{"id":1144674,"date":"2025-01-08T23:01:47","date_gmt":"2025-01-08T15:01:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1144674.html"},"modified":"2025-01-08T23:01:50","modified_gmt":"2025-01-08T15:01:50","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%8f%96%e4%b8%80%e4%b8%aa%e6%95%b0%e7%9a%84%e7%9b%b8%e5%8f%8d%e6%95%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1144674.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\u6765\u5b9e\u73b0\uff0c\u5373\u5c06\u8be5\u6570\u4e58\u4ee5-1\u3002<\/strong> \u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\u7684\u65b9\u6cd5\u975e\u5e38\u76f4\u89c2\u4e14\u6613\u4e8e\u7406\u89e3\uff0c\u9002\u7528\u4e8e\u6574\u6570\u548c\u6d6e\u70b9\u6570\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u770b\u4f3c\u7b80\u5355\uff0c\u4f46\u5728\u5b9e\u9645\u7f16\u7a0b\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u53ef\u4ee5\u7528\u4e8e\u8bb8\u591a\u590d\u6742\u7684\u5e94\u7528\u573a\u666f\uff0c\u6bd4\u5982\u6570\u636e\u5904\u7406\u3001\u56fe\u50cf\u5904\u7406\u548c\u6570\u5b66\u5efa\u6a21\u7b49\u3002\u76f8\u53cd\u6570\u7684\u6982\u5ff5\u5728\u6570\u5b66\u4e0a\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5c24\u5176\u662f\u5728\u5411\u91cf\u8ba1\u7b97\u548c\u7ebf\u6027\u4ee3\u6570\u4e2d\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u57fa\u7840\u65b9\u6cd5\u53ca\u5176\u5e94\u7528<\/h3>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u5c31\u662f\u5c06\u8be5\u6570\u4e58\u4ee5-1\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

number = 5<\/p>\n
opposite_number = -number<\/p>\n
print(opposite_number)  # \u8f93\u51fa -5<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u7b80\u5355\u7684\u64cd\u4f5c\u53ef\u4ee5\u5e94\u7528\u4e8e\u6574\u6570\u548c\u6d6e\u70b9\u6570\uff1a<\/p>\n<\/p>\n
number_float = 5.5<\/p>\n
opposite_number_float = -number_float<\/p>\n
print(opposite_number_float)  # \u8f93\u51fa -5.5<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.1\u3001\u5e94\u7528\u4e8e\u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u6807\u51c6\u5316\u6570\u636e\u6216\u8fdb\u884c\u67d0\u4e9b\u7279\u5b9a\u7684\u6570\u5b66\u53d8\u6362\u3002\u4f8b\u5982\uff0c\u5728\u6570\u636e\u4e2d\u5fc3\u5316\u5904\u7406\u65f6\uff0c\u5e38\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u76f8\u53cd\u6570\u53d8\u6362\uff1a<\/p>\n<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
centered_data = [-x for x in data]<\/p>\n
print(centered_data)  # \u8f93\u51fa [-1, -2, -3, -4, -5]<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u5e94\u7528\u4e8e\u56fe\u50cf\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u56fe\u50cf\u7684\u8d1f\u7247\u6548\u679c\u3002\u8d1f\u7247\u56fe\u50cf\u7684\u6bcf\u4e2a\u50cf\u7d20\u503c\u90fd\u662f\u5176\u539f\u59cb\u50cf\u7d20\u503c\u7684\u76f8\u53cd\u6570\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
def invert_image(image):<\/p>\n
    inverted_image = Image.eval(image, lambda x: 255 - x)<\/p>\n
    return inverted_image<\/p>\n
image = Image.open('example.jpg')<\/p>\n
inverted_image = invert_image(image)<\/p>\n
inverted_image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5411\u91cf\u548c\u77e9\u9635\u4e2d\u7684\u76f8\u53cd\u6570<\/h3>\n<\/p>\n
\u5728\u5411\u91cf\u548c\u77e9\u9635\u8ba1\u7b97\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u540c\u6837\u975e\u5e38\u91cd\u8981\u3002\u5b83\u53ef\u4ee5\u7528\u4e8e\u5411\u91cf\u7684\u53cd\u5411\u64cd\u4f5c\u3001\u77e9\u9635\u53d8\u6362\u7b49\u3002<\/p>\n<\/p>\n
2.1\u3001\u5411\u91cf\u7684\u76f8\u53cd\u6570<\/h4>\n<\/p>\n
