'ValueError: Expected object or value' when reading GeoJSON file with Pandas

Question

I tried to read a GeoJSON file with Pandas, but I got a ValueError message:

'ValueError: Expected object or value'

Here's the approach I used:

import pandas as pd
geojsonPath = r"Z:\dems\address.geojson"
pd_json = pd.io.json.read_json(geojsonPath,lines=True) 
pd_json.head()

Attached is an extract from the file

{
"type": "FeatureCollection",
"name": "cameron-addresses-county",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "X": -78.1422444, "Y": 41.3286117, "hash": "93dd7b7e3ee3e8af", "number": "501", "street": "CASTLE GARDEN RD", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 7579 }, "geometry": { "type": "Point", "coordinates": [ -78.1422444, 41.3286117 ] } },
{ "type": "Feature", "properties": { "X": -78.143584, "Y": 41.3284045, "hash": "853eb0c5f6e70fe3", "number": "64", "street": "BELDIN DR", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 4502 }, "geometry": { "type": "Point", "coordinates": [ -78.143584, 41.3284045 ] } },
{ "type": "Feature", "properties": { "X": -78.1711061, "Y": 41.3282128, "hash": "99a13ba635404d80", "number": "9760", "street": "MIX RUN RD", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 8448 }, "geometry": { "type": "Point", "coordinates": [ -78.1711061, 41.3282128 ] } },
{ "type": "Feature", "properties": { "X": -78.1429278, "Y": 41.3282883, "hash": "70319cf9e435b858", "number": null, "street": null, "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": null }, "geometry": { "type": "Point", "coordinates": [ -78.1429278, 41.3282883 ] } },
{ "type": "Feature", "properties": { "X": -78.1427173, "Y": 41.3282733, "hash": "759f051e7a587eb2", "number": "465", "street": "CASTLE GARDEN RD", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 6447 }, "geometry": { "type": "Point", "coordinates": [ -78.1427173, 41.3282733 ] } },
{ "type": "Feature", "properties": { "X": -78.1433463, "Y": 41.3282308, "hash": "9fbb571fc16a6cb2", "number": "61", "street": "BELDIN DR", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 4466 }, "geometry": { "type": "Point", "coordinates": [ -78.1433463, 41.3282308 ] } },
{ "type": "Feature", "properties": { "X": -78.1432403, "Y": 41.3282179, "hash": "8f837d813626f1e1", "number": null, "street": null, "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": null }, "geometry": { "type": "Point", "coordinates": [ -78.1432403, 41.3282179 ] } },
{ "type": "Feature", "properties": { "X": -78.1715165, "Y": 41.3280965, "hash": "5004ba87bd6e668b", "number": "9736", "street": "MIX RUN RD", "unit": null, "city": null, "district": null, "region": null, "postcode": null, "id": 7434 }, "geometry": { "type": "Point", "coordinates": [ -78.1715165, 41.3280965 ] } }

• Helios: You have a special library to do that, its name is geopandas. Do you work with anaconda?
• Son of a Beach: Please post the full error and the full stack trace.
• Taras: Please, do not forget about "What should I do when someone answers my question?"

Answer 1

There are several things to keep in mind:

• Do not forget to close the GeoJSON with ]}
• There is no need to call the read_json() via pd.io.json.read_json, simply pd.read_json. Even if it is placed in the pandas/pandas/io/json/
• "ValueError: Expected object or value" error comes because in terms of JSON your geojsonPath variable is the right type but with wrong values.

