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Twelve Data VS NumPy

Compare Twelve Data VS NumPy and see what are their differences

HasData
HasData is a top web scraping platform for developers and enterprises. It delivers structured, real-time data from the web using scalable APIs and no-code tools, removing the need to manage proxies, browsers, or anti-bot systems. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Twelve Data logo Twelve Data

The simplest and most effective way to access both realtime and historical stock, forex, cryptocurrency data, and over 100 technical indicators.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Twelve Data Landing page
    Landing page //
    2023-09-23
  • NumPy Landing page
    Landing page //
    2023-05-13

Twelve Data features and specs

  • Comprehensive Data Coverage
    Twelve Data provides access to a wide range of financial data including real-time and historical data for stocks, ETFs, mutual funds, indices, and forex, which is beneficial for comprehensive market analysis.
  • Affordable Pricing
    Twelve Data offers a variety of pricing plans, including a free tier with essential features, which makes it accessible to individual developers and small businesses.
  • Ease of Use
    The API is designed to be user-friendly with well-documented endpoints, making it easy for developers to integrate the service into their applications.
  • High Scalability
    Twelve Data offers robust and scalable API solutions that can handle a large volume of requests, suitable for both small projects and large-scale applications.
  • Global Market Coverage
    The platform provides data from global markets, allowing users to access information from multiple exchanges around the world.

Possible disadvantages of Twelve Data

  • Limited Advanced Features
    While Twelve Data offers a wide range of basic and intermediate features, it may lack some of the advanced analytics and tools needed by experienced traders or larger financial institutions.
  • Rate Limits on Lower Tiers
    The free and lower-tiered plans come with rate limits that can be restrictive for users requiring frequent data access or handling high traffic.
  • Dependency on API Availability
    As with any third-party data provider, users are dependent on the API's uptime and reliability, which can impact applications if there are outages or downtime.
  • Potential Data Lag for Free Plan
    Users on the free plan may experience delays or less frequent updates compared to those on paid plans, which can be a drawback for time-sensitive applications.

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.

Twelve Data videos

+ Add

Twelve Data Demo Review

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 Twelve Data and NumPy)

Twelve Data

NumPy

Finance
100 100%
Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
APIs
100 100%
APIs
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Twelve Data 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 Twelve Data and NumPy

Twelve Data Reviews

  1. Andrew
    Best API for real-time financial data

    Straightforward to use and powerful API to get financial data in real-time and complete historical data. Best in class Python package.

    ๐Ÿ Competitors: Alpha Vantage, Polygon.io
    ๐Ÿ‘ Pros:    Equity data|Forex|Crypto|Affordable|Reliable

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 Twelve Data. While we know about 122 links to NumPy, we've tracked only 8 mentions of Twelve Data. 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.

Twelve Data mentions (8)

  • Building a Gold (XAUUSD) Trend Tracker with Python and SQLite
    I am using TwelveData forex API for Extraction of raw data from the financial markets in JSON format. - Source: dev.to / about 1 year ago
  • Top 5 Free Financial Data APIs for Building a Powerful Stock Portfolio Tracker โšก
    Twelve Data provides extensive financial market coverage, making it ideal for those looking for a free API for building a stock portfolio tracker. - Source: dev.to / over 1 year ago
  • Webflow Crypto Market Data Ticker
    I'd suggest Twelve Data for the financial data feed, if you have a budget for this: https://twelvedata.com/. Source: over 2 years ago
  • Where can I find a free source of historical S&P500 index data at hourly intervals?
    You can use https://twelvedata.com/ they offer an API and their free plan allows 800 credits a day so over a number of days you could get what you need. Source: about 4 years ago
  • Weekend Discussion Thread for the Weekend of May 06, 2022
    Twelve Data has a free one but don't know how good it is. u/flarmster that's the one I was talking about. Do you know a better one? Source: about 4 years ago
View more

NumPy mentions (122)

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What are some alternatives?

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

Alpha Vantage - Alpha Vantage offers free APIs in JSON and CSV formats for realtime and historical stock and forex data, digital/crypto currency data and over 50 technical indicators.

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

Polygon.io - Polygon.io offers streaming realtime data for stocks/equities, ETFs, Indecies and Forex/Currencies including crypto currencies. Our Real-Time Stock Data APIs help you build the future on fintech.

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

Financial Modeling Prep - Access all stocks discounted cash flow statements, market price, stock markets news, and learn more about Financial Modeling. Learn M&A, LBO, DCF, Comps, and Financial Statement Modeling thought concrete examples

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

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