{"id":1105235,"date":"2025-01-08T16:29:02","date_gmt":"2025-01-08T08:29:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1105235.html"},"modified":"2025-01-08T16:29:04","modified_gmt":"2025-01-08T08:29:04","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8a%e6%95%b0%e6%8d%ae%e9%9b%86%e5%8f%af%e8%a7%86%e5%8c%96%e5%87%ba%e6%9d%a5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1105235.html","title":{"rendered":"python\u5982\u4f55\u628a\u6570\u636e\u96c6\u53ef\u89c6\u5316\u51fa\u6765"},"content":{"rendered":"

\"python\u5982\u4f55\u628a\u6570\u636e\u96c6\u53ef\u89c6\u5316\u51fa\u6765\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5c06\u6570\u636e\u96c6\u53ef\u89c6\u5316\u51fa\u6765<\/strong>\uff0c\u4f7f\u7528Matplotlib\u5e93\u3001\u4f7f\u7528Seaborn\u5e93\u3001\u4f7f\u7528Pandas\u81ea\u5e26\u7684\u7ed8\u56fe\u529f\u80fd<\/strong>\u3002\u5176\u4e2d\uff0cMatplotlib\u5e93<\/strong>\u662f\u6700\u57fa\u7840\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u540c\u65f6\u4e5f\u662f\u5176\u4ed6\u53ef\u89c6\u5316\u5e93\u7684\u57fa\u7840\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7Matplotlib\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u6b64\u5916\uff0cSeaborn\u5e93\u662f\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u5c01\u88c5\u7684\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u522b\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002Pandas\u5219\u63d0\u4f9b\u4e86\u4fbf\u6377\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u9002\u5408\u5feb\u901f\u751f\u6210\u56fe\u8868\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u5e93<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/h4>\n<\/p>\n

\u5728\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Matplotlib\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u8d8b\u52bf\u53d8\u5316\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y, marker='o')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("\u6298\u7ebf\u56fe\u793a\u4f8b")<\/p>\n
plt.xlabel("X\u8f74\u6807\u7b7e")<\/p>\n
plt.ylabel("Y\u8f74\u6807\u7b7e")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.plot<\/code>\u51fd\u6570\u521b\u5efa\u4e86\u4e00\u4e2a\u6298\u7ebf\u56fe\uff0c\u4f7f\u7528marker='o'<\/code>\u53c2\u6570\u5728\u6570\u636e\u70b9\u5904\u6dfb\u52a0\u4e86\u6807\u8bb0\u3002<\/p>\n<\/p>\n
3\u3001\u7ed8\u5236\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u663e\u793a\u5404\u7c7b\u522b\u7684\u6570\u503c\u5927\u5c0f\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D']<\/p>\n
values = [4, 7, 1, 8]<\/p>\n
\u521b\u5efa\u67f1\u72b6\u56fe<\/strong><\/h2>\n
plt.bar(categories, values)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("\u67f1\u72b6\u56fe\u793a\u4f8b")<\/p>\n
plt.xlabel("\u7c7b\u522b")<\/p>\n
plt.ylabel("\u503c")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("\u6563\u70b9\u56fe\u793a\u4f8b")<\/p>\n
plt.xlabel("X\u8f74\u6807\u7b7e")<\/p>\n
plt.ylabel("Y\u8f74\u6807\u7b7e")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Seaborn\u5e93<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528Seaborn\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Seaborn\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u7bb1\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 2, 3, 3, 3, 4, 4, 5, 6]<\/p>\n
\u521b\u5efa\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(data=data)<\/p>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
plt.title("\u7bb1\u7ebf\u56fe\u793a\u4f8b")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u70ed\u529b\u56fe<\/h4>\n<\/p>\n
\u70ed\u529b\u56fe\u9002\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u70ed\u5ea6\u60c5\u51b5\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u70ed\u529b\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
\u521b\u5efa\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(data, annot=True, cmap='coolwarm')<\/p>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
plt.title("\u70ed\u529b\u56fe\u793a\u4f8b")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528annot=True<\/code>\u53c2\u6570\u5728\u70ed\u529b\u56fe\u7684\u6bcf\u4e2a\u5355\u5143\u683c\u4e2d\u663e\u793a\u6570\u503c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u81ea\u5e26\u7684\u7ed8\u56fe\u529f\u80fd<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Pandas<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Pandas\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4fbf\u6377\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'X': [1, 2, 3, 4, 5],<\/p>\n
    'Y': [2, 3, 5, 7, 11]<\/p>\n
}<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df.plot(x='X', y='Y', marker='o', title="\u6298\u7ebf\u56fe\u793a\u4f8b")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    '\u7c7b\u522b': ['A', 'B', 'C', 'D'],<\/p>\n
    '\u503c': [4, 7, 1, 8]<\/p>\n
}<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
df.plot(kind='bar', x='\u7c7b\u522b', y='\u503c', title="\u67f1\u72b6\u56fe\u793a\u4f8b")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'X': [1, 2, 3, 4, 5],<\/p>\n
    'Y': [2, 3, 5, 7, 11]<\/p>\n
}<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
df.plot(kind='scatter', x='X', y='Y', title="\u6563\u70b9\u56fe\u793a\u4f8b")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316\u7684\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
1\u3001\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b<\/h4>\n<\/p>\n
\u6839\u636e\u6570\u636e\u7684\u7279\u6027\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    \n
  • \u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u8d8b\u52bf\u53d8\u5316<\/li>\n
  • \u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u663e\u793a\u5404\u7c7b\u522b\u7684\u6570\u503c\u5927\u5c0f<\/li>\n
  • \u6563\u70b9\u56fe\u9002\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb<\/li>\n
  • \u7bb1\u7ebf\u56fe\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5<\/li>\n
  • \u70ed\u529b\u56fe\u9002\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u70ed\u5ea6\u60c5\u51b5<\/li>\n<\/ul>\n
    2\u3001\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/h4>\n<\/p>\n
    \u5728\u56fe\u8868\u4e2d\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u53ef\u4ee5\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u56fe\u8868\u7684\u5185\u5bb9\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    plt.title("\u56fe\u8868\u6807\u9898")<\/p>\n
    plt.xlabel("X\u8f74\u6807\u7b7e")<\/p>\n
    plt.ylabel("Y\u8f74\u6807\u7b7e")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u8c03\u6574\u6837\u5f0f\u548c\u989c\u8272<\/h4>\n<\/p>\n
    \u5408\u7406\u8c03\u6574\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\u53ef\u4ee5\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    plt.plot(x, y, marker='o', linestyle='--', color='r')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u8bbe\u7f6e\u4e86\u6298\u7ebf\u56fe\u7684\u7ebf\u578b\u4e3a\u865a\u7ebf\uff0c\u5e76\u5c06\u7ebf\u6761\u989c\u8272\u8bbe\u7f6e\u4e3a\u7ea2\u8272\u3002<\/p>\n<\/p>\n
    \u7ed3\u8bba<\/h3>\n<\/p>\n
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