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Which is more popular?
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.
social mentions
122 vs 2
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
189 vs 145
Base details
Website, pricing, platforms and company facts side by side.
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
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.
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
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.
Analysis
An editorial look at what each product does well and who it suits.
NumPySplid
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.
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...
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...
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...
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,...
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...
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...
Social recommendations and mentions
Recommendations tracked on public social media and blogs since March 2021.
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages
Familiarity with Python as a language is assumed; if you need a quick...
- Source: dev.to
/
about 1 year ago
AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI,...
- Source: dev.to
/
over 1 year ago
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
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
