Reading a File using Python

Computers use file systems to store and retrieve data. Each file is an individual container of related information. If you’ve ever saved a document, downloaded a song, or even sent an email you’ve created a file on some computer somewhere. Even script.py, the Python program you’re editing in the learning environment, is a file.
So, how do we interact with files using Python?
Let’s say we had a file called hello_python.txt with these contents:

Python is a powerful tool for Data Ananlysis. So, stay at home and learn python for future Data Science.

We could read that file like this:

script.py

with open('hello_python.txt') as python_file:
python_contents = python_file.read()
print(python_contents)

This
opens a file object called python_file and creates a new indented block where you can read the contents of the opened file. We then read the contents of the file python_file using python_file.read() and save the resulting string into the variable python_contents. Then we print python_contents, which outputs the statement written in the above!.

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Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

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Writing a File using Python

In the previous post we have seen that how to open and read a file using python script. Today, I have posting about how to write a file or create your own file using the script.

Reading a file is all well and good, but what if we want to create a file of our own? With Python we can do just that. It turns out that our open() function that we’re using to open a file to read needs another argument to open a file to write to.

script.py

with open('generated_file.txt', 'w') as gen_file:
  gen_file.write("I love python!")

Here we pass the argument 'w' to open() **in order to indicate to open the file in write-mode. The default argument is 'r' and passing 'r' to **open() opens the file in read-mode as we’ve been doing.

This code creates a new file in the same folder as script.py and gives it the text What an incredible file!. It’s important to note that if there is already a file called generated_file.txt it will completely overwrite that file, erasing whatever its contents were before.

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What Is a CSV File?

Text files aren’t the only thing that Python can read, but they’re the only thing that we don’t need any additional parsing library to understand. CSV files are an example of a text file that impose a structure to their data. CSV stands for Comma-Separated Values and CSV files are usually the way that data from spreadsheet software (like Microsoft Excel or Google Sheets) is exported into a portable format. A spreadsheet that looks like the following
Name Username Email
Asibeh Tenager asibeh asibeh@yahoo.com
Asibeh Tenager asibeh asibeh@yahoo.com



In a CSV file that same exact data would be rendered like this:

users.csv

Name,Username,Email, Asibeh Tenager, asibeh, asibeh@yahoo.com

Notice that the first row of the CSV file doesn’t actually represent any data, just the labels of the data that’s present in the rest of the file. The rest of the rows of the file are the same as the rows in the spreadsheet software, just instead of being separated into different cells they’re separated by… well I suppose it’s fair to say they’re separated by commas.


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#Assignment

Guess The Number

Write a programme where the computer randomly generates a number between 0 and 20. The user needs to guess what the number is. If the user guesses wrong, tell them their guess is either too high, or too low. This will get you started with the random library if you haven't already used it.

Post your solution in the comment box

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Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

#FaceMask #KeepDistancing #LearnPython #LearnDataScience

Join @python4fds for more information

Bubble Sort Algorithm Explained! Python Implementation & Step-by-Step Guide
https://www.youtube.com/watch?v=x6WGF8zDWZA

Learn More About Algorithmic Thinking:

If you're interested in diving deeper into algorithmic problem-solving, check out these additional tutorials:

📌 Bubble Sort Algorithm Explained! Python Implementation & Step-by-Step Guide
https://www.youtube.com/watch?v=x6WGF8zDWZA

📌 Linear Search Algorithm: https://www.youtube.com/watch?v=f0KsENxdTGI

📌 Binary Search Algorithm: https://www.youtube.com/watch?v=_MjGCuwFDuw

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💡 Researchers & Beginners in Python!

This step-by-step guide walks you through installing and setting up Python on Windows using the Microsoft Store, along with VS Code setup to get you coding in no time!

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Python podcast with AI Agent

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This beginner-friendly project walks you through:

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🚀 New Python Tutorial Alert!

Boolean logic is the foundation of every programming decision. Whether it’s controlling the flow of your code, building smarter conditions, or making algorithms more efficient—understanding it well is a must for every Python developer.

In my latest tutorial, I break down Boolean logic in Python step by step, with simple explanations and clear examples for beginners.

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If you’re just starting out or want to sharpen your fundamentals, this one’s for you.

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🚀 New Python Tutorial!
Python Lists | Beginner-Friendly Tutorial with Real-World Examples
https://youtu.be/KDZcMK7FoA0

How to import modules for beginners
https://www.youtube.com/watch?v=0GKxOJNRtPA

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🚀 Why and When Should You Use Polynomial Regression?

Polynomial Regression is used when the relationship between variables is not a straight line.
Instead of fitting a simple linear trend, it helps machine learning models capture curves, bends, and more complex patterns in the data.

✅ When to Use Polynomial Regression

• When data shows curved relationships
• When Linear Regression underfits the data
• When prediction accuracy needs improvement
• When patterns change at different rates over time

📌 Common Real-World Applications

• House price prediction
• Sales forecasting
• Population growth analysis
• Weather and climate modeling
• Biological and medical trends

⚠️ Important Tradeoff Higher polynomial degrees can improve fitting… But too much complexity can cause overfitting.

The goal is not to perfectly memorize the data. The goal is to generalize well on unseen data.

💡 Key Idea:
Linear Regression captures straight relationships.

Polynomial Regression captures non-linear relationships.

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🐍 Pickle vs JSON: Which One Should You Use?

When working with Python, you'll often need to save and load data. Two common choices are Pickle and JSON—but they serve different purposes.

✅ JSON
• Human-readable and easy to edit
• Language-independent
• Great for APIs, configuration files, and data exchange
• More secure for sharing data

✅ Pickle
• Stores almost any Python object
• Preserves Python-specific data structures
• Faster and more convenient for Python-to-Python workflows
• Not human-readable and should not be loaded from untrusted sources

📌 Quick Rule:
Use JSON when data needs to be shared, inspected, or used across different systems.
Use Pickle when you need to save and restore complex Python objects within Python applications.

Choosing the right format can make your applications more portable, secure, and maintainable.

Dive Deeper Here:
https://youtu.be/xuOa3vB6gkI?si=sfgVup0my0bQhuz3

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