Free Course on Easy to Advanced Data Structures | Simpliv

Data structures are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding data structures.

Advanced Data Structures 4

Learn and master the most common data structures in this comprehensive course:

  • Static and dynamic arrays
  • Singly and doubly linked lists
  • Stacks
  • Queues
  • Heaps/Priority Queues
  • Binary Trees/Binary Search Trees
  • Union find/Disjoint Set
  • Hash tables
  • Fenwick trees
  • AVL trees

Course contents

Advanced Data Structures 2

This course provides you with high quality animated videos explaining a multitude of data structures and how they are represented visually. You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding of that particular data structure. I will also be posting various coding exercises and multiple choice questions to ensure that you get some hands on experience.

Who is the target audience?

  • Individuals hungry for new knowledge
  • Students who want a fundamental understanding of data structures
Basic knowledge
  • Basic computer science knowledge
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Which is Best for Web Application Development—Dot Net, PHP, Python, Ruby, or Java

When we talk about the web application development technology, web browsers are flashing in our minds at very first moment. It is because web browsers can talk in terms of HTTP or web-sockets with the Internet.

Web Application Development

Supports of Web Browsers

Moreover, web browser technologies are growing rapidly and expanding its supports for new programming languages that we never think of it. Mobile web browsers are behaving differently, and many web services work on mobile platforms with low-level native web browsers like WebView and APIs to give user interface.
The mobile web is hardly depending on de-facto standard desktop web technologies and leave enough room for diversity in web development technologies and frameworks.

Emerging Web Application Development Technologies

Therefore, we cannot rely on traditional Webs Application Development technologies such as PHP and Java, as server-side scripting languages, Ruby, and Python as standard web programming platforms, and .NET as emerging open source from a large proprietary software developer community of Microsoft.

Web Application Development3
We have to think of emerging scripting languages and frameworks such as JavaScript as full-fledged front-end and backend script, jQuery as a substitute of JavaScript in mobile web programming, Scala against Ruby, Elixir with Phoenix framework, Clojure with Datomic database, Haskel for serious functional programming, and Rust as a good alternative to it.
Node.js, Angular.js, Go, Dart, etc. are few names, which are gaining ground in the list of web development technologies. Therefore, sticking with one or two Web Application Development technologies for varying nature, size, and types of projects seems fatal attitude for the web programmers.

Particularly, for those programmers who have to traverse deep ocean of job market and career development in the stiffly competitive world of developers.
However, due to space constraints, we may discuss the traditional and standard web development technologies such as

  • Dot Net
  • PHP
  • Python
  • Ruby
  • Java

All come with their pros and cons and appropriateness in modern contexts, particularly where mobile web is going to dominate, and some new other hardware technologies may wait to arrive on the Web-o-Sphere.

PHP Web Application Development

Hypertext Pre-Processor or PHP is a server-side scripting language developed with the intention to create powerful dynamic and interactive websites in the static website era.
It has rapidly gained momentum, and today nearly, 40% websites or web application in the world of the Internet is running with PHP in their source code. However, we can cite numerous reasons behind such immense popularity, but few are distinguished such as:

  • It can easily embed into HTML code without requiring separate IDE
  • It is with short learning curve and flexible so need frameworks to do organized coding and a lot of frameworks available for little to large-scale programming
  • It is cross-platform and cross-browser compatible, as well as supports all existing web servers
  • It has universal database supports, so integration of any database is possible with PHP
  • It is open source and with huge open source community to support
  • It is at the core of various open source software with great popularity such as WordPress, Joomla, Drupal, Magento, and so on
  • Being the best PHP web application development service provider from India, we cater to hire php developers for different frameworks like Zend, Laravel, CakePHP and more we deliver responsive and cross-platform web applications using these framework with custom solutions.

ASP.NET Web Application Development

Web Application Developmenta

By nature, .NET is a framework, not a programming language. It is a product of the work of the proprietary giant Microsoft. It is supporting various Microsoft languages including VB & C#. The major part of .NET developer uses C# for desktop and mobile web development including cross-platform mobile applications.
Therefore, here we will discuss C# instead of ASP.NET framework. C# is a refined programming language with Object-Oriented Programming properties and follows MVC paradigm for rapid web application development.
ASP.NET offers flexibility and scalability that PHP can give only with various frameworks at different levels. C# has all good things in VB and C++ while support of Visual Studio with myriads of tools is great.

Visual Studio is shifting as open source and free platform gradually, and ASP.NET framework is decoupling from IIS to offer supports for a broad range of servers on the web.
PHP has various frameworks with the loyal community for each framework whereas .NET is a single framework with a comparatively huge community of developers. Therefore, you can collect a big team of desired skill sets easily for ASP.NET Web Application Development projects.

