I have found that 'right' joins on index values raise TypeEerror: Argument 'left' has incorrect type (expected numpy.ndarray, got Int64Index).
Both index types are the same Int64Index index. This works in pandas 0.13.1.
Pandas: 0.17.0
Numpy: 1.9.2
import pandas as pd
import numpy as np
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
s = pd.Series(np.repeat(np.arange(8),2), index=np.repeat(np.arange(8),2), name='TEST')
In []: s.head()
Out[]:
0 0
0 0
1 1
1 1
2 2
dtype: int32
# The following all work as expected
df.join(s, how='inner')
df.join(s, how='outer')
df.join(s, how='left')
# Right Joins Type Error
df.join(s, how='right')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-80-26e8bf54fd8f> in <module>()
----> 1 df.join(s, how='right')
D:\Python27\lib\site-packages\pandas\core\frame.pyc in join(self, other, on, how, lsuffix, rsuffix, sort)
4218 # For SparseDataFrame's benefit
4219 return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
-> 4220 rsuffix=rsuffix, sort=sort)
4221
4222 def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
D:\Python27\lib\site-packages\pandas\core\frame.pyc in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
4232 return merge(self, other, left_on=on, how=how,
4233 left_index=on is None, right_index=True,
-> 4234 suffixes=(lsuffix, rsuffix), sort=sort)
4235 else:
4236 if on is not None:
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)
33 right_index=right_index, sort=sort, suffixes=suffixes,
34 copy=copy, indicator=indicator)
---> 35 return op.get_result()
36 if __debug__:
37 merge.__doc__ = _merge_doc % '\nleft : DataFrame'
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
194 self.left, self.right = self._indicator_pre_merge(self.left, self.right)
195
--> 196 join_index, left_indexer, right_indexer = self._get_join_info()
197
198 ldata, rdata = self.left._data, self.right._data
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in _get_join_info(self)
309 if self.left_index and self.right_index:
310 join_index, left_indexer, right_indexer = \
--> 311 left_ax.join(right_ax, how=self.how, return_indexers=True)
312 elif self.right_index and self.how == 'left':
313 join_index, left_indexer, right_indexer = \
D:\Python27\lib\site-packages\pandas\core\index.pyc in join(self, other, how, level, return_indexers)
2212 if self.is_monotonic and other.is_monotonic:
2213 return self._join_monotonic(other, how=how,
-> 2214 return_indexers=return_indexers)
2215 else:
2216 return self._join_non_unique(other, how=how,
D:\Python27\lib\site-packages\pandas\core\index.pyc in _join_monotonic(self, other, how, return_indexers)
2463 join_index, lidx, ridx = self._left_indexer(sv, ov)
2464 elif how == 'right':
-> 2465 join_index, ridx, lidx = self._left_indexer(other, self)
2466 elif how == 'inner':
2467 join_index, lidx, ridx = self._inner_indexer(sv, ov)
TypeError: Argument 'left' has incorrect type (expected numpy.ndarray, got Int64Index)
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