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ASAP Utilities VS NumPy

Compare ASAP Utilities VS NumPy and see what are their differences

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ASAP Utilities logo ASAP Utilities

ASAP Utilities is a powerful Excel add-in that fills the gaps in Excel.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • ASAP Utilities Landing page
    Landing page //
    2023-04-17
  • NumPy Landing page
    Landing page //
    2023-05-13

ASAP Utilities features and specs

  • Time-Saving Features
    ASAP Utilities offers a wide range of features that automate repetitive tasks in Excel, allowing users to save time on data processing and analysis.
  • User-Friendly Interface
    The add-in integrates seamlessly into Excel and provides an intuitive interface that is easy to navigate, even for users who are not advanced Excel users.
  • Extensive Functionality
    It includes over 300 powerful utilities that cover a variety of functions like data cleaning, formatting, and formula management, enhancing Excel’s built-in capabilities.
  • Regular Updates
    ASAP Utilities is consistently updated with new features and improvements, ensuring compatibility with the latest versions of Excel and addressing user-requested enhancements.
  • Efficient Customer Support
    The software is backed by a responsive customer support team who assist with technical issues and user inquiries, widely praised for their helpfulness and efficiency.

Possible disadvantages of ASAP Utilities

  • Cost
    ASAP Utilities is a paid add-in, which might be a drawback for users who are looking for free solutions or for those with limited budgets.
  • Complexity of Choices
    With over 300 utilities available, users may find it overwhelming to navigate through all the options and identify the most useful tools for their specific needs.
  • Learning Curve
    Even though the interface is user-friendly, the sheer number of features can require a learning curve for new users to become fully proficient with the tool.
  • Compatibility Issues
    There could be occasional compatibility issues with specific Excel versions or other Excel add-ins, potentially leading to software conflicts or reduced functionality.
  • Limited to Excel
    The add-in is specifically designed for Excel and cannot be used in other spreadsheet applications, limiting its utility to Microsoft Office users.

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.

Analysis of ASAP Utilities

Overall verdict

  • ASAP Utilities is generally considered to be a valuable tool for Excel users, especially those who work extensively with spreadsheets and require enhanced functionality beyond what Excel natively offers. Its comprehensive set of features and ease of use make it a worthwhile investment for improving efficiency.

Why this product is good

  • ASAP Utilities is a popular add-in for Microsoft Excel that provides a wide range of tools designed to simplify and enhance spreadsheet tasks. It offers over 300 utilities that help users automate repetitive tasks, improve productivity, and perform advanced data analysis. Users frequently praise its ability to save time and reduce errors in Excel tasks.

Recommended for

    ASAP Utilities is recommended for business professionals, data analysts, accountants, and any individuals or teams who regularly work with large or complex Excel spreadsheets. It's particularly beneficial for users who want to streamline their workflow and enhance the capabilities of Excel through additional tools and automation features.

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.

ASAP Utilities videos

+ Add

Excel Add-in: ASAP Utilities

More videos:

  • Review - Trying out ASAP Utilities
  • Review - CARA CETAK BYNAME DENGAN ASAP UTILITIES DAN EXCEL MUDAH GAMPANG

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

Category Popularity

0-100% (relative to ASAP Utilities and NumPy)

ASAP Utilities

NumPy

Data Dashboard
54 54%
Data Dashboard
46% 46
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Technical Computing
100 100%
Technical Computing
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using ASAP Utilities and NumPy. For example, how are they different and which one is better?
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Reviews

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

ASAP Utilities Reviews

We have no reviews of ASAP Utilities yet.
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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

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 times since March 2021. 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.

ASAP Utilities mentions (0)

We have not tracked any mentions of ASAP Utilities yet. Tracking of ASAP Utilities recommendations started around Mar 2021.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

What are some alternatives?

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

Kutools for Excel - A handy Microsoft Excel add-ins collection to free you from time-consuming operations.

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

Excel Dashboard School - Free Excel add-ins and tools on Excel Dashboard School. Boost your work productivity and save your time! No trials, 100% power!

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

KPI Dashboard in Excel - Professional Management KPI Dashboard. Includes trend charts, past year/target comparisons, monthly & cumulative analysis in performance dashboard.

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

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