Software Alternatives, Accelerators & Startups
Register | Login

When I Work VS NumPy

Compare When I Work VS NumPy and see what are their differences

Insightful.io
The #1 monitoring employee software for clear insights into how your team works. Get real-time and historical data. Understand productivity at every level. 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. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

When I Work logo When I Work

When I Work is an employee scheduling and communication app using the web, mobile apps, text messaging, social media, and email.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • When I Work Landing page
    Landing page //
    2021-07-30
  • NumPy Landing page
    Landing page //
    2023-05-13

When I Work features and specs

  • User-Friendly Interface
    When I Work offers a clean, intuitive interface that both employers and employees find easy to navigate, making scheduling and communication straightforward.
  • Mobile Accessibility
    The mobile app allows employees to check schedules, request time off, and communicate from anywhere, enhancing flexibility and accessibility.
  • Efficient Scheduling
    Automated scheduling features help managers save time by quickly creating and adjusting schedules based on employee availability and business needs.
  • Time Tracking Integration
    When I Work integrates time tracking and attendance, simplifying payroll processes and ensuring accurate timekeeping.
  • Employee Management
    The platform supports streamlined employee management with tools for communication, task assignment, and shift reminders.

Possible disadvantages of When I Work

  • Cost
    While powerful, When I Work can be relatively expensive, especially for smaller businesses with tight budgets.
  • Limited Customization
    Some users have reported that the software offers limited customization options for specific business needs and scheduling intricacies.
  • Learning Curve
    Although generally user-friendly, some features may have a learning curve for new users, particularly those who are not tech-savvy.
  • Customer Support
    While adequate, some users have experienced delays or difficulties in getting timely support from the customer service team.
  • Feature Limitations in Basic Plan
    The basic plan may not include all the advanced features, requiring an upgrade to access the full range of functionalities.

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.

When I Work videos

+ Add

Digital Unboxing: When I Work

More videos:

  • Review - When I Work - Review and Edit Timesheets

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 When I Work and NumPy)

When I Work

NumPy

Employee Scheduling
100 100%
Employee Scheduling
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Time Tracking
100 100%
Time Tracking
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using When I Work and NumPy. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

When I Work Reviews

The 9 Best Paid and Free WhenIWork Alternatives
Whether you’ve grown tired of When I Work or are looking to change things up and see if there’s an app that’s better out there – then this article is for you. In just a moment, we will walk you through nine different software that we believe are fantastic alternatives to When I Work.
Source: everhour.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 When I Work. While we know about 119 links to NumPy, we've tracked only 6 mentions of When I Work. 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.

When I Work mentions (6)

  • Employee calendars that all super admins have access to
    How are the users accessing these calendars if you don't create accounts for them? What you probably want is a work scheduling service like when I work: https://wheniwork.com. Source: over 2 years ago
  • Volunteer schedule with varying hours
    You could try something like this: https://wheniwork.com. Source: almost 3 years ago
  • Labour as a percentage of sales.
    I record all of our takings through a spreadsheet and from this I add our takings into wheniwork.com and get my labour as a percentage of sales. Source: almost 4 years ago
  • Web-Based Time Keeping System Suggestions
    Look at wheniwork.com. We used them a few years ago and they had lots of features. Source: almost 4 years ago
  • Web/App-based Time Tracking Application for Lab use
    We are going to resume our work in few weeks and looking for efficient time tracking applications to keep track of the people working in the lab at any given time. In one lab I am using WhenIWork app and planning to us clockify in the second lab. Both of them are free and have some pros and cons. I was wondering if anybody has experience using any other software (free) in your lab. We are a team of 5-6 people and... Source: almost 4 years ago
View more

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 / 8 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 / 9 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 / 9 months ago
View more

What are some alternatives?

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

Deputy - Deputy is a software for employee scheduling, time and attendance and communication management.

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

ResourceGuru - The fast, simple way to schedule people, equipment, and other resources online.

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

Float - The leading resource management software for agencies, studios, and firms. With a simple, drag and drop interface and powerful editing tools, Float saves you time and keeps projects on track.

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

Loading...