Python Fundamentals Part 3

Online Courses

Overview 
The professional programmer’s Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms.

 

Description 

Python Fundamentals LiveLessons with Paul Deitel is a code-oriented presentation of Python—one of the world’s most popular and fastest growing languages. In the context of scores of real-world code examples ranging from individual snippets to complete scripts, Paul will demonstrate coding with the interactive IPython interpreter and Jupyter Notebooks. You'll quickly become familiar with the Python language, its popular programming idioms, key Python Standard Library modules and several popular open-source libraries. In the Intro to Data Science videos, Paul lays the groundwork for later lessons in which he’ll introduce some of today's most compelling, leading-edge computing technologies, including natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, sentiment analysis through deep learning with recurrent neural networks, big data with Hadoop®, Spark™ streaming, NoSQL databases and the Internet of Things.

 

NOTE this LiveLesson is one in a series of five: 

  • Python Fundamentals LiveLessons Part I
  • Python Fundamentals LiveLessons Part II
  • Python Fundamentals LiveLessons Part III
  • Python Fundamentals LiveLessons Part IV
  • Python Fundamentals LiveLessons Part V

Skill Level 
Beginner-to-Intermediate

 

What you will learn in Part III:

  • Lesson 8–Strings: A Deeper Look–String methods; string formatting; concatenating and repeating strings; stripping whitespace; string comparisons; search strings for substrings and replacing substrings; tokenizing strings; regular expressions for pattern matching, replacing substrings and validating data; manipulating data in pandas.
  • Lesson 9–Files and Exceptions–Text-file processing; serializing objects into the JSON with the json module; with statement for avoiding “resource leaks”; exception handling; tryexcept statement; else clause; executing code when no exceptions occur in a try suite; finally clause; raise exceptions; more details on tracebacks; stack unwinding; CSV file processing via the csv module; loading and manipulating CSV files in pandas.
  • Lesson 10–Object-Oriented Programming–Custom classes; controlling access to attributes; properties for data access; simulating “private” attributes; Python special methods for customizing string representations; inheritance, duck typing and polymorphism; class object; Python special methods for overloading operators; named tuples; Python 3.7 data classes; unit testing with doctest; namespaces and how they affect scope; Introduction to time series and simple linear regression.
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