\u5411\u91cf\u7684\u76f8\u53cd\u6570\u662f\u6307\u5c06\u5411\u91cf\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u53d6\u76f8\u53cd\u6570\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
vector = np.array([1, 2, 3])<\/p>\n
opposite_vector = -vector<\/p>\n
print(opposite_vector)  # \u8f93\u51fa [-1, -2, -3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u64cd\u4f5c\u5728\u5411\u91cf\u52a0\u6cd5\u548c\u51cf\u6cd5\u4e2d\u5c24\u4e3a\u5e38\u89c1\uff0c\u7528\u4e8e\u8ba1\u7b97\u5411\u91cf\u7684\u5dee\u503c\uff1a<\/p>\n<\/p>\n
vector1 = np.array([1, 2, 3])<\/p>\n
vector2 = np.array([4, 5, 6])<\/p>\n
difference = vector1 - vector2<\/p>\n
print(difference)  # \u8f93\u51fa [-3, -3, -3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u77e9\u9635\u7684\u76f8\u53cd\u6570<\/h4>\n<\/p>\n
\u7c7b\u4f3c\u5730\uff0c\u77e9\u9635\u7684\u76f8\u53cd\u6570\u662f\u5c06\u77e9\u9635\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u53d6\u76f8\u53cd\u6570\uff1a<\/p>\n<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
opposite_matrix = -matrix<\/p>\n
print(opposite_matrix)<\/p>\n
\u8f93\u51fa:<\/strong><\/h2>\n
[[-1 -2 -3]<\/strong><\/h2>\n
 [-4 -5 -6]<\/strong><\/h2>\n
 [-7 -8 -9]]<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7b26\u53f7\u64cd\u4f5c\u548c\u7b26\u53f7\u8fd0\u7b97<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u9ad8\u7ea7\u5e94\u7528\u4e2d\uff0c\u5c24\u5176\u662f\u7b26\u53f7\u8fd0\u7b97\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u64cd\u4f5c\u540c\u6837\u91cd\u8981\u3002Python\u4e2d\u7684sympy<\/code>\u5e93\u63d0\u4f9b\u4e86\u7b26\u53f7\u6570\u5b66\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n
3.1\u3001\u4f7f\u7528sympy\u8fdb\u884c\u7b26\u53f7\u8fd0\u7b97<\/h4>\n<\/p>\n
sympy<\/code>\u5e93\u5141\u8bb8\u6211\u4eec\u8fdb\u884c\u7b26\u53f7\u8fd0\u7b97\uff0c\u5305\u62ec\u53d6\u76f8\u53cd\u6570\uff1a<\/p>\n<\/p>\n
import sympy as sp<\/p>\n
x = sp.symbols('x')<\/p>\n
opposite_expression = -x<\/p>\n
print(opposite_expression)  # \u8f93\u51fa -x<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u7b26\u53f7\u8fd0\u7b97\u5728\u89e3\u51b3\u65b9\u7a0b\u3001\u79ef\u5206\u3001\u5fae\u5206\u7b49\u95ee\u9898\u4e2d\u975e\u5e38\u6709\u7528\uff1a<\/p>\n<\/p>\n
expression = x2 + 3*x + 2<\/p>\n
opposite_expression = -expression<\/p>\n
print(opposite_expression)  # \u8f93\u51fa -x2 - 3*x - 2<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u4e8e\u673a\u5668\u5b66\u4e60<\/a>\u548c\u6570\u636e\u79d1\u5b66<\/h3>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u5e38\u5e38\u7528\u4e8e\u6570\u636e\u9884\u5904\u7406\u3001\u7279\u5f81\u5de5\u7a0b\u548c\u6a21\u578b\u4f18\u5316\u7b49\u73af\u8282\u3002<\/p>\n<\/p>\n
4.1\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u9884\u5904\u7406\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u5bf9\u6570\u503c\u8fdb\u884c\u53d8\u6362\uff0c\u4ee5\u589e\u5f3a\u6a21\u578b\u7684\u8868\u73b0\u529b\u3002\u4f8b\u5982\uff0c\u5728\u6807\u51c6\u5316\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u67d0\u4e9b\u7279\u5f81\u53d6\u76f8\u53cd\u6570\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
data = np.array([[1, 2], [3, 4], [5, 6]])<\/p>\n
scaler = StandardScaler()<\/p>\n
scaled_data = scaler.fit_transform(data)<\/p>\n
opposite_scaled_data = -scaled_data<\/p>\n
print(opposite_scaled_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2\u3001\u7279\u5f81\u5de5\u7a0b<\/h4>\n<\/p>\n
\u5728\u7279\u5f81\u5de5\u7a0b\u8fc7\u7a0b\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u65b0\u7684\u7279\u5f81\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u8868\u73b0\u529b\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u53ef\u4ee5\u751f\u6210\u76f8\u53cd\u6570\u7279\u5f81\u4ee5\u6355\u6349\u6570\u636e\u7684\u67d0\u4e9b\u7279\u6027\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
