Say I have a multiindex mi
as follows:
Serial No. Date
A B A B
0 816292 934609 27/01/17 27/01/17
1 983803 683858 25/01/17 26/01/17
2 596573 493741 27/01/17 28/01/17
3 199203 803515 28/01/17 28/01/17
A and B are two parts such that the multiindex contains information about the serial number and build date of multiple instances of the two parts.
I have a dataframe df
containing test information for part A, as follows:
A Test 1 Test 2 Test 3
0 816292 0.934609 0.475035 0.822712
1 983803 0.683858 0.025861 0.691112
2 596573 0.493741 0.397398 0.489101
3 199203 0.803515 0.679537 0.308588
I would like to be able to merge these two and yield something like
Serial No. Date Tests
A B A B Test 1 Test 2 Test 3
0 816292 934609 27/01/17 27/01/17 0.934609 0.475035 0.822712
1 983803 683858 25/01/17 26/01/17 0.683858 0.025861 0.691112
2 596573 493741 27/01/17 28/01/17 0.493741 0.397398 0.489101
3 199203 803515 28/01/17 28/01/17 0.803515 0.679537 0.308588
My initial attempt was
mi = mi.merge(df,left_on=('Serial No.','A'),right_on='A',how='inner')
but that yields ValueError: len(right_on) must equal len(left_on)
. I have tried adding an additional column index 'Tests'
to df
and then doing
mi = mi.merge(df,left_on=('Serial No.','A'),right_on=('Tests','A'),how='inner')
but that yields KeyError: 'A'
The easiest way is to fix df
's columns to match mi
:
In [11]: df
Out[11]:
A Test 1 Test 2 Test 3
0 816292 0.934609 0.475035 0.822712
1 983803 0.683858 0.025861 0.691112
2 596573 0.493741 0.397398 0.489101
3 199203 0.803515 0.679537 0.308588
In [12]: df.columns = pd.MultiIndex.from_arrays([["Serial No.", "Test", "Test", "Test"], df.columns])
In [13]: df
Out[13]:
Serial No. Test
A Test 1 Test 2 Test 3
0 816292 0.934609 0.475035 0.822712
1 983803 0.683858 0.025861 0.691112
2 596573 0.493741 0.397398 0.489101
3 199203 0.803515 0.679537 0.308588
Then a merge will "just work":
In [14]: df.merge(mi)
Out[14]:
Serial No. Test Serial No. Date
A Test 1 Test 2 Test 3 B A B
0 816292 0.934609 0.475035 0.822712 934609 27/01/17 27/01/17
1 983803 0.683858 0.025861 0.691112 683858 25/01/17 26/01/17
2 596573 0.493741 0.397398 0.489101 493741 27/01/17 28/01/17
3 199203 0.803515 0.679537 0.308588 803515 28/01/17 28/01/17
There's a bunch of ways to create the top level of the MultiIndex, here I just wrote the list:
["Serial No.", "Test", "Test", "Test"]
by hand... but you can generate that: it's just a list.
mi.set_index(('Serial No.', 'A')).join(
pd.concat([df.set_index('A')], axis=1, keys=['Tests'])
).reset_index()
Serial No. Date Tests
A B A B Test 1 Test 2 Test 3
0 816292 934609 27/01/17 27/01/17 0.934609 0.475035 0.822712
1 983803 683858 25/01/17 26/01/17 0.683858 0.025861 0.691112
2 596573 493741 27/01/17 28/01/17 0.493741 0.397398 0.489101
3 199203 803515 28/01/17 28/01/17 0.803515 0.679537 0.308588
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