Parsing Json in Python Null data -
suppose have following 2 json.
a={"id": "tuxnqkfhvunbtta0", "name": "campestre 1a. secc.", "city": { "id": "tuxnq0fhvtk2njy", "name": "aguascalientes" }, "state": { "id": "tuxnuefhvtmwnje", "name": "aguascalientes" }, "country": { "id": "mx", "name": "mexico" }, "geo_information": none, "subneighborhoods": [ ] } b={ "id": "tuxntuxnqkfhvtnosg", "name": "aeropuerto aguascalientes (lic. jesus teran peredo)", "city": { "id": "tuxnq0fhvtk2njy", "name": "aguascalientes" }, "state": { "id": "tuxnuefhvtmwnje", "name": "aguascalientes" }, "country": { "id": "mx", "name": "mexico" }, "geo_information": { "location": { "latitude": 21.701155, "longitude": -102.31439 } }, "subneighborhoods": [ ] } print b
and want create table 'locations' next columns:
locations = pandas.dataframe(columns=['city_id', 'city_name', 'name', 'latitud', 'longitud', 'country_id', 'country_name', 'state_id', 'state_name', 'subneighborhoods', 'id'])
expect have following data:
i expect have following table
tuxnqkfhvunbtta0, campestre 1a. secc., tuxnq0fhvtk2njy, aguascalientes, tuxnuefhvtmwnje, aguascalientes, mx, mexico, null, null, [] tuxntuxnqkfhvtnosg, aeropuerto aguascalientes (lic. jesus teran peredo), tuxnq0fhvtk2njy, aguascalientes, tuxnuefhvtmwnje, aguascalientes, mx, mexico, 21.701155, -102.31439, []
as in 'a' geo_information none, can not create table. how con solve issue?
thanks!
did try json_normalizer
? request, dot instead of underline.
in[1]: pandas.io.json import json_normalize in[2]: pd.dataframe(json_normalize([a,b])) out[2]: city.id city.name country.id country.name geo_information \ 0 tuxnq0fhvtk2njy aguascalientes mx mexico nan 1 tuxnq0fhvtk2njy aguascalientes mx mexico nan geo_information.location.latitude geo_information.location.longitude \ 0 nan nan 1 21.701155 -102.31439 id name \ 0 tuxnqkfhvunbtta0 campestre 1a. secc. 1 tuxntuxnqkfhvtnosg aeropuerto aguascalientes (lic. jesus teran pe... state.id state.name subneighborhoods 0 tuxnuefhvtmwnje aguascalientes [] 1 tuxnuefhvtmwnje aguascalientes []
(however, leave subneighborhoods
intact, not want)
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