AI+ Live Training: Idiomatic Pandas: Best Practices for Manipulating Data in Python

Webinar
Artificial Intelligence (AI) & Machine Learning (ML)

Date: 20 Jan '22
Time: 12:00pm to 4:00pm (GMT-05:00) Eastern Time (US and Canada)
Special Offer: Get a 10% discount until Friday 14th January 2022

Visit Event Website


Hands-on Live Training with Matt Harrison, a Stanford University course instructor and author of several python books including Illustrate Guide to Python 3, Learning the Pandas Library, and Machine Learning Pocket Reference.

Pandas can be tricky, and there is a lot of bad advice floating around. This talk will cut through some of the biggest issues I’ve seen with Pandas code after working with the library for a while and writing two books on it.

By undertaking this course, you will be able to understand the benefits of using the correct types, write pandas code that is fit for EDA but also deployment and master the best practices to be more efficient.

Discounted Pricing: $189 (Early-bird offer extends until 14the January 2022)

  • 4 hour immersive session
  • Hands-on Training with Q&A
  • Recording Available on-demand
  • Certification of Completion

LEVEL: INTERMEDIATE

MODULE ONE: LOADING DATA

This section will focus on ingesting data and best practices for working with raw data in Pandas. We will explore the different types and their pros and cons.  Looking and speed and memory performance with strings, numbers, dates and categories. 

MODULE 2: CHAINING & APPLICATION

  • Chaining - The Pandas library encourages chaining operations, yet most articles completely neglect this. We will explore how to get the most out of chaining.

  • Application - Function application is another thorny topic in Pandas. In general it is slow. We will talk about the reasons why, how to speed it up, and when to use application.

MODULE 3: GROUPING & AGGREGATION

One of the most powerful features of Pandas is the ability to group and pivot data. In this section we will show examples and explain how to understand and master this skill.

  • Familiarity with Python
  • Jupyter should be installed

MODULE ONE: LOADING DATA

This section will focus on ingesting data and best practices for working with raw data in Pandas. We will explore the different types and their pros and cons.  Looking and speed and memory performance with strings, numbers, dates and categories. 

MODULE 2: CHAINING & APPLICATION

  • Chaining - The Pandas library encourages chaining operations, yet most articles completely neglect this. We will explore how to get the most out of chaining.

  • Application - Function application is another thorny topic in Pandas. In general it is slow. We will talk about the reasons why, how to speed it up, and when to use application.

MODULE 3: GROUPING & AGGREGATION

One of the most powerful features of Pandas is the ability to group and pivot data. In this section we will show examples and explain how to understand and master this skill.

  • Familiarity with Python
  • Jupyter should be installed

Matt Harrison

Python & Data Science Corporate Trainer
Metasnake
Instructor

Matt Harrison

Python & Data Science Corporate Trainer
Metasnake
Instructor

Stay Updated

Organizer

Open Data Science Conference

Stay Updated

Organizer

Open Data Science Conference

Copyright © 2021 Industry Events. All rights reserved. Site credit.