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Category: Questions

How to return an empty value or None on pandas dataframe?

SAMPLE DATA: https://docs.google.com/spreadsheets/d/1s6MzBu5lFcc-uUZ9B6CI1YR7P1fDSm4cByFwKt3ckgc/edit?usp=sharing I have this function that uses textacy to extract the source attribution. This automatically returns the speaker, cue and content of the quotes. In my dataset, some paragraphs have several quotations, but I only need the first one, that’s why I put the BREAK in the for loop. My problem now is that some of original data

scikit preprocessing across entire dataframe

I have a dataframe: The data is an average response of the same question asked across 4 quarters. I am trying to create a benchmark index from this data. To do so I wanted to preprocess it first using either standardize or normalize. How would I standardize/normalize across the entire dataframe. What is the best way to go about this?

System of equations in python symbolically

I want to solve this system symbolically but it didn’t work. where did I make the mistake? and how can I solve it? I get this error Answer You are attempting to solve such an equation: Ax = b. I don’t think you can mix-up command from different libraries like that, there is some compatibility but you should check the

Enhancing my code that calculates Slope Sign Change (SSC)

I am trying to code this Slope Sign Change (SSC) formula: My attempt below seems to be missing something since it generates -2 instead of 1. I am really stuck in this. Your help is highly appreciated. Answer You can compute the temporal difference with np.diff as you did: x_{n} – x_{n-1} with np.diff(x, prepend=1)[1:-1] or with slicing: x[1:-1] –

How to test a trained model saved in .pth.tar files?

I am working with CORnet-Z and I am building a separate test file. The model seems to be saved as .pth.tar files What would be the best approach to load this model and run evaluation and testing? Answer To test a model you need to load the state dictionary of your trained model and optimizer (if applicable). But, if you

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