#python tutorials- file input output

[atplblog]

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A Beginner's Guide to Learning Python: Essential Steps and Concepts

The journey of learning Python from scratch can be an exciting yet daunting task. With its simple syntax, versatility, and wide range of applications, Python has become one of the most popular programming languages in the world.

In this comprehensive guide, we'll delve into the essential steps and concepts to master as a beginner Python learner, equipping you with the knowledge and skills needed to become proficient in Python programming.

1. Getting Started with Python:

Begin by acquainting yourself with Python's basic syntax and structure. Learn about variables, data types, and control flow statements, such as loops and conditional statements. Practice writing simple Python scripts and executing them to understand how Python code works.

2. Understanding Data Structures:

Dive into fundamental data structures in Python, including lists, tuples, dictionaries, and sets. Explore how these data structures are used to store and manipulate data efficiently.

3. Exploring Functions and Modules:

Familiarize yourself with functions and modules in Python. Learn how to define and call functions, pass arguments, and return values. Explore built-in modules and understand how to import them into your Python scripts to leverage existing functionalities.

4. Embracing Object-Oriented Programming (OOP):

Gain a solid understanding of object-oriented programming (OOP) concepts, such as classes, objects, inheritance, and polymorphism. Practice creating classes and objects to model real-world entities and solve problems more effectively.

5. Mastering File Handling and I/O Operations:

Learn how to read from and write to files using Python's file handling capabilities. Explore input/output (I/O) operations, including reading user input from the console and displaying output.

6. Harnessing the Power of Libraries and Frameworks:

Explore popular Python libraries and frameworks based on your interests and career aspirations. Whether it's data analysis, web development, or machine learning, there's a plethora of libraries and frameworks available in Python's ecosystem. Experiment with libraries like NumPy, Pandas, Flask, Django, TensorFlow, and PyTorch to expand your skill set and solve real-world problems.

7. Practicing Regularly:

Consistent practice is key to mastering Python. Challenge yourself with coding exercises, small projects, and coding puzzles to reinforce your understanding of Python concepts. Engage in coding competitions and collaborate with peers to exchange ideas and learn from each other.

8. Leveraging Resources and Community Support:

Take advantage of online resources, tutorials, and documentation to supplement your learning journey. Websites like Stack Overflow, GitHub, and Python.org offer valuable resources and community support for Python learners. Join online forums, Python user groups, or local meetups to connect with like-minded individuals and seek guidance from experienced developers.

9. Cultivating Curiosity and Continuous Learning:

Python is a vast and dynamic language with endless possibilities. Cultivate a curious mindset and explore new topics, technologies, and best practices in the Python ecosystem. Stay updated with the latest trends, updates, and advancements in Python programming to stay ahead in your Python journey.

Conclusion: Embarking on the journey of learning Python from scratch may seem daunting at first, but with dedication, practice, and the right resources, it can be an enriching and rewarding experience. By mastering the fundamental concepts outlined in this guide and embracing a growth mindset, you'll be well on your way to becoming a proficient Python programmer. So, roll up your sleeves, dive into Python, and embark on an exciting journey of discovery and learning!

[edcater]

Python Programming Demystified: A Step-by-Step Tutorial for Success

Introduction

Python, a versatile and powerful programming language, has become the go-to choice for beginners and seasoned developers alike. Known for its readability, simplicity, and vast community support, Python is an excellent language to learn for anyone aspiring to delve into the world of programming. In this step-by-step tutorial, we will demystify Python programming, breaking down the learning process into manageable chunks to ensure a smooth journey for learners of all levels.

Understanding Python's Appeal

Python's popularity is rooted in its simplicity and readability. Its syntax resembles English, making it easy for beginners to grasp. The language emphasises code readability and encourages developers to write clean and maintainable code. Additionally, Python's versatility extends to a wide range of applications, from web development and data science to artificial intelligence and automation.

Setting Up Your Python Environment

Before diving into Python programming, it's crucial to set up your development environment. Python is compatible with various operating systems, and installing it is a straightforward process. provides detailed instructions for downloading and installing the latest version. Alternatively, you can use package managers like Anaconda, which simplifies the installation process and comes bundled with useful libraries for scientific computing.

Getting Started with Python Basics

Once your environment is set up, it's time to familiarise yourself with Python's basic elements. Start by understanding variables, data types (integers, floats, strings, etc.), and basic operations. Python's interactive shell, known as the Python REPL (Read-Eval-Print Loop), is an excellent tool for experimenting with code snippets and getting immediate feedback.

Control Flow: Making Decisions and Iterating

Python offers intuitive constructs for controlling the flow of your programs. Learn about conditional statements (if, elif, else) for making decisions based on certain conditions. Dive into loops (for and while) to iterate through data structures and perform repetitive tasks. Understanding control flow is fundamental to writing effective and efficient Python code.

Working with Functions

Functions are the building blocks of modular and reusable code. In Python, defining and using functions is straightforward. Learn how to create functions, pass parameters, and return values. Delve into the concept of scope, understanding the difference between global and local variables. Mastering functions will enhance your ability to write organised and scalable code.

Data Structures in Python

Python provides a variety of built-in data structures, each serving specific purposes. Explore lists, tuples, sets, and dictionaries to manipulate and organize data efficiently. Understanding when and how to use each data structure is essential for effective Python programming. Additionally, grasp the concept of list comprehensions, a concise and powerful way to create lists.

