{"id":1140373,"date":"2025-01-08T22:20:07","date_gmt":"2025-01-08T14:20:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1140373.html"},"modified":"2025-01-08T22:20:09","modified_gmt":"2025-01-08T14:20:09","slug":"python%e5%a6%82%e4%bd%95%e5%8f%96%e5%87%ba%e6%95%b0%e7%bb%84%e6%9c%80%e5%90%8e%e4%b8%80%e5%88%97%e6%95%b0%e6%8d%ae%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1140373.html","title":{"rendered":"python\u5982\u4f55\u53d6\u51fa\u6570\u7ec4\u6700\u540e\u4e00\u5217\u6570\u636e\u5e93"},"content":{"rendered":"

\"python\u5982\u4f55\u53d6\u51fa\u6570\u7ec4\u6700\u540e\u4e00\u5217\u6570\u636e\u5e93\"<\/p>\n

Python \u5982\u4f55\u53d6\u51fa\u6570\u7ec4\u6700\u540e\u4e00\u5217\u6570\u636e\u5e93<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u4ece\u6570\u7ec4\u4e2d\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002\u65b9\u6cd5\u662f\uff1a\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528pandas\u5e93<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u7684\u65b9\u6cd5\u6700\u4e3a\u9ad8\u6548\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528NumPy\u5e93\u53d6\u51fa\u6570\u7ec4\u6700\u540e\u4e00\u5217\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4f7f\u7528NumPy\u5e93\uff1a<\/strong><\/p>\n

NumPy\u5e93\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u7684\u5f3a\u5927\u5de5\u5177\u3002\u8981\u53d6\u51fa\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u8fdb\u884c\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7 array[:, -1]<\/code> \u6765\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
array = np.array([[1, 2, 3],<\/p>\n
                  [4, 5, 6],<\/p>\n
                  [7, 8, 9]])<\/p>\n
\u53d6\u51fa\u6700\u540e\u4e00\u5217<\/strong><\/h2>\n
last_column = array[:, -1]<\/p>\n
print(last_column)  # \u8f93\u51fa: [3 6 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u4e0a\u8ff0\u65b9\u6cd5\u4ee5\u53ca\u5176\u4ed6\u76f8\u5173\u7684\u5b9e\u73b0\u548c\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u4e00\u3001\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
1\u3001NumPy\u5e93\u7b80\u4ecb<\/h4>\n<\/p>\n
NumPy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u652f\u6301\u5927\u591a\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u7684\u529f\u80fd\uff0c\u540c\u65f6\u4e5f\u5305\u542b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\u5e93\u3002NumPy\u7684\u6838\u5fc3\u662fndarray\u5bf9\u8c61\uff0c\u5b83\u662f\u4e00\u4e2a\u591a\u7ef4\u6570\u7ec4\uff0c\u7528\u4e8e\u5b58\u50a8\u540c\u7c7b\u578b\u6570\u636e\u3002<\/p>\n<\/p>\n
2\u3001\u521b\u5efaNumPy\u6570\u7ec4<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u5b89\u88c5NumPy\u5e93\uff0c\u5e76\u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u5b89\u88c5\u548c\u521b\u5efa\uff1a<\/p>\n<\/p>\n
# \u5b89\u88c5NumPy\u5e93<\/p>\n
!pip install numpy<\/p>\n
\u5bfc\u5165NumPy\u5e93<\/strong><\/h2>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
array = np.array([[1, 2, 3],<\/p>\n
                  [4, 5, 6],<\/p>\n
                  [7, 8, 9]])<\/p>\n
print(array)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u53d6\u51fa\u6700\u540e\u4e00\u5217<\/h4>\n<\/p>\n
\u8981\u53d6\u51faNumPy\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u7ec4\u5207\u7247\u64cd\u4f5c\u3002\u5207\u7247\u64cd\u4f5c\u5141\u8bb8\u6211\u4eec\u901a\u8fc7\u7d22\u5f15\u6765\u8bbf\u95ee\u6570\u7ec4\u7684\u5b50\u96c6\u3002\u5bf9\u4e8e\u4e8c\u7ef4\u6570\u7ec4\uff0c\u5207\u7247\u64cd\u4f5c\u7684\u8bed\u6cd5\u4e3a array[start_row:end_row, start_col:end_col]<\/code>\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8d1f\u7d22\u5f15 -1<\/code> \u6765\u8868\u793a\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
# \u53d6\u51fa\u6700\u540e\u4e00\u5217<\/p>\n
last_column = array[:, -1]<\/p>\n
print(last_column)  # \u8f93\u51fa: [3 6 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u53d6\u51faNumPy\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528pandas\u5e93<\/h3>\n<\/p>\n
1\u3001pandas\u5e93\u7b80\u4ecb<\/h4>\n<\/p>\n
pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86DataFrame\u548cSeries\u4e24\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u5206\u522b\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u548c\u4e00\u7ef4\u6570\u636e\u3002pandas\u5e93\u5728\u5904\u7406\u6570\u636e\u65f6\u975e\u5e38\u7075\u6d3b\u4e14\u9ad8\u6548\u3002<\/p>\n<\/p>\n
2\u3001\u521b\u5efapandas DataFrame<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528pandas\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u5b89\u88c5pandas\u5e93\uff0c\u5e76\u521b\u5efa\u4e00\u4e2aDataFrame\u5bf9\u8c61\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u5b89\u88c5\u548c\u521b\u5efa\uff1a<\/p>\n<\/p>\n
# \u5b89\u88c5pandas\u5e93<\/p>\n
!pip install pandas<\/p>\n
\u5bfc\u5165pandas\u5e93<\/strong><\/h2>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n
data = {'A': [1, 4, 7],<\/p>\n
        'B': [2, 5, 8],<\/p>\n
        'C': [3, 6, 9]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u53d6\u51fa\u6700\u540e\u4e00\u5217<\/h4>\n<\/p>\n
\u8981\u53d6\u51faDataFrame\u7684\u6700\u540e\u4e00\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u7684 iloc<\/code> \u5c5e\u6027\u3002 iloc<\/code> \u5c5e\u6027\u5141\u8bb8\u6211\u4eec\u4f7f\u7528\u7d22\u5f15\u8fdb\u884c\u884c\u5217\u8bbf\u95ee\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8d1f\u7d22\u5f15 -1<\/code> \u6765\u8868\u793a\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
# \u53d6\u51fa\u6700\u540e\u4e00\u5217<\/p>\n
last_column = df.iloc[:, -1]<\/p>\n
print(last_column)  # \u8f93\u51fa: 0    3<\/p>\n
                    #      1    6<\/p>\n
                    #      2    9<\/p>\n
                    # Name: C, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u53d6\u51faDataFrame\u7684\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u7eafPython\u5b9e\u73b0<\/h3>\n<\/p>\n
\u867d\u7136\u4f7f\u7528NumPy\u548cpandas\u5e93\u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u6570\u7ec4\u548c\u6570\u636e\u6846\uff0c\u4f46\u6709\u65f6\u6211\u4eec\u53ef\u80fd\u5e0c\u671b\u4f7f\u7528\u7eafPython\u4ee3\u7801\u6765\u5b9e\u73b0\u76f8\u540c\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
1\u3001\u4e8c\u7ef4\u5217\u8868<\/h4>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u4e8c\u7ef4\u5217\u8868\u53ef\u4ee5\u7528\u4e8e\u5b58\u50a8\u4e8c\u7ef4\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u5217\u8868\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u89e3\u6790\u6765\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u4e8c\u7ef4\u5217\u8868<\/p>\n
array = [[1, 2, 3],<\/p>\n
         [4, 5, 6],<\/p>\n
         [7, 8, 9]]<\/p>\n
\u53d6\u51fa\u6700\u540e\u4e00\u5217<\/strong><\/h2>\n
last_column = [row[-1] for row in array]<\/p>\n
print(last_column)  # \u8f93\u51fa: [3, 6, 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u904d\u5386\u5217\u8868<\/h4>\n<\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u904d\u5386\u5217\u8868\u6765\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u4e8c\u7ef4\u5217\u8868<\/p>\n
array = [[1, 2, 3],<\/p>\n
         [4, 5, 6],<\/p>\n
         [7, 8, 9]]<\/p>\n
\u521d\u59cb\u5316\u4e00\u4e2a\u7a7a\u5217\u8868\u6765\u5b58\u50a8\u6700\u540e\u4e00\u5217<\/strong><\/h2>\n
last_column = []<\/p>\n
\u904d\u5386\u4e8c\u7ef4\u5217\u8868<\/strong><\/h2>\n
for row in array:<\/p>\n
    last_column.append(row[-1])<\/p>\n
print(last_column)  # \u8f93\u51fa: [3, 6, 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7eafPython\u5b9e\u73b0\u4ece\u4e8c\u7ef4\u5217\u8868\u4e2d\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
1\u3001\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u7ecf\u5e38\u9700\u8981\u4ece\u6570\u636e\u96c6\u4e2d\u63d0\u53d6\u7279\u5b9a\u7684\u5217\u8fdb\u884c\u5206\u6790\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u7279\u5f81\u7684\u9500\u552e\u6570\u636e\u96c6\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u63d0\u53d6\u9500\u552e\u989d\u8fd9\u4e00\u5217\u6765\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
data = {'Product': ['A', 'B', 'C'],<\/p>\n
        'Price': [100, 200, 300],<\/p>\n
        'Sales': [150, 250, 350]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u63d0\u53d6\u9500\u552e\u989d\u8fd9\u4e00\u5217<\/strong><\/h2>\n
sales_column = df['Sales']<\/p>\n
print(sales_column)  # \u8f93\u51fa: 0    150<\/p>\n
                     #      1    250<\/p>\n
                     #      2    350<\/p>\n
                     # Name: Sales, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u673a\u5668\u5b66\u4e60<\/a><\/h4>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u7279\u5f81\u9009\u62e9\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6211\u4eec\u53ef\u80fd\u9700\u8981\u4ece\u6570\u636e\u96c6\u4e2d\u63d0\u53d6\u7279\u5b9a\u7684\u7279\u5f81\u5217\u6765\u8bad\u7ec3\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u7279\u5f81\u7684\u6570\u636e\u96c6\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u63d0\u53d6\u6700\u540e\u4e00\u5217\u7279\u5f81\u4f5c\u4e3a\u6a21\u578b\u7684\u8f93\u5165\u3002<\/p>\n<\/p>\n
# \u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
data = {'Feature1': [1, 2, 3],<\/p>\n
        'Feature2': [4, 5, 6],<\/p>\n
        'Feature3': [7, 8, 9]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u63d0\u53d6\u6700\u540e\u4e00\u5217\u7279\u5f81<\/strong><\/h2>\n
last_feature = df.iloc[:, -1]<\/p>\n
print(last_feature)  # \u8f93\u51fa: 0    7<\/p>\n
                     #      1    8<\/p>\n
                     #      2    9<\/p>\n
                     # Name: Feature3, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u5e93\u64cd\u4f5c<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5e93\u64cd\u4f5c\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u4ece\u67e5\u8be2\u7ed3\u679c\u4e2d\u63d0\u53d6\u7279\u5b9a\u7684\u5217\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u4ece\u6570\u636e\u5e93\u4e2d\u67e5\u8be2\u51fa\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5217\u7684\u6570\u636e\u96c6\uff0c\u6211\u4eec\u9700\u8981\u63d0\u53d6\u6700\u540e\u4e00\u5217\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u3002<\/p>\n<\/p>\n
import sqlite3<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
conn = sqlite3.connect(':memory:')<\/p>\n
cursor = conn.cursor()<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u8868<\/strong><\/h2>\n
cursor.execute('''CREATE TABLE sales<\/p>\n
                  (product TEXT, price INTEGER, sales INTEGER)''')<\/p>\n
\u63d2\u5165\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
cursor.executemany('INSERT INTO sales VALUES (?, ?, ?)',<\/p>\n
                   [('A', 100, 150),<\/p>\n
                    ('B', 200, 250),<\/p>\n
                    ('C', 300, 350)])<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
cursor.execute('SELECT * FROM sales')<\/p>\n
rows = cursor.fetchall()<\/p>\n
\u63d0\u53d6\u6700\u540e\u4e00\u5217<\/strong><\/h2>\n
last_column = [row[-1] for row in rows]<\/p>\n
print(last_column)  # \u8f93\u51fa: [150, 250, 350]<\/p>\n
\u5173\u95ed\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u6570\u636e\u5e93\u67e5\u8be2\u7ed3\u679c\u4e2d\u63d0\u53d6\u6700\u540e\u4e00\u5217\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u6027\u80fd\u6bd4\u8f83<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u53d6\u51fa\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u4e0d\u540c\u7684\u65b9\u6cd5\u5728\u6027\u80fd\u4e0a\u53ef\u80fd\u5b58\u5728\u5dee\u5f02\uff0c\u4e0b\u9762\u6211\u4eec\u5c06\u6bd4\u8f83\u51e0\u79cd\u5e38\u7528\u65b9\u6cd5\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
