'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 ] } }

You have a special library to do that, its name is geopandas. Do you work with anaconda? — Helios, Jun 14 '22 at 01:07
Please, do not forget about "What should I do when someone answers my question?" — Taras, Jun 21 '22 at 05:07

Taras · Answer 1 · 2022-06-15T05:46:52.050

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:

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)

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

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:

This is a high-quality answer. I learned new stuff, thank you — aldo_tapia, Jun 14 '22 at 12:36

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

1 Answers1