I have come across an issue with the np.select section of my code and have reproduced a minimal-reproducible-example to seek some advice as to why ValueError: -1 is not in range is being returned rather than nan Using df[‘number’][3] when number = 1 I would expect to return nan since the value located in df[‘number’][3] is 4 and although number
Tag: numpy
Plot cumulative distribution with networkx and numpy
I want to implement the cumulative distribution for a graph. Here is my code: To plot cumulative I know that I must have at the x-axis the degree and in the y-axis the samples with value > Degree. The result using my code is the following: But the expected result must be something like this: I am not sure if
Python- compress lower end of y-axis in contourf plot
The issue I have a contourf plot I made with a pandas dataframe that plots some 2-dimensional value with time on the x-axis and vertical pressure level on the y-axis. The field, time, and pressure data I’m pulling is all from a netCDF file. I can plot it fine, but I’d like to scale the y-axis to better represent the
Cast a Python class to Numpy Array
Can I cast a python class to a numpy array? In the last step, I would like the to obtain an array np.array([X.x, X.y]). Instead, I get array(X(x=0, y=0), dtype=object). Can I provide method for the dataclass so that the casting works as desired (or overload one of the existing methods of the dataclass)? Answer From docstring of numpy.array we
How to plot the sum of two animated sine waves in python?
Code:- This is the code I have tried to attain the result. But it u is static, that is it prints the final plot, and it only prints in a temporary window and closes after execution of the code. But I need an answer which have three waves in the same output, where the first sine wave and second sine
Faster matrix calculation in numpy
Is there some faster variant of computing the following matrix (from this paper), given a nxn matrix M and a n-vector X: ? I currently compute it as follows: This is very slow, but I suspect there is a nicer “numpythonic” way of doing this instead of looping… EDIT: While all answers work and are much faster than my naive
Unable to use “from numpy import *”
I was trying to import numpy like the below format, but it was not working. It was throwing me some errors. The IDE was VS Code. I already installed NUMPY in pip on CMD. Still it is not working. This is the Screenshot of Error in VS code Answer These are more warnings than errors in your screenshot You’re importing
How to do an outer product of 3 vectors to create a 3d matrix in numpy? (and same for nd)
If i want to do an outer product of 2 vectors to create a 2d matrix, each element a product of the two respective elements in the original vectors: I want the same for 3 (or for n) vectors. An equivalent non numpy answer: out: How to do this with numpy [no for loops]? Also, how to do this for
Python Error RuntimeError: expected scalar type Long but found Double
Firstly, I am fairly new to python/ML in general. I am attempting to utilize the model depicted at stackabuse over my own data set. Everything flows smoothly until I get ready to run the epochs. In debugging I see that it is failing on CrossEntropyLoss function and I get the error expected long found double. The data set it appears
Return a numpy array, with numbers of elements specified in another array
Suppose I have two numpy arrays A, B, with A.shape = (2,4,3) and B.shape = (2,4): Now I would like to get a new array C with C.shape = (2+3+1,3) = (6,3), such that each integer in B specifies the number of the corresponding 3×1 array in A. In other words, A[i,j,:] should appear B[i,j] times in C. In our