Python Programming Bundle: Intro to Python, Pandas, and OOP
Python Programming Bundle: Intro to Python, Pandas, and OOP
Get started with Python programming and the Pandas library while building a deep Object-Oriented Programming foundation!
New | Created by Simon Sez IT
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**This course bundle includes a Python Project and downloadable course instructor and exercise files to work with and follow along.**
If you need a better tool for handling data, it’s time to get to know Python, and this amazing value three-course Python bundle is the perfect place to start.
Python is one of the most popular languages for data analysis and business intelligence, with Pandas as one of the most commonly used Python libraries. At the same time, Object-Oriented Programming is particularly important in Python since every piece of Python code interacts with objects and classes.
What you'll learn
- The basic data types in Python - strings, integers, floats, and Boolean
- Python’s built-in functions and Python keywords
- How to write your first script in Python
- How variables and functions work in Python and how to debug errors
- How to write your first script in Python and use If-Else statements
- Storing complex data, including lists and dictionaries
- All about Python modules and how to install them
- Installing Python locally and completing your first Python project
- Describe the meaning of the object-oriented paradigm and create class hierarchies using the object-oriented design process
- Understand the difference between class variable and instance variable as well as the difference between class method, instance method, and static method.
- Make an object indexable (like lists), callable (like functions), and comparable (like numbers).
- Design and implement Python programs for complex problems, making good use of the language’s features such as classes and inheritance.
- An overview of Pandas and installing Pandas on your computer
- Using the two primary Pandas data structures, Series and DataFrame
- Viewing data imported from an external source and organizing input data using indexing and filtering
- Using Pandas for data preprocessing and addressing missing values and duplicate rows
- Formatting your data most efficiently and processing different data types
- Data manipulation using string functions and date and time formatting
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