Understanding JSON Data and Pandas DataFrame Creation

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In this article, we will explore how to divide a JSON row data into multiple columns and store it as a pandas DataFrame. This is a common task when working with JSON data in Python.

Background Information

JSON (JavaScript Object Notation) is a lightweight data interchange format that is widely used for exchanging data between web servers, web applications, and mobile apps. Pandas is the de facto standard library for data manipulation and analysis in Python.

When working with JSON data in pandas, we often encounter scenarios where we need to convert JSON data into a tabular format, such as a DataFrame. This can be done by using various methods, including json_normalize() or manually creating a DataFrame from individual rows of JSON data.

The Problem

The problem presented is taking JSON data from a URL and converting it into a pandas DataFrame with the desired column structure. However, the original attempts at this conversion resulted in errors, indicating that there may be an issue with how the JSON data was being processed or parsed.

Solution Approach

To solve this problem, we will take a step-by-step approach to understand the correct way of converting JSON data into a pandas DataFrame.

Step 1: Understanding JSON Data

We start by examining the JSON data received from the URL. In this case, the JSON data is represented as a string and contains nested structures.

import json

response = requests.get('url')
data = response.json()

Step 2: Extracting Individual Rows of JSON Data

The next step is to extract individual rows of JSON data from the received string. We can do this by iterating over the value key in the JSON object.

final_data = []
for item in data['value']:
    my_dict = {}
    my_dict['Data'] = item['Data']
    my_dict['Date_Code'] = item['Date_Code']
    my_dict['Date_ItemName_ENG'] = item['Date_ItemName_ENG']
    my_dict['Date_SortOrder'] = item['Date_SortOrder']   
    final_data.append(my_dict)

Step 3: Creating a pandas DataFrame

Finally, we create a pandas DataFrame from the individual rows of JSON data using pd.DataFrame().

df = pd.DataFrame(final_data)

Complete Code Example

Here is the complete code example that combines all the steps outlined above:

import requests
import json
import pandas as pd

response = requests.get('url')
data = response.json()

final_data = []
for item in data['value']:
    my_dict = {}
    my_dict['Data'] = item['Data']
    my_dict['Date_Code'] = item['Date_Code']
    my_dict['Date_ItemName_ENG'] = item['Date_ItemName_ENG']
    my_dict['Date_SortOrder'] = item['Date_SortOrder']   
    final_data.append(my_dict)

df = pd.DataFrame(final_data)

Conclusion

Converting JSON data into a pandas DataFrame is a common task in Python. By following the steps outlined above, we can successfully convert JSON data from individual rows into a tabular format using pd.DataFrame(). This example demonstrates how to extract individual rows of JSON data and create a pandas DataFrame from them, ensuring accurate conversion of complex JSON structures into a tabular format.

Last modified on 2023-07-29