Pandas vs xlwings

Overview

xlwings

Stacks36

Followers125

Votes0

GitHub Stars0

Forks2

Pandas

Stacks2.1K

Followers1.3K

Votes23

Pandas vs xlwings: What are the differences?

Introduction

In this article, we will discuss the key differences between Pandas and xlwings, two popular tools used for data manipulation and analysis in Python.

Installation and Dependencies: Pandas is a Python library that can be easily installed using pip or conda, and it is dependent on other libraries such as NumPy. On the other hand, xlwings is an Excel add-in that requires installation on Windows or Mac environment, along with a license for advanced functionalities.
Data Manipulation and Analysis: Pandas provides a powerful toolkit for manipulating and analyzing structured data, offering functionalities such as data cleaning, grouping, merging, and pivot tables. It has a comprehensive set of functions and methods for handling data efficiently. Xlwings, on the other hand, is primarily used for interacting with Excel and automating Excel tasks using Python. It allows accessing and modifying Excel workbooks, worksheets, and cells, but it does not provide the same level of data manipulation and analysis features as Pandas.
Integration with Excel: Pandas can read and write Excel files using its read_excel and to_excel methods, but it does not have the ability to interact with Excel in real-time. Xlwings, on the other hand, provides direct integration with Excel, allowing users to manipulate and control Excel objects from Python. This includes automating processes, extracting data from Excel, and updating Excel documents with Python calculations.
Performance: Pandas is designed to efficiently handle large datasets and provides optimized functions for data manipulation, which makes it suitable for handling big data analysis tasks. Xlwings, on the other hand, is more focused on Excel integration and automation, and it may not have the same level of performance as Pandas when it comes to data manipulation operations.
Compatibility and Platform: Pandas is a cross-platform library and works on various operating systems, including Windows, Mac, and Linux. It can be used with different IDEs and text editors. Xlwings, on the other hand, is primarily designed for Windows and Mac environments, and it requires Excel installation to work. It is more suitable for users who frequently work with Excel in their data analysis tasks.
Community and Ecosystem: Pandas has a large and active community of users and contributors, which means it has extensive documentation, third-party libraries, and online resources available. Xlwings, although not as popular as Pandas, also has an active community and provides documentation and examples to help users get started with Excel integration in their Python workflow.

In summary, while both Pandas and xlwings are useful tools for data manipulation and analysis, Pandas is more focused on providing comprehensive data manipulation and analysis functionalities, while xlwings is specialized in interacting with Excel and automating Excel tasks using Python.

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Detailed Comparison

xlwings	Pandas
Replace your VBA code with Python, a powerful yet easy-to-use programming language that is highly suited for numerical analysis. Supports Windows & Mac!	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
Easy deployment: The receiver of an xlwings-powered spreadsheets only needs Python with minimal dependencies — or nothing at all when shipped with the Python runtime.;Cross-Platform: xlwings works with Microsoft Excel on Windows and Mac.;Plug-and-Play: No cumbersome installation of Excel add-ins or license keys.;Flexible: Works with pretty much every combination of Excel and Python.;Two way communication: Call Python from Excel or interact with Excel from Python.;Free and open-source: xlwings is released under a permissive BSD-License.	Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Statistics
GitHub Stars 0	GitHub Stars -
GitHub Forks 2	GitHub Forks -
Stacks 36	Stacks 2.1K
Followers 125	Followers 1.3K
Votes 0	Votes 23
Pros & Cons
Cons 3 Very slow and still needs VBA for UDFs	Pros 21 Easy data frame management 2 Extensive file format compatibility
Integrations
No integrations available	Python

What are some alternatives to xlwings, Pandas?

Airtable

Working with Airtable is as fast and easy as editing a spreadsheet. But only Airtable is backed by the power of a full database, giving you rich features far beyond what a spreadsheet can offer.

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Sheetsu

Use spreadsheet as your database. Give data to your users the nice way, directly from the tool you know. Without bothering webdeveloper.

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

Sheety

Power websites, apps, or whatever you like, all from a spreadsheet. Changes to your spreadsheet update your API in realtime.

Sheetlabs

Drag & drop your data, name your API and choose what data people can see - that's it. Documentation is created automatically.

sheet2api

Use any Google Sheets or Excel Online spreadsheet to power a fully-fledged API, no coding required.

SciPy

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

PySpark

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Related Comparisons

Pandas vs xlwings: What are the differences?

Introduction

In this article, we will discuss the key differences between Pandas and xlwings, two popular tools used for data manipulation and analysis in Python.

Installation and Dependencies: Pandas is a Python library that can be easily installed using pip or conda, and it is dependent on other libraries such as NumPy. On the other hand, xlwings is an Excel add-in that requires installation on Windows or Mac environment, along with a license for advanced functionalities.
Data Manipulation and Analysis: Pandas provides a powerful toolkit for manipulating and analyzing structured data, offering functionalities such as data cleaning, grouping, merging, and pivot tables. It has a comprehensive set of functions and methods for handling data efficiently. Xlwings, on the other hand, is primarily used for interacting with Excel and automating Excel tasks using Python. It allows accessing and modifying Excel workbooks, worksheets, and cells, but it does not provide the same level of data manipulation and analysis features as Pandas.
Integration with Excel: Pandas can read and write Excel files using its read_excel and to_excel methods, but it does not have the ability to interact with Excel in real-time. Xlwings, on the other hand, provides direct integration with Excel, allowing users to manipulate and control Excel objects from Python. This includes automating processes, extracting data from Excel, and updating Excel documents with Python calculations.
Performance: Pandas is designed to efficiently handle large datasets and provides optimized functions for data manipulation, which makes it suitable for handling big data analysis tasks. Xlwings, on the other hand, is more focused on Excel integration and automation, and it may not have the same level of performance as Pandas when it comes to data manipulation operations.
Compatibility and Platform: Pandas is a cross-platform library and works on various operating systems, including Windows, Mac, and Linux. It can be used with different IDEs and text editors. Xlwings, on the other hand, is primarily designed for Windows and Mac environments, and it requires Excel installation to work. It is more suitable for users who frequently work with Excel in their data analysis tasks.
Community and Ecosystem: Pandas has a large and active community of users and contributors, which means it has extensive documentation, third-party libraries, and online resources available. Xlwings, although not as popular as Pandas, also has an active community and provides documentation and examples to help users get started with Excel integration in their Python workflow.

Pandas vs xlwings

Overview

Pandas vs xlwings: What are the differences?