So, to get everything working you can either:

1. As was commented by @SalimRodríguez, try to read your GeoJSON with GeoPandas

   Output data format: GeoDataFrame

   import geopandas as gpd
   absolute_path_to_file = 'C:/Documents/Python Scripts/address.geojson'
   addresses = gpd.read_file(absolute_path_to_file)
   print(addresses)
    X Y ... id geometry
   0 -78.142244 41.328612 ... 7579.0 POINT (-78.14224 41.32861)
   1 -78.143584 41.328404 ... 4502.0 POINT (-78.14358 41.32840)
   2 -78.171106 41.328213 ... 8448.0 POINT (-78.17111 41.32821)
   3 -78.142928 41.328288 ... NaN POINT (-78.14293 41.32829)
   4 -78.142717 41.328273 ... 6447.0 POINT (-78.14272 41.32827)
   5 -78.143346 41.328231 ... 4466.0 POINT (-78.14335 41.32823)
   6 -78.143240 41.328218 ... NaN POINT (-78.14324 41.32822)
   7 -78.171516 41.328097 ... 7434.0 POINT (-78.17152 41.32810)
   

2. If geometry is not important, you can can skip it simply by parsing your GeoJSON as a normal JSON

   Output data format: DataFrame

   import json
   import pandas as pd
   absolute_path_to_file = 'C:/Documents/Python Scripts/address.geojson'
   with open(absolute_path_to_file) as f:
    data = json.load(f)
   raw_data = [feature['properties'] for feature in data['features']]
   addresses = pd.DataFrame(raw_data)
   print(addresses)
    X Y hash ... region postcode id
   0 -78.142244 41.328612 93dd7b7e3ee3e8af ... None None 7579.0
   1 -78.143584 41.328404 853eb0c5f6e70fe3 ... None None 4502.0
   2 -78.171106 41.328213 99a13ba635404d80 ... None None 8448.0
   3 -78.142928 41.328288 70319cf9e435b858 ... None None NaN
   4 -78.142717 41.328273 759f051e7a587eb2 ... None None 6447.0
   5 -78.143346 41.328231 9fbb571fc16a6cb2 ... None None 4466.0
   6 -78.143240 41.328218 8f837d813626f1e1 ... None None NaN
   7 -78.171516 41.328097 5004ba87bd6e668b ... None None 7434.0
   

3. If geometry still matters, then parse your GeoJSON as a normal JSON in a little bit different manner

   Output data format: DataFrame

   import json
   import pandas as pd
   from shapely.geometry import Point
   absolute_path_to_file = 'C:/Documents/Python Scripts/address.geojson'
   with open(absolute_path_to_file) as f:
    data = json.load(f)
   raw_data = [feature['properties'] | {'geometry': Point(feature['geometry']['coordinates'])} for feature in data['features']]
   addresses = pd.DataFrame(raw_data)
   print(addresses)
    X Y ... id geometry
   0 -78.142244 41.328612 ... 7579.0 POINT (-78.1422444 41.3286117)
   1 -78.143584 41.328404 ... 4502.0 POINT (-78.143584 41.3284045)
   2 -78.171106 41.328213 ... 8448.0 POINT (-78.1711061 41.3282128)
   3 -78.142928 41.328288 ... NaN POINT (-78.1429278 41.3282883)
   4 -78.142717 41.328273 ... 6447.0 POINT (-78.1427173 41.3282733)
   5 -78.143346 41.328231 ... 4466.0 POINT (-78.1433463 41.3282308)
   6 -78.143240 41.328218 ... NaN POINT (-78.1432403 41.3282179)
   7 -78.171516 41.328097 ... 7434.0 POINT (-78.1715165 41.3280965)
   

If it is still important to obtain a GeoDataFrame as a final output data format, one can achieve it either with

• for option (2):

   gdf = gpd.GeoDataFrame(addresses, geometry=gpd.points_from_xy(addresses["X"], addresses["Y"]))
  

• or for option (3):

   gdf = gpd.GeoDataFrame(addresses, geometry=addresses["geometry"])
  

---

References:

• Convert Geo json with nested lists to pandas dataframe
• ValueError: Expected object or value when reading json as pandas dataframe
• Convert list of dictionaries to a pandas DataFrame
• Converting GeoJSON to Python objects?
• The GeoJSON Specification (RFC 7946)
• wikipedia | GeoJSON

• aldo_tapia: This is a high-quality answer. I learned new stuff, thank you
• Edudzi: How do I split the 'geometry' column into long and lat?