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Qwilr VS NumPy

Compare Qwilr VS NumPy and see what are their differences

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Qwilr logo Qwilr

Turn your quotes, proposals and presentations into interactive and mobile-friendly webpages that...

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Qwilr Landing page
    Landing page //
    2023-10-06

Our aim is to make it as easy as possible for businesses to create epic documents that they can use internally, with their clients and share online. Our templates are not only professional & interactive, but are created as an individual web page that allows for easy shareability & data measuring.

  • NumPy Landing page
    Landing page //
    2023-05-13

Qwilr

Website
qwilr.com
Pricing URL
Official Qwilr Pricing
$ Details
-
Release Date
2014 January
Startup details
Country
Australia
State
New South Wales
City
Redfern
Founder(s)
Dylan Baskind
Employees
10 - 19
Edit details

NumPy

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

Qwilr features and specs

  • Easy to Use
    Qwilr offers a user-friendly interface that simplifies the creation of visually appealing documents without needing extensive design skills.
  • Customization Options
    The platform provides a wide range of customizable templates, allowing users to create tailored proposals, reports, and other business documents.
  • Interactive Content
    Qwilr supports interactive elements like videos, maps, and calendars, enhancing the engagement and readability of documents.
  • Analytics
    The platform includes analytics and tracking capabilities, enabling users to see how recipients interact with their documents.
  • Integrations
    Qwilr integrates with other popular tools such as CRM systems, allowing for seamless workflow integration and automation.

Possible disadvantages of Qwilr

  • Pricing
    Qwilr can be expensive for small businesses or freelancers, as its pricing may not be as competitive as other document creation tools.
  • Learning Curve
    While Qwilr is generally easy to use, new users might experience a learning curve when first getting accustomed to its features and interface.
  • Limited Offline Access
    Qwilr's functionality is primarily online, so users may find it challenging to access or edit documents without an internet connection.
  • Template Restrictions
    Some users may find the available templates somewhat restrictive and not suitable for all types of document needs.
  • Feature Availability
    Certain advanced features and customization options might only be available on higher-tier plans, requiring additional investment.

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 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.

Qwilr videos

+ Add

Qwilr Review - Beginners to Expert Guide PREVIEW by Bizversity.com

More videos:

  • Demo - Qwilr Demo Video

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 Qwilr and NumPy)

Qwilr

NumPy

Document Automation
100 100%
Document Automation
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Document Management
100 100%
Document Management
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Qwilr 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 Qwilr and NumPy

Qwilr Reviews

10 best PandaDoc alternatives & competitors in 2024
By integrating with customer relationship management (CRM) tools, Qwilr can automate many aspects of sales workflows, including generating sales material and personalizing content. Buyer tracking and reporting lets users see how clients engage with proposals and notifies them when a proposal has been viewed or signed.
Source: www.jotform.com

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 a lot more popular than Qwilr. While we know about 119 links to NumPy, we've tracked only 2 mentions of Qwilr. 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.

Qwilr mentions (2)

  • Tell me about your product and I’ll tell you how to market it
    Can you tell me more about it? Is it any different from https://qwilr.com or pandadoc.com or is a direct competitor to those. Source: over 3 years ago
  • Software Recommendations for RFPs & Quotes?
    When we initially researched, we did them independently. For RFP software, we wanted something to help with tracking, analyzing, generating proposals, AI answer suggestion/knowledge base, assigning related tasks etc. Avnio & RFPIO made our shortlist. For Quote software, we wanted something shiny, to make closing faster and easier to understand. Qwilr and PandaDocs were rated pretty high. Source: about 4 years ago

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 Qwilr and NumPy, you can also consider the following products

PandaDoc - Boost your revenue with PandaDoc. A document automation tool that delivers higher close rates and shorter sales cycles. We've helped over 30,000+ companies.

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

Proposify - A simpler way to deliver winning proposals to clients.

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

Conga Contracts - Conga Contracts is management solution designed to accelerate and simplify contract negotiations in Salesforce.

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

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