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Tutorial Python

Rămâneți la curent cu cele mai recente noutăți, tehnici și resurse pentru programarea în Python. Tutorialele noastre sunt pline de ghiduri pas cu pas și studii de caz pe care le puteți folosi pentru a vă perfecționa.
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Groupby, split-apply-combine and pandas

In this tutorial, you'll learn how to use the pandas groupby operation, which draws from the well-known split-apply-combine strategy, on Netflix movie data.

Hugo Bowne-Anderson

26 septembrie 2017

Poker Probability and Statistics with Python

Tackle probability and statistics in Python: learn more about combinations and permutations, dependent and independent events, and expected value.
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Daniel Poston

21 septembrie 2017

Graph Optimization with NetworkX in Python

This NetworkX tutorial will show you how to do graph optimization in Python by solving the Chinese Postman Problem in Python.
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Andrew Brooks

12 septembrie 2017

Detecting Fake News with Scikit-Learn

This scikit-learn tutorial will walk you through building a fake news classifier with the help of Bayesian models.
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Katharine Jarmul

24 august 2017

Python Seaborn Tutorial: A Guide to Statistical Data Visualization

Master statistical data visualization in Python using Seaborn with hands-on examples and best practices.
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Moez Ali

22 mai 2026

Apache Spark Tutorial: ML with PySpark

Apache Spark tutorial introduces you to big data processing, analysis and ML with PySpark.
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Karlijn Willems

28 iulie 2017

Scikit-Learn Tutorial: Baseball Analytics Pt 2

A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hall of Fame
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Daniel Poston

20 iunie 2017

5 Tips to Write Idiomatic Pandas Code

This tutorial covers 5 ways in which you can easily write pandorable or more idiomatic Pandas code.
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DataCamp Team

26 iunie 2025

Asyncio: An Introduction

A short introduction to asynchronous I/O with the asyncio package.
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DataCamp Team

8 mai 2017

Scikit-Learn Tutorial: Baseball Analytics Pt 1

A scikit-learn tutorial to predicting MLB wins per season by modeling data to KMeans clustering model and linear regression models.
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Daniel Poston

4 mai 2017