{"id":1121943,"date":"2025-01-08T19:15:20","date_gmt":"2025-01-08T11:15:20","guid":{"rendered":""},"modified":"2025-01-08T19:15:22","modified_gmt":"2025-01-08T11:15:22","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e4%b8%8d%e5%90%8c%e9%a3%8e%e6%a0%bc%e6%9d%a1%e5%bd%a2%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1121943.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u4e0d\u540c\u98ce\u683c\u6761\u5f62\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u4e0d\u540c\u98ce\u683c\u6761\u5f62\u56fe\"<\/p>\n

\u7528Python\u753b\u4e0d\u540c\u98ce\u683c\u6761\u5f62\u56fe\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Seaborn\u5e93\u3001\u4f7f\u7528Plotly\u5e93\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u548cSeaborn\u8fd9\u4e24\u4e2a\u5e38\u7528\u5e93\u6765\u521b\u5efa\u5404\u79cd\u98ce\u683c\u7684\u6761\u5f62\u56fe\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u5e93<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6761\u5f62\u56fe\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u5e93\u521b\u5efa\u4e0d\u540c\u98ce\u683c\u7684\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n

1\u3001\u57fa\u672c\u6761\u5f62\u56fe<\/h3>\n<\/p>\n

Matplotlib\u7684\u57fa\u672c\u6761\u5f62\u56fe\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u751f\u6210\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
values = [10, 24, 36, 40]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
plt.bar(labels, values)<\/p>\n
plt.xlabel('Labels')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.title('Basic Bar Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u751f\u6210\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6761\u5f62\u56fe\uff0c\u663e\u793a\u4e86\u4e0d\u540c\u6807\u7b7e\u5bf9\u5e94\u7684\u6570\u503c\u3002<\/p>\n<\/p>\n
2\u3001\u6c34\u5e73\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
\u6c34\u5e73\u6761\u5f62\u56fe\u662f\u6761\u5f62\u56fe\u7684\u4e00\u79cd\u53d8\u4f53\uff0c\u6761\u5f62\u56fe\u7684\u6761\u662f\u6c34\u5e73\u653e\u7f6e\u7684\u3002\u4f7f\u7528barh<\/code>\u51fd\u6570\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u521b\u5efa\u6c34\u5e73\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6c34\u5e73\u6761\u5f62\u56fe<\/p>\n
plt.barh(labels, values)<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Labels')<\/p>\n
plt.title('Horizontal Bar Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u7ec4\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u6709\u591a\u4e2a\u6570\u636e\u7cfb\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u5206\u7ec4\u6761\u5f62\u56fe\u6765\u663e\u793a\u3002\u4e0b\u9762\u7684\u793a\u4f8b\u663e\u793a\u4e86\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u5206\u7ec4\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
N = 4<\/p>\n
values1 = [10, 20, 30, 40]<\/p>\n
values2 = [15, 25, 35, 45]<\/p>\n
ind = np.arange(N)    # x\u8f74\u4f4d\u7f6e<\/p>\n
width = 0.35          # \u6761\u5f62\u5bbd\u5ea6<\/p>\n
\u521b\u5efa\u5206\u7ec4\u6761\u5f62\u56fe<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
rects1 = ax.bar(ind, values1, width, label='Series1')<\/p>\n
rects2 = ax.bar(ind + width, values2, width, label='Series2')<\/p>\n
ax.set_xlabel('Labels')<\/p>\n
ax.set_ylabel('Values')<\/p>\n
ax.set_title('Grouped Bar Chart')<\/p>\n
ax.set_xticks(ind + width \/ 2)<\/p>\n
ax.set_xticklabels(('A', 'B', 'C', 'D'))<\/p>\n
