Category: Python

31 Python Projects Which Made An Impact Lately

Posted on by probytesnextjs

Python is undoubtedly one of the most successful programming languages as it is still a very simple language.

It’s practiced in several domains, like web development and automation. These days, Python has further become a default language for machine learning projects and data science as well.

All due to its flexibility and power. This is another reason why many Python projects are initiated frequently every year.

Here’s the list of the top 31 Python projects of 2018.

1. Keras:

Keras is a high-grade neural systems API which is written in Python. It’s equipped for running over TensorFlow, Theano, or CNTK. It was created with an attention on empowering fast experimentation.

Keras is been primarily created for quick and easy prototyping through modularity, user-friendliness, and extensibility.

It sustains recurrent networks and convolutional networks both, and in addition the combinations of the two as well.

2. Pytorch:

Dynamic and Tensors neural systems in Python with robust GPU acceleration. It’s standing on 11019 stars on Github and is developed by the service of Adam Paszke and others at PyTorch Team.

One can reuse their preferred Python packages like SciPy, NumPy, and Cython to enlarge PyTorch when required.

PyTorch project package is used either as a substitution of NumPy to utilize the potential of GPUs or as a deep learning analysis platform which gives the greatest speed and flexibility.

3. Scikit-learn:

It’s a productive and simple tool for data analysis and data mining, available to everyone, and reusable in the different setting.

This project was commenced as a Google Summer of Code project by David Cournapeau in 2007, and from then several volunteers have contributed to this project’s functionality.

It is based on matplotlib, NumPy, SciPy, open source, and commercially usable.

The library includes a plenty of efficient tools for statistical modeling as well as machine learning comprising of classification, clustering, regression, and dimensionality reduction.

It is also beneficial for extracting peculiarities from text and images.

4. The Python Bible:

It is created by Ziyad Yehia which is one of the best python courses available on Udemy. This project-based course provides you to build 11 projects in this course.

For those who appreciate hands-on learning while at the same time dealing with the project as opposed to learning specific ideas.

This is a thorough and carefully prepared course that teaches you everything.

With this course, you will learn to build personalized and meaningful user experiences by using Strings and creating programs which can imagine using logic and data frameworks.

Additionally, it also teaches you to automate coding jobs in making your individual python functions.

5. Som-tsp:

This repository is for solving theproblemsof the traveling salesperson with the help of self-organizing mapsto obtain sub-optimal answers for the problems.

Created by Diego Vicente this project has 343 stars rating on Github.

Read also : Why Future of Python Language is Bright?

6. Theano:

Theano is titled after a Greek mathematician. Theano has launched under the BSD license as an open source project and was created by the MILA (then LISA) group at the University of Montreal, Canada.

It enables you to define, advance, and assess mathematical formulations including multi-dimensional arrays productively.

It understands how to use your structures and convert them into highly dynamic code that utilizes NumPy, native code (C++), effective native libraries such as BLAS to work as quickly as possible on GPUs or CPUs.

7. SimpleCoin:

The individuals who are involved with cryptocurrency and blockchain would discover this project intriguing.

The blockchain project with 779 stars on GitHub will enable the engineers to devise a Bitcoin clone as a feature of the project.

This is bizarre as it enables you to preserve your mined hashes and exchange them out in any of the supported currency.

This excludes the trouble of changing pools to mine various coins or having to shift miners.

8. Gensim:

A free Python library with highlights, for example, analyze down plain-text files for semantic structure, adaptable statistical semantics, and recover semantically related files.

This program is based on two Python packages – Scipy and NumPy for logical computing.Also, it is easy to plug into your own input corpus.

The target audience of this software is the information retrieval (IR) and natural language processing (NLP) community.

It features dynamic multicore enactment of famous algorithms, such as Latent Dirichlet Allocation (LDA), online Latent Semantic Analysis (LSA/LSI/SVD), Hierarchical Dirichlet Process (HDP), Random Projections (RP), or word2vec deep learning.

9. Sentry:

Sentry in a general sense is an administration that supports you manage and fix crashes in the real-time. The supported frameworks and languages of this tool are Python, Ruby, Java, JS, iOS, Django, .NET etc.

The server is in Python; however, it comprises of a full API for transferring events in any application, from any language.

It observes errors and shows when, where, and to whom they befall, ensuring to do so without depending entirely on user review.

Read also : Learn How To Host and Deploy a Web App Using Python and Heroku

10. Pylearn2:

It is a machine learning library. The vast majority of its functionality is based over Theano.

This implies you can write Pylearn2 modules such as algorithms, new models, etc.

utilizing mathematicalexpressions, and Theano will streamline and balance out those expressions, and gather them to your choice of backend i.e. GPU or CPU.

11. Cookiecutter:

It is a command line utility that makes the venture from cookiecutters, such as making a Python package venture from a Python package project layout.  Officially compatible on platforms – Mac, Windows, and Linux and supports Python 3.4, 3.5, 3.6, 2.7, and PyPy.

12. Python-fire:

It is a library for automatically creating command line interfaces from totally any object of the Python. It’s a creation of David Bieber and others at Google Brain with the rating of 7775 stars on Github.

It is a valuable tool for generating and debugging Python code.Just write the functionality you need to be detected at the command line as a function/module/class, and next call Fire. With this extension, your CLI is set to go.

13. Detectron:

Facebook AI Research’s product framework that performs best in class algorithms for object detection, incorporating Mask R-CNN. This is controlled by the Caffe2 deep learning system and is written in Python.

14. Multidiff:

Binary information diffing for various objects or surges of data.

With 188 stars rating on Github under the courtesy of Juha Kivekäs, its motive is to make machine-learning data simpler to comprehend by people that perceive it.

In particular, multi diff serves in the review of the differences inside a vast set of objects by performing diffs between applicable objects and showing them in a sensible way.

15. Face recognition:

It recognizes and controls faces from Python or from the command line with the use of the world’s easiest face recognition library.

This likewise gives a straightforward face recognition command line device that gives you a chance to do face recognition on the folder of pictures from the command line!

16. Rebound:

Rebound is also a command line tool that in a split second brings Stack Overflow results if you get a compiler mistake.

This is an extremely convenient library for software engineers.Rebound is supported on MacOS, Windows, and Linux platforms.

17. Chainer:

A Python-based, independent open source system made for deep learning models. It gives an intuitive, adaptable, and elite methods for performing a full scope of deep learning models, comprising of best in class models, for example, variational auto-encoders and recurrent neural networks.

18. Statsmodels:

It is a Python module that enables users to evaluate statistical models, investigate data, and perform statistical tests.

A broad rundown of descriptive insights, plotting functions, statistical tests, and result statistics are accessible for various kinds of data and every estimator.

19. WhatWaf:

The project is ranking on 554 stars on GitHub and as a major aspect of the arrangement, the programmer should distinguish and bypass web application protection and firewalls frameworks.

20. Flask:

Designed to make beginning easy and fast, with the capacity to scale up the complex apps. Flask is a lightweight WSGI web application system.

It started as a basic wrapper around Jinja and Werkzeug and has turned out to be a standout amongst the most well-known Python web application systems.

Read also : 13 Best Python Web Framework For Web Developers

21. Nilearn:

It is a Python module for simple and quick statistical learning on NeuroImaging data. It influences the scikit-learn Python toolbox for multivariate insights with apps, for example, classification, predictive modeling, connectivity analysis, or decoding.

22. Pipenv:

Createdby Kenneth Reitz, it’s a Python Development Workflow for Humans with 7273 stars ranking on Github.

Pipenv is principally intended to offer developers and users of apps with a simple strategy to configure a working environment. It brings the best of all packaging versions like composer, bundler, npm, yarn, load, and so forth. to the Python world.

23. TensorFlow Models:

An open-source repository to discover numerous models and libraries identified with deep learning. In the event that the Machine learning and Deep learning are your forte, this is for you.It has surged in prevalence with over 1500 projects suggest on Github.

TensorFlow enables programmers to train models quicker, iterate instantly, and run more tests.

24. Mask R-CNN:

Mask R-CNN is for segmentation and object detection. The usage of Mask R-CNN on Keras, Python 3, and TensorFlow.

It depends on a ResNet101 backbone and Feature Pyramid Network (FPN). The model creates the enclosing segmentation and boxes masks for each example of an object in the picture.

25. Snallygaster:

It is a tool created and managed by Hanno Böck. It searches for secret files available on web servers that shouldn’t be public and can represent a security threat.

It’s usually employed for the detection of sensitive information or data leak discovery. Target users for this tool are security professionals, developers, and system administrators.

The types of files snallygastercan discover are the Backup files, Apache server status, Configuration files, Git, Private SSH keys, Web shells, and SVN.

26. spaCy (v2.0):

Tool for industrial-strength Natural Language Processing (NLP) with the help of Cython and Python. The fundamental ease of use you will see in spaCy v2.0 is around explaining, instructing, and loading your own particular components and models.

The new neural system models make it substantially simpler to prepare a model starting from scratch, or refresh a current model with a couple of cases.

27. Pyray:

It’s a 3D rendering library written totally in Python with 83 stars on Github. There is a monotony in this library which limits the reliance on external libraries so individuals can’t waste time anywhere else.

The conditions for this project are almost minimum so that majority of the people can run it easily. These include NumpyScipy, and Python Imaging Library (PIL).

28. Magenta:

It is a research project examining the job of machine learning during the procedure of producing music and art. Basically, this includes generating new reinforcement learning and deep learning algorithms for creating tunes, illustrations, images, and different materials.

Though at the same time it’s a research in building brilliant devices and interfaces that enable musicians and artists to expand their procedures utilizing these models.

29. MicroPython:

A proficient and strong Python project which plans to put a usage of Python 3.x on microcontrollers and small embedded frameworks. It has secured 5728 stars on Github.

MicroPython can produce scripts from the precompiled bytecode or textual source form, either from frozen into the MicroPython executable or an on-device filesystem in both the cases. It executes the whole Python 3.4 syntax structure.

However, this project is in the beta stage and is liable to changes of the code-base.

Read also : Tutorial: Python Database Programming For SQLite3

30. Dash:

The interactive, receptive web applications in unadulterated python created by Chris P, securing 3281 stars rank on Github.

Developed over Plotly.js, Flask, and React, Dash links present-day UI components like sliders, dropdowns, and charts specifically to your expository python code. It does not require any JavaScript.

Moreover, Dash application code is receptive and declarative, which makes it simple to fabricate complex applications that include numerous interactive components.

31. Kivy:

It is a cross-platform, an open source Python system for the advancement of apps that make utilization of inventive, multi-touch UIs.

The objective of this Python project is to permit fast and simple interaction design and fast prototyping while making your code deployable and reusable.

Data Science and Predictive Analytics is Changing Healthcare

Posted on by probytesnextjs

The main reason to use the technology in the healthcare sector is that Data science can be used to save a lot of lives whereas

Predictive Analysis can help in predicting the tumour of a patient with the help of MRI.

This system will also help in the mis-classification of diagnoses via medical reports generated electronically or any sort of risk that is related to the re-admission.

Predictive and Data science model can help in a lot of ways that anyone can assume them to. The system will help in the improvement of life quality of patients and better the health outcomes of an individual.

However, there is no doubt that whole health care has its own sets of opportunities and challenges for the data scientists. Some of them are here while others will be explored with time in the future.

Impact In Healthcare

The things about the healthcare sector are that it has a plenty of data that is way too sensitive.

This whole data set includes a diverse set of medical reports such as diagnoses of illness, demographic and even clinical information incorporated on lab tests.

If a patient is suffering from some chronic disease then the detailed history and information will be much longer than expected.

The data that will be available in the hospitals or clinic will be enlarged with each and every visit of a patient.

There is a huge possibility that the reports will be added up to the external information.

The rich data of a patient including their surrounding can easily be analyzed with the help of models that can train in order to come up with a number of outcomes of awareness.

For instance, for a chronic kidney patient, you can easily use up the model that is made specifically for this task that can help in the prediction of the trajectory about the condition that can help a doctor of a clinician to decide the stage of that condition and come up with reliable recovery medical care.

Predictive model is used to notify the doctor about the patient that might require some extra inter positions that can lessen all the negative outcome risks.

For instance, this type of model is used for the prediction of any type of risk of a patient for hospitalization purpose or even the missed out on dialysis of any treatment.

However, the factors that affect the prediction along with all the predictions made are presented to the doctors so that they can come up with an accurate decision that can help a patient and reduce any sort of risk for them.

Challenges In Healthcare

With the data science and predictive analysis comes a lot of challenges that are faced by the healthcare sector. Here are the major challenges that are faced by the healthcare sectors:

1: The top most challenges of the healthcare industry are that it is far behind any other sector.

Other sectors are easily accepting the data science and predictive analytics where healthcare is facing a lot of problems to adopt the latest advancement in the technology and the tools that are widely used for analytics.

This creates a lot of challenges for the data scientists. However, it is essential to understand that the development environment and data infrastructure will never the dead end of the healthcare sector.

This leads to a lot of opportunities and scope for improvement of each technique in this sector. Even the small and traditional models can be altered in such a way that can open many scopes.

2: Next one is all about the sensitivity of the information onthe healthcare sector.

Hospitals and clinicians are afraid about the data privacy and can’t trust technology with delicate information.

This had made it difficult to access data from any company.

Hence, data scientists are required in the healthcare sector that must require to establish different protocols for the professionals of the data that can give access to information.

However, if there are no protocols then it can be extremely difficult to even access any data and will require a lot of efforts.

3: This point is related to the end-use of any of the predictive model that is used in a system.

There are major cases that show different results as per the false positives and false negatives.

A false positive is the one that will results in unnecessary treatments and costly while false negative is the one that can help in determining the health of a patient.

Hence, it is essential to have an in-depth knowledge of the prediction models and the limitations that can come across end-users.

Another important point is that, it is important to determine that the output that comes out of the prediction model is actionable or not.

However, if you are using a prediction model on a patient and the results show high-risk then it will be useful if and only if the output of a model is inter pretable that can easily elaborate the factors that are mainly responsible to put a patient at risk.

On top of that, if interventions are predicted with the help of these models then the factors must be highlighted so that a doctor can work on the change.

But if the factor is age-related then it cannot be altered but can be diminished at a certain level. This point must be kept in mind if these factors will lower the risk via interventions.

Altered Future by Data Science and Predictive Model

Data science and predictive model have a huge scope for the future in the healthcare sector.

It is promising with the help of wearable devices that can track off the performance of an individual.

These devices are used to track the biometric data and activities making them more sophisticated and extremely common.

The data is streamed with the help of some treatment devices or wearable devices in the real-time that can send of an alert to a doctor about the health status outside the premises of a hospital or clinic.

The major issue that is faced by the providers of the medical field is that the data is tended to exist in the storage tower. The integration is not widely used for these major medical electronic records that result in the fragmented care of data.

This practice results in a doctor or clinicians receiving incomplete or outdated information about a patient. It is also possible for them to receive duplication of treatment.

However, it is possible to integrate this system so that data can be accurate and relevant with the help of data engineering. This single tweaking changes the face of this sector and will vastly impact the work load.

It will increase the impending of data engineers and data scientists. Then they can offer more accurate analytical services that will consider all the health condition of a patient result in the more consistent result.

This system will eliminate the problem of duplication of data, prescription of any dangerous drugs or procedures.

How Data analysis and Predictive Model is Used in Health Care Now

We have read how much potential data science and predictive model will have in the field of healthcare.

It can help in expertise the sector with small tricks and tips as explained above. However, we have summed up a few major ways that can help in enhancing the importance of data science and predictive modelling in healthcare.

  • Now, it is possible to put wearable devices in used that can track, monitor and on top of that prevent any heart-related condition by sending an alert to the respective clinic or hospital.

  • It is a great way to improve the diagnostics of a patient with the help of tracking history and predicting the results accurately with efficiency.

  • Now, it is better to turn out the patient care into the precision medicine that can track and monitor the history including symptoms and treatment before coming up with reliable results.

  • This system is advancing towards the cure of chronic diseases with the help of pharmaceutical research.

  • It will help in the optimization of clinical performance that won’t have to undergo all the data since the machine will give an insight into the problem with the help of diagnosis.

Conclusion

There is so much about data science and predictive analytics that can be offered to the healthcare sector.

These models much are adopted in the system that can end up in the improvement of the health of a patient. This has even increased the opportunities for the data scientist in the healthcare industry.

Python For Business Intelligence and Data Engineering

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Python is a popular multi-paradigm high-level language used for various domains in business as well as technical. It is considered the easiest language for beginners to learn and understand because of its readability and syntax.

Python uses fewer line codes which make the whole concept of developing a lot easier. Data scientists are now involved in forming the link between network applications, web programming or automating data.

If you are looking for a programming language that can carry out these tasks, then Python is the one.

Python is a customary programming language with numerous modules that are used in different tasks such as analyzing or visualizing the data.

Modules like Scikits, SciPy, NumPy, R or Disco can be used in Business Intelligence or Data engineering domain with Python.

What are Business Intelligence and Data Engineering?

To some people, these terms might look similar and serve the same purpose but do not have the same result.

Let us briefly understand what are Business Intelligence and Data Engineering.

Business Intelligence:

Business Intelligence (BI) helps business to make better decisions with various tools and methods. BI alone is a broad category consisting of data analysis, data mining or big data.

BI consists of several methods and procedure through which data collection, sharing, and reporting are carried out easily to ensure the better decision making.

With the technological advancement in BI tools, users can easily produce reports and visualization without any support of the IT firm.

BI usually deals with the historical data through which they can determine the trends using simple reporting and analytics tools.

To make informed decisions, the BI carried out an in-depth analysis of historical data through various resources. It also helps users to get answers to their questions related to data.

The tools of BI are designed to display the result of the analysis in such a way even a layman can be able to understand it properly.

Data Engineering or Data Analytics:

Data Analytics (DA) is the process of examining different data sets and reports to generate information with the help of specialized systems and tools.

Data analytics is mostly used in industries to enable the organization to make informed decisions.

Data analytics can help the business in making informed decisions and optimize their business strategies and policies.

These initiatives generally help organizations to increase their efficiency, strategize marketing or increase their revenue and act more quickly with the latest trends to achieve a competitive edge.

The data which has to be analyzed might consist of historical data or new data from both internal and external sources. It focuses on algorithms and patterns.

To understand both of the terms in simple words, we can say that Business Intelligence helps the organizations in making better decisions by using past data while Data Analytics helps the organizations in making predictions and then make decisions which might help the business in the future.

BI is needed to operate the business while DA is needed to transform the business.

How can Python be used in Business Intelligence or Data Engineering?

