Exporting to JSON in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this JSON export code?

Consider this Python code that exports a DataFrame to JSON format. What is the output string?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
json_str = df.to_json(orient='records')
print(json_str)

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

B {"name":{"0":"Alice","1":"Bob"},"age":{"0":25,"1":30}}

C [{"0":"Alice","1":"Bob"},{"0":25,"1":30}]

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

💡 Hint

The 'records' orient outputs a list of dictionaries, each dictionary is a row.

Using orient='records' in to_json outputs a JSON array where each element is a dictionary representing a row with column names as keys.

❓ data_output

intermediate

How many items are in the JSON array after export?

Given this DataFrame and export code, how many JSON objects are in the resulting JSON string?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'city': ['NY', 'LA', 'SF'], 'population': [8000000, 4000000, 900000]})
json_str = df.to_json(orient='records')
import json
json_data = json.loads(json_str)
print(len(json_data))

A 1

B 0

C 2

D 3

💡 Hint

Each row becomes one JSON object in the list.

The 'records' orient creates a list with one dictionary per DataFrame row. There are 3 rows, so 3 items.

🔧 Debug

advanced

What error does this JSON export code raise?

This code tries to export a DataFrame with a datetime column to JSON. What error occurs?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': [pd.Timestamp('2023-01-01'), pd.Timestamp('2023-01-02')], 'value': [10, 20]})
json_str = df.to_json(orient='records')
print(json_str)

A No error, outputs JSON string with dates as ISO strings

B TypeError: Object of type Timestamp is not JSON serializable

C ValueError: Invalid orient parameter

D KeyError: 'date'

💡 Hint

Pandas handles datetime conversion automatically in to_json.

Pandas converts Timestamp objects to ISO 8601 strings automatically when exporting to JSON, so no error occurs.

🚀 Application

advanced

Which option exports DataFrame to JSON with indentation for readability?

You want to export a DataFrame to a JSON file with indentation (pretty print). Which code snippet does this correctly?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})

A df.to_json('file.json', indent=4)

B df.to_json('file.json', indent=4, orient='records')

C df.to_json('file.json', lines=True)

D df.to_json('file.json', pretty=True)

💡 Hint

to_json supports indent parameter and orient to control format.

to_json accepts indent parameter for pretty printing and orient='records' to output list of dicts. Option B uses both correctly.

🧠 Conceptual

expert

Why might exporting a DataFrame with mixed data types to JSON cause issues?

Consider a DataFrame with columns of different types: numbers, strings, dates, and custom objects. What is the main challenge when exporting this DataFrame to JSON?

A JSON format does not support mixed data types in the same array

B Dates are always lost when exporting to JSON

C Custom objects may not be serializable to JSON without conversion

D Numbers are converted to strings automatically causing data loss

💡 Hint

Think about what JSON can represent and what Python objects need special handling.

JSON supports numbers, strings, arrays, objects, and booleans, but custom Python objects need to be converted to serializable types before export.