356day(call/apply)

本文详细解析了JavaScript中构造函数的使用,通过实例展示了如何利用call和apply实现对象的继承,包括创建对象、属性赋值及方法调用的过程。

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《2018年9月24日》【连续356天】

标题:call/apply;

内容:
 

/ var obj = Object.create(原型);

var obj = Object.create(null); //没有任何原型


// undefined 和 null 不是对象, 并且没法经过包装类
//所以没有toString()方法

// document.write(...);调用的是里面的toString()

//可正常计算的范围:前16,后16位

function Person(name, age,sex)
{
	this.name = name;
	this.age = age;
	this.sex = sex;
}
// call 需要逐个传
// apply需要一个arguments
function Student(name,age,sex,tel,grade)
{
	Person.call(this,name,age,sex);
	//.call(obj)是将内部的this指向obj
	//Person.apply(this,[name,age,sex]);
	this.tel = tel;
	this.grade = grade;
}

var student = new Student("s",100,"male",1,1);

以上代码输出获取SPY的日内数据 (2022-05-09 到 2024-04-22)... 成功获取79015条minute数据 获取SPY的日线数据 (2022-05-09 到 2024-04-22)... 成功获取203条day数据 获取SPY的股息数据并过滤... 计算日内数据指标... Traceback (most recent call last): File "E:\python课\Lib\site-packages\pandas\core\indexes\datetimes.py", line 603, in get_loc parsed, reso = self._parse_with_reso(key) File "E:\python课\Lib\site-packages\pandas\core\indexes\datetimes.py", line 559, in _parse_with_reso parsed, reso = super()._parse_with_reso(label) File "E:\python课\Lib\site-packages\pandas\core\indexes\datetimelike.py", line 293, in _parse_with_reso parsed, reso_str = parsing.parse_datetime_string_with_reso(label, freqstr) File "pandas/_libs/tslibs/parsing.pyx", line 442, in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso File "pandas/_libs/tslibs/parsing.pyx", line 666, in pandas._libs.tslibs.parsing.dateutil_parse pandas._libs.tslibs.parsing.DateParseError: Unknown datetime string format, unable to parse: volume The above exception was the direct cause of the following exception: Traceback (most recent call last): File "E:\python课\Lib\idlelib\idle.py", line 200, in <module> intra_data = calculate_metrics(intra_data.copy()) File "E:\python课\Lib\idlelib\idle.py", line 161, in calculate_metrics df['cum_pv'] = df.groupby('day').transform( File "E:\python课\Lib\site-packages\pandas\core\groupby\generic.py", line 1815, in transform return self._transform( File "E:\python课\Lib\site-packages\pandas\core\groupby\groupby.py", line 2021, in _transform return self._transform_general(func, engine, engine_kwargs, *args, **kwargs) File "E:\python课\Lib\site-packages\pandas\core\groupby\generic.py", line 1732, in _transform_general path, res = self._choose_path(fast_path, slow_path, group) File "E:\python课\Lib\site-packages\pandas\core\groupby\generic.py", line 1834, in _choose_path res = slow_path(group) File "E:\python课\Lib\site-packages\pandas\core\groupby\generic.py", line 1827, in <lambda> slow_path = lambda group: group.apply( File "E:\python课\Lib\site-packages\pandas\core\frame.py", line 10381, in apply return op.apply().__finalize__(self, method="apply") File "E:\python课\Lib\site-packages\pandas\core\apply.py", line 916, in apply return self.apply_standard() File "E:\python课\Lib\site-packages\pandas\core\apply.py", line 1063, in apply_standard results, res_index = self.apply_series_generator() File "E:\python课\Lib\site-packages\pandas\core\apply.py", line 1081, in apply_series_generator results[i] = self.func(v, *self.args, **self.kwargs) File "E:\python课\Lib\site-packages\pandas\core\groupby\generic.py", line 1828, in <lambda> lambda x: func(x, *args, **kwargs), axis=self.axis File "E:\python课\Lib\idlelib\idle.py", line 162, in <lambda> lambda x: (x['volume'] * (x['high'] + x['low'] + x['close']) / 3).cumsum() File "E:\python课\Lib\site-packages\pandas\core\series.py", line 1130, in __getitem__ return self._get_value(key) File "E:\python课\Lib\site-packages\pandas\core\series.py", line 1246, in _get_value loc = self.index.get_loc(label) File "E:\python课\Lib\site-packages\pandas\core\indexes\datetimes.py", line 605, in get_loc raise KeyError(key) from err KeyError: 'volume'基于以上问题重新生成代码
07-04
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