Software Alternatives, Accelerators & Startups
Register | Login
1DS

100 Days of Swift VS NumPy

Compare 100 Days of Swift VS NumPy and see what are their differences

Electe
Discover Electe, our data analytics platform dedicated to SMEs. Don't let your data go unused, take your business into the future! 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

100 Days of Swift logo 100 Days of Swift

Learn Swift by building cool projects

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • 100 Days of Swift Landing page
    Landing page //
    2019-02-03
  • NumPy Landing page
    Landing page //
    2023-05-13

100 Days of Swift features and specs

  • Structured Learning Path
    The 100 Days of Swift provides a clear and organized framework for learning Swift, which is beneficial for those who prefer a guided experience.
  • Daily Commitment
    Committing to a daily routine encourages discipline and regular practice, which are crucial for mastering a new programming language.
  • Community Support
    Participants often engage with a community of learners, offering support, motivation, and a chance to collaborate on problems.
  • Focus on Practical Projects
    The program emphasizes building real-world projects, which helps learners apply theoretical knowledge practically.
  • Comprehensive Coverage
    The tutorial covers a wide range of Swift topics, providing a comprehensive introduction suitable for beginners and intermediate learners.

Possible disadvantages of 100 Days of Swift

  • Time Commitment
    The requirement to engage with the material daily can be challenging for those with busy schedules or who prefer a self-paced learning approach.
  • Pace
    The fast pace may not accommodate slower learners who need additional time to grasp certain concepts.
  • No Direct Instructor Feedback
    As an independent tutorial, learners may miss out on direct feedback or clarification from instructors on challenging topics.
  • Not Suitable for Advanced Users
    Experienced Swift developers might find the program too basic and not challenging enough to enhance their skills significantly.
  • Self-Motivation Required
    Success in the program depends heavily on the learner's ability to stay motivated and complete tasks independently.

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.

100 Days of Swift videos

+ Add

100 Days of Swift

More videos:

  • Review - 100 Days of Swift Challenge!

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 100 Days of Swift and NumPy)

100 Days of Swift

NumPy

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
AI
100 100%
AI
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using 100 Days of Swift 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 100 Days of Swift and NumPy

100 Days of Swift Reviews

We have no reviews of 100 Days of Swift 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.
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 more popular. 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.

100 Days of Swift mentions (0)

We have not tracked any mentions of 100 Days of Swift yet. Tracking of 100 Days of Swift recommendations started around Mar 2021.

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 / 3 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 / 7 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 100 Days of Swift and NumPy, you can also consider the following products

Swift Playgrounds - Learn serious code on your iPad in a seriously fun way

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

A Best-in-Class iOS App - Master accessibility, design, user experience and iOS APIs

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

Code-Free Startup - Learn how to build real apps without coding

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

Loading...