Видео

This is the greatest video on RUclips ever

Hello you Daniel Chen for this insighting video

Excellent talk and very helpful to see good patterns in practice.

Brilliant presentation on decorators. This presentation highly recommended for everyone starting with decorators. My compliments.

This guy makes nerds looks COOL!

Awesome talk! A must watch for anyone doing OOP

watching in 2024 of learnign python

Great talk! Thank you very much!

Lady!! I'll try it for sure!! Thanks a lot!!! 👏🏼👏🏼👏🏼👏🏼

ראובן יא תותח על! הרצאה מצויינת על DECORATORS!

🎯 Key Takeaways for quick navigation: Consider the *limitations and biases of your data when analyzing it, such as missing values and data collection timeframes.* Make your *results understandable and interpretable by choosing appropriate metrics or visualizations, like views per comment instead of comments per view.* When visualizing *the distribution of a numeric variable, consider using a histogram to show the frequency distribution of values.* Adjust the *number of bins in a histogram to reveal more detail in the distribution.* Pay attention *to the interpretation of the visualization, as it may not always match initial assumptions. In this case, the histogram showed that there are more talks with some comments rather than talks with zero comments.* Use the *`PD.to_datetime` function to convert UNIX timestamps to date-time format.* Verify the *correctness of date conversions by randomly sampling and inspecting the results.* Utilize the *`value_counts` method to count the occurrences of each year.* For plotting *data over time, consider using a line plot rather than a bar plot.* Ensure the *proper sorting of data on the x-axis for line plots to avoid misleading visualizations.* Be cautious *of incomplete data when drawing conclusions from visualizations.* To unpack *a stringified list of dictionaries, you can use the `ast.literal_eval` function to convert it into an actual list.* When working *with pandas Series, you can use the `apply` method to apply a custom function to every element in the Series.* You can *also use a lambda function for simple custom functions when using `apply`.* Pay attention *to small sample sizes when calculating statistics.* When dealing *with data limitations, think creatively about how to use the available data to answer your questions. Be aware of the limitations and weaknesses of your chosen approach. If necessary, consider gathering additional data or modifying your question.* To count *the number of funny ratings in the dataset, you can create a function that iterates through the dictionaries in the "ratings" column and extracts the count for "funny."* To calculate *the percentage of funny ratings for each talk, divide the "funny ratings" by the total number of ratings for that talk.* To identify *the funniest occupations, sort the talks by their funny rating percentages and examine the speaker occupations, which should align with common sense expectations.* To analyze *the funny rate by occupation, you can use a groupby operation to calculate the mean funny rate for each speaker occupation. However, be cautious about the small sample sizes for some occupations, which may affect the reliability of the mean.* 03:11:15 You *can use the "describe" function on non-numeric columns to get information about non-null values, unique values, top values, and their frequencies.* 03:11:45 Having *a small sample size in your data can be a weakness, and it's essential to address it.* 03:13:44 Filtering *data based on certain conditions, like including only occupations that appear at least five times, can help mitigate the impact of a small sample size.* 03:17:13 After *filtering data, you can perform groupby and aggregation operations to analyze subsets of the data effectively.* 03:19:26 Dealing *with data where people have multiple occupations listed can be challenging, and it's important to consider how to handle such cases.* 03:20:10 Always *check your assumptions about the data, verify the results for reasonableness, and be aware of small sample sizes and missing data when conducting data analysis.* Made with HARPA AI

Great content covered

Excellent, thank you Luciano

To me the best way to understand what that R function is goes as follows: Suppose you have some slightly crappy version of factorial, call it crappyfact that only works for arguments 0 ... N for some number N, but not for arguments bigger than N. Then R(crappyfact) returns a slightly improved version of factorial - slightly less crappy, because it will work for arguments up to N+1. The actual perfect fact is a fixpoint of R because R cannot improve it further. As a matter of fact (no pun intended), this fixpoint is the so-called LEAST fixpoint. It is the "least crappy" version of factorial that cannot be further improved by R.

Simplification: You don't have to modify ISZERO and you can use the normal TRUE and FALSE. You would still pass thunks as second and third arguments to ISZERO, and then you invoke the thunk at the end after ISZERO returns: lambda n : ISZERO(n)(lambda dummy: ONE)(lambda dummy: MUL(n)(FACT(PRED(n))))(TRUE) (The last TRUE is the dummy argument and could be anything.)

nice talk!

Perfect.

Finally a lecture on programming.

Thanks :))) do we need returning _instance in singleton?

2:40:00 yield from

great lesson

Just now discovering black, can definitely say it changed the way i approach at code formatting

I need to extract document as same where i need to change the code..?

Do we have a github action file/docs for wiley ?

Using it from the terminal to format my files is great. Integration in visual studio code just does not work.

a very good talk indeed. thanks, Andrew!

Great intro to ML and python libraries such as scikit learn.fruitful session...thank you👍👍

thanks, really interesting talk!

The links to slides are dead.

Thank you Reuven! :)

Excellent lecture! very nice and interested topics Question: in case the inner function named "foo" and it can receive a named argument named "cache" in time 16:12, don't you *have* to use nonlocal? since "cache" foo may shadow the local "cache" variable of "memoize " function EDIT: i checked the scenario, and the named variable "cache" of "foo" DON'T shadow the "cache" variable of memoize Thanks again for the great video

where i work we are in the industrial dark ages .we cannot plc programs. everything is keep oem.

Great presentation!

We need more real women in IT.

"The following 'id_vars' are not present in the DataFrame: getting this error

Very good presenter, fun and informative :)

very nice explained w.r.t real world scenario

Well done talk.....

All joking aside, I like the walrus operator. It's a simple and elegant solution for certain expensive calls and it lets me translate C/C++ code (with assingments in conditions) in a logical 1:1 fashion to Python. Moreover, if you don't need it, you don't have to use it. Thanks for this!

Wow, one of the highest-quality programming talks I have ever watched (and I have watched A LOT). This concept is far clearer to me now. Thank you!!

Great talk! Will have to revisit many times

i am honestly saying this, they should scrap this DST bullshit, why cant you people do what asians do?

Caching using pickle'ing was a very nice! Great talk!

Hi jonas how read plc data with pymodbus in rtu metode?

Cool

7:36 ... menciona a una persona "muy buena en visualización" llamada Tamara , el apellido no lo alcanzo a escuchar alguien sabe de quien habla?

I've never felt so excited to implement a SUCC, let me tell you.

This was totally insane. He is a brilliant teacher.

Material is here if you need it. arielortiz.info/s201911/pycon2019/docs/design_patterns.html

1:55:00