Build a Data Analysis Library from Scratch in Python
Immerse yourself in a long, comprehensive project that teaches advanced Python concepts to build an entire library
Development ,Data Science,Data Analysis
Lectures -58
Duration -7.5 hours
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Course Description
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 will 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 a Panda 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 built-in Python functions and operators
Build a Data Frame 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 Data Frame 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 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.
Who this course is for:
- Students who understand the fundamentals of Python and are looking for a longer, more comprehensive project covering advanced topics that they can immerse themselves in.
Goals
- Build a fully-functioning Python library similar to pandas that you can use to do data analysis
- Complete a large, comprehensive project
- Test-driven development with Python
- Environment creation with conda
- Advanced Python topics such as special methods and property decorators
Prerequisites
- Students must know the fundamentals of Python. This is an intermediate/advanced course.
- Must feel comfortable using and iterating through lists, tuples, sets, and dictionaries
- Exposure to numpy and pandas is helpful

Curriculum
Check out the detailed breakdown of what’s inside the course
Project Genesis
3 Lectures
-
Project Overview 09:37 09:37
-
Pandas Cub Examples 13:43 13:43
-
Downloading the Material from GitHub 02:27 02:27
Environment Setup
4 Lectures

Getting Ready to Code
4 Lectures

DataFrame Construction
3 Lectures

Basic Properties and Visual Representation
7 Lectures

Subset Selection
10 Lectures

Basic Methods
6 Lectures

Value Counts
2 Lectures

Other Methods and Operators
8 Lectures

Pivot Tables
5 Lectures

Documentation, Strings, and Reading CSVs
6 Lectures

Instructor Details

Teddy Petrou
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