time_series_data = pd.Series([1, 2, 3, 4, 5])<\/p>\n
opposite_time_series_data = -time_series_data<\/p>\n
print(opposite_time_series_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3\u3001\u6a21\u578b\u4f18\u5316<\/h4>\n<\/p>\n
\u5728\u6a21\u578b\u4f18\u5316\u8fc7\u7a0b\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\u4e2d\u7684\u68af\u5ea6\u53cd\u5411\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
def gradient_descent_step(gradient, learning_rate=0.01):<\/p>\n
    step = -learning_rate * gradient<\/p>\n
    return step<\/p>\n
gradient = np.array([0.1, 0.2, 0.3])<\/p>\n
step = gradient_descent_step(gradient)<\/p>\n
print(step)  # \u8f93\u51fa [-0.001 -0.002 -0.003]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001Python\u4e2d\u7684\u5176\u4ed6\u5b9e\u73b0\u65b9\u5f0f<\/h3>\n<\/p>\n
\u9664\u4e86\u76f4\u63a5\u4f7f\u7528\u8d1f\u53f7\uff08-\uff09\u6765\u53d6\u76f8\u53cd\u6570\uff0cPython\u4e2d\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u5b9e\u73b0\u65b9\u5f0f\uff0c\u6bd4\u5982\u4f7f\u7528lambda\u51fd\u6570\u6216\u81ea\u5b9a\u4e49\u51fd\u6570\u3002<\/p>\n<\/p>\n
5.1\u3001\u4f7f\u7528lambda\u51fd\u6570<\/h4>\n<\/p>\n
\u4f7f\u7528lambda\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u66f4\u52a0\u7075\u6d3b\u7684\u76f8\u53cd\u6570\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
opposite = lambda x: -x<\/p>\n
print(opposite(5))  # \u8f93\u51fa -5<\/p>\n
print(opposite(-3.5))  # \u8f93\u51fa 3.5<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2\u3001\u81ea\u5b9a\u4e49\u51fd\u6570<\/h4>\n<\/p>\n
\u5b9a\u4e49\u4e00\u4e2a\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u53d6\u76f8\u53cd\u6570\uff0c\u53ef\u4ee5\u589e\u52a0\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u590d\u7528\u6027\uff1a<\/p>\n<\/p>\n
def get_opposite_number(number):<\/p>\n
    return -number<\/p>\n
print(get_opposite_number(5))  # \u8f93\u51fa -5<\/p>\n
print(get_opposite_number(-3.5))  # \u8f93\u51fa 3.5<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u662f\u4e00\u4e2a\u7b80\u5355\u4f46\u975e\u5e38\u5b9e\u7528\u7684\u64cd\u4f5c\u3002\u65e0\u8bba\u662f\u5728\u57fa\u7840\u6570\u5b66\u8fd0\u7b97\u3001\u6570\u636e\u5904\u7406\u3001\u56fe\u50cf\u5904\u7406\uff0c\u8fd8\u662f\u5728\u9ad8\u7ea7\u7684\u5411\u91cf\u548c\u77e9\u9635\u8ba1\u7b97\u3001\u7b26\u53f7\u8fd0\u7b97\u3001\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0c\u8fd9\u4e00\u64cd\u4f5c\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u901a\u8fc7\u7406\u89e3\u548c\u638c\u63e1\u53d6\u76f8\u53cd\u6570\u7684\u5404\u79cd\u5b9e\u73b0\u65b9\u5f0f\u548c\u5e94\u7528\u573a\u666f\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u52a0\u9ad8\u6548\u5730\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff0c\u63d0\u5347\u7f16\u7a0b\u6280\u80fd\u548c\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n<\/p>\n
\u6838\u5fc3\u8981\u70b9\u603b\u7ed3\uff1a<\/strong><\/p>\n<\/p>\n
    \n
  • \u53d6\u76f8\u53cd\u6570\u7684\u57fa\u672c\u65b9\u6cd5\u662f\u5c06\u6570\u4e58\u4ee5-1\u3002<\/strong><\/li>\n
  • \u5728\u6570\u636e\u5904\u7406\u3001\u56fe\u50cf\u5904\u7406\u3001\u5411\u91cf\u548c\u77e9\u9635\u8ba1\u7b97\u4e2d\uff0c\u53d6\u76f8\u53cd\u6570\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002<\/strong><\/li>\n
  • \u7b26\u53f7\u8fd0\u7b97\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u4e5f\u5e38\u5e38\u9700\u8981\u4f7f\u7528\u53d6\u76f8\u53cd\u6570\u7684\u64cd\u4f5c\u3002<\/strong><\/li>\n
