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

Created by : Rated :4.1 835 views

Last Updated: 2022-05-06 04:05:31 English Course Type: Self Learning

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 pytest
Environment creation with conda
Advanced Python topics such as special methods and property decorators

Build a Data 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 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 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.

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

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.

Section 1 : Project Generics 3 Lectures 00:25:47
Lecture 1 :
Project Overview Preview
Lecture 2 :
Pandas Cub Examples
Lecture 3 :
Downloading the Material from GitHub
Section 2 : Environment Setup 4 Lectures 00:31:34
Lecture 1 :
Opening the Project in VS Code
Lecture 2 :
Setting up the Development Environment
Lecture 3 :
Test-Driven Development
Lecture 4 :
Installing an IPython Kernel for Jupyter
Section 3 : Getting Ready to Code 4 Lectures 00:22:17
Lecture 1 :
Inspecting the __init__.py File
Lecture 2 :
Importing Pandas Cub
Lecture 3 :
Manually Test in a Jupyter Notebook
Lecture 4 :
Getting Ready to Start
Section 4 : DataFrame Constructions 3 Lectures 00:17:20
Lecture 1 :
Check DataFrame Constructors Input Types
Lecture 2 :
Check Array Lengths
Lecture 3 :
Convert Unicode Arrays to Object
Section 5 : Basic Properties and Visual Representation 7 Lectures 00:54:08
Lecture 1 :
Implementing the __len__ Special Method
Lecture 2 :
Return Columns as a List
Lecture 3 :
Set New Column Names
Lecture 4 :
The shape Property
Lecture 5 :
Visual Notebook Representation
Lecture 6 :
The values Property
Lecture 7 :
The dtypes Property
Section 6 : Subset Selection 10 Lectures 00:45:44
Lecture 1 :
Select a Single Column
Lecture 2 :
Select Multiple Columns
Lecture 3 :
Boolean Selection
Lecture 4 :
Check for Simultaneous Selection
Lecture 5 :
Select a Single Cell
Lecture 6 :
Select Rows as Booleans, Lists, or Slices
Lecture 7 :
Multiple Column Simultaneous Selection
Lecture 8 :
Column Slices
Lecture 9 :
Tab Completion for Columns
Lecture 10 :
Create a New Column
Section 7 : Basic Methods 6 Lectures 00:31:21
Lecture 1 :
head and tail Methods
Lecture 2 :
Generic Aggregation Methods
Lecture 3 :
The isna Method
Lecture 4 :
The count Method
Lecture 5 :
The unique Method
Lecture 6 :
The nunique Method
Section 8 : Value Counts 2 Lectures 00:00:00
Lecture 1 :
Normalize value_counts
Lecture 2 :
The value_counts Method
Section 9 : Others Method and Operations 8 Lectures 00:54:08
Lecture 1 :
The rename Method
Lecture 2 :
The drop Method
Lecture 3 :
Non-Aggregation Methods
Lecture 4 :
The diff Method
Lecture 5 :
The pct_change Method
Lecture 6 :
Arithmetic and Comparison Operators
Lecture 7 :
The sort_values Method
Lecture 8 :
The sample Method
Section 10 : Pivot Tables 5 Lectures 00:42:08
Lecture 1 :
Pivot Tables Part 1
Lecture 2 :
Pivot Tables Part 2
Lecture 3 :
Pivot Tables Part 3
Lecture 4 :
Pivot Tables Part 4
Lecture 5 :
Pivot Tables Part 5
Section 11 : Documentation, String and Reading CSVs 5 Lectures 00:45:13
Lecture 1 :
Automatically Add Documentation
Lecture 2 :
String-only Methods
Lecture 3 :
The read_csv Function part 1
Lecture 4 :
The read_csv Function part 2
Lecture 5 :
Conclusion

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Theodore Petrou,

2905 Course Views

4 Courses

I am the author of Pandas Cookbook and Master Data Analysis with Python, highly rated texts on performing real-world data analysis with Pandas. I am the founder of Dunder Data, a company that teaches the fundamentals of data science and machine learning. I enjoy discovering best practices on how to use and teach data analysis with Python. I also enjoy creating open-source Python libraries and am author of Dexplot, Bar Chart Race, DataFrame Image, and Jupyter to Medium.