Build a Data Analysis Library from Scratch in Python

Build a Data Analysis Library from Scratch in Python, available at $49.99, has an average rating of 4.55, with 57 lectures, based on 140 reviews, and has 8268 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. It is particularly useful 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.

Enroll now: Build a Data Analysis Library from Scratch in Python

Summary

Title: Build a Data Analysis Library from Scratch in Python

Price: $49.99

Average Rating: 4.55

Number of Lectures: 57

Number of Published Lectures: 57

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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.

Target Audiences

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.

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 Master the Fundamentals of Python.

Course Curriculum

Chapter 1: Project Genesis

Lecture 1: Project Overview

Lecture 2: Pandas Cub Examples

Lecture 3: Downloading the Material from GitHub

Chapter 2: Environment Setup

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

Chapter 3: Getting Ready to Code

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

Chapter 4: DataFrame Construction

Lecture 1: Check DataFrame Constructor Input Types

Lecture 2: Check Array Lengths

Lecture 3: Convert Unicode Arrays to Object

Chapter 5: Basic Properties and Visual Representation

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

Chapter 6: Subset Selection

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

Chapter 7: Basic Methods

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

Chapter 8: Value Counts

Lecture 1: The value_counts Method

Lecture 2: Normalize value_counts

Chapter 9: Other Methods and Operators

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

Chapter 10: Pivot Tables

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

Chapter 11: Documentation, Strings, and Reading CSVs

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

Instructors

Ted Petrou
Author of Pandas Cookbook, Founder of Dunder Data

Rating Distribution

1 stars: 0 votes
2 stars: 4 votes
3 stars: 7 votes
4 stars: 43 votes
5 stars: 86 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu