Python loop with loc

Question

I came across the following errors when I ran the codes for performing a loop operation. Would somebody kindly able to point out the mistake I made so I may fix it?

The aim of the codes is trying to see if the next row of "Dividend" equals to zero or not and if not equal to zero, then the next row of "Adjusting Factor" will perform the action on the right hand side of the equation. I really don't know how I should fix it. Please give some help, thank you so much.

for i in range(data.shape[0]):
    if i == (data.shape[0]-1): 
    continue
    data.loc[data['Dividend'].iloc[i+1] != 0, data['Adjusting Factor'].iloc[i+1]] = (data['EQIX US EQUITY'].iloc[i] - data['Dividend'].iloc[i]) 
    data['Adjusted Premium'].iloc[i] = data['Put'].iloc[i] * data['Adjusting Factor']

KeyError                                  Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2896             try:
-> 2897                 return self._engine.get_loc(key)
   2898             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 1

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key, method, tolerance)
   1056         try:
-> 1057             return Index.get_loc(self, key, method, tolerance)
   1058         except (KeyError, ValueError, TypeError):

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2898             except KeyError:
-> 2899                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2900         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 1

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-49-0fba1ee2e5e8> in <module>
     12     if i == (data.shape[0]-1): # skip the last run to avoid error occur
     13         continue
---> 14     data.loc[data['Dividend'].iloc[i+1] != 0, data['Adjusting Factor'].iloc[i+1]] = (data['EQIX US EQUITY'].iloc[i] - data['Dividend'].iloc[i]) / data['EQIX US EQUITY'].iloc[i]
     15     data['Adjusted Premium'].iloc[i] = data['Put'].iloc[i] * data['Adjusting Factor']
     16     data.loc[data['Adjust Factor'].iloc[i] !=data['Adjust Factor'].iloc[i-1], 'Adjusted Contract Multiplier'] = (data['Adjusted Contract Multiplier'].iloc[i-1]) / data['Adjusting Factor'].iloc[i]

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
    202         else:
    203             key = com.apply_if_callable(key, self.obj)
--> 204         indexer = self._get_setitem_indexer(key)
    205         self._setitem_with_indexer(indexer, value)
    206 

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _get_setitem_indexer(self, key)
    180         if isinstance(key, tuple):
    181             try:
--> 182                 return self._convert_tuple(key, is_setter=True)
    183             except IndexingError:
    184                 pass

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _convert_tuple(self, key, is_setter)
    260                 if i >= self.obj.ndim:
    261                     raise IndexingError("Too many indexers")
--> 262                 idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
    263                 keyidx.append(idx)
    264         return tuple(keyidx)

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer(self, obj, axis, is_setter, raise_missing)
   1286         else:
   1287             try:
-> 1288                 return labels.get_loc(obj)
   1289             except LookupError:
   1290                 # allow a not found key only if we are a setter

~\Anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key, method, tolerance)
   1063 
   1064             try:
-> 1065                 stamp = Timestamp(key)
   1066                 if stamp.tzinfo is not None and self.tz is not None:
   1067                     stamp = stamp.tz_convert(self.tz)

pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()

pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_to_tsobject()

TypeError: Cannot convert input [True] of type <class 'numpy.bool_'> to Timestamp

The dataframe has too many columns and rows. I am afraid it might be confusing. However, one thing to be noted is they are all in numerical values. Would this hint be helpful? — Tsz Chun Leung
– Tsz Chun Leung, Commented Aug 19, 2020 at 13:30

Ran Cohen · Accepted Answer · 2020-08-19 17:25:58Z

1

Ok, Im not sure that the "adjustment factor" function calculates correct because I don't know the formula. If you write the formula I'll fix that.

but the method is:
you shift the diffident one row ahead and calculate.
I used to apply because it's easy

data['Dividend_befor']=data['Dividend'].shift(1).fillna(0)
def Adjusted_Premium_dividend(row):
    if(row['Dividend_befor']!=0):
        Adjusted_factor=row['EQIX US EQUITY'] - row['Dividend']
        Adjusted_Premium = row['Put'] * Adjusted_factor
        return Adjusted_Premium
    else:
        return 0
def adjustment_factor_dividend(row):
    if(row['Dividend_befor']!=0):
        Adjusted_factor=row['EQIX US EQUITY'] - row['Dividend']
        return Adjusted_factor
    else:
        return 0
data['Adjusted_factor']  = data.apply(adjustment_factor_dividend,axis=1)
data['Adjusted_Premium']  = data.apply(adjustment_factor_dividend,axis=1)
data

edited Aug 19, 2020 at 17:25

answered Aug 19, 2020 at 17:20

Ran Cohen

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