#Tried to bin the Cnum column using the Cabin and Cnum values. I tried using .apply() method but I needed to check two columns for the binning. I tried using the .iterrows() method but didn't get any satisfactory results. I've been trying these method for three straight hours so a helping hand would really be good.
for i in range(len(training["Forward"])):
if training.loc[i,"B"] & training.loc[i,"Cnum"]>=63 & training[i,"Cnum"]<=100:
training[i,"Forward"]=0
else:
training[i,"Forward"]=1
I get the following Error which didn't give me any useable info:
KeyError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3360 try:
-> 3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
/opt/conda/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
/opt/conda/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'B'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
/tmp/ipykernel_16/1974994097.py in <module>
1 for i in range(len(training["Forward"])):
2
----> 3 if training.loc[i,"B"] & training.loc[i,"Cnum"]>=63 & training[i,"Cnum"]<=100:
4 training[i,"Forward"]=0
5 else:
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
923 with suppress(KeyError, IndexError):
924 return self.obj._get_value(*key, takeable=self._takeable)
--> 925 return self._getitem_tuple(key)
926 else:
927 # we by definition only have the 0th axis
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
1098 def _getitem_tuple(self, tup: tuple):
1099 with suppress(IndexingError):
-> 1100 return self._getitem_lowerdim(tup)
1101
1102 # no multi-index, so validate all of the indexers
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_lowerdim(self, tup)
860 return section
861 # This is an elided recursive call to iloc/loc
--> 862 return getattr(section, self.name)[new_key]
863
864 raise IndexingError("not applicable")
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
929
930 maybe_callable = com.apply_if_callable(key, self.obj)
--> 931 return self._getitem_axis(maybe_callable, axis=axis)
932
933 def _is_scalar_access(self, key: tuple):
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1162 # fall thru to straight lookup
1163 self._validate_key(key, axis)
-> 1164 return self._get_label(key, axis=axis)
1165
1166 def _get_slice_axis(self, slice_obj: slice, axis: int):
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
1111 def _get_label(self, label, axis: int):
1112 # GH#5667 this will fail if the label is not present in the axis.
-> 1113 return self.obj.xs(label, axis=axis)
1114
1115 def _handle_lowerdim_multi_index_axis0(self, tup: tuple):
/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in xs(self, key, axis, level, drop_level)
3774 raise TypeError(f"Expected label or tuple of labels, got {key}") from e
3775 else:
-> 3776 loc = index.get_loc(key)
3777
3778 if isinstance(loc, np.ndarray):
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
-> 3363 raise KeyError(key) from err
3364
3365 if is_scalar(key) and isna(key) and not self.hasnans:
You can use .apply() with the whole row through a lambda function. I'd write a function that does what you want, pass the row to it, and then return the value you want. Something like this:
def binme(row):
if row.B and row.Cum>=63 and row.Cnum<=100:
return 0
return 1
df["Forward"] = df.apply(lambda row: binme(row), axis = 1)