Update 471-pandas-workout.txt

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.
-