ValueError:只能与其他PeriodIndex-ed对象一起调用 [英] ValueError: can only call with other PeriodIndex-ed objects
问题描述
我正在尝试将2个数据帧合并在一起.具有讽刺意味的是，它们最初是同一数据帧的一部分，但我迈出了第一步，有时方向错误. 框架1看起来像这样:
I am trying to merge 2 dataframes together. Ironically, they started out as part of the same dataframe, but I am making baby steps -- sometimes in the wrong direction. Frame 1 looks like this:
Int64Index: 10730 entries, 0 to 10729
Data columns (total 6 columns):
RegionID 10730 non-null int64
RegionName 10730 non-null object
State 10730 non-null object
Metro 10259 non-null object
CountyName 10730 non-null object
SizeRank 10730 non-null int64
dtypes: int64(2), object(4)
第2帧看起来像这样:
Int64Index: 10730 entries, 0 to 10729
Data columns (total 82 columns):
1996Q2 8218 non-null float64
1996Q3 8229 non-null float64
1996Q4 8235 non-null float64
.....
2016Q1 10730 non-null float64
2016Q2 10730 non-null float64
2016Q3 10730 non-null float64
dtypes: float64(82)
请注意，索引具有相同的类型，甚至具有相同的行数.
我试图像这样将数据框合并回去:
Notice that the indexes are of the same type, and they even have the same number of rows.
I am trying to merge the dataframes back together like so:
df4 = pd.merge(df3, df2, how='inner', left_index=True, right_index=True)
我得到的错误是:
ValueError: can only call with other PeriodIndex-ed objects
第二个数据帧中的2016Q1列和类似命名的列为Period类型，但是我没有合并它们-我认为只要索引对齐，合并就可以了吗?我在做什么错了?
The 2016Q1 and similarly named columns in the 2nd dataframe are of Period type, but I am not merging on them -- I thought as long as the indexes line up, merge should work? What am i doing wrong?
推荐答案
假设我们有以下DF:
In [44]: df1
Out[44]:
1996Q2 2000Q3 2010Q4
0 1.5 3.5 1.000000
1 22.0 38.5 2.000000
2 15.0 35.0 4.333333
In [45]: df1.columns
Out[45]: PeriodIndex(['1996Q2', '2000Q3', '2010Q4'], dtype='period[Q-DEC]', freq='Q-DEC')
注意:df1.columns是PeriodIndex dtype
Notice: df1.columns are of the PeriodIndex dtype
In [46]: df2
Out[46]:
a b c
0 a1 b1 c1
1 a2 b2 c2
2 a3 b3 c3
In [47]: df2.columns
Out[47]: Index(['a', 'b', 'c'], dtype='object')
merge和join将返回:ValueError: can only call with other PeriodIndex-ed objects，因为AFAIK，Pandas DF不能具有混合列dtype，如果其中某些类型是PeriodIndex dtype:
merge and join will return: ValueError: can only call with other PeriodIndex-ed objects as, AFAIK, Pandas DF can't have a mixed column dtypes if some of them are of PeriodIndex dtype:
In [48]: df1.join(df2)
...
skipped
...
ValueError: can only call with other PeriodIndex-ed objects
merge引发相同的异常:
In [54]: pd.merge(df1, df2, left_index=True, right_index=True)
...
skipped
...
ValueError: can only call with other PeriodIndex-ed objects
所以我们将不得不将df1.columns转换为字符串:
So we will have to convert df1.columns to strings:
In [49]: df1.columns = df1.columns.values.astype(str)
In [50]: df1.columns
Out[50]: Index(['1996Q2', '2000Q3', '2010Q4'], dtype='object')
现在join和merge将起作用:
In [51]: df1.join(df2)
Out[51]:
1996Q2 2000Q3 2010Q4 a b c
0 1.5 3.5 1.000000 a1 b1 c1
1 22.0 38.5 2.000000 a2 b2 c2
2 15.0 35.0 4.333333 a3 b3 c3
In [52]: pd.merge(df1, df2, left_index=True, right_index=True)
Out[52]:
1996Q2 2000Q3 2010Q4 a b c
0 1.5 3.5 1.000000 a1 b1 c1
1 22.0 38.5 2.000000 a2 b2 c2
2 15.0 35.0 4.333333 a3 b3 c3
合并的DF的dtypes列:
In [58]: df1.join(df2).columns
Out[58]: Index(['1996Q2', '2000Q3', '2010Q4', 'a', 'b', 'c'], dtype='object')
如果合并完成后需要df1.columns作为PeriodIndex，则可以在转换前保存df1.columns，并在完成合并/合并后将其重新设置:
If you need df1.columns as PeriodIndex after the merging is done - you can save df1.columns before you convert them and set them back after you are done with merging / joining:
In [60]: df1.columns
Out[60]: PeriodIndex(['1996Q2', '2000Q3', '2010Q4'], dtype='period[Q-DEC]', freq='Q-DEC')
In [61]: cols_saved = df1.columns
In [62]: df1.columns = df1.columns.values.astype(str)
In [63]: df1.columns
Out[63]: Index(['1996Q2', '2000Q3', '2010Q4'], dtype='object')
# merging (joining) or doing smth else here ...
In [64]: df1.columns = cols_saved
In [65]: df1.columns
Out[65]: PeriodIndex(['1996Q2', '2000Q3', '2010Q4'], dtype='period[Q-DEC]', freq='Q-DEC')
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