Pandas read_json valueerror: expected object or value

Fixing ValueError When Using read_json in Pandas

The error ValueError: Expected object or value occurs when using pd.read_json() in Pandas to read a JSON file or JSON lines file, and the file is improperly formatted or empty. This article explains the causes and solutions to resolve this error effectively.

Why Does This Error Occur?

The ValueError: Expected object or value is typically caused by:

• An empty file or string passed to pd.read_json().
• Malformed JSON content (e.g., missing brackets, incorrect syntax).
• Using the wrong format option when reading JSON lines (lines=True).

Example of the Error

Consider the following malformed or empty JSON file named data.json:

# Malformed or empty JSON content

When attempting to read this file with pd.read_json():

import pandas as pd

# Attempt to read an empty or malformed JSON file
df = pd.read_json('data.json')

Output:

ValueError: Expected object or value

How to Fix the Error

1. Ensure the JSON File is Not Empty

Check if the file contains valid JSON data. For example, the content of data.json should look like this:

[
    {"Name": "Alice", "Age": 25, "Gender": "Female"},
    {"Name": "Bob", "Age": 30, "Gender": "Male"}
]

Then, read the JSON file using:

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

Output:

     Name  Age  Gender
0   Alice   25  Female
1     Bob   30    Male

2. Check for Proper JSON Syntax

Ensure the JSON file has the correct structure. For instance, if the file contains multiple JSON objects, it should either be wrapped in a list or written as JSON lines (one object per line).

3. Reading JSON Lines Format

If your file contains JSON objects written in JSON lines format (one object per line), use the lines=True parameter:

# Example of JSON lines file (data.jsonl)
{"Name": "Alice", "Age": 25, "Gender": "Female"}
{"Name": "Bob", "Age": 30, "Gender": "Male"}

df = pd.read_json('data.jsonl', lines=True)
print(df)

Output:

     Name  Age  Gender
0   Alice   25  Female
1     Bob   30    Male

4. Validate the File Before Loading

Use Python’s json module to validate the JSON content before loading it into Pandas:

import json

# Validate JSON content
with open('data.json', 'r') as file:
    data = json.load(file)
    print(data)
    
# Load into Pandas
df = pd.DataFrame(data)
print(df)

5. Handle Malformed or Partial JSON

If the JSON file is malformed, use jsonlines library or manually preprocess the file to remove errors:

import jsonlines

# Reading a malformed JSON lines file
with jsonlines.open('data.jsonl') as reader:
    data = [obj for obj in reader]

df = pd.DataFrame(data)
print(df)

Conclusion

The ValueError: Expected object or value error in Pandas often results from an empty or improperly formatted JSON file. By ensuring the file contains valid JSON data, using the correct parameters for JSON lines, or validating the content before reading, you can successfully load JSON data into Pandas for analysis.