Exploring pandas 2.0 & Targets for Apache Arrow

1

Introduction

00:00 • 2min

2

How I Got Started With the Pandas Project

01:45 • 2min

3

The Rise and Fall of Pandas in the Data Science Space

03:32 • 3min

4

Pandas 2: A Parallel Branch

06:50 • 2min

5

The Importance of Apache Arrow in Pandas 2.0

09:18 • 4min

6

The Differences Between Nampey and Arrow

12:55 • 5min

7

Python Extensions for Data Types

18:05 • 5min

8

The Pain of Floating Points

23:28 • 4min

9

The Importance of Having a Zone Exone System in Python

27:03 • 3min

10

The Advantages of Python for Data Science

30:19 • 6min

11

The Differences Between a Format and a Field Technology

36:08 • 2min

12

The Differences Between Strings and Embeddings

38:26 • 3min

13

Working With Pandas Data Frames

41:27 • 4min

14

Python Colors: A Python Programming Language for Data Frames

45:22 • 3min

15

The Cost of Serialization

48:30 • 2min

16

The Pandas 2.0 and Arrow Revolution

50:36 • 3min

17

The Importance of a Data Frame Technology

53:28 • 3min

18

The Importance of APIs in Python

56:02 • 4min

19

The Synergies of Decoupling and Keeping Things Modular

01:00:25 • 3min

20

Pandas for Developers Meeting in Switzerland

01:03:07 • 4min

21

How to Be a Successful User on the Web

01:06:44 • 1min

22

The Future of Python

01:08:12 • 3min

23

How to Follow Your Work Online

01:11:11 • 3min