How to Read JSON Files with Pandas

Edition #103 | 24nd Feb 2025

Feb 24, 2025

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Hello !!
Welcome to the new edition of Business Analytics Review!

Today, we’re exploring into a practical topic that many data enthusiasts encounter: Reading JSON files with Pandas.
JSON (JavaScript Object Notation) has become a popular format for data interchange due to its simplicity and readability. Whether you're working with APIs, configuration files, or data exports, knowing how to handle JSON files is essential for any data scientist or analyst.

What is JSON?

JSON is a lightweight data interchange format that is easy for humans to read and write, and easy for machines to parse and generate. It uses a simple key-value pair structure, making it an excellent choice for representing structured data. Here’s a quick example of a JSON object:

json

{
    "name": "Alice",
    "age": 30,
    "city": "New York",
    "skills": ["Python", "Data Analysis", "Machine Learning"]
}

Why Use Pandas?

Pandas is a powerful Python library for data manipulation and analysis. It provides flexible data structures like DataFrames, which are perfect for handling structured data. Reading JSON files into Pandas allows you to leverage its powerful data manipulation capabilities, making it easier to analyze and visualize your data.

How to Read JSON Files with Pandas

Let’s walk through the steps to read a JSON file using Pandas.

Install Pandas: If you haven't already installed Pandas, you can do so using pip
Import the Library: Start by importing the Pandas library in your Python script or Jupyter notebook.
Read the JSON File: Use the pd.read_json() function to read your JSON file. You can specify the path to your JSON file as follows.
Explore Your Data: Once the JSON file is loaded into a DataFrame, you can explore it using various Pandas functions

pip install pandas

import pandas as pd

df = pd.read_json('path/to/your/file.json')

# Display the first few rows of the DataFrame
print(df.head())

Handling Nested JSON

Sometimes, JSON files contain nested structures. In such cases, you might need to normalize the data using pd.json_normalize(). Here’s how you can do it:

import json

# Sample nested JSON

data = '''
{
    "name": "Alice",
    "age": 30,
    "address": {
        "city": "New York",
        "state": "NY"
    },
    "skills": ["Python", "Data Analysis"]
}
'''

# Load the JSON string into a Python dictionary
json_data = json.loads(data)

# Normalize the nested structure
df = pd.json_normalize(json_data)
print(df)

This will flatten the nested structure into a more manageable format.

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Practical Example

Imagine you are working with an API that returns user information in JSON format. By reading this data into a Pandas DataFrame, you can easily filter users based on specific criteria, analyze their skills, or visualize their information using libraries like Matplotlib or Seaborn.

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Recommended Tool for Working with JSON

Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s particularly useful for interactive data analysis and visualization tasks involving libraries like Pandas.
Website: Jupyter Notebook

Thank you for joining me in this exploration of reading JSON files with Pandas! I hope you found this edition informative and engaging. Until next time, keep coding!

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Business Analytics Review

How to Read JSON Files with Pandas

Edition #103 | 24nd Feb 2025

What is JSON?

Why Use Pandas?

How to Read JSON Files with Pandas

Handling Nested JSON

Practical Example

Recommended Reads

Latest News in AI and Data Science

Recommended Tool for Working with JSON

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