You'll produce the code to build a fully-functioning Python library all from scratch.
You'll learn advanced Python topics such as how to implement special methods and decorators.
You'll learn the importance of test-driven development to build robust software by having to pass 100 tests in order to complete the library.
Build a Data Analysis Library from Scratch in Python targets those that have a desire to immerse themselves in a single, long, and comprehensive project that covers several advanced Python concepts. By the end of the project, you'll have built a fully-functioning Python library that is able to complete many common data analysis tasks. The library will be titled Pandas Cub and have similar functionality to the popular pandas library.
This course focuses on developing software within the massive ecosystem of tools available in Python. There are 40 detailed steps that you must complete in order to finish the project. During each step, you will be tasked with writing some code that adds functionality to the library. In order to complete each step, you must pass the unit-tests that have already been written. Once you pass all the unit tests, the project is complete. The nearly 100 unit tests give you immediate feedback on whether or not your code completes the steps correctly.
There are many important concepts that you will learn while building Pandas Cub.
Creating a development environment with conda
Using test-driven development to ensure code quality
Using the Python data model to allow your objects to work seamlessly with builtin Python functions and operators
Build a DataFrame class with the following functionality:
Select subsets of data with the brackets operator
Aggregation methods - sum, min, max, mean, median, etc...
Non-aggregation methods such as isna, unique, rename, drop
Group by one or two columns to create pivot tables
Specific methods for handling string columns
Read in data from a comma-separated value file
A nicely formatted display of the DataFrame in the notebook
It is my experience that many people will learn just enough of a programming language like Python to complete basic tasks, but will not possess the skills to complete larger projects or build entire libraries. This course intends to provide a means for students who are looking for a challenging and exciting project that will take serious effort and a long time to complete.
This course is taught by expert instructor Ted Petrou, author of Pandas Cookbook, Master Data Analysis with Python, and Exercise Python.
"I'm going to say it again: it's a fantastic course for someone like me who wants to go further and work on an intermediate coding/data project in Python! I'm not going to say that I could just write a data analysis library from scratch right now, but I definitely have a much better understanding of Python, NumPy, pandas, data analysis, and the programming itself. Ted Petrou is really a great instructor, he clearly explains the mechanisms behind the library and the way it's all done is very clean, concise and readable. In fact, the code readability is one of the things you can learn here, as well as the test-driven approach, there's a strong focus on that. So that was my course, it was definitely a lot of fun and a lot of new things I didn't know before. I will definitely go for Ted's next course, "Master Data Analysis with Python - Intro to Pandas", and I'm looking forward to finishing reading "Pandas cookbook". Once again, I really recommend this course and I'm very proud and happy to have finished it."