Exporting results to multiple formats in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to export the DataFrame to a CSV file named 'data.csv'.

Pandas

df.to_csv([1])

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

A'data.csv'

B'table.xlsx'

C'output.json'

D'data.txt'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a file extension other than '.csv' for CSV export.

Forgetting quotes around the filename.

To save a DataFrame as a CSV file, use to_csv with the filename ending in '.csv'.

2fill in blank

medium

Complete the code to export the DataFrame to an Excel file named 'report.xlsx'.

Pandas

df.to_excel([1])

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

A'report.json'

B'report.csv'

C'report.xlsx'

D'report.txt'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '.csv' extension with to_excel.

Not using quotes around the filename.

Use to_excel with a filename ending in '.xlsx' to export to Excel format.

3fill in blank

hard

Fix the error in the code to export the DataFrame to a JSON file named 'data.json'.

Pandas

df.[1]('data.json')

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

Ato_json

Bto_csv

Cto_excel

Dsave_json

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_csv or to_excel instead of to_json.

Using a non-existent method like save_json.

The correct method to export a DataFrame to JSON is to_json.

4fill in blank

hard

Fill both blanks to export only the 'Name' and 'Age' columns to a CSV file named 'subset.csv'.

Pandas

df.loc[:, [[1], [2]]].to_csv('subset.csv', index=False)

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

A'Name'

B'Age'

C'Salary'

D'Department'

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting wrong columns.

Forgetting quotes around column names.

Use loc to select columns 'Name' and 'Age' before exporting.

5fill in blank

hard

Fill all three blanks to export rows where 'Score' is greater than 80 to a JSON file named 'high_scores.json'.

Pandas

df_filtered = df[df[[1]] [2] [3]]
df_filtered.to_json('high_scores.json')

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

A'Score'

B>

C80

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column name.

Using wrong comparison operator.

Putting number 80 in quotes.

Filter rows where 'Score' > 80, then export filtered DataFrame to JSON.

Practice

1. Which pandas method is used to save a DataFrame as a CSV file?

easy

A. save_csv()

B. write_csv()

C. export_csv()

D. to_csv()

Solution

1. Step 1: Recall pandas export methods

   pandas provides specific methods to export data, such as to_csv() for CSV files.

2. Step 2: Match method to file type

   The method to_csv() is the standard way to save DataFrames as CSV files.

3. Final Answer:

   to_csv() -> Option D

4. Quick Check:

   CSV export method = to_csv() [OK]

Hint: CSV files use to_csv() method in pandas [OK]

Common Mistakes:

• Using non-existent methods like save_csv()
• Confusing to_csv() with to_excel()
• Trying to use write_csv() which is not a pandas method

2. Which of the following is the correct syntax to save a DataFrame df to an Excel file named data.xlsx?

easy

A. df.to_excel('data.xlsx')

B. df.save_excel('data.xlsx')

C. df.export_excel('data.xlsx')

D. df.write_excel('data.xlsx')

Solution

1. Step 1: Identify the correct pandas method for Excel export

   The method to save a DataFrame to Excel is to_excel().

2. Step 2: Check syntax correctness

   The correct syntax is calling df.to_excel('filename.xlsx') with the filename as a string argument.

3. Final Answer:

   df.to_excel('data.xlsx') -> Option A

4. Quick Check:

   Excel export syntax = df.to_excel(filename) [OK]

Hint: Use to_excel() with filename string to save Excel files [OK]

Common Mistakes:

• Using non-existent methods like save_excel()
• Missing quotes around filename
• Using write_excel() which is not a pandas method

3. What will be the output of the following code?

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.to_json('output.json')
with open('output.json') as f:
    content = f.read()
print(content)

medium

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

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

C. Error: to_json() requires extra parameters

D. Empty file content

Solution

1. Step 1: Understand default to_json() output format

   By default, to_json() saves data in 'columns' orientation. The default is 'columns', which creates a nested JSON with columns as keys.

2. Step 2: Check printed JSON content

   The printed content shows keys 'A' and 'B' with nested indices and values, matching the default 'columns' orientation JSON.

3. Final Answer:

   {"A":{"0":1,"1":2},"B":{"0":3,"1":4}} -> Option A

4. Quick Check:

   Default to_json() output = nested JSON by columns [OK]

Hint: Default to_json() saves nested JSON by columns [OK]

Common Mistakes:

• Expecting list of dicts JSON without setting orient
• Thinking to_json() needs extra parameters
• Assuming file is empty after saving

4. You try to save a DataFrame df to Excel using df.to_excel('file.xlsx') but get an error: ModuleNotFoundError: No module named 'openpyxl'. What is the best way to fix this?

medium

A. Change the filename to .csv and use to_csv()

B. Install the openpyxl library using pip

C. Use df.to_excel() without filename

D. Restart Python without any changes

Solution

1. Step 1: Understand the error cause

   The error means pandas needs the 'openpyxl' library to write Excel files but it is not installed.

2. Step 2: Fix by installing the required library

   Installing 'openpyxl' with pip install openpyxl will provide the needed support for Excel export.

3. Final Answer:

   Install the openpyxl library using pip -> Option B

4. Quick Check:

   Missing Excel engine error = install openpyxl [OK]

Hint: Install openpyxl to export Excel files with pandas [OK]

Common Mistakes:

• Ignoring the missing library error
• Trying to save without filename
• Switching to CSV without reason

5. You have a DataFrame df and want to export it to both CSV and HTML formats in one script. Which code snippet correctly does this?

hard

A. df.save(['data.csv', 'data.html'])

B. df.to_csv('data.csv', 'data.html')

C. df.to_csv('data.csv')\ndf.to_html('data.html')

D. df.export(['data.csv', 'data.html'])

Solution

1. Step 1: Understand pandas export methods

   pandas provides separate methods for each format: to_csv() for CSV and to_html() for HTML.

2. Step 2: Use both methods separately

   Calling df.to_csv('data.csv') and then df.to_html('data.html') saves the DataFrame in both formats correctly.

3. Final Answer:

   df.to_csv('data.csv')\ndf.to_html('data.html') -> Option C

4. Quick Check:

   Separate calls for each format = correct export [OK]

Hint: Call to_csv() and to_html() separately to export both [OK]

Common Mistakes:

• Trying to pass multiple filenames to one method
• Using non-existent export() or save() methods
• Expecting one method to handle multiple formats