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pythonjsonpandasparsing
Jimmy Stack
Jimmy Stack

Reputation: 65

Parse Nested Json in Python to Remove Special Characters in Columns

Here is my Json File

{
    "highest_table": {
        "items": [{
                "key": "Human 1",
                "columns": {
                    "Na$me": "Tom",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "24",
                    "Ge_nder": "M"
                }
            },
            {
                "key": "Human 2",
                "columns": {
                    "Na$me": "John",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "23",
                    "Ge_nder": "M"
                }
                }
        ]
    }
}

The goal is to remove any and all special characters in the column names (or if easier any special character at all in the .json file), and return a .json file. My initial thoughts is to convert it to pandas, remove special characters in the column heading and convert it back to a .json file.

This is what I have tried so far. Both of them print a single line only.

import json
from pandas.io.json import json_normalize    

data_file = r"C:\characters.json"

with open(data_file) as data_file:    
    data = json.load(data_file)  

df = json_normalize(data)

--

data_file = r"C:\characters.json"

df = pd.read_json(data_file)

How can I extract the columns, remove special characters and put them back in a .json file ?

Upvotes: 2

Views: 5500

Answers (2)

bruno desthuilliers
bruno desthuilliers

Reputation: 77902

A bit Q&D - you'll have to provide a complete implementation for fixkey but this should fix your problem.

import json

def fixkey(key):
    # toy implementation
    #print("fixing {}".format(key))
    return key.replace("&", "").replace("$", "")

def normalize(data):
    #print("normalizing {}".format(data))
    if isinstance(data, dict):
        data = {fixkey(key): normalize(value) for key, value in data.items()}
    elif isinstance(data, list):
        data = [normalize(item) for item in data]
    return data

jsdata = """
{
    "highest_table": {
        "items": [{
                "key": "Human 1",
                "columns": {
                    "Na$me": "Tom",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "24",
                    "Ge_nder": "M"
                }
            },
            {
                "key": "Human 2",
                "columns": {
                    "Na$me": "John",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "23",
                    "Ge_nder": "M"
                }
                }
        ]
    }
}
"""


data = json.loads(jsdata)

data = normalize(data)

result = json.dumps(data, indent=2)
print(result)

Upvotes: 3

roganjosh
roganjosh

Reputation: 13175

Frankly this is ugly but I haven't been able to find a more generic approach. This is very specific to your particular JSON (the problem really needs solving in the API).

import json


response = """{
    "highest_table": {
        "items": [{
                "key": "Human 1",
                "columns": {
                    "Na$me": "Tom",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "24",
                    "Ge_nder": "M"
                }
            },
            {
                "key": "Human 2",
                "columns": {
                    "Na$me": "John",
                    "Description(ms/2)": "Table Number One on the Top",
                    "A&ge": "23",
                    "Ge_nder": "M"
                }
                }
        ]
    }
}"""

def fix_json(resp):

    output = {'highest_table': {'items': []}}
    for item in resp['highest_table']['items']:
        inner_dict = item['columns']
        fixed_values = {'Name': inner_dict['Na$me'],
                        'Description(ms/2)': inner_dict['Description(ms/2)'],
                        'Age': inner_dict['A&ge'],
                        'Gender': inner_dict['Ge_nder']
                        }
        new_inner = {'key': item['key'], 'columns': fixed_values}
        output['highest_table']['items'].append(new_inner)
    return output



response = json.loads(response)
fixed = fix_json(response)

Upvotes: 1

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