{"id":1098061,"date":"2025-01-08T15:17:45","date_gmt":"2025-01-08T07:17:45","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1098061.html"},"modified":"2025-01-08T15:17:48","modified_gmt":"2025-01-08T07:17:48","slug":"python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e5%9b%be%e5%bd%a2%e5%8f%af%e8%a7%86%e5%8c%96-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1098061.html","title":{"rendered":"Python\u5982\u4f55\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316"},"content":{"rendered":"

\"Python\u5982\u4f55\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\"<\/p>\n

Python\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\u7684\u6838\u5fc3\u5de5\u5177\u5305\u62ec\uff1aMatplotlib\u3001Seaborn\u3001Plotly\u3001Bokeh\u3002<\/strong> \u5176\u4e2d\uff0cMatplotlib \u662f\u6700\u57fa\u7840\u7684\uff0c\u53ef\u5b9e\u73b0\u5404\u79cd2D\u56fe\u5f62\u7684\u7ed8\u5236\uff1bSeaborn \u5728 Matplotlib \u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u9ad8\u7ea7\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u5f62\uff1bPlotly \u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u9002\u5408\u5b9e\u65f6\u6570\u636e\u53ef\u89c6\u5316\uff1bBokeh \u5219\u4e3b\u8981\u7528\u4e8e\u5927\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib<\/h3>\n<\/p>\n

Matplotlib \u662f Python \u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n

1\u3001\u57fa\u7840\u4f7f\u7528<\/h4>\n<\/p>\n

Matplotlib \u7684\u57fa\u7840\u4f7f\u7528\u975e\u5e38\u7b80\u5355\u3002\u9996\u5148\u9700\u8981\u5bfc\u5165 Matplotlib \u5e93\uff0c\u5e76\u4f7f\u7528 pyplot<\/code> \u6a21\u5757\u8fdb\u884c\u7ed8\u56fe\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Line Plot')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u4f7f\u7528<\/h4>\n<\/p>\n
Matplotlib \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u53ef\u4ee5\u5bf9\u56fe\u5f62\u8fdb\u884c\u8be6\u7ec6\u7684\u8c03\u6574\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y1 = [2, 3, 5, 7, 11]<\/p>\n
y2 = [1, 4, 6, 8, 10]<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
\u7ed8\u5236\u591a\u6761\u6298\u7ebf<\/strong><\/h2>\n
ax.plot(x, y1, label='Prime Numbers')<\/p>\n
ax.plot(x, y2, label='Even Numbers')<\/p>\n
\u6dfb\u52a0\u7f51\u683c<\/strong><\/h2>\n
ax.grid(True)<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
ax.legend()<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
ax.set_title('Multiple Line Plot')<\/p>\n
ax.set_xlabel('X-axis')<\/p>\n
ax.set_ylabel('Y-axis')<\/p>\n
\u4fdd\u5b58\u56fe\u5f62<\/strong><\/h2>\n
plt.savefig('line_plot.png')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Seaborn<\/h3>\n<\/p>\n
Seaborn \u662f\u57fa\u4e8e Matplotlib \u6784\u5efa\u7684\u7edf\u8ba1\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u4f7f\u7ed8\u56fe\u66f4\u7b80\u6d01\u7f8e\u89c2\u3002\u5b83\u7279\u522b\u9002\u5408\u4e8e\u521b\u5efa\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u5982\u70ed\u56fe\u3001\u7bb1\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u77e9\u9635\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u7840\u4f7f\u7528<\/h4>\n<\/p>\n
Seaborn \u7684\u57fa\u7840\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u901a\u5e38\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u521b\u5efa\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(x='total_bill', y='tip', data=tips)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u4f7f\u7528<\/h4>\n<\/p>\n
Seaborn \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u53ef\u4ee5\u5bf9\u56fe\u5f62\u8fdb\u884c\u8be6\u7ec6\u7684\u8c03\u6574\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
\u521b\u5efa\u5e26\u56de\u5f52\u7ebf\u7684\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.lmplot(x='total_bill', y='tip', data=tips, hue='smoker', markers=['o', 'x'])<\/p>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
plt.title('Total Bill vs Tip with Regression Line')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Plotly<\/h3>\n<\/p>\n
Plotly \u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u7684\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u5728\u7f51\u9875\u4e2d\u5d4c\u5165\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u5b83\u7279\u522b\u9002\u5408\u4e8e\u5b9e\u65f6\u6570\u636e\u53ef\u89c6\u5316\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u7840\u4f7f\u7528<\/h4>\n<\/p>\n
Plotly \u7684\u57fa\u7840\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u901a\u5e38\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
df = px.data.iris()<\/p>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u4f7f\u7528<\/h4>\n<\/p>\n
Plotly \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u53ef\u4ee5\u5bf9\u56fe\u5f62\u8fdb\u884c\u8be6\u7ec6\u7684\u8c03\u6574\u3002<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y1 = [2, 3, 5, 7, 11]<\/p>\n
y2 = [1, 4, 6, 8, 10]<\/p>\n
\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
fig = go.Figure()<\/p>\n
fig.add_trace(go.Scatter(x=x, y=y1, mode='lines+markers', name='Prime Numbers'))<\/p>\n
fig.add_trace(go.Scatter(x=x, y=y2, mode='lines+markers', name='Even Numbers'))<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
fig.update_layout(title='Multiple Line Plot', xaxis_title='X-axis', yaxis_title='Y-axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001Bokeh<\/h3>\n<\/p>\n
Bokeh \u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u7684\u7ed8\u56fe\u5e93\uff0c\u7279\u522b\u9002\u5408\u4e8e\u5927\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\uff0c\u53ef\u4ee5\u5728\u56fe\u5f62\u4e2d\u6dfb\u52a0\u5de5\u5177\u63d0\u793a\u3001\u7f29\u653e\u3001\u5e73\u79fb\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u7840\u4f7f\u7528<\/h4>\n<\/p>\n
Bokeh \u7684\u57fa\u7840\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u901a\u5e38\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n
from bokeh.plotting import figure, show<\/p>\n
from bokeh.io import output_notebook<\/p>\n
\u5728 Jupyter Notebook \u4e2d\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
output_notebook()<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
p = figure(title='Simple Line Plot', x_axis_label='X-axis', y_axis_label='Y-axis')<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
p.line(x, y, legend_label='Prime Numbers', line_width=2)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
show(p)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u4f7f\u7528<\/h4>\n<\/p>\n
Bokeh \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u53ef\u4ee5\u5bf9\u56fe\u5f62\u8fdb\u884c\u8be6\u7ec6\u7684\u8c03\u6574\u3002<\/p>\n<\/p>\n
from bokeh.plotting import figure, show<\/p>\n
from bokeh.models import HoverTool<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y1 = [2, 3, 5, 7, 11]<\/p>\n
y2 = [1, 4, 6, 8, 10]<\/p>\n
\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
p = figure(title='Multiple Line Plot', x_axis_label='X-axis', y_axis_label='Y-axis')<\/p>\n
\u7ed8\u5236\u591a\u6761\u6298\u7ebf<\/strong><\/h2>\n
p.line(x, y1, legend_label='Prime Numbers', line_width=2, color='blue')<\/p>\n
p.line(x, y2, legend_label='Even Numbers', line_width=2, color='green')<\/p>\n
\u6dfb\u52a0\u5de5\u5177\u63d0\u793a<\/strong><\/h2>\n
hover = HoverTool()<\/p>\n
hover.tooltips = [('X', '@x'), ('Y', '@y')]<\/p>\n
p.add_tools(hover)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
show(p)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u4ee5\u4e0a\u4ecb\u7ecd\u4e86 Python \u4e2d\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\u7684\u51e0\u79cd\u5e38\u7528\u5de5\u5177\uff0c\u5305\u62ec Matplotlib\u3001Seaborn\u3001Plotly \u548c Bokeh\u3002\u6bcf\u79cd\u5de5\u5177\u90fd\u6709\u5176\u72ec\u7279\u7684\u7279\u70b9\u548c\u4f18\u52bf\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
Matplotlib<\/strong>\uff1a\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u9759\u6001\u56fe\u5f62\u7684\u7ed8\u5236\u3002<\/p>\n
Seaborn<\/strong>\uff1a\u57fa\u4e8e Matplotlib \u6784\u5efa\uff0c\u9002\u5408\u521b\u5efa\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\u3002<\/p>\n
Plotly<\/strong>\uff1a\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u9002\u5408\u5b9e\u65f6\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n
Bokeh<\/strong>\uff1a\u9002\u5408\u5927\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\uff0c\u63d0\u4f9b\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7279\u70b9\u548c\u53ef\u89c6\u5316\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\uff0c\u5145\u5206\u53d1\u6325\u5176\u4f18\u52bf\uff0c\u521b\u5efa\u51fa\u9ad8\u8d28\u91cf\u7684\u56fe\u5f62\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u9002\u5408\u7684Python\u5e93\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u7528\u4e8e\u56fe\u5f62\u53ef\u89c6\u5316\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u548cBokeh\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53d6\u51b3\u4e8e\u60a8\u7684\u9700\u6c42\u3002\u4f8b\u5982\uff0cMatplotlib\u975e\u5e38\u9002\u5408\u57fa\u672c\u7684\u7ed8\u56fe\u9700\u6c42\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u66f4\u52a0\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u5f62\u3002Plotly\u548cBokeh\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u9002\u5408\u9700\u8981\u52a8\u6001\u5c55\u793a\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n
Python\u56fe\u5f62\u53ef\u89c6\u5316\u7684\u5e38\u89c1\u5e94\u7528\u573a\u666f\u6709\u54ea\u4e9b\uff1f<\/strong>
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