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Data Wrangling with Python
Rating: 3.5 out of 5(29 ratings)
129 students

Data Wrangling with Python

Creating actionable data from raw sources
Created byPackt Publishing
Last updated 4/2019
English

What you'll 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

Course content

8 sections • 41 lectures • 3h 18m total length

  • Course Overview3:50

    Data Wrangling with Python starts with the absolute basics of Python, focusing mainly on data structures, and then quickly jumps into the NumPy and pandas libraries as the fundamental tools for data wrangling. You will learn how, using the Python backend, you can extract and transform data from a diverse array of sources. Then, you will also learn how to handle missinsg or incorrect data, and reformat it based on the requirements from the downstream analytics tool. You will learn about these concepts through real-world examples and datasets.

    By the end of this course, you will be confident enough to handle a myriad of sources to extract, clean, transform, and format your data efficiently.

    Follow this link to download the code bundle of this course:

    https://github.com/TrainingByPackt/Data-Wrangling-with-Python-eLearning

  • Lesson Overview0:30

    This lesson describes the importance of data wrangling, identifies the important tasks to be performed in data wrangling, and introduces basic Python data structures. Let us cover the following topics:

    • Importance of Data Wrangling

    • Sets

    • Tuples and Strings

  • Importance of Data Wrangling12:49

    Now, let us understand the importance of data wrangling. We’ll also look at lists with some practical examples.

  • Sets5:06

    A set, mathematically speaking, is just a collection of well-defined distinct objects. Dictionary is a collection of key-value pairs, where the key can be anything that can be hashed. Let us look at these in detail.

  • Tuples and Strings6:02

    Let us now look at tuples and strings in detail with practical examples.

  • Lesson Summary0:50

    Let us now summarize our learning from this lesson.

  • Test Your Knowledge

Requirements

  • Although this course is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational database and SQL.

Description

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.

The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets.

By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

About the Author

Samik Sen is currently working with R on Machine Learning. He has done his Ph.D. in Theoretical Physics. He has Tutored Classes for High-Performance Computing postgraduates and Lecturer at International Conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in Artificial Intelligence and Machine learning from Stanford and MIT.

Shubhadeep Roychowdhury works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying the state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics. He holds a master's degree in computer science from West Bengal University Of Technology and certifications in machine learning from Stanford.

Who this course is for:

  • Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert.

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