{"id":983687,"date":"2024-12-27T07:21:32","date_gmt":"2024-12-26T23:21:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/983687.html"},"modified":"2024-12-27T07:21:34","modified_gmt":"2024-12-26T23:21:34","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e7%94%bb%e5%87%bd%e6%95%b0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/983687.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u753b\u51fd\u6570\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u7ed8\u753b\u51fd\u6570\u56fe\"<\/p>\n

\u4f7f\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u5e93\u5b9e\u73b0\uff0c\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u3002Matplotlib\u662f\u6700\u57fa\u7840\u548c\u5e7f\u6cdb\u4f7f\u7528\u7684\u5e93\u3001Seaborn\u63d0\u4f9b\u66f4\u9ad8\u7ea7\u548c\u7f8e\u89c2\u7684\u56fe\u5f62\u3001Plotly\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u51fd\u6570\u3001\u751f\u6210\u6570\u636e\u3001\u8c03\u7528\u7ed8\u56fe\u51fd\u6570\u6765\u7ed8\u5236\u51fd\u6570\u56fe\u3002<\/strong><\/p>\n<\/p>\n

\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u7ed8\u5236\u51fd\u6570\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7ed8\u5236\u51fd\u6570\u56fe<\/strong><\/h2>\n

1\u3001\u5b89\u88c5\u4e0e\u57fa\u7840\u4f7f\u7528<\/h2>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u3002\u9996\u5148\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6bd5\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165Matplotlib\u7684pyplot<\/code>\u6a21\u5757\u6765\u5f00\u59cb\u7ed8\u56fe\u3002pyplot<\/code>\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eMATLAB\u7684\u7ed8\u56feAPI\uff0c\u975e\u5e38\u6613\u4e8e\u4f7f\u7528\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
def my_function(x):<\/p>\n
    return np.sin(x)<\/p>\n
\u751f\u6210\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = my_function(x)<\/p>\n
\u7ed8\u5236\u51fd\u6570\u56fe<\/strong><\/h2>\n
plt.plot(x, y, label='sin(x)')<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Function Plot')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u81ea\u5b9a\u4e49\u56fe\u5f62\u6837\u5f0f<\/h2>\n<\/p>\n
Matplotlib\u5141\u8bb8\u6211\u4eec\u81ea\u5b9a\u4e49\u56fe\u5f62\u6837\u5f0f\u4ee5\u6ee1\u8db3\u4e0d\u540c\u9700\u6c42\uff0c\u5305\u62ec\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\u3002<\/p>\n<\/p>\n
plt.plot(x, y, color='red', linestyle='--', marker='o', label='sin(x)')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u8c03\u6574\u56fe\u5f62\u7684\u5927\u5c0f\u3001\u5206\u8fa8\u7387\u3001\u5750\u6807\u8f74\u7b49\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 5), dpi=100)<\/p>\n
plt.xlim(0, 10)<\/p>\n
plt.ylim(-1, 1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001SEABORN\u7ed8\u5236\u51fd\u6570\u56fe<\/strong><\/h2>\n
1\u3001\u5b89\u88c5\u4e0e\u57fa\u7840\u4f7f\u7528<\/h2>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u7b80\u5355\u7684API\u3002\u9996\u5148\u786e\u4fdd\u5b89\u88c5Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Seaborn\u7ed8\u5236\u51fd\u6570\u56fe\u4e00\u822c\u9700\u8981\u7ed3\u5408Pandas\u7684\u6570\u636e\u7ed3\u6784\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Seaborn\u7684lineplot<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n
data = pd.DataFrame({'x': x, 'y': y})<\/p>\n
\u7ed8\u5236\u51fd\u6570\u56fe<\/strong><\/h2>\n
sns.lineplot(data=data, x='x', y='y')<\/p>\n
plt.title('Seaborn Function Plot')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u589e\u5f3a\u56fe\u5f62\u6548\u679c<\/h2>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u589e\u5f3a\u56fe\u5f62\u6548\u679c\u7684\u529f\u80fd\uff0c\u5305\u62ec\u81ea\u52a8\u8c03\u8272\u677f\u3001\u4e3b\u9898\u8bbe\u7f6e\u7b49\u3002<\/p>\n<\/p>\n
sns.set_theme(style="darkgrid")<\/p>\n
sns.lineplot(data=data, x='x', y='y', palette="tab10")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001PLOTLY\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/strong><\/h2>\n
1\u3001\u5b89\u88c5\u4e0e\u57fa\u7840\u4f7f\u7528<\/h2>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u5b83\u652f\u6301Web\u6d4f\u89c8\u5668\u4e2d\u663e\u793a\u7684\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u9996\u5148\u786e\u4fdd\u5b89\u88c5Plotly\u5e93\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Plotly\u7ed8\u5236\u51fd\u6570\u56fe\u53ef\u4ee5\u901a\u8fc7plotly.graph_objects<\/code>\u6a21\u5757\u4e2d\u7684Scatter<\/code>\u7c7b\u3002<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
\u7ed8\u5236\u4ea4\u4e92\u5f0f\u51fd\u6570\u56fe<\/strong><\/h2>\n
fig = go.Figure()<\/p>\n
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='sin(x)'))<\/p>\n
fig.update_layout(title='Interactive Function Plot', xaxis_title='x', yaxis_title='y')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4e30\u5bcc\u4ea4\u4e92\u529f\u80fd<\/h2>\n<\/p>\n
