Reading JSON with read_json in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code reading JSON string?

Given the JSON string representing a list of records, what will be the output DataFrame?

Pandas

import pandas as pd
json_str = '[{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]'
df = pd.read_json(json_str)
print(df)

A

   name  age
0  Alice   30
1    Bob   25

B

   0      1
0  name   age
1  Alice  30
2  Bob    25

C

   name  age
0  Alice  25
1  Bob    30

D

   age   name
0  30    Alice
1  25    Bob

💡 Hint

The JSON string is a list of dictionaries, so read_json will create a DataFrame with columns from keys.

The JSON string is a list of objects with keys 'name' and 'age'. pandas.read_json parses it into a DataFrame with columns 'name' and 'age' and rows for each object.

❓ data_output

intermediate

How many rows and columns does this DataFrame have?

Using pandas.read_json on this JSON string, what is the shape of the resulting DataFrame?

Pandas

import pandas as pd
json_str = '{"A": [1, 2, 3], "B": [4, 5, 6]}'
df = pd.read_json(json_str)
print(df.shape)

A (3, 2)

B (6, 1)

C (1, 6)

D (2, 3)

💡 Hint

The JSON string is a dictionary with keys as columns and lists as values for rows.

The JSON string has keys 'A' and 'B' with lists of length 3. pandas.read_json creates a DataFrame with 3 rows and 2 columns.

🔧 Debug

advanced

What error does this code raise when reading JSON?

What error will this code raise when trying to read the JSON string?

Pandas

import pandas as pd
json_str = '{"name": "Alice", "age": 30'
df = pd.read_json(json_str)

A KeyError: 'age'

B TypeError: string indices must be integers

C ValueError: Expected object or value

D JSONDecodeError: Unterminated string starting at: line 1 column 22 (char 21)

💡 Hint

Look carefully at the JSON string syntax.

The JSON string is missing a closing brace and quote, causing a JSONDecodeError for malformed JSON.

🚀 Application

advanced

Which option correctly reads nested JSON into a DataFrame?

Given this nested JSON string, which code correctly reads it into a flat DataFrame?

Pandas

json_str = '{"id": 1, "name": "Alice", "scores": {"math": 90, "english": 85}}'

A pd.json_normalize(pd.read_json(json_str))

B pd.read_json(json_str)

C pd.json_normalize({'id': 1, 'name': 'Alice', 'scores': {'math': 90, 'english': 85}})

D pd.read_json(json_str, orient='records')

💡 Hint

Use json_normalize to flatten nested JSON structures.

pd.read_json cannot flatten nested JSON directly. pd.json_normalize applied to a dict flattens nested fields into columns.

🧠 Conceptual

expert

What is the effect of the 'orient' parameter in pandas.read_json?

Which statement best describes the role of the 'orient' parameter in pandas.read_json?

A It controls the encoding type used to read the JSON file.

B It specifies the format of the JSON string to correctly parse rows and columns.

C It determines whether to convert JSON keys to lowercase.

D It sets the compression algorithm for reading JSON files.

💡 Hint

Think about how JSON data can be structured differently.

The 'orient' parameter tells pandas how the JSON data is organized, such as 'records', 'columns', or 'index', so it can parse it properly.