Since NumPy version 19.0, one must specify dtype=object when creating an array from "ragged" sequences. I'm faced with a large number of array calls from my own
I have an array of distances called dists. I want to select dists which are within a range. dists[(np.where(dists >= r)) and (np.where(dists <= r + dr))]
I have a numpy array of np.shape=(n,) I am attempting to iterate through each value of the array and subtract the 2nd value from the 1st, then see if the differ
I'd like to copy a numpy 2D array into a third dimension. For example, given the 2D numpy array: import numpy as np arr = np.array([[1, 2], [1, 2]]) # arr.shap
I want to scale a dataframe, which raises the error as in the title (or below). My data: df.head() timestamp open high low close volume 0 2020-06-2
I have the following code import matplotlib.pyplot as plt import numpy as np array = np.pad(np.random.rand(300,300),10,'constant', constant_values = nan) fi
I'm trying to implement a function that computes the Relu derivative for each element in a matrix, and then return the result in a matrix. I'm using Python and
Here is the way how numpy.mgrid is used. grid = np.mgrid[x1:y1:100j , x2:y2:100j, ..., xn:yn:100j] However, I find this structure very irritating. Therefore, I
I'm focusing on the special case where A is a n x d matrix (where k < d) representing an orthogonal basis for a subspace of R^d and b is known to be inside t
Seemingly simple question: I have an array with two columns, the first represents an ID and the second a count. I'd like to update it with another, similar arr
If I create an array X = np.random.rand(D, 1) it has shape (3,1): [[ 0.31215124] [ 0.84270715] [ 0.41846041]] If I create my own array A = np.array([0,1,2]
I have searched high and low and just can't find a way to do it. (It's possible I was searching for the wrong terms.) I would like to create a mask (eg: [True F
I use pandas and statsmodels to do linear regression. However, i can't find any possible way to read the results. the results are displayed but i need to do som
I would like to install Python Pandas library (0.8.1) on Mac OS X 10.6.8. This library needs Numpy>=1.6. I tried this $ sudo easy_install pandas Searching
I have been searching for an answer to this question but cannot find anything useful. I am working with the python scientific computing stack (scipy,numpy,matp
Is there a fast way in numpy to add a vector to every row or column of a matrix. Lately, I have been tiling the vector to the size of the matrix, which can use
Given a self-indexing (not sure if this is the correct term) numpy array, for example: a = np.array([3, 2, 0, 1]) This represents this permutation (=> is
I want to read a zipfile into memory and extract its content into a numpy array (as numpy-datatypes). This needs to happen in an extremely efficient/fast manner
I have a NumPy record array of floats: import numpy as np ar = np.array([(238.03, 238.0, 237.0), (238.02, 238.0, 237.01), (238.05
numpy seems to not be a good friend of complex infinities While we can evaluate: In[2]: import numpy as np In[3]: np.mean([1, 2, np.inf]) Out[3]: inf The f
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