Pandas - Performing a transpose of multiple columns
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I have a Dataframe as below:
col1,col2,col3,col4
value1,value2,value3,value4
I am trying to perform a transpose function on them to get the below output:
col1,value1
col2,value2
col3,value3
col4,value4
The problem is the list of columns are dynamic and it depends on user input that pulls the respective columns at the first place.
Use the below query to get the list of columns the user has requested:
cursor.execute('select {} from table'.format(', '.join(str(cols) for cols in col_names)))
Could anyone advice how I could perform a transpose of such a dataframe that has multiple columns but just one row of output. Thanks..
python python-3.x pandas pandas-groupby
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
add a comment |
I have a Dataframe as below:
col1,col2,col3,col4
value1,value2,value3,value4
I am trying to perform a transpose function on them to get the below output:
col1,value1
col2,value2
col3,value3
col4,value4
The problem is the list of columns are dynamic and it depends on user input that pulls the respective columns at the first place.
Use the below query to get the list of columns the user has requested:
cursor.execute('select {} from table'.format(', '.join(str(cols) for cols in col_names)))
Could anyone advice how I could perform a transpose of such a dataframe that has multiple columns but just one row of output. Thanks..
python python-3.x pandas pandas-groupby
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
3
Have you tried usingdf.T?
– dataLeo
Nov 25 '18 at 5:40
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07
add a comment |
I have a Dataframe as below:
col1,col2,col3,col4
value1,value2,value3,value4
I am trying to perform a transpose function on them to get the below output:
col1,value1
col2,value2
col3,value3
col4,value4
The problem is the list of columns are dynamic and it depends on user input that pulls the respective columns at the first place.
Use the below query to get the list of columns the user has requested:
cursor.execute('select {} from table'.format(', '.join(str(cols) for cols in col_names)))
Could anyone advice how I could perform a transpose of such a dataframe that has multiple columns but just one row of output. Thanks..
python python-3.x pandas pandas-groupby
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
I have a Dataframe as below:
col1,col2,col3,col4
value1,value2,value3,value4
I am trying to perform a transpose function on them to get the below output:
col1,value1
col2,value2
col3,value3
col4,value4
The problem is the list of columns are dynamic and it depends on user input that pulls the respective columns at the first place.
Use the below query to get the list of columns the user has requested:
cursor.execute('select {} from table'.format(', '.join(str(cols) for cols in col_names)))
Could anyone advice how I could perform a transpose of such a dataframe that has multiple columns but just one row of output. Thanks..
python python-3.x pandas pandas-groupby
python python-3.x pandas pandas-groupby
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
asked Nov 25 '18 at 5:12
dark horsedark horse
16210
16210
3
Have you tried usingdf.T?
– dataLeo
Nov 25 '18 at 5:40
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07
add a comment |
3
Have you tried usingdf.T?
– dataLeo
Nov 25 '18 at 5:40
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07
3
3
Have you tried using
df.T ?– dataLeo
Nov 25 '18 at 5:40
Have you tried using
df.T ?– dataLeo
Nov 25 '18 at 5:40
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07
add a comment |
1 Answer
1
active
oldest
votes
If your dataframe construct is the way you have shown in your post with the multiple columns and single row then simply set the index to your first column, in your case its "col1" and then transpose it.
DataFrame as per shown:
df = pd.DataFrame({ 'col1':['value1'], 'col2':['value2'], 'col3':['value2'], 'col4':['value4']})
col1 col2 col3 col4
0 value1 value2 value2 value4
You can perform like below:
>>> df.set_index('col1',inplace=True) # First set the Index to `col1` + `inplace=True`
Now allow transpose ..
>>> df.T
col1 value1
col2 value2
col3 value2
col4 value4
In one go all :
>>> df.set_index('col1').T
col1 value1
col2 value2
col3 value2
col4 value4
Note: df.T will not do the Job because it seeks the default index ie zero in this case to be as columns.
Hope this will help you going.
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
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
If your dataframe construct is the way you have shown in your post with the multiple columns and single row then simply set the index to your first column, in your case its "col1" and then transpose it.
DataFrame as per shown:
df = pd.DataFrame({ 'col1':['value1'], 'col2':['value2'], 'col3':['value2'], 'col4':['value4']})
col1 col2 col3 col4
0 value1 value2 value2 value4
You can perform like below:
>>> df.set_index('col1',inplace=True) # First set the Index to `col1` + `inplace=True`
Now allow transpose ..
>>> df.T
col1 value1
col2 value2
col3 value2
col4 value4
In one go all :
>>> df.set_index('col1').T
col1 value1
col2 value2
col3 value2
col4 value4
Note: df.T will not do the Job because it seeks the default index ie zero in this case to be as columns.
Hope this will help you going.
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
add a comment |
If your dataframe construct is the way you have shown in your post with the multiple columns and single row then simply set the index to your first column, in your case its "col1" and then transpose it.
DataFrame as per shown:
df = pd.DataFrame({ 'col1':['value1'], 'col2':['value2'], 'col3':['value2'], 'col4':['value4']})
col1 col2 col3 col4
0 value1 value2 value2 value4
You can perform like below:
>>> df.set_index('col1',inplace=True) # First set the Index to `col1` + `inplace=True`
Now allow transpose ..
>>> df.T
col1 value1
col2 value2
col3 value2
col4 value4
In one go all :
>>> df.set_index('col1').T
col1 value1
col2 value2
col3 value2
col4 value4
Note: df.T will not do the Job because it seeks the default index ie zero in this case to be as columns.
Hope this will help you going.
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
add a comment |
If your dataframe construct is the way you have shown in your post with the multiple columns and single row then simply set the index to your first column, in your case its "col1" and then transpose it.
DataFrame as per shown:
df = pd.DataFrame({ 'col1':['value1'], 'col2':['value2'], 'col3':['value2'], 'col4':['value4']})
col1 col2 col3 col4
0 value1 value2 value2 value4
You can perform like below:
>>> df.set_index('col1',inplace=True) # First set the Index to `col1` + `inplace=True`
Now allow transpose ..
>>> df.T
col1 value1
col2 value2
col3 value2
col4 value4
In one go all :
>>> df.set_index('col1').T
col1 value1
col2 value2
col3 value2
col4 value4
Note: df.T will not do the Job because it seeks the default index ie zero in this case to be as columns.
Hope this will help you going.
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
If your dataframe construct is the way you have shown in your post with the multiple columns and single row then simply set the index to your first column, in your case its "col1" and then transpose it.
DataFrame as per shown:
df = pd.DataFrame({ 'col1':['value1'], 'col2':['value2'], 'col3':['value2'], 'col4':['value4']})
col1 col2 col3 col4
0 value1 value2 value2 value4
You can perform like below:
>>> df.set_index('col1',inplace=True) # First set the Index to `col1` + `inplace=True`
Now allow transpose ..
>>> df.T
col1 value1
col2 value2
col3 value2
col4 value4
In one go all :
>>> df.set_index('col1').T
col1 value1
col2 value2
col3 value2
col4 value4
Note: df.T will not do the Job because it seeks the default index ie zero in this case to be as columns.
Hope this will help you going.
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
answered Nov 25 '18 at 16:26
pygopygo
3,2451721
3,2451721
add a comment |
add a comment |
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3
Have you tried using
df.T?– dataLeo
Nov 25 '18 at 5:40
@dataLeo thanks that helped..
– dark horse
Nov 25 '18 at 18:07