{"id":1024228,"date":"2024-12-30T14:12:03","date_gmt":"2024-12-30T06:12:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1024228.html"},"modified":"2024-12-30T14:12:05","modified_gmt":"2024-12-30T06:12:05","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e8%a1%8c%e6%95%b0%e6%8d%ae-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1024228.html","title":{"rendered":"python\u5982\u4f55\u7edf\u8ba1\u884c\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u7edf\u8ba1\u884c\u6570\u636e\"<\/p>\n

Python\u7edf\u8ba1\u884c\u6570\u636e\u7684\u51e0\u79cd\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528pandas\u3001\u5229\u7528\u5185\u7f6e\u51fd\u6570\u5982len()\u3001\u501f\u52a9numpy\u5e93\u3002<\/strong> <\/p>\n<\/p>\n

\u5728\u6570\u636e\u5904\u7406\u4e2d\uff0c\u7edf\u8ba1\u884c\u6570\u636e\u662f\u4e00\u4e2a\u975e\u5e38\u5e38\u89c1\u7684\u9700\u6c42\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u5904\u7406\u6570\u636e\uff0c\u8fd9\u662f\u4e00\u79cd\u9ad8\u6548\u4e14\u7b80\u4fbf\u7684\u65b9\u6cd5\u3002Pandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5185\u7f6e\u7684\u65b9\u6cd5\u6765\u7edf\u8ba1\u548c\u5206\u6790\u6570\u636e\uff0c\u4f8b\u5982shape<\/code>\u3001len()<\/code>\u3001count()<\/code>\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0cnumpy\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u7edf\u8ba1\u548c\u5904\u7406\u6570\u636e\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u7684\u4f7f\u7528\uff0c\u5e76\u7ed3\u5408\u5177\u4f53\u7684\u793a\u4f8b\u4ee3\u7801\u8fdb\u884c\u8bf4\u660e\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u7edf\u8ba1\u884c\u6570\u636e<\/h2>\n<\/p>\n