Python Web Application Development

Python in web developer community is considering as a general-purpose programming language for high-level designing and expressing concepts in a few lines of code. It has astounding readability, so if you know English, you can understand one-fourth of code written in Python.
Therefore, it is a good choice when a big team is working on a massive scale of the project, and a number of programmers have to read and code in a collaborative environment.
Python is supporting OOP and Functional Programming, as it is supporting multiple ways to create the structure and elements of programs for computing devices.
Its rapid prototyping and dynamic semantic capabilities are unbeatable so you can easily construct web applications by testing and importing vital functions.
Unfortunately, Python has a smaller community in comparison to PHP, NET and Java so find an expert developer is a tough job.

Ruby on Rails Web Application Development

Microservices Spring Boot Tutorial1

Ruby is a high-level programming language, which just like PHP can be embedded into HTML easily. It is open source and pure OOP language for web programming as well as other purposes too.
Technically, it offers encapsulation of data methods within objects while doing OOP and developers can use a super advanced string as well as text manipulation techniques.
It enables developers to write multi-threaded apps using simple API, and it can easily connect to various databases including MySQL, Oracle, Sybase, DB2, and so on.
The more curious thing for Ruby is that it allows programmers to write external libraries in Ruby or C class languages. It also provides a mechanism for powerful string handling and advanced array class.
Some useful features of Ruby include better security coding, flexible syntax, and debugger to create a quality web application.
It is straightforward and easy for fresher to learn and code due to its easy and clean syntax. Of course, enormous learning resources are plus point for its vast developer community and fans.
Ruby has known framework, and it is Rails so sometimes it refers as Ruby on Rails (ROR) among its lovers.

Searching for Ruby on Rails Developers? Hire dedicated Ruby on Rails Developers team with Addon Solutions and meet Meet the world’s top RoR Programmers with affordable rate.

Java Web Application Development

 

Java is a platform-agnostic programming language for web and desktop applications and now used in Android for mobile application development. It is a pure Object-oriented language with strict conventions and typing.
Java is highly preferred programming language for enterprises for big scale projects for the web, intranet as desktop usage, and in modern M2M, IoT, and LBS, as embedded programming language.
It is because it offers high-end security coding, performance designing, and concurrency programming. It brings productivity for developers and eases their life with scalability and interoperability whenever needed.
The best thing for Java is that it is treating as the standard language in universities and courses so finding a Java developer is quite easy and with different levels of skill sets.
Therefore, just like PHP and ASP.NET languages and frameworks, Java is futuristic and give ‘Spring’ and ‘Play’ like programmer friendly frameworks to do rapid Web Application Programming.

Get in touch with Professional Java application development & integration services by Addon Solutions for your all in one requirements with our top multiple skills java developers for hire.

Conclusion:

If you have small-scale projects, selecting a single programming technology is a good option, but with large-scale projects, we cannot restrict with one and should go with a mix of multiple web programming languages, frameworks, and databases.
In big projects, sometimes finding developers with single skill sets is tough and we have to compromise by including multiple languages to serve different purposes.
Therefore, developers should learn more than one Web Application Development languages, and clients should select the web development companies with a mix of skill sets available as development resources.
If you think Addon Solutions as your dependable web development partner, you can access web programmers with expertise in various traditional and modern web development languages.

Click here To join us for more information, get in touch

Top 10 Programming Languages of 2018 You Should Know | Simpliv

The technology world is expanding immensely with each passing year and months, as they are coming up with new trendier smartphones and tablets every other day and the competition too has grown tough in the market to stand at the highest position. That’s the reason programmers and web developers are in tremendous demand nowadays because they have a good knowledge of programming languages. Various programming languages are now available and each of them has distinct functions.

When you are just beginning, you might not know about these languages, but you can certainly make some efforts to learn about them and do mastery on at least one or more languages; then you can certainly gain a high-paid job for yourself in the industry. We have mentioned here 10 excellent programming languages of 2018 which you should learn and have a better idea.

1. Java

Java is considered as the perfect language for the developers and programmers to learn. Currently, it is the top-most programming language and has grabbed the highest position with Android OS yet again, though it was a bit down a few years ago. Java can be utilized for mobile-based applications, enterprise-level purpose, for creating desktop applications, and for establishing Android apps on tablets and smartphones.

2. PHP

The web developers should learn about PHP or Hypertext Preprocessor, a well-known programming language. With the help of PHP, you can enlarge a web app very quickly and effortlessly. PHP is the actual foundation of many strong content management systems, for example, WordPress. PHP is really a valuable programming language for the developers and programmers.