File Handling and Input/Output Operations

Real-world applications often involve reading from and writing to files. Python simplifies file handling with built-in functions and methods. Learn how to open, read, and write files, handling different file formats such as text and CSV. Mastering file operations is crucial for handling data persistence in your Python applications.

Introduction to Object-Oriented Programming (OOP) in Python

Python supports object-oriented programming, a paradigm that promotes the organization of code into classes and objects. Understand the principles of encapsulation, inheritance, and polymorphism. Learn how to create classes, instantiate objects, and leverage the power of OOP to write modular and maintainable code.

Exploring Python Libraries and Frameworks

Python's strength lies in its extensive libraries and frameworks. Explore popular libraries such as NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for data visualization. Additionally, discover web frameworks like Flask and Django for building web applications. Leveraging these tools will significantly enhance your capabilities as a Python developer.

Conclusion

Python programming may initially seem daunting, but by breaking down the learning process into manageable steps, anyone can embark on a successful coding journey. From mastering the basics to exploring advanced topics like OOP and popular libraries, this step-by-step tutorial serves as a comprehensive guide for both beginners and those looking to expand their Python skills. Remember, practice is key, so don't hesitate to experiment with code, tackle real-world projects, and engage with the vibrant Python community for support and inspiration. Happy coding!

[neeljy]

Machine Learning with Python

A Beginner's Guide

Introduction

Machine learning is a rapidly growing field that empowers computers to learn from data and make predictions or decisions without being explicitly programmed. Python, with its user-friendly syntax and a wealth of libraries, has become the language of choice for beginners in machine learning. In this article, we'll provide a beginner's guide to machine learning with Python, covering the fundamental concepts and essential tools to get started on this exciting journey.

What is Machine Learning?

Machine learning is a subfield of artificial intelligence that focuses on creating algorithms and models that allow computers to learn and improve from data. The primary goal is to develop systems that can make predictions or decisions based on patterns or insights found in large datasets.

Why Python for Machine Learning?

Python is the preferred language for machine learning for several reasons:

Ease of Learning: Python's simple and readable syntax makes it an excellent choice for beginners. To learn Python for Data Science, consider taking a course on it.

Abundance of Libraries: Python offers a rich ecosystem of libraries, including NumPy, Pandas, Scikit-Learn, and TensorFlow, designed specifically for machine learning.

Active Community: Python has a large and active community, which means plenty of resources, tutorials, and forums for newcomers.

Key Concepts in Machine Learning

Supervised Learning: In supervised learning, the algorithm is trained on labeled data, where it learns to make predictions by mapping input data to corresponding output labels. It's used for tasks like classification and regression.

Unsupervised Learning: Unsupervised learning deals with unlabeled data, where the algorithm tries to find patterns or structure in the data. Common applications include clustering and dimensionality reduction.

Overfitting and Underfitting: These are common issues in machine learning where models either learn the training data too well (overfitting) or fail to capture the underlying patterns (underfitting). Proper model selection and evaluation are essential to combat these problems.

Feature Engineering: Feature engineering is the process of selecting, transforming, or creating relevant features from the dataset to improve model performance.

Key Python Libraries for Machine Learning

NumPy: NumPy is a fundamental library for numerical operations. It provides support for working with arrays and matrices, which are essential for data handling and manipulation in machine learning.

Pandas: Pandas is used for data manipulation and analysis. It simplifies the handling of data, particularly in structured formats like CSV or Excel files.

Scikit-Learn: Scikit-Learn is a comprehensive library for machine learning. It includes tools for various machine learning algorithms, model selection, and evaluation.

TensorFlow and PyTorch: These libraries are used for deep learning and neural networks. They offer a flexible platform for building and training complex models.

Steps to Get Started with Machine Learning

Data Collection: Begin by gathering the data you'll use to train and evaluate your models.

Data Preprocessing: Clean, transform, and prepare your data for analysis. This step often includes dealing with missing values, scaling, and encoding categorical variables.

Model Selection: Choose a machine learning algorithm suitable for your problem. Start with simpler models, such as linear regression or decision trees, before moving on to more complex ones.

Model Training: Train your selected model using the training dataset.

Model Evaluation: Assess your model's performance using evaluation metrics like accuracy, precision, recall, or mean squared error.

Hyperparameter Tuning: Fine-tune your model by adjusting hyperparameters to optimize performance.

Predictions and Deployment: Once you're satisfied with your model, use it to make predictions on new, unseen data. In some cases, you may deploy your model for real-world use.

Conclusion

Machine learning with Python offers an exciting journey into the world of data-driven decision-making. This beginner's guide introduces the fundamental concepts and essential tools to kickstart your machine learning adventure. By exploring supervised and unsupervised learning, handling data with Python libraries, and following the steps for building and evaluating models, you'll be well on your way to becoming a proficient machine learning practitioner. Python's accessibility and the wealth of resources available in the community make it an excellent choice for beginners looking to explore this dynamic field.

