PYTHON beginner data analysis using the Pandas library to manipulate datasets and automate tasks practical application
What you'll learn
- How to use Python and Pandas to manipulate datasets
- How to use Python as an alternative to Excel
- Learn object-oriented programming using Python
- Understand how functions work
- Understand the difference between Series and Dataframes
- Understand how to clean data using Python Pandas Functions
Requirements
- Python Anaconda installation
- Basic knowledge of programming or coding concepts and logic
Description
In this course, I will help you to simplify and automate your data processing tasks using the Python and the Pandas library. These lectures are the result of my personal crash course Python programming learning experience. I have recently changed jobs and have had the opportunity to learn Python programming to analyse and manipulate data. I have compiled some essential techniques as well as tips to make sure you understand how Python object-oriented programming works.
I am a real world finance professional and I have compiled these lectures on the basis of my experience in my actual job. We will go through the basics as well as some more advanced techniques to help you create a more streamlined process of data manipulation. We will mostly be working with CSV and Excel files, however we also discuss how to import data in other formats.
We start with a background of Python programming language as well as the Pandas library. We will also go through essentials of using the Jupyter Notebooks programming environment. We will then explore ways to import data, and then go through a multitude of examples on how to inspect and manipulate data. We also discuss how to clean data using string functions, as well as how to avoid errors when creating or adding data. Finally, we will look at how to summarise and then export our cleaned and finalised data.
Who this course is for:
- Those wanting to explore alternative data manipulation software aside from Excel
- Beginner Python Pandas data analysts
- Students that don't have much time to learn Python Pandas
- Transitioning students from SAS or SQL to Python Pandas