1\u3001NumPy\u65b9\u6cd5<\/h4>\n<\/p>\n
NumPy\u65b9\u6cd5\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u7ec4\u65f6\u975e\u5e38\u9ad8\u6548\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 timeit<\/code> \u6a21\u5757\u6765\u6d4b\u8bd5\u5176\u6027\u80fd\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import timeit<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5927\u89c4\u6a21\u6570\u7ec4<\/strong><\/h2>\n
array = np.random.rand(1000000, 10)<\/p>\n
\u6d4b\u8bd5NumPy\u65b9\u6cd5\u7684\u6027\u80fd<\/strong><\/h2>\n
time_taken = timeit.timeit('array[:, -1]', globals=globals(), number=1000)<\/p>\n
print(f'NumPy\u65b9\u6cd5\u8017\u65f6: {time_taken:.6f} \u79d2')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001pandas\u65b9\u6cd5<\/h4>\n<\/p>\n
pandas\u65b9\u6cd5\u5728\u5904\u7406\u5927\u89c4\u6a21DataFrame\u65f6\u4e5f\u975e\u5e38\u9ad8\u6548\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 timeit<\/code> \u6a21\u5757\u6765\u6d4b\u8bd5\u5176\u6027\u80fd\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import timeit<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5927\u89c4\u6a21DataFrame<\/strong><\/h2>\n
data = {f'Feature{i}': np.random.rand(1000000) for i in range(10)}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u6d4b\u8bd5pandas\u65b9\u6cd5\u7684\u6027\u80fd<\/strong><\/h2>\n
time_taken = timeit.timeit('df.iloc[:, -1]', globals=globals(), number=1000)<\/p>\n
print(f'pandas\u65b9\u6cd5\u8017\u65f6: {time_taken:.6f} \u79d2')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7eafPython\u65b9\u6cd5<\/h4>\n<\/p>\n
\u7eafPython\u65b9\u6cd5\u5728\u5904\u7406\u5c0f\u89c4\u6a21\u6570\u636e\u65f6\u6027\u80fd\u5c1a\u53ef\uff0c\u4f46\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u53ef\u80fd\u5b58\u5728\u6027\u80fd\u74f6\u9888\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 timeit<\/code> \u6a21\u5757\u6765\u6d4b\u8bd5\u5176\u6027\u80fd\u3002<\/p>\n<\/p>\n
import timeit<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5927\u89c4\u6a21\u4e8c\u7ef4\u5217\u8868<\/strong><\/h2>\n
array = [list(np.random.rand(10)) for _ in range(1000000)]<\/p>\n
\u6d4b\u8bd5\u7eafPython\u65b9\u6cd5\u7684\u6027\u80fd<\/strong><\/h2>\n
time_taken = timeit.timeit('[row[-1] for row in array]', globals=globals(), number=1000)<\/p>\n
print(f'\u7eafPython\u65b9\u6cd5\u8017\u65f6: {time_taken:.6f} \u79d2')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6027\u80fd\u6d4b\u8bd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u4ee5\u4e0b\u7ed3\u8bba\uff1a<\/p>\n<\/p>\n
    \n
  • NumPy\u65b9\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u7ec4\uff0c\u6027\u80fd\u4f18\u5f02\u3002<\/li>\n
  • pandas\u65b9\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21DataFrame\uff0c\u6027\u80fd\u4f18\u5f02\u3002<\/li>\n
  • \u7eafPython\u65b9\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u5c0f\u89c4\u6a21\u6570\u636e\uff0c\u4f46\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u6027\u80fd\u8f83\u5dee\u3002<\/li>\n<\/ul>\n
    \u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u5728Python\u4e2d\uff0c\u53d6\u51fa\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\u6709\u591a\u79cd\u65b9\u6cd5\u53ef\u4f9b\u9009\u62e9\u3002\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528pandas\u5e93\u3001\u4f7f\u7528\u7eafPython\u5b9e\u73b0<\/strong>\u662f\u5e38\u89c1\u7684\u4e09\u79cd\u65b9\u6cd5\u3002NumPy\u548cpandas\u5e93\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u8868\u73b0\u4f18\u5f02\uff0c\u800c\u7eafPython\u65b9\u6cd5\u9002\u7528\u4e8e\u5904\u7406\u5c0f\u89c4\u6a21\u6570\u636e\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u6027\u80fd\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