[saurabhchandrakarbooks]

LEARNING PYTHON FROM SCRATCH IN 2023: A GUIDE WITH SAURABH CHANDRAKAR BOOKS

Python, a versatile and beginner-friendly programming language, continues to be a popular choice for aspiring programmers and seasoned developers alike. Learning Python in 2023 offers a world of opportunities, from web development and data science to automation and artificial intelligence. To embark on this exciting journey, you can leverage the expertise of educators like Saurabh Chandrakar through their books and other resources. In this article, we'll guide you through the process of learning Python from scratch with a focus on using Saurabh Chandrakar books.

1. SETTING UP YOUR PYTHON ENVIRONMENT

The first step in your Python journey is setting up your development environment. In 2023, it's important to ensure you have the latest Python version installed. Visit the official Python website (python.org) to download and install Python on your system. Additionally, choose a code editor or integrated development environment (IDE) to write and run your Python code. Popular choices include Visual Studio Code, PyCharm, and Jupyter Notebook.

2. THE FUNDAMENTALS: SAURABH CHANDRAKAR "PYTHON BASICS" BOOKS

Saurabh Chandrakar books, "Python Basics," is a great resource for beginners. It covers the core concepts of Python in a clear and structured manner. Start with the fundamentals, such as variables, data types, operators, and basic input/output. "Python Basics" provides a solid foundation for your Python journey.

3. CONTROL STRUCTURES AND FUNCTIONS

Once you grasp the basics, it's time to dive into control structures. Learn about if statements, loops (for and while), and exceptions. Saurabh Chandrakar books might also cover these topics in detail. Be sure to understand how to write and use functions - a key aspect of Python programming.

4. DATA STRUCTURES AND OBJECT-ORIENTED PROGRAMMING (OOP)

Explore data structures like lists, tuples, dictionaries, and sets. Understand how to perform operations on these data structures, such as indexing and slicing. Additionally, dive into Object-Oriented Programming (OOP) principles, including classes and objects. "Python Basics" or other books authored by Saurabh Chandrakar may provide insights into these advanced concepts.

5. MODULES, LIBRARIES, AND FILE HANDLING

Python's strength lies in its rich ecosystem of modules and libraries. "Python Basics" or other recommended resources can guide you through using the Python Standard Library and popular external libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Learn how to import and use modules effectively. Also, discover how to read from and write to files, an essential skill for real-world applications.

6. ADVANCED CONCEPTS AND WEB DEVELOPMENT (OPTIONAL)

As you progress, consider delving into advanced Python concepts like list comprehensions, generators, and decorators. These techniques can make your code more efficient and concise. If web development interests you, explore web frameworks like Flask and Django.

7. PRACTICE AND ONLINE RESOURCES

The most effective way to learn Python is through practice. Work on small projects and exercises to reinforce your learning. Take advantage of online Python tutorials, courses, and forums. Saurabh Chandrakar books might offer exercises and projects to apply your knowledge.

8. COMMUNITY ENGAGEMENT

Join Python communities, forums, or local meetups to connect with fellow learners and experienced developers. Sharing your experiences and seeking guidance from others can enhance your learning journey.

9. CONTINUOUS LEARNING

Python is a vast language, and the tech world is constantly evolving. Stay updated with the latest developments and best practices in the Python ecosystem. Saurabh Chandrakar books or online resources can guide you in staying current with Python trends and updates.

Learning Python in 2023 is an exciting endeavor, and leveraging resources like Saurabh Chandrakar books can be an invaluable part of your journey. However, remember that learning to program is a process that requires dedication and persistence. With the right resources and a growth mindset, you can master Python and unlock a world of possibilities in the software development field. Happy coding!

[subashdhoni86]

Linear Regression Single Variable :A Step-by-Step Guide

Predicting Home Prices Using Linear Regression: A Step-by-Step Guide

Introduction:

Welcome to our tutorial on predicting home prices using Linear Regression! In this blog post, we will walk you through the process of building a machine learning model that can accurately predict home prices based on the square footage area of homes in Monroe Township, New Jersey. We will be using Python, the popular library sklearn, and matplotlib for visualization.

Understanding Linear Regression:

Linear Regression is a powerful machine learning algorithm used for predicting numerical values based on input data. In our case, the input data will be the square footage area of homes, and the output will be the corresponding home prices. The fundamental idea behind Linear Regression is to find the best-fitting straight line through the data points, minimizing the sum of errors (residuals) between the actual and predicted values.

Getting Started:

We begin by importing the necessary libraries and loading the dataset into a pandas DataFrame. The dataset contains two columns: 'area' representing the square footage area and 'price' representing the corresponding home prices.

import pandas as pd

import numpy as np

from sklearn import linear_model

import matplotlib.pyplot as plt

df = pd.read_csv('homeprices.csv')

Visualizing the Data:

Before we dive into building the model, let's visualize the data to gain insights and understand the relationship between the square footage area and home prices.

plt.xlabel('area')

plt.ylabel('price')

plt.scatter(df.area, df.price, color='red', marker='+')

plt.show()

Building the Linear Regression Model:

Next, we create a linear regression object and fit it to our data. This process will determine the best-fitting line that represents the relationship between home prices and square footage area.

# Separate the input features (area) and target variable (price)

new_df = df.drop('price', axis='columns')

price = df.price

# Create linear regression object

reg = linear_model.LinearRegression()

reg.fit(new_df, price)

Making Predictions:

Now that our model is trained, we can use it to predict the home price for a given square footage area. Let's predict the price for an area of 3300 square feet and 5000 square feet.

# Predict price for a home with area = 3300 sq. ft.

predicted_price_3300 = reg.predict([[3300]])[0]

print(f"Predicted price for a home with area 3300 sq. ft.: ${predicted_price_3300:.2f}")

# Predict price for a home with area = 5000 sq. ft.

predicted_price_5000 = reg.predict([[5000]])[0]

print(f"Predicted price for a home with area 5000 sq. ft.: ${predicted_price_5000:.2f}")

Generating Predictions for New Data:

Now, let's use our trained model to predict the prices for a list of home areas provided in a separate CSV file named "areas.csv".

area_df = pd.read_csv("areas.csv")

predictions = reg.predict(area_df)

area_df['predicted_prices'] = predictions

area_df.to_csv("prediction.csv", index=False)

Conclusion:

In this tutorial, we successfully built a Linear Regression model to predict home prices based on the square footage area of homes. We learned how to visualize the data, create a Linear Regression object, train the model, and make predictions on new data. Linear Regression is a simple yet powerful algorithm, and it can be used for various prediction tasks beyond home prices.

We hope this tutorial helps you understand the basics of Linear Regression and how it can be applied to real-world problems. Happy predicting!

Note: For a more in-depth understanding, it is essential to explore various evaluation metrics and methods to handle larger datasets. We encourage you to continue your exploration of machine learning to expand your knowledge and skills in this exciting field!

@TalentServe

[inextures]

Is Bokeh the Future of Data Visualization with Interactive Python Plots?

Data visualization plays a crucial role in understanding and communicating insights from data. Python, being a versatile programming language, offers numerous libraries for creating visualizations. Among them, Bokeh stands out as a powerful and user-friendly library that allows you to build interactive visualizations with ease.

Bokeh is an open-source Python library specifically designed for creating interactive visualizations for modern web browsers. It enables you to generate rich, interactive plots, charts, and dashboards that can be easily shared and deployed across different platforms. Whether you’re a data scientist, web developer, or data enthusiast, Bokeh provides a comprehensive set of tools to help you bring your data to life.

With just a few lines of Python code, Bokeh enables you to create interactive, JavaScript-powered visualizations displayable in a web browser.

Its workflow involves two stages selecting visual building blocks and customizing them. Bokeh integrates a Python library responsible for defining visualization content and interactivity with BokehJS, a JavaScript library that handles the display in web browsers. Bokeh automates the generation of JavaScript and HTML code and supports the loading of supplementary JavaScript from Bokeh’s CDN for enhanced functionality.

Key Features of Bokeh:

Interactive Visualizations: Bokeh allows users to create interactive plots that respond to user input, such as mouse movements, clicks, or selections. This interactivity enhances data exploration and enables users to gain deeper insights by dynamically manipulating visual elements.

Multiple Rendering Options: Bokeh offers multiple rendering options, making it flexible for various use cases. It supports rendering visualizations as HTML documents, standalone web applications, or embedded components within other Python frameworks like Flask and Django.

Diverse Plotting Types: Bokeh supports a wide range of plotting types, including scatter plots, line plots, bar plots, histograms, heatmaps, and more. These plot types can be customized extensively to suit specific data visualization requirements.

High-performance Rendering: Bokeh leverages modern web technologies, such as HTML5 Canvas and WebGL, to efficiently render interactive plots. This enables smooth interaction even with large datasets and complex visualizations.

Cross-platform Compatibility: Bokeh visualizations can be displayed on different platforms and devices, including desktops, tablets, and mobile devices. The responsive design ensures that the visualizations adapt to various screen sizes, maintaining their interactivity and readability.

Cross-language Support: Bokeh supports multiple programming languages, including Python, R, and Julia, allowing users to leverage Bokeh’s capabilities in their preferred language environment.

Community and Documentation: Bokeh has an active community of users and developers, providing support, examples, and tutorials. The official Bokeh documentation is comprehensive, making it easier for users to get started and explore its advanced features.

Getting Started with Bokeh:

To start using Bokeh, you need to install it via pip, a package manager for Python. Once installed, you can import Bokeh in your Python script or Jupyter Notebook and begin creating visualizations.

Bokeh provides multiple interfaces for creating plots. The most used interface is the ‘bokeh.plotting’ module, which offers a convenient way to define and customize visual elements. You can create plots, add data, and configure various plot attributes such as titles, axes, legends, and tooltips.

Bokeh follows a declarative approach, allowing users to define plots and visual elements using a concise syntax. Here’s a simple example to illustrate the basic usage of Bokeh:

When you execute these lines of code, Bokeh creates an output file “example.html”. Bokeh also opens a browser to display it.

See the results in browser:

Conclusion:  

Bokeh is a powerful Python library that empowers users to create interactive visualizations for the web. With its intuitive API and extensive customization options, Bokeh enables you to create visually appealing plots, charts, and dashboards that engage and inform your audience. Whether you’re exploring data, presenting insights, or building web applications, Bokeh is a valuable tool in your data visualization toolkit. By leveraging Bokeh’s capabilities, you can effectively communicate complex information, uncover hidden.

Originally published by: Is Bokeh the Future of Data Visualization with Interactive Python Plots?

