{"id":1129811,"date":"2025-01-08T20:32:51","date_gmt":"2025-01-08T12:32:51","guid":{"rendered":""},"modified":"2025-01-08T20:32:53","modified_gmt":"2025-01-08T12:32:53","slug":"python%e5%a6%82%e4%bd%95%e7%a1%ae%e5%ae%9a%e6%9f%90%e4%b8%aa%e5%ad%97%e6%ae%b5%e7%9a%84%e5%88%97%e6%95%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1129811.html","title":{"rendered":"python\u5982\u4f55\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570"},"content":{"rendered":"

\"python\u5982\u4f55\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\"<\/p>\n

\u5728Python\u4e2d\uff0c\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong>\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001Numpy\u5e93\u3001\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6<\/strong> \u7b49\u65b9\u6cd5\u3002 Pandas\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684DataFrame\u7ed3\u6784\uff0c\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6\u548c\u64cd\u4f5c\u6570\u636e\u3002Numpy\u5e93\u5219\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u6807\u51c6\u7684\u6587\u4ef6\u64cd\u4f5c\u6765\u5b9e\u73b0\uff0c\u9002\u7528\u4e8e\u7b80\u5355\u6570\u636e\u683c\u5f0f\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u786e\u5b9a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93<\/strong><\/h2>\n

Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u5229\u5668\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u8868\u683c\u6570\u636e\u3002\u5b83\u63d0\u4f9b\u4e86DataFrame\u548cSeries\u4e24\u79cd\u57fa\u672c\u6570\u636e\u7ed3\u6784\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u8f7b\u677e\u8bfb\u53d6CSV\u3001Excel\u7b49\u6587\u4ef6\uff0c\u5e76\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n

