0:00

Welcome to the Real Python Podcast.

0:02

This. Is episode two hundred for.

0:05

Would you like to get more practice working

0:07

with a P Eyes and Python? How

0:09

about exploring the globe using the

0:12

data from Openstreetmap? Christopher. Trudeau

0:14

is back on the show this week,

0:16

bringing another batch of Pikers Weekly articles

0:18

and projects. We share an

0:21

article from The Pie by blog

0:23

about building queries using the overpass.

0:25

a P I for Openstreetmap. The.

0:28

Post explores the data structures, tags,

0:30

query formats, and how to use

0:32

overpass in Python. Christopher.

0:34

Discussed a real Python article by

0:37

recent Guess Steven group had about

0:39

lazy evaluation in Python. The.

0:41

Peace covers the advantages of

0:43

generator expressions are functions and

0:46

the potential disadvantages of using

0:48

lazy vs. eager evaluation methods.

0:50

We also share several other

0:52

articles and projects for the

0:54

Python community, including a news

0:56

roundup handling control see an

0:59

Ac Geico preventing daily leakage

1:01

and Pandas in Psych It

1:03

Learn discussing the Janko developer

1:05

survey results, asking developers why

1:07

they aren't shipping faster. using.

1:10

You v to install into isolated

1:12

environments and a couple of tools

1:14

for retrying in Python. This.

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Episode is brought to you by

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century. Fix. Applications issues before

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they become downtime with century

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error and performance monitoring for

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Python. All. right? Let's

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get started. The.

1:48

Real Python Podcast is a weekly conversation

1:51

about using Python in the real world.

1:54

My. Name is Christopher Bailey your host. Each.

1:56

week we feature interviews with experts

1:58

in the community and discussions about

2:00

the time articles, and courses found

2:02

at realpython.com. After the podcast,

2:04

join us and learn real-world

2:06

Python skills with a community of

2:08

experts at realpython.com. Hey Christopher, welcome

2:10

back. Hey there. So

2:13

we have a full slate this week.

2:15

We got a few news items, a

2:18

set of articles to cover, and then a couple

2:20

discussion things that we're kind of squishing together, which

2:22

I think will be fun to discuss in

2:25

relation to what's happening in the world of Python.

2:27

And then of course, projects. Who doesn't like

2:30

squishy discussions? Yeah, exactly. All

2:33

right, so you're ready to start it off with

2:35

the news? Sure. First bit

2:38

is PyPy, that's P-Y-P-Y, the

2:40

alternate Python interpreter, has

2:42

released version 7.3.16. This

2:46

one includes Python compatibility for 2.7,

2:49

yes, they still do that, 3.9, and 3.10. So

2:52

if you're into PyPy, go check that out. The

2:55

next piece is PEP 745. This

2:58

is an informational PEP announcing the

3:00

schedule for Python 3.14. Yes,

3:03

that's right. They're already thinking about 3.14. Yeah.

3:07

I doubt I'll be the first to point this out, but that's

3:10

3.14, that's pi, the number, which

3:13

is just gonna be so fun, because

3:15

we'll have PyPy or PyPI to get

3:17

the pi version of Python. So anyways,

3:20

I might be the first to point out the

3:22

obvious, but I'm pretty sure between now and Wednesday,

3:24

October 1st, 2025, which

3:27

is when it comes out, I won't be

3:29

the last. And

3:32

then finally, one little bit of

3:34

not Python, but programming history news,

3:36

the BASIC programming language just turned

3:38

60. BASIC was

3:40

my first programming language. You're

3:43

not gonna catch me waxing nostalgic about

3:45

a lack of functions, required line numbers,

3:47

and a need for GOTO, because yeah,

3:49

I started BASIC before subroutines. I'm

3:52

happy to see that Python's a lot of

3:54

people's first language now, not just because I

3:56

like Python, but because it isn't a toy,

3:59

And that's kind of what. Basic with considered way back in

4:01

the day. How about you? is that where you

4:03

started as well? Yes, And

4:05

Why those people? That. Coded.

4:08

From the back of a magazine a

4:10

little text adventure into. a friend. had

4:12

an Apple two and then I eventually

4:14

got my. Adam computer which is

4:16

an Apple to you and sorry I got to

4:18

play inside basic quite a bit and did lots

4:20

of go to. And

4:23

loops and dancing around to choose your

4:25

own adventure styles that yeah I'd like.

4:27

I I remember both Donkey on know

4:29

the Pc Junior which was hit the

4:31

space bar and move the donkey from

4:33

the left lane to the right lane

4:35

so doesn't get hit by a car

4:37

that code you can actually read and

4:39

understand. and I remember magazine articles for

4:42

the Commodore Sixty Four which essentially was

4:44

just Sprite based. Pete. And

4:46

poke which is like settings set settings and

4:48

removing a said it was just gibberish like

4:50

you typed it in bars like I don't

4:52

know what it's supposed to do. Oh is

4:55

it doing the right thing? I have no

4:57

idea I'd learned no coding from p Could

4:59

poke but yeah you could make pretty sounds

5:01

and make pretty picture. So the most recent

5:04

time I've heard about that was they were

5:06

fixing and voyager and their of have a

5:08

bad ship and I feel about that whole

5:10

of the story. but they were doing something

5:13

similar, moving all that information over and. Repeating

5:16

and poking Cf recipients? it make

5:18

it work across. How many billion

5:20

for a conservative? Reduce Deaths Peter

5:23

Easy. With. Your first

5:25

topic This week I've got an

5:27

article by Jason Brownlee, who writes

5:30

for Superfast Pythons, which focuses on

5:32

concurrent programming, an optimization of your

5:34

favorite language That's Python, not basic

5:37

to. Keep. the thing go

5:39

near the article is called a sink

5:41

io handle control seat now you're probably

5:44

familiar with the fact that hitting control

5:46

see on your keyboard usually stops or

5:48

program what you might not know is

5:51

that this is part of a family

5:53

of signals in unix windows com copies

5:55

some of it but when you hit

5:57

control seen a terminal on unix interrupt

6:00

signal gets sent to your program. This

6:02

signal is often shortened to the name SIGINT,

6:05

not to be confused with signal intelligence for

6:07

anybody who's a big Tom Clancy fan or

6:09

has a military background. So

6:12

your program is actually able to

6:14

trap SIGINT, so the stopping of

6:16

your program only happens if your

6:18

program doesn't trap the signal. This

6:20

is done on purpose so that you can gracefully

6:22

shut your program down if you need to. Say

6:25

you're got to write out a file or need

6:27

to do a database commit, you can trap the

6:29

signal and make sure that those things happen before

6:31

your program actually exits. SIGINT

6:33

isn't the only signal. On Unix there's

6:35

a signal for backgrounding a process and

6:38

a kill signal that isn't trappable so

6:40

that if your signal for trapping signals

6:42

goes awry you can still kill the

6:44

program. Anyway, so

6:46

as you might intuit from the article's title, I

6:49

think IO muddies the signal trapping waters. When

6:51

you've got multiple threads or coroutines running, the

6:53

main process is what gets the interrupt signal

6:55

from the OS. If you do have concurrent

6:58

things going on, that means you now have

7:00

to decide how to handle the signal and

7:02

what to do with all those concurrent activities.