[codeonedigest]

YouTube Shorts - Python File Handling Tutorial for Beginners | Guide to Python File IO operation

Hi, a new #video on #python #file #handling is published on #codeonedigest #youtube channel. Learn #filehandling in python in 1 minute. Enjoy #programming & #coding with #codeonedigest #python #pythontutorial #pythonlibraries #pythonfiles

What is File Handling in Python? File handling is an important part of any web application. Python has several functions for creating, reading, updating, and deleting files. The key function for working with files in Python is the open function. The open function takes two parameters i.e. filename, and mode. There are four different modes for opening a file. “r” – Read – Opens a file for…

[iwebscrapingblogs]

How To Scrape Expedia Using Python And LXML?

When done manually, gathering travel data for planes is a massive undertaking. There are various possible combinations of airports, routes, times, and costs, all of which are always changing. Ticket rates fluctuate on a daily (or even hourly) basis, and there are numerous flights available each day. Web scraping is one method for keeping track of this information. In this Blog, we'll scrape Expedia , a popular vacation booking site, to get flight information. The flight schedules and pricing for a sender and the receiver pair will be extracted by our scraper.

Data Fields that will be extracted:

Arrival Airport

Arrival Time

Departure Airport

Departure Time

Flight Name

Flight Duration

Ticket Price

No. Of Stops

Airline

Below shown is the screenshot of the data fields that we will be extracting:

Scraping Code:

1. Create the URL of the search results from Expedia for instance, we will check the available flights listed from New York to Miami:

https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:New%20York,%20NY%20(NYC-All%20Airports),to:Miami,%20Florida,departure:04/01/2017TANYT&passengers=children:0,adults:1,seniors:0,infantinlap:Y&mode=search

2. Using Python Requests, download the HTML of the search result page.

3. Parse the webpage with LXML — LXML uses Xpaths to browse the HTML Tree Structure. The XPaths for the details we require in the code have already been defined.

4. Save the information in a JSON file. You can change this later to write to a database.

Requirements

We'll need several libraries for obtaining and parsing HTML for this Python 3 web scraping tutorial. The requirements for the package are shown below.

Install Python 3 and Pip

Install Packages

The code is explanatory

You can check the code from the link here.

Executing the Expedia Scraper

Let's say the script's name is expedia.py. In a command prompt or terminal, input the script name followed by a -h.

usage: expedia.py [-h] source destination date positional arguments: source            Source airport code destination       Destination airport code date              MM/DD/YYYY optional arguments: -h, --help show this help message and exit

The input and output arguments are the airline codes for the source and destination airports, respectively. The date parameter must be in the form MM/DD/YYYY

For example, to get flights from New York to Miami, we would use the following arguments:

python3 expedia.py nyc mia 04/01/2017

The nyc-mia-flight-results.json file will be created as a result of this. json, which will be saved in the same directory as the script.

This is what the output file will look like:

{ "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "12:19a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:00p" } ], "airline": "American Airlines", "flight duration": "1 days 3 hours 19 minutes", "plane code": "738", "plane": "Boeing 737-800", "departure": "LaGuardia, New York", "stops": "Nonstop", "ticket price": "1144.21" }, { "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "arrival_time": "11:15a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:11a" }, { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "8:44p", "departure_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "departure_time": "4:54p" } ], "airline": "Republic Airlines As American Eagle", "flight duration": "0 days 11 hours 33 minutes", "plane code": "E75", "plane": "Embraer 175", "departure": "LaGuardia, New York", "stops": "1 Stop", "ticket price": "2028.40" },

You can download the code at:

import json import requests from lxml import html from collections import OrderedDict import argparse def parse(source,destination,date): for i in range(5): try: url = "https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:{0},to:{1},departure:{2}TANYT&passengers=adults:1,children:0,seniors:0,infantinlap:Y&options=cabinclass%3Aeconomy&mode=search&origref=www.expedia.com".format(source,destination,date) headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'} response = requests.get(url, headers=headers, verify=False) parser = html.fromstring(response.text) json_data_xpath = parser.xpath("//script[@id='cachedResultsJson']//text()") raw_json =json.loads(json_data_xpath[0] if json_data_xpath else '') flight_data = json.loads(raw_json["content"]) flight_info  = OrderedDict() lists=[] for i in flight_data['legs'].keys(): total_distance =  flight_data['legs'][i].get("formattedDistance",'') exact_price = flight_data['legs'][i].get('price',{}).get('totalPriceAsDecimal','') departure_location_airport = flight_data['legs'][i].get('departureLocation',{}).get('airportLongName','') departure_location_city = flight_data['legs'][i].get('departureLocation',{}).get('airportCity','') departure_location_airport_code = flight_data['legs'][i].get('departureLocation',{}).get('airportCode','') arrival_location_airport = flight_data['legs'][i].get('arrivalLocation',{}).get('airportLongName','') arrival_location_airport_code = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCode','') arrival_location_city = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCity','') airline_name = flight_data['legs'][i].get('carrierSummary',{}).get('airlineName','') no_of_stops = flight_data['legs'][i].get("stops","") flight_duration = flight_data['legs'][i].get('duration',{}) flight_hour = flight_duration.get('hours','') flight_minutes = flight_duration.get('minutes','') flight_days = flight_duration.get('numOfDays','') if no_of_stops==0:    stop = "Nonstop" else:    stop = str(no_of_stops)+' Stop' total_flight_duration = "{0} days {1} hours {2} minutes".format(flight_days,flight_hour,flight_minutes) departure = departure_location_airport+", "+departure_location_city arrival = arrival_location_airport+", "+arrival_location_city carrier = flight_data['legs'][i].get('timeline',[])[0].get('carrier',{}) plane = carrier.get('plane','') plane_code = carrier.get('planeCode','') formatted_price = "{0:.2f}".format(exact_price) if not airline_name:    airline_name = carrier.get('operatedBy','') timings = [] for timeline in  flight_data['legs'][i].get('timeline',{}):    if 'departureAirport' in timeline.keys():        departure_airport = timeline['departureAirport'].get('longName','')        departure_time = timeline['departureTime'].get('time','')        arrival_airport = timeline.get('arrivalAirport',{}).get('longName','')        arrival_time = timeline.get('arrivalTime',{}).get('time','')        flight_timing = {                            'departure_airport':departure_airport,                            'departure_time':departure_time,                            'arrival_airport':arrival_airport,                            'arrival_time':arrival_time        }        timings.append(flight_timing) flight_info={'stops':stop,    'ticket price':formatted_price,    'departure':departure,    'arrival':arrival,    'flight duration':total_flight_duration,    'airline':airline_name,    'plane':plane,    'timings':timings,    'plane code':plane_code } lists.append(flight_info)    sortedlist = sorted(lists, key=lambda k: k['ticket price'],reverse=False)    return sortedlist except ValueError:    print ("Rerying...")     return {"error":"failed to process the page",} if __name__=="__main__": argparser = argparse.ArgumentParser() argparser.add_argument('source',help = 'Source airport code') argparser.add_argument('destination',help = 'Destination airport code') argparser.add_argument('date',help = 'MM/DD/YYYY') args = argparser.parse_args() source = args.source destination = args.destination date = args.date print ("Fetching flight details") scraped_data = parse(source,destination,date) print ("Writing data to output file") with open('%s-%s-flight-results.json'%(source,destination),'w') as fp: json.dump(scraped_data,fp,indent = 4)

Unless the page structure changes dramatically, this scraper should be able to retrieve most of the flight details present on Expedia. This scraper is probably not going to work for you if you want to scrape the details of thousands of pages at very short intervals.

Contact iWeb Scraping for extracting Expedia using Python and LXML or ask for a free quote!

https://www.iwebscraping.com/how-to-scrape-expedia-using-python-and-lxml.php

[generatour1]

top 10 free python programming books pdf online download 

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[hadoopcourse]

Why R language is used in Big Data

R is a free software environment and interpreted programming language, that is, it executes instructions directly, without prior compilation of the program to instructions in machine language. The term environment, in R, refers to a fully planned and consistent system, rather than an accumulation of specific and inflexible tools, as is often the case in other data analysis software. Are you dreaming to become to certified Pro Python Developer, then stop just dreaming, get your Python certification course from India’s Leading Python training institute?

This environment is commonly used for statistical and graphical computing, since it has a wide variety of statistical techniques (linear and nonlinear models, classical statistical tests, time series analysis, classification, grouping, etc.) and graphs. It works on UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R Features 

Effective management and storage of data.

A set of operators for performing calculations with matrices.

A large collection of tools for data analysis.

Graphical utilities for data visualization.

A well-developed programming language that includes conditional jumps, loops, recursive functions, utilities for data input and output, etc.

It has a LaTeX-based documentation format, which is used to provide complete documentation in both physical and digital formats.

Using R in Big Data

In the field of Big Data, it is used for the manipulation, processing and graphic display of data. R allows us to:

Create high quality data visualizations. 

Create dashboards to visualize and analyze data. 

Create automatic reports. 

Have statistical analysis tools to deepen the knowledge of the data.

R is more than just a programming language . The user does not program properly, but uses R interactively: he tries, makes mistakes and tries again. Only when the cycle ends and the result is satisfactory does it produce a final result that is generally not a program, but a report.

We hope you understand File Handling in Python Tutorial concepts.Get success in your career as a Python developer by being a part of the Prwatech, India’s leading Python training institute in Bangalore.

[luvcoding-blog]

More from LUVCODING python tutorial

[supersecure-blog]

Something awesome - assembly research

For the reversing chapter in my guide, I needed to do some prior research on low level languages, specifically assembly. I have had experience with MIPS in COMP1521, so concepts like registers, code segments, and control flow are all very familiar - I just need to learn the syntax!

What is reverse engineering?

Reverse Engineering in a CTF is typically the process of taking a compiled program like machine code or byte-code and converting it back into a more human readable format. Challenges will often include looking at programs looking written in low-level programming languages.

Low-level vs high-level programming languages

High level programming languages typically allow users to write applications which are portable across various platforms, and are irrespective of architecture. 

Low level programming languages are what is called ‘assembly code’. These languages are more appropriate for developing operating systems and typically take a lot of work to learn and understand how it works.

Assembly Breakdown:

As we’ve said before, learning assembly takes a lot of time and effort to try and understand as it is extremely different from a lot of the programming languages that most people are familiar with like Python or Java.

We’ll be covering some of the basic concepts in assembly below, but for a more in-depth tutorial, visit this site.

An assembly program can be divided into three sections:

The data section

The bss section

The text section

The data section

The data section is used for declaring initialised data or constants. This data does not change at runtime. You can declare various constant values, file names, or buffer size, etc

The bss section

The bss section is used for declaring variables. 

