I’ve read an SQL query into Pandas and the values are coming in as dtype ‘object’, although they are strings, dates and integers. I am able to convert the date ‘object’ to a Pandas datetime dtype, but I’m getting an error when trying to convert the string and integers.
I have the following range of numpy data (deltas of usec timestamps):
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I intend to fit a 2D Gaussian function to images showing a laser beam to get its parameters like FWHM and position. So far I tried to understand how to define a 2D Gaussian function in Python and how to pass x and y variables to it.
I use itertools.product to generate all possible variations of 4 elements of length 13. The 4 and 13 can be arbitrary, but as it is, I get 4^13 results, which is a lot. I need the result as a Numpy array and currently do the following:
What are the differences in performance and behavior between using Python’s native sum function and NumPy’s numpy.sum? sum works on NumPy’s arrays and numpy.sum works on Python lists and they both return the same effective result (haven’t tested edge cases such as overflow) but different types.
I have two arrays of x–y coordinates, and I would like to find the minimum Euclidean distance between each point in one array with all the points in the other array. The arrays are not necessarily the same size. For example:
I would like to get the index of a 2 dimensional Numpy array that matches a row. For example, my array is this:
I try to compile a Python project under Windows 7 using PyInstaller. The project works fine, there are no issues, however when I try to compile it the result doesn’t work. Though I get no warnings during compilation there are many in the warnmain.txt file in the build directory: warnmain.txt
Believe it or not, after profiling my current code, the repetitive operation of numpy array reversion ate a giant chunk of the running time. What I have right now is the common view-based method: