Writing to Excel with to_excel in Pandas - Time & Space Complexity

Time Complexity: Writing to Excel with to_excel

O(n x m)

Understanding Time Complexity

When saving data to an Excel file using pandas, it is important to understand how the time taken grows as the data size increases.

We want to know how the writing process scales when the number of rows or columns grows.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'A': range(1000),
    'B': range(1000, 2000)
})

df.to_excel('output.xlsx', index=False)

This code creates a DataFrame with 1000 rows and 2 columns, then writes it to an Excel file without the index.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Writing each cell's data to the Excel file.
• How many times: Once for each cell in the DataFrame (rows x columns).

How Execution Grows With Input

As the number of rows or columns increases, the number of cells to write grows proportionally.

Input Size (rows x columns)	Approx. Operations
10 x 2 = 20	About 20 write operations
100 x 2 = 200	About 200 write operations
1000 x 2 = 2000	About 2000 write operations

Pattern observation: The time grows roughly in direct proportion to the total number of cells.

Final Time Complexity

Time Complexity: O(n × m)

This means the time to write grows linearly with the number of rows (n) times the number of columns (m).

Common Mistake

[X] Wrong: "Writing to Excel takes the same time no matter how big the DataFrame is."

[OK] Correct: Writing involves saving every cell's data, so more rows or columns mean more work and more time.

Interview Connect

Understanding how data export time grows helps you write efficient data pipelines and manage large datasets smoothly.

Self-Check

What if we added compression or saved to a different file format? How would the time complexity change?

Practice

1. What does the to_excel function in pandas do?

easy

A. Saves a DataFrame to an Excel file

B. Reads data from an Excel file into a DataFrame

C. Deletes an Excel file from disk

D. Converts Excel formulas into Python code

Solution

1. Step 1: Understand the purpose of to_excel

   The to_excel function is used to save or export a pandas DataFrame into an Excel file format.

2. Step 2: Differentiate from other Excel functions

   Functions like read_excel are for reading Excel files, not saving. Deleting files or converting formulas are unrelated.

3. Final Answer:

   Saves a DataFrame to an Excel file -> Option A

4. Quick Check:

   to_excel = Save DataFrame [OK]

Hint: Remember: 'to' means saving out, 'read' means loading in [OK]

Common Mistakes:

• Confusing to_excel with read_excel
• Thinking it deletes files
• Assuming it converts formulas

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. to_excel(df, 'data.xlsx')

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

Solution

1. Step 1: Recall the method call format

   In pandas, saving a DataFrame uses the method to_excel called on the DataFrame object.

2. Step 2: Check the syntax correctness

   The correct syntax is df.to_excel('filename.xlsx'). Other options use incorrect method names or function calls.

3. Final Answer:

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

4. Quick Check:

   Method on DataFrame = to_excel [OK]

Hint: Use df.to_excel('filename.xlsx') to save [OK]

Common Mistakes:

• Using non-existent methods like save_excel
• Calling to_excel as a standalone function
• Using wrong method names like write_excel

3. Given the code below, what will be the content of the Excel file output.xlsx?

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.to_excel('output.xlsx', index=False, sheet_name='Sheet1')

medium

A. Excel file with columns A and B, including row numbers, default sheet name

B. Excel file with columns A and B, no row numbers, sheet named 'Sheet1'

C. Excel file with only column A saved

D. Excel file with columns A and B, but sheet name is 'Sheet2'

Solution

1. Step 1: Analyze parameters in to_excel

   The argument index=False means row numbers are not saved. sheet_name='Sheet1' sets the sheet name explicitly.

2. Step 2: Understand DataFrame content saved

   All columns 'A' and 'B' are saved because no column filtering is done.

3. Final Answer:

   Excel file with columns A and B, no row numbers, sheet named 'Sheet1' -> Option B

4. Quick Check:

   index=False removes row numbers [OK]

Hint: index=False means no row numbers saved [OK]

Common Mistakes:

• Assuming row numbers are saved by default
• Ignoring the sheet_name parameter
• Thinking only one column is saved

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'X': [10, 20]})
df.to_excel('file.xlsx', sheet='Data')

medium

A. File extension should be '.xls' not '.xlsx'

B. DataFrame is empty and cannot be saved

C. Missing import for Excel writer

D. The parameter name 'sheet' is incorrect; it should be 'sheet_name'

Solution

1. Step 1: Check parameter names for to_excel

   The correct parameter to name the sheet is sheet_name, not sheet.

2. Step 2: Validate other parts of the code

   The DataFrame is valid and not empty. The file extension '.xlsx' is correct. No extra import is needed for basic usage.

3. Final Answer:

   The parameter name 'sheet' is incorrect; it should be 'sheet_name' -> Option D

4. Quick Check:

   Use sheet_name to name sheets [OK]

Hint: Use exact parameter names like sheet_name [OK]

Common Mistakes:

• Using wrong parameter names like 'sheet'
• Thinking file extension must be '.xls'
• Assuming extra imports are needed

5. You want to save two DataFrames df1 and df2 into the same Excel file report.xlsx, each on a different sheet named 'Sales' and 'Expenses'. Which code correctly does this?

hard

A. df1.to_excel('report.xlsx')
df2.to_excel('report.xlsx')

B. df1.to_excel('report.xlsx', sheet_name='Sales')
df2.to_excel('report.xlsx', sheet_name='Expenses')

C. with pd.ExcelWriter('report.xlsx') as writer:
df1.to_excel(writer, sheet_name='Sales')
df2.to_excel(writer, sheet_name='Expenses')

D. pd.to_excel([df1, df2], 'report.xlsx', sheets=['Sales', 'Expenses'])

Solution

1. Step 1: Understand saving multiple sheets

   To save multiple DataFrames in one Excel file with different sheets, use pd.ExcelWriter as a context manager.

2. Step 2: Check code correctness

   with pd.ExcelWriter('report.xlsx') as writer: df1.to_excel(writer, sheet_name='Sales') df2.to_excel(writer, sheet_name='Expenses') uses ExcelWriter and calls to_excel with the writer and sheet names correctly. Directly calling to_excel on the same filename multiple times overwrites the file, and pd.to_excel([df1, df2], ...) is not a valid function.

3. Final Answer:

   with pd.ExcelWriter('report.xlsx') as writer: df1.to_excel(writer, sheet_name='Sales') df2.to_excel(writer, sheet_name='Expenses') -> Option C

4. Quick Check:

   Use ExcelWriter for multiple sheets [OK]

Hint: Use ExcelWriter to save multiple sheets [OK]

Common Mistakes:

• Saving multiple DataFrames separately overwrites the file
• Trying to pass multiple DataFrames directly to to_excel
• Using non-existent functions like pd.to_excel with lists