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Use concat with rows before, after and match by condition:

• $graphLookup as per your requirement
• $unwind deconstruct hijos array
• $addFields convert hijos.idUsuario to object id because its an string, if it is already in object id then remove this stage
• $lookup with users collection
• $unwind deconstruct hijos.idUsuario array
• $addFields to remove hijos if it is blank {} object
• $group by _id and reconstruct hijos array

The problem is

You're almost there, you just need to check if the name includes your string, rather than being equal to it:

According to the traceback T[k, k+1] doesn't work because T is a bsr format sparse matrix, which does not implement indexing. (coo is a more common format that doesn't have this either).

Looking at the sp.kron code, https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.kron.html

Since you appear to be a Windows user, I can offer two pieces of advice.

First, make sure your R installation is in a location that you have write permissions to. In some Windows OS, the Program Files locations are by default locked down. If the computer is yours, you should be able to change the permissions to the R folder. How to do so will vary a little by your windows version, so I will not provide more instructions. One other thing you can try is to set R and RStudio to "Run as administrator".

Second, OneDrive is a bad place to put your R installation or your R packages. OneDrive does not support all filenames and file extensions. R and its packages will generate a number of unsupported files, and you will continually be notified that something will not sync and that you should do something about it.

On a Windows machine, I generally install R and set up the library at C:\R where I have appropriate permissions.

Changing FileInputStream to InputStream solved the issue. I have no idea why, though.

Found the answer. Instead of using the "part of the event matched" , I used the input transformer as the following :

Worked properly . I have the json format in the target.

For first you can select only numeric columns and replace non 0 value by 1 in DataFrame.mask, then for second add cumulative sum per axis=1 with compare first 1 values by DataFrame.eq and boolean mask convert to integers by DataFrame.astype: