Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts

Friday, 13 November 2020

Webscrapping of website using python with complete code

Webscrapping of website t get the latest post published by the website.  In this code bs4 is used. 

Using BeautifulSoup and Telegram, All the new post are updated in our telegram channel. 

for any doubt please do comment. 

Complete python code: 

import requests from bs4 import BeautifulSoup page = requests.get("") soup = BeautifulSoup(page.content, 'html.parser') x = soup.find('div',id="content-wrapper") y = x.find('div',class_="post-outer") z = y.find('a',href=True) msg = z['href'] import telegram chat_id =-1001454266592 token_id = '1428056219:AAHN7iwCdMC9tNKjH-3HK2Y0Hx8uloMqSAk' def send(msg,chat_id=chat_id,token=token_id): bot = telegram.Bot(token=token_id) bot.sendMessage(chat_id=chat_id,text=msg) send(f"New Matlab udate on : {msg}")


Thursday, 5 November 2020

Sunday, 1 November 2020

Python Code for CORONA Updates on Telegram

#Python Code for CORONA Updates on Telegram
from bs4 import BeautifulSoup 
import requests  
import pandas as pd 
for k in z: 
for i in z1: 

import telegram
chat_id = 957639882
token_id = '1339570026:AAE39zN86JK6mxgAGpkJo876Mq75IQW5WZU'
def send(msg,chat_id=chat_id,token=token_id):
    bot = telegram.Bot(token=token_id)
send(f" Todays's Corona Update:\n{df}")


Sunday, 31 May 2020

User defined function | Python | part 2

Saturday, 30 May 2020

User defined function | Python | part 1

Friday, 29 May 2020

Logistic Regression in python (part02) | python crash course08

Logistic Regression in python (part02) :

Hello friends, in the previous post we see input data processing methods. In this post we are going to see How create an Array and How to split data into train,test model. Let's start:
We have about forty-Two thousand and odd record. If we use this entire data for model building, we will not left with any data for testings. So generally, we split the entire data set into two part, say 60/40 percentage. We use 60% of the data for model building and the rest for testing the accuracy in prediction of our created model. You may use a different splitting ratios as per your requirements.

Creating Feature Array:

Before we spliting the data, we separate out the data into two array X and Y. The X arrays contain all the feature that we want to analyzed and Y arrays is a single dimensional arrays of boolean value that is the outputs of the prediction. To understand this, let us run some codes.
 Execute the following Python statements to create the X array −
In [06]: X = data.iloc[:,1:]
To examine the content of X use head to print a few initial record. The following screen show the contents of the X arrays.
In [07]: X.head ()
Initial Records
The arrays has several row and column.
Next, we will be create output array containing “y” value.

Creating Outputs Array:

To create an array for the predicted values columns, use the following Python statements−
In [08]: Y = data.iloc[:,0]
Examine its content by calling head. The screen outputs below show the results −
In [09]: Y.head()
Out[09]: 0   0
1    0
2    1
3    0
4    1
Name: y, dtype: int64
Now, splits the data using the following commands −
In [10]: X_train, X_test, Y_train, Y_test = train_test_split(X, Y)
This will be create the four array called X_train, Y_train, X_test, and Y_test. As before, you may examine the content of these array by using the head commands. We will use X_train and Y_train array for training our models and X_test and Y_test array for testing and validating.
Next Step to built Classifier We will see this into it in the next chapter.
                                                        BEST OF LUCK!!!!

Wednesday, 27 May 2020

Professional Python® Frameworks: Web 2.0 Programming with Django® and TurbogearsTM (Programmer to Programmer) Paperback – 16 October 2007 by Dana Moore (Author), Raymond Budd (Author), William Wright (Author)

As two of the leading MVC web frameworks for Python, Django and TurboGears allow you to develop and launch sites in a fraction of the time compared to traditional techniques and they provide greater stability, scalability, and management than alternatives. Packed with examples, this book will help you discover a new methodology for designing, coding, testing, and deploying rich web applications.

A team of expert authors shows you the power of MVC frameworks and the capabilities of the TurboGears and Django packages. The Django chapters show you how to automate production of common web development tasks, portal creation, and content management, so you can focus on higher–level application issues and design. The TurboGears chapters illustrate how to rapidly create modern, highly interactive Web 2.0 applications. For both frameworks, you′ll create useful applications that exemplify common Web 2.0 design paradigms and their solutions. Ultimately, you′ll leverage your Python skills using Django and TurboGears and go from novice to RIA expert.

What you will learn from this book

∗ How you can use frameworks to save you time and frustration in the development cycle

The elements, differences, and similarities of the TurboGears and Django frameworks

Advanced capabilities of both frameworks and how they easily solve issues common to web applications

Approaches to simplifying your client side JavaScript(r) with MochiKit, a Pythonic JavaScript library

How to pair TurboGears with Flash for even more possibilities

Who this book is for

This book is for Python developers who want to learn rapid Web 2.0 development techniques using frameworks and incorporating a model–view–controller architecture.

Wrox Professional guides are planned and written by working programmers to meet the real–world needs of programmers, developers, and IT professionals. Focused and relevant, they address the issues technology professionals face every day. They provide examples, practical solutions, and expert education in new technologies, all designed to help programmers do a better job.

Buy: Professional Python® Frameworks: Web 2.0 Programming with Django® and TurbogearsTM (Programmer to Programmer) Paperback – 16 October 2007 by Dana Moore (Author), Raymond Budd (Author), William Wright (Author)

PDF Download:

Color Transforms | Image Handling | Python

Check this link to explore more: Python for beginners:


Crop Image | Python

Syntax: ImageVariableName.crop(box) Parameters: box – a 4-tuple defining the left, upper, right, and lower pixel coordinate. Return type: Image (Returns a rectangular region as (left, upper, right, lower) tuple). Note: The Python Imaging Library uses a Cartesian pixel coordinate system, with (0,0) in the upper left corner. Prerequisite: Working with Images in Python | Castor Classes Python for beginners:


Working with Images in Python

Python for beginners:


Reading CSV files without using any library | Python

Code: (Note: It will work only if you open your file with variable e) li=[]; for i in e: i=i.replace("\n","").split(","); li.append(i); e.close() Note the values are float , but in above list li , those are stored as string , you can further type cast to float using this below code: (Be careful in converting string to float , the first row can not be converted into float as those are column names , for rest of the rows you can perform conversion) li=[]; temp=0; for i in e: if(temp!=0): i=i.replace("\n","").split(","); gi=[]; for j in i: gi.append(float(j)); li.append(gi); else: i=i.replace("\n","").split(","); li.append(i); temp+=1; e.close() Python for beginners:


Read a file line by line in Python

Python for beginners:


readline & readlines | Python

Prerequisite: File Handling | Python | Castor Classes Writing to file in Python | Castor Classes Append to a file | Python | Castor Classes Python for beginners:


Append to a file | Python

Prerequisite: File Handling | Python | Castor Classes Writing to file in Python | Castor Classes Python for beginners:


Writing to file in Python

Prerequisite: File Handling | Python | Castor Classes Python for beginners:


Tuesday, 26 May 2020

ThE NeaR FuTuRe

Python is a high-level programming language that is widely used in various kinds of programming activities. Python is known for its object-oriented and interpreted features which make it dynamic. Python enables distinct programming on both smaller and large scale as it has a user-friendly and compact module, which increases the efficiency of applications. Python is mainly used for web-building applications and carrying out many automation activities of cloud and system. It has found its latest application in designing Artificial Intelligence.

Future Scope Of Python in India

Python is standout amongst the most common coding dialects of 2015. Close by the being an abnormal state and universally useful programming dialect, Python is likewise a protest arranged and open source. At the comparable time, a commendable number of engineers across the world have been making utilization of Python to make GUI applications and versatile applications.
It is one of the quickest developing dialects and has experienced an effective range of over 25 years to the extent its selection is concerned.

The programming dialect is by and by being utilized by various high-movement sites including GoogleYahoo GroupsYahoo Maps, Shopzilla, and Web Therapy. Essentially, Python additionally finds endless use for making gamingbudgetary, logical, and informative applications.
This accomplishment also reveals a promising future degree of python programming lingo.

Why Python is so Popular?

The main reason behind the immense popularity of python programming language across the globe is the features it provides which can be followed as:-

  1. Python supports multiple programming paradigm - Python is multi-paradigm programming language which has features like object-oriented, imerative, procedural, functional, reflective, etc.
  2. Easy to code and write - Python has a simple, easy and readable code as compared to other programming languages like C, C++, JAVA .
  3. Python has a rich and supportive community - The greatest part of other programming dialects have unpins 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 work for a long time, so there are plenty of documentation, tutorials, guides and so much more to help a programmer. Also, it has a rich and active community who ensures to provide help and supports to the developers. So, the community consist of many experienced developers and programmer who provides support at any time.
  4. Python contains fewer lines of code - In python programming language codes are written complete in fewer lines thus reducing the efforts of programmers.
  5. Standard Library - Python comes with a huge standard library through which eliminate the efforts to write a function or code. The library consists of many inbuilt functions and pre-written codes, so it is not necessary to have to write a code for every single thing.
  6. Python is accessible  -For newcomers and beginners, Python is incredibly easy to learn and use. In fact, it’s one of the most accessible programming languages available. Part of the reason is the simplified syntax with an emphasis on natural language. But it’s also because you can write Python code and execute it much faster. Whatever the case, it’s a great language for beginners, so it’s where a lot of young developers are getting their start. More importantly, experienced developers aren’t left by the wayside, as there’s plenty to do.

The sudden upturn in Python Language

Be that as it may, python has executed in the year 1980 it isn't well known among designer. In the 21st century, Google made a few deviation in python basic rationale which enhanced its execution and power.
The primary explanation behind python acknowledgement is DATA SCIENCE. Information science and machine learning are may be viewed as the principle driver of its quick advancement.
These reasons have given a sudden boost to the scope of python programming language and it is good for you if you are getting trained in it.

