python pandas dataframe，对值的操作

Question

I am trying to understand how Pandas DataFrames works to copy information downward, and then reset until the next variables changes... Specifically below, how do I make Share_Amt_To_Buy reset to 0 once my Signal or Signal_Diff switches from 1 to 0?

Using .cumsum() on Share_Amt_To_Buy ends up bringing down the values and accumulating which is not exactly what I would like to do.

My goal is that when Signal changes from 0 to 1, the Share_Amt_To_Buy is calculated and copied until Signal switches back to 0. Then if Signal turns to 1 again, I want Share_Amt_To_Buy to be recalculated based on that point in time.

Hopefully this makes sense - please let me know.

Signal  Signal_Diff  Share_Amt_To_Buy (Correctly)  Share_Amt_To_Buy (Currently)
0       0            0                             0
0       0            0                             0
0       0            0                             0
1       1            100                           100
1       0            100                           100
1       0            100                           100
0       -1           0                             100
0       0            0                             100
1       1            180                           280
1       0            180                           280

As you can see, my signals alternate from 0 to 1, and this means the following: 0 = no trade (or position) 1 = trade (with a position)

Signal_Diff is calculated as follows

portfolio['Signal_Diff'] = portfolio['Signal'].diff().fillna(0.0)

The column 'Share_Amt_To_Buy' is calculated when signal changes from 0 to 1. I have used the following as an example to calculate this

initial_cap = 100000.0
portfolio['close'] = my stock's closing prices as a float
portfolio['Share_Amt'] = np.where(variables['Signal']== 1.0, np.round(initial_cap / portfolio['close'] *     0.25 * portfolio['Signal']), 0.0).cumsum()

portfolio['Share_Amt_To_Buy'] = (portfolio['Share_Amt']*portfolio['Signal'])

Answer 1

From what I understand, there is no built-in formula module for pandas. You can perform formulas on columns, cells, arrays and generate different arrays or values from them (df[column].count() is an example), and do plenty of work like that, but there is no method for dynamically updating the array itself based on another value in the array (like an Excel formula).

You could always do the procedure iteratively and say:

>>> for index in df.index:
>>>     if df['Signal_Diff'] == 0:
>>>         df.loc[index, 'Signal_Diff'] = some_value
>>>     elif df['Signal_Diff'] == 1:
>>>         df.loc[index, 'Signal_Diff'] = some_other_value

Or you could create a custom function via the map tool: https://stackoverflow.com/a/19226745/4131059

EDIT:

Another solution would be to query for all indexes with a value of 1 in the old array and the new array upon some change to the array:

>>> df_old_list = df[df.Signal_Diff == 1].index.tolist()
>>> ...
>>> df_new_list = df[df.Signal_Diff == 1].index.tolist()
>>>
>>> for x in df_old_list:
>>>     if x in df_new_list:
>>>         df_new_list.remove(x)

Then recalculate for only the indexes in df_new_list.