Matplotlib runs out of memory when plotting in a loop
I have a fairly simple plotting routine that looks like this:
numpy
Is there anything faster than dict()?
I need a faster way to store and access around 3GB of k:v pairs. Where k is a string or an integer and v is an np.array() that can be of different shapes.
Max in a sliding window in NumPy array
I want to create an array which holds all the max()es of a window moving through a given numpy array. I’m sorry if this sounds confusing. I’ll give an example. Input:
Overflow Error in Python’s numpy.exp function
I want to use numpy.exp like this:
Get year, month or day from numpy datetime64
I have an array of datetime64 type:
Using Numpy stride_tricks to get non-overlapping array blocks
I’m trying to using numpy.lib.stride_tricks.as_strided to iterate over non-overlapping blocks of an array, but I’m having trouble finding documentation of the parameters, so I’ve only been able to get overlapping blocks.
Are Decimal ‘dtypes’ available in NumPy?
Are Decimal data type objects (dtypes) available in NumPy?
Efficient way to take the minimum/maximum n values and indices from a matrix using NumPy
What’s an efficient way, given a NumPy matrix (2D array), to return the minimum/maximum n values (along with their indices) in the array?
How to split a numpy array in fixed size chunks with and without overlap?
Lets say I have an array:
Are numpy’s basic operations vectorized, i.e. do they use SIMD operations?
I am doing some performance analysis, and i wonder, whether numpy vectorizes its standard array operations, when the datatype is known (double).