{"id":1036905,"date":"2024-12-31T12:10:38","date_gmt":"2024-12-31T04:10:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1036905.html"},"modified":"2024-12-31T12:10:40","modified_gmt":"2024-12-31T04:10:40","slug":"python%e6%9d%a1%e5%bd%a2%e5%9b%be%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e6%95%b0%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1036905.html","title":{"rendered":"python\u6761\u5f62\u56fe\u5982\u4f55\u6dfb\u52a0\u6570\u503c"},"content":{"rendered":"

\"python\u6761\u5f62\u56fe\u5982\u4f55\u6dfb\u52a0\u6570\u503c\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c\uff1a\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Seaborn\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u3002\u8fd9\u91cc\u4e3b\u8981\u4ee5Matplotlib\u4e3a\u4f8b\uff0c\u8be6\u7ec6\u8bb2\u89e3\u5982\u4f55\u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c\u3002<\/strong><\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5177\u6709\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u8f7b\u677e\u5730\u5728\u56fe\u5f62\u4e0a\u6dfb\u52a0\u6570\u503c\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001Matplotlib\u7ed8\u5236\u6761\u5f62\u56fe\u5e76\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n

1\u3001\u7ed8\u5236\u57fa\u7840\u6761\u5f62\u56fe<\/strong><\/p>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Matplotlib\u5e93\uff0c\u5e76\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6761\u5f62\u56fe\u3002\u4e3a\u4e86\u6f14\u793a\u65b9\u4fbf\uff0c\u6211\u4eec\u4f7f\u7528\u4e00\u4e9b\u968f\u673a\u6570\u636e\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [23, 45, 56, 78, 89]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
plt.bar(categories, values)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c<\/strong><\/p>\n<\/p>\n
\u8981\u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528plt.text<\/code>\u65b9\u6cd5\u3002\u8be5\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5728\u6307\u5b9a\u7684\u4f4d\u7f6e\u6dfb\u52a0\u6587\u672c\u6807\u7b7e\u3002\u6211\u4eec\u53ef\u4ee5\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [23, 45, 56, 78, 89]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
bars = plt.bar(categories, values)<\/p>\n
\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
for bar in bars:<\/p>\n
    yval = bar.get_height()<\/p>\n
    plt.text(bar.get_x() + bar.get_width()\/2, yval + 1, yval, ha='center', va='bottom')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8c03\u6574\u6570\u503c\u683c\u5f0f<\/strong><\/p>\n<\/p>\n
\u6709\u65f6\uff0c\u6211\u4eec\u5e0c\u671b\u8c03\u6574\u6570\u503c\u7684\u683c\u5f0f\uff0c\u4f8b\u5982\u663e\u793a\u6574\u6570\u3001\u5c0f\u6570\u70b9\u6216\u767e\u5206\u6bd4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u529f\u80fd\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [23, 45, 56, 78, 89]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
bars = plt.bar(categories, values)<\/p>\n
\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
for bar in bars:<\/p>\n
    yval = bar.get_height()<\/p>\n
    plt.text(bar.get_x() + bar.get_width()\/2, yval + 1, f'{yval:.2f}', ha='center', va='bottom')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Seaborn\u7ed8\u5236\u6761\u5f62\u56fe\u5e76\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
1\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/p>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5177\u6709\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe\uff0c\u5e76\u6dfb\u52a0\u6570\u503c\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [23, 45, 56, 78, 89]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
sns.barplot(x=categories, y=values)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5728Seaborn\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c<\/strong><\/p>\n<\/p>\n
\u4e0eMatplotlib\u7c7b\u4f3c\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Seaborn\u6761\u5f62\u56fe\u7684\u57fa\u7840\u4e0a\u4f7f\u7528plt.text<\/code>\u65b9\u6cd5\u6dfb\u52a0\u6570\u503c\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [23, 45, 56, 78, 89]<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
ax = sns.barplot(x=categories, y=values)<\/p>\n
\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
for p in ax.patches:<\/p>\n
    ax.annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() \/ 2., p.get_height()), ha='center', va='baseline')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Pandas\u7ed8\u5236\u6761\u5f62\u56fe\u5e76\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
1\u3001\u4f7f\u7528Pandas\u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/p>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u4e0eMatplotlib\u548cSeaborn\u7ed3\u5408\u4f7f\u7528\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7ed8\u5236\u6761\u5f62\u56fe\uff0c\u5e76\u6dfb\u52a0\u6570\u503c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = {'Category': ['A', 'B', 'C', 'D', 'E'], 'Value': [23, 45, 56, 78, 89]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
ax = df.plot(kind='bar', x='Category', y='Value', legend=False)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5728Pandas\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c<\/strong><\/p>\n<\/p>\n
\u4e0eMatplotlib\u548cSeaborn\u7c7b\u4f3c\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Pandas\u6761\u5f62\u56fe\u7684\u57fa\u7840\u4e0a\u4f7f\u7528plt.text<\/code>\u65b9\u6cd5\u6dfb\u52a0\u6570\u503c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = {'Category': ['A', 'B', 'C', 'D', 'E'], 'Value': [23, 45, 56, 78, 89]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
ax = df.plot(kind='bar', x='Category', y='Value', legend=False)<\/p>\n
\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c<\/strong><\/h2>\n
for p in ax.patches:<\/p>\n
    ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/strong><\/h2>\n
\u901a\u8fc7\u4e0a\u8ff0\u793a\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPandas\u7ed8\u5236\u6761\u5f62\u56fe\u5e76\u6dfb\u52a0\u6570\u503c\u7684\u65b9\u6cd5\u3002Matplotlib\u63d0\u4f9b\u4e86\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u6ee1\u8db3\u5927\u90e8\u5206\u9700\u6c42\uff1bSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff1bPandas\u5219\u7ed3\u5408\u4e86\u6570\u636e\u5904\u7406\u548c\u7ed8\u56fe\u529f\u80fd\uff0c\u4f7f\u5f97\u6570\u636e\u5206\u6790\u66f4\u52a0\u65b9\u4fbf\u3002<\/strong>\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u638c\u63e1Python\u7ed8\u56fe\u548c\u6570\u636e\u53ef\u89c6\u5316\u7684\u6280\u5de7\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u7684\u6761\u5f62\u56fe\u4e0a\u663e\u793a\u6570\u503c\u6807\u7b7e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u521b\u5efa\u6761\u5f62\u56fe\u5e76\u6dfb\u52a0\u6570\u503c\u6807\u7b7e\u3002\u9996\u5148\uff0c\u7ed8\u5236\u6761\u5f62\u56fe\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528text<\/code>\u51fd\u6570\u5728\u6bcf\u4e2a\u6761\u5f62\u7684\u9876\u90e8\u6dfb\u52a0\u6570\u503c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528ax.text(x, y, label)<\/code>\u53ef\u4ee5\u5728\u6761\u5f62\u7684\u5750\u6807\u4f4d\u7f6e\u4e0a\u6dfb\u52a0\u5bf9\u5e94\u7684\u6570\u503c\u3002\u786e\u4fdd\u6b63\u786e\u8bbe\u7f6ex\u548cy\u5750\u6807\uff0c\u4ee5\u4fbf\u6807\u7b7e\u4f4d\u7f6e\u6070\u5f53\u3002<\/p>\n
\u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe\u65f6\uff0c\u5982\u4f55\u6dfb\u52a0\u6570\u503c\u663e\u793a\uff1f<\/strong>
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u7ed3\u5408Seaborn\u7684barplot<\/code>\u51fd\u6570\u548cMatplotlib\u7684text<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u5728\u7ed8\u5236\u56fe\u5f62\u540e\uff0c\u904d\u5386\u6bcf\u4e2a\u6761\u5f62\uff0c\u5229\u7528ax.text<\/code>\u6dfb\u52a0\u6570\u503c\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u6761\u5f62\u56fe\u4e0d\u4ec5\u7f8e\u89c2\uff0c\u8fd8\u80fd\u76f4\u89c2\u663e\u793a\u6bcf\u4e2a\u6761\u5f62\u7684\u5177\u4f53\u6570\u503c\u3002<\/p>\n
\u5728\u6761\u5f62\u56fe\u4e2d\u6dfb\u52a0\u6570\u503c\u65f6\uff0c\u6709\u54ea\u4e9b\u6700\u4f73\u5b9e\u8df5\uff1f<\/strong>
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