Why pandas silently ignores .iloc[i, j] assignment with too many indices?
Getting values
If the key is a tuple (as in your example), then the __getitem__ method of the superclass for the loc and iloc objects at some point calls _has_valid_tuple(self, key).
This method has the following code
for i, k in enumerate(key): if i >= self.obj.ndim: raise IndexingError('Too many indexers')This raises an IndexingError you would expect.
Setting values
The superclass's __setitem__ makes a call to _get_setitem_indexer and in turn _convert_to_indexer.
This superclass's implementation of _convert_to_indexer is a bit messy but in this case it returns a numpy array [0, 0].
The class of the iLoc indexer, however, overrides _convert_to_indexer. This method returns the original tuple...
def _convert_to_indexer(self, obj, axis=0, is_setter=False): ... elif self._has_valid_type(obj, axis): return objNow an indexer variable is a numpy array for the .loc case and tuple for the .iloc case. This causes the difference in setting behavior in the subsequent superclass call to _setitem_with_indexer(indexer, value).