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PySpark VS NumPy

Compare PySpark VS NumPy and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

PySpark logo PySpark

PySpark Tutorial - Apache Spark is written in Scala programming language. To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can wor

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • PySpark Landing page
    Landing page //
    2023-08-27
  • NumPy Landing page
    Landing page //
    2023-05-13

PySpark videos

+ Add

Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray

More videos:

  • Tutorial - Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Training | Edureka

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 PySpark and NumPy)

PySpark

NumPy

No Code
100 100%
No Code
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Project Management
100 100%
Project Management
0% 0
Data Science Tools
2 2%
Data Science Tools
98% 98

User comments

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Reviews

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

PySpark Reviews

We have no reviews of PySpark yet.
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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 107 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.

PySpark mentions (0)

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

NumPy mentions (107)

  • Element-wise vs Matrix vs Dot multiplication
    In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
  • JSON in data science projects: tips & tricks
    Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
  • Introducing Flama for Robust Machine Learning APIs
    Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
  • A Comprehensive Guide to NumPy Arrays
    Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
  • Beginning Python: Project Management With PDM
    A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
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What are some alternatives?

When comparing PySpark and NumPy, 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.

xlwings - xlwings is a Python library that makes it easy to call Python from Excel and vice versa

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

PyXLL - PyXLL is an Excel Add-In that enables developers to extend Excel’s capabilities with Python code.

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

Anaconda - Anaconda is the leading open data science platform powered by Python.

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