    \u603b\u4e4b\uff0c\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u3001pandas\u5e93\u548c\u7eafPython\u4ee3\u7801\u6765\u53d6\u51fa\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\uff0c\u5e76\u63a2\u8ba8\u4e86\u8fd9\u4e9b\u65b9\u6cd5\u7684\u5e94\u7528\u573a\u666f\u548c\u6027\u80fd\u6bd4\u8f83\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u63d0\u53d6\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u5904\u7406\u6570\u7ec4\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u65b9\u5f0f\u8f7b\u677e\u63d0\u53d6\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
    import numpy as np\n\n# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u7ec4\narray = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n\n# \u63d0\u53d6\u6700\u540e\u4e00\u5217\nlast_column = array[:, -1]\nprint(last_column)\n<\/code><\/pre>\n
    \u4ee5\u4e0a\u4ee3\u7801\u5c06\u8f93\u51fa\u6570\u7ec4\u7684\u6700\u540e\u4e00\u5217\u3002<\/p>\n
    \u5728\u4f7f\u7528Pandas\u65f6\uff0c\u5982\u4f55\u83b7\u53d6DataFrame\u7684\u6700\u540e\u4e00\u5217\uff1f<\/strong>
    Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u4f7f\u7528DataFrame\u65f6\u53ef\u4ee5\u901a\u8fc7.iloc<\/code>\u5c5e\u6027\u8f7b\u677e\u83b7\u53d6\u6700\u540e\u4e00\u5217\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
    import pandas as pd\n\n# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame\ndf = pd.DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], 'C': [3, 6, 9]})\n\n# \u63d0\u53d6\u6700\u540e\u4e00\u5217\nlast_column = df.iloc[:, -1]\nprint(last_column)\n<\/code><\/pre>\n
    \u6b64\u4ee3\u7801\u5c06\u8fd4\u56deDataFrame\u7684\u6700\u540e\u4e00\u5217\u6570\u636e\u3002<\/p>\n
    \u5982\u4f55\u5728\u6570\u636e\u5e93\u4e2d\u67e5\u8be2\u7279\u5b9a\u5b57\u6bb5\u7684\u6700\u540e\u4e00\u5217\uff1f<\/strong>
    \u5728\u4f7f\u7528SQL\u67e5\u8be2\u6570\u636e\u5e93\u65f6\uff0c\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u9009\u62e9\u7279\u5b9a\u7684\u5217\u6765\u83b7\u53d6\u6700\u540e\u4e00\u5217\u3002\u5728SQL\u4e2d\uff0c\u901a\u5e38\u6ca1\u6709\u76f4\u63a5\u7684\u201c\u6700\u540e\u4e00\u5217\u201d\u6982\u5ff5\uff0c\u4f46\u53ef\u4ee5\u4f7f\u7528ORDER BY<\/code>\u548cLIMIT<\/code>\u7b49\u8bed\u53e5\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff1a<\/p>\n
    SELECT column_name FROM table_name ORDER BY id DESC LIMIT 1;\n<\/code><\/pre>\n
    \u5728\u8fd9\u4e2a\u67e5\u8be2\u4e2d\uff0c\u786e\u4fdd\u66ff\u6362column_name<\/code>\u548ctable_name<\/code>\u4e3a\u5b9e\u9645\u7684\u5b57\u6bb5\u540d\u548c\u8868\u540d\u3002\u8fd9\u6837\u53ef\u4ee5\u83b7\u53d6\u8868\u4e2d\u6700\u540e\u4e00\u6761\u8bb0\u5f55\u7684\u7279\u5b9a\u5b57\u6bb5\u503c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python \u5982\u4f55\u53d6\u51fa\u6570\u7ec4\u6700\u540e\u4e00\u5217\u6570\u636e\u5e93 \u5728Python\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u4ece\u6570\u7ec4\u4e2d\u53d6\u51fa\u6700\u540e\u4e00\u5217\u3002\u65b9 […]","protected":false},"author":3,"featured_media":1140380,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1140373"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1140373"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1140373\/revisions"}],"predecessor-version":[{"id":1140381,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1140373\/revisions\/1140381"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1140380"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1140373"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1140373"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1140373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}