omega | The Last Python Web Framework

Beginning with two pandas dataframes of different shapes, what is the fastest way to select all rows in one dataframe that do not exist in the other (or drop all rows in one dataframe that already exist in the other)? And are the fastest methods different for string-valued columns vs. numeric columns? Operation should be roughly equivalent to the code below

I've mostly worked with MATLAB, and I am converting some of my code that I have written into Python. I am running into an issue where I have a boolean mask that I am calling Omega, and when I apply the mask to different m by n matrices that I call X and M I get different objects. Here is my code

I'm facing a mathematical/programming problem which I don't really understand. In the following α,β,θc are given parameters, ϕ is the variable. I have to solve the equation Cp=0, where:

and:

The solutions that I have computed are:

Coding the above solutions with Python/Numpy, I find that:

• If β=0, then both solutions are correct: Cp(ϕ1)=Cp(ϕ2)=0.
• However, if β!=0 then Cp(ϕ1)!=0 (which is wrong) and Cp(ϕ2)=0. Why? I checked again and again the signs and terms on my coded expressions, they seem fine to me...

Here is the code:

I am trying to fit experimental data

with a function of the form:

Dear stackoverflow community,

I am currently trying to create a custom layer that transforms every value of an input with integers into a vector with length "omega". Currently I combine the tensorflow functions tf.map_fn with the function tf.range to create a ragged tensor and fill that tensor up with zeros afterwards.

The Custom layer is defined as follows:

Here I am using two threads. One thread create a dataframe and pass it to another thread through queue. Another thread collect the dataframe from the queue and append it to a csv file. Here when I run the code ,sometimes I got wrongvalues in entire dataframe. shoudl i have to do any corrections in the code ?

Here is my code:

I have a TABLE sec.sec_secret_tab in the SEC schema

and this function:

After upgrading to android 12, the application is not compiling. It shows

"Manifest merger failed with multiple errors, see logs"

Error showing in Merged manifest:

Merging Errors: Error: android:exported needs to be explicitly specified for . Apps targeting Android 12 and higher are required to specify an explicit value for android:exported when the corresponding component has an intent filter defined. See https://developer.android.com/guide/topics/manifest/activity-element#exported for details. main manifest (this file)

I have set all the activity with android:exported="false". But it is still showing this issue.

My manifest file:

I want to populate a huge sparse matrix in python, with the aim to implement Crank-Nicolson numerical method in 2D.

I did it by lopping through all the interior nodes using two nested for loops, but it is extremely slow. Because it is a nested for loop with matrices, I thought of Numba, but it doesn't work with sparse matrices. I cannot convert the matrix to a dense format before passing it as an argument to a numba-jitted function, because of memory issues.

I want to ask what shall I look for in order to make the function populating the A matrix below quicker?

I tried with scipy.sparse.diags, but I again ended up using two nested for loops, just as in the (naive) code below.

The problem is that the k value is computed from i and j, and I don't know how to manipulate it without the double for loop.

The code which populates the A matrix with the double for loop is: