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Data Wrangling with Python by Packt

Data is the new Oil and it is ruling the modern way of life through incredibly smart tools and transformative technologies. But oil does not come out in its final form from the rig. It has to be refined through a complex processing network. Similarly, data needs to be curated, massaged and refined to be used in intelligent algorithms and consumer products. This is called wrangling and (according to Forbes) all the good data scientists spend almost 60-80% of their time on this, each day, every project. It involves scraping the raw data from multiple sources (including web and database tables), imputing, formatting, transforming – basically making it ready, to be used flawlessly in the modeling process. This course aims to teach you all the core ideas behind this process and to equip you with the knowledge of the most popular tools and techniques in the domain. As the programming framework, we have chosen Python, the most widely used language for data science. We work through real-life examples, not toy datasets. At the end of this course, you will be confident to handle a myriad array of sources to extract, clean, transform, and format your data for the great machine learning app you are thinking of building. Hop on and be the part of this exciting journey.

What you will learn

  • Able to manipulate complex and simple data structure using Python and it’s built-in functions
  • Use the fundamental and advanced level of Pandas DataFrames and numpy.array. Manipulate them at run time.
  • Extract and format data from various formats (textual) – normal text file, SQL, CSV, Excel, JSON, and XML
  • Perform web scraping using Python libraries such as BeautifulSoup4 and html5lib
  • Perform advanced string search and manipulation using Python and RegEX
  • Handle outliers, apply advanced programming tricks, and perform data imputation using Pandas
  • Basic descriptive statistics and plotting techniques in Python for quick examination of data
  • Practice data wrangling and modeling using the random data generation techniques - Bonus Topic

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

  • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of macOS
  • Processor: Intel Core i5 or equivalent
  • Memory: 8GB RAM or more
  • Hard disk: 40GB or more
  • Stable Internet connection

Software requirements

You'll also need the following software installed in advance:

  • Browser: Google Chrome/Mozilla Firefox Latest Version
  • Python 3.4+ (preferably Python 3.6) installed
  • Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so)
  • Notepad++/Sublime Text (latest version), Atom IDE (latest version) or other similar text editor applications.

The following Python libraries are needed:

  • NumPy
  • Pandas
  • SciPy
  • scikit-learn
  • Matplotlib
  • BeautifulSoup4

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