PySpark Vs. Python: A Side-by-Side Analysis

PySpark and Python are two of the most popular tools in data science today. Millions of people worldwide use them to do everything from mining through large datasets to building predictive models of customer behavior and beyond. Read in this content properly about PySpark Vs. Python.

Many enterprises have been trying to make use of Python because of its ease of use and its usefulness across many different industries. However, there are certain issues with Python and its cognitive load compared to PySpark. It makes enterprises wonder if they should use PySpark instead. Thus the question arises: What to choose between them or PySpark vs. Python!

Python Language with the Spark Framework

PySpark is mainly an API facilitating the use of Python language with the Spark framework. So, you get the added benefits of the Spark framework while using Python Language for your development project. 

However, it’s misleading to think that between PySpark Vs. Python, PySpark is always a better option. Before we weigh the pros & cons of choosing either one, let’s have a quick look at the table below that offers insights into PySpark v/s. Python.

Comparison Table: PySpark V/S. Python

I hope the table offers a concise idea of the differences between them. When deciding between PySpark Vs. Python, it’s essential to explore the pros, cons, and detailed comparison of choosing either of the two. So, let’s explore them.

What is Python?

It is a general-purpose programming language, but it has gained popularity recently in some particular segments. For example, developers can use it to automate web scraping websites that use JavaScript to generate a page dynamically or analyze big data sets of thousands of rows with thousands of columns.

There are multiple benefits when you hire python developers for your project. However, that does not mean the programming language is free of drawbacks. 

Let’s explore the pros and cons of Python Language for your project.

Pros of Using Python Language:

It’s Easy to Learn: Python is a great language to learn if you’re new to programming. It has a relatively simple syntax and lots of excellent open-source tools, think NumPy, SciPy, Matplotlib, etc., to handle mathematical computations for you and an ever-expanding community of users.

Ideal for Beginners: Python’s syntax is simple to read, as evidenced by its readable code, which makes it ideal for beginners. It’s also been proven time & again that learning to code first in Python makes you a more well-rounded programmer.

Great for Fast Prototyping: Python is an excellent language for writing fast, production-ready code. It’s often necessary for data science projects because of its robust array of pre-built libraries and frameworks. 

Widely used Across Industries: One of Python’s most significant selling points is its wide use across industries. Learning Python means you’ll be able to apply your coding skills to various career options, from web development to data science. 

It Scales Well: If you’re doing big data, Python makes a lot of sense. There are libraries for pretty much anything you can think of, allowing developers to do computationally heavy tasks efficiently. 

Flexible working environment with lots of tools/frameworks to use/choose from: Python is an open-source language that’s easy to learn, fun to use, and has a wide variety of frameworks to choose from. It has simple constructs but can be used to build very complex solutions. With Python, you can create a computer program without dealing with low-level hardware details or memory management issues.

Growing Community Support: Python is a prevalent programming language with an active community of developers. It means if you run into trouble, chances are you won’t be alone in your struggles. If a particular feature or syntax doesn’t make sense, Google has probably indexed several help forum posts on it.

It eliminates the immediate need to reinvent the wheel for every problem you encounter. Moreover, top Python development companies offer affordable development rates, adding the cherry to the top of the cake.

Cons of Using Python Language

• It lacks private methods that make it a less than ideal choice for some projects.
• Python is slow. The fact that Python performs poorly is a disadvantage. However, using an optimized library or framework can solve these problems by replacing Python with C code parts. Using C-Code enables developers to create a fast application without sacrificing ease of use.

What is PySpark?

PySpark is an acronym for Python Spark API. The Apache Spark group initially developed it to ensure a smooth combination of Python programming language with the renowned Spark framework. The ultimate result is an API that offers a smooth integration & manipulation of RDDs in Python. 

In general, Spark is an open-source framework that allows users to process large amounts of data in distributed clusters with relative ease. And while it was initially designed for extensive data analysis, its creators have since developed algorithms that can work on structured data.

Much like Hadoop or R (statistical language), Spark aims to improve efficiency by speeding up computational times and creating a unified programming environment. It works well for both batch processing jobs and iterative machine learning projects. 

Pros of Using PySpark

Simple to Write:

With PySpark, you can quickly write programs in Python, and along with it, you get full API compatibility with Apache Spark.

Framework handles errors:

The PySpark framework offers a great deal of protection from mistakes. By providing a simple interface for accessing data across many different computing devices, developers can focus on their applications instead of handling technical details.

It automatically handles error messages. There’s no need to create custom exception handling routines. In other words, users don’t have to worry about whether their code will execute correctly or generate nasty error messages down the line.

Algorithms:

Developers programming in PySpark, can code against RDDs (Resilient Distributed Datasets), basically distributed datasets that are fully fault-tolerant. It means if one node crashes, nothing gets lost, and other nodes can still access it. A bonus is that this approach supports iterative algorithms natively.

Learning Curve:

It’s flexible, fast, reliable, and easy to use. For many healthcare, agriculture, manufacturing, and finance businesses, PySpark can be an excellent solution for problems relating to Big Data. 

 Good Libraries Support:

PySpark is a powerful, lightning-fast engine for big data processing that offers a dazzling array of high-level tools.

Cons of Using PySpark

• It is necessary to obtain a Jupyter Notebook on your machine if you do not already have it. Jupyter Notebook is crucial to run Python code when working with PySpark.
• The system tends to run slowly in certain situations, limiting its usefulness depending on your project. It also has trouble dealing with certain types of files, making getting your data into a form that Spark can process challenging at times. 

Having gone through the pros & cons of PySpark & Python, you must have gained a clear idea of how they differ from each other.

Let’s Explore the Differences on Pyspark v/s. Python in Detail

 

1. Licensed Under: Analyzing the Ease of Use

Both Python & PySpark are open source and come under reputable brands. While PySpark gets licensed under Apache Spark, Python gets licensed under the Python Software Foundation License (PSFL). So there’s hardly any difference between them when weighing brand values.

In simple words, both are under General Public License, which hardly leads to any significant differences between them.

PySpark vs. Python – Who’s the winner:  Draw

2. Project Types: Finding compatibility with the project type

Specifics will determine what aspect of PySpark or Python is being analyzed. Are they on pace with the data stream? Or are they a new project that needs to be updated to fit with current market trends? All data sets and analyses must remain unbiased.

Notably, Python is suitable for diverse projects, including Machine Learning, AI, Data Science, etc. It is a simple language to use that allows for easy development. So, if a developer wants to create a new project from scratch or utilize an already existing framework, then Python is preferable.

In contrast, PySpark fits in with cloud computing & Big Data. It provides easier access to computational power without needing much server space or any hardware limitations. So PySpark is useful if the project requires access to large amounts of data and has computational power on its scope.

PySpark vs. Python – Who’s the winner:  Depends upon the project needs

3. Learning Curve: Which One is Easier to Leverage?

One of PySpark’s significant advantages is its built off an already popular programming language – Python. Both have a similar syntax and extensively use standard data structures, like lists and maps. It means that if you’ve written code in one, learning to write in the other will be a breeze.

However, there are times when specialized syntaxes would prove more advantageous. For example, PySpark makes heavy use of complex nested function calls when defining data sets (think files).

Moreover, Python does not require any prior programming skills for a novice. On the other hand, using PySpark requires advanced Python programming language and Spark framework.

PySpark vs. Python – Who’s the winner:  Python

4. Popularity: Which one has a higher brand value?

PySpark is well known as a distributed computing framework in Apache Spark. Since then, it has been included in Spark 1.6 and has had a fast development rate. Although it’s still less popular than Python, PySpark is becoming more popular than R and Scala because of its convenience & ease of use

On the other hand, Python is a worldwide programming language. It holds popularity of 48.24% under the category of most used programming languages worldwide., says Statista.

PySpark vs. Python – Who’s the winner:  Python

Final Words

PySpark and Python are two wonderful options for data analysis. Although both offer a vast range of data manipulation and statistical programming tools, there are a few distinct differences between them that will help you choose which one is best for your own needs. 

Whether it’s tight integration with Apache Spark or greater flexibility in expression and syntax, each offers something unique to its users. Ultimately, it comes down to the relevant work you’re trying to accomplish. 

For example, PySpark has built-in Spark functions for doing things like writing results into cloud databases, while Python gives you more control over your inputs and outputs. Notably, both are complex technologies & must be handled by experienced programmers and developers for ensuring sound project development.

Hire dedicated developers from reputed resources to leverage and make the most of your project effectively.

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