i have the following problem and had an idea to solve it, but it didn't worked: I have the data on DAX Call and Put Options for every trading day in a month .
(我遇到以下问题，并且有解决此问题的想法，但没有成功：我每个月的每个交易日都有 DAX看涨期权和看跌期权的数据。)
After transforming and some calculations I have the following DataFrame: DaxOpt . (经过转换和一些计算之后，我得到了以下DataFrame： DaxOpt 。)
The goal is now to get rid of every row (either Call or Put Option) which does not have the respective pair. (现在的目标是摆脱没有相应对的每一行（看涨期权或看跌期权）。)
With pair I mean a Call and Put Option with the same 'EXERCISE_PRICE' and 'TAU', where 'TAU' = the time to maturity in years. (一对是指具有相同“ EXERCISE_PRICE”和“ TAU”的看涨期权和看跌期权，其中“ TAU” =到期时间（以年为单位）。)
The red boxes in the picture are examples for a pair. (图片中的红色框是一对的示例。)
So either having a DataFrame with only the pairs or having two DataFrames with Call and Put Options where the rows are the respective pairs. (因此，要么具有仅具有一对的DataFrame，要么具有两个具有“调用”和“放置选项”的DataFrame，其中行是各自的对。)
My idea was creating two new DataFrames one which contains only the Call Options and the other the Put Options, sort them after 'TAU' and 'EXERCISE_PRICE' and working my way through with pandas isin function, in order to get rid of the Call or Put Options which do not have the respective pair.
(我的想法是创建两个新的DataFrame，其中一个仅包含调用选项，另一个包含Put选项，将它们排序在“ TAU”和“ EXERCISE_PRICE”之后，并通过pandas isin函数进行操作，以摆脱Call或放置没有相应货币对的期权。)
DaxOptCall = DaxOpt[DaxOpt.CALL_PUT_FLAG == 'C']
(DaxOptCall = DaxOpt [DaxOpt.CALL_PUT_FLAG =='C'])
DaxOptPut = DaxOpt[DaxOpt.CALL_PUT_FLAG == 'P']
(DaxOptPut = DaxOpt [DaxOpt.CALL_PUT_FLAG =='P'])
The problem is that the DaxOptCall and DaxOptPut have different dimensions, so isin function is not applicable.
(问题在于DaxOptCall和DaxOptPut具有不同的尺寸，因此isin函数不适用。)
I am trying to find the most efficient way, since the data I am using now is just a fraction from the real data. (我正在尝试找到最有效的方法，因为我现在使用的数据只是真实数据的一小部分。)
Would appreciate any help or idea.
(将不胜感激任何帮助或想法。)
ask by C0deP0ntage translate from so 与恶龙缠斗过久,自身亦成为恶龙；凝视深渊过久,深渊将回以凝视…
