This tutorial explains in detail how to handle JSON data in Python, especially when the value of a JSON field needs to be another complete and properly escaped JSON string. By solving the specific problems encountered when importing GeoJSON data into BigQuery GIS, the article demonstrates how to use json.dumps for step-by-step serialization to avoid double escaping and ensure the generation of JSON files that meet the target format requirements.

Understanding the needs and challenges of nested JSON strings

When processing data, we sometimes encounter a special need: embedding a complete JSON structure as a string into the value of another JSON field. For example, when importing GeoJSON data into Google BigQuery GIS, BigQuery requires that the data type of the geometry field is GEOGRAPHY, but its content must be a string, and the string itself is a JSON object that conforms to the GeoJSON specification, and the internal double quotes need to be escaped correctly.

Consider the following target JSON format:

 {
  "geometry": "{\"type\": \"LineString\", \"coordinates\": [[25.4907, 35.29833], [25.49187, 35.28897]]}"
}

Here, the value of the geometry field is a Python string that contains a JSON-escaped GeoJSON LineString object.

Common misunderstandings and problems

1. Directly nested dictionaries: If the Python dictionary structure is {"geometry": {"type": "LineString", ...}}, and then directly serialized using json.dumps(), the output will be a nested JSON object instead of a string:

    {
     "geometry": {
       "type": "LineString",
       "coordinates": [[25.4907, 35.29833], [25.49187, 35.28897]]
     }
   }

   This does not comply with the BigQuery GIS specification that geometry fields are required to be strings.

2. Manual string replacement: After trying to convert the geometry dictionary to a string, manually replace the double quotes with a backslash, such as str(obj['geometry']).replace('"', '\\"'). This approach often results in double escaping, since json.dumps() will escape existing backslashes again on final serialization, producing \\":

    {
     "geometry": "{\\\"type\\\": \\\"LineString\\\", \\\"coordinates\\\": ...}"
   }

   This is obviously not what we want because BigQuery or other parsers will interpret it as the literal \" instead of ".

The core of the problem is that Python's json.dumps() function automatically handles the necessary escaping (such as converting " to \") when serializing a Python string into a JSON string. We need to take advantage of this feature, but make sure that the escape happens only once, and in the right place.

Core Solution: Step-by-Step JSON Serialization

The key to solving this problem is to understand the behavior of json.dumps() and perform step-by-step serialization. We first serialize the internal JSON structure (such as the geometry dictionary) into a normal Python string, which already contains the correct JSON escaping internally. Then, we use this escaped Python string as the value of the external JSON field and serialize it as a whole again.

Detailed explanation of steps

1. Identify the internal JSON structure: Determine which dictionary or list needs to be embedded as a string.
2. First serialization: Use json.dumps() to convert this internal JSON structure to a Python string. At this time, json.dumps() will automatically escape the internal double quotes as \" to generate a string representation that conforms to the JSON specification.
3. Build the external structure: Use the Python string generated in step 2 as the value of the corresponding field in the external dictionary.
4. Final serialization: Use json.dumps() to serialize the external dictionary containing the processed strings in its entirety. At this point, the external json.dumps() will process the string generated in step 2 as a whole and add external double quotes to it without escaping the existing \" again.

Practical exercise: processing GeoJSON data

Below we will use a GeoJSON FeatureCollection example to demonstrate how to apply the above solution to convert the geometry dictionary in each feature into a properly escaped JSON string.

Sample data

Suppose we have the following GeoJSON data (simplified version, the actual data structure can be found in the complete example in the problem description):

 {
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "LineString",
        "coordinates": [
          [121.51749976660096, 25.04609631049641],
          [121.51870845722954, 25.045781689873138]
        ]
      },
      "properties": {
        "model": {
          "RoadClass": "3",
          "RoadName": "Taiwan Line 1"
        }
      }
    }
    // ... more features
  ]
}

Python code implementation

 import json
from pathlib import Path

# Simulate original GeoJSON data # In actual applications, this may come from file reading, API response, etc. original_geojson_data = {
    "type": "FeatureCollection",
    "features": [
        {
            "type": "Feature",
            "geometry": {
                "type": "LineString",
                "coordinates": [
                    [121.51749976660096, 25.04609631049641],
                    [121.51870845722954, 25.045781689873138]
                ]
            },
            "properties": {
                "model": {
                    "RoadClass": "3",
                    "RoadClassName": "Provincial highway general road",
                    "RoadID": "300010",
                    "RoadName": "Taiwan 1 Line",
                    "RoadNameID": "10",
                    "InfoDate": "2015-04-01T00:00:00"
                }
            }
        },
        {
            "type": "Feature",
            "geometry": {
                "type": "LineString",
                "coordinates": [
                    [121.51913536000893, 25.045696164346566],
                    [121.51938079578713, 25.045646605406546]
                ]
            },
            "properties": {
                "model": {
                    "RoadClass": "3",
                    "RoadClassName": "Provincial highway general road",
                    "RoadID": "300010",
                    "RoadName": "Taiwan 1 Line",
                    "RoadNameID": "10",
                    "InfoDate": "2015-04-01T00:00:00"
                }
            }
        }
    ]
}

