20.12.2022
Manipulating Python Strings as part of the Working with Text Data in Python provide a clear basic understanding of the Python Programming.
Great learning!
Intermediate Python Programming
with Martin Ganchev
This course teaches you how to work with text data, list comprehensions, anonymous (Lambda) functions, and other Python tools that will be your indispensable stepping stones to becoming an advanced Python user.
13 lessons 1h
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Course Overview
This course is short yet intense. We assume you’re no stranger to Jupyter and Python fundamentals, such as conditionals, functions, sequences, and iterations. Nevertheless, a lack of experience working with text data, list comprehensions, and anonymous (Lambda) functions would immediately reveal to your data science colleagues that coding in Python hasn’t become second nature. In the following sections, we explain these intermediate programming tools in detail and provide an extensive set of exercises to ensure your ability to comply with today’s best practices in the field.
13 High Quality Lessons
1 Practical Tasks
1 Hour of Video
Certificate of Achievement
Skills you will gain
Anonymous (Lambda) FunctionsJupyterlist comprehensionsNested LoopsProgrammingPythonWorking with Text Data
What You'll Learn
This course allows you to dig deeper than Python fundamentals. It focuses on intermediate tools that prove effective and efficient when processing large amounts of text and quantitative data.
Work with text data and argument specifiers
Learn how to use string accessors
Apply the .split(), .strip(), and .format() methods
Work with nested for loops
Study and practice list comprehensions
Understand and apply anonymous (Lambda) functions
Curriculum
- Intermediate Python Programming - Introduction2 Lessons 3 Min
What are intermediate Python tools, and why do we need to learn and practice them?
Course Introduction Free Python Refresher and Setting Up the Environment - Working with Text Data in Python6 Lessons 41 Min
Whether you use Python for general programming or analytics, you’ll not only manage quantitative data; you’ll also need to clean and preprocess vast amounts of text data. This section covers various tools—such as argument specifiers and string accessors—to help you manipulate text data. We’ll also work with widely-applicable Python string methods, such as .split(), .strip(), and .format().
Dealing with Text Data and Argument Specifiers Free Working with Python Strings at the Next Level Free Exploring Python String Methods - Part I Exploring Python String Methods - Part II Learning How to Use String Accessors Working with the .format() Method - Nested for Loops, List Comprehensions, and Anonymous Functions5 Lessons 32 Min
Once accustomed to manipulating text data, doors will open for you to employ several tools that will optimize the use of iterations and functions to the highest level. You’ll learn and apply iteration over range objects, nested for loops, list comprehensions, and anonymous (Lambda) functions.
The Concept of Iterating Over Range Objects in Python Free Introduction to Nested For Loops Working with Triple Nested For Loops Using List Comprehensions Working with Anonymous (Lambda) Functions
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“If you don’t want to be labeled as a Python beginner or newcomer by your colleagues, you mustn’t rely on fundamentals to achieve impressive analytic or programming results. This course is devoted to teaching efficient tools for working with text data, lists, and functions that are more sophisticated to learn but yet provide limitless application.”
Intermediate Python Programming
with Martin Ganchev