CSV\u6587\u4ef6\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\u4e4b\u4e00\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_name = 'your_column_name'<\/p>\n
column_length = df[column_name].shape[0]<\/p>\n
print(f"The length of column '{column_name}' is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6Excel\u6587\u4ef6<\/h3>\n<\/p>\n
Excel\u6587\u4ef6\u5728\u5f88\u591a\u4e1a\u52a1\u573a\u666f\u4e2d\u975e\u5e38\u5e38\u89c1\u3002Pandas\u63d0\u4f9b\u4e86read_excel<\/code>\u51fd\u6570\u6765\u8bfb\u53d6Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_excel('data.xlsx')<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_name = 'your_column_name'<\/p>\n
column_length = df[column_name].shape[0]<\/p>\n
print(f"The length of column '{column_name}' is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bfb\u53d6SQL\u6570\u636e\u5e93<\/h3>\n<\/p>\n
\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728SQL\u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_sql<\/code>\u51fd\u6570\u6765\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import sqlite3<\/p>\n
\u8fde\u63a5\u5230SQLite\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = sqlite3.connect('database.db')<\/p>\n
\u8bfb\u53d6SQL\u6570\u636e<\/strong><\/h2>\n
df = pd.read_sql('SELECT * FROM your_table', conn)<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_name = 'your_column_name'<\/p>\n
column_length = df[column_name].shape[0]<\/p>\n
print(f"The length of column '{column_name}' is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Numpy\u5e93<\/strong><\/h2>\n
Numpy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u6570\u636e\u7684\u57fa\u7840\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Numpy\u6765\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n
1\u3001\u4ece\u5217\u8868\u521b\u5efaNumpy\u6570\u7ec4<\/h3>\n<\/p>\n
\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728\u5217\u8868\u4e2d\uff0c\u53ef\u4ee5\u5c06\u5176\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff0c\u5e76\u786e\u5b9a\u5217\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efaNumpy\u6570\u7ec4<\/strong><\/h2>\n
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_index = 1<\/p>\n
column_length = data[:, column_index].shape[0]<\/p>\n
print(f"The length of column {column_index} is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6Numpy\u6570\u7ec4<\/h3>\n<\/p>\n
Numpy\u63d0\u4f9b\u4e86\u8bfb\u53d6\u6587\u672c\u6587\u4ef6\u548cCSV\u6587\u4ef6\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u76f4\u63a5\u8bfb\u53d6\u6570\u636e\u5e76\u786e\u5b9a\u5217\u6570\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = np.genfromtxt('data.csv', delimiter=',')<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_index = 1<\/p>\n
column_length = data[:, column_index].shape[0]<\/p>\n
print(f"The length of column {column_index} is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6<\/strong><\/h2>\n
\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528\u6807\u51c6\u7684\u6587\u4ef6\u64cd\u4f5c\u6765\u8bfb\u53d6\u6587\u4ef6\uff0c\u5e76\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u6587\u672c\u6587\u4ef6<\/h3>\n<\/p>\n
\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728\u6587\u672c\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\uff0c\u5e76\u7edf\u8ba1\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6\u6587\u672c\u6587\u4ef6<\/p>\n
with open('data.txt', 'r') as file:<\/p>\n
    lines = file.readlines()<\/p>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570<\/strong><\/h2>\n
column_index = 1<\/p>\n
column_length = len([line.split()[column_index] for line in lines])<\/p>\n
print(f"The length of column {column_index} is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u7b80\u5355\u7684CSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684CSV\u6a21\u5757\u6765\u8bfb\u53d6\u6587\u4ef6\uff0c\u5e76\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u3002<\/p>\n<\/p>\n
import csv<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('data.csv', 'r') as file:<\/p>\n
    reader = csv.reader(file)<\/p>\n
    column_length = len([row for row in reader])<\/p>\n
print(f"The length of the column is {column_length}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/strong><\/h2>\n
\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u9700\u6c42\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u8bfb\u53d6\u548c\u64cd\u4f5c\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u590d\u6742\u7684\u8868\u683c\u6570\u636e\u3002Numpy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u9ad8\u6027\u80fd\u8ba1\u7b97\u573a\u666f\u3002\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6\u5219\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\u3002<\/strong><\/p>\n<\/p>\n
1\u3001\u63a8\u8350\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u5e93\u662f\u5904\u7406\u6570\u636e\u7684\u9996\u9009\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u7b80\u6d01\u7684API\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u591a\u79cd\u6570\u636e\u6e90\uff0c\u5e76\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
2\u3001Numpy\u5e93\u7684\u9ad8\u6548\u6027<\/h3>\n<\/p>\n
Numpy\u5e93\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u7ec4\u6570\u636e\u65f6\u6027\u80fd\u4f18\u8d8a\uff0c\u9002\u7528\u4e8e\u9ad8\u6027\u80fd\u8ba1\u7b97\u573a\u666f\u3002\u901a\u8fc7Numpy\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u64cd\u4f5c\u591a\u7ef4\u6570\u7ec4\uff0c\u5e76\u8fdb\u884c\u590d\u6742\u7684\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
3\u3001\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6\u7684\u7b80\u4fbf\u6027<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\uff0c\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6\u662f\u4e00\u79cd\u7b80\u4fbf\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u6807\u51c6\u7684\u6587\u4ef6\u64cd\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\uff0c\u5e76\u8fdb\u884c\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
\u65e0\u8bba\u4f7f\u7528\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u6700\u9002\u5408\u7684\u5de5\u5177\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u7406\u89e3\u548c\u638c\u63e1\u5982\u4f55\u786e\u5b9a\u67d0\u4e2a\u5b57\u6bb5\u7684\u5217\u6570\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u7279\u5b9a\u5b57\u6bb5\u7684\u5217\u6570\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8f7b\u677e\u83b7\u53d6\u7279\u5b9a\u5b57\u6bb5\u7684\u5217\u6570\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff08\u5982CSV\uff09\u5e76\u4f7f\u7528.shape<\/code>\u5c5e\u6027\u6765\u83b7\u53d6\u5b57\u6bb5\u7684\u5217\u6570\u3002\u4f8b\u5982\uff1adataframe.shape[1]<\/code>\u5c06\u8fd4\u56de\u5217\u6570\u3002\u6b64\u5916\uff0c\u4f7f\u7528.columns<\/code>\u5c5e\u6027\u4e5f\u80fd\u5f97\u5230\u5217\u540d\u5217\u8868\uff0c\u4ece\u800c\u5e2e\u52a9\u4f60\u786e\u8ba4\u7279\u5b9a\u5b57\u6bb5\u7684\u5b58\u5728\u3002<\/p>\n
\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\uff0c\u5982\u4f55\u6709\u6548\u83b7\u53d6\u7279\u5b9a\u5b57\u6bb5\u7684\u5217\u6570\uff1f<\/strong>
\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\uff0c\u6548\u7387\u662f\u4e00\u4e2a\u5173\u952e\u56e0\u7d20\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv<\/code>\u51fd\u6570\u4e2d\u7684usecols<\/code>\u53c2\u6570\u6765\u4ec5\u52a0\u8f7d\u9700\u8981\u7684\u5217\uff0c\u8fd9\u6837\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u6d88\u8017\u3002\u52a0\u8f7d\u540e\uff0c\u4f7f\u7528.shape<\/code>\u5c5e\u6027\u6765\u83b7\u53d6\u8be5\u5b57\u6bb5\u7684\u5217\u6570\uff0c\u4f1a\u663e\u8457\u63d0\u5347\u5904\u7406\u901f\u5ea6\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u901a\u8fc7\u5176\u4ed6Python\u5e93\u83b7\u53d6\u5b57\u6bb5\u7684\u5217\u6570\uff1f<\/strong>
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