{"id":1036308,"date":"2024-12-31T12:05:58","date_gmt":"2024-12-31T04:05:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1036308.html"},"modified":"2024-12-31T12:06:01","modified_gmt":"2024-12-31T04:06:01","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%ae%a1%e7%ae%97%e5%87%a0%e5%b9%b4%e5%86%85%e5%b9%b3%e5%9d%87","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1036308.html","title":{"rendered":"\u5982\u4f55\u7528python\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747\"<\/p>\n

\u7528Python\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u5229\u7528numpy\u5e93\u3001\u4f7f\u7528pandas\u5e93\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pandas\u5e93\u6765\u8ba1\u7b97\u51e0\u5e74\u5185\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570<\/p>\n<\/p>\n

Python\u5185\u7f6e\u51fd\u6570\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u7b80\u5355\u4e14\u5feb\u6377\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u51e0\u5e74\u5185\u7684\u5e73\u5747\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

years_data = [2015, 2016, 2017, 2018, 2019]<\/p>\n
values = [10, 20, 30, 40, 50]<\/p>\n
average = sum(values) \/ len(values)<\/p>\n
print(f"Average value over the years is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u6bcf\u4e00\u5e74\u7684\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u5e76\u901a\u8fc7\u5185\u7f6e\u7684sum()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u603b\u548c\uff0c\u518d\u9664\u4ee5\u6570\u636e\u7684\u957f\u5ea6\u6765\u83b7\u5f97\u5e73\u5747\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u63a5\uff0c\u4f46\u5e76\u4e0d\u9002\u7528\u4e8e\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5229\u7528numpy\u5e93<\/p>\n<\/p>\n
Numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002\u4ee5\u4e0b\u662f\u5229\u7528numpy\u5e93\u6765\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747\u503c\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
years_data = np.array([2015, 2016, 2017, 2018, 2019])<\/p>\n
values = np.array([10, 20, 30, 40, 50])<\/p>\n
average = np.mean(values)<\/p>\n
print(f"Average value over the years is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy\u7684mean()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3002Numpy\u5e93\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u51fd\u6570\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528pandas\u5e93<\/p>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528pandas\u5e93\u6765\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747\u503c\u7684\u8be6\u7ec6\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5e74\u4efd\u548c\u76f8\u5e94\u503c\u7684\u6570\u636e\u6846<\/strong><\/h2>\n
data = {<\/p>\n
    'Year': [2015, 2016, 2017, 2018, 2019],<\/p>\n
    'Value': [10, 20, 30, 40, 50]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
average = df['Value'].mean()<\/p>\n
print(f"Average value over the years is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5e74\u4efd\u548c\u76f8\u5e94\u503c\u7684\u6570\u636e\u6846\uff0c\u7136\u540e\u4f7f\u7528pandas\u7684mean()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3002Pandas\u5e93\u4e0d\u4ec5\u53ef\u4ee5\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u66f4\u591a\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684\u8be6\u7ec6\u4ecb\u7ecd\uff1a<\/strong><\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u6570\u636e<\/p>\n<\/p>\n
Pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u8bfb\u53d6\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4eceCSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u591a\u79cd\u683c\u5f0f\u8bfb\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528read_csv()<\/code>\u51fd\u6570\u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u4f7f\u7528head()<\/code>\u51fd\u6570\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u6570\u636e\u5f80\u5f80\u662f\u4e0d\u5b8c\u6574\u6216\u4e0d\u4e00\u81f4\u7684\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u7684\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
df = df.dropna()<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df = df.fillna(0)<\/p>\n
\u5220\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
df = df.drop_duplicates()<\/p>\n
\u66f4\u6539\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
df['Year'] = df['Year'].astype(int)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790<\/p>\n<\/p>\n
Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u3001\u5206\u7ec4\u6c47\u603b\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/p>\n
print(df.describe())<\/p>\n
\u6309\u5e74\u4efd\u5206\u7ec4\u5e76\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
average_by_year = df.groupby('Year')['Value'].mean()<\/p>\n
print(average_by_year)<\/p>\n
\u8ba1\u7b97\u6eda\u52a8\u5e73\u5747\u503c<\/strong><\/h2>\n
df['Rolling_Avg'] = df['Value'].rolling(window=3).mean()<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8ba1\u7b97\u4e86\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\uff0c\u7136\u540e\u6309\u5e74\u4efd\u5206\u7ec4\u5e76\u8ba1\u7b97\u4e86\u6bcf\u5e74\u7684\u5e73\u5747\u503c\uff0c\u6700\u540e\u8ba1\u7b97\u4e86\u6eda\u52a8\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
4\u3001\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
Pandas\u5e93\u4e0ematplotlib\u5e93\u96c6\u6210\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u53ef\u89c6\u5316\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(df['Year'], df['Value'], label='Value')<\/p>\n
plt.plot(df['Year'], df['Rolling_Avg'], label='Rolling_Avg')<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.legend()<\/p>\n
plt.xlabel('Year')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.title('Value over the Years')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u4e86\u4e00\u4e2a\u6298\u7ebf\u56fe\uff0c\u5c55\u793a\u4e86\u6bcf\u5e74\u7684\u6570\u636e\u503c\u548c\u6eda\u52a8\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
\u603b\u4e4b\uff0c\u4f7f\u7528Python\u8ba1\u7b97\u51e0\u5e74\u5185\u5e73\u5747\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u6570\u636e\u548c\u9700\u6c42\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u6216numpy\u5e93\uff1b\u5bf9\u4e8e\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u63a8\u8350\u4f7f\u7528pandas\u5e93\u3002\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\u548c\u793a\u4f8b\uff0c\u5e0c\u671b\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u8fdb\u884c\u6570\u636e\u7684\u8ba1\u7b97\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728\u7528Python\u8ba1\u7b97\u51e0\u5e74\u7684\u5e73\u5747\u503c\u65f6\uff0c\u5e94\u8be5\u4f7f\u7528\u54ea\u4e9b\u6570\u636e\u7ed3\u6784\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u51e0\u79cd\u6570\u636e\u7ed3\u6784\u5305\u62ec\u5217\u8868\u3001\u5143\u7ec4\u548c\u5b57\u5178\u3002\u5217\u8868\u662f\u6700\u5e38\u7528\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u8f7b\u677e\u5b58\u50a8\u548c\u5904\u7406\u4e00\u7cfb\u5217\u6570\u5b57\u3002\u4f60\u53ef\u4ee5\u5c06\u6bcf\u5e74\u7684\u6570\u636e\u5b58\u5165\u4e00\u4e2a\u5217\u8868\uff0c\u7136\u540e\u4f7f\u7528\u5185\u7f6e\u7684sum()<\/code>\u548clen()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3002\u6b64\u5916\uff0cPandas\u5e93\u4e5f\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u9002\u5408\u5904\u7406\u66f4\u590d\u6742\u7684\u60c5\u51b5\u3002<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u7b80\u5316Python\u4e2d\u7684\u5e73\u5747\u503c\u8ba1\u7b97\uff1f<\/strong>
Pandas\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684DataFrame<\/code>\u548cSeries<\/code>\u5bf9\u8c61\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u5e73\u5747\u503c\u8ba1\u7b97\u3002\u4f7f\u7528Pandas\u65f6\uff0c\u4f60\u53ea\u9700\u8c03\u7528.mean()<\/code>\u65b9\u6cd5\u5373\u53ef\u8ba1\u7b97\u51fa\u67d0\u4e00\u5217\u7684\u5e73\u5747\u503c\u3002\u6b64\u5916\uff0cNumPy\u4e5f\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86numpy.mean()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5feb\u901f\u8ba1\u7b97\u6570\u7ec4\u6216\u5217\u8868\u7684\u5e73\u5747\u503c\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u786e\u4fdd\u8ba1\u7b97\u7684\u51c6\u786e\u6027\uff1f<\/strong>
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