Reading Excel files (read_excel) in Data Analysis Python - Time & Space Complexity

Time Complexity: Reading Excel files (read_excel)

O(n x m)

Understanding Time Complexity

When we read Excel files using data tools, we want to know how the time it takes changes as the file gets bigger.

We ask: How does reading time grow when the Excel file has more rows or columns?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

data = pd.read_excel('data.xlsx')

# data now holds the Excel file content as a DataFrame

This code reads an Excel file into a table-like structure for analysis.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Reading each cell in the Excel file to load data.
• How many times: Once for every cell in the file (rows x columns).

How Execution Grows With Input

As the number of rows and columns grows, the time to read grows roughly by the total number of cells.

Input Size (rows x columns)	Approx. Operations
10 x 5 = 50	About 50 cell reads
100 x 5 = 500	About 500 cell reads
1000 x 10 = 10,000	About 10,000 cell reads

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 read grows roughly with the number of rows times the number of columns.

Common Mistake

[X] Wrong: "Reading an Excel file takes the same time no matter how big it is."

[OK] Correct: The program reads every cell, so bigger files with more rows or columns take more time.

Interview Connect

Understanding how file size affects reading time helps you explain performance in data tasks clearly and confidently.

Self-Check

"What if the Excel file has many empty cells? Would the time complexity change?"

Practice

1. What does the pandas.read_excel() function do in Python?

easy

A. It reads data from an Excel file into a DataFrame.

B. It writes data from a DataFrame to an Excel file.

C. It converts a DataFrame to a CSV file.

D. It creates a new Excel file from scratch.

Solution

1. Step 1: Understand the purpose of read_excel

   The function pandas.read_excel() is designed to load data from Excel files into Python as a DataFrame.

2. Step 2: Differentiate from other file operations

   Writing data to Excel or converting to CSV are done by other functions like to_excel() or to_csv(), not read_excel().

3. Final Answer:

   It reads data from an Excel file into a DataFrame. -> Option A

4. Quick Check:

   read_excel loads Excel data [OK]

Hint: Remember: read_excel loads data, to_excel saves data [OK]

Common Mistakes:

• Confusing read_excel with to_excel
• Thinking it writes data instead of reading
• Mixing up CSV and Excel functions

2. Which of the following is the correct syntax to read the sheet named 'Sales' from an Excel file 'data.xlsx' using pandas?

easy

A. pd.read_excel('data.xlsx', sheet='Sales')

B. pd.read_excel('data.xlsx', sheet_name='Sales')

C. pd.read_excel('data.xlsx', sheetname='Sales')

D. pd.read_excel('data.xlsx', sheetname='sales')

Solution

1. Step 1: Identify the correct parameter name

   The correct parameter to specify the sheet name in read_excel is sheet_name, not sheet or sheetname.

2. Step 2: Check case sensitivity and spelling

   Parameter names are case-sensitive and must be spelled exactly as documented. Also, sheet names are case-sensitive, so 'Sales' is correct if that is the sheet name.

3. Final Answer:

   pd.read_excel('data.xlsx', sheet_name='Sales') -> Option B

4. Quick Check:

   Use sheet_name to select sheet [OK]

Hint: Use exact parameter name 'sheet_name' to select sheets [OK]

Common Mistakes:

• Using 'sheet' instead of 'sheet_name'
• Misspelling 'sheet_name' as 'sheetname'
• Ignoring case sensitivity in sheet names

3. What will be the output of this code snippet?

import pandas as pd
df = pd.read_excel('data.xlsx', usecols='A:C')
print(df.columns.tolist())

Assuming columns A, B, C in the Excel file are named 'Name', 'Age', 'City'.

medium

A. ['A', 'B', 'C']

B. Error: Invalid usecols parameter

C. ['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2']

D. ['Name', 'Age', 'City']

Solution

1. Step 1: Understand usecols='A:C'

   The parameter usecols='A:C' selects columns A to C from the Excel file, which correspond to the columns named 'Name', 'Age', and 'City'.

2. Step 2: Check what df.columns contains

   After reading, df.columns contains the actual column names from the Excel file, so printing df.columns.tolist() outputs ['Name', 'Age', 'City'].

3. Final Answer:

   ['Name', 'Age', 'City'] -> Option D

4. Quick Check:

   usecols selects columns by letter, output shows column names [OK]

Hint: usecols='A:C' picks columns A to C by letter [OK]

Common Mistakes:

• Expecting column letters instead of names in output
• Confusing usecols syntax causing errors
• Assuming default numeric column indexing

4. You run this code but get an error:

import pandas as pd
df = pd.read_excel('data.xlsx', sheet_name=1, usecols=[0, 2, 5])

What is the most likely cause of the error?

medium

A. The file path 'data.xlsx' is incorrect or file missing.

B. The usecols list contains an invalid column index.

C. The sheet index 1 does not exist in the Excel file.

D. The parameter 'sheet_name' must be a string, not an integer.

Solution

1. Step 1: Check sheet_name parameter

   Using sheet_name=1 is valid to select the second sheet by index (0-based), so this is not an error cause.

2. Step 2: Validate usecols parameter

   The list [0, 2, 5] is valid to select columns by zero-based index, so this is not an error cause.

3. Step 3: Consider file path and existence

   If the file 'data.xlsx' does not exist or path is wrong, pandas raises a FileNotFoundError, which is a common cause of errors when reading Excel files.

4. Final Answer:

   The file path 'data.xlsx' is incorrect or file missing. -> Option A

5. Quick Check:

   FileNotFoundError usually means wrong file path [OK]

Hint: Check file path first if read_excel errors [OK]

Common Mistakes:

• Assuming sheet_name must be string only
• Thinking usecols list indexes are invalid
• Ignoring file existence or path errors

5. You want to read only the columns 'Date', 'Sales', and 'Profit' from the sheet named '2023' in an Excel file 'report.xlsx'. Which code snippet correctly does this?

hard

A. pd.read_excel('report.xlsx', sheet_name='2023', usecols='Date,Sales,Profit')

B. pd.read_excel('report.xlsx', sheet='2023', columns=['Date', 'Sales', 'Profit'])

C. pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit'])

D. pd.read_excel('report.xlsx', sheet_name='2023', usecols=[0,1,2])

Solution

1. Step 1: Use correct parameter names

   The parameter to select the sheet is sheet_name, and to select columns by name is usecols with a list of column names.

2. Step 2: Check option correctness

   pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit']) uses sheet_name='2023' and usecols=['Date', 'Sales', 'Profit'], which is the correct syntax to read only those columns from the specified sheet.

3. Step 3: Identify errors in other options

   pd.read_excel('report.xlsx', sheet='2023', columns=['Date', 'Sales', 'Profit']) uses wrong parameter names sheet and columns. pd.read_excel('report.xlsx', sheet_name='2023', usecols='Date,Sales,Profit') uses a string for usecols which is invalid for column names. pd.read_excel('report.xlsx', sheet_name='2023', usecols=[0,1,2]) selects columns by index, not by name.

4. Final Answer:

   pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit']) -> Option C

5. Quick Check:

   Use sheet_name and list in usecols for named columns [OK]

Hint: Use list of column names in usecols to select columns [OK]

Common Mistakes:

• Using wrong parameter names like 'sheet' or 'columns'
• Passing column names as a comma string instead of list
• Selecting columns by index when names are needed