#pythoncoder

[sanjaymhatreblog]

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[trialn1error]

Python Notes and Resources

Hey! I made a bunch of Python notes a bit ago and decided to share them. They're really beginner friendly, and include a bunch of external sources that I used and continue using at the end. Hope this helps someone!

Notion Notes

[d0nutzgg]

I am currently coding a browser right now in pure Python. Let me explain all the steps it has taken at 300+ lines of code except in no particular order because I need to reorganize.

Fernet Encryption SSL Certificate Check Implementing a UI - Chose PyQt because its a prettier layout than Tkinter. Implementing a toolbar for the UI Implementing browsing history + browsing history tab + a clear button to clear the history - Done in the GUI / UI as well. Adding a privacy browsing mechanism - routed through Tor Added PyBlocker for Ad Blocking and Anti-Tracking on websites Added a feature to make Google not track your searches because fuck you Google you nosey assholes. Added a sandbox to avoid user getting infected with viruses Added additional functionalities including optimization that way the browser wasn't slow as fuck: Using JavaScript v 8 engine to load JavaScript Using slight mem caching <100mb to avoid slowing the computer but also let the program browse quicker Using one http request to send multiple http requests - need to set limit. Encrypted global security features. I am at over 300 lines of code. I am ready to pay someone to debug this shit when I am finished with it (lol) May the force be with you my #Pythonistas

[shivadmads]

Top Free Python Courses & Tutorials Online Training | NareshIT

Top Free Python Courses & Tutorials Online Training | NareshIT

In today’s tech-driven world, Python has emerged as one of the most versatile and popular programming languages. Whether you're a beginner or an experienced developer, learning Python opens doors to exciting opportunities in web development, data science, machine learning, and much more.

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Free Python Basics Tutorials: Get started with our easy-to-follow Python tutorials designed for beginners.

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[xpc-web-dev]

Studying code calmly and avoiding a burnout. Day 2

(27/06/2023)

Hello everyone.

How are you? I hope well.

Today I continued my saga of studying calmly avoiding a burnout (it's serious but it's funny at the same time kkkkkk)

I finished module 1 of javascript and did well in the logic exercises.

And here I've been reflecting and comparing myself a lot with the June 2022 bea, she couldn't do a logic exercise and today I've mastered it well. Sometimes I get choked up. But I can always work it out if I really try.

I installed python 3.11 and here was another overcoming because as I have the linux terminal (I will never recommend it for beginners) I kept having to install and update the version. But today I got it.

(This exercise was to only test my terminal in vscode)

Hey, you must ask yourself, but why python if you have a front-end?

Because I need to learn function and ordering algorithms and I find it easier with python than with javascript. (precisely because I know more about python than js)

With this I started my introductory computer science course and I hope to finish it by Thursday. And how are your studies?

I wish you can overcome your obstacles to achieve your goals, discipline, constancy.

And my personal advice, when my goals aren't enough to motivate me, I decide to win in the power of hate.

Recommend, anger is good if you use it responsibly and intelligently. UEUEHHEUEEU. Drink water.

[msrlunatj]

Primeros Pasos en Programación: Guía Completa

Introducción

Bienvenido al mundo de la programación. Si estás aquí, probablemente estás dando tus primeros pasos en el vasto campo del desarrollo de software. Puede parecer abrumador al principio, con tantos lenguajes, herramientas y conceptos desconocidos, pero no te preocupes. Este blog está diseñado para guiarte en este viaje, ofreciéndote una introducción clara y consejos prácticos para que puedas empezar con buen pie.

1. ¿Qué es la Programación?

La programación es el proceso de crear instrucciones que una computadora puede seguir para realizar tareas específicas. Estas instrucciones se escriben en un lenguaje de programación, que es un conjunto de reglas y sintaxis que los humanos pueden usar para comunicarse con las computadoras.

Lenguajes de Programación Populares:

Python: Fácil de aprender y ampliamente utilizado en ciencia de datos, desarrollo web, automatización y más.

JavaScript: El lenguaje del web, esencial para desarrollar aplicaciones y sitios interactivos.

Java: Famoso por su uso en aplicaciones empresariales y móviles (especialmente en Android).

C++: Utilizado en desarrollo de software de sistemas, juegos, y aplicaciones de alto rendimiento.

2. Conceptos Básicos de Programación

a) Variables y Tipos de Datos

Variables: Son contenedores que almacenan valores que pueden cambiar durante la ejecución del programa.

Ejemplo en Python: x = 5 asigna el valor 5 a la variable x.

Tipos de Datos: Representan la naturaleza de los valores almacenados en las variables.

Enteros: int (números sin decimales)

Flotantes: float (números con decimales)

Cadenas: str (secuencias de caracteres)

Booleanos: bool (True o False)

b) Estructuras de Control

Condicionales: Permiten que un programa tome decisiones.

Ejemplo: if x > 0: print("x es positivo")

Bucles: Ejecutan un bloque de código repetidamente.

Ejemplo: for i in range(5): print(i) imprimirá los números del 0 al 4.

c) Funciones

Las funciones son bloques de código reutilizables que realizan una tarea específica.

Ejemplo en Python: def suma(a, b): return a + b print(suma(2, 3)) # Salida: 5

3. Elige tu Primer Lenguaje de Programación

Si eres nuevo en la programación, te recomiendo empezar con Python por las siguientes razones:

Sintaxis Simple: La sintaxis de Python es clara y fácil de entender, lo que permite concentrarte en aprender conceptos básicos de programación sin enredarte en detalles complejos.

Comunidad Amplia: Hay muchos recursos de aprendizaje disponibles, incluyendo tutoriales, foros y documentación oficial.

Versatilidad: Python se utiliza en una amplia gama de aplicaciones, desde desarrollo web hasta inteligencia artificial.

4. Herramientas Esenciales

a) Entornos de Desarrollo Integrados (IDEs)

VS Code (Recomendado): Un editor de código ligero y personalizable que soporta múltiples lenguajes.

PyCharm: Un IDE robusto para Python que ofrece herramientas avanzadas para el desarrollo y depuración.

b) Control de Versiones

Git: Una herramienta esencial para el control de versiones, que te permite rastrear cambios en tu código y colaborar con otros desarrolladores.

GitHub: Un servicio basado en la nube que facilita la colaboración y el alojamiento de proyectos.

5. Primeros Proyectos para Principiantes

Comenzar con pequeños proyectos es una excelente manera de aplicar lo que has aprendido y adquirir confianza. Aquí tienes algunas ideas de proyectos:

Calculadora Básica:

Crea una calculadora que pueda realizar operaciones básicas como suma, resta, multiplicación y división.

Juego de Adivinanza de Números:

Un programa que elige un número al azar y pide al usuario que lo adivine. Puedes agregar funciones como limitar el número de intentos y dar pistas si el número es mayor o menor.

Lista de Tareas (To-Do List):

Una aplicación simple que permite a los usuarios agregar, eliminar y marcar tareas como completadas.

6. Consejos Útiles para Principiantes

a) Practica Regularmente

La programación es una habilidad práctica. Cuanto más código escribas, mejor entenderás los conceptos.

Utiliza plataformas como LeetCode o HackerRank para resolver problemas de programación.

b) No Tengas Miedo de Cometer Errores

Cometer errores es parte del proceso de aprendizaje. Cada error que cometes es una oportunidad para aprender algo nuevo.

c) Aprende a Buscar Información

Saber cómo buscar respuestas a tus preguntas es una habilidad vital. Stack Overflow es un recurso invaluable donde puedes encontrar soluciones a problemas comunes.

d) Colabora y Comparte tu Trabajo

Participa en comunidades de desarrolladores, como GitHub o Reddit. Compartir tu trabajo y colaborar con otros te expondrá a nuevas ideas y te ayudará a mejorar.

e) Mantente Curioso

La tecnología está en constante evolución. Mantente al día con las últimas tendencias y tecnologías para seguir creciendo como desarrollador.

7. Recursos Adicionales

a) Cursos y Tutoriales

CódigoFacilito (Página web): Ofrece una amplia variedad de cursos gratuitos en español sobre programación, desarrollo web, bases de datos y más. Además, cuenta con tutoriales y una comunidad activa que apoya el aprendizaje colaborativo.

freeCodeCamp (Página web): Un excelente recurso gratuito que cubre desde conceptos básicos hasta proyectos avanzados.

Desarrolloweb.com: Un portal completo que ofrece artículos, tutoriales y guías sobre programación y desarrollo web. Es una excelente fuente para aprender HTML, CSS, JavaScript, PHP, y otros lenguajes de programación.

Píldoras Informáticas (Canal de YouTube): Explica conceptos de programación y desarrollo de software en videos cortos y fáciles de entender.

HolaMundo (Canal de YouTube): Un canal dedicado a enseñar programación en español, con cursos completos de Java, Python, C++, y más.

Fazt Code (Canal de YouTube): Ofrece tutoriales y guías sobre desarrollo web, especialmente en JavaScript, Node.js, y frameworks modernos.

b) Libros Recomendados

“Python para todos” de Raúl González Duque: Este libro es una excelente introducción a Python, diseñado para principiantes. Está escrito de manera sencilla y práctica, ideal para quienes quieren aprender a programar desde cero.

“Aprende JavaScript desde cero” de Victor Moreno: Un libro que te guía paso a paso en el aprendizaje de JavaScript. Es perfecto para principiantes que desean entender el lenguaje desde sus fundamentos y aplicar lo aprendido en proyectos reales.

“Programación en C” de Luis Joyanes Aguilar: Este es un clásico en la literatura técnica en español, ideal para quienes desean aprender el lenguaje C, uno de los más fundamentales y poderosos en la programación.

“Introducción a la programación con Python” de Jesús Conejo: Otro excelente recurso para aprender Python, este libro está enfocado en estudiantes y autodidactas que desean adquirir una base sólida en programación utilizando Python.

“El gran libro de HTML5, CSS3 y JavaScript” de Juan Diego Gauchat: Este libro cubre los fundamentos del desarrollo web moderno, incluyendo HTML5, CSS3 y JavaScript. Es una guía completa para aquellos que quieren empezar a construir sitios y aplicaciones web.

Conclusión

Adentrarse en la programación es una experiencia emocionante y gratificante. Con paciencia, práctica y los recursos adecuados, estarás bien encaminado hacia convertirte en un desarrollador competente. Recuerda que cada experto fue una vez un principiante, y lo más importante es disfrutar del proceso de aprendizaje.

[the-grim-jester]

PYTHONNN

GRRRR I HAVEN’T DRAWN HIM IN FOREVER!!! BUT I LOVE HIMMMMM

[fortunatelycoldengineer]

[free-python-code]

YouChat new ai can help you in your code

[codeparttime]

Sort Dictionaries by Key, or Value, in Python – Asc, Desc

Python includes a built-in function, the sorted() function, that we can utilize for efficiently sorting dictionaries by keys or values(in Asc or Desc order).

[python-bot-creator]

YouTube Automation

I'm deign a YouTube shorts automation bot that will create a shorts video for you. If you are looking for this

click here: https://www.fiverr.com/businessmark645

[eyescananalyze]

PYTHON - Dictionary - Methods - copy #PythonTips #viral #dictionary #vir...

[statisticshelpdesk]

Building Predictive Models with Regression Libraries in Python Assignments

Introduction

Predictive modeling serves as a fundamental method for data-driven decisions that allows to predict outcomes, analyze trends, and forecast likely scenarios from the existing data. Predictive models are the ones that forecast the future outcomes based on historical data and helps in the understanding of hidden patterns. Predictive modeling is an essential technique in data science for applications in healthcare, finance, marketing, technology, and virtually every area. Often such models are taught to students taking statistics or Data Science courses so that they can utilize Python’s vast libraries to build and improve regression models for solving real problems.