Use of Python in:

Python is an open source and object-oriented programming language which is used for many several purposes:
➤Website programming development
➤Desktop application development
➤GUI application development
➤Writing system administration software
➤Used as the scripting language for SIMP, blender, open office etc
➤Use of python in software testing

Integrating Python with Other Languages:

Python can also be integrating with other languages. There are some mechanisms used to integrate python with another language such as

Iron Python – Implementation of Python running on the CLR.
Jython – Provides an implementation of the JVM

Improved Wrapper and Interface Generator – permits you to interop between C based languages and others, including.Net and Java.

Future Technologies Counting On python

For the most part we have seen that python programming dialects is probably utilized for wave improvement applications advancement Framework organisation creating recreations and so on.

Artificial intelligence python programming dialect is without a doubt rulling alternative dialects when future advances like Artificial Intelligence(AI) comes into the play.There is a long list of Python frameworks, libraries, and tools that are created to direct Artificial Intelligence to reduce human efforts with enhanced accuracy and efficiency for development.

With the help of AI, speech recognition, autonomous cars and data interpretation have become possible. 

Big Data The future extent of Python is clearly evident as it has helped big data technology to grow.

Python is successfully contributing to analyzing the number of data sets across computer clusters with the high-performance toolkit and libraries.

Python libraries and toolkits, Pandas, Scikit-Learn, NumPy, Bokeh, Agate, Dask.

 Networking - Networking is where Python has a future scope as the language is used to read, write and configure routers while performing other networking functions cost-effectively.
Systems administration is another feild in which python has a more briliant extension later on.
Undoubtedly, the awesome benefits and performance of python and its libraries are working as the root of its constant growth.

This foundation is so strong, that almost all top-notch companies are using this language in their codebase.

Moreover, future technologies like AI and its subsets, big data and networking ensure a bright future for this programming tool.  It’s quite a secure and cost-effective programming language.

By seeing such large scale demand skilled professionals, it is rightly said that Python is actually the language of the FUTURE

Monday, 25 May 2020

Learning Python: Powerful Object-Oriented Programming Kindle Edition by Mark Lutz (Author) pdf

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.

Explore Python’s major built-in object types such as numbers, lists, and dictionaries
Create and process objects with Python statements, and learn Python’s general syntax model
Use functions to avoid code redundancy and package code for reuse
Organize statements, functions, and other tools into larger components with modules
Dive into classes: Python’s object-oriented programming tool for structuring code
Write large programs with Python’s exception-handling model and development tools
Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
Buy: Learning Python: Powerful Object-Oriented Programming Kindle Edition by Mark Lutz (Author)


Sunday, 24 May 2020

Logistic Regression in Python(part01) | python crash course_07

Logistic Regression in Python: part-01

Welcome to python crash course, Today we are going to start Logistic Regression. basically, in this post you will learn How to encoding data so let's start:
As the amount of available data, the strength of computing power, and the number of algorithmic improvements continue to rise, so does the importance of data science and machine learningClassification is among the most important area of machine learning, and logistic regression is one of its basic methods. By the end of this tutorial, you will have learned about classification in general and the fundamentals of logistic regression in particular, as well as how to implement logistic regressions in Python.
Supervised machine learning algorithms define models that capture relationships among data. Classification is an area of supervised machine learning that tries to predict which class or category some entity belongs to, based on its features.
For example, you might analyze the employees of some company and try to establish a dependence on the features or variables, such as the level of education, number of years in a current position, age, salary, odds for being promoted. The features or variable can take one of two forms:
  1. Independent variable, also called input or predictor, doesn’t depend on other features of interest (or at least you assume so for the purpose of the analysis).
  2. The dependent variable, also called output or responses, depending on the independent variables.
Encoding Data

In [01]: # creating one hot encoding of categorical column.
data = pd.get_dummies(df, columns =['job', 'marital', 'default', 'housing', 'loan', 'poutcome'])

In [02]: data.head()

You will see the following outputs −
Created Data

Dropping the “unknown”

In [03]: data.columns[12]
Out[03]: 'job_unknown'
In [04]: data.drop(data.columns[[12, 16, 18, 22, 24]], axis=1, inplace=True)
After dropping the undesired columns, you can see the final list of columns as shown in the output below −
In [05]: data.columns
Out[16]: Index(['y', 'job_admin.', 'job_bluecollar', 'jobentrepreneur',
'jobhousemaid', 'job_management', 'job_retired', 'job_self-employed',
'jobservices', 'job_student', 'job_technician', 'job_unemployed',
'marital_divorced', 'marital_married', 'marital_single', 'default_no',
'default_yes', 'housingno', 'housing_yes', 'loan_no', 'loan_yes',
'poutcome_failure', 'poutcome_nonexistent', 'poutcomesuccess'],
our data is ready for model buildings.
In the next post, we will see how to split the data.
If you want to learn more about python then click here.
                                              Best of Luck!!!!!!

Friday, 22 May 2020

File Handling | Python

Topics Discussed: 1)Opening a File 2)Reading from a file 3)Closing a file Python for beginners:


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