3. JavaScript

While you are expanding your site, JavaScript is extremely functional as this language can immensely assist you in generating the communication for your website. You can utilize various in style frameworks in JavaScript for constructing the superb user interface. When you’re into web development, it’s very important to know about JavaScript for making interactive web pages. JavaScript is applied for including animations on the web pages, loading fresh images, scripts or objects on web page, and craft hugely responsive user interfaces.

4. Python

For becoming skilled at all-in-one language, you should begin learning Python language that has the ability to expand web apps, data analysis, user interfaces, and much more, and frameworks are also available for these tasks. Python is utilized by bigger companies mostly that can evaluate vast data sets, thus this is a huge chance to learn it and be a Python programmer.

5. Objective-C

If you are the one who is interested in constructing apps for iOS, then you have to know about Objective-C language efficiently. The most preferred choice for all the web developers is Objective-C. When you have learned Objective-C, you can begin applying XCode that is known to be the authorized software development tool from Apple. This you can quickly produce an iOS app that can be noticeable in App Store.

6. Ruby

Another popular programming language is Ruby and Ruby on Rails. This can be learned easily, and also very strong and clear-cut. If you’ve small time in hand and still want to craft any project, then you can surely utilize Ruby language. This programming language is applied massively for web programming and hence turned out to be the ideal selection for the beginner companies.

7. Perl

Perl is also a well-accepted programming language that offers distinct tools for various obscure setbacks such as system programming. Though this programming language is a bit puzzling, it is really a strong one that you can learn for this year and renew your knowledge. Perl is mainly used for sites and web app expansion, desktop app development and system administration, and test automation that can be applied to testing databases, web apps, networking devices, and much more.

8. C, C++ and C#

You can increase your knowledge by learning about C this year that is a unique programming language. Being the oldest, it should be learned first when you start up, and it is mainly applied in forming different software.

C++ or C plus plus is a bit more progressive than C and utilized immensely in forming hardware speeded games. It is an ideal selection for strong desktop software as well as apps for mobiles and desktop. Known to be the strongest language, C++ is applied in vital operating systems, such as Windows.

After learning these 2, you can go ahead in knowing about C# language. It won’t be difficult for you to get accustomed with C# after knowing C and C++. C# is actually the prime language for Microsoft applications and services. While executing with .Net and ASP technologies, you are required to be familiar with the C# accurately.

9. SQL

When you are executing on databases such as Microsoft SQL Server, Oracle, MySQL, etc, you should be aware of SQL programming language or Standard Query Language. From this language, you can achieve the proficiency of acquiring the needed data from big and multifaceted databases.

10. Swift

Swift is reflected upon as the trendiest program language for expanding apps for Apple products. This language can be utilized by you for building up apps for iOS activated devices and Apple’s MAC in a quick and simple method. When you are keen to expand a superb iOS application, then it is better for you to gain knowledge of Swift programming language.

Hence, the above programming languages are known to be the best ones of 2018. So the developers and programmers should ensure that they’re updated regarding them. Knowing such programming languages will certainly take them to a greater level altogether in their career!

 

Best Machine Learning and Data Science Courses for 2018

Statistics and Data Science in R

Data Science in Rg

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What’s Covered:

  • Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
  • Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
  • Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
  • Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
  • Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Click here continue to improve your Knowledge

 

Complete iOS 11 Machine Learning Masterclass

iOS 11 Machine Learningadd.jpg

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

Click here continue to improve your Knowledge

 

Introduction to Data Science with Python

Data Science with Pythongs.png

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here continue to improve your Knowledge

 

Introduction to Data Science with R

Data Science with Rgfaw.jpg

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here continue to improve your Knowledge

 

Machine Learning In The Cloud With Azure Machine Learning

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The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

Or Amazon’s recommendation engine which recommends products based on buying patterns of millions of consumers.

In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.

This progress in the field of machine learning is great news for the tech industry and humanity in general.

But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.

Well, what if there was an easy to use a web service in the cloud – which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?

The answer – is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.

The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

 

 

5 Best Python Online Courses on Simpliv

Learn Python Programming

Learn Python.jpg

A Note on the Python versions 2 and 3: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

What’s Covered:

  • Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
  • Database operations: Just as much database knowledge as you need to do data manipulation in Python
  • Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
  • Text processing and NLP: Python’s powerful tools for text processing – nltk and others.
  • Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
  • Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
  • Hundreds of lines of code with hundreds of lines of comments
  • Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
  • Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
  • Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
  • Drill #4: Scrape newspapers and a blog and apply several machine learning techniques – classification and clustering to these

How We Need to Keep Grow Up

 

Spark for Data Science with Python

Data Science in R

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Get your data to fly using Spark for analytics, machine learning and data science

Let’s parse that.

  • What’s Spark? If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
  • Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
  • Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

How We Need to Keep Grow Up

 

Machine Learning, NLP & Python-Cut to the Chase

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Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible – but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won’t tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art – all shown by studies to improve cognition and recall.

 

How We Need to Keep Grow Up

 

Image Processing Applications on Raspberry Pi – From Scratch

Image Processing Applications on Raspberry Pi - From Scratch

Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.

The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.

You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.

This course will take beginners without any coding skills to a level where they can write their own programs.

Basics of Python programming language are well covered in the course.

Building Image Processing applications are taught in the simplest manner which is easy to understand.

Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.

The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.

Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.

The course will enable you to independently build Image Processing applications using Raspberry Pi.

This course is the easiest way to learn and become familiar with the Raspberry Pi platform.

By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.

The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.

 

How We Need to Keep Grow Up

 

Python for Beginners 2017

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See why over 350,000 Simpliv members learn coding from Mark Lassoff and LearnToProgram.tv!

Few programming languages provide you with the flexibility and pure power of Python.

If you’re becoming a professional developer, or are early in your development career, adding the Python skill set isn’t just a resume embellishment. It’s an empowering language that will allow you to write procedural code in many types of environments and for many uses.

Python is commonly used for server side programming for complex web applications or as a middle tier language providing web services or a communication layer with larger ecommerce systems. That being said, it’s also a great language for beginners. The clear syntax makes it very easy to learn, and the powerful libraries make all types of programming possible. There are libraries for everything from games and graphics to complex mathematics to network and embedded programming.

 

How We Need to Keep Grow Up

Learn Python Programming

 

#cyber #onlinesecurityA Note on the Python versions 2 and 3: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

What’s Covered:

  • Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
  • Database operations: Just as much database knowledge as you need to do data manipulation in Python
  • Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
  • Text processing and NLP: Python’s powerful tools for text processing – nltk and others.
  • Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
  • Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
  • Hundreds of lines of code with hundreds of lines of comments
  • Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
  • Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
  • Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
  • Drill #4: Scrape newspapers and a blog and apply several machine learning techniques – classification and clustering to these

Using discussion forums

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Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

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  • Yep! Folks with zero programming experience looking to learn a new skill
  • Machine Learning and Language Processing folks looking to apply concepts in a full-fledged programming language
  • Yep! Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you 🙂
Basic knowledge
  • No prior programming experience is needed 🙂
  • The course will use a Python IDE (integrated development environment) called iPython from Anaconda. We will go through a step-by-step procedure on downloading and installing this IDE.
What you will learn
  • Pick up programming even if you have NO programming experience at all
  • Write Python programs of moderate complexity
  • Perform complicated text processing – splitting articles into sentences and words and doing things with them
  • Work with files, including creating Excel spreadsheets and working with zip files
  • Apply simple machine learning and natural language processing concepts such as classification, clustering and summarization
  • Understand Object-Oriented Programming in a Python context

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Basic Python Interview Questions and Answers

Q1. What are the key features of Python?

Ans: These are the few key features of Python:

  • Python is an interpreted language. That means that, unlike languages like C and its variants, Python does not need to be compiled before it is run. Other interpreted languages include PHP and Ruby.
  • Python is dynamically typed, this means that you don’t need to state the types of variables when you declare them or anything like that. You can do things like x=111 and then x="I'm a string" without error
  • Python is well suited to object orientated programming in that it allows the definition of classes along with composition and inheritance. Python does not have access specifiers (like C++’s publicprivate), the justification for this point is given as “we are all adults here”
  • In Python, functions are first-class objects. This means that they can be assigned to variables, returned from other functions and passed into functions. Classes are also first class objects
  • Writing Python code is quick but running it is often slower than compiled languages. Fortunately,Python allows the inclusion of C based extensions so bottlenecks can be optimized away and often are. The numpy package is a good example of this, it’s really quite quick because a lot of the number crunching it does isn’t actually done by Python
  • Python finds use in many spheres – web applications, automation, scientific modelling, big data applications and many more. It’s also often used as “glue” code to get other languages and components to play nice.

Q2. What is the difference between deep and shallow copy?

Ans: Shallow copy is used when a new instance type gets created and it keeps the values that are copied in the new instance. Shallow copy is used to copy the reference pointers just like it copies the values. These references point to the original objects and the changes made in any member of the class will also affect the original copy of it. Shallow copy allows faster execution of the program and it depends on the size of the data that is used.

Deep copy is used to store the values that are already copied. Deep copy doesn’t copy the reference pointers to the objects. It makes the reference to an object and the new object that is pointed by some other object gets stored. The changes made in the original copy won’t affect any other copy that uses the object. Deep copy makes execution of the program slower due to making certain copies for each object that is been called.

Q3. What is the difference between list and tuples?

Ans: Lists are mutable i.e they can be edited. Syntax: list_1 = [10, ‘Chelsea’, 20]

Tuples are immutable (tuples are lists which can’t be edited). Syntax: tup_1 = (10, ‘Chelsea’ , 20)

Q4. How is Multithreading achieved in Python?

Ans:

  1. Python has a multi-threading package but if you want to multi-thread to speed your code up.
  2. Python has a construct called the Global Interpreter Lock (GIL). The GIL makes sure that only one of your ‘threads’ can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread.
  3. This happens very quickly so to the human eye it may seem like your threads are executing in parallel, but they are really just taking turns using the same CPU core.
  4. All this GIL passing adds overhead to execution. This means that if you want to make your code run faster then using the threading package often isn’t a good idea.

Q5. How can the ternary operators be used in python?

Ans: The Ternary operator is the operator that is used to show the conditional statements. This consists of the true or false values with a statement that has to be evaluated for it.

Syntax:

The Ternary operator will be given as:
[on_true] if [expression] else [on_false]x, y = 25, 50big = x if x < y else y

Example:

The expression gets evaluated like if x<y else y, in this case if x<y is true then the value is returned as big=x and if it is incorrect then big=y will be sent as a result.

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Q6. How is memory managed in Python?

Ans:

  1. Python memory is managed by Python private heap space. All Python objects and data structures are located in a private heap. The programmer does not have an access to this private heap and interpreter takes care of this Python private heap.
  2. The allocation of Python heap space for Python objects is done by Python memory manager. The core API gives access to some tools for the programmer to code.
  3. Python also have an inbuilt garbage collector, which recycle all the unused memory and frees the memory and makes it available to the heap space.

Q7. Explain Inheritance in Python with an example.

Ans: Inheritance allows One class to gain all the members(say attributes and methods) of another class. Inheritance provides code reusability, makes it easier to create and maintain an application. The class from which we are inheriting is called super-class and the class that is inherited is called a derived / child class.

They are different types of inheritance supported by Python:

  1. Single Inheritance – where a derived class acquires the members of a single super class.
  2. Multi-level inheritance – a derived class d1 in inherited from base class base1, and d2 is inherited from base2.
  3. Hierarchical inheritance – from one base class you can inherit any number of child classes
  4. Multiple inheritance – a derived class is inherited from more than one base class.

Q8. Explain what Flask is and its benefits?

Ans: Flask is a web micro framework for Python based on “Werkzeug, Jinja2 and good intentions” BSD license. Werkzeug and Jinja2 are two of its dependencies. This means it will have little to no dependencies on external libraries.  It makes the framework light while there is little dependency to update and less security bugs.

A session basically allows you to remember information from one request to another. In a flask, a session uses a signed cookie so the user can look at the session contents and modify. The user can modify the session if only it has the secret key Flask.secret_key.

Q9. What is the usage of help() and dir() function in Python?

Ans: Help() and dir() both functions are accessible from the Python interpreter and used for viewing a consolidated dump of built-in functions.

  1. Help() function: The help() function is used to display the documentation string and also facilitates you to see the help related to modules, keywords, attributes, etc.
  2. Dir() function: The dir() function is used to display the defined symbols.

Q10. Whenever Python exits, why isn’t all the memory de-allocated?

Ans:

  1. Whenever Python exits, especially those Python modules which are having circular references to other objects or the objects that are referenced from the global namespaces are not always de-allocated or freed.
  2. It is impossible to de-allocate those portions of memory that are reserved by the C library.
  3. On exit, because of having its own efficient clean up mechanism, Python would try to de-allocate/destroy every other object.

Q11. What is dictionary in Python?

Ans: The built-in datatypes in Python is called dictionary. It defines one-to-one relationship between keys and values. Dictionaries contain pair of keys and their corresponding values. Dictionaries are indexed by keys.

Let’s take an example:

The following example contains some keys. Country, Capital & PM. Their corresponding values are India, Delhi and Modi respectively.

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dict={'Country':'India','Capital':'Delhi','PM':'Modi'}
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print dict[Country]
India
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print dict[Capital]
Delhi
1
print dict[PM]
Modi

Q12. What is monkey patching in Python?

Ans: In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.

Consider the below example:

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# m.py
class MyClass:
def f(self):
print "f()"

We can then run the monkey-patch testing like this:

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import m
def monkey_f(self):
print "monkey_f()"
m.MyClass.f = monkey_f
obj = m.MyClass()
obj.f()

The output will be as below:

monkey_f()

As we can see, we did make some changes in the behavior of f() in MyClass using the function we defined, monkey_f(), outside of the module m.

Q13. What does this mean: *args**kwargs? And why would we use it?

Ans: We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function. **kwargsis used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments. The identifiers args and kwargs are a convention, you could also use *bob and **billy but that would not be wise.

Q14. Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory.

Ans:  Let us first write a multiple line solution and then convert it to one liner code.

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with open(SOME_LARGE_FILE) as fh:
count = 0
text = fh.read()
for character in text:
    if character.isupper():
count += 1

We will now try to transform this into a single line.

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count sum(1 for line in fh for character in line if character.isupper())

Q15. What are negative indexes and why are they used?

Ans: The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses ‘0’ that is uses as first index and ‘1’ as the second index and the process goes on like that.

The index for the negative number starts from ‘-1’ that represents the last index in the sequence and ‘-2’ as the penultimate index and the sequence carries forward like the positive number.

The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order.

Q16. How can you randomize the items of a list in place in Python?

Ans: Consider the example shown below:

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from random import shuffle
x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High']
shuffle(x)
print(x)

The output of the following code is as below.

['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']

Q17. What is the process of compilation and linking in python?

Ans: The compiling and linking allows the new extensions to be compiled properly without any error and the linking can be done only when it passes the compiled procedure. If the dynamic loading is used then it depends on the style that is being provided with the system. The python interpreter can be used to provide the dynamic loading of the configuration setup files and will rebuild the interpreter.

The steps that is required in this as:

  1. Create a file with any name and in any language that is supported by the compiler of your system. For example file.c or file.cpp
  2. Place this file in the Modules/ directory of the distribution which is getting used.
  3. Add a line in the file Setup.local that is present in the Modules/ directory.
  4. Run the file using spam file.o
  5. After successful run of this rebuild the interpreter by using the make command on the top-level directory.
  6. If the file is changed then run rebuildMakefile by using the command as ‘make Makefile’.

Q18. Write a sorting algorithm for a numerical dataset in Python.

Ans: The following code can be used to sort a list in Python:

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list = ["1", "4", "0", "6", "9"]
list = [int(i) for i in list]
list.sort()
print (list)

Q19. Looking at the below code, write down the final values of A0, A1, …An.

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A0 = dict(zip(('a','b','c','d','e'),(1,2,3,4,5)))
A1 = range(10)A2 = sorted([i for i in A1 if i in A0])
A3 = sorted([A0[s] for s in A0])
A4 = [i for i in A1 if i in A3]
A5 = {i:i*i for i in A1}
A6 = [[i,i*i] for i in A1]
print(A0,A1,A2,A3,A4,A5,A6)

Ans: The following will be the final outputs of A0, A1, … A6

A0 = {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4} # the order may vary
A1 = range(0, 10) 
A2 = []
A3 = [1, 2, 3, 4, 5]
A4 = [1, 2, 3, 4, 5]
A5 = {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
A6 = [[0, 0], [1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49], [8, 64], [9, 81]]

Q20. Explain split(), sub(), subn() methods of “re” module in Python.

Ans: To modify the strings, Python’s “re” module is providing 3 methods. They are:

  • split() – uses a regex pattern to “split” a given string into a list.
  • sub() – finds all substrings where the regex pattern matches and then replace them with a different string
  • subn() – it is similar to sub() and also returns the new string along with the no. of replacements.

Q21. How can you generate random numbers in Python?

Ans: Random module is the standard module that is used to generate the random number. The method is defined as:

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import random
random.random

The statement random.random() method return the floating point number that is in the range of [0, 1). The function generates the random float numbers. The methods that are used with the random class are the bound methods of the hidden instances. The instances of the Random can be done to show the multi-threading programs that creates different instance of individual threads. The other random generators that are used in this are:

  1. randrange(a, b): it chooses an integer and define the range in-between [a, b). It returns the elements by selecting it randomly from the range that is specified. It doesn’t build a range object.
  2. uniform(a, b): it chooses a floating point number that is defined in the range of [a,b).Iyt returns the floating point number
  3. normalvariate(mean, sdev): it is used for the normal distribution where the mu is a mean and the sdev is a sigma that is used for standard deviation.
  4. The Random class that is used and instantiated creates an independent multiple random number generators.

Q22. What is the difference between range & xrange?

Ans: For the most part, xrange and range are the exact same in terms of functionality. They both provide a way to generate a list of integers for you to use, however you please. The only difference is that range returns a Python list object and x range returns an xrange object.

This means that xrange doesn’t actually generate a static list at run-time like range does. It creates the values as you need them with a special technique called yielding. This technique is used with a type of object known as generators. That means that if you have a really gigantic range you’d like to generate a list for, say one billion, xrange is the function to use.

This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program. It’s a memory hungry beast.

Q23. What is pickling and unpickling?

Ans: Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.

Django – Python Interview Questions

Q24. Mention the differences between Django, Pyramid and Flask.

Ans:

  • Flask is a “microframework” primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use.
  • Pyramid is built for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable.
  • Django can also used for larger applications just like Pyramid. It includes an ORM.

Q25. Discuss the Django architecture.

Ans: Django MVT Pattern:

Django Architecture - Python Interview Questions - EdurekaFigure:  Python Interview Questions – Django Architecture

The developer provides the Model, the view and the template then just maps it to a URL and Django does the magic to serve it to the user.

Q26. Explain how you can set up the Database in Django.

Ans: You can use the command edit mysite/setting.py , it is a normal python module with module level representing Django settings.

Django uses SQLite by default; it is easy for Django users as such it won’t require any other type of installation. In the case your database choice is different that you have to the following keys in the DATABASE ‘default’ item to match your database connection settings.

  • Engines: you can change database by using ‘django.db.backends.sqlite3’ , ‘django.db.backeneds.mysql’, ‘django.db.backends.postgresql_psycopg2’, ‘django.db.backends.oracle’ and so on
  • Name: The name of your database. In the case if you are using SQLite as your database, in that case database will be a file on your computer, Name should be a full absolute path, including file name of that file.
  • If you are not choosing SQLite as your database then settings like Password, Host, User, etc. must be added.

Django uses SQLite as default database, it stores data as a single file in the filesystem. If you do have a database server—PostgreSQL, MySQL, Oracle, MSSQL—and want to use it rather than SQLite, then use your database’s administration tools to create a new database for your Django project. Either way, with your (empty) database in place, all that remains is to tell Django how to use it. This is where your project’s settings.py file comes in.

We will add the following lines of code to the setting.py file:

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DATABASES = {
     'default': {
          'ENGINE' : 'django.db.backends.sqlite3',
          'NAME' : os.path.join(BASE_DIR, 'db.sqlite3'),
     }
}

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Q27. Give an example how you can write a VIEW in Django?

Ans: This is how we can use write a view in Django:

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from django.http import HttpResponse
import datetime
def Current_datetime(request):
     now = datetime.datetime.now()
     html = "<html><body>It is now %s</body></html>" % now
     return HttpResponse(html)

Returns the current date and time, as an HTML document

Q28. Mention what the Django templates consists of.

Ans: The template is a simple text file.  It can create any text-based format like XML, CSV, HTML, etc.  A template contains variables that get replaced with values when the template is evaluated and tags (% tag %) that controls the logic of the template.

Django Template - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Template

Q29. Explain the use of session in Django framework?

Ans: Django provides session that lets you store and retrieve data on a per-site-visitor basis. Django abstracts the process of sending and receiving cookies, by placing a session ID cookie on the client side, and storing all the related data on the server side.

Django Framework - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Framework

So the data itself is not stored client side. This is nice from a security perspective.

Q30. List out the inheritance styles in Django.

Ans: In Django, there is three possible inheritance styles:

  1. Abstract Base Classes: This style is used when you only wants parent’s class to hold information that you don’t want to type out for each child model.
  2. Multi-table Inheritance: This style is used If you are sub-classing an existing model and need each model to have its own database table.
  3. Proxy models: You can use this model, If you only want to modify the Python level behavior of the model, without changing the model’s fields.

Web Scraping – Python Interview Questions

Q31. How To Save An Image Locally Using Python Whose URL Address I Already Know?

Ans: We will use the following code to save an image locally from an URL address

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import urllib.request
urllib.request.urlretrieve("URL", "local-filename.jpg")

Q32. How can you Get the Google cache age of any URL or web page?

Ans: Use the following URL format:

http://webcache.googleusercontent.com/search?q=cache:URLGOESHERE

Be sure to replace “URLGOESHERE” with the proper web address of the page or site whose cache you want to retrieve and see the time for. For example, to check the Google Webcache age of edureka.co you’d use the following URL:

http://webcache.googleusercontent.com/search?q=cache:edureka.co

Q33. You are required to scrap data from IMDb top 250 movies page. It should only have fields movie name, year, and rating.

Ans: We will use the following lines of code:

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from bs4 import BeautifulSoup
import requests
import sys
response = requests.get(url)
soup = BeautifulSoup(response.text)
tr = soup.findChildren("tr")
tr = iter(tr)
next(tr)
for movie in tr:
title = movie.find('td', {'class': 'titleColumn'} ).find('a').contents[0]
year = movie.find('td', {'class': 'titleColumn'} ).find('span', {'class': 'secondaryInfo'}).contents[0]
rating = movie.find('td', {'class': 'ratingColumn imdbRating'} ).find('strong').contents[0]
row = title + ' - ' + year + ' ' + ' ' + rating
print(row)

The above code will help scrap data from IMDb’s top 250 list

Data Analysis – Python Interview Questions

Q34. What is map function in Python?

