pythonic | Testing examples of Raymond Hettinger 's python idioms

I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).

Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.

Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)

x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:

(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))

Below are two versions of the same create_dir function that creates a directory from a file path supplied as file_path argument.

The Version A checks if there is a directory in the supplied as a function argument file_path and only then tries to create a directory. The second Version B skips the extra check and feeds the output of the os.path.dirname(file_path) command straight into the os.makedir. Also, please note, that there are three if statements used in Version A, while version B doesn't have any.

Which version A or B is more preferred or more Pythonic?

Version A:

If I have the following matrix, which the input format is a list of lists:

How can I construct the following python matrix:

I have been trying to replace part of the texts in a Pandas dataframe column with keys from a dictionary based on multiple values; though I have achieved the desired result, the process or loop is very very slow in large dataset. I would appreciate it if someone could advise me of a more 'Pythonic' way or more efficient way of achieving the result. Pls see below example:

I have a list of integers. Now I want to sort from the middle. By this I mean:

1. starts from the number in the middle if the length of the list is an odd number;
2. starts from the larger of the two numbers in the middle if the length of the list is an even number;
3. alternatingly take the numbers on the left and right (or right and left, depending on their magnitudes). Always first take the larger of the two.

I managed to write the following code, but only works for odd-size lists. It will not be hard to write an even version for this, but I feel like there has to be more general and "Pythonic" way to code this. I'm actually surprised that this trivial question has never been asked before.

I would like to change a range of values in a 2d NumPy array in a simple way. For example, imagine I have a 2d array of shape (5,5). If I want to change the values of the upper 2x2 'block', I can use the NumPy put function. This is an example:

I have an np.array(<>,dtype = object) of floats that are visually grouped by Nones between them. For example:

I have a Pandas dataframe with an ID, a Timestamp, and a Value. There are multiple rows per ID, and it is sorted by ID and Timestamp ascending. I need to identify the IDs where two values - 'A' and 'B' - occur any two rows, per ID, in that order. For example:-

From the docs it seems the recommended way to kickstart a asyncio application is to use asyncio.run(), so my application looks like this: