From d06fe17246b63ceeb6cc03b2978ae1a410ec9829 Mon Sep 17 00:00:00 2001 From: Manikantagit Date: Thu, 25 Jul 2024 11:48:53 +0530 Subject: [PATCH] Update 471-pandas-workout.txt --- transcripts/471-pandas-workout.txt | 33 +++++++++++++++--------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/transcripts/471-pandas-workout.txt b/transcripts/471-pandas-workout.txt index 90216dcf..f29bcf83 100644 --- a/transcripts/471-pandas-workout.txt +++ b/transcripts/471-pandas-workout.txt @@ -58,7 +58,7 @@ 00:02:25 me on the socials and I'm happy to talk about it. Hope to see you there. -00:02:27 Ruven, welcome back to Talk Python To Me. How are you doing? +00:02:27 Reuven, welcome back to Talk Python To Me. How are you doing? 00:02:32 I'm doing great. Great to be back here. Nice to see you. @@ -112,7 +112,7 @@ 00:04:28 waited a while, cause it'd been like a year between the ban and me noticing. So they couldn't -00:04:33 do anything about it. So I was like half laughing and half like, you gotta be kidding me about this. +00:04:33 do anything about it. So I was like half laughing and half like, you got to be kidding me about this. 00:04:39 and I posted on my blog about this and you guys picked it up. on Python bites, @@ -192,7 +192,7 @@ 00:07:48 I'm going to bleep that part. Every time the word pandas is said, we're bleeping it out on -00:07:51 the YouTube version. Well, this could be entertaining then. Wow. Reuben was really +00:07:51 the YouTube version. Well, this could be entertaining then. Wow. Reuven was really 00:07:57 testy. Like all those bleeps. No, seriously though. You know, let's, let's catch up. We'll @@ -228,7 +228,7 @@ 00:09:11 for example, and they had this short, cute article about the number of animals that go -00:09:16 through Heathrow airport every year. I was like, wait, there's gotta be a dataset for that. And +00:09:16 through Heathrow airport every year. I was like, wait, there's got to be a dataset for that. And 00:09:21 sure enough, the Heathrow airport authority publishes a dataset in CSV of how many animals @@ -350,7 +350,7 @@ 00:13:34 learned, like some of the idioms from Python are not appropriate. So I was giving a class in like -00:13:39 optimizing pandas, like a short class, we'll call it microclass, like 90 minutes long, +00:13:39 optimizing pandas, like a short class, we'll call it micro class, like 90 minutes long, 00:13:43 about a year or so ago. And at the end, I was like, oh, and by the way, obviously just never do for loops. And everyone's like, wait, wait, wait, what? @@ -396,7 +396,7 @@ 00:15:28 has its own idiomatic style that is different than what you would call Pythonic, right? Like -00:15:34 it's Pandonic. I don't know what the name is, but idiomatic pandas, right? Where there's things that +00:15:34 it's Pydantic. I don't know what the name is, but idiomatic pandas, right? Where there's things that 00:15:40 are specific to pandas, like this vectorization stuff, right? Instead of looping over, right? @@ -450,7 +450,7 @@ 00:17:51 I initialized the logger with the string info for the level rather than the enumeration dot info, -00:17:58 which was an integer-based enum. So the logging statement would crash, saying that I could not +00:17:58 which was an integer-based Enum. So the logging statement would crash, saying that I could not 00:18:04 use less than or equal to between strings and ints. Crazy town. But with Sentry, I captured it, @@ -522,9 +522,9 @@ 00:20:52 vast. Yeah. Or pandas too comes out or something like that. Yes. Yes, indeed. I mean, I've been -00:20:57 exploring, I mean tomorrow, tomorrow I head off to Prague for EuroPython, where I'm giving a talk +00:20:57 exploring, I mean tomorrow, tomorrow I head off to Prague for Euro Python, where I'm giving a talk -00:21:02 on a pyarrow in pandas. And so I've been looking into that a lot and oh boy, right. I mean, I've +00:21:02 on a Pyarrow in pandas. And so I've been looking into that a lot and oh boy, right. I mean, I've 00:21:08 been using it for say a year or so, but it's amazing. And yet there are all these subtle @@ -690,7 +690,7 @@ 00:27:48 to like multi-step it. And I would just love to see more of it, but let's talk. -00:27:51 Well, I'll just, I'll just say there on that front. So CanDoes like does have the option to +00:27:51 Well, I'll just, I'll just say there on that front. So Condos like does have the option to 00:27:58 either get back a new data frame or to say in place equals true. And then it does it locally, @@ -930,7 +930,7 @@ 00:37:37 you know, send taxis at different places. - Sure, have a special program for long distance -00:37:41 stuff or whatever. Yeah. - Right. Right. Or if you're Uber, you know where to place, they actually used to have the geog, the longitude and latitude of where +00:37:41 stuff or whatever. Yeah. - Right. Right. Or if you're Uber, you know where to place, they actually used to have the geography, the longitude and latitude of where 00:37:48 people were picked up and dropped off and they got rid of that. And I'm sure both for privacy @@ -1054,7 +1054,7 @@ 00:42:52 Precisely. Precisely. And so another nice way to do this also is not just read this one and read -00:43:00 that one, but you can use a list comprehension with something like glob. So glob.glob on star.csv, +00:43:00 that one, but you can use a list comprehension with something like glob. So glob. Glob on star.csv, 00:43:06 get back a list of data frames and then just hand that to pd.concat. And so that's where @@ -1090,7 +1090,7 @@ 00:44:27 I don't think that like they need to learn that. So and like a lot of the standard library there, -00:44:31 it's hard to say. Right. So as I said, I love glob, right? Globbing is fantastic. +00:44:31 it's hard to say. Right. So as I said, I love glob, right? Globing is fantastic. 00:44:36 But that's definitely not in like my intro class. I would say, oh, by the way. Yeah. I would bet @@ -1198,7 +1198,7 @@ 00:48:51 I read this fantastic book a few years ago called Cork Dork by this journalist who decided to become -00:48:57 a Somalier and she took the exam and her journey toward there, she convinced me these words actually +00:48:57 a Sommelier and she took the exam and her journey toward there, she convinced me these words actually 00:49:03 have real meaning and people are very serious about it. So I will not roll my eyes quite as @@ -1228,7 +1228,7 @@ 00:49:55 break that into a list. But now what? Now I have a series of lists. Now what do I do? -00:49:59 And so one of the key methods to know here is something called explode. And explode is +00:49:59 And so one of the key methods to know here is something called Explode. And Explode is 00:50:05 let's take a series of lists and turn that into a very, very, very long series. And so basically @@ -1422,7 +1422,7 @@ 00:57:55 have more big cities than I realized? And you know, where's New York and New Jersey? Like it's -00:58:00 way down the line. You think of those as having like pretty megatropolis type places. Massachusetts. +00:58:00 way down the line. You think of those as having like pretty metropolis type places. Massachusetts. 00:58:06 That's right. That's right. But it's how many cities, right? So, right, right, right. That's @@ -1573,4 +1573,3 @@ 01:03:47 host, Michael Kennedy. Thanks so much for listening. I really appreciate it. Now get out there and 01:03:51 write some Python code. -