首页 \ 问答 \ 在javascript中,如何区分没有arg传递和未定义的arg传递(in javascript, how to distinguish between no arg passed and undefined arg passed)

在javascript中,如何区分没有arg传递和未定义的arg传递(in javascript, how to distinguish between no arg passed and undefined arg passed)

在一个函数中,如何识别非arg和未定义的arg?

myFunc( 'first' );

var obj = { a: 123 };
myFunc( 'first', obj.b );
_or_
myFunc( 'first', undefined )

arguments.length指的是通过命名参数的参数,所以它没有帮助 可以用arguments.length轻松解决 - 抱歉大脑放屁!

function myFunc( a, b ) {

  // Case A: if no second arg, provide one
  // should be: if( arguments.length < 2 ) ...
  if( b === undefined ) b = anotherFunc;

  // Case B: if b is not resolved - passed but undefined, throw
  else if( b === undefined ) throw( 'INTERNAL ERROR: undefined passed' );

  // Case C: if b not a function, resolve by name
  else if( typeof b != 'function' ) { ... }

  ...
}

myFunc捕获案例A案例B的正确方法是什么?


within a function, how to discern between a non-arg and an undefined arg?

myFunc( 'first' );

var obj = { a: 123 };
myFunc( 'first', obj.b );
_or_
myFunc( 'first', undefined )

arguments.length refers to arguments past the named arguments, so it's no help Can be solved easily with arguments.length - sorry about the brain fart!

function myFunc( a, b ) {

  // Case A: if no second arg, provide one
  // should be: if( arguments.length < 2 ) ...
  if( b === undefined ) b = anotherFunc;

  // Case B: if b is not resolved - passed but undefined, throw
  else if( b === undefined ) throw( 'INTERNAL ERROR: undefined passed' );

  // Case C: if b not a function, resolve by name
  else if( typeof b != 'function' ) { ... }

  ...
}

What is the correct way to capture Case A and Case B in myFunc?


原文:https://stackoverflow.com/questions/7750577
更新时间:2023-03-22 12:03

最满意答案

虽然你可以使用

In [85]: pd.DataFrame({'test':test.tolist(), 'test2':test2.tolist()})
Out[85]: 
     test   test2
0  [1, 2]  [2, 4]
1  [2, 3]  [2, 5]

NumPy数组上的计算可能比在包含Python列表的Pandas DataFrame上进行的等效计算快得多。


Although you could use

In [85]: pd.DataFrame({'test':test.tolist(), 'test2':test2.tolist()})
Out[85]: 
     test   test2
0  [1, 2]  [2, 4]
1  [2, 3]  [2, 5]

computation on the NumPy arrays would probably be much faster than an equivalent computation done on a Pandas DataFrame whose columns contain Python lists.

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