ax.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u5806\u53e0\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
\u5806\u53e0\u6761\u5f62\u56fe\u53ef\u4ee5\u7528\u6765\u663e\u793a\u591a\u4e2a\u6570\u636e\u7cfb\u5217\u5728\u540c\u4e00\u4e2a\u6761\u5f62\u4e0a\u53e0\u52a0\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u6570\u636e<\/p>\n
values3 = [5, 15, 25, 35]<\/p>\n
\u521b\u5efa\u5806\u53e0\u6761\u5f62\u56fe<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.bar(labels, values1, label='Series1')<\/p>\n
ax.bar(labels, values2, bottom=values1, label='Series2')<\/p>\n
ax.bar(labels, values3, bottom=np.array(values1) + np.array(values2), label='Series3')<\/p>\n
ax.set_xlabel('Labels')<\/p>\n
ax.set_ylabel('Values')<\/p>\n
ax.set_title('Stacked Bar Chart')<\/p>\n
ax.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u5e26\u6709\u8bef\u5dee\u6761\u7684\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
\u4e3a\u4e86\u663e\u793a\u6570\u636e\u7684\u4e0d\u786e\u5b9a\u6027\uff0c\u53ef\u4ee5\u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u8bef\u5dee\u6761\u3002<\/p>\n<\/p>\n
# \u6570\u636e<\/p>\n
errors = [2, 3, 4, 1]<\/p>\n
\u521b\u5efa\u5e26\u6709\u8bef\u5dee\u6761\u7684\u6761\u5f62\u56fe<\/strong><\/h2>\n
plt.bar(labels, values, yerr=errors, capsize=5)<\/p>\n
plt.xlabel('Labels')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.title('Bar Chart with Error Bars')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Seaborn\u5e93<\/p>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\uff0c\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
Seaborn\u7684\u57fa\u672c\u6761\u5f62\u56fe\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u751f\u6210\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'Labels': ['A', 'B', 'C', 'D'],<\/p>\n
    'Values': [10, 24, 36, 40]<\/p>\n
})<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
sns.barplot(x='Labels', y='Values', data=data)<\/p>\n
plt.xlabel('Labels')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.title('Basic Bar Chart with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6c34\u5e73\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
\u6c34\u5e73\u6761\u5f62\u56fe\u5728Seaborn\u4e2d\u4e5f\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6c34\u5e73\u6761\u5f62\u56fe<\/p>\n
sns.barplot(x='Values', y='Labels', data=data, orient='h')<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Labels')<\/p>\n
plt.title('Horizontal Bar Chart with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u7ec4\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
Seaborn\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u521b\u5efa\u5206\u7ec4\u6761\u5f62\u56fe\uff0c\u53ea\u9700\u8981\u5c06\u6570\u636e\u6574\u7406\u6210\u9002\u5f53\u7684\u683c\u5f0f\u3002<\/p>\n<\/p>\n
# \u6570\u636e<\/p>\n
data = pd.DataFrame({<\/p>\n
    'Labels': ['A', 'B', 'C', 'D'] * 2,<\/p>\n
    'Values': [10, 20, 30, 40, 15, 25, 35, 45],<\/p>\n
    'Series': ['Series1'] * 4 + ['Series2'] * 4<\/p>\n
})<\/p>\n
\u521b\u5efa\u5206\u7ec4\u6761\u5f62\u56fe<\/strong><\/h2>\n
sns.barplot(x='Labels', y='Values', hue='Series', data=data)<\/p>\n
plt.xlabel('Labels')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.title('Grouped Bar Chart with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u5e26\u6709\u8bef\u5dee\u6761\u7684\u6761\u5f62\u56fe<\/h3>\n<\/p>\n
