pandas 重新索引并填充缺少的值:“索引必须是单调的" [英] Pandas reindex and fill missing values: "Index must be monotonic"

问题描述

在回答这个stackoverflow问题，我在重新索引数据帧时使用fill方法时发现了一些有趣的行为.

In answering this stackoverflow question, I found some interesting behavior when using a fill method while reindexing a dataframe.

此旧错误报告在熊猫中表示，df.reindex(newIndex,method='ffill')应该等于df.reindex(newIndex).ffill()，但这不是我所看到的行为

This old bug report in pandas says that df.reindex(newIndex,method='ffill') should be equivalent to df.reindex(newIndex).ffill(), but that is NOT the behavior I'm witnessing

下面是说明行为的代码段

Here's a code snippet that illustrates the behavior

df = pd.DataFrame({'values': 2}, index=pd.DatetimeIndex(['2016-06-02', '2016-05-04', '2016-06-03']))
newIndex = pd.DatetimeIndex(['2016-05-04', '2016-06-01', '2016-06-02', '2016-06-03', '2016-06-05'])
print(df.reindex(newIndex).ffill())
print(df.reindex(newIndex, method='ffill'))

第一个打印语句按预期工作.第二个提出一个

The first print statement works as expected. The second raises a

ValueError: index must be monotonic increasing or decreasing

这是怎么回事?

请注意，样本df 故意具有非单调索引.该问题与df.reindex(newIndex, method='ffil')中的操作顺序有关.我的期望是，如bug报告所言，它应该可以工作-首先用新索引重新索引，然后填充.

Note that the sample df intentionally has a non-monotonic index. The question pertains to the order of operations in df.reindex(newIndex, method='ffil'). My expectation is as the bug-report says it should work- first reindex with the new index and then fill.

如您所见，newIndex.is_monotonic是True，并且填充被单独调用时起作用，但是当作为reindex的参数调用时失败.

As you can see, the newIndex.is_monotonic is True, and the fill works when called separately but fails when called as a parameter to reindex.

推荐答案

看来这也需要在列上完成.

It seems that this needs to be done on the columns as well.

In[76]: frame = DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'],columns=['Ohio', 'Texas', 'California'])

In[77]: frame.reindex(index=['a','b','c','d'],method='ffill',columns=states)
---> ValueError: index must be monotonic increasing or decreasing

In[78]: frame.reindex(index=['a','b','c','d'],method='ffill',columns=states.sort())

Out[78]:
  Ohio  Texas  California
a     0      1           2
b     0      1           2
c     3      4           5
d     6      7           8

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