There are lots of question about json to pandas dataframe but none of them solved my issues. I am practicing on this complex json file which looks like this
{
"type" : "FeatureCollection",
"features" : [ {
"Id" : 265068000,
"type" : "Feature",
"geometry" : {
"type" : "Point",
"coordinates" : [ 22.170376666666666, 65.57273333333333 ]
},
"properties" : {
"timestampExternal" : 1529151039629
}
}, {
"Id" : 265745760,
"type" : "Feature",
"geometry" : {
"type" : "Point",
"coordinates" : [ 20.329506666666667, 63.675425000000004 ]
},
"properties" : {
"timestampExternal" : 1529151278287
}
} ]
}
I want to convert this json directly to pandas dataframe using pd.read_json()
My Primary Goal is to extract Id, Coordinates and timestampExternal. As this is very complex json, normal way of pd.read_json()
, simply doesnt give correct output. Can you suggest me, how can i approach to solve in this kind of situations. Expected output is something like this
Id,Coordinates,timestampExternal
265068000,[22.170376666666666, 65.57273333333333],1529151039629
265745760,[20.329506666666667, 63.675425000000004],1529151278287
You can read the json to load it into a dictionary. Then, using dictionary comprehension, extract the attributes you want as columns -
import json
import pandas as pd
_json = json.load(open('/path/to/json'))
df_dict = [{'id':item['Id'], 'coordinates':item['geometry']['coordinates'],
'timestampExternal':item['properties']['timestampExternal']} for item in _json['features']]
extracted_df = pd.DataFrame(df_dict)
>>>
coordinates id timestampExternal
0 [22.170376666666666, 65.57273333333333] 265068000 1529151039629
1 [20.329506666666667, 63.675425000000004] 265745760 1529151278287
You can read the json directly, and then given the features
array to pandas as a dict like:
import json
with open('test.json', 'rU') as f:
data = json.load(f)
df = pd.DataFrame([dict(id=datum['Id'],
coords=datum['geometry']['coordinates'],
ts=datum['properties']['timestampExternal'],
)
for datum in data['features']])
print(df)
coords id ts
0 [22.170376666666666, 65.57273333333333] 265068000 1529151039629
1 [20.329506666666667, 63.675425000000004] 265745760 1529151278287
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