Introduction:

1. Python is a popular programming language created by Guido van Rossum and released in the year 1991. 

2. Python is a more powerful language that offers great tools for data crunching and preparation, as well as for complex scientific data analysis and modeling. 

3. Python today has multiple implementations including Jython, scripted in Java language for Java Virtual Machine.

4. IronPython is the new variety that is written in C# for the Common Language Infrastructure, and the PyPy version is written in RPython and translated into C. 

5. Most of the Python modules work on a community development model and are open-source and free.

Zip Function in Python:

1. The zip() function in Python is basically used to return a zip object.

2. The zip object is basically an iterator of tuples. 

3. Here in the iterator the first item or value in each passed iterator is paired together, and then the second item in each passed iterator is paired together, etc.

4. During the process, if the passed iterators have different lengths, the iterator with the least items decides the length of the new iterator.

Syntax:

zip(iterator1, iterator2, iterator3 ...)

Note:  Here the Iterator objects that will be joined together.

Example:

We can have the following example which shows how the zip function is being used in Python. Here it should be noted that If one tuple contains more items, these items are ignored.

a = ("John", "Charles", "Mike")
b = ("Jenny", "Christy", "Monica", "Vicky")

x = zip(a, b)

Zip in Python 3:

 As you know Python released version 3.0 which is also called Python 3 in December 2008. Technically this version was mainly released in order to fix the major problems that are found in Python 2. As you know Python 3 was incompatible with Python 2 in many aspects. It is backward incompatible and Some features of Python 3 have been backported to Python 2. x versions to make the migration process easy in Python 3.

When we are going to consider the zip function utility Python 3 doesn’t have many differences in terms of syntax but yes in terms of values, it does have. Let us consider the following example which will let you know how the zip function is applied in Python3.

Syntax:
zip(*iterators)
Parameters:
Here the Python iterables or containers that we used to have are ( list, string, etc ).
Return Value :
It is used to return a single iterator object which is used to contains the mapped values from all the containers that are present.

Example:

# Python code to demonstrate the working of zip() 

# initializing lists 

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 

roll_no = [ 4, 1, 3, 2 ] 

marks = [ 40, 50, 60, 70 ] 

# using zip() to map values 

mapped = zip(name, roll_no, marks) 

# converting values to print as set 

mapped = set(mapped) 

# printing resultant values  

print ("The zipped result is : ",end="") 

print (mapped) 

Note: If we are going to compare with Python2 and Python3 then you will find the similarity in maximum cases.

Unzipping in Python:

Similar to zip() the Unzipping is also get implemented. The Unzipping operation in Python means we are going to convert the zipped values back to the individual self as they were initially before the zip() was applied. This is done with the help of “*” operator.

Let us consider the following example which will let you know how the unzipping will work. Here I am going to consider the same example that I have taken in the zip() implementation so that you can easily understand the zip() and unzip().

Example:

# Python code to demonstrate the working of  unzipping 

# First we need to initialize the lists 

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 

roll_no = [ 4, 1, 3, 2 ] 

marks = [ 40, 50, 60, 70 ] 

# using zip() to map values 

mapped = zip(name, roll_no, marks) 

# converting values to print as list 

mapped = list(mapped) 

# printing resultant values  

print ("The zipped result is : ",end="") 

print (mapped) 

print("\n") 

# unzipping values 

namz, roll_noz, marksz = zip(*mapped) 

print ("The unzipped result: \n",end="") 

# printing initial lists 

print ("The name list is : ",end="") 

print (namz) 

print ("The roll_no list is : ",end="") 

print (roll_noz) 

print ("The marks list is : ",end="") 

print (marksz)

Note:

It should be noted that there are many possible applications present that can be said to be expected using Zip. 

We have many relevant examples where we are going to apply such functionality in a wider range. For example, student databases scorecards, or any other utility that requires mapping of groups. 

Scope @ NareshIT:

1. At Naresh IT you will get a good Experienced faculty who will guide you, mentor you, and nurture you to achieve your dream goal.

2. Here you will get good hands-on practice in terms of the practical industry-oriented environment which will definitely help you a lot to shape your future.

3. During the designing process of the application, we will let you know about the other aspects of the application too. 

4. Our Expert trainer will let you know about every in and out of the problem scenario.

Achieving your dream goal is our motto. Our excellent team is working restlessly for our students to click their target. So, believe in us and our advice, and we assure you about your success.

The Python libraries offer great tools for data crunching and preparation, as well as for complex scientific data analysis and modeling. Here I am going to discuss the list of top Python frameworks that allow you to carry out complex mathematical computations and create sophisticated models that make sense of your data.

Introduction:

1. As Python is already a proven language in the data science industry and is widely accepted by most of the industry, it is now taken the lead as the toolkit for scientific data analysis and modeling. 

2. Here I would like to highlight some of the most popular and go-to Python libraries for data science. 

3. These are open-sourced libraries, offering alternate ways of deriving the same output. 

4. As technology nowadays gets more and more competitive, data scientists and engineers are continually striving for ways to process information, extract insights, and model, by processing massive datasets. 

5. Python is the only platform where we can be able to explore the various, so you need to be well-versed in the various Python libraries that support your data science tasks and the benefits they offer to make your outputs more robust and speedier.

Here I would like to discuss some important libraries which is mostly required by Python developers.

TensorFlow:

1. It is the best and the Ultimate Machine Learning and Deep Learning Framework, which consists of many libraries that use a system of multi-layered nodes to enable the setting up, training, and deployment of artificial neural networks when working with large datasets. 

2. It was set up by Google Brain and is written in C++ but can be called in Python. 

3. The most prolific applications of TensorFlow are object identification, speech recognition, word embedding, and recurrent neural networks.

4. It is also used for sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. 

5. It also supports production prediction at scale, using the same models used for training.

6. It has many features such as a high level of performance, flexible architecture, and the ability to run on any target like a local machine, a cluster in the cloud, iOS and Android devices, CPUs, or GPUs.

Keras:

1. It is the Library for Neural Networks.

2. Keras is a high-performing library for working with neural networks, running on top of TensorFlow, Theano, and CNTK (Microsoft’s Cognitive Toolkit). 

3. Keras is user-friendly, with simple APIs and easy fast experimentation, making it possible to work on more complex models. 

4. Its modular and extendable nature allows you to use a variety of modules from neural layers, to optimizers, and activation functions to develop a new model. 

5. This makes Keras a good option for data scientists when they want to add a new module as classes and functions.

NumPy :

1. It is being considered as the Core Numeric and Scientific Computation Library.

2. The NumPy is also refer as Numerical Python and it is the core library that forms the mainstay of the ecosystem of data science tools in Python. 

3. It supports scientific computing with high-quality mathematical functions and logical operations on built-in multi-dimensional arrays and matrices. 

4. Besides n-dimensional array objects, NumPy provides functionality in basic algebraic functions, random numbers, basic Fourier transforms, sophisticated random number capabilities, and tools for integrating Fortran code and C/C++ code. 

5. The Array interface of NumPy also allows multiple options to reshape large datasets.

6. It is one of the best data science toolkits and is being used by most other data science or machine learning Python packages (SciPy, MatplotLib, ScikitLearn, etc.) are built on it.

SciPy:

1. As we have already discussed above regarding the NumPy, the SciPy is the Numeric and Scientific Computation Library.

2. SciPy is an important Python library for researchers, developers, and data scientists

3. SciPy is also referred to as Scientific Python which is considered as another core library for scientific computing with algorithms and complex mathematical tools for Python.

4. It contains tools for numerical integration, interpolation, optimization, etc., and helps to solve problems in linear algebra, probability theory, integral calculus, fast Fourier transform, signal processing, and other such tasks of data science.

5. The SciPy key data structure is also a multidimensional array, implemented by NumPy.

6. It basically gets set up after the NumPy installation is done on the environment.

7. It offers an edge to NumPy by improving useful functions for regression, minimization, Fourier transformation, and more.

Pandas:

1. It is considered the Data Analysis Library and is a dedicated library for data analysis, data cleaning, data handling, data discovery, and steps executed prior to machine learning projects.

2. It is basically used to provide tools for shaping, merging, reshaping, and slicing datasets.

3. Here we have three types of data structures such as “series” (single-dimensional, homogenous array), “data frames” (two-dimensional, heterogeneous columns), and “panel” (three-dimensional, size mutable array). 

4. These are used to enable merging, grouping, filtering, slicing, and combining data, besides providing a built-in time-series functionality. Data in multiple formats such as CSV, SQL, HDFS, or Excel can also be processed easily.

5. The Panda is the go-to library for data analysis in domains like finance, statistics, social sciences, and engineering. 

6. Its easy adaptability, and ability to work well with incomplete, unstructured, and uncategorized data, make it popular among data scientists.

SciKit-Learn:

1. It is basically used for the Data Analysis and Machine Learning Library to solve complex machine learning problems.

2. It basically used to provide algorithms for common machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, feature extraction, image processing, model selection, and pre-processing. 

3. It is built on the top of SciPy, Numpy, and Matplotlib.

4. SciKit-Learn has great supporting documentation that makes it user-friendly.

5. The various functionalities of SciKit-Learn help data scientists in use cases like spam filters, image recognition, drug response, stock pricing, and customer segmentation.

PyTorch:

1. It is the other Largest Machine Learning Framework used to solve the more complex problems.

2. The PyTorch library has several features that make it the ultimate choice for data science. 

3. It is the largest machine learning library supporting complex tasks like dynamic computational graphs design and fast tensor computations with GPU acceleration. 

4. For applications calling for neural network algorithms, PyTorch offers a rich API. It supports a cloud-based ecosystem for scaling of resources used in deployment and testing.

5. PyTorch allows you to define your computational graph dynamically and transition in graph mode for optimization. 

6. It is a great library for your deep learning research projects as it provides great flexibility and native support for establishing P2P communication.

LightGBM:

1. It is another important concept that is being used in Python.

2. Using the Light Gradient Boosting Machine model to find important features in a dataset with many features.

3. If you look in the lightgbm docs for the feature_importance function, you will see that it has a parameter importance_type. 

4. The two valid values for these parameters are split(default one) and gain. 

5. It is not necessarily important that both split and gain produce the same feature importance. There is a new library for feature importance shape. 

6. Here you should use verbose_eval and early_stopping_rounds to track the actual performance of the model upon training. 

Eli5:

1. For sklearn-compatible estimators, eli5 provides PermutationImportance wrapper.

2. This method can be useful not only for introspection but also for feature selection - one can compute feature importances using PermutationImportance, then drop unimportant features using e.g. sklearn’s SelectFromModel or RFE.

3.  Here the permutation importance should be used for feature selection with care (like many other feature importance measures). 

4. For example, if several features are correlated, and the estimator uses them all equally, permutation importance can be low for all of these features: 

5. Dropping one of the features may not affect the result, as estimator still has access to the same information from other features. 

6. So if features are dropped based on importance threshold, such correlated features could be dropped all at the same time, regardless of their usefulness.

7. The eli5 provides a way to compute feature importances for any black-box estimator by measuring how the score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.

Theano:

1. Theano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently. 

2. It is mostly used in building Deep Learning Projects. 

3. It works away faster on a Graphics Processing Unit (GPU) rather than on the CPU. 

4. Theano attains high speeds that give tough competition to C implementations for problems involving large amounts of data. 

5. It can take advantage of GPUs which makes it perform better than C on a CPU by considerable orders of magnitude under certain circumstances.

6. It is mainly designed to handle the types of computation required for large neural network algorithms used in Deep Learning.

Scope @ NareshIT:

1. At NareshIT’s Python application Development program, you will be able to get extensive hands-on training in front-end, middleware, and back-end technology.

2. It skilled you along with phase-end and capstone projects based on real business scenarios.

3. Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

4. An end-to-end application with exciting features

5. Earn an industry-recognized course completion certificate.

Who Is A Python Developer?

The Python application development is a process where a developer is allowed to deal with the both Front-end and Back-end development of the application. 

1. To be a python developer one should be comfortable with the python library component too as it is being considered as a foundation for developing the application.

2. In Python application Development both the front-end and Back-end development are to be get done like server and client-side applications. 

3. The front-end and Back-end development in Python application involves the logics and thus they are used to get paid highly. 

Here I am going to discuss the few tips which are surely makes you to prepare yourself as Python developer whose primary job is to write web applications using Python technology.

Why Become a Python Developer?

1. The Learning of python is very much essential because it is being indexed in 3rd important ranking in programming approach.

2. Python has great analytical capabilities and multiple libraries geared for Various domains and is a very powerful general-purpose language.

3. In addition, with this it is also being highly recommended by most of the industries in now a days. 

4. It even better is that Python is being preferred for domains like Data Science, Machine Learning, and Artificial Intelligence.

5. It is being expected by the expert that the modern requirement of the industry is in the field of Data Science, Machine Learning and AI.

Job Roles

Being a python developer, you need to do the following.

1. You should to learn how Python works on a framework.

2. As Python has some very powerful frameworks like Django, Flask, and CherryPy so you to learn them also.

3. You must have the understanding how to use ORM libraries like SQLAlchemy and Django ORM. This is easier and faster than writing SQL.

4. You must be comfortable with the technologies like HTML5, CSS3, and JavaScript/jQuery are not a requirement to be a Python developer.

5. But if you can, try to gain a basic understanding of these, and they will let you understand how things work and what is possible.

6. As a Python developer, you may need to work with the front-end team.

7. You should learn GitHub and its simple terms like push, pull, fork, and commit if you want to implement version control

How To Become A Python Developer?

1. If you want to persue your career in Python then decide to learn Python first and you should be very clear regarding why you want to do it.

2. You need to Follow online tutorials. DataFlair has this comprehensive list of Python tutorials where you’ll find everything at one place. Keep practicing as you learn.

3. 3.You need to go through the good books on Python and enroll in an online Python course if possible.

4. Through the continuous learning and proper guidance, you’ll understand the concepts, solve practical’s and assignments, and even work on some exciting projects.

5. Keep reading the official Python documentation for different constructs.

6. Take a peek into the source code of your favourite Python package to know how it works.

Projects for Practice

1. In this particular field the most basic question is What to Learn in Python? So to solve this complex question I need to suggest you some prerequisites as below.

2. Learn the basics. Learn about its history, syntax, installation, and some basic constructs like statements, variables, and operators.

3. Find out about the applications of Python. Also, understand the differences between Python 2 and Python 3.

4. Learn about basic data structures like lists, sets, and dictionaries and Understand the important concepts like decision making and loops.

5. Learn how to create a virtual environment and Move on to functions and recursion.

6. Try to learn how to generate and use random numbers and regular expressions.

7. Learn about more complex topics like networking, XML processing, and multiprocessing.

8. Learn to build GUIs with Python.

9. Find out about exceptions and how to handle them.

10. Learn to use SciPy, NumPy, and Pandas.

11. Learn to debug, unit-test, log, serialize, and access the database.

Program Advantage at NareshIT:

1. At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

2. It skilled you along with phase-end and capstone projects based on real business scenarios.

3. Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

4. An end-to-end application with exciting features

5. Earn an industry-recognized course completion certificate.