Five years later, when I Google “how to create a kernel density plot using python”, this thread still shows up at the top! Today, a much easier way to do this is to use seaborn, a package that provides many convenient plotting functions and good style management. import numpy as np import seaborn as sns … Read more
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
By default, “import scipy” does not import any subpackage. There are too many subpackages with large Fortran extension modules that are slow to load. I do not recommend doing import scipy or the abbreviated import scipy as sp. It’s just not very useful. Use from scipy import special, from scipy import linalg, etc.
After great help from @Brenlla the code was modified to: def sigmoid(x, L ,x0, k, b): y = L / (1 + np.exp(-k*(x-x0))) + b return (y) p0 = [max(ydata), np.median(xdata),1,min(ydata)] # this is an mandatory initial guess popt, pcov = curve_fit(sigmoid, xdata, ydata,p0, method=’dogbox’) The parameters optimized are L, x0, k, b, who are … Read more
Pandas is aware of the object datetime but when you use some of the import functions it is taken as a string. So what you need to do is make sure the column is set as the datetime type not as a string. Then you can make your query. df[‘date’] = pd.to_datetime(df[‘date’]) df_masked = df[(df[‘date’] … Read more
In scipy 0.14 or later, there is a new function scipy.interpolate.RegularGridInterpolator which closely resembles interp3. The MATLAB command Vi = interp3(x,y,z,V,xi,yi,zi) would translate to something like: from numpy import array from scipy.interpolate import RegularGridInterpolator as rgi my_interpolating_function = rgi((x,y,z), V) Vi = my_interpolating_function(array([xi,yi,zi]).T) Here is a full example demonstrating both; it will help you understand … Read more
My guess is that you want to estimate the shape parameter and the scale of the Weibull distribution while keeping the location fixed. Fixing loc assumes that the values of your data and of the distribution are positive with lower bound at zero. floc=0 keeps the location fixed at zero, f0=1 keeps the first shape … Read more
Edit: bbtrb’s method (using coo_matrix) is much faster than my original suggestion, using nonzero. Sven Marnach’s suggestion to use itertools.izip also improves the speed. Current fastest is using_tocoo_izip: import scipy.sparse import random import itertools def using_nonzero(x): rows,cols = x.nonzero() for row,col in zip(rows,cols): ((row,col), x[row,col]) def using_coo(x): cx = scipy.sparse.coo_matrix(x) for i,j,v in zip(cx.row, cx.col, … Read more
df.values is a numpy array, and accessing values that way is always faster than np.array. scipy.sparse.csr_matrix(df.values) You might need to take the transpose first, like df.values.T. In DataFrames, the columns are axis 0.
It’s possible to install on regular arm64 brew python, you need to compile it yourself. If numpy is already installed (from wheels) you’ll need to uninstall it: pip3 uninstall -y numpy pythran I had to compile numpy, which requires cython and pybind11: pip3 install cython pybind11 Then numpy can be compiled: pip3 install –no-binary :all: … Read more