python - AttributeError with an outer join in pandas 0.15.1 -

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  in [26]:  xyz = temp_val_ns.join(temp_ref_ns, how='outer')    traceback (most recent call last):    file "<ipython-input-26-e10ed4b1946b>", line 1, in <module>     xyz = temp_val_ns.join(temp_ref_ns, how='outer')    file "c:\anaconda\lib\site-packages\pandas\core\frame.py", line 3867, in join     rsuffix=rsuffix, sort=sort)    file "c:\anaconda\lib\site-packages\pandas\core\frame.py", line 3881, in _join_compat     suffixes=(lsuffix, rsuffix), sort=sort)    file "c:\anaconda\lib\site-packages\pandas\tools\merge.py", line 39, in merge     return op.get_result()    file "c:\anaconda\lib\site-packages\pandas\tools\merge.py", line 187, in get_result     join_index, left_indexer, right_indexer = self._get_join_info()    file "c:\anaconda\lib\site-packages\pandas\tools\merge.py", line 260, in _get_join_info     left_ax.join(right_ax, how=self.how, return_indexers=true)    file "c:\anaconda\lib\site-packages\pandas\core\index.py", line 1729, in join     elif self.is_monotonic , other.is_monotonic:    file "c:\anaconda\lib\site-packages\pandas\core\index.py", line 577, in is_monotonic     return self._engine.is_monotonic_increasing  attributeerror: 'pandas.index.int64engine' object has no attribute 'is_monotonic_increasing'

i didn't have problems in 0.15 might related this change. curious if having similar problems , if there workaround it. in advance.

edit: adding in reproducible example. 

aaa = {'bbot_sampler_ref': {1413180063086001221: true, 1413180063086915835: true, 1413180063086998237: true, 1413180063087746824: true, 1413180063089530483: true}, 'bw_ref': {1413180063086001221: 128.04550264550264, 1413180063086915835: 128.04553191489362, 1413180063086998237: 128.04559139784948, 1413180063087746824: 128.04556756756756, 1413180063089530483: 128.04492822966506}} temp_ref_ns = pd.dataframe(aaa) bbb = {  'agg': {1413180063080171210: 1,   1413180063080280537: 1,   1413180063080365279: 1,   1413180063080440876: 1,   1413180063080514973: 1},  'last_trade': {1413180063080171210: 150.75,   1413180063080280537: 150.75,   1413180063080365279: 150.75,   1413180063080440876: 150.75,   1413180063080514973: 150.75},  'mid': {1413180063080171210: 150.745,   1413180063080280537: 150.745,   1413180063080365279: 150.745,   1413180063080440876: 150.745,   1413180063080514973: 150.745},  'pcap_seq': {1413180063080171210: 17613,   1413180063080280537: 17615,   1413180063080365279: 17617,   1413180063080440876: 17619,   1413180063080514973: 17621},  'timestamp': {1413180063080171210: 1413180063080171210,   1413180063080280537: 1413180063080280537,   1413180063080365279: 1413180063080365279,   1413180063080440876: 1413180063080440876,   1413180063080514973: 1413180063080514973}}  temp_val_ns = pd.dataframe(bbb)

then, fail error above:

xyz = temp_val_ns.join(temp_ref_ns, how='outer')

worked me in 0.15.1. error receiving because updated source didn't recompile (if 'manually' installed code), function called is_monotonic_increasing new in 0.15.1.

in [11]: temp_ref_ns out[11]:                      bbot_sampler_ref      bw_ref 1413180063086001221             true  128.045503 1413180063086915835             true  128.045532 1413180063086998237             true  128.045591 1413180063087746824             true  128.045568 1413180063089530483             true  128.044928  in [12]: temp_val_ns out[12]:                       agg  last_trade      mid  pcap_seq            timestamp 1413180063080171210    1      150.75  150.745     17613  1413180063080171210 1413180063080280537    1      150.75  150.745     17615  1413180063080280537 1413180063080365279    1      150.75  150.745     17617  1413180063080365279 1413180063080440876    1      150.75  150.745     17619  1413180063080440876 1413180063080514973    1      150.75  150.745     17621  1413180063080514973  in [13]: temp_val_ns.join(temp_ref_ns, how='outer') out[13]:                       agg  last_trade      mid  pcap_seq     timestamp bbot_sampler_ref      bw_ref 1413180063080171210    1      150.75  150.745     17613  1.413180e+18              nan         nan 1413180063080280537    1      150.75  150.745     17615  1.413180e+18              nan         nan 1413180063080365279    1      150.75  150.745     17617  1.413180e+18              nan         nan 1413180063080440876    1      150.75  150.745     17619  1.413180e+18              nan         nan 1413180063080514973    1      150.75  150.745     17621  1.413180e+18              nan         nan 1413180063086001221  nan         nan      nan       nan           nan             true  128.045503 1413180063086915835  nan         nan      nan       nan           nan             true  128.045532 1413180063086998237  nan         nan      nan       nan           nan             true  128.045591 1413180063087746824  nan         nan      nan       nan           nan             true  128.045568 1413180063089530483  nan         nan      nan       nan           nan             true  128.044928

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