How to convert this JSON file into a Pandas DataFrame. PYTHON
'''
URL="https://ressources.data.sncf.com/api/records/1.0/search/?dataset=sncf-gares-et-arrets-transilien-ile-de-france&q=&rows=9999&start=0&sort=libelle&facet=code_uic&facet=libelle_point_d_arret&facet=nom_gare&facet=commune&facet=zone_navigo&facet=gare_non_sncf"
req = requests.get(URL )
#text_data= req.text
json_dict= json.loads(text_data)
json_dict
'''
Use pd.json_normalize:
data = requests.get(URL).json()
df = pd.json_normalize(data['records'])
print(df)
# Output
datasetid recordid ... geometry.type geometry.coordinates
0 sncf-gares-et-arrets-transilien-ile-de-france 297f7655c89068015a0e90d1bcfdef6400c575b3 ... Point [2.41924456072, 48.7253771442]
1 sncf-gares-et-arrets-transilien-ile-de-france 42d1c6c90a2bbd0b0b2d86d35ff71b168ad5d92f ... Point [2.09202131475, 48.9551807035]
2 sncf-gares-et-arrets-transilien-ile-de-france dc6c79b461241cb64998f044d097a223c0cf43c1 ... Point [2.07739969014, 48.9700946335]
3 sncf-gares-et-arrets-transilien-ile-de-france 1be3bf1799e2a15402537e1609c147b73eb27d34 ... Point [2.56069879391, 49.0097447522]
4 sncf-gares-et-arrets-transilien-ile-de-france 39fea382bfe5bb656e8f87615e10151985770f48 ... Point [2.57154696503, 49.0040985205]
.. ... ... ... ... ...
510 sncf-gares-et-arrets-transilien-ile-de-france 3dadd55b81461fce9450f2d0a79882717903c696 ... Point [2.40241090458, 48.800528137]
511 sncf-gares-et-arrets-transilien-ile-de-france 4c95f1133da62fae7e25d9a1125566a79f36e50f ... Point [2.59909325048, 48.5149781204]
512 sncf-gares-et-arrets-transilien-ile-de-france 6ff2bd5aa5a583b66395c2974d4864edd3c3ffad ... Point [1.62217099624, 48.274974514]
513 sncf-gares-et-arrets-transilien-ile-de-france 48499659fd75dc7bb8d95e3c5beae5c0076da341 ... Point [2.75278446885, 48.4305611079]
514 sncf-gares-et-arrets-transilien-ile-de-france 23f0787d9a956964169cba6703b0ea216cfdea3a ... Point [2.48337131052, 48.7067634252]
[515 rows x 20 columns]