The text section

The text section is used for keeping the actual code. This section must begin with the declaration global _start, which tells the kernel where the program execution begins.

The syntax for declaring text section is −

section.text   global _start _start:

Registers

In assembly, registers store data elements for processing without having to access the memory. A limited number of registers are built into the processor chip.

Four 32-bit data registers are used for arithmetic, logical, and other operations.

EAX -  is the primary accumulator; it is used in input/output and most arithmetic instructions. For example, in multiplication operation, one operand is stored in EAX or AX or AL register according to the size of the operand.

EBX - is known as the base register, as it could be used in indexed addressing.

ECX - is known as the count register, as the ECX, CX registers store the loop count in iterative operations.

EDX -  is known as the data register. It is also used in input/output operations. It is also used with AX register along with DX for multiply and divide operations involving large values.

Instructions:

The MOV Instruction

The MOV instruction is used for moving data from one storage space to another. The MOV instruction takes two operands.

Syntax:

The syntax of the MOV instruction is:

MOV  destination, source

The MOV instruction may have one of the following five forms:

MOV  register, register MOV  register, immediate MOV  memory, immediate MOV  register, memory MOV  memory, register

Note that however:

Both the operands in MOV operation should be of same size

The value of source operand remains unchanged

The ADD and SUB Instructions

The ADD and SUB instructions are used for performing simple addition/subtraction of binary data in byte, word and doubleword size, i.e., for adding or subtracting 8-bit, 16-bit or 32-bit operands, respectively.

Syntax

The ADD and SUB instructions have the following syntax:

ADD/SUB destination, source

The ADD/SUB instruction can take place between:

Register to register

Memory to register

Register to memory

Register to constant data

Memory to constant data

However, like other instructions, memory-to-memory operations are not possible using ADD/SUB instructions. An ADD or SUB operation sets or clears the overflow and carry flags.

Control Flow

Unconditional Jump

This is performed by the JMP instruction. Conditional execution often involves a transfer of control to the address of an instruction that does not follow the currently executing instruction. Transfer of control may be forward, to execute a new set of instructions or backward, to re-execute the same steps.

Syntax:

The JMP instruction provides a label name where the flow of control is transferred immediately. The syntax of the JMP instruction is:

JMP label

Conditional Jump

If some specified condition is satisfied in conditional jump, the control flow is transferred to a target instruction. There are numerous conditional jump instructions depending upon the condition and data.

Following are the conditional jump instructions used on signed data used for arithmetic operations:

JE/JZ - Jump Equal or Jump Zero

JNE/JNZ - Jump not Equal or Jump Not Zero

JG/JNLE - Jump Greater or Jump Not Less/Equal

JGE/JNL - Jump Greater/Equal or Jump Not Less

JL/JNGE - Jump Less or Jump Not Greater/Equal

JLE/JNG - Jump Less/Equal or Jump Not Greater

[thestudy24x7]

Beginner's Guide to the Fundamentals of Coding

Coding, also known as programming, is the process of creating computer software, websites, mobile apps, and other digital products by writing instructions in a programming language that a computer can understand. With the rise of technology, learning how to code has become an essential skill for many people. In this article, we will discuss the basics of coding for beginners.

Choose a Programming Language

There are many programming languages to choose from, and each has its unique features and purposes. Some popular programming languages include Python, Java, C++, Ruby, and JavaScript. To start coding, you need to select a programming language that matches your goals and interests. For example, if you want to develop websites, HTML, CSS, and JavaScript are the primary languages to learn.

Learn the Fundamentals

Before you start coding, it is essential to understand the fundamentals of programming. This includes learning programming concepts such as variables, functions, loops, and conditional statements. These concepts are the building blocks of programming and are used in almost every programming language.

Get a Code Editor

A code editor is a software application that is used to write, test, and debug code. There are many free and paid code editors available, such as Visual Studio Code, Atom, and Sublime Text. Code editors come with features such as code highlighting, auto-completion, and error highlighting, which make coding easier and more efficient.

Practice, Practice, Practice

Like acquiring any other skill, learning to code involves practice. Write simple programmes at first, then progress to more complicated ones. Several online sources, like Codecademy, Udemy, and Study24x7, provide tutorials and courses to assist beginners in learning to code.

Debugging

The process of debugging involves locating and repairing flaws in your code. Every programmer needs this ability, but it takes practise to master. Debugging entails looking over your code to identify the error's cause. Once you have located the issue, you may update your code to resolve it.

Collaboration and Learning from Others

Collaboration and learning from others are important aspects of coding. Joining online coding communities, attending coding events, and participating in coding challenges are great ways to learn from others and improve your coding skills. You can also work on coding projects with others, which can help you learn new coding techniques and approaches.

Basic programming concepts are fundamental ideas and principles that underpin all software development. Here are some of the most essential concepts:

Variables: A variable is a named storage location in a program's memory where data can be stored and retrieved later. Variables can hold different types of data, including numbers, text, and Boolean values.

Control structures: Control structures are constructs in programming languages that enable developers to control the flow of program execution. The most common control structures are loops (for, while, do-while), conditional statements (if-else, switch-case), and functions.

Data types: A data type defines the kind of data that a variable can hold, such as integers, floating-point numbers, characters, and strings.

