#how to create and add data to excel files in python

[govind-singh]

Learn Python with the Best Python Training Online: A Complete Python Training Course for Beginners and Professionals

In today’s digital world, programming has become a vital skill that opens doors to countless career opportunities. Among the various programming languages, Python stands out as one of the easiest and most powerful languages to learn. If you are searching for Python training online or a comprehensive python training course, you are making a smart choice for your future.

This blog will explain why Python is so popular, the advantages of online training, the skills you will acquire, and how this course can boost your career.

What is Python and Why Should You Learn It?

Python is a high-level, interpreted programming language created by Guido van Rossum in 1991. It is known for its simple and readable syntax, making it an excellent language for beginners. However, Python is also powerful enough to be used in advanced areas such as:

Web development

Data science and analytics

Artificial intelligence and machine learning

Automation and scripting

Scientific computing

Python’s versatility and growing demand in the industry have made it one of the top languages to learn today.

Why Choose a Python Training Course?

While you can find many free resources online, enrolling in a structured Python training course offers significant advantages:

Step-by-step learning: Courses guide you from the basics to advanced topics in a logical sequence.

Hands-on practice: Real-world projects and coding exercises help you apply what you learn.

Expert guidance: Instructors provide explanations, feedback, and support to clear doubts.

Certification: Completing a course often comes with a certificate that adds value to your resume.

A well-designed Python course ensures you learn efficiently and become job-ready.

Benefits of Python Training Online

1. Learn at Your Own Pace

Online Python training lets you study whenever and wherever you want. Whether you are a student, professional, or homemaker, you can balance your learning with other responsibilities.

2. Access to Quality Content

Online courses provide a rich mix of videos, tutorials, quizzes, and assignments curated by experts. This helps deepen your understanding of Python concepts.

3. Interactive Learning Experience

Many platforms offer live sessions, doubt clearing forums, and peer-to-peer discussions, making learning more engaging and interactive.

4. Cost-Effective Learning

Compared to traditional classroom training, online courses are more affordable and save you time and travel costs.

What Will You Learn in a Python Training Course?

A comprehensive Python training course covers essential topics like:

Python syntax, variables, and data types

Conditional statements and loops

Functions and modules

Object-oriented programming (OOP) concepts

File handling and exception management

Working with libraries such as Pandas, NumPy, and Matplotlib

Basics of web frameworks like Django and Flask

Introduction to data science and machine learning

Automation and scripting

These skills prepare you for various real-world applications and industries.

Career Opportunities After Python Training

Python skills open up a variety of career paths, including:

Python Developer

Data Scientist

Machine Learning Engineer

Web Developer

Automation Tester

DevOps Engineer

Python professionals are highly sought after worldwide, and their salaries reflect the demand.

How to Choose the Best Python Training Online?

Here are some tips to select the right Python training course for you:

Look for updated and comprehensive course content

Check if the course includes hands-on projects and assignments

Prefer courses taught by experienced instructors

Ensure the course provides certification upon completion

Read reviews and ratings from past learners

Choosing the right course can make your learning journey smooth and effective.

Conclusion

Python is undoubtedly one of the best programming languages to learn in 2025 and beyond. By enrolling in a python training online program, you gain flexibility, expert knowledge, and practical skills that can launch or advance your career in tech.

Don’t wait! Start your Python training course today and take the first step towards becoming a confident and skilled programmer.

[digitalmarketingtrainer]

🏨 Build a Hotel Management Website in Python: A Practical Project for Students and Beginners

In today's digital era, hands-on projects are crucial for mastering programming skills. The Hotel Management Website Project in Python offers an excellent opportunity for students, educators, and aspiring developers to delve into real-world application development.

🔍 Project Overview

This project simulates a comprehensive hotel management system, enabling users to manage bookings, customer details, and room availability efficiently. Developed using Python with a graphical user interface (GUI), it integrates essential features that mirror actual hotel operations.

🛠️ Key Features

Customer Management: Add customer details, including ID proof uploads and nationality verification for foreign guests.

Room Booking: Book rooms for specific dates and room types, with automatic exclusion of already booked rooms.

Automated Billing: Calculate costs with applicable taxes (e.g., GST) during booking.

Receipt Generation: Print or save booking receipts in PDF format.

Data Validation: Ensure accurate data entry and prevent booking conflicts.projectsforeveryone.com+1Dailyaspirants+1

🎓 Educational Value

This project serves as an excellent educational tool, offering insights into:

GUI Development: Learn to create user-friendly interfaces using Python's GUI libraries.

Database Integration: Understand how to connect and interact with databases for data storage and retrieval.

Real-World Application: Gain experience in developing applications that simulate real-life scenarios.

Modular Programming: Implement a modular approach for scalable and maintainable code.GitHub

📦 What's Included

Complete Python source code

Database files

Comprehensive documentation

Free remote installation support

🚀 Get Started Today

Embark on your programming journey with the Hotel Management Website Project in Python. Whether you're a student aiming to enhance your skills, an educator seeking practical teaching tools, or an enthusiast eager to delve into application development, this project is tailored for you.

🔗 Download Now

[fromdevcom]

Pandas DataFrame Tutorial: Ways to Create and Manipulate Data in Python Are you diving into data analysis with Python? Then you're about to become best friends with pandas DataFrames. These powerful, table-like structures are the backbone of data manipulation in Python, and knowing how to create them is your first step toward becoming a data analysis expert. In this comprehensive guide, we'll explore everything you need to know about creating pandas DataFrames, from basic methods to advanced techniques. Whether you're a beginner or looking to level up your skills, this tutorial has got you covered. Getting Started with Pandas Before we dive in, let's make sure you have everything set up. First, you'll need to install pandas if you haven't already: pythonCopypip install pandas Then, import pandas in your Python script: pythonCopyimport pandas as pd 1. Creating a DataFrame from Lists The simplest way to create a DataFrame is using Python lists. Here's how: pythonCopy# Creating a basic DataFrame from lists data = 'name': ['John', 'Emma', 'Alex', 'Sarah'], 'age': [28, 24, 32, 27], 'city': ['New York', 'London', 'Paris', 'Tokyo'] df = pd.DataFrame(data) print(df) This creates a clean, organized table with your data. The keys in your dictionary become column names, and the values become the data in each column. 2. Creating a DataFrame from NumPy Arrays When working with numerical data, NumPy arrays are your friends: pythonCopyimport numpy as np # Creating a DataFrame from a NumPy array array_data = np.random.rand(4, 3) df_numpy = pd.DataFrame(array_data, columns=['A', 'B', 'C'], index=['Row1', 'Row2', 'Row3', 'Row4']) print(df_numpy) 3. Reading Data from External Sources Real-world data often comes from files. Here's how to create DataFrames from different file formats: pythonCopy# CSV files df_csv = pd.read_csv('your_file.csv') # Excel files df_excel = pd.read_excel('your_file.xlsx') # JSON files df_json = pd.read_json('your_file.json') 4. Creating a DataFrame from a List of Dictionaries Sometimes your data comes as a list of dictionaries, especially when working with APIs: pythonCopy# List of dictionaries records = [ 'name': 'John', 'age': 28, 'department': 'IT', 'name': 'Emma', 'age': 24, 'department': 'HR', 'name': 'Alex', 'age': 32, 'department': 'Finance' ] df_records = pd.DataFrame(records) print(df_records) 5. Creating an Empty DataFrame Sometimes you need to start with an empty DataFrame and fill it later: pythonCopy# Create an empty DataFrame with defined columns columns = ['Name', 'Age', 'City'] df_empty = pd.DataFrame(columns=columns) # Add data later new_row = 'Name': 'Lisa', 'Age': 29, 'City': 'Berlin' df_empty = df_empty.append(new_row, ignore_index=True) 6. Advanced DataFrame Creation Techniques Using Multi-level Indexes pythonCopy# Creating a DataFrame with multi-level index arrays = [ ['2023', '2023', '2024', '2024'], ['Q1', 'Q2', 'Q1', 'Q2'] ] data = 'Sales': [100, 120, 150, 180] df_multi = pd.DataFrame(data, index=arrays) print(df_multi) Creating Time Series DataFrames pythonCopy# Creating a time series DataFrame dates = pd.date_range('2024-01-01', periods=6, freq='D') df_time = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['A', 'B', 'C', 'D']) Best Practices and Tips Always Check Your Data Types pythonCopy# Check data types of your DataFrame print(df.dtypes) Set Column Names Appropriately Use clear, descriptive column names without spaces: pythonCopydf.columns = ['first_name', 'last_name', 'email'] Handle Missing Data pythonCopy# Check for missing values print(df.isnull().sum()) # Fill missing values df.fillna(0, inplace=True) Common Pitfalls to Avoid Memory Management: Be cautious with large datasets. Use appropriate data types to minimize memory usage:

pythonCopy# Optimize numeric columns df['integer_column'] = df['integer_column'].astype('int32') Copy vs. View: Understand when you're creating a copy or a view: pythonCopy# Create a true copy df_copy = df.copy() Conclusion Creating pandas DataFrames is a fundamental skill for any data analyst or scientist working with Python. Whether you're working with simple lists, complex APIs, or external files, pandas provides flexible and powerful ways to structure your data. Remember to: Choose the most appropriate method based on your data source Pay attention to data types and memory usage Use clear, consistent naming conventions Handle missing data appropriately With these techniques in your toolkit, you're well-equipped to handle any data manipulation task that comes your way. Practice with different methods and explore the pandas documentation for more advanced features as you continue your data analysis journey. Additional Resources Official pandas documentation Pandas cheat sheet Python for Data Science Handbook Real-world pandas examples on GitHub Now you're ready to start creating and manipulating DataFrames like a pro. Happy coding!

[advancedexcelinstitute]

A Beginner’s Guide to NVH Testing in India’s Automotive Industry

In today’s fast-paced world of data analytics, staying relevant means knowing how to turn raw data into smart decisions—and fast. Sure, tools like Python, SQL, and Power BI are gaining popularity, but if there’s one tool that still stands strong in 2025, it’s Microsoft Excel.

Whether you’re just starting out or you’ve been crunching numbers for years, Excel for data analyst roles remains one of the most practical and in-demand skills. It strikes that perfect balance between simplicity and capability, making it the go-to for countless data tasks.

In this post, we’ll look at why Excel isn’t going anywhere, the most valuable Excel job skills right now, and how you can sharpen your expertise to keep up with the latest demands in data analytics.

The Modern-Day Data Analyst: More Than Just a Number Cruncher

Back in the day, data analysts were mostly behind the scenes—collecting numbers, making charts, and maybe sending the occasional report. Fast forward to 2025, and their role is far more central. Today’s analysts are storytellers, business advisors, and problem solvers.

Here’s what a typical day might include:

Pulling raw data from different platforms (think CRMs, ERPs, databases, web analytics tools)

Cleaning and organizing that data so it actually makes sense

Analyzing trends to help forecast what’s coming next

Creating reports and dashboards that communicate findings clearly

Presenting insights to decision-makers in a way that drives action

And you guessed it—Excel shows up in almost every one of these steps.

Why Excel Still Matters (a Lot)

Some might argue that Excel is “old-school,” but here’s the reality: it’s still everywhere. And for good reason.

1. It’s Familiar to Everyone

From finance teams to marketing departments, most professionals have at least a basic grasp of Excel. That makes collaboration easy—no need to explain a tool everyone’s already using.

2. Quick Results, No Coding Required

Need to filter a dataset or run a few calculations? You can do it in Excel in minutes. It’s great for ad-hoc analysis where speed matters and there’s no time to build complex code.

3. Plays Nice with Other Tools

Excel isn’t an island. It connects smoothly with SQL databases, Google Analytics, Power BI, and even Python. Power Query is especially useful when pulling in and reshaping data from different sources.

4. It’s on Every Work Computer

You don’t need to install anything or get IT involved. Excel is ready to go on pretty much every company laptop, which makes it incredibly convenient.

Top Excel Skills Every Data Analyst Needs in 2025

To really stand out, you’ll want to move past the basics. Employers today expect you to do more than just sum a column or build a pie chart. Here’s where to focus your energy:

1. Data Cleaning and Transformation

Use functions like CLEAN(), TRIM(), and Text to Columns to fix messy data.

Power Query is a game-changer—it lets you clean, merge, and reshape large datasets without writing a line of code.

2. Advanced Formulas

Learn how to use INDEX, MATCH, XLOOKUP, IFERROR, and dynamic arrays. These help you build smarter, more flexible spreadsheets.

Nesting formulas (formulas within formulas) is super helpful for building logic into your models.

3. PivotTables and PivotCharts

Still one of the fastest ways to analyze large data sets.

Great for grouping, summarizing, and drilling into data—all without writing any SQL.

4. Power Query and Power Pivot

These tools turn Excel into a mini-BI platform.

You can pull in data from multiple tables, define relationships, and use DAX for more advanced calculations.

5. Interactive Dashboards

Combine charts, slicers, and conditional formatting to build dashboards that update as data changes.

Form controls (like drop-downs or sliders) add a professional touch.

6. Automation with Macros and VBA

Automate tasks like data formatting, report generation, and file creation.

Even basic VBA scripts can save hours each week on repetitive tasks.

Real-World Excel Use Cases That Still Matter

Let’s get practical. Here’s how Excel is still making an impact across industries:

Sales & Marketing: Track campaign performance, customer engagement, and conversion rates—all in a single dashboard.

Finance: Build cash flow models, scenario forecasts, and budget reports that help CFOs make data-driven calls.

Healthcare: Monitor key performance indicators like patient wait times or readmission rates.

Logistics: Analyze delivery times, shipping costs, and supplier performance to streamline operations.

These aren’t theoretical use cases—they’re actual day-to-day tasks being done in Excel right now.

Excel vs. Other Tools

Let’s be real: no single tool does it all. Excel fits into a broader ecosystem of data tools. Here’s a quick breakdown:TaskBest ToolHow Excel ContributesQuick AnalysisExcelFast and easy to useDashboardsPower BI / TableauExcel dashboards are perfect for internal or lightweight reportsData CleaningSQL / Power QueryExcel connects and transforms with Power QueryBig DataPython / RUse Excel for summary views and visualizations of Python output

Excel’s strength lies in how easily it fits into your workflow—even when you’re working with more advanced tools.

How to Get Better at Excel in 2025

If you’re serious about leveling up, here’s how to grow your skills:

1. Take a Course That Focuses on Analytics

Pick one that emphasizes real business problems and gives you projects to work on. Case studies are gold.

2. Practice on Real Data

Websites like Kaggle, data.gov, or even your company’s historical data (with permission, of course) are great places to start.

3. Learn Keyboard Shortcuts

You’ll work faster and feel more confident. Start with common ones like Ctrl + Shift + L for filters or Alt + = for autosum.

4. Dive into Power Query and Power Pivot

Once you get the hang of them, you’ll wonder how you ever worked without them.

5. Build Mini Projects

Create dashboards or models that solve specific business problems—like tracking customer churn or sales performance. These can become portfolio pieces for your next job interview.

Conclusion

Excel isn’t going anywhere. It’s deeply woven into how businesses run, and in 2025, it’s still one of the best tools in a data analyst’s toolkit. It might not be as flashy as Python or as powerful as Tableau, but it gets the job done—and done well.

If you’re aiming to future-proof your career, investing in advanced Excel job skills is a smart move. From dashboards to data modeling, the possibilities are endless. And when paired with other tools, Excel helps you deliver even more value to your team.

So keep practicing, keep building, and remember—being great at Excel can set you apart in the data world.

FAQs

Is Excel still worth learning for data analysis in 2025?Yes! Excel remains one of the top skills hiring managers look for in data analyst roles. It’s everywhere—from startups to large enterprises.

What are the most useful Excel features for analysts? Advanced formulas, PivotTables, Power Query, Power Pivot, and dashboard design are the big ones. Knowing VBA is a bonus.

Can Excel handle big datasets?To an extent. While Excel has limits, features like Power Query and Power Pivot help it manage more data than it could in the past. For really massive data, combine it with tools like SQL or Power BI.

Should I learn Excel or Python?Both. Excel is great for quick analysis and reporting. Python is better for automation, data science, and machine learning. Together, they’re a powerful combo.

How can I show off my Excel skills to employers? Create dashboards or reports based on real data and include them in a portfolio. Show how you used Excel to solve actual business problems on your resume.

[data-analytics-masters]

Top Tools Used in Data Analytics in 2025 – A Complete Guide for Beginners

Data is everywhere. When you order food, shop online, watch a movie, or use an app, you are creating data. Companies use this data to understand people’s behavior, improve their services, and make better decisions. But how do they do it? The answer is data analytics.

To perform data analytics, we need the right tools. These tools help us to collect, organize, analyze, and understand data.

If you are a beginner who wants to learn data analytics or start a career in this field, this guide will help you understand the most important tools used in 2025.

What Is Data Analytics

Data analytics means studying data to find useful information. This information helps people or businesses make better decisions. For example, a company may study customer buying patterns to decide what products to sell more of.

There are many types of data analytics, but the basic goal is to understand what is happening, why it is happening, and what can be done next.

Why Tools Are Important in Data Analytics

To work with data, we cannot just use our eyes and brain. We need tools to:

Collect and store data

Clean and organize data

Study and understand data

Show results through graphs and dashboards

Let us now look at the most popular tools used by data analysts in 2025.

Most Used Data Analytics Tools in 2025

Microsoft Excel

Excel is one of the oldest and most common tools used for data analysis. It is great for working with small to medium-sized data.

With Excel, you can:

Enter and organize data

Do basic calculations

Create charts and tables

Use formulas to analyze data

It is simple to learn and a great tool for beginners.

SQL (Structured Query Language)

SQL is a language used to talk to databases. If data is stored in a database, SQL helps you find and work with that data.

With SQL, you can:

Find specific information from large datasets

Add, change, or delete data in databases

Combine data from different tables

Every data analyst is expected to know SQL because it is used in almost every company.

Python

Python is a programming language. It is one of the most popular tools in data analytics because it can do many things.

With Python, you can:

Clean messy data

Analyze and process large amounts of data

Make graphs and charts

Build machine learning models

Python is easy to read and write, even for beginners. It has many libraries that make data analysis simple.

Tableau and Power BI

Tableau and Power BI are tools that help you create visual stories with your data. They turn data into dashboards, charts, and graphs that are easy to understand.

