Why “numpy.any” has no short-circuit mechanism?
I don’t understand why a so basic optimization has not yet be done:
numpy
Binary numpy array to list of integers?
I have a binary array, and I would like to convert it into a list of integers, where each int is a row of the array.
Filtering a list based on a list of booleans
I have a list of values which I need to filter given the values in a list of booleans:
Generalise slicing operation in a NumPy array
This question is based on this older question:
Shift elements in a numpy array
This question contains its own answer at the bottom. Use preallocated arrays.
numpy get index where value is true
>>> ex=np.arange(30) >>> e=np.reshape(ex,[3,10]) >>> e array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]]) >>> e>15 array([[False, False, False, False, False, False, False, False, False, False], [False, False, False, False, False, False, … Read more
pd.Timestamp versus np.datetime64: are they interchangeable for selected uses?
This question is motivated by an answer to a question on improving performance when performing comparisons with DatetimeIndex in pandas.
Populate a Pandas SparseDataFrame from a SciPy Sparse Matrix
I noticed Pandas now has support for Sparse Matrices and Arrays. Currently, I create DataFrame()s like this:
Python: slicing a multi-dimensional array
I’m new to Python and numpy. I’ve figured out how to slice 1 dimensional sequence: arr[start:end], and access an element in the array: el = arr
[col].
Fitting a Normal distribution to 1D data
I have a 1 dimensional array. I can compute the “mean” and “standard deviation” of this sample and plot the “Normal distribution” but I have a problem: