Python Pandas - Flatten Nested JSON
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Working with Nested JSON data that I am trying to transform to a Pandas dataframe. The json_normalize function offers a way to accomplish this.
{
"locations" : [ {
"timestampMs" : "1542654",
"latitudeE7" : 3777321,
"longitudeE7" : -122423125,
"accuracy" : 17,
"altitude" : -10,
"verticalAccuracy" : 2,
"activity" : [ {
"timestampMs" : "1542652",
"activity" : [ {
"type" : "STILL",
"confidence" : 100
} ]
}]
}]
}
I utilized the function to normalize locations, however, the nested part 'activity' is not flat.
Here's my attempt:
activity_data = json_normalize(d, 'locations', ['activity','type', 'confidence'],
meta_prefix='Prefix.',
errors='ignore')
DataFrame:
[{u'activity': [{u'confidence': 100, u'type': ... -10.0 NaN 377777377 -1224229340 1542652023196
The Activity column still has nested elements which I need unpacked in its own column.
Any suggestions/tips would be much appreciated.
python json pandas geopandas
asked Nov 21 at 21:42
keval
629
add a comment |
up vote
0
down vote
favorite
Working with Nested JSON data that I am trying to transform to a Pandas dataframe. The json_normalize function offers a way to accomplish this.
{
"locations" : [ {
"timestampMs" : "1542654",
"latitudeE7" : 3777321,
"longitudeE7" : -122423125,
"accuracy" : 17,
"altitude" : -10,
"verticalAccuracy" : 2,
"activity" : [ {
"timestampMs" : "1542652",
"activity" : [ {
"type" : "STILL",
"confidence" : 100
} ]
}]
}]
}
I utilized the function to normalize locations, however, the nested part 'activity' is not flat.
Here's my attempt:
activity_data = json_normalize(d, 'locations', ['activity','type', 'confidence'],
meta_prefix='Prefix.',
errors='ignore')
DataFrame:
[{u'activity': [{u'confidence': 100, u'type': ... -10.0 NaN 377777377 -1224229340 1542652023196
The Activity column still has nested elements which I need unpacked in its own column.
Any suggestions/tips would be much appreciated.
python json pandas geopandas
asked Nov 21 at 21:42
keval
629
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Working with Nested JSON data that I am trying to transform to a Pandas dataframe. The json_normalize function offers a way to accomplish this.
{
"locations" : [ {
"timestampMs" : "1542654",
"latitudeE7" : 3777321,
"longitudeE7" : -122423125,
"accuracy" : 17,
"altitude" : -10,
"verticalAccuracy" : 2,
"activity" : [ {
"timestampMs" : "1542652",
"activity" : [ {
"type" : "STILL",
"confidence" : 100
} ]
}]
}]
}
I utilized the function to normalize locations, however, the nested part 'activity' is not flat.
Here's my attempt:
activity_data = json_normalize(d, 'locations', ['activity','type', 'confidence'],
meta_prefix='Prefix.',
errors='ignore')
DataFrame:
[{u'activity': [{u'confidence': 100, u'type': ... -10.0 NaN 377777377 -1224229340 1542652023196
The Activity column still has nested elements which I need unpacked in its own column.
Any suggestions/tips would be much appreciated.
python json pandas geopandas
asked Nov 21 at 21:42
keval
629
Working with Nested JSON data that I am trying to transform to a Pandas dataframe. The json_normalize function offers a way to accomplish this.
{
"locations" : [ {
"timestampMs" : "1542654",
"latitudeE7" : 3777321,
"longitudeE7" : -122423125,
"accuracy" : 17,
"altitude" : -10,
"verticalAccuracy" : 2,
"activity" : [ {
"timestampMs" : "1542652",
"activity" : [ {
"type" : "STILL",
"confidence" : 100
} ]
}]
}]
}
I utilized the function to normalize locations, however, the nested part 'activity' is not flat.
Here's my attempt:
activity_data = json_normalize(d, 'locations', ['activity','type', 'confidence'],
meta_prefix='Prefix.',
errors='ignore')
DataFrame:
[{u'activity': [{u'confidence': 100, u'type': ... -10.0 NaN 377777377 -1224229340 1542652023196
The Activity column still has nested elements which I need unpacked in its own column.
Any suggestions/tips would be much appreciated.
python json pandas geopandas
python json pandas geopandas
asked Nov 21 at 21:42
keval
629
asked Nov 21 at 21:42
keval
629
edited Nov 22 at 1:30
asked Nov 21 at 21:42
keval
629
asked Nov 21 at 21:42
keval
629
asked Nov 21 at 21:42
keval
629
629
add a comment |
add a comment |
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