11 Useful Excel Python Libraries for Data Management

Python is likely one of the hottest programming languages ​​for knowledge administration and evaluation. One in all its strengths is that it may possibly learn knowledge in varied codecs, reminiscent of JSON, CSV, and Excel spreadsheets.

This text covers a number of the most helpful Python libraries for dealing with knowledge, particularly Excel spreadsheets.

Why use Python for knowledge administration?

  • Python has an intuitive syntax that makes it a easy language. This additionally makes it simpler to be taught and subsequently highly regarded with programmers.
  • Python is flexible as a result of it may be used for quite a lot of functions, from synthetic intelligence to internet growth, from knowledge analytics to desktop growth.
  • Python has a big group that creates sources to make use of and be taught from. This makes Python dependable as a result of issues are recognized and resolved quicker, and growth occurs quicker.
  • Python additionally has a big ecosystem of libraries that you should utilize for knowledge administration. These embrace NumPy, Pandas and others that we are going to talk about on this article.

Now we’ll discover the info administration libraries in Python.


OpenPyXL is a Python library for studying information from Microsoft Excel 2010 or later. Supported file extensions embrace .xlsx, .xlsm, .xltm, and .xltx information. It’s one in all Python’s hottest libraries for Excel Information Administration.

The library permits you to open information, create worksheets, change the metadata, and browse and write knowledge. This makes it potential to simply handle your Excel knowledge from Python.



pandas is an immensely well-liked knowledge administration, evaluation, and manipulation library in Python. It is free, open-source and gives unimaginable flexibility, ease of use and pace.

It will possibly learn knowledge from varied codecs, together with Excel. The library is highly effective and stays probably the most necessary instruments in a knowledge scientist’s toolbox.

Learn additionally: For this reason Pandas is the preferred Python knowledge evaluation library


xlrd is a Python library extensively used for studying and formatting Excel workbooks. Like the opposite libraries on this checklist, it’s free and open supply. Nonetheless, it solely helps spreadsheets within the conventional .xls file format. Regardless of this, it stays a preferred knowledge administration library.


pyexcel goals to offer a single API for working with totally different excel/spreadsheet file codecs. These embrace csv, ods, xls, xlsx and different file codecs.

pyexcel offers a simple approach to import the info from all these information, convert them to arrays and in-memory dictations and vice versa. The library can be free and open supply.


PyExcelerate is a library used to put in writing spreadsheets rapidly and effectively. It’s extremely optimized for pace. PyExcelerate solely helps writing spreadsheets. Nonetheless, not like many of the libraries on this checklist, it additionally helps including types. This library could be very helpful if you must generate a variety of spreadsheets rapidly.


xlwings is an open core bundle that works with Microsoft Excel and Google Sheets. It’s a spreadsheet automation answer that provides a wholesome different to VBA macros and Energy Question.

Being open-core signifies that the core model is free and open-source. Nonetheless, there’s a professional model that provides extra options and help however is paid for. XLwings customers embrace corporations reminiscent of Accenture, Nokia, Shell and the European Fee.


xlSlim permits you to work with spreadsheets as in the event that they have been Jupyter notebooks. xlSlim permits you to write code in interactive cells in your spreadsheets. This code can work together with knowledge in your workbook and carry out calculations.

xlSlim additionally offers a built-in editor on your Python code. You’ll be able to name VBA features out of your Python and use features outlined in your spreadsheet as you’d different Excel features.


NumPy is a Python numerical computation library that’s extremely well-liked for its pace and knowledge processing capabilities.

NumPy permits you to import knowledge from CSV information into NumPy arrays. As soon as that is achieved, you are able to do as a lot knowledge administration as you need from the consolation of your Python program. Additionally it is potential to put in writing the info again to CSV information.


Pycel compiles your Excel workbooks right into a Python chart that may be run exterior of Excel. This makes it helpful for performing complicated calculations exterior of Excel, for instance in Python on a Linux server.

The generated calculation chart comprises nodes for all cells within the workbook and their relationships. These relationships and dependencies can then be used to dynamically calculate all values ​​when the worth of 1 cell modifications.



Formulation is one other interpreter on your excel workbooks. The open-source Python bundle reads your Excel workbooks, parses your Excel formulation, and compiles them to Python. This Python could make quicker calculations on totally different computer systems with out putting in an Excel COM server.


PyXLL offers a front-end person interface for utilizing Python in Excel. This bundle permits you to write Python code that interacts with the info in your spreadsheets. As well as, you possibly can outline features that you should utilize in your spreadsheet cells.

Primarily, it features as a alternative for VBA. The benefit of VBS is that it permits you to leverage your entire Python ecosystem and the varied libraries it gives inside your Microsoft Excel.

Final phrases

This text discusses the totally different Python libraries used for knowledge administration in Excel spreadsheets. These libraries will let you ingest and use knowledge in probably the most frequent knowledge show codecs: Excel spreadsheets.

These libraries will let you carry out extra complicated duties and use the wealthy Python ecosystem to handle your knowledge.

Subsequent, see the way to create a Pandas DataFrame.

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