Reading Excel Spreadsheets & Multiple Sheets0%
Reading JSON, SQL Databases & Parquet Files

Reading Excel Spreadsheets & Multiple Sheets

Beginner12 min read•Updated: Jul 11, 2026
Study MaterialsCheatsheet

Reading Excel Spreadsheets & Multiple Sheets

Reading Time: 8 Mins | Level: Beginner to Advanced

1. Reading Excel Files into Pandas

Microsoft Excel (.xlsx, .xls) is the enterprise standard for business reporting. Pandas leverages the openpyxl (for .xlsx) and xlrd (for legacy .xls) libraries behind the scenes to parse complex workbooks.

Bash / Terminal
# Ensure required engine is installed
pip install openpyxl

2. Basic Excel Reading

Python
import pandas as pd
 
# Load the first sheet of an Excel file
df = pd.read_excel('Q3_Financial_Model.xlsx')
print(df.head())

3. Reading Specific Sheets & Multiple Worksheets

A. Load by Sheet Name or Index

Python
# By sheet name
df_sales = pd.read_excel('Company_Data.xlsx', sheet_name='Sales_North')
 
# By zero-indexed position (e.g., 2nd sheet)
df_expenses = pd.read_excel('Company_Data.xlsx', sheet_name=1)

B. Load ALL Sheets Simultaneously

Passing sheet_name=None returns an ordered dictionary where the keys are sheet names and values are the corresponding DataFrames:

Python
all_sheets = pd.read_excel('Company_Data.xlsx', sheet_name=None)
 
# Inspect available sheets
print("Found sheets:", list(all_sheets.keys()))
 
# Access individual sheet DataFrame
df_hr = all_sheets['HR_Headcount']
print(df_hr.shape)

4. Advanced Ingestion: Skipping Metadata Rows

Many corporate Excel templates contain company logos, title banners, or blank rows at the top before the actual table header begins:

Python
df_clean = pd.read_excel(
'Monthly_Report.xlsx',
sheet_name='Summary',
skiprows=4, # Skip first 4 rows containing company banner & blank space
usecols="B:G", # Excel column range syntax (columns B through G)
nrows=50 # Read only the first 50 data rows
)

5. Writing to Multi-Sheet Excel Workbooks (ExcelWriter)

To export multiple DataFrames into different tabs of a single Excel file, use pd.ExcelWriter:

Python
with pd.ExcelWriter('Final_Audit_Report.xlsx', engine='openpyxl') as writer:
df_sales.to_excel(writer, sheet_name='Sales_Summary', index=False)
df_expenses.to_excel(writer, sheet_name='Expense_Breakdown', index=False)
 
print("Workbook with multiple sheets saved successfully!")
Interactive Quiz

Multiple Choice Questions

Question 1
Which Python library is required by Pandas to read and write modern .xlsx Excel files?
Question 2
What does pd.read_excel('report.xlsx', sheet_name=None) return?
Question 3
How do you skip the first 3 non-data banner rows when loading an Excel sheet in Pandas?
Question 4
Which class should you use to write multiple DataFrames to different worksheets within a single Excel file?
Question 5
In pd.read_excel(), what does usecols="B:F" specify?
Next Lesson

Reading JSON, SQL Databases & Parquet Files

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous LessonNext Lesson
Reading & Writing CSV and TSV FilesReading JSON, SQL Databases & Parquet Files

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

PreviousNext
PrevNext