10_Python_Pandas_Module | level data manipulation tool developed by Wes McKinney

I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).

I am trying to achieve a calculation involving geometric progression (split). Is there any effective/efficient way of doing it. The data set has millions of rows. I need the column "Traded_quantity"

turtle = 2 (User defined)

base_quantity = 1 (User defined)

I have a multi index df, with column "Turtle"

I have a scenario in which I have a peptide frame having 9 AA. I want to generate all possible peptides by replacing a maximum of 3 AA on this frame ie by replacing only 1 or 2 or 3 AA.

The frame is CKASGFTFS and I want to see all the mutants by replacing a maximum of 3 AA from the pool of 20 AA.

we have a pool of 20 different AA (A,R,N,D,E,G,C,Q,H,I,L,K,M,F,P,S,T,W,Y,V).

I am new to coding so Can someone help me out with how to code for this in Python or Biopython.

output is supposed to be a list of unique sequences like below:

CKASGFTFT, CTTSGFTFS, CTASGKTFS, CTASAFTWS, CTRSGFTFS, CKASEFTFS ....so on so forth getting 1, 2, or 3 substitutions from the pool of AA without changing the existing frame.

I am not able to access jupyter lab created on google cloud

I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

I tried the diagnostic tool, which gave the following result:

but didn't fix it.

Thanks in advance.

I am trying to work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model.

As part of this exercise I am trying to execute the below code for plotting a histogram and got "TypeError: import_optional_dependency() got an unexpected keyword argument 'errors' ". Please help resolve this TypeError.

I am working on Fine-Tuning Pretrained Model on custom (using HuggingFace) dataset I will copy all code correctly from the one youtube video everything is ok but in this cell/code:

I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

Table example: