
m.save('Heatmap.html')
print("热力图已保存到 Heatmap.html")

# 显示地图
m

热力图已保存到 Heatmap.html

Make this Notebook Trusted to load map: File -> Trust Notebook

TipFolium 底图选择

新版本 folium 推荐使用以下方式指定底图：

# 默认 OpenStreetMap（推荐）
folium.Map(location=[lat, lon])

# 使用其他底图提供商
folium.Map(location=[lat, lon], tiles='OpenStreetMap')
folium.Map(location=[lat, lon], tiles='CartoDB positron')
folium.Map(location=[lat, lon], tiles='CartoDB dark_matter')

注意：Stamen 底图已不再免费提供，建议使用 OpenStreetMap 或 CartoDB。