如何用Python实现含空值/空字符串记录的错误JSON文件迁移（支持任意列数）

Hey there! Let's get your JSON record filtering logic working properly. Your current code is just printing column values—let's expand it to actually validate each record, split them into valid/error sets, and save them to the right files. Since your data has nested structures (like arrays and objects), we'll use Python's built-in json module instead of pandas—it's more flexible for handling arbitrary nested data.

Here's the complete solution: #

First, we'll create a helper function to recursively check if any value in a record (including nested arrays/objects) is an empty string or null. Then we'll split the records and save them.

import json

def has_invalid_value(value):
    """Check if a value (or any nested value) is empty string or null"""
    # Check basic case: empty string or null
    if value == "" or value is None:
        return True
    # If it's a list, check every item in the list
    elif isinstance(value, list):
        for item in value:
            if has_invalid_value(item):
                return True
    # If it's a dictionary, check every value in the dict
    elif isinstance(value, dict):
        for val in value.values():
            if has_invalid_value(val):
                return True
    # No invalid values found
    return False

# Read input JSON
with open("Input_Files/find_error_record.json", "r") as input_file:
    all_records = json.load(input_file)

# Split records into valid and error sets
valid_records = []
error_records = []

for record in all_records:
    if has_invalid_value(record):
        error_records.append(record)
    else:
        valid_records.append(record)

# Save valid records to output file
with open("output.json", "w") as output_file:
    json.dump(valid_records, output_file, indent=4)

# Save error records to error file
with open("error.json", "w") as error_file:
    json.dump(error_records, error_file, indent=4)

How this works: #

• Recursive Validation: The has_invalid_value function checks every part of a record, no matter how nested it is. For example, it'll catch empty strings inside the org_names array or parsed_address object, just like in your input example.
• Flexible for Any Columns: Since we're iterating through all keys/values dynamically, this code works with any number of columns or nested structures—no need to hardcode field names.
• Clean File Output: We use json.dump with indent=4 to keep the output JSON formatted nicely, matching your example files.

Testing with your sample input: #

• The first record has empty strings in first_name, last_name, and org_names—it'll go to error.json.
• The second record has all valid values—it'll go to output.json.
• The third record has no empty/null values—it'll go to output.json (matches your expected output).

If you ever need to exclude specific fields from the validation (e.g., allow empty strings in a certain column), you can modify the has_invalid_value function to skip those fields by name.

内容的提问来源于stack exchange，提问作者Naveen