Tableau is used more in global companies and is known for beautiful visuals.

Power BI is made by Microsoft and is used a lot in Indian companies.

These tools are helpful for presenting your analysis to managers and clients.

Google Sheets

Google Sheets is like Excel but online. It is simple and free to use. You can share your work easily and work with others at the same time.

It is good for:

Small projects

Group work

Quick calculations and reports

Many startups and small businesses use Google Sheets for simple data tasks.

R Programming Language

R is another programming language like Python. It is mainly used in research and academic fields where you need strong statistics.

R is good for:

Doing detailed statistical analysis

Working with graphs

Writing reports with data

R is not as easy as Python, but it is still useful if you want to work in scientific or research-related roles.

Apache Spark

Apache Spark is a big data tool. It is used when companies work with a large amount of data, such as millions of customer records.

It is good for:

Processing huge data files quickly

Using Python or Scala to write data analysis code

Working in cloud environments

Companies like Amazon, Flipkart, and banks use Apache Spark to handle big data.

Which Tools Should You Learn First

If you are just getting started with data analytics, here is a simple learning path:

Start with Microsoft Excel to understand basic data handling.

Learn SQL to work with databases.

Learn Python for deeper analysis and automation.

Then learn Power BI or Tableau for data visualization.

Once you become comfortable, you can explore R or Apache Spark based on your interests or job needs.

Final Thoughts

Learning data analytics is a great decision in 2025. Every company is looking for people who can understand data and help them grow. These tools will help you start your journey and become job-ready.

The best part is that most of these tools are free or offer free versions. You can start learning online, watch tutorials, and practice with real data.

If you are serious about becoming a data analyst, now is the best time to begin.

You do not need to be an expert in everything. Start small, stay consistent, and keep practicing. With time, you will become confident in using these tools.

[greenplumbboblover]

THIS ISSUE HAS BEEN RESOLVED!

(Source: https://modthesims.info/showthread.php?t=687747 )

TL;DR: What happened?

Two creators had unfortunately been victim to their passwords being leaked. The people who are behind these types of TS4 malware issues tend to find leaked passwords and then sharing their Trojan file.

IF you downloaded any of these 4 items in the last 24 hours: 1. No Mosaic / Censor Mod for The Sims 4 - Toddler Compatibility Update! 2. AllCheats - Get your cheats back! 3. CAS FullEditMode Always On (Updated 6/26/18) 4. Full House Mod - Increase your Household Size! [Still Compatible as of 1/25/18] Just know that they were only live for 1,5 hours. The chances that you downloaded something malware are quite low due to this. However, just to be safe, it's good to delete them anyways if you did download them 24 hours before as of this reblog post.

So: Just a reminder to, well, everyone using the internet: Make sure to change your passwords periodically! (and, if possible, use an authentication app).

As far as I know, MTS is working on making it much harder to update posts when you've been inactive for a while! So in the future, the hackers would need access to your email provider to include malware in your mods. I believe this code is already live as we speak.

How to stay safe downloading anything CC related in the future:

Know that this issue is seemingly a big issue in The sims 4 community! While the other communities are certainly not ruled out to be able to have malware in them, it seems this group of hackers are really focused on The Sims 4 community as a whole.

What files are the issue?

ts4script files. Because it's raw python AND TS4 doesn't have great restrictions for script mods in place, these people can modify the python file to create a .dll file on running the game. That's how they get information if they're lucky.

.exe files or files that look like another file type but are an .exe file. (or some executable file like a bash script, etc). MTS does check these things before approving, but do be careful when downloading these things from tumblr or github. Make sure to check the comments there instead.

What files CANNOT ever get malware in them?

Simply said: .Package files. Exception for maybe the .package files that are actually ts4script files, but that's really from the ancient TS4 days.

With other words, your: CasParts, Lots, Cosmetics, Hair, Sims, Recolours, Objects CANNOT have malware in them

The only "kind of" malware we saw back in the days in Package files was the infamous TS3 Doll corruption bug. But that didn't collect your data, just corrupted your save/game 😉

What ways can I detect if something is malware at first sight?

99% of script modders, when updating their mods, WILL add WHY they updated their mod in the first place. If you do NOT see any update reasons in the description, it's probably malware.

Check the comments! If you're not sure, always check if someone left a comment (or in Tumblr's case, a Reblog).

Trust your gut feeling! Does something seem strange? A bit out of place from the usual? Give it a few days before you download the mod.

Package files SHOULD NEVER have a way of "installing your content" through an .exe file "For simplicity", because 99% of the cases, it's malware to trick you. Unless there is a excellent reason for it (and I mean REALLY good reason).

More or less a download site related thing: If a download site has a billion buttons saying "Download". Please don't press these. They are most likely Malware too, but definitely shady ads. For those pages, it would be best to leave the item alone, unless you really know what you're doing!

Conclusion

While these discord server announcements mean well, it frustrates me to see that they mention that EVERYTHING is compromised. Whereas in reality it's only TS4Scripts and .exe files that can do harm.

I know they mean well! And wanting to protect people! But at the same time, it also spreads a sense of misinformation that can harm creators, websites, you name it.

So, instead, I would love to advise them to educate their members instead on what files can be the problem! And how to detect them. The more we get this into the world, the better we will be able to protect one another from downloading bad things!

And of course, websites that share CC, should make an effort to prevent this in the future. I'm happy MTS is doing this at the moment.

Stay safe!

(Sourced from the Sims After Dark discord server)

DO NOT DOWNLOAD ANY MODS FROM MODTHESIMS! Numerous mods there (including those by TwistedMexi) are being compromised by hackers adding a malicious file with the mods

Please reblog!!

[learning-code-ficusoft]

Exploring Jupyter Notebooks: The Perfect Tool for Data Science

Exploring Jupyter Notebooks: The Perfect Tool for Data Science Jupyter Notebooks have become an essential tool for data scientists and analysts, offering a robust and flexible platform for interactive computing.

 Let’s explore what makes Jupyter Notebooks an indispensable part of the data science ecosystem. 

What Are Jupyter Notebooks? 

Jupyter Notebooks are an open-source, web-based application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. 

They support multiple programming languages, including Python, R, and Julia, making them versatile for a variety of data science tasks. 

Key Features of Jupyter Notebooks Interactive Coding Jupyter’s cell-based structure lets users write and execute code in small chunks, enabling immediate feedback and interactive debugging. 

This iterative approach is ideal for data exploration and model development. 

Rich Text and Visualizations Beyond code, Jupyter supports Markdown and LaTeX for documentation, enabling clear explanations of your workflow. 

It also integrates seamlessly with libraries like Matplotlib, Seaborn, and Plotly to create interactive and static visualizations.

 Language Flexibility With Jupyter’s support for over 40 programming languages, users can switch kernels to leverage the best tools for their specific task. 

Python, being the most popular choice, often integrates well with other libraries like Pandas, NumPy, and Scikit-learn. 

 Extensibility Through Extensions Jupyter’s ecosystem includes numerous extensions, such as JupyterLab, nbconvert, and nbextensions, which add functionality like exporting notebooks to different formats or improving UI capabilities. 

Collaborative Potential Jupyter Notebooks are easily shareable via GitHub, cloud platforms, or even as static HTML files, making them an excellent choice for team collaboration and presentations. 

Why Are Jupyter Notebooks Perfect for Data Science? Data Exploration and Cleaning Jupyter is ideal for exploring datasets interactively. 

You can clean, preprocess, and visualize data step-by-step, ensuring a transparent and repeatable workflow. 

Machine Learning Its integration with machine learning libraries like TensorFlow, PyTorch, and XGBoost makes it a go-to platform for building and testing predictive models. 

Reproducible Research The combination of narrative text, code, and results in one document enhances reproducibility and transparency, critical in scientific research. 

Ease of Learning and Use The intuitive interface and immediate feedback make Jupyter a favorite among beginners and experienced professionals alike. 

Challenges and Limitations While Jupyter Notebooks are powerful, they come with some challenges: 

Version Control Complexity: 

Tracking changes in notebooks can be tricky compared to plain-text scripts. 

Code Modularity: 

Managing large projects in a notebook can lead to clutter. 

Execution Order Issues:

 Out-of-order execution can cause confusion, especially for newcomers. 

Conclusion 

Jupyter Notebooks revolutionize how data scientists interact with data, offering a seamless blend of code, visualization, and narrative. 

Whether you’re prototyping a machine learning model, teaching a class, or presenting findings to stakeholders, Jupyter Notebooks provide a dynamic and interactive platform that fosters creativity and productivity.

[itview2024]

How Python Training Can Help You Land Your First Job in Tech

The tech industry is booming, and one of the most sought-after skills across the globe is Python programming. Known for its simplicity, readability, and versatility, Python is used in various fields, from web development to artificial intelligence and data science. If you’re aiming to break into the tech industry, Python training in Pune can provide the foundation, skills, and confidence you need to succeed. Here’s how Python training in Pune can help you secure your first job in tech.

1. Why Python? The Perfect Beginner-Friendly Language

Python is one of the most beginner-friendly programming languages, which makes it an excellent choice for those new to coding. Its syntax is clear and concise, closely resembling everyday English, making it easier for newcomers to understand programming concepts without getting bogged down by complex syntax.

Simple Syntax: Python’s straightforward syntax allows beginners to focus on learning problem-solving skills and logic.

