korla 发表于 2023-5-24 16:40:20

为什么给全局变量x赋值会报错?

ef f1():
    global x
    print(x**2)
    x=5
    print(x)
x=3
SyntaxError: invalid syntax

isdkz 发表于 2023-5-24 16:41:43

你少了一个 d

def f1():
    global x
    print(x**2)
    x=5
    print(x)
x=3

korla 发表于 2023-5-24 16:51:09

isdkz 发表于 2023-5-24 16:41
你少了一个 d

def f1():




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

isdkz 发表于 2023-5-24 16:53:32

korla 发表于 2023-5-24 16:51


新鱼油传图片:https://fishc.com.cn/thread-168190-1-1.html

sfqxx 发表于 2023-5-24 17:50:22

在给全局变量x赋值之前,你需要先定义ef f1()函数。正确的代码应该如下所示:


x=3

def f1():
    global x
    print(x**2)
    x=5
    print(x)

f1()

请注意全局变量需要在函数之外定义,而且在函数内部修改全局变量需要使用global关键字。我帮你修正了代码,现在执行f1()函数,输出结果应该是 9 和 5。

歌者文明清理员 发表于 2023-5-24 18:42:34

isdkz 发表于 2023-5-24 16:53
新鱼油传图片:https://fishc.com.cn/thread-168190-1-1.html

https://fishc.com.cn/forum.php?mod=viewthread&tid=226351
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查看完整版本: 为什么给全局变量x赋值会报错?