json_normalize appears to delete entries
I imported some json data from an API and tried to flatten it into a dataframe, but it appears that this process deletes all the entries.
# show contents of d
d
[{'20': {'id': '20',
'k1a[q1]': '5',
'k1c[q15]': '5',
'ok4': ''}},
{'22': {'id': '22',
'k1a[q1]': '5',
'ok4': ''}},
# flatten that thing
data = json_normalize(d)
# show data of first column
data['100.k1a[q1]']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
It would appear that there is something that I'm overlooking, and there are a few things that confuse me:
- the index seems to be lost
- the resulting columns all have "100." appended to the front, why can't I see that in the json source?
- Most importantly, all the values are now NaN
How can I transform this data more thoroughly?
python json pandas nan
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
add a comment |
I imported some json data from an API and tried to flatten it into a dataframe, but it appears that this process deletes all the entries.
# show contents of d
d
[{'20': {'id': '20',
'k1a[q1]': '5',
'k1c[q15]': '5',
'ok4': ''}},
{'22': {'id': '22',
'k1a[q1]': '5',
'ok4': ''}},
# flatten that thing
data = json_normalize(d)
# show data of first column
data['100.k1a[q1]']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
It would appear that there is something that I'm overlooking, and there are a few things that confuse me:
- the index seems to be lost
- the resulting columns all have "100." appended to the front, why can't I see that in the json source?
- Most importantly, all the values are now NaN
How can I transform this data more thoroughly?
python json pandas nan
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
add a comment |
I imported some json data from an API and tried to flatten it into a dataframe, but it appears that this process deletes all the entries.
# show contents of d
d
[{'20': {'id': '20',
'k1a[q1]': '5',
'k1c[q15]': '5',
'ok4': ''}},
{'22': {'id': '22',
'k1a[q1]': '5',
'ok4': ''}},
# flatten that thing
data = json_normalize(d)
# show data of first column
data['100.k1a[q1]']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
It would appear that there is something that I'm overlooking, and there are a few things that confuse me:
- the index seems to be lost
- the resulting columns all have "100." appended to the front, why can't I see that in the json source?
- Most importantly, all the values are now NaN
How can I transform this data more thoroughly?
python json pandas nan
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
I imported some json data from an API and tried to flatten it into a dataframe, but it appears that this process deletes all the entries.
# show contents of d
d
[{'20': {'id': '20',
'k1a[q1]': '5',
'k1c[q15]': '5',
'ok4': ''}},
{'22': {'id': '22',
'k1a[q1]': '5',
'ok4': ''}},
# flatten that thing
data = json_normalize(d)
# show data of first column
data['100.k1a[q1]']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
It would appear that there is something that I'm overlooking, and there are a few things that confuse me:
- the index seems to be lost
- the resulting columns all have "100." appended to the front, why can't I see that in the json source?
- Most importantly, all the values are now NaN
How can I transform this data more thoroughly?
python json pandas nan
python json pandas nan
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
asked Nov 23 '18 at 9:48
DataWizDataWiz
165
165
add a comment |
add a comment |
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