Machine Learning with Python

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🐼 20 of the most used Pandas + PDF functions

👨🏻‍💻 The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data.

✔️ Since then I decided to prepare a list of the most used Pandas functions that I use on a daily basis. Now this list is ready! In the following, I will introduce 20 of the best and most used Pandas functions:

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read_csv(): Fast data upload from CSV files

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head(): look at the first five rows of the database to start..

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info(): Checking data structure such as data type and empty values.

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describe(): Generate descriptive statistics for numeric columns.

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loc[ ]: accesses rows and columns by label or condition.

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iloc[ ]: Access data by row number.

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merge(): Merge dataframes with common columns.

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groupby(): Grouping for easier analysis.

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pivot_table(): Summarize data in pivot table format.

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to_csv(): Save data as a CSV file.

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pd.concat(): Concatenate multiple dataframes in rows or columns.

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pd.melt(): Convert wide format data to long format.

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pd.pivot_table(): Create a pivot table with multiple levels.

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pd.cut(): Split the data into specific intervals.

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pd.qcut(): Sort data by percentage.

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pd.merge(): Merge data in database style for advanced linking.

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DataFrame.apply(): Apply a custom function to the data.

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DataFrame.groupby(): Analyze grouped data.

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DataFrame.drop_duplicates(): Drop duplicate rows.

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DataFrame.to_excel(): Save data directly to Excel file.

┌ 🐼 Pandas Functions
└ 📄 PDF

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

http://t.me/codeprogrammer ⭐️

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Machine Learning with Python

Regression & Classification Loss Functions

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

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9.69K viewsedited 08:27

Machine Learning with Python

Python Network Programming Cheat Sheet 🖥

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

http://t.me/codeprogrammer

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Machine Learning with Python

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Machine Learning with Python

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Machine Learning with Python

In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

more explain: https://hackmd.io/@husseinsheikho/imageprocessing

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

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Machine Learning with Python

Forwarded from Machine Learning

👣 Rust Interview Deep Dive 🦀🔍

A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. 💼📚

Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. 💻🏗️ Not "guess the program's output", but the mechanics on which real services are built. 🛠️🚀

Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. 🔒⚡ And it all starts with the basics. Ownership, borrowing, lifetimes. 🧱🔄 Those who want can start from scratch or at the staff level. 🚶‍♂️👨‍💻

https://github.com/Develp10/rustinterviewquiestions 🔗

#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth

GitHub

GitHub - Develp10/rustinterviewquiestions: Rust вопорсы с собеседований

Rust вопорсы с собеседований . Contribute to Develp10/rustinterviewquiestions development by creating an account on GitHub.

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