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Hub Planner VS NumPy

Compare Hub Planner VS NumPy and see what are their differences

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.

Hub Planner logo Hub Planner

Transparent Resource Scheduling, Timesheets, Vacation, Resource Requesting, Project Management & powerful Reports in an agile designed, feasible & intuitive software for simple planning

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Hub Planner Landing page
    Landing page //
    2023-03-21

14 Day Free Trial

BLOG

Schedule, Track & Plan with Hub Planner Resource Management Software

Innovative Resource Management Solution Get the ultimate birds eye view of your resource management and team with Hub Planner. Quickly view resources availability, utilization and schedule your team on projects using Hub Planners interactive drag and drop scheduler. Capacity planning made easy with the skills matching and capacity finder.

Integrated Time Tracking & Approval Engage Teams with Timesheets. Measuring the actual time reported via timesheets versus the forecasted time via the resource scheduler gives you valuable insight into your teams performance.

Team Analytics & Generate Dynamic Reports View instant project and resource stats in real time via the dashboards and reports. Build, save and share custom made reports using the powerful Hub Planner reports. Use the dashboard to track down to the individual resource or project performance making resource management a painless task. The Audit Log will help track all scheduling changes.

Plan & Calculate Project Budgets & Spend with Phases and Milestones Within the Hub Planner scheduler, you can plan your project budgets, use dynamic billing rates and calculate project spend..

Vacation, Annual Leave and PTO Request & Approval Flow Allow your team to transparently schedule and request time off directly from the scheduler or request forms with the Vacation & Annual Leave requesting functionality.

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

Hub Planner

$ Details
paid Free Trial $7.0 / Monthly (Plug & Play )
Platforms
Browser Web REST API Windows Safari Twitter iPhone Windows Phone Google Chrome Internet Explorer Facebook

Hub Planner features and specs

  • Easy to Set-up and use
    Intuitive UI
  • Analytics and Reporting
    Yes - Full Reporting Platform included free in all subscriptions
  • Attendance Recording/Monitoring
  • Absence Management
  • Free Trial
    60 Day Free Trial
  • Free Trial
    Explore for free no credit card required
  • Billing
    Yes - Internal & External, Non Billable features
  • Clean UI
    Drag & Drop Interface
  • Integrations
    Yes - Basecamp, Zapier & API
  • Mobile prioritized UI
    Responsive design optimized for mobile devices
  • Clean UI
    Easy to navigate and use
  • Clean UI
    Simple and intuitive user interface for rapid and accurate search
  • Demo
  • GDPR Compliant
  • Google Calendar Sync
  • Dashboards
    Powerful charts and graphs show trends in your data and recent and relevant records right at your fingertips.
  • Dashboards
  • Help Centre
  • Help Documentation
    A comprehensive,step-by-step user manual is available to help you get started, stay productive and fully utilize all that SimpleRent has to offer.
  • Invoices
  • Knowledge Base
  • Metrics
  • News & Announcements
  • Pricing
    Powerful product for a great value. From $7
  • Reports
  • Headquarters
    Stockholm, Sweden
  • Analytics and Reporting
    Group Reports
  • Real time collaboration
  • Responsive Design
  • Teams
    Customize Your Team's Settings and Permissions
  • Users
    Unlimited
  • Unlimited projects (Design)
  • Security
    Single Sign On
  • Security
    Sign up / Login with Google
  • Data Import/Export
    Yes - Projects, Resources, Bookings & Time Entries
  • Data Import/Export
    Export data, scheduler and analytics
  • Blogs
    Yes, all news and feature updates
  • Budgeting and Allocation
    Full Billing and Budget Management features
  • Customizable
    Scheduler Customization
  • Customizable
    Settings Customization
  • Customizable
    Build and Customize Reports
  • Customer Support
    Yes - Excellent Customer Support
  • Custom Domains
  • Drag & Drop Interface
  • Drag and Drop Form Builder
    Report Builder
  • Easy to Set-up and use
    Yes + Tutorials
  • Email notifications
  • Slack integration
  • Project Tracking
  • Project Management
  • Project Accouting
  • Project Planning
  • Project duplication
  • Team Collaboration
  • Team Management
  • Team Collaboration and Permission Control
  • Team members' availability
  • Security
    Permissions
  • Security
    Role Rights
  • Resource Library
  • Manage Budgets
  • Employee Management
  • Transparent Pricing
  • Transparent workflow & distribution
  • Permission Management
  • Role Based Access Control
  • Roles & Permissions
  • Resource Management
  • Resource Planning
  • Capacity management
  • Audit Log
  • Billable / Non Billable Work
  • MFA
  • Fixed costs
  • Security
    MFA
  • Expenses
  • Skills matching
  • Capacity planning
  • Dependencies
  • Deadlines

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.

Hub Planner videos

Introduction to the Hub Planner Scheduler and How to Schedule & Create Bookings

More videos:

  • Tutorial - How to use Timesheets in Hub Planner
  • Tutorial - How to use the Resource Availability and Capacity Bar in Hub Planner Resource Scheduler
  • Tutorial - How to find Resource Availability based on Skill Set, Location and Role using Hub Planner
  • Review - Resource Planner & Scheduler - Hub Planner 2016

NumPy videos

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 Hub Planner and NumPy)
Project Management
100 100%
0% 0
Data Science And Machine Learning
Resource Scheduling
100 100%
0% 0
Data Science Tools
0 0%
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 Hub Planner and NumPy

Hub Planner Reviews

We have no reviews of Hub Planner yet.
Be the first one to post

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.
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 Hub Planner. While we know about 119 links to NumPy, we've tracked only 1 mention of Hub Planner. 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.

Hub Planner mentions (1)

  • Any good examples of Employee Personnel / Workforce & Planning Dashboards?
    We use https://hubplanner.com/ . It is a little clumsy but better than most. Source: almost 3 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 / 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 Hub Planner and NumPy, you can also consider the following products

teamdeck - Teamdeck is a SaaS resource management tool with resource scheduling, leave management, time tracking and timesheet, and customizable reports features. Selected by Hill-Knowlton, Stormind Games, Wunderman Thompson. $3.60/per member.

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

Runn - Runn is a real-time resource management platform with integrated time tracking and forecasting. Intuitively plan projects and schedule resources across the short and long term.

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

Keyedin Projects - Keyedin Projects is a cloud-based project and portfolio management software.

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