# Target output file path output_filepath = Path("processed_geojson_for_bigquery.json")

#Create a list to store processed features
processed_features = []

# Traverse each feature in the original data
for feature in original_geojson_data["features"]:
    # 1. Extract the current geometry dictionary geometry_dict = feature["geometry"]

    # 2. Serialize the geometry dictionary into a JSON string # This step is key, it will correctly escape the double quotes in the dictionary to \"
    geometry_as_string = json.dumps(geometry_dict)

    # 3. Reassign the serialized string to feature['geometry']
    # At this time, the value of feature['geometry'] is a Python string whose content is escaped JSON
    feature["geometry"] = geometry_as_string

    # Add processed features to the list processed_features.append(feature)

# Build the final output dictionary structure # Reassemble the original "type" and "features" output_data = {
    "type": original_geojson_data["type"],
    "features": processed_features
}

# Write the final data to the JSON file # indent=2 is used to beautify the output, ensure_ascii=False ensures that non-ASCII characters (such as Chinese) are displayed normally with output_filepath.open(mode="w", encoding="utf-8") as fp:
    json.dump(output_data, fp, indent=2, ensure_ascii=False)

print(f"The processed GeoJSON has been successfully saved to: {output_filepath.resolve()}")

# Verify the contents of the output file (optional, you can manually open the file to view)
# with output_filepath.open(mode="r", encoding="utf-8") as fp:
# print("\n---Example of output file content---")
# print(fp.read())

Example of output results

After running the above code, the contents of the processed_geojson_for_bigquery.json file will look like this (only the geometry part of the first feature is shown):

 {
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": "{\"type\": \"LineString\", \"coordinates\": [[121.51749976660096, 25.04609631049641], [121.51870845722954, 25.045781689873138]]}",
      "properties": {
        "model": {
          "RoadClass": "3",
          "RoadClassName": "Provincial highway general road",
          "RoadID": "300010",
          "RoadName": "Taiwan 1 Line",
          "RoadNameID": "10",
          "InfoDate": "2015-04-01T00:00:00"
        }
      }
    },
    {
      "type": "Feature",
      "geometry": "{\"type\": \"LineString\", \"coordinates\": [[121.51913536000893, 25.045696164346566], [121.51938079578713, 25.045646605406546]]}",
      "properties": {
        "model": {
          "RoadClass": "3",
          "RoadClassName": "Provincial highway general road",
          "RoadID": "300010",
          "RoadName": "Taiwan 1 Line",
          "RoadNameID": "10",
          "InfoDate": "2015-04-01T00:00:00"
        }
      }
    }
  ]
}

As you can see, the value of the geometry field is now a string wrapped in double quotes, and the double quotes in the internal JSON structure are correctly escaped as \", meeting the requirements of the target format.

Notes and Summary

• The functions of json.dumps() and json.loads():
  • json.dumps(): Serializes Python objects (such as dictionaries, lists) into JSON format strings.
  • json.loads(): Deserializes JSON-formatted strings into Python objects.
  • Understanding the behavior of these two functions in handling string escapes is key to solving problems like this.
• Avoid manual escaping: Never try to manually add backslashes to a string for escaping. Python's json module already handles these details for you, manual intervention will only result in double escaping or other errors.
• ensure_ascii=False: Using the ensure_ascii=False parameter in json.dump() or json.dumps() can ensure that non-ASCII characters (such as Chinese) are not escaped into \uXXXX form when output, but are displayed in their original character form to improve readability.
• Application scenarios: This step-by-step serialization method is not only applicable to scenarios where GeoJSON is imported into BigQuery GIS, but also applicable to any situation where a JSON structure needs to be embedded as a string into another JSON field, such as the parameters of some API requests, fields in the database that store JSON strings, etc.
• Data verification: After processing the data, it is recommended to perform data verification to ensure that the generated file meets the requirements of the target system. For example, you can use json.loads() to try to load the generated JSON file to check whether the structure is correct.

Through this tutorial, we learned how to cleverly utilize the json.dumps() function of the Python json module to generate complex JSON structures containing correctly escaped JSON strings through step-by-step serialization. This method avoids the tediousness and errors of manual escaping and ensures that the output data meets the strict requirements of the specific system.

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