Chet  Lubowitz

Chet Lubowitz

1599080040

Scaling Pandas: Dask vs Ray vs Modin vs Vaex vs RAPIDS

Python and its most popular data wrangling library, Pandas, are soaring in popularity. Compared to competitors like Java, Python and Pandas make data exploration and transformation simple.

But both Python and Pandas are known to have issues around scalability and efficiency.

Python loses some efficiency right off the bat because it’s an interpreted, dynamically typed language. But more importantly, Python has always focused on simplicity and readability over raw power. Similarly, Pandas focuses on offering a simple, high-level API, largely ignoring performance. In fact, the creator of Pandas wrote “ The 10 things I hate about pandas,” which summarizes these issues:

Image for post

Performance issues and lack of flexibility are the main things Pandas’ own creator doesn’t like about the library. (source)

So it’s no surprise that many developers are trying to add more power to Python and Pandas in various ways. Some of the most notable projects are:

  • Dask: a low-level scheduler and a high-level partial Pandas replacement, geared toward running code on compute clusters.
  • Ray: a low-level framework for parallelizing Python code across processors or clusters.
  • Modin: a drop-in replacement for Pandas, powered by either Dask or Ray.
  • Vaex: a partial Pandas replacement that uses lazy evaluation and memory mapping to allow developers to work with large datasets on standard machines.
  • RAPIDS**: **a collection of data-science libraries that run on GPUs and include cuDF, a partial replacement for Pandas.

There are others, too. Below is an overview of the Python data wrangling landscape:

Image for post

Dask, Modin, Vaex, Ray, and CuDF are often considered potential alternatives to each other. Source: Created with this tool

So if you’re working with a lot of data and need faster results, which should you use?

#pandas #data-science #programming #machine-learning #python

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Buddha Community

Scaling Pandas: Dask vs Ray vs Modin vs Vaex vs RAPIDS
Paula  Hall

Paula Hall

1623396211

Making Pandas fast with Dask parallel computing

So you, my dear Python enthusiast, have been learning Pandas and Matplotlib for a while and have written a super cool code to analyze your data and visualize it. You are ready to run your script that reads a huge file and all of a sudden your laptop starts making un ugly noise and burning like hell. Sounds familiar?

Well, I have got a couple of good news for you: this issue doesn’t need to happen anymore and you no, you don’t need to upgrade your laptop or your server.

Introducing Dask:

Dask is a flexible library for parallel computing with Python. It provides multi-core and distributed parallel execution on larger-than-memory datasets. It figures out how to break up large computations and route parts of them efficiently onto distributed hardware.

A massive cluster is not always the right choice

Today’s laptops and workstations are surprisingly powerful and, if used correctly, can handle datasets and computations for which we previously depended on clusters. A modern laptop has a multi-core CPU, 32GB of RAM, and flash-based hard drives that can stream through data several times faster than HDDs or SSDs of even a year or two ago.

As a result, Dask can empower analysts to manipulate 100GB+ datasets on their laptop or 1TB+ datasets on a workstation without bothering with the cluster at all.

The project has been a massive plus for the Python machine learning Ecosystem because it democratizes big data analysis. Not only can you save money on bigger servers, but also it copies the Pandas API so you can run your Panda script changing very few lines of code.

#making pandas fast with dask parallel computing #dask parallel computing #pandas #pandas fast #dask #dask parallel

Chet  Lubowitz

Chet Lubowitz

1599080040

Scaling Pandas: Dask vs Ray vs Modin vs Vaex vs RAPIDS

Python and its most popular data wrangling library, Pandas, are soaring in popularity. Compared to competitors like Java, Python and Pandas make data exploration and transformation simple.

But both Python and Pandas are known to have issues around scalability and efficiency.

Python loses some efficiency right off the bat because it’s an interpreted, dynamically typed language. But more importantly, Python has always focused on simplicity and readability over raw power. Similarly, Pandas focuses on offering a simple, high-level API, largely ignoring performance. In fact, the creator of Pandas wrote “ The 10 things I hate about pandas,” which summarizes these issues:

Image for post

Performance issues and lack of flexibility are the main things Pandas’ own creator doesn’t like about the library. (source)

So it’s no surprise that many developers are trying to add more power to Python and Pandas in various ways. Some of the most notable projects are:

  • Dask: a low-level scheduler and a high-level partial Pandas replacement, geared toward running code on compute clusters.
  • Ray: a low-level framework for parallelizing Python code across processors or clusters.
  • Modin: a drop-in replacement for Pandas, powered by either Dask or Ray.
  • Vaex: a partial Pandas replacement that uses lazy evaluation and memory mapping to allow developers to work with large datasets on standard machines.
  • RAPIDS**: **a collection of data-science libraries that run on GPUs and include cuDF, a partial replacement for Pandas.

There are others, too. Below is an overview of the Python data wrangling landscape:

Image for post

Dask, Modin, Vaex, Ray, and CuDF are often considered potential alternatives to each other. Source: Created with this tool

So if you’re working with a lot of data and need faster results, which should you use?

#pandas #data-science #programming #machine-learning #python

Kasey  Turcotte

Kasey Turcotte

1623927960

Pandas DataFrame vs. Spark DataFrame: When Parallel Computing Matters

With Performance Comparison Analysis and Guided Example of Animated 3D Wireframe Plot

Python is famous for its vast selection of libraries and resources from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as NumpyPandasScikit-learnKeras, and TensorFlow. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing Big Data, such as Apache Spark. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a comparison between the Pandas DataFrame and Spark DataFrame. My hope is to provide more conviction on choosing the right implementation.

Pandas DataFrame

Pandas has become very popular for its ease of use. It utilizes DataFrames to present data in tabular format like a spreadsheet with rows and columns. Importantly, it has very intuitive methods to perform common analytical tasks and a relatively flat learning curve. It loads all of the data into memory on a single machine (one node) for rapid execution. While the Pandas DataFrame has proven to be tremendously powerful in manipulating data, it does have its limits. With data growing at an exponentially rate, complex data processing becomes expensive to handle and causes performance degradation. These operations require parallelization and distributed computing, which the Pandas DataFrame does not support.

Introducing Cluster/Distribution Computing and Spark DataFrame

Apache Spark is an open-source cluster computing framework. With cluster computing, data processing is distributed and performed in parallel by multiple nodes. This is recognized as the MapReduce framework because the division of labor can usually be characterized by sets of the mapshuffle, and reduce operations found in functional programming. Spark’s implementation of cluster computing is unique because processes 1) are executed in-memory and 2) build up a query plan which does not execute until necessary (known as lazy execution). Although Spark’s cluster computing framework has a broad range of utility, we only look at the Spark DataFrame for the purpose of this article. Similar to those found in Pandas, the Spark DataFrame has intuitive APIs, making it easy to implement.

#pandas dataframe vs. spark dataframe: when parallel computing matters #pandas #pandas dataframe #pandas dataframe vs. spark dataframe #spark #when parallel computing matters

Jamison  Fisher

Jamison Fisher

1620293605

Pandas Vs Numpy: Difference Between Pandas & Numpy [2021]

Python is undoubtedly one of the most popular programming languages in the software development and Data Science communities. The best part about this beginner-friendly language is that along with English-like syntax. It comes with a wide range of libraries. Pandas and NumPy are two of the most popular Python libraries.

Today’s post is all about exploring the differences between Pandas and NumPy to understand their features and aspects that make them unique.

Pandas vs. NumPy: What are they?

Pandas vs. NumPy: The core difference between Pandas and NumPy

#data science #comparison #difference between pandas and numpy #numpy #pandas #pandas vs numpy

Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native