Plotly\u652f\u6301\u4e30\u5bcc\u7684\u4ea4\u4e92\u529f\u80fd\uff0c\u5305\u62ec\u7f29\u653e\u3001\u5e73\u79fb\u3001\u60ac\u505c\u63d0\u793a\u7b49\u3002\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u6dfb\u52a0\u591a\u4e2aScatter<\/code>\u5bf9\u8c61\u6765\u7ed8\u5236\u591a\u6761\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
y2 = np.cos(x)<\/p>\n
fig.add_trace(go.Scatter(x=x, y=y2, mode='lines', name='cos(x)'))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001PYTHON\u7ed8\u5236\u51fd\u6570\u56fe\u7684\u6700\u4f73\u5b9e\u8df5<\/strong><\/h2>\n
1\u3001\u9009\u62e9\u5408\u9002\u7684\u5e93<\/h2>\n<\/p>\n
\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u662f\u7ed8\u5236\u51fd\u6570\u56fe\u7684\u7b2c\u4e00\u6b65\u3002Matplotlib\u9002\u5408\u9759\u6001\u56fe\uff0cSeaborn\u9002\u5408\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\uff0cPlotly\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u51c6\u5907\u4e0e\u5904\u7406<\/h2>\n<\/p>\n
\u5728\u7ed8\u56fe\u4e4b\u524d\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u6b63\u786e\u6027\u3002\u4f7f\u7528Numpy\u751f\u6210\u6570\u636e\uff0cPandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u7ba1\u7406\u3002<\/p>\n<\/p>\n
3\u3001\u56fe\u5f62\u7f8e\u89c2\u4e0e\u5e03\u5c40<\/h2>\n<\/p>\n
\u901a\u8fc7\u81ea\u5b9a\u4e49\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u7b7e\u7b49\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002\u4f7f\u7528\u7f51\u683c\u3001\u6ce8\u91ca\u548c\u6807\u7b7e\u4f7f\u56fe\u5f62\u4fe1\u606f\u66f4\u52a0\u660e\u786e\u3002<\/p>\n<\/p>\n
4\u3001\u4ea4\u4e92\u4e0e\u52a8\u6001<\/h2>\n<\/p>\n
\u5982\u679c\u9700\u8981\u4ea4\u4e92\u529f\u80fd\uff0c\u4f18\u5148\u9009\u62e9Plotly\u3002\u5728Web\u5e94\u7528\u4e2d\u96c6\u6210\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u4ee5\u63d0\u9ad8\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u5e0c\u671b\u4f60\u5bf9\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u6709\u4e86\u5168\u9762\u7684\u4e86\u89e3\u3002\u65e0\u8bba\u662f\u9759\u6001\u56fe\u8fd8\u662f\u4ea4\u4e92\u5f0f\u56fe\uff0cPython\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u6765\u6ee1\u8db3\u5404\u79cd\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\uff1f<\/strong>
\u8981\u5728Python\u4e2d\u7ed8\u5236\u51fd\u6570\u56fe\uff0c\u5e38\u7528\u7684\u5e93\u662fMatplotlib\u3002\u60a8\u9700\u8981\u5b89\u88c5\u8fd9\u4e2a\u5e93\uff0c\u7136\u540e\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u60a8\u7684\u51fd\u6570\u3002\u53ef\u4ee5\u901a\u8fc7numpy<\/code>\u5e93\u751f\u6210\u6570\u636e\u70b9\uff0c\u4f7f\u5f97\u56fe\u5f62\u66f4\u52a0\u5e73\u6ed1\u548c\u51c6\u786e\u3002<\/p>\n
\u5728\u7ed8\u5236\u51fd\u6570\u56fe\u65f6\uff0c\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u5750\u6807\u8303\u56f4\uff1f<\/strong>
\u9009\u62e9\u5750\u6807\u8303\u56f4\u8981\u6839\u636e\u51fd\u6570\u7684\u7279\u6027\u8fdb\u884c\u8c03\u6574\u3002\u53ef\u4ee5\u901a\u8fc7\u67e5\u770b\u51fd\u6570\u7684\u5b9a\u4e49\u57df\u548c\u56fe\u50cf\u7684\u7279\u5f81\uff0c\u4f7f\u7528numpy.linspace()<\/code>\u751f\u6210\u9002\u5f53\u7684x\u503c\u8303\u56f4\u3002\u786e\u4fdd\u6db5\u76d6\u4e86\u51fd\u6570\u7684\u5173\u952e\u70b9\u548c\u53d8\u5316\u8d8b\u52bf\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5c55\u793a\u56fe\u5f62\u3002<\/p>\n
\u80fd\u5426\u5728Python\u4e2d\u6dfb\u52a0\u591a\u4e2a\u51fd\u6570\u5230\u540c\u4e00\u5f20\u56fe\u8868\uff1f<\/strong>
\u53ef\u4ee5\u7684\uff01\u901a\u8fc7\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u591a\u6b21\u8c03\u7528plt.plot()<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u5c06\u591a\u4e2a\u51fd\u6570\u7ed8\u5236\u5230\u540c\u4e00\u5f20\u56fe\u8868\u4e0a\u3002\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u548c\u6807\u8bb0\u6765\u533a\u5206\u5404\u4e2a\u51fd\u6570\uff0c\u6dfb\u52a0\u56fe\u4f8b\u53ef\u4ee5\u5e2e\u52a9\u89c2\u4f17\u7406\u89e3\u6bcf\u6761\u66f2\u7ebf\u4ee3\u8868\u7684\u542b\u4e49\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u7ed8\u5236\u51fd\u6570\u56fe\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u5e93\u5b9e\u73b0\uff0c\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly […]","protected":false},"author":3,"featured_media":983690,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/983687"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=983687"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/983687\/revisions"}],"predecessor-version":[{"id":983692,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/983687\/revisions\/983692"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/983690"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=983687"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=983687"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=983687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}