Wide Applications: Python is used in web development, data science, machine learning, automation, and more.

Strong Community Support: The Python community is vast, with plenty of tutorials, forums, and resources available online.

Learning Python as a first language provides you with a versatile foundation, allowing you to explore various career paths in tech. Python training in Pune specifically can connect you with local tech opportunities and industry connections.

2. Building In-Demand Skills

Python is a highly sought-after skill in the tech job market, and having it on your resume is a major advantage. Through Python training in Pune, you’ll gain practical skills in:

Data Handling and Analysis: Learn to work with popular libraries like Pandas and NumPy, essential for data manipulation and analysis.

Web Development: Gain knowledge of frameworks like Django and Flask, used to create scalable web applications.

Automation: Python’s capabilities in automating repetitive tasks make it highly valuable, especially in roles focused on DevOps and testing.

Machine Learning and AI: Familiarize yourself with Python’s robust machine learning libraries like TensorFlow and scikit-learn, a significant asset in data science and AI fields.

These skills are highly transferable, allowing you to fit into a variety of tech roles.

3. Access to High-Quality Projects and Hands-On Learning

Most Python training programs in Pune emphasize hands-on learning, which allows you to work on real projects, gain practical experience, and build a portfolio that showcases your skills to potential employers. Common projects in Python training include:

Data Analysis Projects: Analyzing and visualizing datasets to solve real-world problems.

Web Applications: Developing functional web applications from scratch.

Automation Scripts: Building scripts that automate tedious tasks, like data entry or file management.

Machine Learning Models: Creating predictive models that can analyze trends and make data-driven predictions.

These projects can serve as strong additions to your portfolio, demonstrating to employers that you’re capable of handling real-world coding tasks.

4. Certification for Credibility

Many Python training programs in Pune offer certifications that validate your knowledge and skills. A certification from a reputable course or institution can set you apart from other candidates, showing hiring managers that you’ve put in the effort to gain solid Python skills. Python certifications can add credibility to your profile, especially when applying for entry-level positions.

Some popular certifications include:

Certified Entry-Level Python Programmer (PCEP)

Microsoft Certified: Azure AI Fundamentals

Google IT Automation with Python Professional Certificate

Adding these certifications to your resume gives you an advantage when applying for Python-related roles.

5. Career Versatility and Diverse Job Opportunities

Python training in Pune opens the doors to various entry-level roles, such as:

Junior Developer: Work on small projects, writing clean, efficient code under the guidance of senior developers.

Data Analyst: Collect, clean, and interpret data, transforming it into actionable insights for business decisions.

Automation Engineer: Develop scripts to automate routine tasks, enhancing operational efficiency.

Quality Assurance (QA) Tester: Use Python scripting to automate testing processes, identifying bugs and ensuring product quality.

With these career options, Python serves as a gateway to multiple fields within the tech industry, allowing you to choose a path that aligns with your interests.

6. Networking Opportunities in Python Training Programs in Pune

Python training in Pune often connects you with like-minded peers, instructors, and industry professionals, providing valuable networking opportunities. Engaging with a network of fellow learners, mentors, and professionals can:

Expand Your Professional Network: Meet people who can offer job leads, career advice, and mentorship.

Offer Project Collaboration Opportunities: Collaborate on group projects or contribute to open-source projects, showcasing your teamwork and problem-solving skills.

Provide Access to Job Referrals: A network of industry contacts can recommend you for job openings and provide a strong reference.

Networking is a key component in landing a job, especially in the tech industry where referrals and connections often play a significant role in hiring.

7. Personalized Career Support

Many Python training programs in Pune include career support services such as resume workshops, mock interviews, and job placement assistance. These resources can be invaluable for newcomers to the tech industry, providing guidance on:

Resume Building: Tailoring your resume to highlight relevant Python skills, projects, and certifications.

Interview Preparation: Practicing commonly asked interview questions and technical tests, increasing your confidence during job interviews.

Portfolio Development: Learning to showcase your projects effectively, emphasizing your skills and problem-solving abilities.

With these career support services, Python training programs help you navigate the job search process more effectively, enhancing your chances of landing a role.

8. Confidence and Problem-Solving Skills

Mastering Python through training builds confidence and sharpens your problem-solving skills. Coding requires logical thinking, patience, and attention to detail — all of which are valuable traits in the tech industry. As you practice coding, debug errors, and solve real-world problems, you’ll become more comfortable tackling complex tasks. This confidence will come through in interviews, demonstrating to employers that you’re prepared to learn, adapt, and contribute to their team.

Conclusion

Python training in Pune offers a solid foundation for launching a tech career, equipping you with in-demand skills, a strong portfolio, and valuable certifications. By focusing on hands-on projects, networking opportunities, and career support, Python training prepares you to take on your first tech role with confidence. Whether you aspire to be a developer, data analyst, or automation engineer, Python training in Pune can pave the way to your dream job in the tech industry.

[schoolofcoreai01]

How to Use Excel for Artificial Intelligence Online Data Analysis

In the age of big data and advanced analytics, Excel remains a powerful tool for handling various aspects of data analysis, including those related to artificial intelligence. Many professionals are unaware of the potential that lies in combining Excel with AI techniques. This guide will help you understand how to use Excel for artificial intelligence online data analysis effectively.

Getting Started with Excel for AI Data Analysis

Before diving into artificial intelligence online data analysis, it's crucial to ensure that you have the latest version of Excel installed. Microsoft regularly updates Excel with features that enhance its data handling and analysis capabilities. Here are the steps to get started:

Install Excel: Make sure you have the latest version of Microsoft Excel.

Understand the Basics: Familiarize yourself with basic Excel functions such as sorting, filtering, and basic formulas.

Add-Ins and Tools: Install necessary add-ins like the Data Analysis Toolpak and Power Query.

Importing and Cleaning Data

The first step in artificial intelligence online data analysis using Excel is to import and clean your data. Data cleaning is crucial as it ensures the accuracy and reliability of your analysis.

Import Data: Use the 'Get Data' feature to import data from various online sources such as databases, CSV files, or directly from websites.

Clean Data: Remove duplicates, handle missing values, and ensure consistency in your data. Use Excel functions like CLEAN, TRIM, and REMOVE DUPLICATES to streamline this process.

Organize Data: Structure your data in a tabular format with clear headers, making it easier to analyze.

Utilizing Excel Functions for AI Data Analysis

Excel offers a multitude of functions that are particularly useful in the initial stages of artificial intelligence online data analysis. These functions help in summarizing and interpreting data effectively.

Statistical Functions: Use functions like AVERAGE, MEDIAN, STDEV, and VAR to get a statistical overview of your data.

Logical Functions: Functions like IF, AND, and OR are essential for creating conditional statements that can help in data segmentation and filtering.

Lookup Functions: Use VLOOKUP, HLOOKUP, and INDEX-MATCH to search and retrieve data from large datasets efficiently.

Leveraging Excel Add-Ins for AI Analysis

Several Excel add-ins can enhance its capabilities for artificial intelligence online data analysis. Here are a few notable ones:

Power Query: This tool allows for advanced data transformation and connection to multiple data sources, making it easier to manipulate and prepare data for analysis.

Solver: This add-in helps in optimizing complex problems by finding the best solution from a set of possible solutions, which is particularly useful in predictive analytics.

Excel Data Analysis Toolpak: It includes various statistical analysis tools like regression analysis, histograms, and t-tests that are essential for data analysis.

Implementing Machine Learning Models in Excel

Though Excel is not primarily designed for machine learning, it can still be used for simple machine learning tasks with the help of add-ins and custom functions.

Excel R and Python Integration: Use the Excel add-ins to integrate R and Python scripts. This integration allows you to perform more advanced machine learning tasks that Excel alone cannot handle.

Azure Machine Learning: Connect Excel with Azure Machine Learning to access more powerful machine learning models and services. This setup allows for real-time data analysis and model implementation.

Visualizing Data in Excel

Effective visualization is key to understanding the results of your artificial intelligence online data analysis. Excel offers a wide range of visualization tools.

Charts and Graphs: Use various chart types like bar charts, pie charts, and scatter plots to represent your data visually.

PivotTables: PivotTables are incredibly powerful for summarizing large datasets and identifying patterns and trends.

Conditional Formatting: Highlight key data points and trends using conditional formatting, making it easier to interpret the data at a glance.

Conclusion

Excel is a versatile tool that, when combined with the right techniques and add-ins, can be incredibly effective for artificial intelligence online data analysis. By leveraging Excel’s powerful functions, add-ins, and integration capabilities, you can perform comprehensive data analysis and gain valuable insights. Whether you are a beginner or an experienced data analyst, understanding how to use Excel for artificial intelligence online data analysis will enhance your analytical capabilities.

At Schoolofcoreai, we provide comprehensive training on leveraging tools like Excel for advanced data analysis and artificial intelligence. Join us to master these skills and stay ahead in the rapidly evolving field of data science and AI.

By following these guidelines and integrating Excel into your data analysis workflow, you can maximize your efficiency and effectiveness in artificial intelligence online data analysis.

