Edureka Fan

Edureka Fan


Data Structures & Algorithms in Python

Data Structures & Algorithms in Python | Data Structures in Python

This Edureka video on Data Structures & Algorithms in Python will help you understand the various fundamental concepts in Python and how they are used in Python programming along with examples.  

00:00 Introduction
01:42 Data Structures in Python
04:53 User Defined Data Structures
05:14 What are Algorithms
08:25 Elements of a Good Algorithm
09:30 Algorithm Classes
11:08 Tree Traversal Algorithm
15:44 Sorting Algorithms
19:00 Hands On
24:05 Algorithm Analysis
26:55 Searching Algorithms

#datastructures #algorithms #python


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Data Structures & Algorithms in Python
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Virgil  Hagenes

Virgil Hagenes


Python+ 101: Most useful data structures and algorithms

Python is undoubtedly one of the most widely used programming languages of this century that has given a whole new perspective to programming in terms of ease of use and intuitiveness. Besides having a rich set of the most common data structures, it provides a number of standard libraries that can greatly reduce the burden of developing codes from scratch for a variety of problems pertaining to different fields. In this tutorial, I will focus on some of the most common problems or should I say, encounters, and their solutions using data structures and algorithms of standard python modules. So let’s dive into the world of “Python+” which is all about adding something more into what we know of Python.

1. N largest or smallest elements in a list, tuple, set or numpy array

In many scenarios, we need to extract the largest or smallest elements in a collection. The nlargest and nsmallest methods of the heapq module of python can be used to obtain N largest and smallest elements in a list. nlargest(n,iterable) and nsmallest(n,iterable) where n is the number of largest or smallest elements that need to be obtained and iterable can be a list, a tuple, a set or a numpy array can be used as follows.

Figure 1. Smallest and largest elements in a list, tuple and array

These methods are faster and more efficient in case more than one largest or smallest elements in a collection is needed. If the single largest or smallest element is needed, the min and max functions in python are more efficient. Note that the _nlargest_ and _nsmallest_ functions always return a list irrespective of the type of the collection.

#python-standard-library #python #data-structures #algorithms #data-science

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

Data Structures & Algorithm in Python: Everything You Need to Know

Data structures and algorithms in Python are two of the most fundamental concepts in computer science. They are indispensable tools for any programmer. Data structures in Python deal with the organization and storage of data in the memory while a program is processing it. On the other hand, Python algorithms refer to the detailed set of instructions that helps in the processing of data for a specific purpose.

Alternately, it can be said that different data structures are logically utilized by algorithms to work out a particular problem of data analysis. Be it a real-world problem or a typical coding-related question, an understanding of data structures and algorithms in Python is crucial if you want to come up with an accurate solution. In this article, you will find a detailed discussion of different Python algorithms and data structures.

Learn more: The Six Most Commonly Used Data Structures in R

**What are data structures in Python? **

Data structures are a way of organizing and storing data; they explain the relationship between data and various logical operations that can be performed on the data. There are many ways in which data structures can be classified. One way is to categorize them into primitive and non-primitive data types.

While the primitive data types include Integers, Float, Strings and Boolean, the non-primitive data types are Array, List, Tuples, Dictionary, Sets and Files. Some of these non-primitive data types, such as List, Tuples, Dictionaries and Sets, are in-built in Python. There is another category of data structures in Python that is user-defined; that is, users define them. These include Stack, Queue, Linked List, Tree, Graph and HashMap.

#data science #algorithms in python #data structures #python