Chaining melt and groupby in pandas causes integer values to be cast to boolean
up vote
1
down vote
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I'm trying to reshape the following dataframe:
df = pd.DataFrame({'feature': [True,False,False,True],
'id': [1,0,1,2]})
...to create a dataframe similar to the example below. Column names should now be the index, and the frequency of each unique value should be provided as a count.
Using melt and groupby almost achieves this, except that 0 and 1 (integers) are cast to False and True (boolean).
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Any suggestions for achieving the desired dataframe (without the 0 and 1 being cast to boolean) would be greatly appreciated!
python pandas pandas-groupby
asked Nov 19 at 23:26
tomp
435313
add a comment |
up vote
1
down vote
favorite
I'm trying to reshape the following dataframe:
df = pd.DataFrame({'feature': [True,False,False,True],
'id': [1,0,1,2]})
...to create a dataframe similar to the example below. Column names should now be the index, and the frequency of each unique value should be provided as a count.
Using melt and groupby almost achieves this, except that 0 and 1 (integers) are cast to False and True (boolean).
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Any suggestions for achieving the desired dataframe (without the 0 and 1 being cast to boolean) would be greatly appreciated!
python pandas pandas-groupby
asked Nov 19 at 23:26
tomp
435313
1
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I'm trying to reshape the following dataframe:
df = pd.DataFrame({'feature': [True,False,False,True],
'id': [1,0,1,2]})
...to create a dataframe similar to the example below. Column names should now be the index, and the frequency of each unique value should be provided as a count.
Using melt and groupby almost achieves this, except that 0 and 1 (integers) are cast to False and True (boolean).
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Any suggestions for achieving the desired dataframe (without the 0 and 1 being cast to boolean) would be greatly appreciated!
python pandas pandas-groupby
asked Nov 19 at 23:26
tomp
435313
I'm trying to reshape the following dataframe:
df = pd.DataFrame({'feature': [True,False,False,True],
'id': [1,0,1,2]})
...to create a dataframe similar to the example below. Column names should now be the index, and the frequency of each unique value should be provided as a count.
Using melt and groupby almost achieves this, except that 0 and 1 (integers) are cast to False and True (boolean).
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Any suggestions for achieving the desired dataframe (without the 0 and 1 being cast to boolean) would be greatly appreciated!
python pandas pandas-groupby
python pandas pandas-groupby
asked Nov 19 at 23:26
tomp
435313
asked Nov 19 at 23:26
tomp
435313
asked Nov 19 at 23:26
tomp
435313
asked Nov 19 at 23:26
tomp
435313
asked Nov 19 at 23:26
tomp
435313
435313
1
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29
add a comment |
1
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29
1
1
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
Using dtype convert id to str
df.id=df.id.astype(str)
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Out[81]:
freq
variable value
feature False 2
True 2
id 0 1
1 2
2 1
answered Nov 19 at 23:43
W-B
96k72961
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Using dtype convert id to str
df.id=df.id.astype(str)
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Out[81]:
freq
variable value
feature False 2
True 2
id 0 1
1 2
2 1
answered Nov 19 at 23:43
W-B
96k72961
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
add a comment |
up vote
1
down vote
accepted
Using dtype convert id to str
df.id=df.id.astype(str)
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Out[81]:
freq
variable value
feature False 2
True 2
id 0 1
1 2
2 1
answered Nov 19 at 23:43
W-B
96k72961
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Using dtype convert id to str
df.id=df.id.astype(str)
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Out[81]:
freq
variable value
feature False 2
True 2
id 0 1
1 2
2 1
answered Nov 19 at 23:43
W-B
96k72961
Using dtype convert id to str
df.id=df.id.astype(str)
df.melt().groupby(['variable','value']).size().to_frame(name='freq')
Out[81]:
freq
variable value
feature False 2
True 2
id 0 1
1 2
2 1
answered Nov 19 at 23:43
W-B
96k72961
answered Nov 19 at 23:43
W-B
96k72961
answered Nov 19 at 23:43
W-B
96k72961
answered Nov 19 at 23:43
W-B
96k72961
96k72961
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
add a comment |
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
This answers the question as it is phrased, but changing the data type to a string isn't ideal. e.g. df.id.sum() now returns '1012', rather than 4.
– tomp
Nov 20 at 15:52
@tomp that is true
– W-B
Nov 20 at 16:25
@tomp that is true
– W-B
Nov 20 at 16:25
add a comment |
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1
Index with type object. Since you have bool and int , it will convert to object , and in pandas 0 treat as False 1 treat as True for object conversion .
– W-B
Nov 19 at 23:42
makes sense, thanks for the explanation!
– tomp
Nov 20 at 2:29