Ans: map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions.

Q35. How to get indices of N maximum values in a NumPy array?

Ans: We can get the indices of N maximum values in a NumPy array using the below code:

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import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])

Output

[ 4 3 1 ]

Q36. How do you calculate percentiles with Python/ NumPy?

Ans: We can calculate percentiles with the following code

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import numpy as np
a = np.array([1,2,3,4,5])
p = np.percentile(a, 50)  #Returns 50th percentile, e.g. median
print(p)

Output

3

Q37. What advantages do NumPy arrays offer over (nested) Python lists?

Ans:

  1. Python’s lists are efficient general-purpose containers. They support (fairly) efficient insertion, deletion, appending, and concatenation, and Python’s list comprehensions make them easy to construct and manipulate.
  2. They have certain limitations: they don’t support “vectorized” operations like elementwise addition and multiplication, and the fact that they can contain objects of differing types mean that Python must store type information for every element, and must execute type dispatching code when operating on each element.
  3. NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.
  4. NumPy array is faster and You get a lot built in with NumPy, FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.

Q38. Explain the use of decorators.

Ans: Decorators in Python are used to modify or inject code in functions or classes. Using decorators, you can wrap a class or function method call so that a piece of code can be executed before or after the execution of the original code. Decorators can be used to check for permissions, modify or track the arguments passed to a method, logging the calls to a specific method, etc.

Q39. What is the difference between NumPy and SciPy?

Ans:

  1. In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera.
  2. All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors.
  3. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms.
  4. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

Q40. How do you make 3D plots/visualizations using NumPy/SciPy?

Ans: Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy. Matplotlib provides basic 3D plotting in the mplot3d subpackage, whereas Mayavi provides a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.

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Multiple Choice Questions

Q41. Which of the following statements create a dictionary? (Multiple Correct Answers Possible)

a) d = {}
b) d = {“john”:40, “peter”:45}
c) d = {40:”john”, 45:”peter”}
d) d = (40:”john”, 45:”50”)

Answer: b, c & d.

Dictionaries are created by specifying keys and values.

Q42. Which one of these is floor division?

a) /
b) //
c) %
d) None of the mentioned

Answer: b) //

When both of the operands are integer then python chops out the fraction part and gives you the round off value, to get the accurate answer use floor division. For ex, 5/2 = 2.5 but both of the operands are integer so answer of this expression in python is 2. To get the 2.5 as the answer, use floor division using //. So, 5//2 = 2.5

Q43. What is the maximum possible length of an identifier?

a) 31 characters
b) 63 characters
c) 79 characters
d) None of the above

Answer: d) None of the above

Identifiers can be of any length.

Q44. Why are local variable names beginning with an underscore discouraged?

a) they are used to indicate a private variables of a class
b) they confuse the interpreter
c) they are used to indicate global variables
d) they slow down execution

Answer: a) they are used to indicate a private variables of a class

As Python has no concept of private variables, leading underscores are used to indicate variables that must not be accessed from outside the class.

Q45. Which of the following is an invalid statement?

a) abc = 1,000,000
b) a b c = 1000 2000 3000
c) a,b,c = 1000, 2000, 3000
d) a_b_c = 1,000,000

Answer: b) a b c = 1000 2000 3000

Spaces are not allowed in variable names.

Q46. What is the output of the following?

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try:
    if '1' != 1:
        raise "someError"
    else:
        print("someError has not occured")
except "someError":
    print ("someError has occured")

a) someError has occured
b) someError has not occured
c) invalid code
d) none of the above

Answer: c) invalid code

A new exception class must inherit from a BaseException. There is no such inheritance here.

Q47. Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ?

a) Error
b) None
c) 25
d) 2

Answer: c) 25

The index -1 corresponds to the last index in the list.

Q48. To open a file c:\scores.txt for writing, we use

a) outfile = open(“c:\scores.txt”, “r”)
b) outfile = open(“c:\\scores.txt”, “w”)
c) outfile = open(file = “c:\scores.txt”, “r”)
d) outfile = open(file = “c:\\scores.txt”, “o”)

Answer: b) The location contains double slashes ( \\ ) and w is used to indicate that file is being written to.

Q49. What is the output of the following?

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f = None
for i in range (5):
    with open("data.txt", "w") as f:
        if i > 2:
            break
print f.closed

a) True
b) False
c) None
d) Error

Answer: a) True

The WITH statement when used with open file guarantees that the file object is closed when the with block exits.

Q50. When will the else part of try-except-else be executed?

a) always
b) when an exception occurs
c) when no exception occurs
d) when an exception occurs in to except block

Answer: c) when no exception occurs

The else part is executed when no exception occurs.

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