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Splid VS NumPy

Compare Splid VS NumPy and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Splid logo Splid

Splid helps friends manage their money.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Splid Landing page
    Landing page //
    2018-09-30
  • NumPy Landing page
    Landing page //
    2023-05-13

Splid features and specs

  • User-Friendly Interface
    Splid offers an intuitive and easy-to-use interface that makes it simple for users to split expenses without any confusion.
  • Offline Functionality
    Users can log expenses and use the app even without an internet connection, which is useful in remote areas.
  • Multiple Currency Support
    Splid allows users to add expenses in different currencies, making it convenient for international travel and expenses.
  • No Sign-Up Required
    The app does not require users to create an account, simplifying access and reducing privacy concerns.
  • Easy Sharing
    Users can easily share expense reports with group members via a link, simplifying communication and transparency.

Possible disadvantages of Splid

  • Limited Integration
    Splid lacks integration with other financial apps or services, which can limit its functionality for some users.
  • Manual Expense Entry
    All expenses must be entered manually, which can be time-consuming compared to apps that offer receipt scanning.
  • No Real-Time Sync
    Changes are not updated in real-time across devices, potentially leading to discrepancies if multiple users are managing a list.
  • Basic Features Free
    While the basic app is free, advanced features require a paid upgrade, which might not be ideal for all users.
  • Limited to Group Expense
    Splid is focused on group expense sharing, lacking features for individual finance management or budgeting.

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.

Splid videos

+ Add

"Splid" by Kvelertak (ALBUM OF THE YEAR CONTENDER?) | ALBUM REVIEW

More videos:

  • Review - Album Review/Reaction: Kvelertak - Splid
  • Review - Kvelertak: Splid -- ๐Ÿ’ฟ album review ๐Ÿ’ฟ

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 Splid and NumPy)

Splid

NumPy

Personal Finance
100 100%
Personal Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Bill-Splitting Apps
100 100%
Bill-Splitting Apps
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Splid 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 Splid and NumPy

Splid Reviews

  1. dee parkes
    ยท retired at retired ยท
    great! easy to use, versatile, flexible. Recommended.

    I prefer this app to similar ones I've tried. It seems clearer, more straightforward and simpler to use for normal holidays etc. But if you need more complex arrangements it's versatile too: in that you can have multiple currencies; and groups containing different people.

12 Best Bill Splitting Apps in 2023
Splid is an excellent app for those who want to take their bill splitting to the next level. It is among the popular bill splitting apps that supports advanced features like location-based payment suggestions, detailed accounts of shared expenses, and a user-friendly interface, Splid app makes splitting bills easier than ever before.
Source: www.mobileappdaily.com
Best Bill-Splitting Apps
Splitting up the cost of group trips can be tough. Splid allows you to add in all the expenses of a trip and then split it up among each person on the trip. The app is useful for splitting up non-trip expenses as well. Multiple payees can be added to each expense, for example, if two people covered the cost of groceries upfront, but five people need to chip in on the bill....
Source: www.thebalancemoney.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 Splid. While we know about 122 links to NumPy, we've tracked only 2 mentions of Splid. 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.

Splid mentions (2)

  • Running an Open Source App: Usage, Costs and Community Donations
    Would be interesting to see how this compares to https://splid.app/. - Source: Hacker News / almost 2 years ago
  • Show HN: An alternative to Splitwise, more minimalist, no ads, no account
    Https://splid.app/ is a great no-account alternative. - Source: Hacker News / about 4 years ago

NumPy mentions (122)

View more

What are some alternatives?

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

Splitwise - Splitwise is a free tool for friends and roommates to track bills and other shared expenses, so that everyone gets paid back. On the web, iPhone, and Android!

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

Tricount - Manage and share expenses with friends

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

Settle Up - SETTLE UP is an indispensable app for friends and flatmates who need to keep track of shared bills...

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

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