  • \u81ea\u5b9a\u4e49\u51fd\u6570\u548clambda\u51fd\u6570\u53ef\u4ee5\u589e\u52a0\u4ee3\u7801\u7684\u7075\u6d3b\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/strong><\/li>\n<\/ul>\n
    \u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u60a8\u80fd\u591f\u5bf9Python\u4e2d\u5982\u4f55\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u6709\u66f4\u6df1\u5165\u7684\u7406\u89e3\uff0c\u5e76\u80fd\u591f\u5728\u5b9e\u9645\u7f16\u7a0b\u4e2d\u7075\u6d3b\u5e94\u7528\u8fd9\u4e00\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5728Python\u4e2d\uff0c\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u7684\u65b9\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\u6765\u83b7\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u53ea\u9700\u5c06\u8be5\u6570\u4e58\u4ee5-1\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u6709\u4e00\u4e2a\u53d8\u91cfx<\/code>\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528-x<\/code>\u6765\u5f97\u5230\u5176\u76f8\u53cd\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u6574\u6570\u3001\u6d6e\u70b9\u6570\u7b49\u6570\u503c\u7c7b\u578b\u3002<\/p>\n
    \u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u8d1f\u6570\u7684\u76f8\u53cd\u6570\uff1f<\/strong>
    \u5bf9\u4e8e\u8d1f\u6570\uff0c\u53d6\u76f8\u53cd\u6570\u7684\u8fc7\u7a0b\u540c\u6837\u9002\u7528\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u76f8\u540c\u7684\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u5982\u679cy = -5<\/code>\uff0c\u90a3\u4e48\u5176\u76f8\u53cd\u6570\u53ef\u4ee5\u7528-y<\/code>\u6765\u8868\u793a\uff0c\u8fd9\u6837\u5f97\u5230\u7684\u7ed3\u679c\u662f5\u3002\u8fd9\u79cd\u5904\u7406\u65b9\u5f0f\u5728\u903b\u8f91\u4e0a\u662f\u4e00\u81f4\u7684\uff0c\u9002\u7528\u4e8e\u6240\u6709\u6570\u503c\u7c7b\u578b\u3002<\/p>\n
    \u5728Python\u4e2d\uff0c\u5982\u679c\u6211\u60f3\u540c\u65f6\u53d6\u591a\u4e2a\u6570\u7684\u76f8\u53cd\u6570\uff0c\u8be5\u600e\u4e48\u505a\uff1f<\/strong>
    \u82e5\u60f3\u540c\u65f6\u83b7\u53d6\u591a\u4e2a\u6570\u7684\u76f8\u53cd\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u6216map()<\/code>\u51fd\u6570\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u4f60\u6709\u4e00\u4e2a\u5217\u8868numbers = [1, -2, 3, -4]<\/code>\uff0c\u53ef\u4ee5\u4f7f\u7528[-n for n in numbers]<\/code>\u6765\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u5217\u8868[ -1, 2, -3, 4]<\/code>\uff0c\u6216\u8005\u4f7f\u7528list(map(lambda x: -x, numbers))<\/code>\u6765\u5b9e\u73b0\u540c\u6837\u7684\u6548\u679c\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u6d01\u660e\u4e86\uff0c\u9002\u5408\u5904\u7406\u591a\u4e2a\u6570\u503c\u7684\u60c5\u51b5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53d6\u4e00\u4e2a\u6570\u7684\u76f8\u53cd\u6570\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\u6765\u5b9e\u73b0\uff0c\u5373\u5c06\u8be5\u6570\u4e58\u4ee5-1\u3002 \u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\u7684\u65b9\u6cd5\u975e\u5e38\u76f4\u89c2 […]","protected":false},"author":3,"featured_media":1144686,"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\/1144674"}],"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=1144674"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1144674\/revisions"}],"predecessor-version":[{"id":1144688,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1144674\/revisions\/1144688"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1144686"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1144674"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1144674"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1144674"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}