Reading JSON (read_json) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of reading a JSON file with nested objects?

Given the JSON file content:

{"name": "Alice", "details": {"age": 30, "city": "New York"}}

What will be the output of pd.read_json('file.json')?

Data Analysis Python

import pandas as pd
from io import StringIO
json_data = '{"name": "Alice", "details": {"age": 30, "city": "New York"}}'
df = pd.read_json(StringIO(json_data), typ='series')
print(df)

A DataFrame with columns 'name' and 'details', where 'details' column contains dictionaries

B Raises ValueError: Expected object or value

C DataFrame with columns 'name', 'age', and 'city' flattened automatically

D Empty DataFrame with no columns

💡 Hint

Think about how pandas handles nested JSON objects by default.

Pandas reads the JSON and creates columns for top-level keys. Nested objects become entries in cells as dictionaries, not automatically flattened.

❓ data_output

intermediate

How many rows are in the DataFrame after reading a JSON array?

Given this JSON array:

[{"id": 1, "score": 88}, {"id": 2, "score": 92}, {"id": 3, "score": 75}]

What is the number of rows in the DataFrame after pd.read_json()?

Data Analysis Python

import pandas as pd
from io import StringIO
json_data = '[{"id": 1, "score": 88}, {"id": 2, "score": 92}, {"id": 3, "score": 75}]'
df = pd.read_json(StringIO(json_data))
print(len(df))

A 1

B 2

C 3

D 0

💡 Hint

Each object in the JSON array becomes one row.

Each dictionary in the JSON array is converted to one row in the DataFrame, so there are 3 rows.

🔧 Debug

advanced

What error occurs when reading malformed JSON with pd.read_json?

What error will this code raise?

import pandas as pd
from io import StringIO
json_data = '{"name": "Bob", "age": 25,'
df = pd.read_json(StringIO(json_data))

Data Analysis Python

import pandas as pd
from io import StringIO
json_data = '{"name": "Bob", "age": 25,'
df = pd.read_json(StringIO(json_data))

A No error, returns empty DataFrame

B ValueError: Expected object or value

C TypeError: read_json() missing 1 required positional argument

D JSONDecodeError: Expecting property name enclosed in double quotes

💡 Hint

Look at the JSON string carefully for syntax issues.

The JSON string is incomplete and malformed, causing pandas to raise a ValueError when parsing.

🚀 Application

advanced

How to read a JSON file with multiple JSON objects separated by newlines?

You have a file where each line is a separate JSON object:

{"id": 1, "val": 10}
{"id": 2, "val": 20}
{"id": 3, "val": 30}

Which option correctly reads this file into a DataFrame?

A pd.read_json('file.json')

B pd.read_json('file.json', chunksize=1)

C pd.read_json('file.json', orient='records')

D pd.read_json('file.json', lines=True)

💡 Hint

Look for the parameter that handles JSON lines format.

The 'lines=True' parameter tells pandas to treat each line as a separate JSON object and combine them into a DataFrame.

🧠 Conceptual

expert

What is the effect of the 'typ' parameter in pd.read_json?

What does setting typ='series' do when reading JSON with pd.read_json()?

A Returns a pandas Series instead of a DataFrame

B Returns a DataFrame with only one column

C Raises an error because 'typ' is not a valid parameter

D Flattens nested JSON objects automatically

💡 Hint

Think about the difference between Series and DataFrame in pandas.

The 'typ' parameter controls the type of object returned. Setting it to 'series' returns a Series instead of a DataFrame.