Exporting to JSON in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to export the DataFrame to a JSON file named 'data.json'.

Data Analysis Python

df.to_json('[1]')

Drag options to blanks, or click blank then click option'

Adata.csv

Bdata.json

Cdata.txt

Ddata.xlsx

Attempts:

3 left

💡 Hint

Common Mistakes

Using a filename with the wrong extension like .csv or .txt

Forgetting to put the filename in quotes

The to_json method saves the DataFrame as a JSON file. The filename must end with .json.

2fill in blank

medium

Complete the code to export the DataFrame to a JSON string instead of a file.

Data Analysis Python

json_str = df.to_json([1]=None)

Drag options to blanks, or click blank then click option'

Ato_string

Bforce_ascii

Corient

Dpath_or_buf

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'orient' or 'to_string' which are unrelated to output destination

Not passing None but a filename string

Setting path_or_buf=None returns the JSON as a string instead of saving to a file.

3fill in blank

hard

Fix the error in the code to export only the 'name' and 'age' columns to JSON.

Data Analysis Python

df[['name', '[1]']].to_json('subset.json')

Drag options to blanks, or click blank then click option'

Aage

Bsalary

Cgender

Dcity

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting columns not present in the DataFrame

Forgetting to include the second column

To export 'name' and 'age' columns, both must be selected. The missing column is 'age'.

4fill in blank

hard

Fill both blanks to export the DataFrame to JSON with records orientation and indentation of 2 spaces.

Data Analysis Python

df.to_json('output.json', orient='[1]', indent=[2])

Drag options to blanks, or click blank then click option'

Arecords

Bcolumns

C2

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong orient like 'columns' which changes JSON structure

Using indent values that are too large or missing

Setting orient='records' outputs JSON as a list of records. indent=2 formats JSON with 2 spaces indentation.

5fill in blank

hard

Fill all three blanks to export the DataFrame to JSON with index excluded, using split orientation, and ASCII characters only.

Data Analysis Python

df.to_json('final.json', [1]=False, orient='[2]', force_ascii=[3])

Drag options to blanks, or click blank then click option'

Aindex

Bsplit

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to exclude index causing extra data

Using wrong orient value

Not setting force_ascii correctly

Setting index=False excludes the index from JSON. orient='split' formats JSON with separate keys for index, columns, and data. force_ascii=True ensures ASCII characters only.

Practice

1. What is the main purpose of exporting data to JSON format in data analysis?

easy

A. To save data in a format that is easy to share and widely used

B. To make data unreadable for security

C. To convert data into an Excel spreadsheet

D. To compress data to reduce file size

Solution

1. Step 1: Understand JSON format use

   JSON is a text format that is easy to share and read by many programs.

2. Step 2: Identify the purpose of exporting

   Exporting to JSON helps save data in this easy-to-share format.

3. Final Answer:

   To save data in a format that is easy to share and widely used -> Option A

4. Quick Check:

   Exporting to JSON = Easy sharing [OK]

Hint: JSON is for sharing data simply and widely [OK]

Common Mistakes:

• Thinking JSON is for data compression
• Confusing JSON with Excel format
• Believing JSON makes data unreadable

2. Which of the following is the correct way to export a pandas DataFrame df to a JSON file named data.json?

easy

A. df.write_json('data.json')

B. df.export_json('data.json')

C. df.to_json('data.json')

D. df.save_json('data.json')

Solution

1. Step 1: Recall pandas export method

   The correct pandas method to export JSON is to_json.

2. Step 2: Match method with filename

   Using df.to_json('data.json') saves the DataFrame to the file.

3. Final Answer:

   df.to_json('data.json') -> Option C

4. Quick Check:

   pandas export JSON = to_json() [OK]

Hint: Use df.to_json(filename) to save DataFrame as JSON [OK]

Common Mistakes:

• Using non-existent methods like export_json
• Confusing save_json or write_json with to_json
• Forgetting to provide the filename as a string

3. Given the DataFrame df = pd.DataFrame({'A':[1,2], 'B':[3,4]}), what is the output of df.to_json(orient='records')?

medium

A. '{"A":{"0":1,"1":2},"B":{"0":3,"1":4}}'

B. '[{"A":1,"B":3},{"A":2,"B":4}]'

C. '{"0":{"A":1,"B":3},"1":{"A":2,"B":4}}'

D. '[1,2,3,4]'

Solution

1. Step 1: Understand orient='records'

   This option converts each row to a JSON object in a list.

2. Step 2: Convert DataFrame rows to JSON objects

   Rows become [{"A":1,"B":3}, {"A":2,"B":4}].

3. Final Answer:

   '[{"A":1,"B":3},{"A":2,"B":4}]' -> Option B

4. Quick Check:

   orient='records' = list of row dicts [OK]

Hint: orient='records' makes list of row dictionaries [OK]

Common Mistakes:

• Confusing 'records' with default 'columns' orient
• Expecting a nested dictionary instead of list
• Misreading JSON string format

4. What is wrong with this code snippet for exporting a DataFrame df to JSON?

df.to_json('output.json', orient='wrong')

medium

A. It raises a ValueError because 'wrong' is not a valid orient

B. It will save the file but with empty content

C. It ignores the orient and uses default silently

D. It saves the file but with incorrect JSON format

Solution

1. Step 1: Check valid orient options

   Valid options include 'records', 'columns', 'index', etc., but not 'wrong'.

2. Step 2: Understand error behavior

   Using an invalid orient raises a ValueError immediately.

3. Final Answer:

   It raises a ValueError because 'wrong' is not a valid orient -> Option A

4. Quick Check:

   Invalid orient = ValueError [OK]

Hint: Invalid orient causes ValueError, check spelling [OK]

Common Mistakes:

• Assuming invalid orient defaults silently
• Expecting empty file instead of error
• Thinking JSON format is saved anyway

5. You want to export a DataFrame df with nested dictionaries in a column to JSON, preserving the nested structure. Which orient option should you use to keep the nested data intact?

hard

A. 'records'

B. 'split'

C. 'index'

D. 'table'

Solution

1. Step 1: Understand nested data export needs

   Nested dictionaries require a format that preserves complex structure.

2. Step 2: Identify orient='table' features

   Orient='table' exports data with schema and supports nested structures well.

3. Step 3: Compare other options

   'records' flattens rows, 'split' and 'index' do not preserve nested well.

4. Final Answer:

   'table' -> Option D

5. Quick Check:

   Nested data export = orient='table' [OK]

Hint: Use orient='table' to keep nested JSON structure [OK]

Common Mistakes:

• Using 'records' which flattens nested data
• Choosing 'split' or 'index' which lose nested info
• Not specifying orient and losing structure