How to extend an array in-place in Numpy?
Currently, I have some code like this
scipy
How to add a new row to an empty numpy array
Using standard Python arrays, I can do the following:
What do all the distributions available in scipy.stats look like?
Visualizing scipy.stats distributions A histogram can be made of the scipy.stats normal random variable to see what the distribution looks like. % matplotlib inline import pandas as pd import scipy.stats as stats d = stats.norm() rv = d.rvs(100000) pd.Series(rv).hist(bins=32, normed=True) What do the other distributions look like? Answers: Thank you for visiting the Q&A section … Read more
Multiple variables in SciPy’s optimize.minimize
According to the SciPy documentation it is possible to minimize functions with multiple variables, yet it doesn’t tell how to optimize on such functions.
Python curve_fit with multiple independent variables
Python’s curve_fit calculates the best-fit parameters for a function with a single independent variable, but is there a way, using curve_fit or something else, to fit for a function with multiple independent variables? For example:
Generating Discrete random variables with specified weights using SciPy or NumPy
I am looking for a simple function that can generate an array of specified random values based on their corresponding (also specified) probabilities. I only need it to generate float values, but I don’t see why it shouldn’t be able to generate any scalar. I can think of many ways of building this from existing functions, but I think I probably just missed an obvious SciPy or NumPy function.
matplotlib – extracting data from contour lines
I would like to get data from a single contour of evenly spaced 2D data (an image-like data).
Use scipy.integrate.quad to integrate complex numbers
I’m using right now the scipy.integrate.quad to successfully integrate some real integrands. Now a situation appeared that I need to integrate a complex integrand. quad seems not be able to do it, as the other scipy.integrate routines, so I ask: is there any way to integrate a complex integrand using scipy.integrate, without having to separate the integral in the real and the imaginary parts?
Installing NumPy and SciPy on 64-bit Windows (with Pip)
I found out that it’s impossible to install NumPy/SciPy via installers on Windows 64-bit, that’s only possible on 32-bit. Because I need more memory than a 32-bit installation gives me, I need the 64-bit version of everything.