Seaborn\u4e5f\u652f\u6301\u5e26\u6709\u8bef\u5dee\u6761\u7684\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n
# \u6570\u636e<\/p>\n
data = pd.DataFrame({<\/p>\n
    'Labels': ['A', 'B', 'C', 'D'],<\/p>\n
    'Values': [10, 24, 36, 40],<\/p>\n
    'Errors': [2, 3, 4, 1]<\/p>\n
})<\/p>\n
\u521b\u5efa\u5e26\u6709\u8bef\u5dee\u6761\u7684\u6761\u5f62\u56fe<\/strong><\/h2>\n
sns.barplot(x='Labels', y='Values', data=data, ci='sd')<\/p>\n
plt.xlabel('Labels')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.title('Bar Chart with Error Bars using Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Plotly\u5e93<\/p>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684\u7ed8\u56fe\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u521b\u5efa\u4e92\u52a8\u56fe\u8868\u548c\u5c55\u793a\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u53ea\u7b80\u5355\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Plotly\u521b\u5efa\u57fa\u672c\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'Labels': ['A', 'B', 'C', 'D'],<\/p>\n
    'Values': [10, 24, 36, 40]<\/p>\n
}<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
fig = px.bar(data, x='Labels', y='Values', title='Basic Bar Chart with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u51e0\u79cd\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u521b\u5efa\u591a\u79cd\u98ce\u683c\u7684\u6761\u5f62\u56fe\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u6761\u5f62\u56fe\u3001\u6c34\u5e73\u6761\u5f62\u56fe\u3001\u5206\u7ec4\u6761\u5f62\u56fe\u8fd8\u662f\u5806\u53e0\u6761\u5f62\u56fe\uff0cMatplotlib\u548cSeaborn\u90fd\u80fd\u591f\u6ee1\u8db3\u4f60\u7684\u9700\u6c42\u3002\u5982\u679c\u4f60\u9700\u8981\u521b\u5efa\u66f4\u590d\u6742\u548c\u4e92\u52a8\u7684\u56fe\u8868\uff0c\u4e5f\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528Plotly\u5e93\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u6761\u5f62\u56fe\u98ce\u683c\u4ee5\u4f20\u8fbe\u6570\u636e\u7684\u542b\u4e49\uff1f<\/strong>
\u9009\u62e9\u6761\u5f62\u56fe\u7684\u98ce\u683c\u65f6\uff0c\u9700\u8003\u8651\u6570\u636e\u7684\u7c7b\u578b\u548c\u53d7\u4f17\u3002\u6bd4\u5982\uff0c\u4f7f\u7528\u660e\u4eae\u7684\u989c\u8272\u548c\u7b80\u5355\u7684\u8bbe\u8ba1\u53ef\u4ee5\u5438\u5f15\u5e74\u8f7b\u89c2\u4f17\uff0c\u800c\u6b63\u5f0f\u7684\u62a5\u544a\u5219\u53ef\u80fd\u9700\u8981\u66f4\u4e3a\u7b80\u6d01\u548c\u4e13\u4e1a\u7684\u98ce\u683c\u3002\u6b64\u5916\uff0c\u4f7f\u7528\u9634\u5f71\u3001\u6e10\u53d8\u6216\u56fe\u6848\u586b\u5145\u53ef\u4ee5\u589e\u52a0\u56fe\u8868\u7684\u89c6\u89c9\u5438\u5f15\u529b\uff0c\u4f7f\u6570\u636e\u66f4\u6613\u4e8e\u7406\u89e3\u3002<\/p>\n
Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u6761\u5f62\u56fe\uff1f<\/strong>
\u5e38\u7528\u7684Python\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002Matplotlib\u63d0\u4f9b\u4e86\u57fa\u7840\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u5408\u521b\u5efa\u7b80\u5355\u7684\u6761\u5f62\u56fe\uff1bSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u9ad8\u7ea7\u7684\u529f\u80fd\uff1bPlotly\u5219\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u7528\u6237\u4e0e\u56fe\u8868\u8fdb\u884c\u4e92\u52a8\u7684\u573a\u666f\u3002<\/p>\n
\u5982\u4f55\u5728\u6761\u5f62\u56fe\u4e2d\u6dfb\u52a0\u6570\u636e\u6807\u7b7e\u4ee5\u63d0\u9ad8\u53ef\u8bfb\u6027\uff1f<\/strong>
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