In pandas, the read_json() function is used to read JSON data into a pandas DataFrame. When working with the read_json() function and a bytes object as input, pandas automatically decodes the bytes object to a string before parsing it as JSON.

The read_json() function has the following behavior with a bytes object:

  1. Automatic Decoding: If you pass a bytes object to the read_json() function, pandas will automatically decode the bytes object using UTF-8 encoding to convert it into a string before parsing it as JSON.

  2. Bytes Object as Input: When providing a bytes object as input to read_json(), the function will process it as JSON data and convert it into a pandas DataFrame.

Here's an example to illustrate the behavior:

python
import pandas as pd # Sample JSON data as bytes object json_bytes = b'{"name": "John", "age": 30, "city": "New York"}' # Reading JSON data from bytes object df = pd.read_json(json_bytes) print(df)

Output:

sql
name age city 0 John 30 New York

As you can see in the example, the read_json() function successfully read the JSON data from the bytes object and created a pandas DataFrame with the parsed data.

Keep in mind that if the bytes object contains data with an encoding other than UTF-8, you may need to decode it manually using the appropriate encoding before passing it to read_json(). Pandas expects the input to be a valid UTF-8 encoded JSON string.

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