Software Alternatives, Accelerators & Startups

Snowball Analytics VS NumPy

Compare Snowball Analytics 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.

Snowball Analytics logo Snowball Analytics

Simple and powerful portfolio tracker for investors. Dividend tracker, portfolio performance and quick portfolio rebalancing. Supports thousands of stocks, funds and cryptocurrencies from all over the world.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Snowball Analytics Landing page
    Landing page //
    2022-09-14

Snowball Analytics is an investment tracking app for any thoughtful long-term investor. Get overview of all your investments in one place - portfolio performance, dividends, company fundamentals, benchmarking and more.

๐Ÿ“‹ Lose the spreadsheet โ€“ we make investment tracking easy and hassle-free

๐Ÿ’ฐ All your investments in one place - stocks, crypto, funds, real estate, etc.

๐ŸŒ Multiple currencies and stock exchanges

โฑ๏ธ Easy data import - link your brokerage account in a few minutes (US, EU, Asia, ...). 1000+ brokers supported

๐Ÿ“Š Benchmarking - compare your results with popular funds and indices

๐Ÿช™ Comprehensive dividend analytics, future dividends and our own rating of dividend companies

๐Ÿ–ฑ๏ธ One-click portfolio rebalancing

๐Ÿ“ˆ Company fundamentals

๐Ÿค Community - see how other investors are navigating their portfolio in current market conditions

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

Snowball Analytics

$ Details
freemium $7.99 / Monthly (1 portfolio, automatic brokerage connection)
Platforms
Web
Release Date
2022 August

Snowball Analytics features and specs

  • Number of portfolios
    10
  • Number of holdings
    Unlimited
  • Automatic brokerage connection
  • Stocks, Funds, Cryptocurrencies
  • Dividend calendar
  • Returns analytics
  • IRR calculations
  • Benchmarking
  • Portfolio rebalancing
  • Multiple currencies and stock exchnages
  • Custom investments (real estate, deposits)
  • Binance API support
  • Top dividend stocks

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.

Snowball Analytics videos

Snowball Analytics overview

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 Snowball Analytics and NumPy)
Finance
100 100%
0% 0
Data Science And Machine Learning
Investing
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

Share your experience with using Snowball Analytics 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 Snowball Analytics and NumPy

Snowball Analytics Reviews

We have no reviews of Snowball Analytics 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 should be more popular than Snowball Analytics. It has been mentiond 122 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.

Snowball Analytics mentions (33)

  • Replacement for StockMarketEye?
    Https://snowball-analytics.com has categories, but about $150 a year for multiple portfolios. Source: almost 3 years ago
  • Portfolio rebalancing
    If you're contributing monthly, use those funds to top up. Otherwise, there are online tools you can use to get rebalancing very close. I use https://snowball-analytics.com/ but there are others I'm sure. Source: about 3 years ago
  • Website or app to track all retirement accounts?
    You can try Snowball Analytics. Have been using the free edition for a while now and its OK but its not as comprehensive as other tools. Source: about 3 years ago
  • Dividends across multiple brokerages
    You can use โ€œstock eventsโ€ or https://snowball-analytics.com to manually add your positions from all your brokers and see a total overview. Source: about 3 years ago
  • $150/month milestone! Still not buying schd and jepi (but added OKE and NEE)
    For trading/broker I use M1 Finance. The app on the screenshot is https://snowball-analytics.com/ but itโ€™s not a trading app, itโ€™s just to track dividends and stuff. Source: over 3 years ago
View more

NumPy mentions (122)

View more

What are some alternatives?

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

Sharesight - Online stock portfolio tracker that automatically tracks prices, dividends, performance and tax.

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

Kubera - Protect your wealth

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

getquin - Track all your investments in one place

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