Pandas Read JSON

Welcome to The Coding College, your trusted platform for coding and programming tutorials! This guide will help you master the Pandas read_json function, enabling you to seamlessly load and analyze JSON data in Python.

What is JSON?

JSON (JavaScript Object Notation) is a lightweight data format often used to exchange data between systems, especially in APIs and web applications. JSON structures data using key-value pairs, making it easy to read and parse.

Why Use Pandas to Read JSON?

Pandas makes it simple to:

  • Load JSON data into a tabular format (DataFrame).
  • Handle nested JSON structures efficiently.
  • Integrate JSON data with Python’s robust data analysis capabilities.

How to Read JSON with Pandas

The basic syntax for reading a JSON file:

import pandas as pd

df = pd.read_json('file_name.json')

Examples of Reading JSON Data

1. Reading a Simple JSON File

# data.json
[
  {"Name": "Alice", "Age": 25, "Country": "USA"},
  {"Name": "Bob", "Age": 30, "Country": "UK"},
  {"Name": "Charlie", "Age": 35, "Country": "Canada"}
]
import pandas as pd

df = pd.read_json('data.json')
print(df)

Output:

      Name  Age Country
0    Alice   25     USA
1      Bob   30      UK
2  Charlie   35  Canada

2. Reading JSON Strings

If your JSON data is stored as a string:

json_data = '[{"Name": "Alice", "Age": 25, "Country": "USA"}, {"Name": "Bob", "Age": 30, "Country": "UK"}]'

df = pd.read_json(json_data)
print(df)

3. Handling Nested JSON

For JSON files with nested structures:

# nested_data.json
[
  {
    "Name": "Alice",
    "Details": {"Age": 25, "Country": "USA"}
  },
  {
    "Name": "Bob",
    "Details": {"Age": 30, "Country": "UK"}
  }
]
df = pd.read_json('nested_data.json')
df_normalized = pd.json_normalize(df['Details'])
print(df_normalized)

Output:

   Age Country
0   25     USA
1   30      UK

4. Reading JSON Lines (ndjson)

For JSON data in newline-delimited format:

{"Name": "Alice", "Age": 25, "Country": "USA"}
{"Name": "Bob", "Age": 30, "Country": "UK"}
df = pd.read_json('data.ndjson', lines=True)
print(df)

Customizing read_json

  • Specifying the Orientation of JSON Data: Use the orient parameter to handle different JSON structures. 
df = pd.read_json('data.json', orient='records')  # Default
  • Dealing with Missing Data: Replace missing values directly: 
df = pd.read_json('data.json', na_values=["null", "NA"])
  • Specifying Data Types: Enforce column data types using dtype after reading: 
df['Age'] = df['Age'].astype(int)

Real-World Applications of Pandas with JSON

  1. API Integration: Easily import and analyze data from REST APIs.
  2. Data Storage: Read JSON files used for configuration or data storage.
  3. Web Scraping: Parse JSON responses from web scraping tasks.

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Conclusion

Using Pandas to read JSON files is an essential skill for anyone working with data. The versatility of JSON and the power of Pandas make it easy to load, clean, and analyze data efficiently.

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