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NumPy VS Daily Time Tracking

Compare NumPy VS Daily Time Tracking and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Daily Time Tracking logo Daily Time Tracking

Daily shows what you have been working on and for how long. It creates accurate timesheets by asking what you are doing, so no more timers or switching tasks. Use its data to submit your hours, create invoices or simply increase your productivity.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Daily Time Tracking Landing page
    Landing page //
    2020-10-30

Daily is a 5 star-rated time tracker for Mac that works by asking what you are working on. It provides a better way to track your daily activities without the hassle of toggling timers, switching tasks or taking notes. Use its accurate timesheets to submit your hours, create better invoices not missing any work or simply increase your productivity.

Underneath Daily’s user-friendly interface supporting both light and dark mode, you will find dozens of useful features. Examples include synchronisation via iCloud, automation using AppleScript, exporting to CSV, JSON and more, a tracking scheduler and system-wide keyboard shortcuts.

Try Daily for free by downloading it from the Mac App Store and join thousands of other employees, freelancers, founders and professionals.

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.

Daily Time Tracking features and specs

  • User-Friendly Interface
    Daily Time Tracking offers a simple and intuitive interface making it easy for users to navigate and log their time.
  • Detailed Reporting
    The platform provides comprehensive reporting features that allow users to analyze their productivity and time allocation.
  • Cross-Platform Compatibility
    It supports multiple platforms including web, iOS, and Android, enabling users to track time on the go.
  • Integration with Other Tools
    Daily Time Tracking integrates with popular productivity tools such as Asana, Trello, and Slack, enhancing its utility.
  • Customizable Settings
    Users can customize settings to suit their specific workflow requirements, including creating custom task categories and labels.

Possible disadvantages of Daily Time Tracking

  • Subscription Costs
    The platform requires a subscription, which may be a barrier for individual users or small teams with limited budgets.
  • Learning Curve
    Despite its user-friendly design, there is still a learning curve for users who are not familiar with time-tracking tools.
  • Limited Offline Functionality
    The app requires an internet connection for most features, which can be limiting in areas with poor connectivity.
  • Potential for Overhead
    Constantly logging time can become an administrative overhead, detracting from actual productive work.
  • Data Security Concerns
    Storing time-tracking data on a third-party service may raise concerns about data privacy and security for some users.

NumPy videos

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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

Daily Time Tracking videos

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Daily Time Tracking

Category Popularity

0-100% (relative to NumPy and Daily Time Tracking)

NumPy

Daily Time Tracking

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Productivity
0 0%
Productivity
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Time Tracking
0 0%
Time Tracking
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 Daily Time Tracking

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

Daily Time Tracking Reviews

We have no reviews of Daily Time Tracking yet.
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Social recommendations and mentions

Based on our record, NumPy should be more popular than Daily Time Tracking. 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.

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

Daily Time Tracking mentions (56)

  • Time Tracking
    Check out Daily if you don't like manually toggling timers. Instead, it periodically asks what you are doing. Source: almost 2 years ago
  • "Blind" Time-tracker idea
    Just for an app reference, a quick google reference I found this https://dailytimetracking.com not sure if this helps, but seems pretty simple and not intrusive/invasive. Source: almost 2 years ago
  • Add work log for another user via API
    I'm the developer behind a time-tracking app, and I'm looking to build a Zapier integration for a larger customer who uses Jira. They want tracked time to automatically be pushed to Jira using their work log capability. They want to avoid using a (way more expensive) organization plan of Zapier, though. Source: almost 2 years ago
  • Looking for a good time tracking app with lots of statistics and graphs
    If you're on a Mac, you might want to try out DailyTry out Daily if you're on a Mac. Although it focuses more on simplicity, you might like its way of tracking time: by periodically asking what you are doing. For other options, check out this blog post. Source: about 2 years ago
  • Time tracker free
    Not free, unfortunately, but check out Daily. It tracks time by periodically asking what you are doing instead of requiring you to toggle timers when you switch tasks. Alternatively, check out this blog post for other options. Source: about 2 years ago
View more

What are some alternatives?

When comparing NumPy and Daily Time Tracking, 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.

Zoom - Equip your team with tools designed to collaborate, connect, and engage with teammates and customers, no matter where you’re located, all in one platform.

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

GoToMeeting - GoToMeeting is a web conferencing service offering a range of services which are available on Mac, PC, iOS and Android devices.

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

join.me - Instant screen sharing. Instant Aha!

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