7:05

What the article does is walk you through

7:07

step-by-step as to how to build a custom

7:09

signal handler, where in your

7:11

coroutines to trap it, and how to deal

7:14

with such a graceful shutdown to make sure

7:16

that all the coroutines finish doing what they're

7:18

needed doing, and receive what's called a cancel

7:21

inside of Python to handle all this. So

7:24

if you're doing some async IO, this article

7:26

is definitely worth the read so that you

7:28

don't leave little orphaned processes happening and have

7:30

weird stuff going on in your system just

7:33

because you're doing concurrency. Well,

7:38

my first one is

7:41

about APIs, actually a very specific

7:43

API for open street maps. It's

7:45

called Overpass API and this

7:48

is on the PyBytes blog.

7:50

That's P-Y-B-I-T-E-S.

7:54

I was on their podcast recently and so

7:56

we've been following their blog and they have

7:58

a variety of people contributing. And this

8:00

one is from Judith Valkenrath, and

8:02

it's kind of a tour of

8:04

what this API can do. We

8:07

talk a lot about tools that can create

8:09

an API, fast API, Django, DRF, and Django

8:11

and Ninja, which we talk about a little

8:13

bit more later. But often

8:16

people are interested in consuming the data

8:18

and pulling it out of an API,

8:20

and maybe you're interested

8:23

in exploring that or practicing that. And I think

8:25

this is a really good overview, not only of how

8:27

this particular API works, generally how

8:29

they might be organized, sort of data structures

8:31

they have and so forth. And this is

8:33

a very rich one. There's a lot of

8:35

data, the whole planet, if you will, of

8:38

data. And one of the best

8:40

ways to get familiar with it is to

8:42

sort of just play around with going to

8:44

the website, which is, I think it's just

8:47

openstreetmap.org. As you bring up

8:49

the map, you're looking around, you can right click on any

8:52

particular element or node, and

8:54

you can see the information about it. And

8:56

she has some visuals there kind of indicating

8:58

that stuff. So like she clicked on

9:01

a particular bus stop, and

9:03

there's a bunch of tags that are associated

9:05

with that. So you can kind of see

9:07

the structure there. Does it have

9:09

a bench? Does it have a trash can? Does it

9:11

lit? Does it have a shelter? Stuff like that. And

9:13

then it says things like, oh, it's part of this

9:16

way. And so this connection of

9:18

nodes and how they are connected, the nodes,

9:20

the way and the relations, all

9:23

these sort of tags that are connected there. You

9:25

also kind of get an insight of how this, all

9:27

this information is sort of crowdsourced. You can actually see

9:29

the people that contributed, which I think is really interesting.

9:32

I brought up my own personal neighborhood and there's

9:34

a lot of bike paths,

9:37

walking paths around and somebody

9:40

is really into doing that stuff, which I

9:42

think is kind of cool. I guess they want

9:44

that information for themselves. And so

9:46

they've added all those around the particular area I

9:48

live using, I guess, satellite imagery or other things

9:51

like that to kind of get an idea of

9:53

it. How do you get

9:55

into consuming this? Well, you can play around

9:57

with it directly in the URL if you've

9:59

ever. done that before, you might have seen

10:01

the question mark and build a query string out to

10:03

kind of see the different elements that are in there.

10:06

She describes that process, but also then

10:08

describes using wget and the type of

10:11

output that you would get in that

10:13

particular case. And then it's

10:15

intriguing the list of like how the tags kind of

10:18

connect. And there's a lot of really

10:21

deep information that I wasn't expecting,

10:23

like tourism, amenities, historic

10:25

stuff. One of the fun things that

10:27

she found is a public oven. I'm

10:30

kind of intrigued by that idea. Actually a little

10:32

more intrigued by the idea of listings

10:34

for public bookcases, but this is probably

10:36

Europe and not so much a US

10:38

kind of stuff. So the

10:40

rest of the article digs into using

10:43

this thing called Overpass, which is

10:45

the API sort of connection to

10:47

it, and then a

10:49

Python wrapper for that. I think

10:52

this is a really good resource. She talks about

10:55

using that overpass API

10:57

first with using requests and then

10:59

using the Overpass API

11:01

Python wrapper. And I

11:03

found it a good resource. Like I said, if

11:06

you're into consuming APIs, it's nice to have

11:08

building from, okay, show you what it

11:10

looks like as far as the web, URLs,

11:13

and then manually building on a query and

11:15

then sort of graduating into using Python in

11:17

a library. She

11:19

shares another Python based tutorial that is

11:21

a medium one that actually

11:24

continues on this idea of using the Overpass

11:26

API in Python. So if you want to

11:28

continue on that, you can. And then we

11:31

at RealPython have another, if you are

11:33

looking for even more sort

11:35

of general ideas on how to use

11:37

APIs, it's called, this particular

11:39

tutorial is called Python and APIs, the

11:41

winning combo for reading public data. And

11:43

it has a variety of other types

11:45

of things that you might explore, like

11:47

GitHub, you can kind of practice using

11:49

these public data resources. So I'm just

11:51

check out what's your next one. I've

11:54

got a new article from a

11:56

Steven group on RealPython called what

11:59

lazy evaluation. in Python. And

12:02

it starts out by telling you as programmers

12:04

you're all lazy and you don't work at

12:06

no not at all. Lazy

12:09

evaluation is the idea of not

12:18

generating a value until you actually

12:20

need it. The most common example

12:23

that inside of Python is with

12:25

generators. So if like say

12:27

you've got a process where you want

12:29

a series of values a list gives

12:32

you all those values altogether whereas a

12:34

generator only creates those instances as

12:36

you process them as you go along. And

12:39

that's really where the article digs in.

12:41

It starts out by sort of showing

12:44

a small example of generating a bunch

12:46

of random numbers and the

12:48

difference between say creating a list of those

12:50

versus getting each one of those numbers generated

12:52

as you use them inside a or loop

12:54

or somewhere else where you're using them. There

12:56

are a lot of examples in this article.

12:59

So he starts out talking about built-in

13:02

data types and how they work. The

13:04

built-in functions zip and enumerate and how

13:06

they work. Enumerate is a great

13:08

example. You typically use it around a list

13:11

and you get back pairs. The pair being a

13:13

counter of how far you are through

13:16

the enumeration and then a value from

13:18

it. And it is lazy evaluation. It

13:20

does not reprocess the list. And

13:22

so as you go along you get

13:25

little pairs out and if you just

13:27

instantiate enumerate what you'll get back is

13:29

an enumeration object rather than the set.

13:32

And so you then loop through that and use it

13:34

as you go along. He starts there and

13:36

then digs in a lot

13:38

deeper. There's a lot of stuff.

13:40

There's a library inside of Python in the standard

13:42

library called iter tools. It provides

13:44

a whole bunch of functions that allow

13:47

you to iterate through things and this

13:49

iteration process is typically done in a

13:51

lazy fashion. So the

13:53

first example he uses from iter tools

13:55

is chain which allows you to treat

13:57

two sets of sequences as if they

13:59

were one. sequence and again

14:01

because it fits in the example with the

14:03

article you go through the first piece and

14:05

then it goes through the second piece and

14:07

it's not generating a new thing it's lazily

14:09

iterating through each of those together so you

14:12

don't have to think about them you don't have to squish them

14:14

together and what generally

14:16

the big advantage of this is that

14:18

means you do not have to create

14:20

a duplication there so if you just

14:22

added two lists together now you've got

14:24

to take up all the memory of

14:26

both the first list the second list

14:29

and this third combined list with

14:31

iteration and with lazy evaluation you

14:33

don't need to create that third

14:35

list which saves you a whole

14:37

bunch of memory for a

14:39

large lists this can make a big difference

14:42

the article goes on to show you

14:44

generator expressions and generator functions which are

14:46

things you can write yourself in order

14:49

to build this kind of functionality and

14:51

then does deep dives into all sorts of tools

14:53

that you can use to go along as you

14:56

go there and finally the

14:58

article finishes off showing you sort of

15:00

the advantages versus the disadvantages of lazy

15:02

evaluation I already mentioned sort of the

15:04

advantage there of saving you the memory

15:06

the disadvantages is you can't do things

15:08

like slice into them because you can't

15:10

go immediately to the 50th thing because

15:12

that's not how it works you kind of have to go through in

15:14

sequence so this as with everything

15:16

in programming this is a trade-off and you

15:18

pick the trade-off that works for your situation

15:20

the best Stephen writes great stuff

15:23

he's got a very very approachable article

15:25

style and this one is no different

15:27

it's an easy read so if

15:29

you're interested in this kind of stuff it's a good place to

15:31

start yeah just had him

15:33

on the show that was very fun to talk to

15:35

him and we talked about his book I

15:38

titled the episode about it being like friendly and

15:41

approachable and you know that's definitely his

15:43

style and then what's funny is that

15:45

this I feel is very related to

15:47

the article that just came out this

15:49

week just after the lazy evaluation one

15:51

which is about sequences which kind of

15:53

makes sense and if you've dug

15:56

at all into generators

15:58

I think this is a really good build

16:00

on top of that, like kind of what's happening

16:02

and how they work with this lazy evaluation one.

16:04

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16:07

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Check it out at sentry.io. All

17:17

right, well, my next one is

17:19

a data science topic. It is

17:22

by Kevin Markham and

17:24

his site is dataskool.io.

17:26

This is not something that I've

17:29

personally run into and I wonder

17:31

how much controversy and potential p-snashing

17:33

goes into this idea and

17:35

this concept, but the

17:37

article is titled, How to Prevent Data

17:39

Leakage in Pandas and Scikit-Learn. There's

17:41

a couple concepts being covered. He starts

17:44

with this premise that you can pretend

17:46

you're working on a supervised machine learning problem. Your

17:49

data is in the pandas data frame and you

17:51

discover, oh, we got some missing values here in

17:53

a column And you really

17:55

think that column is important to what you're modeling and

17:58

so you want it to be a feature. Well.

18:00

How should you to? Do This.

18:03

So. There's this. Idea. Of

18:05

including are basically having a tool

18:07

like pandas serve guess values for

18:10

you inside their which I think

18:12

is a really interesting idea. So.

18:15

In this process option one he has his

18:17

are you just fill in the missing guys

18:19

and pandas in shooting the values and then

18:22

you can have continue on. And

18:25

then there's another way that he is

18:27

General. Meaning. For this

18:29

entire article is you should probably

18:31

leave it to a tool that

18:33

is designed. For creating models and

18:35

and doing stuff like that which a psychic

18:38

learn as you ask that original data and

18:40

as I can learn and. Then

18:42

perform all the data transformations including

18:44

the missing value in station with

18:46

like Hitler. I hadn't heard

18:49

the term daily Kids again. I haven't been

18:51

in a situation where I wanted to just

18:53

say still in some values because I truly

18:55

wanted to use a column like this. But.

18:57

That concept. Is. A little

19:00

weird. In. The leakage is not like.

19:02

Data is disappearing is

19:04

that it's crossing over

19:06

into your other. Process.

19:09

Of you start by training. Your

19:12

information but if that same

19:15

included data is over. When.

19:17

You're testing. In

19:19

that data. Also, then you got

19:21

a problem because it's basically. Sort

19:24

of verifying for what you've created

19:26

by the computer data and so.

19:28

I. Found a. Kind of a

19:30

another example that's a little better. In

19:33

the idea of it, this one's actually

19:35

called group leakage, but it it's a

19:37

similar idea. There. Is this example

19:39

on this or and I'll could the Wikipedia

19:42

on it. By. not having i

19:44

of a grouping inside of a

19:46

split column this experiment that was

19:48

looking at x rays to try

19:50

to determine if they hadn't the

19:52

patient had pneumonia they were one

19:54

hundred thousand x rays of thirty

19:56

thousand patients meaning if there were

19:58

about three him just per patient

20:01

by not having the grouping and

20:03

by having this mix

20:05

of patients records

20:08

being both in the training data in

20:10

the testing data, what

20:12

their model did was identify

20:14

people. It

20:16

did not do what it's supposed to do, which is

20:18

predict what pneumonia is supposed to

20:20

look like. So you should have

20:22

kept all those particular patients in

20:25

one group or the other and not have it

20:27

sort of randomly cut across to. So that made

20:29

a little more sense to me in the idea,

20:32

but I still kind of feel a little weird about this idea of

20:34

like coming up with random data.

20:36

So what

20:38

Scikit-learn will do is

20:41

it will actually, in the

20:43

process of creating the split of

20:45

test train split, it

20:47

will actually only create the

20:49

values in the training data

20:52

and then leave the testing part alone. He

20:54

talks about a couple different tools for

20:57

doing that inside Scikit-learn. It's a pretty short

20:59

article. I wonder how much

21:01

of this happens and I wonder if

21:03

this is a

21:06

problem with reproducibility and replication of

21:08

results by imputing values. And I

21:10

would wonder why you wouldn't

21:12

just maybe leave that value out. Yeah,

21:16

I think it comes down to like

21:18

if you think that piece of data

21:20

has strong correlation to the training, but

21:23

then it's missing in 10%

21:25

of the cases, you don't want to throw

21:27

that value out as part of the training

21:30

information, but now you've got to deal with

21:32

those 10% where you don't have it

21:34

for whatever. Your

21:36

choices are guess at it, which

21:40

is really what it is, like fancy guess. There's

21:42

guessing, there's fancy guessing. No, it's called

21:44

imputation. Sorry. And

21:46

there's dropping stuff. And honestly, I think

21:50

this might be a bit of an oversimplification, but

21:52

it kind of reminds me of like

21:54

deciding when to do rounding. Yeah, right.

21:57

So if you round too early or

22:00

round too frequently, you

22:02

can end up with a starkly

22:04

different answer than if you round

22:06

at the end. Right. And

22:08

I think really what my takeaway from this was, particularly

22:11

when you're trying to do machine learning training,

22:13

whether it's imputation or anything else, the

22:15

order of these things can make a difference. The

22:19

machine learning industry is filled with examples of,

22:21

oh, we trained it to recognize there was

22:23

a ruler in the picture rather than what

22:25

was and what wasn't a tumor. Right.

22:28

Exactly. And that's something that you've got to

22:30

be careful with when you operate in this space. Yeah.

22:33

This is sort of a related thing. I'm

22:35

going to be talking to Brett Kennedy about

22:38

his book, Outlier Dimension in Python. And

22:40

I thought maybe he'd be interested in talking a little

22:42

bit about this, at least add a little bit of light to it. He's

22:45

kind of working on the opposite end of the spectrum

22:47

though. In looking for outliers, he's

22:49

trying to find literally the anomalies,

22:52

like financial fraud, bot activity, and

22:54

malicious network attacks and stuff like

22:56

that. It can also be used

22:58

for cleanliness though, because statistics, it's a very

23:00

common practice in statistics to drop the top

23:02

10% and the bottom 10%. So

23:04

that you're dealing with the middle, right? Sure. So

23:07

you don't swing your average in a certain direction or something.

23:10

So again, identifying the outliers is

23:12

kind of the opposite of imputing. It's

23:15

the find the crap that's destroying our

23:17

results. Yeah, it's pulling us off. So

23:20

how do you... And then you decide to chop

23:22

them or do whatever you want to do with them. Yeah.

23:25

I'm not going to say that too, but I think

23:27

that he's actually focusing on actually truly like, I want

23:29

to locate those anomalies because they're actually showing

23:32

me problems and things that we

23:34

want to discover. Yeah. I

23:38

seem to remember there being an example in the

23:40

book too where the outliers could cause data

23:43

grouping to create artificial groups.

23:47

It makes it look like there's two groups

23:49

when there actually isn't Because

23:51

the outliers are pulling your split line

23:53

in the wrong direction. So It's a

23:55

related kind of thing. It's the how

23:57

do we make sure our data is

23:59

helpful. the nurse saw the actual problem

24:01

rather than getting in our way. We

24:03

have real life is messy. Great real

24:05

life. He ate. As always, messy is

24:07

a problem and utterly. Go. Like

24:10

a Texas right into our discussion. We've

24:12

mentioned a couple times are under cover.

24:14

A couple things here. Yep. First one

24:16

is the Janko developer survey result. Yeah,

24:19

yeah. so so we've got two topics.

24:21

I tried to come up with some

24:23

convoluted way of saying they're related and

24:25

decided against it's. About

24:28

the topic at both topics involve developers

24:30

and pretty sure I could say that

24:32

about every discussion we've ever had. so

24:34

famous? Yes, And about surveys of sorts,

24:36

Exactly Sir, Yes, It right So that

24:39

the first topic. Is are the results

24:41

for the Twenty Twenty Three Jangle Developer

24:43

Survey have come out. We've discussed the

24:45

Python Developer survey before and like that

24:47

one. This one is sponsored by Jeff

24:49

Rain, so thanks most folks for doing

24:51

this as some interesting data in here.

24:53

There a couple things that kind of

24:55

stuck out for me personally. First off,

24:57

there's almost a seventy percent adoption rate

24:59

of the latest version, which at the

25:01

time the survey was four Point Two

25:04

sir. Impressive. actually. Yeah, A on one

25:06

hand, I'm come. It was kind of

25:08

surprised and then I stopped and sort

25:10

of thought about it. And honestly, the

25:12

difference between Four Point One, four Point

25:14

Two were minimal, so it was a

25:16

relatively painless progression. I think that makes

25:18

it easier for database adoption be big.

25:20

Winner was Posts Greste. seventy six percent

25:22

of respondents said they were using that,

25:24

followed by Forty Three Four Sq a

25:26

late. Ah, Do note this was

25:28

multi selects on the percentages. don't add up

25:30

to one hundred, don't If you're wondering why

25:32

to be seventy six and forty three? Math

25:34

doesn't quite work there. Yes, I. Was

25:37

a little disappointed to see that

25:39

twenty one percent of folks don't

25:41

do automated testing. I'm hoping that's

25:43

partially because of things like hobby

25:45

sites young my cookbooks site which

25:47

is toppling Saddam Topic Janko does

25:49

not is not testing their So

25:51

spread. The flip side of it

25:53

is my consulting career has told me that

25:56

that's probably overly generous. I'm it's always depressed

25:58

when com a sense of gun. The large

26:00

institution like a banker insurance company to

26:02

find out. oh you're still doing manual

26:04

que a isn't that great. mm the

26:06

other one that was. Bit. Of

26:08

a pleasant surprise was the prevalence

26:10

of Mac O S, so thirty

26:12

six percent of respondents said they'd

26:14

use Linux as thirty two percent

26:16

said Mac O S, and that's

26:19

almost three times the adoption rate

26:21

of Max and the general public.

26:23

They're only about ten percent generally,

26:25

and it's also three times the

26:27

reported usage rate of Windows without

26:29

the linux subsystem. So pure Windows

26:31

without it as a entry point

26:33

to linux. And I suspect

26:35

that's the the same reason Linux is

26:37

out there. that where are the most

26:39

people doing web development? the? they're doing

26:42

Unix on the backend. And.

26:44

Mac is a very pretty version of

26:46

Unix is really when it comes down

26:48

to but tie yeah Was just are

26:50

surprised at the adoption rate them and

26:52

in the last one that was for

26:54

me was said. There were two questions

26:56

that sort of depress me. First, my

26:59

treasured Vim only has a seven percent

27:01

adoption rate and second I'm to Sigma

27:03

to the right of the mean on

27:05

the Aids graph. So from one old

27:07

fogy to another, assess assess assess what

27:09

stood out for you. I like the

27:11

term lovably Vince's have. A.

27:16

Lovely used yes, I'm

27:18

younger than basic assistance

27:20

if it affects or

27:22

yeah, that two things

27:24

that are related to.

27:27

You were talking about testing their one about.

27:30

Continuous. Integration Systems. Which.

27:33

is i'd probably higher value it's like

27:35

only like a quarter of them say

27:37

none and then there's a configuration tools

27:39

which is they're actually pretty low like

27:41

having tools that can do that stuff

27:43

for you automatically set up those ricotta

27:45

extinct i thought the third party framework

27:47

stuff is is interesting to go rest

27:50

framework has dropped a little bit not

27:52

lot but i would guess that may

27:54

be jingle ninjas creeping in there and

27:56

taking away some of that i was

27:58

surprised it didn't show Yeah, it's,

28:00

I think somebody mentioned it and so

28:02

it's, it's, it's growing. I

28:05

had a question about models versus views. I

28:07

know this is like really Django nerdery, but

28:09

I don't know. Is, are you in the,

28:11

with the 69% that says models and the

28:14

favorite core components and maybe don't use

28:16

the view based stuff as

28:18

much? I, yeah, I think

28:21

I probably, I'm trying to remember what

28:23

boxes I checked. I suspect it was

28:25

probably the admin. Yeah. Would,

28:27

would be my guess, which is very

28:30

handy. Probably authentication. I

28:32

can't remember. I, you know, core

28:35

components are core components. I use all, almost

28:37

all those things all the time. So

28:40

I don't know what difference it makes.

28:43

You know, the models for non-Gen and

28:45

Go folks, the model is what sits in front

28:47

of the database. So, uh, if you're not using

28:49

the database, you're probably more likely to use something

28:52

like fast API. So the fact that you're using

28:54

Django kind of tells you that you're there anyways.

28:56

So it's not surprising. And that's part of, you

28:58

know, why I checked the box for admin. It's

29:01

the same thing. It's a built-in tool that allows

29:03

you to muck with the models. And it means

29:05

you don't have to do very much coding to

29:07

get a GUI to interface with the database. Yeah.

29:10

I found it almost

29:12

an odd question. Yeah,

29:15

sure. So, but yeah, the other

29:17

one that I thought was interesting is sort

29:19

of the growth of a HTMX as a

29:21

quote unquote Java framework, um, how a lot

29:24

of these other ones are kind of dipping

29:27

a little bit, uh, react and view and

29:29

so forth and whatever the flavor dues you

29:31

are. I think people are not as interested

29:33

in doing with the

29:36

overhead of having to like have all that

29:38

JavaScript in your head to work with it.

29:40

Yeah. I, before I discovered HTMX myself, I

29:42

was using view.js a lot and

29:45

it wasn't because I really wanted to, it

29:47

was because sometimes your pages need some dynamic

29:49

stuff. Right. And what I really like about

29:51

view is the fact that it allows you

29:53

to, you can adopt varying

29:55

degrees of it. So if you just need a

29:57

tiny little bit of dynamicism on a page. you

30:00

don't have to all or nothing. Whereas,

30:02

you know, the React and Angular of

30:04

the world tend to be, uh, rather

30:06

opinionated in your entire site. It has

30:08

to zow-shelf type script

30:11

rather than, but all

30:13

I needed was an auto-select here and

30:16

so I was, I was very, I tended

30:18

to use view because of that and the

30:20

HTML just makes that so much easier. There's

30:22

just so much less code. And because of

30:24

the way HTML works with partial pages, it

30:27

integrates very nicely with Django because Django can feed

30:29

out partial pages to your heart content.

30:31

Right. So you're basically taking advantage of the

30:33

templating system and it's, it's, it's

30:35

very 1.0 style coding with very

30:38

2.0 style, uh,

30:40

interfaces. So yeah, it doesn't surprise me

30:42

that it's taking off. That's nice. Are

30:45

you, I thought it was interesting. I looked at

30:47

the CSS thing frameworks. I thought that was interesting

30:50

too. That bootstrap is kind of dropping off after

30:52

a long time at the top. I would argue

30:54

it's still at the top, but the, there's something called

30:56

tailwind. Yeah. That's growing. And then there's a few others

30:59

that I've seen inside there, but yeah, bootstraps been around

31:01

for a long time, I would say that you can

31:03

definitely tell the age of a site that's using it

31:05

now. Yes. So yeah,

31:08

cool. It's good to see that. You know,

31:10

I was mentioning to this to

31:12

you before we started that, you know,

31:14

I've found right years, I've been kind

31:17

of paying attention on the side of watching Django and

31:19

having done a little project myself and tried to set

31:21

up some stuff at work and just

31:23

paying some attention to it. And it just

31:25

looks so healthy. Yeah.

31:27

Growing and you know, the Django

31:29

not thing that we mentioned earlier for onboarding

31:32

contributors and just looks good. I, I

31:35

like the batteries included of it in a

31:37

lot of ways. So, you know, of

31:40

course we're not going to get through this

31:42

topic without, you know, the old joke about

31:44

marathoners and how do you know somebody ran

31:46

a marathon? You know, they'll effing tell you

31:48

or went to Harvard. Yeah. Well, so

31:51

it goes the same for authors. So we can't

31:53

get to the bottom of this without me shilling a

31:55

little bit, but, I was

31:57

pleased to have Michael Kennedy.

31:59

of TalkPython acquiesced to

32:01

writing my forward for the

32:04

book. Oh, nice. One of the

32:06

things he mentions in it is

32:08

just how deep the Django community

32:10

is, right? Like outside of most,

32:12

most of the time you see

32:14

language conferences, you very, very seldom

32:16

see library conferences. Yeah. Frameworks. Yeah.

32:19

Or if they are their libraries,

32:21

like they're, they're a track inside

32:23

of conference, whereas, you

32:25

know, DjangoCon is huge, right? Yeah.

32:27

This is European versions and stuff

32:29

out there. There's a North American one, European

32:31

one, and an African one now. Oh,

32:33

wow. And so like there's, and the

32:36

community is very, very vibrant and there's a lot of

32:38

sort of support there. And one of the things he

32:40

kind of said in the, in the forward is sort

32:42

of, you know, welcome to this world. Like it's, uh,

32:45

it really is a very vibrant thing and,

32:47

and people are, they're actively trying to

32:49

figure out how to get more contributors,

32:51

which, you know, you see in very,

32:53

very few places, uh, in the open

32:55

source world often. So I go away,

32:57

go away little boy, you're bothering me.

32:59

Whereas here it does seem to be

33:01

very, uh, an accepting sort of community.

33:03

So, uh, I think that's, I think that's definitely part

33:05

of it. Yeah. Cool. This

33:11

week, I want to shine a spotlight

33:13

on another real Python video course. It's

33:15

connected to one of our discussions this

33:17

week. When you start building any new

33:20

Django web application, there's the

33:22

fundamental step you need to tackle first. The

33:24

course is titled how to set up

33:26

a Django project. And it's

33:28

based on an article by former guest and

33:31

real Python team member, Martin Breus. In

33:33

the video course, Martin takes you through setting

33:36

up a virtual environment, installing

33:38

Django, pinning your project

33:40

dependencies, setting up that

33:42

Django project and starting your first

33:45

Django app. It also

33:47

covers techniques to manage a slimmer structure

33:49

for your Django project. I

33:51

think it's a worthwhile investment of your time

33:53

to learn the fundamental steps to properly set

33:55

up a Django project and to

33:57

get a solid understanding of this structure of

33:59

a Django project. Django application. Like

34:01

most of the video courses on RealPython,

34:03

the course is broken in the easily

34:06

consumable sections. And all RealPython

34:08

courses have a transcript, including closed captions. Check

34:10

out the video course, you can find a

34:12

link in the show notes, or

34:14

you can find it using the enhanced

34:17

search tool on realpython.com. So

34:22

shall we move on to topic two? Yeah, let's

34:24

go ahead. All right, so... A survey. Sort

34:27

of, yeah. Our next

34:29

source of discussion is an article by

34:32

Dachsh Gupta, and it's titled, I asked

34:34

100 devs why they aren't shipping faster.

34:37

Dachsh is the founder of GrepTile, which

34:39

is an AI coding tool. So I

34:41

suspect he talks to devs all the

34:43

time as part of that whole sales

34:45

process. The article name checks a couple

34:48

dozen companies, almost all of which are

34:50

household names, or would be household names

34:52

for houses containing programmers. The

34:55

article goes into detail about these kinds

34:57

of conversations he's had. But I'll start

34:59

out with the two things that surprised

35:01

him. First, nobody had to pause

35:03

for an answer. You ask them, you know, why aren't

35:05

you shipping faster? And everybody immediately went, oh, yeah, off

35:08

the top of my head, here are our reasons. Yeah,

35:11

the reason this is a bit of a surprise

35:13

to him was that there's sort of this aspect

35:15

of, well, if you know that, why wouldn't you

35:17

be fixing it? Well, and as

35:19

we'll get into in a minute, sometimes it's out of our

35:21

control to fix. And then

35:23

the second surprise he had was how

35:26

many of the answers were actually tool

35:28

chain answers. So slow to build, slow

35:30

to compile, slow deploy times. I

35:32

find it interesting because the rest of the

35:35

article spends very little time there. But

35:38

it's definitely one of the topics that comes up.

35:40

And I think it comes up because it's sort

35:42

of the immediate thing that annoys us code junkies.

35:44

So what stood out for you here? Oh,

35:48

it started with some of the stuff that's

35:50

really hard to solve, in my opinion, like dependency bugs,

35:52

like, you know, hitting random mysterious

35:54

bugs with libraries and reading

35:57

into Stack Overflow to try to

35:59

find out what's happening or GitHub issues and so forth.

36:01

And that rolls

36:03

into this next one of a complicated code

36:05

base, you know, where they're not

36:08

updating their documentation and it becomes like a

36:10

vicious cycle of like shipping

36:12

features versus like keeping the docs up to date and

36:14

so forth. I wonder about

36:16

that, like this idea of cross

36:18

languages. We just mentioned, you know, the idea of

36:21

using JavaScript with Django and so forth and how

36:23

that isn't like doubling the

36:25

complexity. It's sometimes like multiplying it

36:28

in a much larger fashion. So,

36:30

but the really interesting stuff is like, as

36:32

you get further and further into

36:35

it, it becomes more about the

36:37

people and less about the

36:39

technology. And

36:41

so, you know, stuff like the QA loops,

36:44

you know, meetings, reviews, weird stuff,

36:47

like developers having too much free

36:49

time and personal vendettas and prejudice.

36:51

And it's just like, I

36:53

have only been in corporate America for a short period of

36:55

time here and there. And so I

36:57

only, I kept saying this to

36:59

people at different jobs that had that were inside

37:02

large buildings that had lots

37:04

of offices where I would say, is

37:07

this normal? And I guess every,

37:10

I dunno, what is the, what is the saying? Every

37:13

family is broken

37:15

in its own way or whatever. I

37:19

don't know the coin as well as I wanted to, but

37:21

you know, and I think that's it. Like, you know, every

37:24

unhappy family is unhappy in its own way.

37:26

And so it just makes me like kind

37:29

of like, I don't have

37:31

a lot of ideas about how you would solve these

37:33

things. Mostly, I think about like, man, I would not

37:36

be interested in them. I thought of some

37:39

things that I've talked to people in the industry

37:42

about at

37:44

least increasing communication and sort

37:46

of making sure the engineering

37:48

teams can potentially work together.

37:50

And that was like the conversations that

37:52

I had with Pablo about Bloomberg. We

37:54

talked about guilds and this idea that

37:56

they have internal guilds. This

38:00

is where a bunch of people that are interested in Python

38:03

can go and learn more about that and so forth. And

38:05

so people came from other teams

38:08

and then that kind of spawned

38:10

conversations across different silos,

38:12

if you will. I thought that was interesting.

38:14

And I don't know if it solves that.

38:17

Well, so one of the reasons guilds

38:19

have become more popular in the last

38:21

decade or so was the

38:23

old waterfall way of doing things was you had the

38:25

development department and they were on a different floor than

38:27

the QA department who were on a different floor from

38:29

the BA department. It's a good form of

38:31

waterfall. And

38:33

the agile space very much tries

38:36

to have research team driven

38:38

pieces. So you get a group of people together

38:40

who are solving the same problem, which means

38:42

inevitably you have some developers, some QA's,

38:44

some BA folks working together. We

38:47

can debate for hours as to what works,

38:49

what doesn't work, but at least then you've

38:51

got a team who are concentrating on that

38:53

problem and solving the problem. The

38:56

downside of that is it becomes a

38:58

lot harder to cross pollinate ideas because

39:00

now all the developers aren't on the

39:02

same floor. So the guild

39:04

concept kind of came out of that

39:06

where you end up sort of lunch

39:08

and learns or whatever where the developer

39:11

folks can all leave their

39:13

feature focused teams and go to

39:15

a process focused area. Some

39:17

organizations will try to put, I don't

39:19

want to call them management and they're not really

39:22

leaders, but sort of architect type

39:24

people who are watching

39:26

what's happening on the different teams and are in

39:28

charge of trying to make sure the best practices

39:30

are being handled so that you

39:32

can get growth across the team. And

39:35

it's a hard problem. So some of

39:37

the things that you have by having those development

39:40

silos, these one set of

39:42

problems doesn't show up, but you get a different

39:44

set of problems and then you get rid of

39:46

those silos and make them feature focused and that

39:48

solves one set of problems and creates a different

39:50

one. Yeah,

39:52

it's always these things are always

39:54

a trade off, right? You know, it's

39:57

take the next thing I'm about to say with a grain of salt because

39:59

of course this is. how I make my money. I

40:01

have found that consulting can help in this.

40:04

And the reason for that is

40:07

kind of counterintuitive. Oftentimes

40:09

what consultants are doing is they're being

40:11

paid an awful lot of money. And

40:13

because of that, they're listened to. So

40:16

I can't tell you how. Yeah, I could

40:18

tell you that how often they listen to

40:20

them and not people that are there. Yeah.

40:22

Yeah. So I can't tell you how often

40:24

my job has been go in, talk

40:27

to everybody, write down what they say

40:29

the problems are and then report those

40:32

problems to management. And you

40:34

know, and it's stupid, right? Because if I wasn't there,

40:37

yeah, if I

40:39

wasn't there, they definitely could have

40:41

still solved the problem. Right. And just by listening

40:43

to their own people. But coming

40:45

in as an outsider and providing a little

40:47

bit of guidance and, you know, massaging a

40:49

little bit of data together, magically

40:52

all of a sudden management listens.

40:54

And so that can actually make

40:57

a bit of a difference. I

40:59

would almost like it if the world

41:01

didn't work that way, but you know,

41:03

it's something you can almost take advantage

41:07

of. I mentioned

41:09

some other things to you kind of before we started

41:11

too, just that I was thinking about, because

41:13

we were going to discuss this. And I feel

41:17

like maybe we're in a cycle, as far

41:20

as, you know, technology, you know,

41:22

the world that we're in. I mean,

41:24

there's definitely this whole AI hype thing, which is kind

41:27

of becoming a little bit much. There's

41:31

a lot of these things that are coming out where

41:34

they sure did ship it, but

41:36

it didn't have the software and it

41:38

wasn't working and all this other sort

41:40

of stuff. And so

41:43

who is pushing so hard to ship

41:46

that stuff that isn't done? And I

41:49

feel like, you know, like they're

41:51

answering to a whole different set of

41:53

people and it's not necessarily like an

41:55

overall company goal. It's like the sales

41:57

people, the advertising people, the marketers and

41:59

just... saying, Oh, well, we'll just fix it later.

42:02

I mean, we see it in so many industries, the video game

42:04

industry, that that's, you know, we're beta

42:07

testing games now, you know, through

42:09

the pre-release cycle and so forth,

42:11

and sometimes that's okay, which

42:13

is interesting to see things get developed later,

42:15

but another time is when it's like a

42:17

physical product and you're paying potentially a monthly

42:19

fee for something that doesn't really work

42:21

is kind of nuts, you

42:23

know? The balancing act is really, really

42:26

difficult. If your technical

42:28

people are too much in

42:30

charge, nothing will ever go out

42:32

the door because they want it perfect. And

42:35

if you're marketing people are too much in charge,

42:38

then the deadline becomes

42:40

about everything and, you know, we

42:43

bought the ads already. Yeah. There's

42:46

definitely an assumption that first

42:48

mover gets an advantage. I'd

42:51

love somebody to actually do a study on that because

42:54

sure. There are some of

42:56

the key, several

42:58

of the letters in fang, most

43:02

of them weren't first movers, Facebook

43:04

was not, Google was not, and

43:07

yet they're dominant, right? I don't know, you

43:09

know, that's anecdotal, right? So I don't know

43:11

how often first mover actually makes a difference,

43:14

but business-wise that often tends to be the

43:16

answer. We have to be first mover. And

43:18

so then you get half baked going out

43:20

the door. The other part

43:22

of it too is, you know, and this

43:24

comes from sort of the organic

43:26

processes that happen in the different

43:28

departments that happen inside of an

43:30

organization, you always hear developers swearing

43:32

about sales because they've promised something

43:35

in order to, you know, Oh

43:37

yeah, of course it does that. And then it doesn't, and then

43:39

development are the ones who are, have to clean it up. Well,

43:44

sales wants to make

43:46

sales and if sales should

43:48

want happy customers, right, because that allows them

43:50

to make more sales. If

43:52

your management is properly thinking about how

43:54

that happens and making sure that the

43:56

salespeople are, you know, one of

43:58

the things that I've. I've worked with

44:00

organizations where the commission doesn't happen unless the cuss

44:03

SAT numbers of a certain size, right? And so

44:05

like you get half your commission when you make

44:07

the sale and you get the other half of

44:09

the commission once it's actually been implemented and your

44:11

cuss SAT score is above 8

44:15

or whatever the number is. And it's

44:18

a completely different dynamic because the salespeople

44:20

are, they want to work with

44:23

the engineers because they're not going to get

44:25

their money unless, right? And

44:28

so there are things management can do to try and

44:30

help this kind of stuff, but

44:32

we have a long history of not

44:34

doing it. And unfortunately the

44:37

vast majority of C-suite

44:39

are not engineers. And

44:42

so the MBAs rule the world.

44:47

Definitely what seems to be happening in a

44:49

lot of Silicon Valley and then

44:52

just the idea of let's

44:55

make sure that we just pay attention

44:57

to the stock

44:59

and so forth. And I feel like

45:01

there was a little bit more of a wall, but

45:04

it's just gone now. It's like, it seems

45:06

like it's completely fallen apart. And

45:08

well, speaking of falling apart, like Boeing's a case

45:11

study in this, right? Oh God. Yeah.

45:14

Yeah. Right. So they

45:16

were an engineering driven company and then the merger happened and

45:19

the MBAs took over and it's

45:21

been downhill since then. So

45:24

I heard about the

45:27

slide just this morning. I don't know

45:29

if you heard that one. No, I

45:31

missed this. There's emergency exit

45:33

slide that got detached in

45:35

flight or whatever. Oh, fine. Another Boeing.

45:39

And then they couldn't find it. And then they

45:42

found it at the one of the lawyers

45:44

who's suing Boeing. At least that's what I

45:46

understand. So yeah, that's pretty bananas. Hey,

45:51

you guys lost this. Anyway,

45:56

I guess it's sort of sobering in a way.

