Rupert  Beatty

Rupert Beatty


Introduction to Spark with Python

Introduction to Spark with Python – PySpark for Beginners

Apache Spark is one the most widely used framework when it comes to handling and working with Big Data AND Python is one of the most widely used programming languages for Data Analysis, Machine Learning and much more. So, why not use them together? This is where Spark with Python also known as PySpark comes into the picture.

With an average salary of $110,000 pa for an Apache Spark Developer, there’s no doubt that Spark is used in the industry a lot. Because of its rich library set, Python is used by the majority of Data Scientists and Analytics experts today. Integrating Python with Spark was a major gift to the community. Spark was developed in Scala language, which is very much similar to Java. It compiles the program code into bytecode for the JVM for spark big data processing. To support Spark with python, the Apache Spark community released PySpark. Ever since, PySpark Certification has been known to be one of the most sought-after skills throughout the industry due of the wide range of benefits that came after combining the best of both these worlds. In this Spark with Python blog, I’ll discuss the following topics.

Introduction to Apache Spark

Apache Spark is an open-source cluster-computing framework for real-time processing developed by the Apache Software Foundation. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.

Below are some of the features of Apache Spark which gives it an edge over other frameworks:

Spark Features - Spark with Python - Edureka

  • Speed: It is 100x faster than traditional large-scale data processing frameworks.
  • Powerful Caching: Simple programming layer provides powerful caching and disk persistence capabilities.
  • Deployment: Can be deployed through Mesos, Hadoop via Yarn, or Spark’s own cluster manager.
  • Real Time: Real-time computation & low latency because of in-memory computation.
  • Polyglot: It is one of the most important features of this framework as it can be programmed in Scala, Java, Python and R.

Why go for Python?

Although Spark was designed in scala, which makes it almost 10 times faster than Python, but Scala is faster only when the number of cores being used is less. As most of the analysis and process nowadays require a large number of cores, the performance advantage of Scala is not that much.

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For programmers Python is comparatively easier to learn because of its syntax and standard libraries. Moreover, it’s a dynamically typed language, which means RDDs can hold objects of multiple types.

Although Scala has SparkMLlib it doesn’t have enough libraries and tools for Machine Learning and NLP purposes. Moreover, Scala lacks Data Visualization.

Setting up Spark with Python (PySpark)

I hope you guys know how to download spark and install it. So, once you’ve unzipped the spark file, installed it and added it’s path to .bashrc file, you need to type in source .bashrc

export SPARK_HOME = /usr/lib/hadoop/spark-2.1.0-bin-hadoop2.7
export PATH = $PATH:/usr/lib/hadoop/spark-2.1.0-bin-hadoop2.7/bin

To open pyspark shell you need to type in the command  ./bin/pyspark

Pyspark shell - Spark with python - Edureka

Spark in Industry

Apache Spark because of it’s amazing features like in-memory processing, polyglot and fast processing are being used by many companies all around the globe for various purposes in various industries:

Companies using Spark - Spark with Python - Edureka

Yahoo uses Apache Spark for its Machine Learning capabilities to personalize its news, web pages and also for target advertising. They use Spark with python to find out what kind of news – users are interested to read and categorizing the news stories to find out what kind of users would be interested in reading each category of news.

TripAdvisor uses apache spark to provide advice to millions of travelers by comparing hundreds of websites to find the best hotel prices for its customers. The time taken to read and process the reviews of the hotels in a readable format is done with the help of Apache Spark.

One of the world’s largest e-commerce platform Alibaba runs some of the largest Apache Spark jobs in the world in order to analyze hundreds of petabytes of data on its e-commerce platform.

PySpark SparkContext and Data Flow

Talking about Spark with Python, working with RDDs is made possible by the library Py4j. PySpark Shell links the Python API to spark core and initializes the Spark Context. Spark Context is the heart of any spark application.

  1. Spark context sets up internal services and establishes a connection to a Spark execution environment.
  2. The sparkcontext object in driver program coordinates all the distributed process and allows resource allocation.
  3. Cluster Managers provide Executors, which are JVM process with logic.
  4. SparkContext object sends the application to executors.
  5. SparkContext executes tasks in each executor.

Pyspark Sparkcontext - Spark with Python - Edureka

PySpark KDD Use Case

Now Let’s have a look at a Use Case of KDD’99 Cup (International Knowledge Discovery and Data Mining Tools Competition). Here we will take a fraction of the dataset because the original dataset is too big

import urllib
f = urllib.urlretrieve ("<a href=""></a>", "kddcup.data_10_percent.gz")

Now we can use this file to create our RDD.

data_file = "./kddcup.data_10_percent.gz"
raw_data = sc.textFile(data_file)


Suppose We want to count how many normal. interactions we have in our dataset. We can filter our raw_data RDD as follows.

normal_raw_data = raw_data.filter(lambda x: 'normal.' in x)


Now we can count how many elements we have in the new RDD.

from time import time
t0 = time()
normal_count = normal_raw_data.count()
tt = time() - t0
print "There are {} 'normal' interactions".format(normal_count)
print "Count completed in {} seconds".format(round(tt,3))


There are 97278 'normal' interactions
Count completed in 5.951 seconds


In this case we want to read our data file as a CSV formatted one. We can do this by applying a lambda function to each element in the RDD as follows. Here we will use the map() and take() transformation.

from pprint import pprint
csv_data = x: x.split(","))
t0 = time()
head_rows = csv_data.take(5)
tt = time() - t0
print "Parse completed in {} seconds".format(round(tt,3))


Parse completed in 1.715 seconds


Now we want to have each element in the RDD as a key-value pair where the key is the tag (e.g. normal) and the value is the whole list of elements that represents the row in the CSV formatted file. We could proceed as follows. Here we use the line.split() and map().

def parse_interaction(line):
elems = line.split(",")
tag = elems[41]
return (tag, elems)
key_csv_data =
head_rows = key_csv_data.take(5)




Here we are going to use the collect() action. It will get all the elements of RDD into memory. For this reason, it has to be used with care when working with large RDDs.

t0 = time()
all_raw_data = raw_data.collect()
tt = time() - t0
print "Data collected in {} seconds".format(round(tt,3))


Data collected in 17.927 seconds

That took longer as any other action we used before, of course. Every Spark worker node that has a fragment of the RDD has to be coordinated in order to retrieve its part and then reduce everything together.

As a last example combining all the previous, we want to collect all the normal interactions as key-value pairs.

# get data from file
data_file = "./kddcup.data_10_percent.gz"
raw_data = sc.textFile(data_file)
# parse into key-value pairs
key_csv_data =
# filter normal key interactions
normal_key_interactions = key_csv_data.filter(lambda x: x[0] == "normal.")
# collect all
t0 = time()
all_normal = normal_key_interactions.collect()
tt = time() - t0
normal_count = len(all_normal)
print "Data collected in {} seconds".format(round(tt,3))
print "There are {} 'normal' interactions".format(normal_count)


Data collected in 12.485 seconds
There are 97278 normal interactions

So this is it, guys!

I hope you enjoyed this Spark with Python blog. If you are reading this, Congratulations! You are no longer a newbie to PySpark. Try out this simple example on your systems now.

Now that you have understood basics of PySpark, check out the Python Spark Certification Training using PySpark by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. Edureka’s Python Spark Certification Training using PySpark is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175).

Got a question for us? Please mention it in the comments section and we will get back to you.

Original article source at:

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Introduction to Spark with Python
Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python