如何将带筛选条件的JSON路径Python字符串转换为含字典的列表？

Got it, let's tackle this problem step by step. You need to turn that path string into the structured list with embedded dictionaries for your Spark RDD lookups, right? Here's a straightforward way to do it in Python:

Step-by-Step Approach #

1. First split the original path string by / to get your initial segments.
2. Loop through each segment:
   • If the segment has [ and ], split it into the base key (like line) and the filter condition inside the brackets.
   • Parse the filter condition into a dictionary: split on = to separate key and value, strip the quotes from the value, then build the key-value pair dict.
   • Add both the base key and the new dictionary to your result list.
   • If the segment has no brackets, just add it directly to the list.

Python Code Implementation #

def parse_json_path(path_str):
    parsed_path = []
    # Split the original path into segments
    segments = path_str.split('/')
    
    for seg in segments:
        # Check if the segment contains a filter condition
        if '[' in seg and ']' in seg:
            # Split into the base key and the filter part
            base_key, filter_raw = seg.split('[', 1)
            # Remove the closing bracket from the filter string
            filter_clean = filter_raw.rstrip(']')
            # Split into key and value, strip quotes from the value
            filter_key, filter_val = filter_clean.split('=', 1)
            filter_val = filter_val.strip('"')
            # Add both the base key and the filter dict to the result
            parsed_path.append(base_key)
            parsed_path.append({filter_key: filter_val})
        else:
            # No filter, add the segment directly
            parsed_path.append(seg)
    return parsed_path

# Test with your example string
st = """data/policy/line[Type="BusinessOwners"]/risk/coverage[Type="FuelHeldForSale"]/id"""
paths = parse_json_path(st)
print(paths)

Output #

When you run this code, you'll get exactly the target list you need:

['data', 'policy', 'line', {'Type': 'BusinessOwners'}, 'risk', 'coverage', {'Type': 'FuelHeldForSale'}, 'id']

Quick Extension Note #

If you ever need to handle multiple filter conditions in one bracket (like line[Type="BusinessOwners",Status="Active"]), you can tweak the code to split the filter string by , and process each key-value pair individually. But for your current Spark RDD use case, the above code should work perfectly.

内容的提问来源于stack exchange，提问作者mdeonte001