scipy (scientific) package in Python
import scipy
from scipy.linalg import inv as my_inv
linalg.solve(A, b) ==> Dense matrix solver
linalg.lstsq(F, E) ==> Linear least-square solver
la, v = linalg.eig(myMatrix) ==> Solve eigenvalue problem
linalg.eigvals(myMatrix)
la, v = sparse.linalg.eigs(myMatrix, 1)
U, s, Vh = linalg.svd(myMatrix)
Sig = linalg.diagsvd(s, M, N)
print(scipy.linalg.inv(a))
from scipy.stats import zscore
turnout_zscore = zscore(election['turnout'])
>>> b = stats.norm.pdf(bins) # norm is a distribution
>>> loc, std = stats.norm.fit(a)
>>> stats.scoreatpercentile(a, 50)
>>> stats.ttest_ind(a, b)
>>> from scipy import linalg, optimize
>>> optimize.fmin_bfgs(f, 0)
>>> optimize.fmin_bfgs(f, 3, disp=0)
>>> optimize.basinhopping(f, 0)
>>> xmin_local = optimize.fminbound(f, 0, 10)
>>> root = optimize.fsolve(f, 1) # our initial guess is 1
>>> params, params_covariance = optimize.curve_fit(f2, xdata, ydata, guess)
sol = minimize(obj, x0, method='SLSQP', bounds=bnds, constraints=cons)
img= data.camera()
>>> from scipy import io as spio
>>> spio.savemat('file.mat', {'a': a}) # savemat expects a dictionary
>>> data = spio.loadmat('file.mat', struct_as_record=True)
>>> from scipy import misc
>>> misc.imread('fname.png')
im=misc.imread("/resources/data/lena.png").astype(np.float)
>>> face = misc.face(gray=True)
f = misc.face()
misc.imsave('face.png', f) # uses the Image module (PIL)
>>> from scipy import fftpack
>>> sample_freq = fftpack.fftfreq(sig.size, d=time_step)
>>> sig_fft = fftpack.fft(sig)
>>> main_sig = fftpack.ifft(sig_fft)
>>> from scipy.interpolate import interp1d
>>> linear_interp = interp1d(measured_time, measures)
>>> linear_results = linear_interp(computed_time)
>>> cubic_results = cubic_interp(computed_time)
>>> from scipy.integrate import quad
>>> res, err = quad(np.sin, 0, np.pi/2)
ans, err = scipy.integrate.quad(lambda x: x**2, 0., 4)
>>> I2 = integrate.simps(y2, x)
>>> from scipy.integrate import odeint
yvec, info = odeint(calc_derivative, 1, time_vec, args=(counter,), full_output=True)
>>> from scipy import signal
>>> wiener_face = signal.wiener(noisy_face, (5, 5))
>>> from scipy import ndimage
>>> shifted_face = ndimage.shift(face, (50, 50))
>>> rotated_face = ndimage.rotate(face, 30)
>>> zoomed_face = ndimage.zoom(face, 2)
>>> el = ndimage.generate_binary_structure(2, 1)
>>> ndimage.binary_opening(a, structure=np.ones((3, 3))).astype(np.int)
>>> ndimage.binary_opening(a).astype(np.int)
>>> closed_mask = ndimage.binary_closing(opened_mask)
from scipy.spatial import distance_matrix
dist_matrix = distance_matrix(X2,X2)
from scipy.cluster import hierarchy
hierarchy.linkage(dist_matrix, 'complete')
dendro = hierarchy.dendrogram(Z)
import scipy.stats as spstats
from scipy.stats import mode
mode(data['Gender'])
from scipy import stats
from scipy.stats import norm
res = stats.probplot(df_train['SalePrice'], plot=plt)
