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.

Practice

1. What does the pandas function read_json do?

easy

A. It loads JSON data into a pandas DataFrame for analysis.

B. It converts a DataFrame into a JSON string.

C. It saves a DataFrame as a CSV file.

D. It reads CSV files into a DataFrame.

Solution

1. Step 1: Understand the purpose of read_json

   The function read_json is designed to load JSON formatted data into a pandas DataFrame.

2. Step 2: Compare with other pandas functions

   Functions like to_json convert DataFrames to JSON, and read_csv reads CSV files, so they do not match the description.

3. Final Answer:

   It loads JSON data into a pandas DataFrame for analysis. -> Option A

4. Quick Check:

   read_json loads JSON to DataFrame = C [OK]

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

Common Mistakes:

• Confusing read_json with to_json
• Thinking it reads CSV files
• Assuming it saves data instead of loading

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

easy

A. pd.read_json(data.json)

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

C. pd.readjson('data.json')

D. pd.read_json['data.json']

Solution

1. Step 1: Check the function name and usage

   The correct pandas function is read_json with an underscore, called as a function with parentheses.

2. Step 2: Validate the argument format

   The file name must be a string inside quotes, so 'data.json' is correct.

3. Final Answer:

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

4. Quick Check:

   Correct function call with string filename = B [OK]

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

Common Mistakes:

• Missing quotes around filename
• Using wrong function name like readjson
• Using square brackets instead of parentheses

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

medium

A. An error because the JSON string is not a file path.

B. A Series object with the JSON string as a single entry.

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

D. A DataFrame with one column containing the entire JSON string.

Solution

1. Step 1: Understand input format

   The JSON string is a list of objects, each with 'name' and 'age' keys, suitable for conversion to a DataFrame.

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

   Passing a JSON string with list of dicts creates a DataFrame with columns matching keys and rows for each dict.

3. Final Answer:

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

4. Quick Check:

   List of dicts JSON string -> DataFrame rows and columns = A [OK]

Hint: List of dicts JSON string becomes DataFrame rows [OK]

Common Mistakes:

• Expecting error for string input
• Thinking it returns Series instead of DataFrame
• Assuming entire JSON is one column

4. What is wrong with this code snippet?

import pandas as pd
json_data = '{"name": "Alice", "age": 30}\n{"name": "Bob", "age": 25}'
df = pd.read_json(json_data)

medium

A. The function read_json cannot read JSON strings.

B. The JSON string is invalid because it uses double quotes.

C. The variable json_data should be a file path, not a string.

D. The JSON string is multiline but lines=True is missing in read_json.

Solution

1. Step 1: Identify JSON format

   The JSON string contains multiple JSON objects separated by newlines, which is a JSON lines format.

2. Step 2: Check pandas parameter for JSON lines

   To read JSON lines, read_json requires lines=True to parse each line as a JSON object.

3. Final Answer:

   The JSON string is multiline but lines=True is missing in read_json. -> Option D

4. Quick Check:

   Multiline JSON needs lines=True in read_json = D [OK]

Hint: Use lines=True for newline-separated JSON objects [OK]

Common Mistakes:

• Ignoring the need for lines=True with JSON lines
• Thinking double quotes are invalid in JSON
• Assuming read_json only reads files

5. You have a JSON file where data is stored as a dictionary of lists like this:

{"name": ["Alice", "Bob"], "age": [30, 25]}

Which read_json parameter should you use to correctly load this into a DataFrame?

hard

A. orient='columns'

B. orient='index'

C. orient='records'

D. orient='split'

Solution

1. Step 1: Identify JSON structure

   The JSON is a dictionary with keys as column names and values as lists of column data, matching the 'columns' orientation.

2. Step 2: Match pandas orient parameter

   Using orient='columns' tells pandas to interpret keys as columns and values as column data lists.

3. Final Answer:

   orient='columns' -> Option A

4. Quick Check:

   Dict of lists JSON needs orient='columns' = A [OK]

Hint: Use orient='columns' for dict of lists JSON format [OK]

Common Mistakes:

• Using orient='records' which expects list of dicts
• Using orient='index' which expects dict of dicts
• Not specifying orient and getting wrong DataFrame