Converting pandas DataFrame to JSON Object Column

In this article, we will explore the process of converting a pandas DataFrame to a JSON object column. This can be particularly useful when working with PostgreSQL databases and need to query or manipulate data in a JSON format.

Background and Context

Pandas is a popular Python library used for data manipulation and analysis. It provides an efficient way to handle structured data, including tabular data such as spreadsheets and SQL tables. However, when dealing with semi-structured or unstructured data, pandas may not provide the most convenient way to store or query it.

JSON (JavaScript Object Notation) is a lightweight data interchange format that has become widely used in recent years. It provides a flexible and human-readable way to represent structured data, making it an ideal choice for storing and querying data in databases such as PostgreSQL.

PostgreSQL, in particular, has built-in support for JSON data types, which allows us to store and query JSON data directly within the database.

Problem Statement

Given a pandas DataFrame with information about users and their orders, we want to convert it to a JSON object column that can be queried in PostgreSQL. The original approach involves using the to_json method with various options to produce the desired output format.

However, we notice that this approach does not produce the exact format we need, which has multiple levels of nesting for the item details. In this article, we will explore an alternative solution using vanilla Python and demonstrate how to achieve the desired format.

Solution Overview

Our solution involves the following steps:

1. Convert the pandas DataFrame to a dictionary using the to_dict method.
2. Process the item details by creating a nested dictionary with the item IDs as keys and their descriptions as values.
3. Initialize an empty dictionary with the required keys (New-Data, order_number, and user_id) and add the processed item details.
4. Add any remaining data from the original DataFrame to the dictionary.

Solution in Detail

Here is a step-by-step explanation of our solution:

Step 1: Convert DataFrame to Dictionary

We start by converting the pandas DataFrame to a dictionary using the to_dict method with the 'records' option.

M = df.to_dict("records")

This will produce a list of dictionaries, where each dictionary represents a row in the original DataFrame.

Step 2: Process Item Details

We then process the item details by creating a nested dictionary with the item IDs as keys and their descriptions as values. We use list comprehension to achieve this.

items = [
    {key: value 
     for key, value in entry.items() 
     if key not in ("user_id", "order_num")}
    for entry in M
]

item_details = [{str(num + 1): entry} 
                for num, entry 
                in enumerate(items)]

This will produce a list of dictionaries with item IDs as keys and their descriptions as values.

Step 3: Initialize Dictionary

Next, we initialize an empty dictionary with the required keys (New-Data, order_number, and user_id).

d = dict()
d['New-Data'] = item_details

We also add any remaining data from the original DataFrame to this dictionary.

d['order_number'] = M[0]['order_num']
d['user_id'] = M[0]['user_id']

Step 4: Add Remaining Data

Finally, we add any additional data that needs to be included in the final output. In our case, it’s just a list containing the processed dictionary.

wrapper = [d]

Code Example

Here is the complete code example:

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({
    'user_id': [1, 1, 1],
    'order_num': [1, 1, 1],
    'item_id': [1, 2, 3],
    'item_desc': ['red', 'blue', 'green']
})

# Convert DataFrame to dictionary
M = df.to_dict("records")

# Process item details
items = [
    {key: value 
     for key, value in entry.items() 
     if key not in ("user_id", "order_num")}
    for entry in M
]

item_details = [{str(num + 1): entry} 
                for num, entry 
                in enumerate(items)]

# Initialize dictionary
d = dict()
d['New-Data'] = item_details

# Add remaining data
d['order_number'] = M[0]['order_num']
d['user_id'] = M[0]['user_id']

# Add wrapper list
wrapper = [d]

# Print output
for item in wrapper:
    print(item)

Output

The final output will be a JSON object with the desired format:

{
  "New-Data": [
    {
      "1": {"item_id": "1", "item_desc": "red"}
    },
    {
      "2": {"item_id": "2", "item_desc": "blue"}
    },
    {
      "3": {"item_id": "3", "item_desc": "green"}
    }
  ],
  "order_number": 1,
  "user_id": 1
}

This JSON object can be stored in a PostgreSQL database and queried using standard SQL syntax.

Last modified on 2023-12-04