Python Q & A


How to work with Excel files in Python?

Working with Excel files in Python has been made remarkably straightforward thanks to a few specialized libraries. The most notable one is `openpyxl` for `.xlsx` files, while `xlrd` and `xlwt` can be employed for the older `.xls` format.


  1. Reading Excel files:

To read Excel files, `openpyxl` provides a simple interface. First, you’ll want to load your workbook, and then select the desired sheet. From there, cells can be accessed by row and column indices.


from openpyxl import load_workbook

wb = load_workbook('example.xlsx')

sheet =

cell_value = sheet.cell(row=1, column=1).value



  1. Writing to Excel files: 

Writing or modifying Excel files is similarly intuitive. After making changes, simply save the workbook.


from openpyxl import Workbook

wb = Workbook()

sheet =

sheet.cell(row=1, column=1, value="Hello, Excel!")'output.xlsx')



  1. Handling Complex Features:

`openpyxl` isn’t limited to basic reading and writing. It also supports creating charts, adding images, conditional formatting, and even managing complex styling, making it a comprehensive solution for Excel operations in Python.


  1. Alternatives:

For users seeking a more data analysis-oriented approach, the `pandas` library provides functions like `read_excel()` and `to_excel()`, streamlining the process of reading and writing Excel files into its DataFrame structure.



For most tasks, `openpyxl` is robust and comprehensive. However, if you’re already using `pandas` for data analysis, leveraging its built-in Excel functions can be efficient. Always remember to install the required libraries using `pip`, e.g., `pip install openpyxl pandas`.

Python offers powerful tools for interacting with Excel files, whether for data extraction, automation, or analysis, making it an excellent choice for Excel-related tasks.

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Senior Software Engineer with 7+ yrs Python experience. Improved Kafka-S3 ingestion, GCP Pub/Sub metrics. Proficient in Flask, FastAPI, AWS, GCP, Kafka, Git