How to Read JSON File in Pandas

To read a JSON file in pandas, you can use the pandas.read_json() function. This function allows you to load JSON data into a pandas DataFrame.

Common Use Cases and Examples

1. Reading a JSON File ```python import pandas as pd

# Assuming 'data.json' is a JSON file in the same directory df = pd.read_json('data.json') print(df.head()) ```

1. Reading a JSON String python json_data = '{"name": ["Alice", "Bob"], "age": [25, 30]}' df = pd.read_json(json_data) print(df)

2. Reading JSON from a URL python url = "https://api.example.com/data.json" df = pd.read_json(url) print(df.head())

3. Specifying JSON Orientations JSON can have different structures. Use the orient parameter to specify the format.

4. records: A list of dictionaries (one dictionary per row).

5. index: A dictionary with keys as index and values as dictionaries of column data.

Example for orient='records': python json_data = '[{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}]' df = pd.read_json(json_data, orient='records') print(df)

1. Working with Nested JSON If your JSON contains nested data, you might need to normalize it using pandas.json_normalize():

Example: python json_data = ''' { "employees": [ {"name": "Alice", "details": {"age": 25, "city": "New York"}}, {"name": "Bob", "details": {"age": 30, "city": "Los Angeles"}} ] } ''' import json data = json.loads(json_data) df = pd.json_normalize(data['employees']) print(df)

Key Parameters for pd.read_json()

• path_or_buf: Path to the JSON file or a JSON string.
• orient: Format of the JSON data ('split', 'records', 'index', 'columns', 'values').
• lines: Set to True if the JSON data is in newline-delimited format.
• dtype: Specify the data type of the resulting DataFrame.

If you share your JSON file or its structure, I can provide tailored guidance!