411 | An Alert Management Web Application

You should have those two indexes to speed up your query :

Found this issue, pytube v11.0.1. It's a little late for me, but if no one has submitted a fix tomorrow I'll check it out.

in C:\Python38\lib\site-packages\pytube\parser.py

Change this line:

152: func_regex = re.compile(r"function\([^)]+\)")

to this:

152: func_regex = re.compile(r"function\([^)]?\)")

The issue is that the regex expects a function with an argument, but I guess youtube added some src that includes non-paramterized functions.

In golang I have taken an array ARR1 which represents a time series ( could be audio or in my case an image ) where each element of this time domain array is a floating point value which represents the height of the raw audio curve as it wobbles ... I then fed this floating point array into a FFT call which returned a new array ARR2 by definition in the frequency domain where each element of this array is a single complex number where both the real and the imaginary parts are floating points ... when I then fed this array into an inverse FFT call ( IFFT ) it gave back a floating point array ARR3 in the time domain ... to a first approximation ARR3 matched ARR1 ... needless to say if I then took ARR3 and fed it into a FFT call its output ARR4 would match ARR2 ... essentially you have this time_domain_array --> FFT call -> frequency_domain_array --> InverseFFT call -> time_domain_array ... rinse N repeat

I know Web Audio API has a FFT call ... do not know whether it has an IFFT api call however if no IFFT ( inverse FFT ) you can write your own such function here is how ... iterate across ARR2 and for each element calculate the magnitude of this frequency ( each element of ARR2 represents one frequency and in the literature you will see ARR2 referred to as the frequency bins which simply means each element of the array holds one complex number and as you iterate across the array each successive element represents a distinct frequency starting from element 0 to store frequency 0 and each subsequent array element will represent a frequency defined by adding incr_freq to the frequency of the prior array element )

Each index of ARR2 represents a frequency where element 0 is the DC bias which is the zero offset bias of your input ARR1 curve if its centered about the zero crossing point this value is zero normally element 0 can be ignored ... the difference in frequency between each element of ARR2 is a constant frequency increment which can be calculated using

Use read_html for the DataFrame creation and then drop the na rows

using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.

It seems like np.swapaxes does the trick you need: arr.reshape(2,3,4,4).swapaxes(2,3).reshape(2,3,16)

The main idea is to swap the axes in the most inner data:

One option to achieve your desired result would be to use stat="bin" in geom_text too. Additionally we have to group by year so that each year is a separate "block". The tricky part is to get the year labels for which I make use of after_stat. However, as the groups are stored internally as an integer sequence we have them back to the corresponding years for which I make use of a helper vector.

If I understood correctly

Do you want something like this: