#402: Polars: A Lightning-fast DataFrame for Python [updated audio]

1

Introduction

00:00 • 2min

2

How Rust Makes Writing Concurrent Programming Easier

01:55 • 2min

3

Python Is Great to Use, but It's Harder to Write Correct Code

03:51 • 2min

4

Is There a Shift in Modern Python?

05:34 • 2min

5

How Did You Get Into Programming and Rust?

07:53 • 1min

6

GitHub Stars: The Fastest Growing Data Tool

09:22 • 2min

7

Is Rust a Better Way to Learn Programming?

11:52 • 2min

8

GitHub Sponsors

13:26 • 2min

9

Talkpython Amaze - Taipei

15:05 • 2min

10

Python and Rust C++ C vs WebAssembly

16:41 • 3min

11

Python Query Planner

19:28 • 3min

12

Is the Promise API Really Good for Descriptive Query Optimization?

22:00 • 2min

13

Using Lazy APIs in a Database?

24:06 • 3min

14

Can IDEs and Editors Be More Helpful?

27:09 • 1min

15

How to Earn Extra Income From User Interviews

28:39 • 3min

16

The Lazy Frames, Lazy APIs, and the Data Frames in Pandas

31:48 • 2min

17

TPCH Benchmarks

33:54 • 3min

18

Dask to Distributed Can Work Really Well

36:55 • 2min

19

Scaling on a Single Machine

38:44 • 2min

20

Is It a GPU or a CPU?

40:45 • 2min

21

Is There a Difference Between PIP and PIP?

42:23 • 3min

22

Apache Arrow Database Connector

45:42 • 2min

23

The Challenge Is, Are We Gonna Push Back Our Optimizations?

47:13 • 2min

24

Using Parquet Files Versus CSV Files?

48:58 • 2min

25

Using Polish Expressions on a Query Engine?

51:20 • 2min

26

Alonda

52:55 • 2min

27

How Many People We Got Here?

54:28 • 2min

28

Paula d'Arson - The Most Fun Experience

56:03 • 2min

29

Talk Python - Part 2

57:51 • 2min