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Tag: lambda

Weird nested Python lambda

How does this python lambda work? My understanding: The lambda v3: v1(lambda v4: v3(v3)(v4)) becomes the argument v1, and the statement becomes lambda v2: v1(v2(v2)) with v1 binding to the lambda v3: v1(lambda v4: v3(v3)(v4)) But what is the status of v1 in lambda v3: v1(lambda v4: v3(v3)(v4))? An reference to itself? Answer You can usually figure out things like

How to upload pandas, sqlalchemy package in lambda to avoid error “Unable to import module ‘lambda_function’: No module named ‘importlib_metadata'”?

I’m trying to upload a deployment package to my AWS lambda function following the article https://korniichuk.medium.com/lambda-with-pandas-fd81aa2ff25e. My final zip file is as follows: https://drive.google.com/file/d/1NLjvf_-Ks50E8z53DJezHtx7-ZRmwwBM/view but when I run my lambda function I get the error Unable to import module ‘lambda_function’: No module named ‘importlib_metadata’ My handler is named lambda_function.lambda_handler which is the file name and the function to run. I

Lambda path parameters are embedded inside path dictionary

I have some python AWS lambdas which are deployed using serverless framework and I was able to retrieve the path variables using: event.get(“variable”) I am not sure what has changed but now I need to retrieve these path parameters using: event.get(“path”).get(“variable”) I am using lambda integration and my serverless configuration has not changed and looks like: I want to retrieve

Lambda and vectorize in python

I’m learning data analysis while performing vectorized operation with lambda function it run at first but again run it shows error as TypeError: <lambda>() takes 1 positional argument but 2 were given sample data of tips.csv file This is the image that I run first which doesn’t show any error Answer You can vectorize solution different way – with numpy.where

How to write a function to fit data to a sum of N Gaussian-like peaks without explicitly defining the expression for every possible N?

I am trying to fit a progression of Gaussian peaks to a spectral lineshape. The progression is a summation of N evenly spaced Gaussian peaks. When coded as a function, the formula for N=1 looks like this: where A, e0, hf, S and fwhm are to be determined from the fit with some good initial guesses. Importantly, the parameter i

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