[myresellerhome]

The Beginner's Guide to Website Development

Imagine entering a massive digital city where every building is a separate website with its own purpose and design. Imagine becoming an architect in this vast information city instead of a visitor. Welcome to website development, the thrill! Start building your digital domain with this beginner's guide. Website design opens us unlimited opportunities for sharing your passion, skills, or business. In this article, we'll teach you how to convert that blank digital canvas into a masterpiece that draws international visitors. Join us on this exciting quest to turn ideas into reality, one pixel at a time.

1. Web Development

How are websites made?

Websites are files and code on a computer connected to the Internet. Chrome, Firefox, or Safari load pages. This is the client. These two form the "server-client model."

In real life, a friend sends you a humorous movie link. When you click the link, your browser (client) requests the video file from the video website (server). The website server transfers the file to your PC when requested. 

Clients are the "front end," and servers are the "back end." Front end web developers build client-side features, whereas back-end developers develop server-side features. They understand both front-end and back-end technologies and use the "full stack" of programming technology. 

Understanding their differences can help you choose one for your career. Discuss the differences between front-end, back-end, and full-stack web development. 

How is front-end development done?

Front-end developers produce website content. This group can run menus, buttons, and animations on a client's computer. Front-end developers use HTML, CSS, and JavaScript. HTML builds a website's structure, CSS changes its appearance, and JavaScript adds interactivity. 

How is back-end development done?

Back end developers work on websites' back ends. This includes managing web servers, databases, and data analysis, which site visitors don't see. Back end authors can use SQL and Python to control website functionality. 

What is the meaning of "Full Stack" Stuff?

Website developers who use the "full stack" of tools are termed "full stack developers." 

There's no predetermined time to learn full-stack development, but this versatile work is easier. Tech fans at full-time schools can learn job-related skills in three to six months. Still, those who want the breadth of information a degree provides should plan to study for at least four years. 

What do full stack developers do, and are they accountable for what?

Full stack web developers handle various website tasks. Some examples include: 

Website front-end creation

Creating user interfaces and experiences

Creating technical documentation

Website structure creation

Implementing data protection rules

Setting up servers and files

Optimizing mobile devices across platforms

2. Get the things you need to begin.

Basic Computer Specifications 

Web creation can begin on any Windows, macOS, or Linux machine. The basics of writing are: 

Apple Silicon CPU or Intel i5 or i7 processor on subsequent Macs

A Full HD display or laptop with a 1920x1080 resolution and 8GB of memory is recommended.

Basic Text Editor

You'll need a text editor to organize your code. Many free options are available to start. 

Notepad ++ is a nice text editor for beginners, but only on Windows. Atom is another amazing Linux, Mac, and Windows option. 

The browser's

coding requires a browser therefore, you're reading this, you probably have one! Installing multiple browsers enables you to make sure your site looks excellent on all devices; therefore, you should have several. Popular browsers include Chrome, Safari, Edge, Firefox, Brave, and Opera. 

Local Web Server

A local web server lets you test code on your computer without putting it online. You can test layouts, scripts, and features while building the site. 

XAMPP runs servers on Mac, Windows, and Linux. Other options include MAMP, which works solely on Mac and Windows. 

Graphic editors

Developers can use graphics editors to create and modify website graphics. Adobe Creative Suite is the industry standard, but inexperienced designers may find the monthly charge too high. Check out GIMP, a free Photoshop, and Inkscape, a free Illustrator for vector graphics. 

3. Understand how front end development works from the ground up

Front-end writers make the site look good when loaded. They create and build all visible website elements. This list includes languages all front-end writers should know before their first job. 

HTML

HTML is an acronym for Hyper Text Markup Language. Coders can create a website's fundamental layout and design. HTML files instruct browsers on how to display text, lists, and images. 

HTML is easy to learn and may help beginners start coding. This is a crucial tool for web developers that all websites start with. Dynamic web content is created using JavaScript on modern websites. Report Although HTML is an ancient technology, some websites still use it. 

Are you looking to learn this "building block" programming language? HTML learning strategy.

CSS lets you style HTML elements on the screen. CSS can help websites seem amazing on all major devices. Layout, colors, fonts, and page effects can be changed. 

CSS

HTML and CSS let you create and style websites. It is complex but easy to grasp. Anyone who wants to develop beautiful, mobile-friendly websites must master CSS, since it controls how an HTML page looks to users. 

JavaScript

Every front-end web developer needs JavaScript. JavaScript, HTML, and CSS are client-side languages for dynamic, flexible web pages. It's easy to learn and flexible so developers may work on any website design or function. Writing in JavaScript is an excellent approach to learning to code because many of your required skills are also useful in Python and Java.

4. Learn more front end development tools 

Before starting large programming projects, seek out front-end development tools to organize and manage code writing. 

Package managers 

Tool and dependency packages can be installed via package managers for front-end development. Dependencies are external software that aids the program.

Make Tools 

Some web development can be automated using build tools. These tools are handy for small, risky tasks like altering text strings and moving or combining files, and they're helpful for aspiring coders. 

Task runners like Gulp automate JavaScript development tasks. They run a local server, improve images, and prepare CSS files. They are powerful yet complicated to set up. 

JavaScript programmers utilize Webpack. It separates your code into chunks based on app usage and makes it web-friendly. Bugs are easier to manage now. 

Changing Control 

Version control systems track code changes and enable you to revert if you make a mistake. 

You'll probably start studying version control with Git. Every major web development company uses Git for version management, and every front-end worker needs this simple toolbox piece. 

Microsoft owns GitHub, a Git file-storing and sharing service that is like a social network for programmers and web writers. Many large companies use GitHub to track code changes and project versions. You can also showcase your work and develop a résumé there. 

Responding Design 

Consider your laptop, desktop, tablet, and phone, which you use daily. Every website you design should work on any device. 

Responsive design means websites should adapt to users and devices. Websites that are responsive look nice on 32-inch monitors and 5.5-inch smartphones. As more people use phones to browse the web, web authors must understand responsive design. 

Frameworks for JavaScript 

JavaScript frameworks are libraries with pre-written code for common tasks. Using a framework instead of starting from scratch speeds up site development. 

Say you're developing a local business's "About Us" website and want a movie to play when someone mouses over an employee photo. Spend less time starting from scratch with a structure.

5. Learn basic back end construction. 

Back-end development involves computers, databases, and data processing that users don't see. 

Getting to Know Servers 

Back end professionals must understand server management. 

Standard, centralized servers are first. This Windows or Linux PC stores all its data in one place. Large companies select centralized server architecture because it allows them more control over data storage and viewing. 

Programming languages 

Also, back end developers should know several computer languages, like 

Java: Java is a general-purpose programming language for client—and server-side apps. After being in use for over 20 years, it is considered one of the easiest to learn. The open-source Java language has a large developer community and many building resources and tools. As it matured, Java became strong, reliable, and scalable. Big companies use it, so learning it now can help you when you start working. 

Python: Python is another popular back-end programming language and one of the most popular computer languages. It is also a beginner language, flexible, easy to learn, and useful in data science, back-end development, and data analysis. Read our Python beginner guide to learn more! 

Node.js: Node.js allows basic JavaScript back end programming. A server-based runtime environment lets writers create dynamic web pages using JavaScript code and scripts. Microsoft, PayPal, and Uber use Node.js. Because so many tech giants use it, learning Node.js will help you professionally. 

PHP: Write computer scripts with PHP. PHP retrieves data from a website's server and displays it on a user's computer. For example, a PHP script can instantly display your three most recent blog posts on your homepage. Server-stored posts are called when users visit your home page, so no page refresh is needed. PHP powers custom WordPress themes and plugins, so WordPress developers must know it. 

Ruby: Ruby was created in the mid-1990s as a versatile language. Ruby is popular with companies and small businesses because it's easy to start a project and make product prototypes. Ruby is less popular than other back end languages, but many developers use it. New developers or those seeking company employment should consider this. 

Using Databases 

Databases matter when building the back end. To improve as a back-end worker, you must handle and access data. 

Data scientists and back-end writers prefer SQL. It creates, updates, and adds to databases. SQL lets coders query relational databases' table-based data. MySQL and MS SQL Server are popular. 

NoSQL skills are also required. These systems lack tables, columns, and rows. Instead, these databases are fast, simple, and customizable. Businesses with large amounts of data that can't be organized in a table should use them. 

6. Website building and design basics 

Your focus has been on technical website construction so far. As a web developer, you should know how to build websites. 

Consider your site, users, and goals broadly. Who should read your site? They come from where? Are you asking them to buy, fill out a form, or do something else? Not only should some people like your site, but your target audience should. 

The layout and organization of website pages are called website design. This can have these pages: 

Index page 

The "About" page 

A landing page with "Contact us." 

Your site needs distinct pages, depending on its purpose. E-commerce sites may display product sections, pages, and categories. 

Predict how visitors will navigate your site. One page is the plan. Do you want visitors to explore your site and learn about your business? Make sure your site's design makes it simple and easy to use. 

Another consideration is design. Web designers must make websites seem beautiful and easy to use. Your site's designs, colors, and images form its appearance. Layout is the arrangement of content on each page. Show off your creativity now. 

General design principles underpin good web design. Think about: 

A color 

Color can accomplish many things and convey a lot about your site quickly. A nature blog might employ earth-toned greens, browns, and grays. A few color theory basics can help. 

Font 

Like colors, fonts are plentiful. Font choice can define your site. Fonts convey diverse messages; therefore, choosing the proper one is crucial. Google's online font selection guide has more information. 

Layout 

Your website should be visually arranged. Why are certain ideas necessary to see first? Many individuals read websites in an F- or Z-shaped design. Knowing how people use the web helps you design pages. 

Start thinking about design by visiting your greatest websites. Note their types, colors, and layouts. Outside, how do you feel? They discuss what business aspects? Examine colors, fonts, and portion layouts. This may alter how you view your pages.

Conclusion

Website development can be challenging, but with the correct advice and resources, anyone can do it. Building functioning and attractive websites requires knowledge of website layout, coding languages, user experience, and optimization. Remember, website development success requires constant learning and adapting to new technology and trends. With time, ingenuity, and determination, you may construct personal blogs or commercial e-commerce sites.

Janet Watson MyResellerHome MyResellerhome.com We offer experienced web hosting services that are customized to your specific requirements.

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[edcater]

Python Demystified: A Beginner's Approach to Programming Prowess

Introduction

Embarking on the journey of learning Python can be both exciting and rewarding for beginners. This versatile programming language is known for its simplicity and readability, making it an excellent choice for those who are just starting their coding adventure.

Why Learn Python?

Python is renowned for its user-friendly syntax, making it easy for beginners to grasp the basics of programming without getting bogged down by complicated commands. Learning Python opens up a world of possibilities, from web development to data science and artificial intelligence.

Setting Up Your Python Environment

Before diving into Python, you need to set up your coding environment. Download and install Python, choose a code editor, and start experimenting with simple programs to get a feel for how Python works.

Understanding Variables and Data Types

In Python, variables are like containers that store information. Learn about different data types, such as integers, floats, strings, and booleans, and understand how to use them in your programs.

Mastering Basic Operations

Explore the fundamental operations in Python, such as arithmetic operations, comparisons, and logical operations. Understanding these basics lays the groundwork for more complex coding tasks.

Control Flow: Making Decisions with Python

Learn how to control the flow of your program using conditional statements like if, else, and elif. This allows your code to make decisions based on specific conditions, enhancing its flexibility.

Repetition and Loops

Discover the power of loops in Python. From for loops to while loops, these structures help you repeat tasks efficiently, reducing the need for redundant code and making your programs more dynamic.

Functions: Building Blocks of Code

Understand the concept of functions, which are reusable pieces of code that perform specific tasks. Learning to create and use functions enhances the modularity of your code, making it easier to manage and understand.

Working with Lists and Dictionaries

Explore Python's built-in data structures like lists and dictionaries. These versatile containers allow you to store and manipulate data in various ways, providing essential tools for more complex programming tasks.

File Handling in Python

Learn how to read from and write to files in Python. File handling is a crucial skill, especially when dealing with data input and output, and it adds a practical dimension to your programming knowledge.

Introduction to Object-Oriented Programming (OOP)

Delve into the basics of OOP, a programming paradigm widely used in Python. Understand the concepts of classes and objects, which enable you to structure your code in a more organized and efficient manner.

[grotechminds]

Data-Driven Testing with Selenium and Python

Data-Driven Testing with Selenium and Python

In the realm of test automation, the ability to execute tests with different sets of data is a game-changer. Data-driven testing allows testers to run the same test scenario with multiple inputs, making scripts more versatile and efficient. When it comes to Selenium test automation testing with Python, harnessing the power of data-driven testing can significantly enhance the effectiveness of your test suites.

Understanding Data-Driven Testing:

Data-driven testing is an approach where test scripts are designed to run with multiple sets of data. Instead of hardcoding values within the script, data-driven testing separates the test logic from the test data. This approach offers several advantages, including:

Versatility: The same test script can be executed with different input data.

Scalability: Easily scale test suites by adding more data sets.

Maintainability: Changes to test data don't require modifications to the test script.

Implementing Data-Driven Testing with Selenium and Python:

Let's explore how to implement data-driven testing using Selenium and Python. We'll use a simple example of a login functionality to demonstrate the concept.

1. Data Preparation:

Create a separate file or data source containing the test data. This can be a CSV file, Excel sheet, or even a Python list or dictionary.

python

Copy code

# Example data in a Python list

login_data = [

 {"username": "user1", "password": "pass1"},

 {"username": "user2", "password": "pass2"},

 # Add more data sets as needed

]

2. Test Script Modification:

Modify the test script to read test data from the external source. Here, we use a simple loop to iterate through the data sets and perform the login test.

python

Copy code

from selenium import webdriver

# Assuming login_data is defined as mentioned above

def test_login():

 driver = webdriver.Chrome()

 for data_set in login_data:

 username = data_set["username"]

 password = data_set["password"]

 # Your login test steps using Selenium

 driver.get("login_page_url")

 driver.find_element_by_id("username").send_keys(username)

 driver.find_element_by_id("password").send_keys(password)

 driver.find_element_by_id("login_button").click()

 # Add assertions or verifications as needed

 driver.quit()

3. Parameterized Testing with Pytest:

Using a testing framework like Pytest makes parameterized testing even more straightforward. Pytest's @pytest.mark.parametrize decorator allows you to easily iterate through different data sets.

python

Copy code

import pytest

# Assuming login_data is defined as mentioned above

@pytest.mark.parametrize("username, password", [(d["username"], d["password"]) for d in login_data])

def test_login(username, password):

 driver = webdriver.Chrome()

 # Your login test steps using Selenium

 driver.get("login_page_url")

 driver.find_element_by_id("username").send_keys(username)

 driver.find_element_by_id("password").send_keys(password)

 driver.find_element_by_id("login_button").click()

 # Add assertions or verifications as needed

 driver.quit()

Best Practices for Data-Driven Testing:

Separate Test Data from Test Logic:

Keep test data in external files or sources, ensuring easy updates without modifying the test script.

Handle Data Variations:

Include diverse data sets to cover different scenarios and edge cases.

Logging and Reporting:

Implement comprehensive logging to capture data-driven test execution details.

Randomize Data Order:

Randomizing the order of test data sets helps identify any dependencies between data sets.

Handle Data-Driven Frameworks:

Consider implementing more sophisticated data-driven frameworks for larger projects, such as using a database to store test data.

Conclusion:

Data-driven testing is a potent strategy to maximize the efficiency and coverage of your Selenium test scripts in Python. Whether you're enrolled in a Selenium Python course or independently exploring automation testing with Python, incorporating data-driven principles will undoubtedly elevate your testing capabilities.

By embracing the versatility of data-driven testing, you pave the way for scalable and maintainable test suites, making your Selenium Python Automation Testing with cucumber framework.

[computerguru-blogs]

How Do I Create A Text File In Python?

Introduction

In python, Creating a new text file is a simple process using  the built-in file handling function. Before creating a file we need to know about file handling function. file handling is a in-built method in python , which is used to handle different types of file operations like read , write, update, delete etc .

Know We have to see different types of file operations,

1. How do I create a text file in python?

 Suppose we will create a text file exam.txt in python.( Read More)

[shinelikethunder]

Fandom Userscript Cookbook: Five Projects to Get Your Feet Wet

Target audience: This post is dedicated, with love, to all novice, aspiring, occasional, or thwarted coders in fandom. If you did a code bootcamp once and don’t know where to start applying your new skillz, this is for you. If you're pretty good with HTML and CSS but the W3Schools Javascript tutorials have you feeling out of your depth, this is for you. If you can do neat things in Python but don’t know a good entry point for web programming, this is for you. Seasoned programmers looking for small, fun, low-investment hobby projects with useful end results are also welcome to raid this post for ideas.

You will need:

The Tampermonkey browser extension to run and edit userscripts

A handful of example userscripts from greasyfork.org. Just pick a few that look nifty and install them. AO3 Savior is a solid starting point for fandom tinkering.

Your browser dev tools. Hit F12 or right click > Inspect Element to find the stuff on the page you want to tweak and experiment with it. Move over to the Console tab once you’ve got code to test out and debug.

Javascript references and tutorials. W3Schools has loads of both. Mozilla’s JS documentation is top-notch, and I often just keep their reference lists of built-in String and Array functions open in tabs as I code. StackOverflow is useful for questions, but don’t assume the code snippets you find there are always reliable or copypastable.

That’s it. No development environment. No installing node.js or Ruby or Java or two different versions of Python. No build tools, no dependency management, no fucking Docker containers. No command line, even. Just a browser extension, the browser’s built-in dev tools, and reference material. Let’s go.

You might also want:

jQuery and its documentation. If you’re wrestling with a mess of generic spans and divs and sparse, unhelpful use of classes, jQuery selectors are your best bet for finding the element you want before you snap and go on a murderous rampage. jQuery also happens to be the most ubiquitous JS library out there, the essential Swiss army knife for working with Javascript’s... quirks, so experience with it is useful. It gets a bad rap because trying to build a whole house with a Swiss army knife is a fool’s errand, but it’s excellent for the stuff we're about to do.

Git or other source control, if you’ve already got it set up. By all means share your work on Github. Greasy Fork can publish a userscript from a Github repo. It can also publish a userscript from an uploaded text file or some code you pasted into the upload form, so don’t stress about it if you’re using a more informal process.

A text editor. Yes, seriously, this is optional. It’s a question of whether you’d rather code everything right there in Tampermonkey’s live editor, or keep a separate copy to paste into Tampermonkey’s live editor for testing. Are you feeling lucky, punk?