Operators: Operators are symbols that represent specific operations to be performed on data. Examples of operators include arithmetic operators (+, -, *, /), comparison operators (==, !=, >, <), and logical operators (&&, ||, !).

Functions: A function is a block of code that performs a specific task. Functions can be called multiple times from different parts of a program, making code more modular and reusable.

Input/output: Input/output (I/O) refers to the process of exchanging data between a program and the user or the system. Common I/O operations include reading from and writing to files, displaying output on the screen, and accepting input from the user.

Debugging: Debugging is the process of finding and fixing errors in a program's code. Debugging tools and techniques include print statements, breakpoints, and step-through debugging.

These basic programming concepts are essential building blocks that are used in almost all programming languages and applications. Understanding the basics of coding for beginners is critical for anyone who wants to learn programming and build software.

In conclusion, coding is a valuable skill that can open up many opportunities in today's digital world. To get started with coding, choose a programming language, learn the fundamentals, get a code editor, practice, practice, practice, debug your code, and collaborate and learn from others. With patience and dedication, anyone can learn to code and become a successful programmer.

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Arduino camera code

#Arduino camera code how to#

#Arduino camera code serial#

#Arduino camera code software#

Hope this will give you some idea about using ultrasonic sensor with arduino using Python. Once all these settings are done, When you run the program Ultrasonic sensor will find the obstacles in an interval and capture the images using the camera. 2) Go to Tools > Port and select the COM port the ESP32-CAM is connected to. Otherwise, this board won’t show up on the Boards menu. You must have the ESP32 add-on installed. In this tutorial we will interface most widely used camera module OV7670 with Arduino UNO. After finish uploading demo code, we can take a photo now, just press the button.

#Arduino camera code how to#

Arduino port name is shown in arduino ide choose Tools => Port => Port name is shown in ide To upload code to the ESP32-CAM using Arduino IDE, follow the next steps: 1) Go to Tools > Board and select AI-Thinker ESP32-CAM. How to Use OV7670 Camera Module with Arduino. Its a great camera for Arduino centered image recognition projects. If data python “Your python project name”/arduino port name(example : python self.py /dev/ttys0 ). One string is typically output on every video frame, that is, these strings are coming out of the JeVois camera at rates of 30 per second, 60 per second, or. String, in order to process futher, it is converted

#Arduino camera code serial#

The value received through serial interface would be Starts the camera, Captures the image, saves it &amp amp amp amp amp amp amp amp stopsįile_name = home_dir + ‘/image_captured/image_’ + str(dt.now()) + ‘.jpg’Įstablishes a connection to Arduino board through serial interfaceĪrduino_board = serial.Serial(sys.argv, 9600)Įnters an infite loop that runs until it receives Keyboard Interrupt Python program is used for getting the input signal from sensor via arduino, so that it can capture the obstacle according to the sensor detection.Ĭam = (“/dev/video0″, (640, 480)) // Here we declare the arduino portĪdjust the value of this variable to set the distance Open unzipped file of CameraVC0706TEST, and the program the code. Arduino codeĪrduino will receive the signal from Ultrasonic and given the signal input to python. Put the unzipped file of CameraVC0706lib into the Arduino IDE folder of Libraries. Similar to ArduCAM-Mini example, see section 3.1. 4 ArduCAM Shield V2 Examples 4.1 ArduCAMCameraPlayback. And examples also work with ArduCAM-Nano-ESP8266 module. Open arduino ide and paste the arduino code into ide and upload the program into UNO. All of the examples are designed for different camera modules, and will take effect automatically according to the Macro definition in the memorysaver.h file. Connection :Ĭonnect your arduino 12th and 11th pin to sensor Trigger pin and Echo pin, arduino +5v and Gnd pin into ultrasonic positive pin and Gnd pin circuit diagram. Ultrasonic generates high frequency sound waves so the echo is received back to the sensor in between the transmit time and receiveing time is calculated by the arduino and it will give the input to python. Ultrasonic sensor converts sound wave into electrical signal, they do both transmitting and receiving the signal, It will act like as an transducer. The project goal is to capture the obstacle for security purpose using ultrasonic sensor with a camera. So I thought of creating a small project. The thing with the original QuickCam, though, was the fact that it only had 16 shades of grey at 320x200 resolution - so it was only needing to push 32000 bytes per frame (roughly, not counting overhead).I have been in IOT space for quite few months and trying to integrate things with Arduino board, Recently I came across Ultrasonic sensor, it is interesting. I suppose if you bumped the serial speed of the USB/FTDI connection on the Arduino, and wrote some really tight code on both ends to parse things as fast as possible, you might be able to get a decent frame rate (10fps or so). What Arduino code can be used for a any camera module There are hundreds of connection protocols and there are hundred different possible connections to an. Required Components The OV7670 camera module (without the FIFO chip) An Arduino Uno or Uno compatible board Or an Arduino Nano or Nano compatible board A.

#Arduino camera code software#

Well, back in the day (~'95 or '96) there was a connectix quick cam made for a mac that I think used a serial connection.Īctually, most likely (though I never saw it, so I don’t know for sure) it used the ADB connection on the Mac while such a connection theoretically allowed for a higher-speed data rate than a regular serial port - supposedly it wasn’t really possible, unless they were doing some software trickery to get the speed up (certainly a possibility).