45:59

I wish there was more. solutions

46:02

or ideas in there

46:04

for, I mean, it's,

46:07

you know, first step spot the problem, sure.

46:09

But from there, like, you know, what can you

46:11

do to kind of get beyond it? And I

46:13

don't know if things are so endemic,

46:16

you know, inside of a company that you

46:18

can't quite get past it. I

46:20

don't know. I just feel like we

46:22

are definitely in a cycle right now where a

46:24

lot of this is happening and the push to

46:27

ship things is like so strong. And

46:29

then the push to cut the bottom line is there.

46:32

And like, okay, it's just not really sustainable.

46:35

Well, you know, it's one of

46:37

the things I try to coach

46:39

inside organizations is make

46:42

sure that the tech side understands

46:44

that without the sales side, they

46:47

don't get paid. And make

46:49

sure that the sales side understands that without

46:52

the tech side, you don't have a product

46:54

to sell. So let's start talking about this

46:56

as a problem we're solving together rather than,

46:58

you know, creating these fiefdoms,

47:00

right? Yeah. Anyway,

47:03

shall we move on to projects? Yeah, I think

47:05

digging into projects is probably a good idea. We're

47:09

not going to solve this. So yeah, yeah.

47:11

So mine, it uses two things

47:14

that we talk about a lot. One is

47:16

PipEx, which you've mentioned, maybe even

47:18

last week, I think. And

47:20

the other is UV, which you've mentioned a few

47:22

times, which is a tool that

47:25

in some ways can be for packaging,

47:27

but also can in some ways replace

47:29

what Pip does. So this

47:31

is a project called PipExU. And

47:35

it's installing and running Python applications

47:37

in isolated environments using UV. The

47:39

project's by Mark Blakendy.

47:41

And he goes by Bullet Mark. And

47:43

then the other person that is on the project is Patrick

47:46

Zajja. Each package

47:48

and its dependencies are insulated from

47:51

other applications and from the system

47:53

Python. That's kind of a hallmark of

47:55

PipEx, which is really kind of nice. It's

47:58

a good way to install. tools that you

48:00

use across a

48:03

system that you want to have access,

48:05

but it won't be embedded in other

48:07

particular projects you have. So again,

48:10

if you're familiar using pipx, pipx use just

48:13

kind of a thing that provides a

48:15

speed improvement in a lot of ways.

48:17

It also is lighter weight

48:19

in the sense that pip is one of

48:21

these dependencies that

48:24

does take up some space and also

48:26

is updating itself pretty often. And

48:28

so this is something that on

48:30

the UV side is going to be a little quicker.

48:32

I installed it, played around with

48:34

it. I played around with a couple different tools

48:37

that I like to use and yeah, it just

48:39

worked really, really simply. In some ways it was

48:41

a little more verbose and it's a installation which

48:43

was kind of nice kind of showing me things

48:46

that were in there. I don't use

48:48

pipx all the time. So maybe I would you know could

48:50

turn on a different flag or two and I would have

48:52

seen similar sort of stuff, but I

48:54

like the defaults that it provided. So yeah,

48:56

if you're interested in pipx and you're interested

48:58

in maybe speeding it up a little bit.

49:00

This is taking advantage of

49:02

the enhancements of UV. What's

49:05

your project? I've got two which are

49:07

related to each other. Okay, the first

49:10

one is called Tenacity and it's by

49:12

Julian Dandjou. This library is

49:15

for doing retries in your code. So it

49:17

started out as a fork of a library

49:19

actually called retrying which is no longer maintained.

49:22

So let's say you've got a flaky network

49:24

connection and you want to try sending something

49:26

a few times before giving up. Tenacity

49:29

ships with decorators that you can apply to

49:31

a function that give you this functionality. For

49:34

example, the decorator can catch exceptions and then

49:36

retry the function again. And if the function

49:38

passes then Tenacity does nothing and you can

49:41

go along your merry way. You

49:43

can control how many times to

49:45

retry. You can control how long

49:48

to wait between retries, conditional retries

49:50

based on specific exceptions, or

49:52

you can call a truthy function that decides

49:54

whether or not to retry it. And

49:57

in fact, it also has tools to

49:59

hook in with managing laws. and monitors

50:01

in case you want to give information

50:03

about retrying. So neat little library. And

50:05

then the second project is a wrapper

50:07

to Tenacity and it's called Stamina. It's

50:10

by Heineck Schlawak. We've

50:13

mentioned him on the show before back in

50:15

episode 145. We featured his

50:17

article on the Knox testing tool. The

50:20

easiest way to explain stamina is to

50:22

quote the README. Stamina is

50:24

an opinionated wrapper around the

50:26

great but unopinionated Tenacity package.

50:29

So essentially it tries its best

50:31

to pick reasonable defaults for things

50:33

and to provide some helpers. So

50:35

for example, out of the box

50:38

stamina rather than retrying on all

50:40

exceptions, it only retries on certain

50:42

exceptions. So you know that network

50:44

example I was giving you, IO error is probably a

50:46

common one to do, but if it throws up a

50:48

key error then you wouldn't. Likewise

50:51

it uses exponential back off to wait

50:53

between retries, which is a good philosophy

50:56

and limits the number of retries and the

50:58

total time taken. So it's essentially taken good

51:01

ideas about how to do a retry

51:04

and implemented it using Tenacity. The one

51:06

feature I really like is it also

51:08

has a helper method that allows you

51:11

to globally disable the retry mechanism, which

51:13

can be really really handy when you're

51:15

testing. And the documentation shows

51:17

you how to do this with a

51:19

PyTest mixture. So if you need retry

51:22

stuff, these libraries are great. If

51:24

you're already a Tenacity person, I definitely

51:26

recommend taking a look at stamina because it might

51:28

actually save you some coding. You mean

51:30

tenacious person, right? I

51:34

like the attempts and so forth. You can

51:36

kind of like put values in for that

51:39

and I guess you would in the testing

51:42

runs of these things, you would just maybe lower

51:44

it to one or something like that just to

51:46

make it go through. It has

51:48

a global setter getter kind

51:51

of function in the library and you

51:53

essentially just say turn it on

51:55

turn it off and it's as if the

51:57

decorator is not there. Okay awesome. Well,

52:00

thanks for bringing all these PyCoders

52:03

Weekly articles and projects this

52:05

week and discussion topics. Thanks,

52:08

Christopher. Great. I'll see you in a

52:10

couple weeks. Okay. And

52:15

don't forget, installing Sentry is just

52:17

five lines of code. And real

52:19

Python listeners get three full months

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signup. Check it out

52:28

at sentry.io. I

52:31

want to thank Christopher Trudeau for coming on the show

52:33

again. And I want to thank

52:36

you for listening to the real Python podcast. Make

52:38

sure that you click that follow button in your

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