1\u3001\u5bfc\u5165\u6570\u636e\u5e76\u521b\u5efaDataFrame<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u975e\u5e38\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165pandas\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2aDataFrame\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],<\/p>\n
    'Age': [24, 27, 22, 32, 29],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
shape<\/code>\u5c5e\u6027\u53ef\u4ee5\u8fd4\u56deDataFrame\u7684\u884c\u6570\u548c\u5217\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528shape[0]<\/code>\u6765\u83b7\u53d6\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = df.shape[0]<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
len()<\/code>\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8fd4\u56deDataFrame\u7684\u884c\u6570\uff0c\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u7b80\u4fbf\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = len(df)<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u4f7f\u7528count()\u51fd\u6570\u7edf\u8ba1\u975e\u7a7a\u884c\u6570<\/h3>\n<\/p>\n
count()<\/code>\u51fd\u6570\u53ef\u4ee5\u7edf\u8ba1\u6bcf\u4e00\u5217\u7684\u975e\u7a7a\u503c\u7684\u4e2a\u6570\u3002\u5982\u679c\u60f3\u7edf\u8ba1\u884c\u6570\uff0c\u53ef\u4ee5\u9009\u62e9\u5176\u4e2d\u4efb\u610f\u4e00\u5217\u8fdb\u884c\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528count()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = df['Name'].count()<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Numpy\u7edf\u8ba1\u884c\u6570\u636e<\/h2>\n<\/p>\n
Numpy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528numpy\u6765\u7edf\u8ba1\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165numpy\u5e93\u5e76\u521b\u5efa\u6570\u7ec4<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165numpy\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
data = np.array([<\/p>\n
    ['Alice', 24, 'New York'],<\/p>\n
    ['Bob', 27, 'Los Angeles'],<\/p>\n
    ['Charlie', 22, 'Chicago'],<\/p>\n
    ['David', 32, 'Houston'],<\/p>\n
    ['Eve', 29, 'Phoenix']<\/p>\n
])<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
\u4e0ePandas\u7c7b\u4f3c\uff0cnumpy\u7684shape<\/code>\u5c5e\u6027\u4e5f\u53ef\u4ee5\u8fd4\u56de\u6570\u7ec4\u7684\u884c\u6570\u548c\u5217\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528shape[0]<\/code>\u6765\u83b7\u53d6\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = data.shape[0]<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
len()<\/code>\u51fd\u6570\u540c\u6837\u53ef\u4ee5\u76f4\u63a5\u8fd4\u56de\u6570\u7ec4\u7684\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = len(data)<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5176\u4ed6\u7edf\u8ba1\u884c\u6570\u636e\u7684\u65b9\u6cd5<\/h2>\n<\/p>\n
1\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u7eaf\u5217\u8868\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684sum()<\/code>\u51fd\u6570\u914d\u5408\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u6765\u7edf\u8ba1\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u5217\u8868<\/p>\n
data = [<\/p>\n
    ['Alice', 24, 'New York'],<\/p>\n
    ['Bob', 27, 'Los Angeles'],<\/p>\n
    ['Charlie', 22, 'Chicago'],<\/p>\n
    ['David', 32, 'Houston'],<\/p>\n
    ['Eve', 29, 'Phoenix']<\/p>\n
]<\/p>\n
\u4f7f\u7528sum()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/strong><\/h2>\n
row_count = sum(1 for row in data)<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528enumerate\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
enumerate<\/code>\u51fd\u6570\u53ef\u4ee5\u5728\u904d\u5386\u5217\u8868\u7684\u540c\u65f6\uff0c\u63d0\u4f9b\u4e00\u4e2a\u8ba1\u6570\u5668\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528\u8fd9\u4e2a\u7279\u6027\u6765\u7edf\u8ba1\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528enumerate\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = sum(1 for _, _ in enumerate(data))<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528SQLalchemy\u7edf\u8ba1\u6570\u636e\u5e93\u884c\u6570\u636e<\/h2>\n<\/p>\n
\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SQLalchemy\u5e93\u6765\u7edf\u8ba1\u884c\u6570\u3002<\/p>\n<\/p>\n
1\u3001\u8fde\u63a5\u6570\u636e\u5e93\u5e76\u67e5\u8be2\u6570\u636e<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165SQLalchemy\u5e93\uff0c\u7136\u540e\u8fde\u63a5\u5230\u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
engine = create_engine('sqlite:\/\/\/example.db')<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
data = engine.execute("SELECT * FROM users")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528count()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u5728SQL\u67e5\u8be2\u8bed\u53e5\u4e2d\u4f7f\u7528count()<\/code>\u51fd\u6570\u6765\u7edf\u8ba1\u884c\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528SQL\u67e5\u8be2\u8bed\u53e5\u7edf\u8ba1\u884c\u6570<\/p>\n
result = engine.execute("SELECT COUNT(*) FROM users")<\/p>\n
row_count = result.fetchone()[0]<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Dask\u7edf\u8ba1\u5927\u6570\u636e\u96c6\u884c\u6570\u636e<\/h2>\n<\/p>\n
Dask\u662fPython\u4e2d\u7684\u4e00\u4e2a\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Dask\u6765\u7edf\u8ba1\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165Dask\u5e76\u521b\u5efaDataFrame<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165Dask\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2aDask DataFrame\u3002<\/p>\n<\/p>\n
import dask.dataframe as dd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDask DataFrame<\/strong><\/h2>\n
df = dd.from_pandas(pd.DataFrame(data), npartitions=2)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
\u4e0ePandas\u7c7b\u4f3c\uff0cDask DataFrame\u7684shape<\/code>\u5c5e\u6027\u4e5f\u53ef\u4ee5\u8fd4\u56de\u884c\u6570\u548c\u5217\u6570\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528shape\u5c5e\u6027\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = df.shape[0].compute()<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/h3>\n<\/p>\n
len()<\/code>\u51fd\u6570\u540c\u6837\u9002\u7528\u4e8eDask DataFrame\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528len()\u51fd\u6570\u7edf\u8ba1\u884c\u6570<\/p>\n
row_count = len(df)<\/p>\n
print(f"\u884c\u6570: {row_count}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u51e0\u79cd\u5728Python\u4e2d\u7edf\u8ba1\u884c\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u3001Numpy\u3001\u5185\u7f6e\u51fd\u6570\u3001SQLalchemy\u548cDask\u7b49\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u9002\u7528\u7684\u573a\u666f\u548c\u4f18\u7f3a\u70b9\u3002<\/p>\n<\/p>\n
\u4f7f\u7528Pandas\u5904\u7406\u5c0f\u5230\u4e2d\u7b49\u89c4\u6a21\u7684\u6570\u636e\u96c6\u975e\u5e38\u65b9\u4fbf\u3001\u4f7f\u7528Numpy\u5904\u7406\u6570\u503c\u6570\u636e\u9ad8\u6548\u3001\u5bf9\u4e8e\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u53ef\u4ee5\u4f7f\u7528SQLalchemy\u3001\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u53ef\u4ee5\u9009\u62e9Dask\u3002<\/strong><\/p>\n<\/p>\n
\u5e0c\u671b\u672c\u6587\u80fd\u5bf9\u60a8\u5728\u6570\u636e\u5904\u7406\u4e2d\u7edf\u8ba1\u884c\u6570\u636e\u6709\u6240\u5e2e\u52a9\u3002\u5982\u679c\u60a8\u6709\u4efb\u4f55\u95ee\u9898\u6216\u5efa\u8bae\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6\u548c\u5904\u7406\u884c\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406\u884c\u6570\u636e\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86pandas\u5e93\u3002\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\u53ef\u4ee5\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u5bf9\u8c61\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7DataFrame\u7684\u884c\u7d22\u5f15\u6765\u8bbf\u95ee\u548c\u7edf\u8ba1\u884c\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528df.shape<\/code>\u6765\u83b7\u53d6\u884c\u6570\uff0c\u6216\u4f7f\u7528df.iloc[]<\/code>\u6765\u9009\u62e9\u7279\u5b9a\u884c\u3002<\/p>\n
\u5728Python\u4e2d\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u7edf\u8ba1\u884c\u6570\u636e\u7684\u6570\u91cf\uff1f<\/strong>
\u7edf\u8ba1\u884c\u6570\u636e\u7684\u6570\u91cf\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b8c\u6210\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528DataFrame\u7684shape<\/code>\u5c5e\u6027\uff0clen()<\/code>\u51fd\u6570\uff0c\u6216count()<\/code>\u65b9\u6cd5\u3002\u4f8b\u5982\uff0clen(df)<\/code>\u53ef\u4ee5\u8fd4\u56de\u884c\u7684\u603b\u6570\uff0c\u800cdf.count()<\/code>\u5219\u8fd4\u56de\u6bcf\u5217\u975e\u7a7a\u503c\u7684\u8ba1\u6570\uff0c\u5e2e\u52a9\u4f60\u4e86\u89e3\u6570\u636e\u7684\u5b8c\u6574\u6027\u3002<\/p>\n
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