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https://github.com/trainingbypackt/data-wrangling-with-python-elearning
Creating actionable data from raw sources
https://github.com/trainingbypackt/data-wrangling-with-python-elearning
beautifulsoup4 data-science data-wrangling matplotlib numpy pandas python web-scraping
Last synced: 2 days ago
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Creating actionable data from raw sources
- Host: GitHub
- URL: https://github.com/trainingbypackt/data-wrangling-with-python-elearning
- Owner: TrainingByPackt
- License: mit
- Created: 2019-04-27T08:07:26.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-04-27T08:20:12.000Z (over 5 years ago)
- Last Synced: 2023-03-02T20:56:42.719Z (over 1 year ago)
- Topics: beautifulsoup4, data-science, data-wrangling, matplotlib, numpy, pandas, python, web-scraping
- Language: Jupyter Notebook
- Size: 8.24 MB
- Stars: 2
- Watchers: 4
- Forks: 5
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[![GitHub issues](https://img.shields.io/github/issues/TrainingByPackt/Data-Wrangling-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Wrangling-with-Python-eLearning/issues)
[![GitHub forks](https://img.shields.io/github/forks/TrainingByPackt/Data-Wrangling-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Wrangling-with-Python-eLearning/network)
[![GitHub stars](https://img.shields.io/github/stars/TrainingByPackt/Data-Wrangling-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Wrangling-with-Python-eLearning/stargazers)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/TrainingByPackt/Data-Wrangling-with-Python-eLearning/pulls)# Data Wrangling with Python [eLearning]
Data is the new oil, but it comes crude. To do anything meaningful - modeling, visualization, machine learning, for predictive analysis – you first need to wrestle and wrangle with data. Data Wrangling with Python teaches you the essentials that will get you up and running with data wrangling in no time.## What you will learn
* Use and manipulate complex and simple data structures
* Harness the full potential of DataFrames and numpy.array at run time
* Perform web scraping with BeautifulSoup4 and html5lib
* Execute advanced string search and manipulation with RegEX
* Handle outliers and perform data imputation with Pandas
* Use descriptive statistics and plotting techniques
* Practice data wrangling and modeling using data generation techniques### Hardware requirements
For an optimal learning experience, we recommend the following hardware configuration:
* Processor: Intel Core i5 or equivalent
* Memory: 4GB RAM (8 GB Preferred)
* Storage: 35 GB available space### Software requirements
You must also install in advance the following software:
* OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
* Browser: Google Chrome/Mozilla Firefox Latest Version
* Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser)
* Python 3.4+ (latest is Python 3.7) installed (from https://python.org)
* Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so)