Understanding JSON Normalization with Pandas in Python

JSON normalization is a process of converting a nested JSON data structure into a flat table format, which can be easily manipulated and analyzed. In this article, we’ll delve into the world of JSON normalization using the pandas library in Python.

Introduction to JSON Normalization

JSON (JavaScript Object Notation) is a lightweight data interchange format that has become widely used for exchanging data between web servers, web applications, and mobile apps. However, when dealing with nested JSON data structures, it can be challenging to extract the desired information. This is where JSON normalization comes in.

JSON normalization involves flattening a nested JSON object into a flat table format, which consists of rows and columns. Each row represents a single record or item, while each column represents a field or attribute associated with that record.

Using Pandas for JSON Normalization

The pandas library provides an efficient way to perform JSON normalization using the json_normalize function. This function takes in a JSON object as input and returns a flat table format.

However, when dealing with nested JSON objects, we often encounter issues where dictionaries at specific paths do not get flattened as expected.

Understanding the Issue

In the provided Stack Overflow question, the user is trying to normalize a nested JSON data structure using the json_normalize function from pandas. The issue arises because the record_path parameter is used to specify the path under which dictionaries should be flattened. However, in this case, the dictionaries at the specified path ("SupplyDetail.member" ) are not being flattened as expected.

Solution Overview

To overcome this issue, we need to rethink our approach and manipulate the JSON data structure before passing it to the json_normalize function. One way to achieve this is by converting the JSON data structure into a string format using the to_json method, loading it back into a pandas DataFrame using json.loads, and then normalizing it again.

Step-by-Step Solution

Let’s break down the solution into step-by-step instructions:

1. Load the JSON Data Structure

We start by loading the JSON data structure into a Python variable:

import json

json_data = [
    {
        "ASIN": "B0773V2Z6",
        "Condition": "NewItem",
        "EarliestAvailability": {
            "TimepointType": "Immediately"
        },
        "FNSKU": "B0773V2Z6",
        "InStockSupplyQuantity": "18",
        "SellerSKU": "30237",
        "SupplyDetail.member": [
            {
                "EarliestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "LatestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "Quantity": "1",
                "SupplyType": "InStock"
            },
            {
                "EarliestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "LatestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "Quantity": "1",
                "SupplyType": "InStock"
            }
        ],
        "TotalSupplyQuantity": "18"
    }
]

2. Convert the JSON Data Structure into a String Format

Next, we convert the JSON data structure into a string format using the to_json method:

re_data = json_normalize(json_data, record_path="SupplyDetail.member", meta=["ASIN"], errors='ignore').to_json(orient='records')

3. Load the String Format Back into a Pandas DataFrame

We load the string format back into a pandas DataFrame using json.loads:

df_new = json.loads(re_data)

4. Normalize the DataFrame Again

Finally, we normalize the DataFrame again using the json_normalize function:

df_new = json_normalize(df_new)

Full Code Example

Here’s the full code example that demonstrates how to solve the issue using the proposed solution:

import pandas as pd
import json

# Load the JSON data structure
json_data = [
    {
        "ASIN": "B0773V2Z6",
        "Condition": "NewItem",
        "EarliestAvailability": {
            "TimepointType": "Immediately"
        },
        "FNSKU": "B0773V2Z6",
        "InStockSupplyQuantity": "18",
        "SellerSKU": "30237",
        "SupplyDetail.member": [
            {
                "EarliestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "LatestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "Quantity": "1",
                "SupplyType": "InStock"
            },
            {
                "EarliestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "LatestAvailableToPick": {
                    "TimepointType": "Immediately"
                },
                "Quantity": "1",
                "SupplyType": "InStock"
            }
        ],
        "TotalSupplyQuantity": "18"
    }
]

# Convert the JSON data structure into a string format
df = pd.json_normalize(json_data, record_path="SupplyDetail.member", meta=["ASIN"], errors='ignore')
re_data = df.to_json(orient='records')

# Load the string format back into a pandas DataFrame
df_new = json.loads(re_data)

# Normalize the DataFrame again
df_new = json_normalize(df_new)
print(df_new)

The final output of this code should match the desired result, where dictionaries at the specified path ("SupplyDetail.member" ) are flattened as expected.

Last modified on 2024-03-18