Project #1: Hack on an existing userscript

Install some nifty-looking scripts for websites you visit regularly. Use them. Ponder small additions that would make them even niftier. Take a look at their code in the Tampermonkey editor. (Dashboard > click on the script name.) Try to figure out what each bit is doing.

Then change something, hit save, and refresh the page.

Break it. Make it select the wrong element on the page to modify. Make it blow up with a huge pile of console errors. Add a console.log("I’m a teapot"); in the middle of a loop so it prints fifty times. Savor your power to make the background wizardry of the internet do incredibly dumb shit.

Then try a small improvement. It will probably break again. That's why you've got the live editor and the console, baby--poke it, prod it, and make it log everything it's doing until you've made it work.

Suggested bells and whistles to make the already-excellent AO3 Savior script even fancier:

Enable wildcards on a field that currently requires an exact match. Surely there’s at least one song lyric or Richard Siken quote you never want to see in any part of a fic title ever again, right?

Add some text to the placeholder message. Give it a pretty background color. Change the amount of space it takes up on the page.

Blacklist any work with more than 10 fandoms listed. Then add a line to the AO3 Savior Config script to make the number customizable.

Add a global blacklist of terms that will get a work hidden no matter what field they're in.

Add a list of blacklisted tag combinations. Like "I'm okay with some coffee shop AUs, but the ones that are also tagged as fluff don't interest me, please hide them." Or "Character A/Character B is cute but I don't want to read PWP about them."

Anything else you think of!

Project #2: Good Artists Borrow, Great Artists Fork (DIY blacklisting)

Looking at existing scripts as a model for the boilerplate you'll need, create a script that runs on a site you use regularly that doesn't already have a blacklisting/filtering feature. If you can't think of one, Dreamwidth comments make a good guinea pig. (There's a blacklist script for them out there, but reinventing wheels for fun is how you learn, right? ...right?) Create a simple blacklisting script of your own for that site.

Start small for the site-specific HTML wrangling. Take an array of blacklisted keywords and log any chunk of post/comment text that contains one of them.

Then try to make the post/comment it belongs to disappear.

Then add a placeholder.

Then get fancy with whitelists and matching metadata like usernames/titles/tags as well.

Crib from existing blacklist scripts like AO3 Savior as shamelessly as you feel the need to. If you publish the resulting userscript for others to install (which you should, if it fills an unmet need!), please comment up any substantial chunks of copypasted or closely-reproduced code with credit/a link to the original. If your script basically is the original with some key changes, like our extra-fancy AO3 Savior above, see if there’s a public Git repo you can fork.

Project #3: Make the dread Tumblr beast do a thing

Create a small script that runs on the Tumblr dashboard. Make it find all the posts on the page and log their IDs. Then log whether they're originals or reblogs. Then add a fancy border to the originals. Then add a different fancy border to your own posts. All of this data should be right there in the post HTML, so no need to derive it by looking for "x reblogged y" or source links or whatever--just make liberal use of Inspect Element and the post's data- attributes.

Extra credit: Explore the wildly variable messes that Tumblr's API spews out, and try to recreate XKit's timestamps feature with jQuery AJAX calls. (Post timestamps are one of the few reliable API data points.) Get a zillion bright ideas about what else you could do with the API data. Go through more actual post data to catalogue all the inconsistencies you’d have to catch. Cry as Tumblr kills the dream you dreamed.

Project #4: Make the dread Tumblr beast FIX a thing

Create a script that runs on individual Tumblr blogs (subdomains of tumblr.com). Browse some blogs with various themes until you've found a post with the upside-down reblog-chain bug and a post with reblogs displaying normally. Note the HTML differences between them. Make the script detect and highlight upside-down stacks of blockquotes. Then see if you can make it extract the blockquotes and reassemble them in the correct order. At this point you may be mobbed by friends and acquaintainces who want a fix for this fucking bug, which you can take as an opportunity to bury any lingering doubts about the usefulness of your scripting adventures.

(Note: Upside-down reblogs are the bug du jour as of September 2019. If you stumble upon this post later, please substitute whatever the latest Tumblr fuckery is that you'd like to fix.)

Project #5: Regular expressions are a hard limit

I mentioned up above that Dreamwidth comments are good guinea pigs for user scripting? You know what that means. Kinkmemes. Anon memes too, but kinkmemes (appropriately enough) offer so many opportunities for coding masochism. So here's a little exercise in sadism on my part, for anyone who wants to have fun (or "fun") with regular expressions:

Write a userscript that highlights all the prompts on any given page of a kinkmeme that have been filled.

Specifically, scan all the comment subject lines on the page for anything that looks like the title of a kinkmeme fill, and if you find one, highlight the prompt at the top of its thread. The nice ones will start with "FILL:" or end with "part 1/?" or "3/3 COMPLETE." The less nice ones will be more like "(former) minifill [37a / 50(?)] still haven't thought of a name for this thing" or "title that's just the subject line of the original prompt, Chapter 3." Your job is to catch as many of the weird ones as you can using regular expressions, while keeping false positives to a minimum.

Test it out on a real live kinkmeme, especially one without strict subject-line-formatting policies. I guarantee you, you will be delighted at some of the arcane shit your script manages to catch. And probably astonished at some of the arcane shit you never thought to look for because who the hell would even format a kinkmeme fill like that? Truly, freeform user input is a wonderful and terrible thing.

If that's not enough masochism for you, you could always try to make the script work on LiveJournal kinkmemes too!

[janenguyenstudies]

What I think Biotech freshmen should learn during your first year at IU

So my first year has finally ended. The curriculum for freshman year is pre-determined by the Office of Academics so I did not have a chance to change the schedule. I took about 7 subjects each semester, with a total of 2 semesters. If you do not have to take IE1 and IE2 classes, you can “jump” directly into the main curriculum. So in my first year I took: Calculus 1&2, Physics 1&2, General Biology and Chemistry plus 2 Bio and Chem labs, Organic Chemistry, Academic English 1&2 (Reading, Writing, Speaking, Listening), Critical Thinking, Introduction to Biotechnology, P.E. It’s a relief that I could work through the courses although I was not excelling at STEM subjects in highschool. But college taught me all the amazing skills to study on my own and discover knowledge for my self-growth. Apart from schoolwork, I think any Biotech freshmen should also keep an eye on learning other extra skills of a scientist/professional, which I will list below. Do not worry because a year ago I entered this school while being a completely blank state, having seen so many of my friends succeeded in getting scholarships, leading extracurricular activities,... I felt hopeless sometimes but I believe in grinding one step at a time until I could accomplish the job. So my general experience boils down to being humble and let others teach you the skills, then practice slowly but firmly. You will be able to grow so much faster. And do not compare yourself with others’ success stories because everyone has their strengths and their own clock.

These are the lists of skills I have learnt and will continue to improve in the future. I will be expanding and giving more details about each point. This is in no chronological order:

 - Learn to make a positive affirmations/ orienting articles book: 

            During your whole college career, you will have a lot of moments of self-doubt, for example when your grades are not good, you've failed some classes, your part-time job application got turned down too many times (trust me I am so familiar with such rejections), some experiments got messy and returned no results, you wonder what your future in the field would look like. These are all scenarios that have happened to me in freshman year. 

Therefore, I have found a way to cope with self-doubt and boost my confidence, which is to make a collection of positive affirmations and orienting articles. I would form an imagined overview of my own career path reading all these writings and finally came to recording my own path . I use all forms of note-jotting tools to record them. I tend to record 1) Experience snippets from influencing scientists in my field, whom I happen to follow on Facebook 2) Lists of “What college kids need to practice before they graduate” (Dr.Le Tham Duong 's Facebook) 3) Ybox 's Shared tips for career orientation column (Link) 4) Short paragraphs from the books that I have read. The paragraphs often contain insights into what successful people (in Biotech or in Finance) have thought, have planned, and have acted on. For digital copy of the books, I save the snippets into a file called "Clippings" and later export them through the website called clippings.io

- Have an online note-taking tools for jotting down important thinking (recommend Keep or Evernote for quick jot, while Onenote is more suitable for recording lectures thanks to its structure that resembles a binder)

A snippet of my Evernote, where I store career advice:

- Learn to do research properly (what is a journal, what is a citation management software, what is the structure of a paper[abstract, introduction, methods, data analysis, discussion, conclusion], poster, conferences). You can begin to search for academic papers using Google Scholar scholar.google.com. However, there are countless of other websites for published journals that serve different sience fields. You need to dig into Google further to find them.

- Learn to write essays (basic tasks are covered in Writing AE courses including brainstorming, reading, citings, argumentative essay, process essay, preparing thesis statement)

- Join a lab: learn the safety guidelines, learn who is in control of the lab, what researches/projects are being carried there). As for this, you need to make contact with professors from our department. Most professors here are friendly and willing to help if you just come up and ask a question after class. To be eligible to join their labs,  - Learn to write a proper email to a professor: to ask for what will be on the next test, whether you can be a volunteer in their lab (usually by cold-email, and the professor will likely ask for your background: your highschool grades, your motivation,...); write email to an employer asking for a temporary position. - Learn to write a meeting minute: a sum-up of your meeting with other team members - Learn to answer basic questions for a volunteer opportunity: what is your strength, what is your past experience, what do you know about us, what is your free-time - Learn to write a CV: using topcv,... or downloading free templates on the Internet and write a motivation letter: What you are currently doing, what problems have you solved for your employers, and what can you do to help your new employers solve their problems.

- Learn to create an attractive presentation by using Canvas and learn to deliver ideas effectively (recommended book Think on your feet [Lib 1]).

I have discovered Canvas only for a year, but its graphics are so attractive and appealing to my taste, so here is my presentation for Finals using Canvas:

- Learn a programming language or a second language: recommend Python or R, and any foreign language that you feel interested in, but your ultimate goal would be to comfortably use that language in academic reading and exchanging ideas through writing and speaking (which is a long journey of 4+ years learning), so choose wisely. - Learn to create an online presence through a blog (Tumblr, Wordpress, Github, StackExchange,...), stalk your favorite experts on Quora and Reddit, make a habit to have a journal article delivered to your inbox every morning (me being Medium, Pocket, Nature); then Instagram or YouTube - Recommended books for incomming freshmen: How to be a straight-A student, Do not eat alone (socializing skills) - Recommended Medium sites: - Recommended Newspapers: The Economist, The Guardian, The New York Times Opinions Columns, Nature’s columns, The Scientific American. - Recommended Youtube Channels:  - Recommended Podcasts: listen passively on the bus, but try to paraphrase in your own words what you have understood about their conversation: - Recommended study spots: Den Da Coffeeshop, The Coffeehouse, Library of [...] in District 1, Central Library (Thu Duc District), IU Library.

- Learn the Pomodoro technique and Forest app: - Important websites for study resources: libgen, khanacademy, Coursehero (post only a short paragraph to get 1 free upload), scripd organic chemistry tutor, for jobs: ybox.vn - If your laptop is capable (with decent hardware), learn Adobe Tools (Video editing, Photoshop,...). My laptop can only run Linux Mint, so I chose to learn the skill of citation management and research (using less resource). Basically do not become computer-illiterate. - To reduce eye-strain, buy an e-reader to read scientific papers, do not print out all of them. - One exception to IU: you can bring one two-sided A4 paper into certain exams: this is my note for Calculus class:

- Learn to write a grand summary of formulas for Physics 2, meta-sum of all exercise questions - Prepare for IELTS (if you haven't taken IELTS already): you'll need it to pass IE classes, or apply for an exchange program. Ultimately, you need at least 6.0 in IELTS to graduate. I stumbled upon this careful list of tips from a senior student in our BT department who got an 8.0 => Link

Basically, follow Ngoc Bach’s page on Facebook to receive fully-solved exam materials, add ielts-simon.com into the mix, learn 560 academic word list, listen to Ted Talk and podcasts, do tests on ieltsonlinetests.com, do Cambridge IELTS book 9-14 and you’re good to go. - Have your eyes on competitions that spark your interest (innovative competition, writing contests, speaking and debating contests,...)

- Develop your fitness routine to protect your sanity when academic coursework overwhelm you and make you gain 15 pounds.

I do home HIIT exercises on Fitness Blender’s Youtube channel, Emi Wong, Chloe Ting home workouts in the beginning. 

Later I went to the gym and do split routines with weights, then threw in squats, deadlifts, lunges and HIIT on treadmill. This is how my current routine look like: (I work out only 4 times/week)

- Learn to use flashcards. (Quizlet has premade flashcards for biology class)

- Learn to manage personal finance: what is budget, expenses, income,... - Learn some google tweaks to pirate stuff. (especially textbook files and solutions files) - Learn to make handwritten A4 notes. I will post my own handwritten notes for Critical Thinking Mid (final is taken), Calculus II Mid and Final. - Learn to create meta sets for formulas and problems. I will post my formula set for Physics II and Problem/Skill set for Organic Chemistry. For Physics II, I learned my hard lesson is that it is better to do past exams than to solve advance textbook problems, so I stuck with past exams posted by TA and learnt by heart all the formulas, SI units. For Organic Chemistry you need a skillset checklist (like in the Wade textbook). Also there are questions from the slides such as the connection of amylopectin,... But they won't challenge you to think much. Only the amount of information to be memorized is deemed challenging here. - Intro to Biotech was quite easy and you could prepare in advance. 3 faculty members (from 3 fields: plant, animal, pharma) will take turn giving you an overview lecture. The exam will ask “Write what you know about those fields and their applications”, openbook-style. So hear me out and search for Overview powerpoints of that field, then write your own essay, print it out and bring it with you into the exam. Your power should be spent on Calculus and Physics, not on memorizing the essay.

[peggysalting8-blog]

Convert Any Audio File To Mp3 With Python

Is a person-friendly free mp3 download website and online youtube converter. There isn't a doubt that music is an indispensable a part of life. Simply as the old saying goes, music is the perfect aspect of life. Because of the fast improvement of the internet, we have been capable of finding numerous sorts of music on different websites with out hassle. Apart from listening to music online, you can even choose to obtain MP3 albums to COMPUTER or www.goodreads.com cell phones and play them handily. Therefore, this article aims at sharing the highest sites to obtain full albums free. - Save audio and data tracks to recordsdata (.wav,wma,ogg,mp3 andiso). To conclude, the conversion service is one of the rarest to be completly limitless, you can obtain very long videos with none period limit, without each day downloading limit and that from a plethora of video and music-sharing and diffusion websites. To obtain any given piece of the music to your laptop, simply right-click on the link below its participant and select save hyperlink as." Then select the placement in your system the place you wish to save it. Earlier than you add the MP3 or AAC file to your iOS gadget, you may have so as to add it to your iTunes music library. Fortunately, that is extremely simple. Superior Download Manager shouldn't be a music downloader app. Nonetheless, it will possibly assist you in downloading music files using lots of its download features. ADM isn't idol app for downloading music. 1. Insert the CD that comprises the music you wish to merge. You probably have a bunch of random songs you wish to merge together then burn them to a CD first. Simply in case, set the hole to zero seconds. There are some wonderful music gamers you may download on your phone, however, one of the major reasons why you should get the waptick music obtain app is because this is likely one of the apps that has a number of the most popular songs commonly up to date. There are also some genres that you may select depending in your taste. With this app now you can just remember to save rather a lot on cell knowledge and hearken to songs even in low community areas. The compressed information on this app assist make it more efficient. This is what makes it stand out amongst other apps. Codec: Many audio file codecs are wrapper" or container codecs, which implies that the file extension alone does not tell how the contained audio stream is encoded. The audio stream in a WAV file for example may be encoded in an unlimited number of completely different compressed or uncompressed formats, it could actually even include an audio stream encoded as MP3. MP4 and OGG are container formats, which may include audio streams of different formats, in addition to video streams, metadata and for example subtitles. The one other excellent software I like to recommend is Streaming Audio Recorder In addition to primary recording perform, this tool additionally offers customers with constructed-in audio converter. It allows you to convert M4A file to various codecs like MP3, WMA, AAC, FLAC, WAV, and so on. which caters multiple calls for of users. What makes it distinguishable from others is that this program empowers you to transform audio information in batch and can help customers to merge mp3 songs series of recordsdata into one file. Converting M4A to MP3 isn't any extra an issue by using this highly effective utility. Now go to Format" button and click on the drop down menu down it. From there, scroll down and click on on MP3" format. You can even set the bitrate of the audio. RockMyRun not only will freshen up your mind but it will additionally carry you back to form. It presents a superb collection of DJ-combined playlists for exercise soundtracks. Customers can obtain the tracks to listen to them offline. Users can synchronize their heartbeats to filter a playlist that suits you. My beat function mechanically creates a playlist to suit your work style. Going jogging, give a attempt to this the most effective music downloader apps for Android. This app permits simple music downloading and administration and faucets into the catalogs of Outly and Jamendo. Total, Obtain MP3 Music affords about one million royalty-free tracks for obtain throughout a variety of different genres. Sound Cloud Downloader Professional: Like Sound Cloud on Android, this is one of the hottest streaming companies accessible within the mobile music app industry. You cannot obtain songs from Sound Cloud Downloader Pro as it is a streaming device, but when you download and set up this, you'll be able to search and obtain the desired songs. Apple Lossless: Also called ALAC, Apple Lossless is just like FLAC. It is a compressed lossless file, although it is made by Apple. Its compression isn't quite as environment friendly as FLAC, so your files may be a bit larger, but it's totally supported by iTunes and iOS (while FLAC is just not). Thus, you'd wish to use this in the event you use iTunes and iOS as your main music listening software program. Music Download Paradise is a sound search engine for Android smartphones. Much like MP3 Music Download, Music Obtain Paradise allows you to get your hands on every thing from sound effects ringtones and other brief clips and then obtain them. You may even find free songs to download, but keep in mind that copyright legal guidelines shield many songs.

It's looking like my best guess right now is to make use of Audio Grabber to join all of the tracks into one WAV file, after which use a format converter to transform from WAV to MP3. WinAmp seems like something that might do this, in addition to perhaps increase the amount of the recording. That will be my next test. Click the crimson "Begin" button on the highest-left to make Leawo Music Recorder begin to report. Leawo Music Recorder may routinely capture audio and charleyshuman.pen.io start recording. Nevertheless, it must be observed that you should run Leawo Music Recorder earlier than taking part in an MP3 audio.