Mixing
How to read, transpose, and stack rows based on whether a row value is the same across them
I have a raw dataset in an Excel spreadsheet that looks like this (snippet):
SampleNbr A B C
1 2.0 5.0 0.6
1 6.0 7.0 0.3
2 9.2 4.0 0.5
2 7.3 5.5 0.4
What I would like to do is convert this to something that looks like this:
A 2.0 9.2
B 5.0 4.0
C 0.6 0.5
A 6.0 7.3
B 7.0 5.5
C 0.3 0.4
In other words, where the sample number is the same, read the row values, transpose into a column, and stack them. It seems I need a mask to represent groups of rows where the sample number value is the same, but I'm not sure if I need to write a function to do the read, transpose, append.
pandas
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
add a comment |
I have a raw dataset in an Excel spreadsheet that looks like this (snippet):
SampleNbr A B C
1 2.0 5.0 0.6
1 6.0 7.0 0.3
2 9.2 4.0 0.5
2 7.3 5.5 0.4
What I would like to do is convert this to something that looks like this:
A 2.0 9.2
B 5.0 4.0
C 0.6 0.5
A 6.0 7.3
B 7.0 5.5
C 0.3 0.4
In other words, where the sample number is the same, read the row values, transpose into a column, and stack them. It seems I need a mask to represent groups of rows where the sample number value is the same, but I'm not sure if I need to write a function to do the read, transpose, append.
pandas
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03
add a comment |
I have a raw dataset in an Excel spreadsheet that looks like this (snippet):
SampleNbr A B C
1 2.0 5.0 0.6
1 6.0 7.0 0.3
2 9.2 4.0 0.5
2 7.3 5.5 0.4
What I would like to do is convert this to something that looks like this:
A 2.0 9.2
B 5.0 4.0
C 0.6 0.5
A 6.0 7.3
B 7.0 5.5
C 0.3 0.4
In other words, where the sample number is the same, read the row values, transpose into a column, and stack them. It seems I need a mask to represent groups of rows where the sample number value is the same, but I'm not sure if I need to write a function to do the read, transpose, append.
pandas
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
I have a raw dataset in an Excel spreadsheet that looks like this (snippet):
SampleNbr A B C
1 2.0 5.0 0.6
1 6.0 7.0 0.3
2 9.2 4.0 0.5
2 7.3 5.5 0.4
What I would like to do is convert this to something that looks like this:
A 2.0 9.2
B 5.0 4.0
C 0.6 0.5
A 6.0 7.3
B 7.0 5.5
C 0.3 0.4
In other words, where the sample number is the same, read the row values, transpose into a column, and stack them. It seems I need a mask to represent groups of rows where the sample number value is the same, but I'm not sure if I need to write a function to do the read, transpose, append.
pandas
pandas
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
edited Jan 2 at 21:31
MikeTheLiar
3,52873660
3,52873660
asked Jan 2 at 21:30
juliebjulieb
53
asked Jan 2 at 21:30
juliebjulieb
53
asked Jan 2 at 21:30
juliebjulieb
53
53
is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03
add a comment |
is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03
is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03
is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03
add a comment |
1 Answer
1
active
oldest
votes
The following should work:
your_data = {
'SampleNbr': {0: 1, 1: 1, 2: 2, 3: 2},
'A': {0: 2.0, 1: 6.0, 2: 9.2, 3: 7.3},
'B': {0: 5.0, 1: 7.0, 2: 4.0, 3: 5.5},
'C': {0: 0.6, 1: 0.3, 2: 0.5, 3: 0.4}
}
df = pd.DataFrame(your_data)
df['order'] = df.groupby('SampleNbr').cumcount()
df = (df
.groupby(['order','SampleNbr'])['A','B','C'].sum()
.stack(0)
.unstack(1)
.reset_index(level=0, drop=True)
)
print(df)
SampleNbr 1 2
A 2.000 9.200
B 5.000 4.000
C 0.600 0.500
A 6.000 7.300
B 7.000 5.500
C 0.300 0.400
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The following should work:
your_data = {
'SampleNbr': {0: 1, 1: 1, 2: 2, 3: 2},
'A': {0: 2.0, 1: 6.0, 2: 9.2, 3: 7.3},
'B': {0: 5.0, 1: 7.0, 2: 4.0, 3: 5.5},
'C': {0: 0.6, 1: 0.3, 2: 0.5, 3: 0.4}
}
df = pd.DataFrame(your_data)
df['order'] = df.groupby('SampleNbr').cumcount()
df = (df
.groupby(['order','SampleNbr'])['A','B','C'].sum()
.stack(0)
.unstack(1)
.reset_index(level=0, drop=True)
)
print(df)
SampleNbr 1 2
A 2.000 9.200
B 5.000 4.000
C 0.600 0.500
A 6.000 7.300
B 7.000 5.500
C 0.300 0.400
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
add a comment |
The following should work:
your_data = {
'SampleNbr': {0: 1, 1: 1, 2: 2, 3: 2},
'A': {0: 2.0, 1: 6.0, 2: 9.2, 3: 7.3},
'B': {0: 5.0, 1: 7.0, 2: 4.0, 3: 5.5},
'C': {0: 0.6, 1: 0.3, 2: 0.5, 3: 0.4}
}
df = pd.DataFrame(your_data)
df['order'] = df.groupby('SampleNbr').cumcount()
df = (df
.groupby(['order','SampleNbr'])['A','B','C'].sum()
.stack(0)
.unstack(1)
.reset_index(level=0, drop=True)
)
print(df)
SampleNbr 1 2
A 2.000 9.200
B 5.000 4.000
C 0.600 0.500
A 6.000 7.300
B 7.000 5.500
C 0.300 0.400
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
add a comment |
The following should work:
your_data = {
'SampleNbr': {0: 1, 1: 1, 2: 2, 3: 2},
'A': {0: 2.0, 1: 6.0, 2: 9.2, 3: 7.3},
'B': {0: 5.0, 1: 7.0, 2: 4.0, 3: 5.5},
'C': {0: 0.6, 1: 0.3, 2: 0.5, 3: 0.4}
}
df = pd.DataFrame(your_data)
df['order'] = df.groupby('SampleNbr').cumcount()
df = (df
.groupby(['order','SampleNbr'])['A','B','C'].sum()
.stack(0)
.unstack(1)
.reset_index(level=0, drop=True)
)
print(df)
SampleNbr 1 2
A 2.000 9.200
B 5.000 4.000
C 0.600 0.500
A 6.000 7.300
B 7.000 5.500
C 0.300 0.400
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
The following should work:
your_data = {
'SampleNbr': {0: 1, 1: 1, 2: 2, 3: 2},
'A': {0: 2.0, 1: 6.0, 2: 9.2, 3: 7.3},
'B': {0: 5.0, 1: 7.0, 2: 4.0, 3: 5.5},
'C': {0: 0.6, 1: 0.3, 2: 0.5, 3: 0.4}
}
df = pd.DataFrame(your_data)
df['order'] = df.groupby('SampleNbr').cumcount()
df = (df
.groupby(['order','SampleNbr'])['A','B','C'].sum()
.stack(0)
.unstack(1)
.reset_index(level=0, drop=True)
)
print(df)
SampleNbr 1 2
A 2.000 9.200
B 5.000 4.000
C 0.600 0.500
A 6.000 7.300
B 7.000 5.500
C 0.300 0.400
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
answered Jan 3 at 20:48
Sander van den OordSander van den Oord
715520
715520
add a comment |
add a comment |
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is the answer i gave below useful for u?
– Sander van den Oord
Jan 4 at 20:03