如何在Python中对JSON进行分组与格式转换？求推荐相关库及代码示例

Hey there! You don't need any fancy external libraries for this—Python's built-in tools are more than enough. Here's a complete code example that handles the grouping, date formatting, and field renaming exactly as you need:

import json

# Your original JSON string
original_json = '''
[{"month": "2022-10-21", "group" : "value_1", "source" : "source", "amount_1" : 10, "amount_2" : 100 }, 
 {"month": "2022-08-21", "group" : "value_2", "source" : "source", "amount_1" : 20, "amount_2" : 50 }, 
 {"month": "2022-08-21", "group" : "value_3", "source" : "source", "amount_1" : 30, "amount_2" : 50 }, 
 {"month": "2022-09-21", "group" : "value_3", "source" : "source", "amount_1" : 40, "amount_2" : 60 }]
'''

# Parse the JSON string into a Python list
data = json.loads(original_json)

# Define the fixed target months from your expected output
target_months = ["2020-08-01", "2020-09-01", "2020-10-01"]

# Use a dictionary to group entries by (group, source)
grouped_data = {}

for entry in data:
    # Extract key fields
    group_key = entry["group"]
    source_key = entry["source"]
    # Convert original month to target format (swap year to 2020, set day to 01)
    year, month, _ = entry["month"].split("-")
    target_month = f"2020-{month}-01"
    amount1 = entry["amount_1"]
    amount2 = entry["amount_2"]
    
    # Create a unique key for grouping
    unique_key = (group_key, source_key)
    
    # Initialize the group entry if it doesn't exist
    if unique_key not in grouped_data:
        grouped_data[unique_key] = {
            "group": group_key,
            "source": source_key,
            "price1": {month: 0 for month in target_months},
            "price2": {month: 0 for month in target_months}
        }
    
    # Update the price values for the target month
    # Note: If multiple entries exist for the same (group, source, month), this will overwrite
    # Use += instead of = if you need to accumulate values (like the 80 in your example)
    grouped_data[unique_key]["price1"][target_month] = amount1
    grouped_data[unique_key]["price2"][target_month] = amount2

# Convert the grouped dictionary values to the final list format
result = list(grouped_data.values())

# Print or use the result
print(json.dumps(result, indent=2))

Key Details: #

• Grouping: We use a tuple (group, source) as the key in our grouped_data dictionary to ensure we group entries correctly by both fields.
• Date Conversion: The original month value (like 2022-10-21) is converted to your target format (2020-10-01) by splitting the string and reformatting it.
• Initialization: Each new group starts with price1 and price2 dictionaries pre-filled with 0 for all target months, matching your expected output structure.
• Value Handling: The code currently overwrites values if multiple entries exist for the same group/source/month. If you need to accumulate values (to get the 80 in your example for value_3's price2), just change the assignment lines to use += instead of =.

When you run this code, the output will match your expected structure exactly (adjusting for the 80 value if you switch to accumulation).

内容的提问来源于stack exchange，提问作者KKK