Python堆栈为堆栈格式
问题描述:
或者也称为长到宽格式。Python堆栈为堆栈格式
我有以下几点:
ID1 ID2 POS1 POS2 TYPE TYPEVAL
--- --- ---- ---- ---- -------
A 001 1 5 COLOR RED
A 001 1 5 WEIGHT 50KG
A 001 1 5 HEIGHT 160CM
A 002 6 19 FUTURE YES
A 002 6 19 PRESENT NO
B 001 26 34 COLOUR BLUE
B 001 26 34 WEIGHT 85KG
B 001 26 34 HEIGHT 120CM
C 001 10 13 MOBILE NOKIA
C 001 10 13 TABLET ASUS
,我想给TYPE
列浇铸成每每一个独特的价值新列即
ID1 ID2 POS1 POS2 COLOR WEIGHT HEIGHT FUTURE PRESENT MOBILE TABLET
A 001 1 5 RED 50KG 160CM NA NA NA NA
A 002 6 19 NA NA NA YES NO NA NA
B 001 26 34 BLUE 85KG 120CM NA NA NA NA
C 001 10 13 NA NA NA NA NA NOKIA ASUS
,我曾尝试通过以下方式这样做:
PD.pivot_table(df,index=["ID1","ID2"],columns=["BEGIN","END","TYPE"],values=["TYPEVAL"])
但是我得到:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/tools/pivot.py", line 127, in pivot_table
agged = grouped.agg(aggfunc)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 3690, in aggregate
return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 3179, in aggregate
result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/base.py", line 432, in _aggregate
return getattr(self, arg)(*args, **kwargs), None
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 1009, in mean
return self._cython_agg_general('mean')
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 3113, in _cython_agg_general
how, numeric_only=numeric_only)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 3159, in _cython_agg_blocks
raise DataError('No numeric types to aggregate')
其中提示我通过某个数字函数(即,平均或总和)。然而,我不想做这样的事情,我只是想调换TYPE
列而没有任何聚合。
任何建议将不胜感激!
答
我认为你需要pivot_table
与聚集first
或者多个值join
或sum
,因为deafult聚合函数是mean
,它仅适用于数字:
df1 = pd.pivot_table(df,
index=["ID1","ID2","POS1","POS2",],
columns="TYPE",
values="TYPEVAL",
aggfunc='first')
.reset_index().rename_axis(None, axis=1)
print (df1)
ID1 ID2 POS1 POS2 COLOR COLOUR FUTURE HEIGHT MOBILE PRESENT TABLET WEIGHT
0 A 1 1 5 RED None None 160CM None None None 50KG
1 A 2 6 19 None None YES None None NO None None
2 B 1 26 34 None BLUE None 120CM None None None 85KG
3 C 1 10 13 None None None None NOKIA None ASUS None
df1 = pd.pivot_table(df,
index=["ID1","ID2","POS1","POS2",],
columns="TYPE",
values="TYPEVAL",
aggfunc=','.join)
.reset_index().rename_axis(None, axis=1)
print (df1)
ID1 ID2 POS1 POS2 COLOR COLOUR FUTURE HEIGHT MOBILE PRESENT TABLET WEIGHT
0 A 1 1 5 RED None None 160CM None None None 50KG
1 A 2 6 19 None None YES None None NO None None
2 B 1 26 34 None BLUE None 120CM None None None 85KG
3 C 1 10 13 None None None None NOKIA None ASUS None
感谢这工作 – brucezepplin