To calculate all the p-values at once, you can use calculate_pvalues function (code below): df = pd.DataFrame({‘A’:[1,2,3], ‘B’:[2,5,3], ‘C’:[5,2,1], ‘D’:[‘text’,2,3] }) calculate_pvalues(df) The output is similar to the corr() (but with p-values): A B C A 0 0.7877 0.1789 B 0.7877 0 0.6088 C 0.1789 0.6088 0 Details: Column D is automatically ignored as it … Read more
The value line in each box is telling you how many samples at that node fall into each category, in order. That’s why, in each box, the numbers in value add up to the number shown in sample. For instance, in your red box, 91+212+113=416. So this means if you reach this node, there were … Read more
If I read the source code right (Numpy 1.8.2, Scipy 0.14.1 ), numpy.linalg.lstsq() uses the LAPACK routine xGELSD and scipy.linalg.lstsq() usesxGELSS. The LAPACK Manual Sec. 2.4 states The subroutine xGELSD is significantly faster than its older counterpart xGELSS, especially for large problems, but may require somewhat more workspace depending on the matrix dimensions. That means … Read more
The distributions in scipy are coded in a generic way wrt two parameter location and scale so that location is the parameter (loc) which shifts the distribution to the left or right, while scale is the parameter which compresses or stretches the distribution. For the two parameter lognormal distribution, the “mean” and “std dev” correspond … Read more
This constraint t[0] + t[1] = 1 would be an equality (type=”eq”) constraint, where you make a function that must equal zero: def con(t): return t[0] + t[1] – 1 Then you make a dict of your constraint (list of dicts if more than one): cons = {‘type’:’eq’, ‘fun’: con} I’ve never tried it, but … Read more
As @AbhranilDas mentioned, just use a linear method. There’s no need for a non-linear solver like scipy.optimize.lstsq. Typically, you’d use numpy.polyfit to fit a line to your data, but in this case you’ll need to do use numpy.linalg.lstsq directly, as you want to set the intercept to zero. As a quick example: import numpy as … Read more
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
You can also check if all the eigenvalues of matrix are positive. If so, the matrix is positive definite: import numpy as np def is_pos_def(x): return np.all(np.linalg.eigvals(x) > 0)
EDIT Changed aspect=”normal to aspect=”auto’ since that changed in more recent versions of matplotlib (thanks to @Luke19). Assuming : import matplotlib.pyplot as plt To make a figure without the frame : fig = plt.figure(frameon=False) fig.set_size_inches(w,h) To make the content fill the whole figure ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) Then draw your … Read more
>>> import math >>> import numpy as np >>> import scipy >>> math.pi == np.pi == scipy.pi True So it doesn’t matter, they are all the same value. The only reason all three modules provide a pi value is so if you are using just one of the three modules, you can conveniently have access … Read more