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.

Practice

1. What does the pandas.read_json() function do in data analysis?

easy

A. It saves a DataFrame to a CSV file.

B. It converts a DataFrame into a JSON string.

C. It loads JSON data into a DataFrame for easy analysis.

D. It visualizes JSON data as a chart.

Solution

1. Step 1: Understand the purpose of read_json

   The function pandas.read_json() is designed to read JSON formatted data and convert it into a DataFrame.

2. Step 2: Compare options with function behavior

   Only It loads JSON data into a DataFrame for easy analysis. correctly describes this behavior. Options B, C, and D describe other unrelated functions.

3. Final Answer:

   It loads JSON data into a DataFrame for easy analysis. -> Option C

4. Quick Check:

   read_json loads JSON to DataFrame [OK]

Hint: Remember: read_json reads JSON into DataFrame [OK]

Common Mistakes:

• Confusing read_json with to_json (writing JSON)
• Thinking it saves data instead of loading
• Assuming it creates visualizations

2. Which of the following is the correct syntax to read a JSON file named data.json into a DataFrame using pandas?

easy

A. df = pd.read_json(data.json)

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

C. df = pd.read_json("data.json", header=None)

D. df = pd.read_json[data.json]

Solution

1. Step 1: Check correct function call syntax

   The function pd.read_json() requires the filename as a string argument inside parentheses.

2. Step 2: Identify correct string usage

   df = pd.read_json('data.json') uses single quotes correctly. df = pd.read_json(data.json) misses quotes, causing a NameError. df = pd.read_json("data.json", header=None) uses an invalid header parameter (not supported by read_json). df = pd.read_json[data.json] uses square brackets which is invalid syntax.

3. Final Answer:

   df = pd.read_json('data.json') -> Option B

4. Quick Check:

   Use parentheses and quotes for filename [OK]

Hint: Use parentheses and quotes for file name in read_json [OK]

Common Mistakes:

• Forgetting quotes around filename
• Using square brackets instead of parentheses
• Passing variable without defining it
• Using invalid parameters like header= (confusing with read_csv)

3. Given the JSON string '{"name": ["Alice", "Bob"], "age": [25, 30]}', what will be the output of pd.read_json(json_str)?

medium

A. A DataFrame with columns 'name' and 'age' and two rows for Alice and Bob.

B. A DataFrame with two rows and columns named 0 and 1.

C. A Series object with keys 'name' and 'age'.

D. An error because JSON string is invalid.

Solution

1. Step 1: Understand JSON structure

   The JSON string has keys 'name' and 'age' with lists as values, representing columns with two entries each.

2. Step 2: Behavior of pd.read_json on this JSON

   By default, pd.read_json() interprets this as columns with lists of values, creating a DataFrame with columns 'name' and 'age' and two rows.

3. Final Answer:

   A DataFrame with columns 'name' and 'age' and two rows for Alice and Bob. -> Option A

4. Quick Check:

   JSON keys become columns, lists become rows [OK]

Hint: Keys become columns, lists become rows in DataFrame [OK]

Common Mistakes:

• Thinking JSON keys become rows
• Expecting a Series instead of DataFrame
• Assuming JSON string is invalid

4. What is wrong with this code snippet?

import pandas as pd
json_str = '{"0": {"name": "Alice", "age": 25}, "1": {"name": "Bob", "age": 30}}'
df = pd.read_json(json_str)
print(df)

medium

A. The JSON string is missing quotes around keys.

B. The print statement is incorrect syntax.

C. pd.read_json cannot read JSON strings, only files.

D. The JSON string format requires orient='index' to read correctly.

Solution

1. Step 1: Analyze JSON structure

   The JSON string has numeric keys '0' and '1' with nested dictionaries representing rows.

2. Step 2: Understand read_json orient parameter

   By default, read_json expects columns orientation. Here, the data is oriented by index, so orient='index' is needed to parse correctly.

3. Final Answer:

   The JSON string format requires orient='index' to read correctly. -> Option D

4. Quick Check:

   Use orient='index' for nested dicts keyed by row labels [OK]

Hint: Use orient='index' for JSON keyed by row labels [OK]

Common Mistakes:

• Ignoring orient parameter causing wrong DataFrame
• Assuming JSON keys need quotes (they have quotes)
• Thinking read_json can't read strings
• Misreading print syntax

5. You have a JSON file where each entry is a list of values representing rows, like this:

[ ["Alice", 25], ["Bob", 30] ]

How do you read this JSON into a DataFrame with columns 'name' and 'age'?

hard

A. Use pd.read_json('file.json', orient='records') then assign columns manually.

B. Use pd.read_json('file.json', orient='values', columns=['name', 'age'])

C. Use pd.read_json('file.json', orient='split') with columns parameter.

D. Use pd.read_json('file.json', orient='records', names=['name', 'age'])

Solution

1. Step 1: Understand JSON structure and orient options

   The JSON is a list of lists, which matches the 'records' orient but without keys for columns.

2. Step 2: Read JSON and assign columns

   Using orient='records' reads the data as rows but columns are unnamed, so columns must be assigned manually after reading.

3. Final Answer:

   Use pd.read_json('file.json', orient='records') then assign columns manually. -> Option A

4. Quick Check:

   Assign columns manually when JSON has no keys [OK]

Hint: Assign columns after reading list-of-lists JSON [OK]

Common Mistakes:

• Using non-existent orient values like 'values'
• Expecting read_json to assign columns automatically
• Confusing 'split' orient with list-of-lists JSON