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NumPy VS Excelize

Compare NumPy VS Excelize and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Excelize logo Excelize

Go language library for reading and writing Microsoft Excelโ„ข (XLAM / XLSM / XLSX / XLTM / XLTX) spreadsheets
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Excelize Official Documentation WebSite
    Official Documentation WebSite //
    2025-11-07

Excelize is a library written in pure Go providing a set of functions that allow you to write to and read from XLSX / XLSM / XLTM / XLTX files. Supports reading and writing spreadsheet documents generated by Microsoft Excelโ„ข 2007 and later. Supports complex components by high compatibility, and provided streaming API for generating or reading data from a worksheet with huge amounts of data.

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

Excelize

Website
xuri.me
Pricing URL
Official Excelize Pricing
$ Details
freemium $5.0 / One-off
Platforms
Cross Platform Windows Mac OSX Linux Go Android iOS Cloud Docker
Release Date
2016 August
Edit details

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Excelize features and specs

  • XLAM, XLSM, XLSX, XLTM, XLTX
  • Encryption and Decryption
  • Charts
    Over 53 kind of chart types and combo chart
  • Streaming API
  • Pivot Table
  • Sparkline
  • Merge Cells
  • Embed VBA

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Excelize

Overall verdict

  • Excelize is a robust, well-maintained open-source Go library for reading, writing, and manipulating Excel (XLSX/XLSM) files, making it a solid choice for Go developers who need spreadsheet functionality.

Why this product is good

  • Pure Go implementation with no external dependencies, making it easy to integrate into Go projects
  • Comprehensive feature set supporting cell styling, charts, pivot tables, images, formulas, and data validation
  • Active development and maintenance with frequent updates and a responsive community on GitHub
  • Good documentation and plenty of code examples to help developers get started quickly
  • High performance and streaming API support for handling large spreadsheets efficiently
  • Open source under a permissive BSD license, allowing free use in commercial and personal projects

Recommended for

  • Go developers who need to generate or parse Excel files programmatically
  • Backend services that produce reports, invoices, or data exports in XLSX format
  • Applications requiring advanced Excel features like charts, pivot tables, and complex styling
  • Projects that need to process large spreadsheets with efficient memory usage via streaming
  • Teams looking for a free, open-source alternative to commercial spreadsheet libraries

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Excelize videos

+ Add

Excelize Tutorial in English

More videos:

  • Tutorial - Excelize ๅŸบ็ก€ๆ•™็จ‹
  • Tutorial - Reading Data from Excel using Excelize in Golang | Dr Vipin Classes
  • Review - Reports Generation using Go with Excelize and Mysql - Karen Irene Matala

Category Popularity

0-100% (relative to NumPy and Excelize)

NumPy

Excelize

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Spreadsheets
0 0%
Spreadsheets
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Excel
0 0%
Excel
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Excelize

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Excelize Reviews

We have no reviews of Excelize yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Excelize. While we know about 122 links to NumPy, we've tracked only 1 mention of Excelize. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (122)

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Excelize mentions (1)

  • Excelize 2.5.0 is Released โ€“ Go language API for spreadsheet (Excel) document
    Documentation website with multilingual: Arabic, German, Spanish, English, French, Russian, Chinese, Japanese, and Korean, which has been updated. Source: over 4 years ago

What are some alternatives?

When comparing NumPy and Excelize, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

UniDoc.io - PDF & Office Libraries In Pure Golang.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Microsoft Office Excel - Microsoft Office Excel is a commercial spreadsheet application.

OpenCV - OpenCV is the world's biggest computer vision library

LibreOffice - Calc - LibreOffice Calc is the spreadsheet program you've always needed. A fork of OpenOffice.

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