Convert name value pairs into new pandas data columns
How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.
The column (attrs) with the named value pairs has values like :
[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false
Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns
python json pandas
asked Nov 19 '18 at 3:57
user468648user468648
152312
add a comment |
How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.
The column (attrs) with the named value pairs has values like :
[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false
Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns
python json pandas
asked Nov 19 '18 at 3:57
user468648user468648
152312
If these are row-specific, it might be better to unpack theattr_idinto a column andvalinto a column, though I'm not sure ifvalwould have a namespace collision withinpandasor not, so you may want to proceed with caution on that particular column name
– C.Nivs
Nov 19 '18 at 4:07
add a comment |
How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.
The column (attrs) with the named value pairs has values like :
[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false
Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns
python json pandas
asked Nov 19 '18 at 3:57
user468648user468648
152312
How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.
The column (attrs) with the named value pairs has values like :
[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false
Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns
[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}][{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]python json pandas
python json pandas
asked Nov 19 '18 at 3:57
user468648user468648
152312
asked Nov 19 '18 at 3:57
user468648user468648
152312
edited Nov 19 '18 at 5:10
user468648
asked Nov 19 '18 at 3:57
user468648user468648
152312
asked Nov 19 '18 at 3:57
user468648user468648
152312
asked Nov 19 '18 at 3:57
user468648user468648
152312
152312
If these are row-specific, it might be better to unpack theattr_idinto a column andvalinto a column, though I'm not sure ifvalwould have a namespace collision withinpandasor not, so you may want to proceed with caution on that particular column name
– C.Nivs
Nov 19 '18 at 4:07
add a comment |
If these are row-specific, it might be better to unpack theattr_idinto a column andvalinto a column, though I'm not sure ifvalwould have a namespace collision withinpandasor not, so you may want to proceed with caution on that particular column name
– C.Nivs
Nov 19 '18 at 4:07
If these are row-specific, it might be better to unpack the
attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name– C.Nivs
Nov 19 '18 at 4:07
If these are row-specific, it might be better to unpack the
attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name– C.Nivs
Nov 19 '18 at 4:07
add a comment |
1 Answer
1
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oldest
votes
Maybe something like the below:
import pandas as pd
import numpy as np
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
d =
for i in l:
q={}
for x in i:
q['attr_id{}'.format(x['attr_id'])]=x['val']
d.append(q)
df = pd.DataFrame(d)
print(df)
.
attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
0 false false 4.00 2.50 1750
1 false false 2.00 1.00 NaN
2 false false NaN NaN NaN
answered Nov 19 '18 at 4:20
tengteng
817721
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
Maybe something like the below:
import pandas as pd
import numpy as np
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
d =
for i in l:
q={}
for x in i:
q['attr_id{}'.format(x['attr_id'])]=x['val']
d.append(q)
df = pd.DataFrame(d)
print(df)
.
attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
0 false false 4.00 2.50 1750
1 false false 2.00 1.00 NaN
2 false false NaN NaN NaN
answered Nov 19 '18 at 4:20
tengteng
817721
add a comment |
Maybe something like the below:
import pandas as pd
import numpy as np
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
d =
for i in l:
q={}
for x in i:
q['attr_id{}'.format(x['attr_id'])]=x['val']
d.append(q)
df = pd.DataFrame(d)
print(df)
.
attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
0 false false 4.00 2.50 1750
1 false false 2.00 1.00 NaN
2 false false NaN NaN NaN
answered Nov 19 '18 at 4:20
tengteng
817721
add a comment |
Maybe something like the below:
import pandas as pd
import numpy as np
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
d =
for i in l:
q={}
for x in i:
q['attr_id{}'.format(x['attr_id'])]=x['val']
d.append(q)
df = pd.DataFrame(d)
print(df)
.
attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
0 false false 4.00 2.50 1750
1 false false 2.00 1.00 NaN
2 false false NaN NaN NaN
answered Nov 19 '18 at 4:20
tengteng
817721
Maybe something like the below:
import pandas as pd
import numpy as np
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
d =
for i in l:
q={}
for x in i:
q['attr_id{}'.format(x['attr_id'])]=x['val']
d.append(q)
df = pd.DataFrame(d)
print(df)
.
attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
0 false false 4.00 2.50 1750
1 false false 2.00 1.00 NaN
2 false false NaN NaN NaN
answered Nov 19 '18 at 4:20
tengteng
817721
answered Nov 19 '18 at 4:20
tengteng
817721
answered Nov 19 '18 at 4:20
tengteng
817721
answered Nov 19 '18 at 4:20
tengteng
817721
817721
add a comment |
add a comment |
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If these are row-specific, it might be better to unpack the
attr_idinto a column andvalinto a column, though I'm not sure ifvalwould have a namespace collision withinpandasor not, so you may want to proceed with caution on that particular column name– C.Nivs
Nov 19 '18 at 4:07