Extracting Data from JSON File into Excel Using Python's Pandas Library

Extracting Data from JSON File into Excel

Overview

In this article, we’ll explore a step-by-step guide on how to extract data from a JSON file and populate it into an Excel spreadsheet using Python’s pandas library.

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy to read and write. It is commonly used for exchanging data between web servers and web applications. However, it can be challenging to work with JSON data directly in Excel, especially when dealing with complex data structures like nested arrays and objects.

On the other hand, Excel is an excellent spreadsheet software for data analysis, visualization, and manipulation. While it has its own data formats, such as CSV (Comma Separated Values), it’s still possible to import and export JSON data from within Excel using various workarounds.

In this article, we’ll explore a Python-based solution that uses the pandas library to extract data from a JSON file and populate it into an Excel spreadsheet.

Prerequisites

Before we begin, make sure you have the following installed:

Python 3.x (preferably the latest version)
pandas library (pip install pandas)
json library (built-in Python library)
openpyxl library (pip install openpyxl) for writing to Excel files
numpy library (pip install numpy)

Step 1: Importing Libraries and Loading JSON Data

import json
import pandas as pd

First, we need to import the required libraries. We’ll use the json library to load the JSON data from a file.

Create a new Python script or open an existing one, and add the following code:

with open('data.json', 'r', encoding='utf-8') as f:
    data = json.loads(f.read())

This code loads the JSON data from a file named data.json into a Python dictionary called data.

Step 2: Normalizing JSON Data

The next step is to normalize the JSON data using pandas’ json_normalize() function.

df = pd.json_normalize(data['IdentityDocuments'], record_path='IdentityDocumentFields')

This code normalizes the nested arrays and objects in the JSON data, converting them into a flat table-like structure that can be easily manipulated by pandas.

Step 3: Renaming Columns

After normalization, we need to rename some columns to match the expected Excel column names.

df = df.rename(columns={'Type.Text': 'Type', 'ValueDetection.Text': 'Text', 'ValueDetection.Confidence': 'Confidence'})

This code renames three specific columns in the normalized DataFrame: Type, Text, and Confidence.

Step 4: Writing to Excel

Finally, we can write the normalized data to an Excel spreadsheet using pandas’ to_excel() function.

df.to_excel("output.xlsx")

This code writes the normalized data to a file named output.xlsx. You can specify any other filename you prefer.

Step 5: Viewing the Output

To view the output, open the resulting Excel file and verify that the data has been successfully extracted from the JSON file.

print(df)

You can also print the normalized DataFrame using this code to verify its contents.

Conclusion

In this article, we’ve explored a step-by-step guide on how to extract data from a JSON file and populate it into an Excel spreadsheet using Python’s pandas library. We covered topics such as loading JSON data, normalizing JSON data, renaming columns, and writing to Excel. With these steps, you can easily import JSON data into Excel and perform various analysis tasks.

Additional Tips

Here are some additional tips for working with JSON data in Python:

When working with nested arrays and objects, make sure to use the correct record path to access the desired data.
Consider using pandas’ melt() function to reshape your data if you need to work with it in a different format.
For larger datasets, consider using more efficient data structures like NumPy arrays or Pandas DataFrames instead of Python dictionaries.

Common Issues

Here are some common issues you might encounter when working with JSON data in Python:

Invalid JSON data: If your JSON data is malformed, the json.loads() function will raise an error. Make sure to double-check your JSON data for any syntax errors.
Missing keys: When using pandas’ json_normalize() function, missing keys can cause issues. Consider adding default values or handling missing keys explicitly.
Data type mismatches: Be aware that pandas will convert data types automatically based on the source data. If you’re working with numeric data, make sure to handle any potential data type mismatches.

By following these tips and being mindful of common issues, you can successfully extract JSON data into an Excel spreadsheet using Python’s pandas library.

Last modified on 2023-10-28