How to Convert JSON to Excel?

JSON and Excel are two widely used formats for storing and exchanging data. This blog aims to explore the process of converting JSON data to Excel and make this process accessible and user-friendly. It will empower you to efficiently manage and manipulate your JSON data within Excel spreadsheets.

Check out this video on Excel to learn it in one go:

What is JSON?

JSON, or JavaScript Object Notation, offers a standardized structure that enables the representation and transmission of data in a readable manner. JSON finds widespread application in web development as it facilitates data exchange between servers and web applications. It utilizes key-value pairs and accommodates diverse data types, including strings, numbers, booleans, arrays, and objects.JSON’s simplicity, versatility, and compatibility with multiple programming languages have made it a popular choice for storing, retrieving, and transmitting data in modern applications.

Applications of JSON files

Below, we will discuss some common applications of JSON files:

• Web Development: JSON is extensively used for data exchange between servers and web applications. It enables efficient communication between client-side JavaScript and server-side programming languages.
• API Integration: Many web services and APIs (Application Programming Interfaces) utilize JSON as a standard data format for transmitting and receiving data. JSON’s lightweight and readable structure makes it ideal for seamless integration between different software systems.
• Configuration Files: They are employed for storing configuration settings in various applications. It allows developers to define and modify settings in a structured manner, facilitating easy configuration management.
• Data Storage: These files serve as a lightweight alternative to traditional databases. They are used for storing and retrieving structured data, providing a flexible and portable storage solution for small to medium-sized datasets.
• Mobile App Development: JSON is commonly used in mobile app development to exchange data between the app and server. It allows for efficient synchronization and real-time updates, enhancing the user experience.
• Data Interchange: It is a standard format for data interchange between different systems and platforms. It enables seamless data transfer and interoperability across diverse applications.

What is Excel?

Excel is a robust spreadsheet application created by Microsoft, offering a complete solution for data organization, analysis, and manipulation. It is a widely used tool due to its intuitive interface and extensive features, catering to diverse needs like budgeting, inventory management, and report generation. 

Users can effortlessly create worksheets comprising rows and columns to store and arrange data efficiently within Excel. 

Additionally, it provides a vast array of functions, formulas, and tools. This empowers users to perform calculations, generate visually appealing charts and graphs, and automate repetitive tasks. 

Applications of Excel

Several applications of Excel can be utilized across different fields and industries. Here are some common applications of it.

• Data Analysis: Excel provides powerful tools for analyzing and visualizing data. Users can perform calculations, apply statistical functions, and create charts and graphs to gain insights from their data.
• Project Management: It offers project planning, task tracking, and resource management features. It enables users to create Gantt charts, track progress, and allocate resources efficiently.
• Reporting and Dashboards: Excel’s ability to organize and format data makes it an ideal tool for creating professional reports and interactive dashboards. Users can present data visually and summarize information effectively.
• Inventory Management: This can track inventory levels, manage stock, and generate inventory reports. It provides a convenient way to monitor and control inventory in businesses.
• Data Entry and Organization: Excel simplifies data entry and organization tasks. Users can create structured worksheets, use formulas for data manipulation, and sort/filter data to streamline information management.
• Academic and Research Purposes: Excel helps in data analysis, statistical calculations, and creating charts for academic and research purposes. Students, researchers, and educators often use it in various disciplines.

How to Import JSON to Excel: Step-By-Step Guide

To convert JSON data into Excel, you can follow the steps outlined below:

Step 1: Prepare your JSON Data

Before importing JSON data into Excel, ensure the JSON file or data is ready. JSON is a popular format for storing and exchanging data. It uses a key-value pair structure similar to dictionaries in Python or objects in JavaScript. Ensure your JSON data is appropriately formatted and contains the necessary information to import into Excel.

Step 2: Open Excel and Create a New Worksheet

To start, open Microsoft Excel on your computer. Click on the “File” menu at the top, then choose “New.” From the options provided, select “Blank Workbook.” This will create a fresh Excel workbook for your tasks.

Step 3: Use Power Query (Built-in for Excel 2016 and later)

In modern versions of Microsoft Excel (2016 and later, including Office 365), Power Query is built-in and does not require separate installation. It allows users to import and transform data from various sources, including JSON.

Step 4: Import the JSON data

Once the Power Query add-in is enabled, proceed to import the JSON data into Excel. Locate the “Data” tab in the Excel ribbon at the top and click the “Get Data” button. A drop-down menu will appear—select “From File” and then pick “From JSON.” This action will prompt a file explorer window to appear. Use it to navigate to the location where your JSON file is stored. Once you’ve found the JSON file, click “Open” to proceed.

Step 5: Transform the JSON data

After importing the JSON file, Excel will automatically launch the Power Query Editor window. This specialized tool allows you to transform the data according to your needs. Within the Power Query Editor, you can perform various tasks, such as filtering data, removing unnecessary columns, renaming columns, and more. Utilize the available options and tools in the Power Query Editor to modify and shape the JSON data per your requirements.

Step 6: Load the Data into Excel

Once you have finished transforming the data, click the “Close & Load” button in the Power Query Editor to load the data into Excel. By default, it will create a new worksheet with the imported data. Depending on your preferences, you can load the data into an existing worksheet or create a data connection instead.

Alternative Methods to Import JSON into Excel

Besides Power Query, there are several other ways to convert and import JSON data into Excel:

1. Using Python (Pandas Library)

If you’re familiar with programming, Python’s pandas library offers a powerful method to convert JSON into Excel:

import pandas as pd

# Load JSON
df = pd.read_json('yourfile.json')

# Export to Excel
df.to_excel('output.xlsx', index=False)

2. Using VBA (Visual Basic for Applications)

Advanced Excel users can also use a simple VBA script to parse JSON data and insert it into cells. Several open-source VBA JSON parsers are available for this purpose.

3. Using Online Converters

Many free online tools are available to convert JSON to Excel without installing any software. Simply upload your JSON file and download the Excel output:

• ConvertCSV.com
• JSON-CSV.com
• AConvert.com

4. Using Google Sheets (Cloud-Based Option)

Google Sheets with Apps Script or third-party add-ons can parse and display JSON data in a spreadsheet format:

• Integrate with APIs directly for live data pulls.
• Use IMPORTDATA() or UrlFetchApp in Apps Script to fetch and parse JSON.

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Conclusion

Understanding how to convert JSON to Excel opens up data management and analysis possibilities. By importing JSON into Excel, users can seamlessly integrate and leverage the strengths of both formats. With the help of tools like Python’s Pandas or online converters, the conversion process can be simplified, allowing for efficient data integration and analysis. This knowledge empowers learners to optimize their data-handling capabilities and make decisions effectively.