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18 Python libraries to work with Excel

September 2024 (3078 Words, 18 Minutes)

This note focuses on 18 libraries for working with Excel. Each one of the 18 libraries below has a specific focus and can be used for different use cases.

It’s not really worth reading after the “What do you want to do” section which is sufficient to understand anything Excel-related is possible with Python

What do you want to do ?

  1. I want to create a new Excel file. (Use xlsxWriter)
  2. I want to read data from both .xls and .xlsx files. (Use xlrd)
  3. I want to automate Excel tasks using Python’s features. (Use xlwings)
  4. I want to write data to .xls files with custom formatting. (Use xlwt)
  5. I want to read and write across multiple Excel formats easily. (Use pyexcel)
  6. I want to convert complex Excel formulas to Python code. (Use pycel)
  7. I want ultra-fast writing to Excel files for large datasets. (Use pyExcelerate)
  8. I want advanced manipulation of Excel files. (Use xlutils)
  9. I want to convert Jupyter Notebooks into Excel files. (Use nb2xls)
  10. I need a lightweight solution for simple Excel file reading and writing. (Use pylightxl)
  11. I want to translate Excel formulas into Python for calculations. (Use formulas)
  12. I want to read, write, and modify .xlsx files. (Use openpyxl)
  13. I need to convert sophisticated Excel models into Python functions. (Use koala)
  14. I need to read large Excel .xlsb files efficiently. (Use pyxlsb)
  15. I want to convert Visual Basic code into Python. (Use vb2py)
  16. I need to create complex Excel tables programmatically. (Use xltable)
  17. I want to convert Excel spreadsheets to CSV. (Use xlsx2csv)
  18. I need to transform Excel files into images. (Use excel2img)
  19. I want to merge several Excel files into one. (Combine xlrd and xlwt)
  20. I need to split a large Excel file into smaller ones. (Use xlrd and pyExcelerate)
  21. I want to generate Excel charts with Python. (Use xlsxWriter)
  22. I need to apply conditional formatting in Excel. (Use openpyxl)
  23. I want to create dropdowns in Excel files. (Use openpyxl)
  24. I need to automate data entry into Excel. (Use xlwings)
  25. I want to extract images from Excel files. (Use openpyxl)
  26. I need to manipulate Excel pivot tables. (Use openpyxl)
  27. I want to handle Excel files without Excel installed. (Use openpyxl)
  28. I want to work with Excel files on a server. (Use openpyxl)
  29. I need to validate data in Excel sheets. (Use openpyxl)
  30. I want to automate report generation in Excel. (Use xlsxWriter)
  31. I want to create interactive Excel dashboards. (Use xlwings)
  32. I need to synchronize data between Excel and other formats. (Use pyexcel)
  33. I want to integrate Excel with web services or APIs. (Use xlwings)
  34. I want to use machine learning algorithms on Excel data. (Use pyxlsb and openpyxl)
  35. I want to visualize data directly from Excel. (Use openpyxl)
  36. I want to manage large datasets in Excel. (Use pyExcelerate)
  37. I want to create Excel templates with Python. (Use xlsxWriter)
  38. I need to encrypt or protect Excel files. (Use openpyxl)
  39. I want to convert CSV or JSON to Excel format. (Use pyexcel)
  40. I need to perform batch operations on multiple Excel files. (Use xlutils)
  41. I want to create Excel files with multiple worksheets. (Use xlsxWriter)
  42. I want to copy or move worksheets between Excel files. (Use xlutils)
  43. I want to filter or sort data in Excel sheets. (Use openpyxl)
  44. I want to handle Excel files with a lot of styling. (Use openpyxl)
  45. I want to create conditional formatting in Excel sheets. (Use openpyxl)
  46. I want to debug or test Excel files with Python. (Use xlwings)

xlsxWriter: A Python library for creating feature-rich .xlsx files

xlsxWriter is a useful tool for handling data and creating reports. It’s good at making Excel files with data, formulas, formatting, and charts. xlsxWriter is helpful for automating complex reports and dealing with large datasets. It includes features like conditional formatting and charting, which are useful for tasks like financial reporting where clear and professional formatting is important. Overall, if you’re working with Python and need to create detailed Excel files, xlsxWriter is a practical choice for data analysis and reporting.

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pyexcel: Straightforward reading and writing across multiple Excel formats

Pyexcel is a handy and easy-to-use Python library that works with various Excel formats like xls, xlsx, ods, and csv. Unlike more specialized libraries such as xlrd, xlwt, and xlsxWriter, Pyexcel supports a wide range of formats and makes tasks simpler across these formats. It allows you to read and write data in different spreadsheet formats and is great for data manipulation and analysis, especially when used with libraries like Pandas. Its ability to efficiently transfer data between different spreadsheet formats makes it really useful for tasks involving data conversion and interoperability.

Pyexcel is particularly useful in situations where format flexibility and ease of use are important, such as when a company needs to manage inventory in Excel and generate reports in various formats. By offering a unified interface for different Excel formats, Pyexcel helps streamline Excel-related workflows in Python, especially in environments where versatility and straightforward data processing are key.

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pycel: Converts complex Excel formulas into executable Python code

Pycel distinguishes itself as a unique Python library, focusing on translating complex Excel formulas into executable Python code. Unlike pyexcel or xlwings, which facilitate reading, writing, and integrating with Excel files, pycel specifically targets the conversion of Excel’s formulae into Python, enabling intricate Excel models to be seamlessly integrated into Python environments. It excels in handling complex formulas that are cumbersome to replicate in Python, thereby improving performance in scenarios like simulations and iterative calculations. Pycel is particularly beneficial for tasks requiring the advanced analytical capabilities of Python while retaining existing Excel models, as seen in use cases like a financial analyst integrating sophisticated Excel forecasting models into Python for enhanced simulations and analysis. In essence, pycel serves as a crucial bridge, allowing users to leverage Python’s powerful features without abandoning their intricate Excel-based workflows, making it invaluable for complex data processing and analysis scenarios.

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pyExcelerate: Ultra-fast writing to Excel files, perfect for large datasets

PyExcelerate sets itself apart in the Python-Excel ecosystem, specifically engineered for ultra-fast writing to Excel files, a standout feature when compared to libraries like xlwt or pyexcel. While xlwt and pyexcel focus on creating and handling Excel files with emphasis on compatibility and formatting, pyExcelerate prioritizes speed, especially for large datasets. It excels in scenarios requiring rapid Excel file generation, efficiently handling bulk data without compromising performance. This makes it an ideal choice for data-intensive applications where time is of the essence, such as a data analyst at a large retail company needing to quickly generate sales reports from vast transaction datasets. PyExcelerate not only speeds up the Excel file creation process but also integrates smoothly into broader data processing workflows, offering a solution for Python users facing performance bottlenecks in Excel file generation. In essence, pyExcelerate is a key tool for swiftly converting large volumes of data into Excel format, crucial for time-sensitive and data-heavy applications.

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xlutils: A suite of utilities for advanced manipulation of Excel files

Xlutils stands out in the Python-Excel landscape as a suite of utilities designed for sophisticated manipulation of Excel files, bridging the functionalities of xlrd and xlwt. Unlike libraries focused solely on reading (xlrd) or writing (xlwt) Excel files, xlutils complements these by offering advanced operations like copying, modifying, and filtering within Excel files. Its ability to preserve Excel-specific features like formatting and formulas while manipulating data makes it a go-to choice for complex Excel tasks. A typical use case involves a business analyst who, using xlutils in conjunction with xlrd and xlwt, can efficiently update financial reports with complex requirements like maintaining existing formats and applying new data filters. Xlutils thus emerges as a key component in Python-based projects that demand intricate handling of Excel data, offering a more robust and error-resistant approach to Excel file manipulation. In summary, xlutils provides an enriched toolkit for Python users needing to perform advanced Excel file operations, making it indispensable in scenarios that require maintaining the integrity of sophisticated Excel features.

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nb2xls: Transforms Jupyter Notebooks into Excel files effortlessly

Nb2xls uniquely caters to the need of converting Jupyter Notebooks into Excel files, a niche yet essential task in the Python-Excel ecosystem. Unlike libraries like xlwt or pyexcel that are focused on writing or handling Excel files in various formats, nb2xls specifically targets the conversion of the rich, interactive content of Jupyter Notebooks into a more universally accessible Excel format. This makes it invaluable for data scientists and analysts who conduct complex analyses in Jupyter Notebooks but need to share their findings with others who prefer or require data in Excel. Nb2xls maintains the structure and content of notebooks, including code outputs, markdown, and visualizations, ensuring the narrative and analytical logic is preserved in the Excel format. It bridges the interactive, code-based environment of Jupyter with the widespread use of Excel, thus enhancing the accessibility and distribution of data analyses. In summary, nb2xls is a critical tool for effectively communicating complex data analyses from Jupyter Notebooks to a broader audience, especially in business or educational settings where Excel is the preferred medium.

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pylightxl: A lightweight solution for simple Excel file reading and writing

Pylightxl carves its niche in the Python-Excel integration space as a lightweight and efficient library for basic reading and writing of Excel files. In contrast to more feature-rich libraries like pyexcel or xlrd, which cater to a broad range of Excel formats and complex data manipulations, pylightxl focuses on simplicity and minimalism, ideal for small to medium-sized datasets. It stands out for its ease of installation and use, with no external dependencies, making it a go-to choice for quick projects or those new to Python-Excel interactions. While it doesn’t support advanced functionalities like cell formatting or formulas, its optimized speed and efficiency make it a practical solution for basic data tasks. Pylightxl is especially suitable for scenarios where a lightweight, straightforward tool is needed for Excel file manipulation, such as a small business owner updating and analyzing sales data. In summary, pylightxl is the optimal choice for Python users seeking a simple, no-frills approach to Excel file handling, particularly effective in resource-constrained environments or for straightforward data tasks.

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formulas: Translates Excel formulas into Python for advanced calculations

The ‘formulas’ library uniquely enables the translation of Excel formulas into Python, filling a specific niche in Python-Excel interactions. Unlike libraries like pylightxl or pyexcel, which focus on simpler Excel file handling, ‘formulas’ specializes in parsing and executing complex Excel formulas within a Python environment. This makes it particularly valuable for integrating Excel’s robust formula-based logic with Python’s data processing and analytical power, ideal for data scientists and analysts working extensively with Excel-driven models. ‘Formulas’ addresses the challenge of replicating complex Excel calculations in Python, offering a seamless bridge between the two platforms and improving performance with large datasets. It’s especially useful in scenarios where intricate Excel models, such as an engineer’s cost estimation calculations, need to be integrated into Python applications for enhanced data processing, automation, and scalability. In summary, ‘formulas’ is a crucial tool for leveraging sophisticated Excel formulas within Python, making it indispensable for advanced data analysis and modeling in Python-centric workflows.

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openpyxl: A one-stop solution for reading, writing, and modifying .xlsx files

Openpyxl emerges as a robust, all-encompassing Python library for dealing with .xlsx files, standing out in the Python-Excel integration arena. Unlike more specialized libraries like pylightxl or ‘formulas’ that focus on lightweight operations or formula translation, openpyxl provides extensive capabilities for reading, writing, and modifying .xlsx files. It supports a wide array of advanced Excel features, from data manipulation to complex tasks like handling formulas, filters, and creating charts. This makes it particularly suitable for handling modern Excel files that require not just data entry but also sophisticated spreadsheet operations. Openpyxl is an invaluable tool for automating Excel tasks, such as report generation and data analysis, illustrated by use cases like a marketing analyst automating monthly performance reports. Its ability to streamline repetitive tasks and handle advanced spreadsheet features positions openpyxl as a key resource for Python users engaged in extensive Excel file manipulation and analysis, especially where automation and advanced functionalities are crucial.

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koala: Converting sophisticated Excel models into Python functions

Koala uniquely specializes in converting intricate Excel formulas into executable Python code, offering a distinct solution in the Python-Excel integration landscape. Unlike openpyxl, which provides broad functionality for .xlsx file manipulation, or ‘formulas’ that focuses on translating formulas for computational purposes, Koala specifically targets the translation of sophisticated Excel models into Python functions. This capability is invaluable for scenarios involving complex spreadsheet calculations that need to be integrated into Python for advanced analysis or automation. Koala effectively bridges the gap between Excel’s user-friendly formula environment and Python’s powerful programming and data analysis capabilities. It is particularly useful for professionals like financial analysts who have developed intricate Excel models and seek to leverage these in a more flexible and robust Python environment. In essence, Koala enhances the performance and scalability of Excel models by translating them into Python, thus facilitating a higher level of analysis and automation for complex data tasks.

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pyxlsb: Makes reading large Excel .xlsb files in Python a breeze

Pyxlsb stands out in the Python-Excel landscape for its specific ability to read Excel files in the Binary Workbook (.xlsb) format, a niche not typically covered by other libraries like openpyxl or pyexcel. This makes it especially valuable for handling large Excel files, where the .xlsb format offers better performance and smaller file sizes. Unlike libraries focused on broader file manipulation or formula translation, pyxlsb is tailored to efficiently access and process data from large, performance-intensive Excel files. It’s a crucial tool for data analysts working with extensive datasets, enabling seamless integration of large Excel files into Python-based data processing workflows. This capability is exemplified in scenarios like a corporate analyst analyzing years of sales data stored in a .xlsb file, where pyxlsb facilitates efficient data extraction, allowing for advanced analysis using Python’s data libraries. In summary, pyxlsb is essential for Python users dealing with large Excel files in the .xlsb format, providing a much-needed solution for efficient data reading and integration into Python for comprehensive analysis and reporting.

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vb2py: Converts Visual Basic code into Python, modernizing legacy applications.

Vb2py offers a unique solution in the realm of programming language conversion, specifically targeting the transition from Visual Basic (VB) to Python. Unlike libraries within the Python-Excel scope, such as openpyxl or pyxlsb, vb2py focuses on codebase conversion, playing a crucial role in modernizing legacy applications originally written in VB. This tool is essential for developers seeking to update older systems to leverage Python’s more modern capabilities and extensive ecosystem. Vb2py automates the conversion process, reducing the time and potential errors associated with manual code translation. It not only facilitates the migration of legacy systems but also ensures the integration of VB functionalities into Python projects, maintaining the core logic while enhancing flexibility and scalability. In scenarios like a financial institution updating its VB-based risk assessment tools, vb2py enables seamless integration into Python-based platforms, preserving trusted logic while expanding capabilities. In summary, vb2py is invaluable for organizations transitioning from VB to Python, offering a pathway to modernize and enhance legacy systems while tapping into Python’s advanced programming environment.

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xltable: Streamlines the creation of complex Excel tables in Python

Xltable uniquely focuses on building sophisticated Excel tables in Python, differing from libraries like openpyxl or xlsxwriter, which handle broader Excel file manipulations. While xltable itself doesn’t directly write to Excel files, it excels in constructing detailed tables with formulas, styles, and various data types, seamlessly integrating with writing libraries for final output. This specialization makes xltable particularly effective in automating Excel table creation, especially valuable in scenarios involving dynamic data or regular report generation. It streamlines the process of creating complex tables, enhancing efficiency and accuracy in report generation. A practical application, like a healthcare data analyst generating monthly clinical trial reports, showcases xltable’s capability to define table structures, integrate dynamic data, and work in tandem with libraries like xlsxwriter or openpyxl for efficient report creation. In summary, xltable offers a streamlined, scalable solution for constructing intricate Excel tables within Python, ideal for data-driven applications requiring structured and dynamic Excel report generation.

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xlsx2csv: Converts Excel spreadsheets into shareable images

Xlsx2csv specializes in converting Excel (.xlsx) files into CSV format, a crucial process for enhancing data portability and exchange. This conversion is particularly beneficial for environments where CSV’s simplicity and wide compatibility are essential. Unlike tools focused on direct Excel file manipulation, xlsx2csv addresses the need for easy conversion to a more universally compatible format, aiding in sharing data across different platforms and integrating Excel data into various data processing workflows. It’s an invaluable tool for scenarios like a business analyst needing to analyze Excel data in systems that prefer CSV, streamlining data preparation for analysis, sharing, or integration with other data sources.

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excel2img: Effortlessly transforms .xlsx files into universally compatible CSV format

Excel2img serves a unique function by converting content from Excel (.xlsx) files into image formats like PNG, JPEG, or BMP. This tool is particularly valuable for creating visual representations of Excel data for use in presentations, reports, or web content. Unlike excel2csv, which converts Excel files to a text-based CSV format, excel2img focuses on preserving the visual layout and formatting of Excel data in image form, ensuring readability and integrity when shared. It’s ideal for scenarios such as a marketing manager needing to incorporate Excel charts into a newsletter or presentation, offering a practical solution for sharing Excel data with a wider audience, including those without access to Excel.

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