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NumPy VS Stata

Compare NumPy VS Stata and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Stata logo Stata

Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Stata Landing page
    Landing page //
    2023-09-27

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.

Stata features and specs

  • Comprehensive Statistical Tool
    Stata offers a wide array of built-in statistical procedures, making it ideal for complex data analysis and research.
  • User-Friendly Interface
    With a graphical user interface and command syntax, Stata caters to both novice and experienced users, improving ease of use and flexibility.
  • Extensive Documentation
    Stata provides thorough documentation and a vast range of tutorials, which can help users quickly find solutions and learn new techniques.
  • Strong Community Support
    Stata has an active user community and mailing list, enabling users to share knowledge, scripts, and advice efficiently.
  • Cross-Platform Compatibility
    Stata is available for Windows, Mac, and Linux, allowing users to work on their preferred operating system without any compromise.
  • Reproducible Research
    Stata promotes reproducible research by providing tools for scripting and automation, ensuring that analyses can be easily replicated and verified.

Possible disadvantages of Stata

  • High Cost
    Compared to some other statistical software, Stata can be expensive, particularly for individual users or small organizations without access to institutional licenses.
  • Steep Learning Curve
    Despite its user-friendly interface, mastering Stata's full capabilities requires time and a considerable learning effort, which can be daunting for beginners.
  • Limited Graphical Capabilities
    While adequate for many purposes, Stata's graphical capabilities are not as advanced as some other software options like R or Python's visualization packages.
  • Less Flexible for Custom Development
    Compared to open-source languages like R or Python, Stata is less flexible for custom development and integration with other software, which might limit advanced users.
  • Resource Intensive
    Stata can be resource-heavy, requiring substantial computing power for large datasets or complex operations, potentially limiting its use on lower-end machines.

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

Stata videos

+ Add

What's it like–Getting started in Stata

More videos:

  • Review - Stata's dyndoc review
  • Review - 【Stata小课堂】第2讲：界面介绍

Category Popularity

0-100% (relative to NumPy and Stata)

NumPy

Stata

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Technical Computing
0 0%
Technical Computing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Data Dashboard
50 50%
Data Dashboard
50% 50

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Stata

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

Stata Reviews

25 Best Statistical Analysis Software
Stata is a robust statistical software widely utilized by professionals across various fields for efficient data management, in-depth statistical analysis, and comprehensive data visualization.
Source: www.scijournal.org
9 Best Analysis Software for PC 2023
Stata is statistical software that provides almost all the tools you need in data analysis and visualization. The software is crucial in data manipulation, computing statistics queries, visualization, and generating analytical reports. The software is owned by the StataCorp company and has several applications in various fields like science, engineering, biomedicine,...
Source: pdf.wps.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.

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 / 8 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

Stata mentions (0)

We have not tracked any mentions of Stata yet. Tracking of Stata recommendations started around Mar 2021.

What are some alternatives?

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

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

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

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

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

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

GraphPad Prism - Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

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