Python has been the popular default language for predictive modeling owing to its ease of use, flexibility, and availability of libraries that are specific to data analysis and machine learning. From cleaning to building models, and even evaluating the performance of models, you can do all of these with Python tools like sci-kit-learn and stats models, as well as for data analysis using the pandas tool. Getting acquainted with these tools requires following certain procedures, writing optimized codes, and consistent practice. Availing of Python help service can be helpful for students requiring extra assistance with assignments or with coding issues in predictive modeling tasks.

In this article, we take you through techniques in predictive modeling with coding illustrations on how they can be implemented in Python. Specifically, the guide will be resourceful for students handling data analysis work and seeking python assignment help.

Why Regression Analysis?

Regression analysis is one of the preliminary methods of predictive modeling. It enables us to test and measure both the strength and the direction between a dependent variable [that is outcome variable] and one or more independent variables [also referred to as the predictors]. Some of the most commonly used regression techniques have been mentioned below: • Linear Regression: An easy-to-understand but very effective procedure for predicting the value of a dependent variable as the linear combination of the independent variables. • Polynomial Regression: This is a linear regression with a polynomial relationship between predictors and an outcome. • Logistic Regression: Especially popular in classification problems with two outcomes, logistic regression provides the likelihood of the occurrence of specific event. • Ridge and Lasso Regression: These are the more standardized types of linear regression models that prevent overfitting.

Step-by-Step Guide to Building Predictive Models in Python

1. Setting Up Your Python Environment

First of all: you need to prepare the Python environment for data analysis. Jupyter Notebooks are perfect as it is a platform for writing and executing code in small segments. You’ll need the following libraries:

# Install necessary packages

!pip install numpy pandas matplotlib seaborn scikit-learn statsmodels

2. Loading and Understanding the Dataset

For this example, we’ll use a sample dataset: ‘student_scores.csv’ file that consists of records of Study hours and Scores of the students. It is a simple one, but ideal for the demonstration of basics of regression. The dataset has two columns: Numerical variables include study hours referred to as Hours; and exam scores referred as Scores.

Download the students_scores.csv file to follow along with the code below.

import pandas as pd

# Load the dataset

data = pd.read_csv("students_scores.csv")

data.head()

3. Exploratory Data Analysis (EDA)

Let us first understand the data before we perform regression in python. Let us first explore the basic relationship between the two variables – the number of hours spent studying and the scores.

import matplotlib.pyplot as plt

import seaborn as sns

# Plot Hours vs. Scores

plt.figure(figsize=(8,5))

sns.scatterplot(data=data, x='Hours', y='Scores')

plt.title('Study Hours vs. Exam Scores')

plt.xlabel('Hours Studied')

plt.ylabel('Exam Scores')

plt.show()

While analyzing the scatter plot we can clearly say the higher the hours studied, the higher the scores. With this background, it will be easier to build a regression model.

4. Building a Simple Linear Regression Model

Importing Libraries and Splitting Data

First, let’s use the tool offered by the sci-kit-learn to split the data into training and testing data that is necessary to check the performance of the model

from sklearn.model_selection import train_test_split

# Define features (X) and target (y)

X = data[['Hours']]

y = data['Scores']

# Split data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Training the Linear Regression Model

Now, we’ll fit a linear regression model to predict exam scores based on study hours.

from sklearn.linear_model import LinearRegression

# Initialize the model

model = LinearRegression()

# Train the model

model.fit(X_train, y_train)

# Display the model's coefficients

print(f"Intercept: {model.intercept_}")

print(f"Coefficient for Hours: {model.coef_[0]}")

This model equation is Scores = Intercept + Coefficient * Hours.

Making Predictions and Evaluating the Model

Next, we’ll make predictions on the test set and evaluate the model's performance using the Mean Absolute Error (MAE).

from sklearn.metrics import mean_absolute_error

# Predict on the test set

y_pred = model.predict(X_test)

# Calculate MAE

mae = mean_absolute_error(y_test, y_pred)

print(f"Mean Absolute Error: {mae}")

A lower MAE indicates that the model's predictions are close to the actual scores, which confirms that hours studied is a strong predictor of exam performance.

Visualizing the Regression Line

Let’s add the regression line to our initial scatter plot to confirm the fit.

# Plot data points and regression line

plt.figure(figsize=(8,5))

sns.scatterplot(data=data, x='Hours', y='Scores')

plt.plot(X, model.predict(X), color='red')  # Regression line

plt.title('Regression Line for Study Hours vs. Exam Scores')

plt.xlabel('Hours Studied')

plt.ylabel('Exam Scores')

plt.show()

If you need more assistance with other regression techniques, opting for our Python assignment help services provides the necessary support at crunch times.

5. Improving the Model with Polynomial Regression

If the relationship between variables is non-linear, we can use polynomial regression to capture complexity. Here’s how to fit a polynomial regression model.

from sklearn.preprocessing import PolynomialFeatures

# Transform the data to include polynomial features

poly = PolynomialFeatures(degree=2)

X_poly = poly.fit_transform(X)

# Split the transformed data

X_train_poly, X_test_poly, y_train_poly, y_test_poly = train_test_split(X_poly, y, test_size=0.2, random_state=42)

# Fit the polynomial regression model

model_poly = LinearRegression()

model_poly.fit(X_train_poly, y_train_poly)

# Predict and evaluate

y_pred_poly = model_poly.predict(X_test_poly)

mae_poly = mean_absolute_error(y_test_poly, y_pred_poly)

print(f"Polynomial Regression MAE: {mae_poly}")

6. Adding Regularization with Ridge and Lasso Regression

To handle overfitting, especially with complex models, regularization techniques like Ridge and Lasso are useful. Here’s how to apply Ridge regression:

from sklearn.linear_model import Ridge

# Initialize and train the Ridge model

ridge_model = Ridge(alpha=1.0)

ridge_model.fit(X_train, y_train)

# Predict and evaluate

y_pred_ridge = ridge_model.predict(X_test)

mae_ridge = mean_absolute_error(y_test, y_pred_ridge)

print(f"Ridge Regression MAE: {mae_ridge}")

Empowering Students in Python: Assignment help for improving coding skills

Working on predictive modeling in Python can be both challenging and rewarding. Every aspect of the service we offer through Python assignment help is precisely designed to enable students not only to work through the assignments but also to obtain a better understanding of the concepts and the use of optimized Python coding in the assignments. Our approach is focused on student learning in terms of improving the fundamentals of the Python programming language, data analysis methods, and statistical modeling techniques.

There are a few defined areas where our service stands out

First, we focus on individual learning and tutoring.

Second, we provide comprehensive solutions and post-delivery support. Students get written solutions to all assignments, broken down into steps of the code and detailed explanations of the statistical method used so that the students may replicate the work in other projects.

As you choose our service, you get help from a team of professional statisticians and Python coders who will explain the complex concept, help to overcome technical difficulties and give recommendations on how to improve the code.

In addition to predictive analytics, we provide thorough consultation on all aspects of statistical analysis using Python. Our services include assistance with key methods such as:

• Descriptive Statistics

• Inferential Statistics

• Regression Analysis

• Time Series Analysis

• Machine Learning Algorithms

Hire our Python assignment support service, and you will not only get professional assistance with your tasks but also the knowledge and skills that you can utilize in your future assignments.

Conclusion In this guide, we introduced several approaches to predictive modeling with the use of Python libraries. Thus, by applying linear regression, polynomial regression, and Ridge regularization students will be able to develop an understanding of how to predict and adjust models depending on the complexity of the given data. These techniques are very useful for students who engage in data analysis assignments as these techniques are helpful in handling predictive modeling with high accuracy. Also, take advantage of engaging with our Python assignment help expert who can not only solve your Python coding issues but also provide valuable feedback on your work for any possible improvements.