Python can be very easy to learn and apply to achieve data analysis. If you are thinking you don’t have prior knowledge of Python to start with data analysis. You need to change your mind first.

How much Python you need to understand to perform data analysis? There is no need for you to expertise in Python programming language to work with data sets.

Also Read : 7 Kick-ass Games Built Using Python Language

Thus you need a basic knowledge of Python and need to learn Python libraries. Python libraries consist of several features which offer the user to evaluate and analyze the data sets and produce effective outcomes.

The Python programming language has turned into a robust and powerful tool for data analysis with the help of these libraries. The libraries which are used in DA are listed below:

1. NumPy:

NumPy is a fundamental package for the Python is used generally for scientific computing. With the use NumPy the object for multidimensional arrays, matrices and routines are introduced. These allow the developer in performing the task of advanced mathematical and statistical functions on those arrays and matrices with the minimum code as possible.

2. SciPy:

The SciPy is an open-source Python module which is a collection of mathematical algorithms built on NumPy data structures by adding sets of algorithms, patterns, and high-level commands. These are later used for manipulating and visualizing the data for the analytics process. This library usually helps in solving differential and integrals numerically, optimization and more.

3. Pandas:

Pandas library is used for data manipulation which is based on NumPy data structure. It also provides various functions in the analysis of finance, statistics, social sciences, and This library offers tools which can shape the raw data into useful datasets. It also provides several functions for accessing, indexing, merging or grouping data easily.

4. IPython:

IPython is a higher version of the Python interpreter which provides great features to data scientists. It helps in creating clean and clear reports and statistics for the data analysis. IPython is also an embeddable interpreter for the programs.

5. Matplotlib:

Matplotlib library is used in Python to create graphs and visual representation of the data. It creates interactive 2D and 3D plots which can be very easily You can easily create a graph with little commands and is very flexible to work with statistical analysis.

These libraries will enable the user to handle the raw, incomplete, big data or datasets with less effort. There is no limit to size which you can analyze using Python libraries.

To perform data analysis with Python you need to import Python module i.e. Pandas. Pandas is a software module written for Python programming language which is used for data manipulation and data analysis.

Also Read : Python Development Trends 2018 – [Infographic]

It can perform fairly at a high-performance rate when it is compared to other Python procedures.

Creating a Simple Dataset Using Python by Using Pandas:

Code:

import pandas as pd

dataxyz = {‘Day’:[1, 2, 3, 4, 5], “Visitors”: [1500, 600, 5000, 2000, 4500], ‘Bounce_Rate’: [20, 50, 25, 20, 15]}

df = pd.DataFrame(dataxyz)

print(df)

Output:

We have used import syntax to import Pandas tool. ‘dataxyz’ consists of the data sets such as visitors, day and bounce rate of the website. The dictionary which we have prepared shall be converted to a data frame with the help of pd.dataframe(name of the dictionary).

Pandas module in Python can help in various operations such as:

  1. Slicing the DataFrame: If you want only a part of a particular frame you can easily slice it.
  2. Changing the Index: It also enables in changing the index value of the data frame.
  3. Data Conversion: You can also easily convert the data into a different format.
  4. Changing the column headers: it can also help in changing the column headers of the data.
  5. Concatenation: You can also interlink multiple data frames with the help of Pandas.
  6. Joining and merging: It can also perform an operation like joining and merging of two or more data frames.

Slicing the Data frame:

import pandas as pd

dataxyz = {‘Day’:[1, 2, 3, 4, 5, 6], “Visitors”: [1000, 700, 6000, 1000, 400, 350], ‘Bounce_Rate’: [20, 20, 25, 22, 15, 22]}

df = pd.DataFrame(dataxyz)

print(df.head(2))

Output:

With this help, you can print only a part of data and if you want to print the last part of the data set you can change print(df.head(2)) to print(df.tail(2))

Output:

Merging of Data Frame:

import pandas as pd

data1 = {‘Day’:[1, 2, 3], “Visitors”: [1000, 700, 6000], ‘Bounce_Rate’: [20, 20, 25]}

data2 = {‘Day’:[4, 5, 6], “Visitors”: [100, 7000, 2000], ‘Bounce_Rate’: [30, 25, 45]}

merge = pd.merge(data1, data2)

print(merge)

Output:

Changing the index and column header:

      import pandas as pd

dataxyz = {‘Day’:[1, 2, 3, 4, 5], “Visitors”: [1000, 700, 6000, 1000, 400], ‘Bounce_Rate’: [20, 20, 25, 22, 15]}

data1.set_index(“Day”, inplace=True)

print(data1)

Output:

The Day has now become the index value of the data frame.

Why Future of Python Language is Bright?

Posted on by probytesnextjs

Python programming language is dominating other programming languages such as C, C++ or Java. Python is an Object-Oriented, High-Level multi-paradigm programming language with dynamic features.

It was designed by Guido Van Rossum who was a Dutch programmer. It is one of the most favored programming languages used worldwide.

It has undergone more than 25 years of the successful span and it is one of the fastest growing programming languages. Python itself reveals its success story and a promising future ahead.

Python programming language is best used for application development, web application or web development, game development, system administration, scientific computing etc.

Why has Python become more popular?

Python has gained more popularity than ever. Python provides significant features which catch every programmer’s attention.

Python is very simple to read and write hence, it reduces the confusion among the programmers.

Surprisingly, one of the biggest tech company Google uses Python for their numerous applications and has a full devoted portal to Python.

Here are some features of Python that can project the reasons why it has become so popular.

1. Python Has A Rich And Supportive Community

Most of the other programming languages have supports issues. Also, some of them lack in the documentation which makes it difficult for a programmer to build his project.

Python doesn’t have these issues. It has been around for a long time, so there are plenty of documentation, tutorials, guides and much more to help a programmer.

Also, it has an active and rich community who ensures to provide help and supports to the developers. The community consists of many experienced developers and programmer who provides support at any time.

2. Easy To Code And Write

Python has a simple and readable code as compared to other programming languages like Java, C or C++. The code is expressed in an easy manner which can be simply interpreted even by a beginner programmer.

Although to master Python programming, it will require a lot of effort and time, but to learn this language from scratch is easy for a novice. Even looking at the code he can tell what the code is supposed to do.

3. Open-Source And Availability

Python is an open-source programming language that means its source code is publically available.

You can either modify or use its code directly.

It is freely available and you can download it using this link https://www.Python.org/downloads/ you can start with the Python by simply installing it.

4. Standard Library

Python comes with a huge standard library. These libraries eliminate the effort to write a function or code.

The library consists of many inbuilt functions and pre-written codes, so you don’t have to write a code for every single thing.

This contains expressions, unit-testing, web browsers, databases, threading and much more.

5. Cross-Platform Language

Python can run smoothly on different operating systems such as Windows, Linux, Ubuntu, etc. so it can be interpreted that it is a portable language.

That means if you’ve written your code for the Windows platform, you can also run it on a Mac platform.

There would be no need to make changes to your code to run it on any other platform.

Career Opportunities Associated With Python

With many different programming languages available, Python has tremendously outraced the other languages.

Career opportunities associated with Python have also grown significantly as its popularity has increased by 40%.

Many IT businesses are looking for more candidates with experience and skills in Python programming languages.

This has illustrated the better career scope for the Python programmers in the near future.

Here is the list of the Job profiles for the Python programmers with their salary respectively.

  • Python Developer: plus 300,000k per annum
  • Software Engineer: plus 520,000k per annum
  • Senior Software Engineer: plus 900,000 per annum
  • Software Developer: plus 500,000 per annum
  • DevOps Engineer: plus 600,000 per annum
  • Data Scientist: plus 800,000 per annum

A recent job search has shown, over 40,000 Python jobs were inquired in the USA.

Also, the Data on the Stack Overflow has shown that the Python was the most visited tag across in US and UK.

One of the major reasons for a good career opportunity is the combination of Python language and Data Science.

Top Companies Embracing Python Programming Language

Not only small companies but even top companies are using Python as their business application development.

Even the Central Intelligence Agency (CIA) is using Python to maintain their websites. We have listed a few top companies which are continuous deploying Python’s application in their organizations.

companies using python

Google: Google’s first search engine and the entire stack were written in Python. It was developed in the late 90s which uses old-school They still use Python extensively.

Facebook: Facebook uses the Python language in their Production Engineering.

NASA: NASA uses Workflow Automation Tool which is written in Python.

Nokia: Nokia which is a Finnish company is a popular telecommunication industry. It uses Python for its platform such as S60.

IBM: IBM an American-based multinational computer manufacturer also uses Python for their factor tool control applications.

SGI Inc: SGI (Silicon Graphics International) uses Python for its Linux installer.

Walt Disney Feature Animation: This animation studio uses Python as a scripting language for their animations.

Yahoo! Maps: It is a map developed by Yahoo! and many of its services are also written in Python.

Websites based on Python

Disqus: Disqus is a commenting forum and the entire forum uses the Django Django is a web development tool written in Python.

Dropbox: The entire stack of Dropbox was written in Python.

Quora: Quora is a popular social commenting websites which is also written in Python.

Instagram: Instagram is another website made with Django framework. It uses Python for its front-end

Bit Torrent: The original Bit Torrent client was written in Python.

YouTube: YouTube also uses scripted Python for their websites.

This has clearly shown that most of the biggest MNCs use Python programming language in their applications. Thus it has a vast scope in the future.

Applications of Python

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

  • Web Development
  • Data Science (including Machine Learning)
  • Data Analysis/Visualization

Also, it has applications in game development, desktop applications, embedded applications or scripting.

python applications

1. Web Development:

Web frameworks basically help you to create server-side code that runs on your server.

With the use of web frameworks, it has helped in building common backend logic.

You can use either of the frameworks to built web applications.

Flask has fewer components, simpler and more flexible, while Django has more components, a specific way to deal with databases.

2. Machine Learning:

Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

To deeply understand the role of the Python in machine learning, let’s take an example. Suppose you have to develop a code to determine the picture whether it is a car or a bike.

To write a code which determines the shape and size of the image would become a lot trickier. That is where the machine learning comes into play. ML typically implements an algorithm that automatically detects a pattern in the given inputs.

In this example, you can give thousand of images to the ML algorithm defining which a car is and which a bike is. It will learn the difference between the two. And when you’ll give the image of either a car or a bike, it will be able to recognize which one it is.

You can use the same idea to apply machine learning to things like Recommendation systems, Face recognition and voice recognition. Popular ML algorithms are neural networks, deep learning, support vector machines, and Random forest.

3. Data Analysis and Data Visualization:

Python is also better for data manipulation and repeated tasks.

The future scope of Python is bright as it also helps in the analysis of a large amount of data through its high-performance libraries and tools.

One of the most popular Python libraries for the data visualization is Matplotlib. It’s easy to start with it and also other libraries depend on it. Also, there are various libraries which can be used for data analysis.

  • PySpark
  • IPython
  • PySpark
  • SciPy
  • Scikit-Learn
  • Bokeh

Why Python has a bright future ahead?

Python has been voted for the most favorite programming language of all time. It is undoubtedly beating other programming languages. It has been used for developing almost every kind of applications whether it is web applications or game applications.

Read also : How to Hire a Talented Python Developer?

Numerous programmers have increased the use of Python programming languages and it is certainly used worldwide.

Python programmers would be the most demandable in the future of IT industries which makes Python future brighter.

How to Create Vivid graphs using Python Language

Posted on by probytesnextjs

Plotting graphs in python can be a tricky affair, but a few simple steps can help you generate a graph easily. To generate graphs in Python you will need a library called Matplotlib. It helps in visualizing your data and makes it easier for you to see the relationship between different variables. Before starting with the graph, it is important to first understand Matplotlib and its functions in Python.

Why is data Visualization needed?

Visualization of data is a practice of presenting the data in a simple manner (in graphical or pictorial format), through which even a non-technical person can understand it easily as the human brain can process information easily when it is in pictorial or graphical form.

It allows us to quickly interpret data and adjust different variables to observe their effects. You can simply interpret the information from data visualization which is very helpful for a person (a Non-technical person) to understand.

Introduction to Matplotlib

Matplotlib is a library used in Python to generate graphs and lines for 2D graphics. Matplotlib package is totally written in Python. Matplotlib uses simple commands to generate simple plots for your data.

Installation

The first step is to install the Matplotlib using the pip command given below.

pip install matplotlib

Using pip command it will take care of dependencies while installing the library in Python.

Matplotlib Python Plot

You might be thinking, to start with the plotting graphs in python there would be some typical commands which you will be using to generate graphs. Matplotlib has tremendously reduced that effort which provides a flexible library and much built-in defaults to simply generate graphs. You need to make sure that you make the necessary imports, prepare data and start with the plot() function.

Use this import to get started with your Matplotlib plot:

>>> import matplotlib.pyplot as plt

To generate data later also NumPy import will be used. To import NumPy use this syntax:

>>> import numpy as np

Also, use show() function to show the resulting graph. Let us see a simple example of how we can start generating a graph.

  • # Make sure to import the necessary packages and modules
  • import pyplot as plt
  • import numpy as np
  • # Prepare your data
  • z = np.linspace(0, 10, 100)
  • # Plotting the data
  • plot(z, z, label=’linear’)
  • # Adding a legend
  • legend()
  • # Result
  • plt.show()

Run your code and see the resulting plot.

Result:

plotting graphs in python

You can also look for another example to generate a most simple graph using Matplotlib.

Simple graph

  • from Matplotlib import pyplot as plt
  • # Plotting of graph
  • plot([1,2,3],[4,5,1])
  • # Showing the result
  • plt.show()

Result:

plotting graphs in python

In the above example, we’ve just plotted a simple graph without any title, x-axis or y-axis. Moving forward we will be learning how to add title and labels to the graph.

Adding label and titles to your graph

  • from matplotlib import pyplot as plt
  • x=[5,8,10]
  • y=[12,16,6]
  • plot(x,y)
  • title(‘info’)
  • ylabel(‘Y axis’)
  • xlabel(‘X axis’)
  • show()

Result:

plotting graphs in python

In the above example, we’ve shown the x-axis and y-axis by a simple command plt.ylabel() and title by plt.title(). We have used plot(x,y) instead of using direct numbers for plotting the X and Y axis.

 This graph doesn’t include any style or color. What if you want to add some style or change the width of the line or add color to the graph? We’ll see a simple code to generate a graph with different styles and colors.

Adding style to the graph

  • from matplotlib import pyplot as plt
  • from matplotlib import style
  • use(‘ggplot’)
  • x=[5,8,10]
  • y=[12,16,6]
  • x1=[6,9,11]
  • y1=[6,15,7]
  • plot(x,y,’g’,label=’line one’,linewidth=5)
  • plot(x1,y1,’c’,label=’line two’,linewidth=5)
  • title(‘Epic info’)
  • ylabel(‘Y axis’)
  • xlabel(‘X axis’)
  • show()

Result:

plotting graphs in python

To introduce color in different lines we have used ‘g’ for green and ‘c’ for cyan. We can also introduce the thickness of the line by using linewidth function. As we have only used default grid lines, to change the color of the grid line use this simple command before plt.show()

Result:

plotting graphs in python

If you want to add a highlight to the graph which shows the details of the line you can use legend() function by using this simple command.

Result:

After adding legend() and grid() function code will look like this.

  • from matplotlib import pyplot as plt
  • from matplotlib import style
  • use(‘ggplot’)
  • x=[5,8,10]
  • y=[12,16,6]
  • x1=[6,9,11]
  • y1=[6,15,7]
  • plot(x,y,’g’,label=’line one’,linewidth=5)
  • plot(x1,y1,’c’,label=’line two’,linewidth=5)
  • title(‘Epic info’)
  • ylabel(‘Y axis’)
  • xlabel(‘X axis’)
  • grid(True,color=’K’)
  • legend()
  • show()

In the above examples we have learned how to change width line, style, and grid or add a highlighter and now we’ll see how we can plot different types of graphs using Matplotlib in Python

Types of plots

There are several types of the plot which we will generate in this section using Matplotlib.

  • Bar Graph
  • Histograms
  • Scatter Plot
  • Stack Plot
  • Pie Plot

Bar graph:

Bar graphs are used generally to compare different groups using visualizations. Whether it be a change of market or change in revenue, using a bar graph we can easily determine and compare the actual results.

Code to generate Bar graph in Python:

  • Import pyplot as plt
  • bar([1,3,5,7,9],[5,2,7,8,2], label=“Example one”)
  • bar([2,4,6,8,10],[8,6,2,5,6], label=“Example two”,color=‘g’)
  • legend()
  • xlabel(‘bar number’)
  • ylabel(‘bar height’)
  • title(‘Bar Graph’)
  • show()

Result:

plotting graphs in python

Histogram: Histogram graph is generally used to display the statistical information or the distribution of successive process data set. The histogram is generally used for continuous data. Histogram or Bar graph may seem similar but a general difference between histogram plot and bar graph plot is that a histogram plot is used to display the distribution of variables while bar graph is used to display the comparison between variables.

Code to generate the Histogram graph in Python:

  •  import matplotlib pyplot as plt
  • population_ages=[22,55,62,45,21,22,34,42,42,4,99,102,110,120,121,122,130,111,115,112,80,75,65,54,44,43,42,48]
  • bins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]
  • hist(population_ages, bins, histtype=’bar’,r width=0.8)
  • xlabel(‘x’)
  • ylabel(‘y’)
  • title(‘Histogram’)
  • legend()
  • show()

Result:plotting graphs in python

  1. Scatter Plot: Using a scatter plot you can compare two variables and can determine the correlation between them. The values of the variables are represented in the form of a dot. Example of a scatter plot is shown in the image.

Code to generate Scatter plot:

  • import pyplot as plt
  • x=[1,2,3,4,5,6,7,8]
  • y=[5,2,4,2,1,4,5,2]
  • scatter(x,y, label=’skitscat’, color=’k’, s=25, marker=“o”)
  • xlabel(‘x’)
  • ylabel(‘y’)
  • title(‘Scatter Plot’)
  • legend()
  • show()

Result:

plotting graphs in python

Stack Plot: Stack plot or area plot is similar to the line graphs. They can be used to track changes of one or more variables. Stack plot is good to use when you are tracking changes in two or more related group that make up a whole category. Example of stack plots:

plotting graphs in python

Code to generate Stack Plot

  • importpyplot as plt
  • days = [1,2,3,4,5]
  • sleeping =[7,8,6,11,7]
  • eating = [2,3,4,3,2]
  • working =[7,8,7,2,2]
  • playing = [8,5,7,8,13]
  • plot([],[],color=’m’, label=’Sleeping’, linewidth=5)
  • plot([],[],color=’c’, label=’Eating’, linewidth=5)
  • plot([],[],color=’r’, label=’Working’, linewidth=5)
  • plot([],[],color=’k’, label=’Playing’, linewidth=5)
  • stackplot(days, sleeping,eating,working,playing, colors=[‘m’,’c’,’r’,’k’])
  • xlabel(‘x’)
  • ylabel(‘y’)
  • title(‘Interesting Graph\n Check it out’)
  • legend()
  • show()

Result:

 

Pie Chart: Pie chart is used to display the statistical data in the form of a circular Different variables are represented in the form of ‘pie slices’. Pie chart generally shows the percentage of different categories by dividing the circle into proportional pie slices. A pie chart can be useful to show the exact quantity which has been consumed by the category with representation. It is also useful when comparing more than two variables using a graph.

Code to generate Pie chart:

  • Import pyplot as plt
  • x=[7,2,2,13]
  • activities=[‘sleeping’,’eating’,’working’,’playing’]
  • cols=[‘c’,’m’,’r’,’b’]
  • pie(x,
  • labels=activites,
  • colors=cols,
  •  startangle=90,
  •   shadw=True,
  •   explode=(0,0.1,0,0),
  •    autopct=’%1.1f%%’)
  • title(‘Pie Plot’)
  • show()

Result:

Working with Multiple Plots

As we have discussed various types of plots in the above section, we are going to see how we can work with multiple graphs.

Code:

  • import numpy as np
  • import pyplot as plt
  • def f(t):
  •  returnexp(-t) * np.cos(2*np.pi*t)
  • t1 = np.arange(0.0, 5.0, 0.1)
  • t2 = np.arange(0.0, 5.0, 0.02)
  • subplot(221)
  • plot(t1, f(t1), ‘bo’, t2, f(t2))
  • subplot(222)
  • plot(t2, np.cos(2*np.pi*t2))
  • show()

Result:

plotting graphs in python

Now, you have learned how plotting graphs in python used to be done and what are the various types of plots which you can generate using Matplotlib in Python.

Learn How To Host and Deploy a Web App Using Python and Heroku

Posted on by probytesnextjs

You have developed a web application. It’s ultimately a time to share your wonderful work with the world. Getting your idea or dream project online is really a genuinely basic process with Google App Engine. But how?

How would you deploy it, how would you scale it, how would you control it?

What are the deployment alternatives accessible? Is a shared server sufficient for your web application or do you need a VPS?

Hosting a web application requires the arrangement of three performing artists –

  • The Application

  • The Gateway

  • The Web Server

The purpose behind building a Python-inspired web application is that a person can apply Python code to resolve what content to display to the user and what steps to take. Actually, the code is run by the web server which hosts your site. Thus, the user doesn’t require to install anything to utilize your app; as the user has an Internet connection and a browser, so everything else will be streamed online.

We have created an easy and straightforward guide that shows how you can deploy your Python web application to a server. This will lessen your trouble of searching for the solution on the web.

Here, we’ll learn to publish the Python application online on Heroku.

Deploy web app on Heroku –

We will be utilizing the git tool to transfer the local files to either the cloud or online web server whatever you like. Then, the first step is to download git from https://git-scm.com/downloads and install it.

Step 1. Getting started

  • Make an account in Heroku

  • Install the Python version on your local system.

  • Then Download Heroku CLI or Toolbelt.

Step 2. List Python Dependencies

We have to list the dependencies which are needed for the Heroku environment. To list them run pip freeze from a terminal window.

We want to put those dependencies in a file named requirements.txt. We can make that with one command –

Step 3. Head to Command Prompt

Go to your working directory and hit forthcoming commands.

Open your command line when you are within the myblog folder then type:

This will require you to insert email id and password. Enter your heroku account details as it asks about them.

Step 4. heroku create

This will build an application in Heroku that you can view on Heroku Dashboard.

Heroku does not possess a webserver. Rather, it assumes the application to handle its own webserver. A Python webserver to be employed is gunicorn.

Gunicorn appears as a Python library, then you’ve to install it with pip. To do this in the command line, type –

 

Step 5. Create Procfile

 

Build an empty file and name it Procfile in your current folder.

Afterward in the empty fileenter this line: web: gunicornapp.hello:app The file ought not to have any extension. Thus, ensure the file name is not capturing a .txt extension.

Make a requirements.txt file by typing as follows:

This will start creating Python libraries which are established in your virtual environment and write the list within the requirements.txt file. Then that file will be transmitted and read by the webserver as a result the webserver will know which libraries to install so that application works precisely.

Presently, Heroku by default may run Python 2 for the apps that are sent to it. Hence, it would be a great idea to tell what Python version your application has been created to work with. If you are using Python 3.5.1. then to do that.You must build a runtime.txt file and include this line in there: python-3.5.1

This will show Heroku what Python to apply when running your application.

You have presently developed all your files for deployment with the two files (requirements.txt and Procfile) you built.

We can confirm that our app operates by running python app.py and later go to 127.0.0.1:5000 in the browser to view it in operation.

Step 6. git add.

Now we will add all the files.

Append all your local files to the online repository by typing -git add .

Ensure to incorporate the dot after add. This dot indicates that you are adding the whole directory to the repository.

The following step comprises of building an empty app on heroku and conveying our local files to that app. We will be sending the files utilizing git.

But prior to applying the git commands for sending our files to Heroku, you require to report git who you are. So, type this in the command line:

Ensure to substitute your email address and name properly in the above lines retaining the double quotes.

Form a local git repository by typing:git init

Step 7. git commit -m “App ready to

We took python app deployment as the name for our app. Kindly ensure that you choose your own needed name. When the command is successfully performed, you will notice that your application has been built in the Heroku account beneath the menu called Personal Apps.

Step 8. git push heroku master

Now, the final step!

It’ll push the whole app on Heroku Server.Here we will push our app to Heroku –

git push heroku master

Finally, ensure minimum one situation of the app is running with:

herokups:scale web=1

If you got here easily without any errors, your website must be now live. Ours is at live pythonappdeployment.heroku.com. Yours will be as per the name that was chosen by you.

Step 9. heroku open

Hit the app through created URL or with the above-given command.

Step 10. heroku logs

In case that anything goes wrong than it’s used to verify the logs.

Conclusion

So, why was Heroku an ideal choice in this tutorial?

Heroku, a cloud platform that supports Python web apps is created with different programming languages along with applications developed with Python flask. Heroku make deployment simpler by managing administrative duties on its own. Thus, a person can concentrate on the programming section.

Another great feature is that you can also host your Python web applications free of cost.  While you get more traffic after some time, you may need to sign up for the better plan in order to let your web application function adequately during high traffic.

You can likewise have your personal free subdomain on top of herokuapp.com or utilize your own domain in case you have purchased one.

Why is Python the best option for your Startup?

Posted on by probytesnextjs

As a start-up, the first crucial and pivotal decision to take would to choose the programming language for the development of your particular project. Before choosing one or nodding to the language that you have been using so far.

There are certain questions that you need to ask yourself.

  • What is the best option for your team?

  • What will be easy to learn for your developer’s?

  • What is going to work well in the context?

  • Is cross-platform ability of any help?

  • What is the number of tools that can be used with the programming languages that you are considering?

  • How are general aspects and security going to work in the scenario of a particular language?

Despite all, time is the biggest constraint in this world that will make you feel to complete a project with 100 per cent productivity and reliability that can add value to any business. This is making developers to change their direction to the tool that can help them to prototype at a pater rate – Python.

Why Are Developers Bending Towards Python?

Python is a programming language that is offering so much to a developer such as attributes and various features than any other platform. With the high level, object-oriented language and dynamic semantics, python have managed to grab a lot more attention than predicted.

It is used to develop application at a faster rate with dynamic typing and binding along with built-in data structures. Not only this, if you are looking for a scripting language or glue to connect existing components then Python is the best option.

The best thing is that it will reduce the cost of maintenance of a project due to its syntax that can be learned and managed easily with the readability factor on top. In addition to this, python is among those programming languages that are promoting program modularity and reusability due to its support of modules and packages.

You can even distribute it evenly with the help of interpreters and standard library that are available in the source form or binary digits for all the main platform without any cost.

Let us also keep the fact about the market research in mind for a business. Among all the programming languages, every single one won’t be the perfect platform for your clients and projects. You need to understand that the success or failure of a business will depend on the market.

Why is Python the language for start-ups?

Are you juggling right now with so much to grasp? Well, it is a start-up and it will take some time but don’t let this intimidate you. All the worries are answered with Python.

Innovation

Not only start-ups but the giant platforms like Google, Instagram and Quora also writes their code in Python. It brings so much innovation and versatility to a project that made people fall for it easily. It will take the service to new heights and will help in crossing your milestone by elevating the product and service.

Robustness

There is no doubt that everything is turning to web-based, from social networks to streaming media projects. Big data is the only reason that companies are actually driving a huge amount of data at once. However, it can difficult and complex sometimes to process the data. But Python has it covered in the number of layers and allows developers to deal with such small challenges (for it). This is an essential reason that makes people jump on to python without thinking about anything else.

Scalability

If your start-up is a success only when it will go further. Hence, the main agenda of companies is to make a market reputation and work to maintain it. However, it is essential to see that your business is ready to do so or not. If it can’t handle the growth then it will be difficult to attain the success. Python will help you out with it.

Read also : Python is the Industry’s Most Preferred Language. Know Why?

This will allow you to win against any obstacle that will come out with the simplicity of the language to make sure that is growing as per your goals. This will mark the year’s long journey for you in the industry without many challenges as it will be covered as soon as it arises.

Ubiquitous

From YouTube to Reddit, everyone is bending towards python which has rapidly increased its popularity among individuals. The developers are giving their support to this ever-growing platform that makes it the top choice for the companies. If you are trying to proof your future and stay here for a long run then it will be better to go for a language that is here to stay for a longer period of time which can be forever.

User-Friendly

Python holds a lot of respect and value among all the programming languages due to the easy to use and intuitive nature. Developers appreciate the language because of its wide range of inviting qualities that work as the deciding factors for the start-upsthat end up choosing python without regrets.

Conclusion

Python has managed to excel in all the points with the trusted web framework. Especially when it comes down to prove itself against challenges with the help of speed, efficiency and quality that can’t be matched with any other language available in the market.

So, if you are planning to start-up then you don’t have to think much about the platform to follow.

Read also : 7 Kick-ass Games Built Using Python Language

Python Web Scraping Tutorial

Posted on by probytesnextjs

Did it take the trouble to copy the content of the web page and extract that data directly to your local computer? Or ever gave a thought, how the data is extracted from millions of URLs?

If you are wondering what could be the possible process behind this technique? This article will provide you with the essential information.

Web Scraping and how it works?

Web scraping is also known as data scraping or data extraction technique. This software application is used to extract information from the websites and WebPages. The main focus of this technique is to transform the unstructured data (typically in HTML format) into structured data (useful data).

The work of the web scraper is carried out by a code called ‘scraper’. To gather the useful data from the HTML document, it first sends a GET query using HTTP protocol to a targeted website and then based on the result received it allows you to read the HTML of that web page which you can store on your computer and then shows the result which you are looking for.

Web scraping can be performed by various methods which include every programming language. To make web scrapping easier Python programming language is used.

As the Python provides more ease of use and work environment, web scraping using Python can be very effective.

Web Scraping Vs Web Crawling

The basic difference between the terms can be easily defined by their names itself. Scraping is generally meant scraping or extracting the data from the specified websites whereas, Crawling means to crawl and look for the numerous websites content and then index accordingly in the search engine.

They are used to build an index page for the user and show the useful websites URLs by indexing them. There is no need of web crawling if you want to do web scraping but if you are doing web crawling there is a need of a small portion of web scraping.

Introduction to Web Scraping using Python

For web scraping technique an open source web crawling framework is used. This tool is known as Scrapy which is built on the Python library. As this tool is easy and has a fast access to a library, it can be very useful for web scraping. We can also use beautiful soap which is a library to extract XML or HTML.

Let us start with web scraping with the help of an example. Suppose there are 10 fastest cars in the world and we like to see the top 5 fastest cars based on their views and popularity. We’ll see which sports car has greater views and followers.

We’ll use Python 3 and Python virtual environments for this example. Through web scraping and Python, it can be very easy to achieve. Web scraping selects some of the data that you’ve downloaded from the web and passes it along the other process.

  • Initializing the Python web scraper

To start with the web scraper, you need to set the virtual environment for the Python 3. Use this method to set the following.

You’ll also need to install these packages using pip.

  1. To perform an HTTP request, a requests package has to be installed.

  2. To handle all the HTML processing BeautifulSoap4 has to be installed.

Use this code to install these packages.

After installation of these packages, create your file as cars.py and also include these import statements at the top.

  • Making your Web Requests

The first step is to download the web pages, thus requests package provides the help. This package will help you to do all the tasks of HTTP in Python. For this example, you are only going to need requests.get () function.

This sentence will help you to get the content of the particular URL by making an HTTP GET request. It will return the text content if the URL is some kind of HTML or XML. If not, it will return none.

If the response will be in HTML, it will return true otherwise it will return false

This function will print the log errors which can be useful for you.

The function simple_get() takes a single URL argument and then makes a GET request to that URL. If everything goes smoothly, it will return the content of that particular URL in a raw HTML. If there would be problems like server down or URL is denied then the function will return none.

  • HTML with BeautifulSoap

After collecting the raw HTML data from the URL you can select and extract the document structure from the raw HTML.  We will be using BeautifulSoup for this purpose. BeautifulSoap will produce a structured document of the Raw HTML by parsing them. To see how the BeautifulSoap works let us take a quick example of HTML.

Save this file as example.html. After saving this file you can use BeautifulSoup as:

If we break down this example, we’ll see that the raw HTML data was passed through BeautifulSoup constructor. The html.parser is the second argument supplied here. BeautifulSoup accepts different back-end parser but only the standard back-end parser is html.parser.

By using select() method it will let you use CSS selectors. To locate the elements in the document, this method is used in the html object. In the given example, html.select(‘p’) returns a list. As in the line if p[‘id’] = =’car’, ‘p’ has an HTML attribute which can be accessed like a directory.  The <p id=”car”> attribute in HTML corresponds to id attribute is equal to string ‘car’.

  • Car names

Now, it is time to provide the information to the select () function. When you’ll see the names of the car in your web browser, the name appears in <li> tag and inside this tag, there is a car’s name. Generally, we’ll look for the class element or id element attributes or any other source which provides the unique identification of the information which we want to extract.

You can search the top fastest cars on your web browser and then examine their attributes. Let us consider this look with python.

In these sentences, there are various names which are separated by a newline character. Keeping this in mind, you can extract their names in a single list. You can use this code to generate the list.

This sentence will find the list of the cars and download that specific page and returns a list of strings. It will return one car name at a time.

# This syntax will raise an exception if there is a failure in retrieving the data from the url.

The get_names () function will download the page and get the name of <li> elements and iterates over. To ensure there are no duplicates names, you can add each name in python set and convert this set into a list and returns it.

  • Getting the number of views of the car

Now we have a list of the names and the last thing to do is to gather their views and followers. The code to be used to get the number of views is similar to the code which we have used to get the list of names. In this function, we have to provide the name of the car and the pick the integer value from the web page.

For reference, you can view the example page in the browser’s developer tools. There you can find the text appearance in <a> element which has a href attribute that contains a substring ‘latest-40’. You can start with the function as:

This syntax will accept the name of the car and returns a number of hits or insights on that specific page of the car name. The hits on the page will be received in integer form from the last 40 days as ‘int’

  • Finding errors and overcoming it

The last step would be to find the simple errors in the retrieval of data. To find out the proper structure of the data from an unstructured data can sometimes be messy. So it is wise to keep the track of the errors in this retrieval of data. You can also print the message which shows there are number of cars which were left out from the ranking list. You can write a code as follows:

Everything has done, all that left is to run the script and find the detailed report of the following codes.

  • Reaching the end

Let us take a quick review of what we have completed. First, we have created a list of car names. Second, we have run the iteration on the list of the name individually to generate the number of hits or their popularity.

Third, we have finished the script by sorting the car names with their number of views. After all these things, run the script and review your output.

These are the list of the top 5 cars which are most popular among the people. We are pretty much sure that you’ve learned how the web scraping works and how it can be used with python.

13 Best Python Web Framework For Web Developers

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Python is a progressively favored language. It is widely used high-level programming language for general purpose programming.

It is often used by system administrators, machine learning engineers, desktop and web developers, and data scientists.  It is easily understandable and a powerful language to develop any type of system.

The large user platform of Python provides an upstanding circle. The open source community provides additional support for building programmers seeking help.

A framework is a code library which makes it easier to build scalable, maintainable and reliable web applications by providing re-usable code or extensions for regular operations.

Its low time-consuming feature allows developers to focus on application logic instead of tedious elements. There is numerous, downloadable open source Python web frameworks available online.

Best Python Frameworks:  Django, TurboGears, Tornado, web2py, Zope, Grok, CherryPy, Flask, Quixote, Nevow, BlueBream, Bottle, Pyramid are some of the top Python Web Framework Software.

Django

Django is a high level and most popular web framework for Python.  This high-level and well-organized framework simplifies the development of big and complicated web applications by giving a number of strong features.

It keeps upgrading on regular basis to match the latest trends in web application development. This feature also helps developers to achieve common web developments tasks such as user authentication, sitemap, administration, and RSS feeds.

The developers of Django can take benefit from the in-built security features given by cross-site scripting, clickjacking, Django prevents SQL injection and cross-site request forgery. On contrary, Django also helps programmers to control the website instantly to tackle a sudden spike in traffic.

TurboGears

TurboGears is designed in a way that it overcomes the deficiency of various widely used web frameworks. Python also consists of all written, data-driven web application frameworks. It allows developers and programmers to start creating web applications with the nominal setup.

TurboGears supports numerous databases and data exchange formats and horizontal data division. It also allows developers to ease web application development by taking in use different JavaScript development tools. The programmers can also use Pylons as a web server along with getting benefited by SQLAlchemy and an ORM system.

Tornado

Tornado is a Python web framework. It is was originally developed at FriendFeed. Tornado uses the non-blocking network I/O, by which it can scale thousands of open connections making it just perfect for WebSockets, long polling and other apps that need a long-lived connection to every user.

Tornado cab installed with pip or easy install. Tornado runs on any Unix-like platform but Linux and BSD are recommended for the best performance and scalability.

Web2Py

Web2Py eases down custom web application development, including practical batteries such as a web server, SQL database, and an interface based on a web. It allows programmers to build, modify, manage and change the web application’s efficiency by web browsers.

The developers can also operate Web2Py smoothly on major operating systems and web servers. They can even create web applications driven by web applications by working with various widely used relational database management systems. Simultaneously, Web2Py supports programmers to execute MVC programming and prevent common security failure.

Zope

Zope is also a Python-based, open source web application server. The programmers can further enhance Zope as per their requirement through Python code. Zope is completely different from other web frameworks. It is an object-oriented web application development platform.

Zope’s features help users to create custom web applications as per various business needs. Additionally, Zope also supports both versions of Python programming language, i.e. 2.x and 3.x. Zope 4 further gives you access to take benefit from page templates based on Chameleon and upgrade the performance of web application by minimizing memory utilization.

Grok

Zope Toolkit technology is used to develop a web framework for Python, in Grok. It allows Python users to boost their web application development by using toolkits of Zope libraries, as a set. The programmers have an option to select from a broad range of standalone and group libraries according to the need of the specific project.

The constituent planning used by Gork helps Python developers to reduce complexity in web application development by taking benefit of views, controller, and content objects.  Grok also helps by providing all the necessary resources required to create custom web applications as per the various business needs.

CherryPy

CherryPy is also designed as an object-oriented web framework for Python. It boosts web application development by giving developers an access to write short and brief code, based on object-oriented programming (OOPS) principles. 

But programmers can still simplify custom web application development by getting benefited through the in-built tools offered by CherryPy for static content, sessions, caching, and sessions.

CherryPy is now more than ten years old, and its stability and efficiency is just the same. It is used by many sites for production. CherryPy is one of the oldest we frameworks available for Python, yet it is unknown for many people. It is so because CherryPy is not equipped with in-built support. It also supports profiling, testing, and coverage.

Flask

In Flask, the web framework for python is developed based on Jinja 2 templating language. Flask boosts web application development by providing an in-built debugger and development server.

It also helps in securing integrated unit testing, Jinja 2 templating, RESTful request dispatching, and cookies. The programmers can use particular extensions to extend Flask as per the project’s specific needs.

Quixote

Quixote is a framework used for writing Web-based applications using Python. Its objectives are flexible and high performance when taken into consideration with developing a web application. It includes two major versions, version 1 and version 2 and uses two outsourced libraries,  Jinja2 template engine, and the Werkzeug WSGI toolkit.

They are quite similar but incompatible with each other. These are actively maintained and are frequently used by various public sites. It also has a demo as part of the Quixote distribution.

It offers a basic example of a Quixote application and serves as a template for new applications. Quixote is an open source framework.

Divmod Nevow

Divmod Nevow is a web application toolkit written in Python. It allows the developer to demonstrate the view logic as desired in Python, including a clear Python XML expression syntax named stan to ease this.

However, it also provides great help for designer-edited templates by using mini XML attribute language to give bi-directional template manipulation ability.

Divmod also comprises of Divmod Athena, which is a “two-way web” or “COMET” execution. This acts as a two-way bridge between the server’s Python code and JavaScript code on the client.

BlueBream

BlueBream is also an open source web application framework, server, and library for web developers. It is created by the Zope community and presently known as Zope 3.

This framework is best suitable for both medium and large projects divided into many re-usable and interchangeable components. BlueBream is based on Zoop Toolkit (ZTK). It holds years of experience ensuring that it meets the necessary requirement for consistent, stable and scalable software.

BlueBream uses the Buildout system coded in Python. It employs the ZODB (Zope Object Database) which is a transactional object database offering extremely powerful and easily –usable perseverance.

Bottle

The bottle is a simple, light and fast WSGI micro web-framework for Python. It is issued as a file and does not rely on any library other than the python standard library.

Bottle takes into use a world-wide list of search tracks (bottle.TEMPLATE_PATH) to discover templates on the file system. The Bottle’s  Routing requests function-call mapping along with the support for pure and flexible URLs.

The Bottle’s Routing Template is fast and Python-based and supports Jinja2, cheetah templates, and mako. The Bottle’s Routing Utilities gives convenient access to file uploads, form cookies, headers and other metadata related to HTTP.

Pyramid

Writing web applications is easier with Pyramid. Programmers have the freedom to begin small with “hello world” minimal response web application.

This is very useful and can take you quite far while learning. Pyramid simultaneously offers you many features as your application grows. It makes writing complicated software easy and effortless.

The pyramid is compatible with all supported versions of Python. In-Built value is provided by Full-stack frameworks that tell it what to do. On contrary, the thought of doing something unique or using something better leads to unexpected challenges in the framework.

Pyramid has a very small and slow start but it provides you with many high-quality choices. It is distinctively equipped to scale.

In future, Web developers will have the opportunity to choose from a more wide range of Python frameworks. Some of the above-mentioned frameworks are full-stack, whereas others are not.

Similarly, some of these web frameworks are getting an update which will for sure; compliment the emerging web application development shift.  Therefore, programmers will be able to develop a web application in a more accurate, efficient, and convenient way.

Top 100 Python Interview Questions and Answers

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Are you a Python Developer? Or looking for a job in Python Development? Or is there an interview you want to crack no matter what? Well, you don’t have to waste your time anymore! This post contains 100 Python interview questions and answers which will surely help you.

These sample questions for the interview are framed by our various experienced Python developers and experts. This will give you an idea of what can be asked in the interview related to Python development.

Python Development Interview Questions

1. What is Python?

Python is a general-purpose programming language which is object-oriented and high-level programming mainly used for web applications and development.

2. What are the features Python can offer?

Python can be used to create software, games and web application using several frameworks.

3. Can you compare Java and Python?

Yes, the biggest difference between Java and Python, Python is easier and simpler whereas Java uses more complex codes.

4. Is Python an interpreted language?

Yes, Python has interpreted language because Python programs run directly from the source code.

5. Who was the founder of Python and when was it released?

Guido Van Rossum has founded the Python and it was released in December 1989.

6. Name any programming paradigms which Python include?

Object-oriented, imperative, functional and procedural are the programming paradigms which Python include.

7. Why is Python said to be a High-Level Programming Language?

Python is said to be High-Level Programming language because the language which Python uses is closer to human languages thus which makes it easier for a human to interpret.

8. What do you know about PEP 8? Why is it important?

PEP 8 is Python’s style guide which has set of rules for how to format your Python code. It is important because it shows how the Python code should be formatted.

9. Can you differentiate between pickling and Un-pickling in Python?

Pickling in Python is the process of converting Python object to byte stream whereas unpicking is the reverse operation of the pickling.

10. How will you find the bugs and errors in a Python code?

To find the bugs and errors in Python code we’ll use Python Debugger tool or PDB.  

11. Can you state the names of the tool which are used to find bugs in Python?

PyChecker is used to find bugs in Python. Pylint is another tool which can be used to detect errors.

12. How can you alter functions in Python syntax?

By using Python decorators we can alter the functions easily.

13. What is the use of dictionaries in Python?

Dictionaries in Python are used for mapping of unique keys to values.

14. What are the major two versions of Python Programming language?

Python 2.0 and Python 3.0 are the two versions used Python programming language.

15. What is “The Zen of Python”?

It is the principle which influences the design of the python programming.

16. What is the use of functions help ( ) and dir ( )? How are they different?

Help () and dir () both are used to view the attributes of built-in functions in Python.

Help (): this function is used to display built-in functions as well as to provide help related to the module.

Dir (): dir () can show the methods and attributes of the class.

17. Which command do you use to exit the help window in Python programming?

To exit the help window in Python programming ‘press q’.

18. How would you list all the built-in functions and variables in Python?

By using help () function or dir () function we can list all the built-in functions that are available in the Python.

19. Explain module in Python?

The Module is used to structure a Python program. Modules basically contain a set of functions which you want to use in a Python program.

20. How will you find methods or attributes of an object?

We can find methods or attributes of an object by using built-in functions. Help () or dir () can be used to find the attributes of an object.

21. What do you know about mutable and immutable types in Python?

They are the built-in types of Python.

Mutable built-in types are list, sets, and dictionaries.

Immutable built-in types are strings, tuples, and numbers.

22. Why isn’t memory freed whenever the Python exits?

Memory isn’t freed because Python does not try to destroy every single of its object. Also, certain bits of memory are distributed by the C library which is impossible to get free.

23. Can you explain the term monkey patching in Python?

Monkey Patching is a method by which we can extend our programming while runtime. We can modify the code or extends it while runtime.

24. What does “*args” and “**kwargs” means and why do have to use it?

*args and **kwargs are the special syntax used for the function to pass a variable number of arguments to a function in a Python.

25. Write a single line code to count the capitals letters in a file in Python.

Single line code: count sum(1 for line in cl for character in line if character.isupper())

26. Write a code to randomize the items of a list in their accurate place in Python.

Output:
[‘y‘, ‘v ‘, ‘z ‘, ‘x ‘, ‘w ‘ ]

27. Can you explain the term compilation and linking in Python?

Compilation: allows the new extensions to properly compile without any error.

Linking: After the new extensions are properly compiled without any error, the linking is done.

28. Write a code in Python to sort the numbers in a list.

Output: 1 3 5 6 10

29. How will you modify the strings in Python? Can you name all of them?

We can use different functions which can be used to modify the strings. Split(), sub(), and subn() are the functions used to modify the strings in Python.

30. How will you generate random numbers in Python?

import random

print(random.randint(0,5))

31. Can u state the difference between range and xrange in Python? State their uses.

Both are used to generate a list of number for the user to use. Thus only difference is that xrange only returns the xrange object whereas range returns the Python list of object.

32. Explain the term unittest. What is the use of unittest in Python?

Unittest in Python provides a unit testing framework. It helps in automation testing, shutdown code for tests, aggregation of tests and independence tests.

33. Can you convert a string to a number and how?

Yes, we can convert a string to a number by using built-in functions in Python. For example, ch = “4789” is a string which can be converted to an integer by using

num = int(ch)

34. Can you copy an object in Python and how?

You cannot copy most of the objects but still by using copy.copy() or copy.deepcopy() we can copy an object in Python.

35. What do you understand by the term local and global variables in Python?

Local variables are set to be local when they are declared in the function’s body. Global variables are those variables which are declared outside the function’s body which has to be used.

36. Can you share global variables across the modules?

Yes, we can share a global variable across the modules by creating a special configuration module often called config or cfg.

37. When was the Python 2.0 and Python 3.0 released?

Python 2.0 was released on 16 October 2000 and Python 3.0 was released on 3 December 2008.

38. Name the types of divisions which are used in Python?

True division and floor division are the types of division used in Python.

39. Name few platforms which Python supports?

Python supports various platforms like Windows, Linux, MacOS or any other .NET framework.

40. Define the term pass in Python?

The pass is a placeholder in a statement where nothing has to be written. It should be left blank.

41. What are the conditional statements in Python?

Conditional statements in Python are basically a syntax used to check whether the given condition is true or not.

42. Define the term slicing in Python.

Slicing is a process to select a range of items from a sequence. The Sequence can be list, tuple, strings etc.

43. What is the Lambda expression in Python?

It is used to create a one-time or small function objects in Python.

44. What are the applications of Python?

Python can be used in developing games, web applications, and software.

45. Define the libraries in Python. How is library used in Python?

Libraries are basically collections of functions in Python. It helps the user to perform actions without writing code.

46. State the advantages and disadvantages of Python?

Python is easy to learn and supports various platforms and system. It is object-Oriented Language

Python is slow as well as not a very good language for mobile development. It is impossible to create a high graphics 3D game using Python.

47. How will you represent a statement in Python?

Newline (enter) is used to represent a statement in Python. Use of semicolon at the end of the statement is optional.

48. Can you explain the use of “with” statement?

Using ‘with’ statement in Python, we get the better syntax and exceptions handling. It simplifies exception handling by cleanup tasks.

49. State the difference between modules and packages in Python.

A module is a single Python file whereas package is a collection of modules.

50. If you are a beginner, which programming language would you prefer between Java and Python to develop games and why?

I would prefer Python over Java to develop games. As a beginner Python can be easier as it already supports various platforms in which we can easily create games.

51. Can you suggest me some good frameworks which are used in Python? Can you give an example where they are used?

PyGame, Django, flask are some good frameworks which are used in Python. Pygame is used to create games and Django is used in web application.

52. Name few applications which are developed using the Python language?

Battlefield 2, Quora, BitTorrent, Dropbox, Instagram and many more which are developed using Python Language.

53. Why enumerate ( ) is used in Python?

It is used in Python to simplify the programmer’s task by providing a built-in function which adds a counter to an iterable object.

54. Suggest me the built-in function which can be used to display the contents in reverse order?

Reverse () function is used to display the contents in reverse order.

55. Explain the term List Comprehension.

List comprehension is a method of creating a list while performing some operation on the data through which it can use an iterator.

56. Can you explain the term OOP? Is Python object-oriented language?

OOP stands for Object-Oriented Programming is a programming model in which the programmer defines the data structure and also the types of operations which can be applied to the data structure.

57. Explain the term inheritance in Python.

Inheritance in Python is defining a new class with no modification to an existing class. Hence it is a powerful feature which can be used in OOP.

58. What is super in Python?

The super function in Python is used to gain access from parent to sibling to inherited method.

59. Name some companies which are using Python?

Google, Mozilla, IBM, Yahoo, NASA and many others which are using Python programming language.

60. What are the variables in Python?

In Python to store values, the memory location is reserved, that reserved memory location is known as variable.

61. Can u differentiate between .py and .pyc files? What is the use of these files? 

.py file is used in compilation whereas .pyc is the saved file in Python.

62. Can you retrieve a data from a database? How?

Yes, we can retrieve a data from a database in Python. By using MySQL or SQLite we can retrieve any data from the database.

63. If you want to retrieve a data from a table in MySQL database, which query will you use?

First, we’ll use execute () query than to fetch the data we’ll use fetchone() query.

64. State the difference between copy.copy(x) and copy.deepcopy(x).

Copy.copy(x) returns a shallow copy of x whereas copy.deepcopy(x) returns a deep copy of x.

65. Explain between the terms append ( ) and extend ( ) methods.

Append () is used to add an element in Python whereas extend () is used to merge multiple elements.

66. Can you use Python for web development? Name some web frameworks which can be useful?

Yes, Python can be used for web development. Django, flask or pyramid can be used for web development.

67. State the difference between Django and Flask?

Flask is web-framework generally used to build a small application with a  simpler requirement whereas Django is used to build larger applications.

68. What is Django?

Django is a Python web framework which is used for the web development. It is a high-level and open-source framework which follows the MVT (Model View Template) pattern.

69. Can you explain me the term map function in Python?

Map functions are generally used to apply functions the sequences and other iterables. It is the simple built-in used in Python.

70. What is numPy and sciPy in Python?

SciPy is Python-based system of open-source software for science, engineering or mathematics whereas NumPy is a package used to compute scientific calculations in Python.

71. What is the use of Pygame in Python?

Pygame is an open-source library in Python which is used to make games and multimedia application.

72. Assume list is [1, 22, 33, 44, 55], what is list [-1]?

The answer is

55

73. Which code should be used to open a file Python.txt?

File = open (“Python.txt”,”w+”)

74. Explain Package in Python?

A package in Python is a special directory which contains a special file and multiple modules.

75. Does Python have scope in a programming language?

As it is the most versatile programming language, Python does have a great scope for building web applications.

76. Can you check the file existence in Python?

To check the file existence in Python the most common way is to use exist() or isfile() syntax.

77. Define the term multithreading in Python?

Multithreading in Python is a technique by which several processors can use a single set of code at different stages of execution.

78. What is the syntax of using “else” statement in Python?

if expression:

statement(x)

else:

statement(x)

79. Define the term iteration? Why is iteration used in Python?

Iteration can be defined as the control flow statement which allows the code to be executed repeatedly. Iteration is used for a loop which can be used to execute array.

80. How can you define a class in Python?

A class can be defined as an object constructor. It consists of several attributes which are used for creating objects.

81. Explain Dash framework?

It is an open-source Python framework used for creating analytical web applications. It’s good for beginners who aren’t familiar with the web development.

82. What is the use of a web framework in Python?

The web applications like web services, web resources, and web APIs are supported and developed using web framework

83. Are there any predefined functions available in Python?

Yes, there are numerous predefined functions which are available in Python. bool(), compile(), dict(), float() etc.

84. How would you take the input from the user, state the syntax?

To take the input from the user we’ll use function input() or raw_input().

Syntax: printf “enter the number”
x= raw_input()

85. Is Python good for web development or game development?

Python is good for both the development. As it is easy and simple, this supports multiple frameworks which can be used to create applications.

86. State the difference between list and tuple.

The only difference is that list is mutable type while tuple is an immutable type.

87. Can u list some games which were developed using Python?

Yes, there are many 3D games which use the Python language. Battlefield, EVE online, Pydance or openRTS are some of the games which were developed using Python.

88. Describe index in Python?

Python can be the index in positive and negative numbers. Starting from the positive index, 0 is the first index, 1 is the second index and so on. Whereas negative index starts from -1 and goes on.

89. Do you know any other programming languages which are influenced by Python?

Yes, there are several languages which are influenced by Python. C, C++, ABC, Dylan and many others.

90. How many ways are there to copy an object in Python?

We can use copy.copy() or copy.deepcopy() to make the copies of an object in Python.

91. Can u define a term generator in Python?

Yes, the process for implementing the iterators in Python is known as generators.

92. Explain the term docstring in Python.

Docstring is the way of documenting the Python functions, packages and classes. Docstring is basically a Python documentation string.

93. Write a code to delete a file in Python.

The syntax which is can be used to delete a file in Python:

Import os
Os.remove(“ File_name.txt”)

94. Why ‘ // ’ operator is used in Python?

It is a floor division type, which is used for division of two numbers. The result of the quotient shows only digits before the decimal point.

95. Can we use Python as a scripting language?

Yes, we can use Python as a scripting language. It can be used for web programming like PHP, ruby which is a scripted language.

96. Which framework does Python use to make a web application?

Django, Flask or pyramid are the most popular web framework used to make a web application.

97. What do you know about PDB?

PDB is generally known as Python Debugger. It is a debugger tool which is used to find bugs and errors while runtime of a program.

98. Define the terms split() in Python?

This function is used to split the string into the smaller string using the defined separator.

99. Is Python easy to learn and easy to write?

Yes, Python is very easy to learn and is not complex as compared to other programming languages.

100. What is the use of Flask in Python?

Flask is used to make a simple and small web application. It is independent of external libraries.

Top 100 Python Interview Questions And Answers

Python Programming based jobs are now in high demand. But to crack the initial Q and A interview you need to know certain aspects of Python. Go through this Sl…

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