{"id":975116,"date":"2024-12-27T06:10:39","date_gmt":"2024-12-26T22:10:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/975116.html"},"modified":"2024-12-27T06:10:42","modified_gmt":"2024-12-26T22:10:42","slug":"python%e7%bb%98%e5%9b%be%e5%a6%82%e4%bd%95%e7%bc%a9%e6%94%be%e5%9d%90%e6%a0%87","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/975116.html","title":{"rendered":"python\u7ed8\u56fe\u5982\u4f55\u7f29\u653e\u5750\u6807"},"content":{"rendered":"

\"python\u7ed8\u56fe\u5982\u4f55\u7f29\u653e\u5750\u6807\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
\u5728Python\u4e2d\u7ed8\u56fe\u65f6\u7f29\u653e\u5750\u6807\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\u3001\u4f7f\u7528\u7f29\u653e\u529f\u80fd\u7684\u5e93\u3001\u4fee\u6539\u56fe\u5f62\u5c3a\u5bf8\u3001\u4ea4\u4e92\u5f0f\u7f29\u653e<\/strong>\u7b49\u65b9\u6cd5\u5b9e\u73b0\u3002\u5176\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\uff0c\u901a\u8fc7\u8bbe\u5b9a\u8f74\u7684\u4e0a\u4e0b\u9650\u6765\u63a7\u5236\u7f29\u653e\u6bd4\u4f8b\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u63a5\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u9759\u6001\u56fe\u7684\u7f29\u653e\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u901a\u8fc7set_xlim()<\/code>\u548cset_ylim()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6ex\u8f74\u548cy\u8f74\u7684\u8303\u56f4\uff0c\u4ece\u800c\u5b9e\u73b0\u5750\u6807\u7684\u7f29\u653e\u3002\u8fd9\u4e2a\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u81ea\u5b9a\u4e49\u663e\u793a\u7684\u6570\u636e\u8303\u56f4\uff0c\u8fd8\u80fd\u6709\u6548\u63d0\u5347\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4<\/p>\n<\/p>\n

\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\u662f\u4e00\u79cd\u6700\u5e38\u7528\u7684\u7f29\u653e\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u8bbe\u5b9a\u5750\u6807\u8f74\u7684\u4e0a\u4e0b\u9650\u6765\u5b9e\u73b0\u5bf9\u56fe\u5f62\u7684\u7f29\u653e\u3002\u8fd9\u4e2a\u65b9\u6cd5\u901a\u5e38\u7528\u4e8e\u9759\u6001\u56fe\u3002<\/p>\n<\/p>\n

1.1 \u4f7f\u7528Matplotlib\u5e93\u8fdb\u884c\u8f74\u8303\u56f4\u8c03\u6574<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\u53ef\u4ee5\u901a\u8fc7\u5176\u63d0\u4f9b\u7684set_xlim()<\/code>\u548cset_ylim()<\/code>\u65b9\u6cd5\u5b9e\u73b0\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
x = [0, 1, 2, 3, 4, 5]<\/p>\n
y = [0, 1, 4, 9, 16, 25]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlim(0, 5)  # \u8bbe\u7f6ex\u8f74\u8303\u56f4<\/p>\n
plt.ylim(0, 30)  # \u8bbe\u7f6ey\u8f74\u8303\u56f4<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cx\u8f74\u88ab\u9650\u5236\u57280\u52305\u4e4b\u95f4\uff0c\u800cy\u8f74\u5219\u88ab\u9650\u5236\u57280\u523030\u4e4b\u95f4\u3002\u8fd9\u79cd\u65b9\u5f0f\u975e\u5e38\u7b80\u5355\u4e14\u9ad8\u6548\u3002<\/p>\n<\/p>\n
1.2 \u4f7f\u7528Seaborn\u5e93\u8fdb\u884c\u8f74\u8303\u56f4\u8c03\u6574<\/p>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u9002\u7528\u4e8e\u7edf\u8ba1\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002\u867d\u7136\u4e3b\u8981\u7528\u4e8e\u7edf\u8ba1\u56fe\u5f62\uff0c\u4f46\u540c\u6837\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684\u63a5\u53e3\u8bbe\u7f6e\u8f74\u8303\u56f4\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
tips = sns.load_dataset("tips")<\/p>\n
sns.scatterplot(data=tips, x="total_bill", y="tip")<\/p>\n
plt.xlim(0, 60)  # \u8bbe\u7f6ex\u8f74\u8303\u56f4<\/p>\n
plt.ylim(0, 12)  # \u8bbe\u7f6ey\u8f74\u8303\u56f4<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Seaborn\u7ed8\u5236\u4e86\u4e00\u4e2a\u6563\u70b9\u56fe\uff0c\u5e76\u901a\u8fc7Matplotlib\u7684\u65b9\u6cd5\u5bf9\u5750\u6807\u8f74\u8fdb\u884c\u4e86\u8303\u56f4\u9650\u5236\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u7f29\u653e\u529f\u80fd\u7684\u5e93<\/p>\n<\/p>\n
\u9664\u4e86\u76f4\u63a5\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\uff0c\u8fd8\u6709\u4e00\u4e9bPython\u5e93\u63d0\u4f9b\u4e86\u66f4\u52a0\u7075\u6d3b\u7684\u7f29\u653e\u529f\u80fd\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u5f62\u65f6\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528Bokeh\u5e93\u8fdb\u884c\u7f29\u653e<\/p>\n<\/p>\n
Bokeh\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u5e93\uff0c\u7279\u522b\u9002\u5408\u5927\u89c4\u6a21\u6570\u636e\u7684\u7ed8\u56fe\u3002Bokeh\u63d0\u4f9b\u4e86\u5185\u7f6e\u7684\u5de5\u5177\u680f\uff0c\u5176\u4e2d\u5305\u62ec\u7f29\u653e\u5de5\u5177\u3002<\/p>\n<\/p>\n
from bokeh.plotting import figure, show<\/p>\n
from bokeh.io import output_notebook<\/p>\n
output_notebook()<\/p>\n
x = [0, 1, 2, 3, 4, 5]<\/p>\n
y = [0, 1, 4, 9, 16, 25]<\/p>\n
p = figure(title="Simple line example", x_axis_label='x', y_axis_label='y', tools="pan,wheel_zoom,box_zoom,reset")<\/p>\n
p.line(x, y, legend_label="Line", line_width=2)<\/p>\n
show(p)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728Bokeh\u4e2d\uff0c\u901a\u8fc7tools<\/code>\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u9700\u8981\u7684\u5de5\u5177\uff0c\u6bd4\u5982wheel_zoom<\/code>\u5c31\u53ef\u4ee5\u4f7f\u7528\u9f20\u6807\u6eda\u8f6e\u6765\u7f29\u653e\u56fe\u5f62\u3002<\/p>\n<\/p>\n
2.2 \u4f7f\u7528Plotly\u5e93\u8fdb\u884c\u7f29\u653e<\/p>\n<\/p>\n
Plotly\u662f\u53e6\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u7279\u522b\u662f\u5728Web\u5e94\u7528\u4e2d\u4f7f\u7528\u5e7f\u6cdb\u3002Plotly\u9ed8\u8ba4\u652f\u6301\u56fe\u5f62\u7f29\u653e\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
df = px.data.iris()<\/p>\n
fig = px.scatter(df, x="sepal_width", y="sepal_length")<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728Plotly\u4e2d\uff0c\u4f60\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u9f20\u6807\u62d6\u52a8\u6765\u7f29\u653e\u56fe\u5f62\uff0c\u4e14\u4e0d\u9700\u8981\u8fdb\u884c\u989d\u5916\u7684\u914d\u7f6e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4fee\u6539\u56fe\u5f62\u5c3a\u5bf8<\/p>\n<\/p>\n
\u8c03\u6574\u56fe\u5f62\u5c3a\u5bf8\u4e5f\u662f\u4e00\u79cd\u5b9e\u73b0\u7f29\u653e\u7684\u624b\u6bb5\u3002\u901a\u8fc7\u6539\u53d8\u8f93\u51fa\u56fe\u5f62\u7684\u5927\u5c0f\uff0c\u53ef\u4ee5\u95f4\u63a5\u8fbe\u5230\u7f29\u653e\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
3.1 \u4fee\u6539Matplotlib\u56fe\u5f62\u5c3a\u5bf8<\/p>\n<\/p>\n
\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7figure<\/code>\u51fd\u6570\u7684figsize<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure(figsize=(10, 5))  # \u8bbe\u7f6e\u56fe\u5f62\u5927\u5c0f\u4e3a10x5\u82f1\u5bf8<\/p>\n
x = [0, 1, 2, 3, 4, 5]<\/p>\n
y = [0, 1, 4, 9, 16, 25]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574figsize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u5728\u4fdd\u8bc1\u5750\u6807\u8f74\u8303\u56f4\u4e0d\u53d8\u7684\u60c5\u51b5\u4e0b\u7f29\u653e\u6574\u4e2a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
3.2 \u4fee\u6539Seaborn\u56fe\u5f62\u5c3a\u5bf8<\/p>\n<\/p>\n
\u540c\u6837\u5730\uff0c\u5728Seaborn\u4e2d\u4e5f\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684figure<\/code>\u51fd\u6570\u6765\u8c03\u6574\u56fe\u5f62\u5c3a\u5bf8\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.figure(figsize=(12, 6))  # \u8bbe\u7f6e\u56fe\u5f62\u5927\u5c0f\u4e3a12x6\u82f1\u5bf8<\/p>\n
tips = sns.load_dataset("tips")<\/p>\n
sns.scatterplot(data=tips, x="total_bill", y="tip")<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
Seaborn\u4e2d\uff0cfigsize<\/code>\u7684\u8c03\u6574\u540c\u6837\u53ef\u4ee5\u5f71\u54cd\u5230\u6700\u7ec8\u8f93\u51fa\u56fe\u5f62\u7684\u5c3a\u5bf8\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4ea4\u4e92\u5f0f\u7f29\u653e<\/p>\n<\/p>\n
\u5bf9\u4e8e\u9700\u8981\u52a8\u6001\u4ea4\u4e92\u7684\u573a\u666f\uff0c\u4ea4\u4e92\u5f0f\u7f29\u653e\u662f\u975e\u5e38\u6709\u7528\u7684\u529f\u80fd\u3002\u901a\u8fc7\u4ea4\u4e92\u5f0f\u7f29\u653e\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u7531\u653e\u5927\u6216\u7f29\u5c0f\u7279\u5b9a\u533a\u57df\u3002<\/p>\n<\/p>\n
4.1 \u4f7f\u7528ipywidgets\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u7f29\u653e<\/p>\n<\/p>\n
ipywidgets\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u5c0f\u90e8\u4ef6\u7684\u5e93\uff0c\u53ef\u4ee5\u4e0eJupyter Notebook\u7ed3\u5408\u4f7f\u7528\uff0c\u63d0\u4f9b\u4ea4\u4e92\u5f0f\u529f\u80fd\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
from ipywidgets import interact<\/p>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
def plot_func(xmin, xmax):<\/p>\n
    plt.figure(figsize=(8, 4))<\/p>\n
    plt.plot(x, y)<\/p>\n
    plt.xlim(xmin, xmax)<\/p>\n
    plt.show()<\/p>\n
interact(plot_func, xmin=(0, 10, 0.1), xmax=(0, 10, 0.1))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7interact<\/code>\u51fd\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u62d6\u52a8\u6ed1\u5757\u6765\u6539\u53d8\u663e\u793a\u7684x\u8f74\u8303\u56f4\uff0c\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u7f29\u653e\u3002<\/p>\n<\/p>\n
4.2 \u4f7f\u7528Holoviews\u8fdb\u884c\u4ea4\u4e92\u5f0f\u7f29\u653e<\/p>\n<\/p>\n
Holoviews\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u7684\u9ad8\u5c42\u63a5\u53e3\uff0c\u652f\u6301\u4ea4\u4e92\u5f0f\u529f\u80fd\uff0c\u5e76\u4e0eBokeh\u7ed3\u5408\u4f7f\u7528\u3002<\/p>\n<\/p>\n
import holoviews as hv<\/p>\n
import numpy as np<\/p>\n
hv.extension('bokeh')<\/p>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
curve = hv.Curve((x, y))<\/p>\n
curve.opts(width=800, tools=['hover', 'box_zoom', 'wheel_zoom'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728Holoviews\u4e2d\uff0c\u76f4\u63a5\u901a\u8fc7opts<\/code>\u9009\u9879\u53ef\u4ee5\u914d\u7f6e\u4ea4\u4e92\u5de5\u5177\uff0c\u4f8b\u5982box_zoom<\/code>\u548cwheel_zoom<\/code>\uff0c\u4ece\u800c\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u7f29\u653e\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/p>\n<\/p>\n
\u5728Python\u4e2d\u5b9e\u73b0\u7ed8\u56fe\u7f29\u653e\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5177\u4f53\u9009\u62e9\u53d6\u51b3\u4e8e\u4f7f\u7528\u573a\u666f\u548c\u9700\u6c42\u3002<\/p>\n<\/p>\n
    \n
  • \u9759\u6001\u56fe<\/strong>\uff1a\u5982\u679c\u53ea\u662f\u9700\u8981\u7b80\u5355\u7684\u9759\u6001\u56fe\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u5750\u6807\u8f74\u8303\u56f4\u6765\u5b9e\u73b0\u3002<\/li>\n
  • \u4ea4\u4e92\u5f0f\u56fe<\/strong>\uff1a\u5bf9\u4e8e\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u573a\u666f\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528Bokeh\u6216Plotly\u7b49\u4ea4\u4e92\u5f0f\u5e93\u3002<\/li>\n
  • \u5927\u89c4\u6a21\u6570\u636e<\/strong>\uff1a\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u9009\u62e9\u652f\u6301\u4ea4\u4e92\u4e14\u9ad8\u6027\u80fd\u7684\u5e93\uff08\u5982Holoviews\u4e0eBokeh\u7ed3\u5408\uff09\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/li>\n<\/ul>\n
    \u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u4e86\u89e3\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\uff0c\u5e76\u6839\u636e\u5b9e\u9645\u9700\u6c42\u6765\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u6280\u672f\uff0c\u5c06\u6709\u52a9\u4e8e\u66f4\u6709\u6548\u5730\u5b9e\u73b0\u76ee\u6807\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u7ed8\u56fe\u4e2d\u5b9e\u73b0\u5750\u6807\u7684\u7f29\u653e\u529f\u80fd\uff1f<\/strong>
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