Question

  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 and 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'

我在0.15中没有任何问题，因此它可能与this change有关。只是好奇是否有人有类似的问题，如果有一个解决方法。提前谢谢。

编辑：添加可重复的示例。

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)

然后，这将失败并出现上述错误：

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

Answer 1

在0.15.1为我工作。您收到的错误可能是因为您更新了源但未重新编译（如果您手动安装了代码），因为名为is_monotonic_increasing的函数是0.15中的新功能0.1。

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

在pandas 0.15.1中具有外连接的AttributeError

1 个答案: