The goal is both to offer a quick reference for new and old users and to -provide also a set of exercices for those who teach. If you remember having -asked or answered a (short) problem, you can send a pull request. The format -is:
--#. Find indices of non-zero elements from [1,2,0,0,4,0] - - .. code:: python - - # Author: Somebody - - print(np.nonzero([1,2,0,0,4,0])) --
Here is what the page looks like so far: -http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html
-Repository is at: https://github.com/rougier/numpy-100
-Thanks to Michiaki Ariga, there is now a -Julia version.
-Import the numpy package under the name np (★☆☆)
--import numpy as np --
Print the numpy version and the configuration (★☆☆)
--print(np.__version__) -np.show_config() --
Create a null vector of size 10 (★☆☆)
--Z = np.zeros(10) -print(Z) --
How to find the memory size of any array (★☆☆)
--Z = np.zeros((10,10)) -print("%d bytes" % (Z.size * Z.itesize)) --
How to get the documentation of the numpy add function from the command line? (★☆☆)
-
-python -c "import numpy; numpy.info(numpy.add)"
-
-Create a null vector of size 10 but the fifth value which is 1 (★☆☆)
--Z = np.zeros(10) -Z[4] = 1 -print(Z) --
Create a vector with values ranging from 10 to 49 (★☆☆)
--Z = np.arange(10,50) -print(Z) --
Reverse a vector (first element becomes last) (★☆☆)
--Z = np.arange(50) -Z = Z[::-1] --
Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)
--Z = np.arange(9).reshape(3,3) -print(Z) --
Find indices of non-zero elements from [1,2,0,0,4,0] (★☆☆)
--nz = np.nonzero([1,2,0,0,4,0]) -print(nz) --
Create a 3x3 identity matrix (★☆☆)
--Z = np.eye(3) -print(Z) --
Create a 3x3x3 array with random values (★☆☆)
--Z = np.random.random((3,3,3)) -print(Z) --
Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)
--Z = np.random.random((10,10)) -Zmin, Zmax = Z.min(), Z.max() -print(Zmin, Zmax) --
Create a random vector of size 30 and find the mean value (★☆☆)
--Z = np.random.random(30) -m = Z.mean() -print(m) --
Create a 2d array with 1 on the border and 0 inside (★☆☆)
--Z = np.ones((10,10)) -Z[1:-1,1:-1] = 0 --
How to add a border (filled with 0's) around an existing array ? (★☆☆)
--Z = np.ones((5,5)) -Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0) --
What is the result of the following expression? (★☆☆)
--0 * np.nan -np.nan == np.nan -np.inf > np.nan -np.nan - np.nan -0.3 == 3 * 0.1 --
Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)
--Z = np.diag(1+np.arange(4),k=-1) -print(Z) --
Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)
--Z = np.zeros((8,8),dtype=int) -Z[1::2,::2] = 1 -Z[::2,1::2] = 1 -print(Z) --
Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?
--print(np.unravel_index(100,(6,7,8))) --
Create a checkerboard 8x8 matrix using the tile function (★☆☆)
--Z = np.tile( np.array([[0,1],[1,0]]), (4,4)) -print(Z) --
Normalize a 5x5 random matrix (★☆☆)
--Z = np.random.random((5,5)) -Zmax, Zmin = Z.max(), Z.min() -Z = (Z - Zmin)/(Zmax - Zmin) -print(Z) --
Create a custom dtype that describes a color as four unisgned bytes (RGBA) (★☆☆)
--color = np.dtype([("r", np.ubyte, 1), - ("g", np.ubyte, 1), - ("b", np.ubyte, 1), - ("a", np.ubyte, 1)]) --
Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)
--Z = np.dot(np.ones((5,3)), np.ones((3,2))) -print(Z) --
Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)
--# Author: Evgeni Burovski - -Z = np.arange(11) -Z[(3 < Z) & (Z <= 8)] *= -1 --
What is the output of the following script? (★☆☆)
--# Author: Jake VanderPlas - -print(sum(range(5),-1)) -from numpy import * -print(sum(range(5),-1)) --
Consider an integer vector Z, which of these expressions are legal? (★☆☆)
--Z**Z -2 << Z >> 2 -Z <- Z -1j*Z -Z/1/1 -Z<Z>Z --
What are the result of the following expressions?
--np.array(0) / np.array(0) -np.array(0) // np.array(0) -np.array([np.nan]).astype(int).astype(float) --
How to round away from zero a float array ? (★☆☆)
--# Author: Charles R Harris - -Z = np.random.uniform(-10,+10,10) -print (np.trunc(Z + np.copysign(0.5, Z))) --
How to find common values between two arrays? (★☆☆)
--Z1 = np.random.randint(0,10,10) -Z2 = np.random.randint(0,10,10) -print(np.intersect1d(Z1,Z2)) --
How to ignore all numpy warnings (not recommended)? (★☆☆)
--# Suicide mode on -defaults = np.seterr(all="ignore") -Z = np.ones(1)/0 - -# Back to sanity -np.seterr(**defaults) --
Is the following expressions true? (★☆☆)
--np.sqrt(-1) == np.emath.sqrt(-1) --
How to get the dates of yesterday, today and tomorrow? (★☆☆)
--yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D') -today = np.datetime64('today', 'D') -tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D') --
How to get all the dates corresponding to the month of July 2016? (★★☆)
--Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]') -print(Z) --
How to compute ((A+B)*(-A/2)) in place (without copy)? (★★☆)
--A = np.ones(3)*1 -B = np.ones(3)*2 -C = np.ones(3)*3 -np.add(A,B,out=B) -np.divide(A,2,out=A) -np.negative(A,out=A) -np.multiply(A,B,out=A) --
Extract the integer part of a random array using 5 different methods (★★☆)
--Z = np.random.uniform(0,10,10) - -print (Z - Z%1) -print (np.floor(Z)) -print (np.ceil(Z)-1) -print (Z.astype(int)) -print (np.trunc(Z)) --
Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)
--Z = np.zeros((5,5)) -Z += np.arange(5) -print(Z) --
Consider a generator function that generates 10 integers and use it to build an -array (★☆☆)
--def generate(): - for x in xrange(10): - yield x -Z = np.fromiter(generate(),dtype=float,count=-1) -print(Z) --
Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)
--Z = np.linspace(0,1,12,endpoint=True)[1:-1] -print(Z) --
Create a random vector of size 10 and sort it (★★☆)
--Z = np.random.random(10) -Z.sort() -print(Z) --
How to sum a small array faster than np.sum? (★★☆)
--# Author: Evgeni Burovski - -Z = np.arange(10) -np.add.reduce(Z) --
Consider two random array A anb B, check if they are equal (★★☆)
--A = np.random.randint(0,2,5) -B = np.random.randint(0,2,5) -equal = np.allclose(A,B) -print(equal) --
Make an array immutable (read-only) (★★☆)
--Z = np.zeros(10) -Z.flags.writeable = False -Z[0] = 1 --
Consider a random 10x2 matrix representing cartesian coordinates, convert -them to polar coordinates (★★☆)
--Z = np.random.random((10,2)) -X,Y = Z[:,0], Z[:,1] -R = np.sqrt(X**2+Y**2) -T = np.arctan2(Y,X) -print(R) -print(T) --
Create random vector of size 10 and replace the maximum value by 0 (★★☆)
--Z = np.random.random(10) -Z[Z.argmax()] = 0 -print(Z) --
Create a structured array with x and y coordinates covering the -[0,1]x[0,1] area (★★☆)
--Z = np.zeros((10,10), [('x',float),('y',float)]) -Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,10), - np.linspace(0,1,10)) -print(Z) --
Given two arrays, X and Y, construct the Cauchy matrix C (Cij = 1/(xi - yj))
--# Author: Evgeni Burovski - -X = np.arange(8) -Y = X + 0.5 -C = 1.0 / np.subtract.outer(X, Y) -print(np.linalg.det(C)) --
Print the minimum and maximum representable value for each numpy scalar type (★★☆)
--for dtype in [np.int8, np.int32, np.int64]: - print(np.iinfo(dtype).min) - print(np.iinfo(dtype).max) -for dtype in [np.float32, np.float64]: - print(np.finfo(dtype).min) - print(np.finfo(dtype).max) - print(np.finfo(dtype).eps) --
How to print all the values of an array? (★★☆)
--np.set_printoptions(threshold=np.nan) -Z = np.zeros((25,25)) -print(Z) --
How to find the closest value (to a given scalar) in an array? (★★☆)
--Z = np.arange(100) -v = np.random.uniform(0,100) -index = (np.abs(Z-v)).argmin() -print(Z[index]) --
Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)
-- Z = np.zeros(10, [ ('position', [ ('x', float, 1), - ('y', float, 1)]), - ('color', [ ('r', float, 1), - ('g', float, 1), - ('b', float, 1)])]) -print(Z) --
Consider a random vector with shape (100,2) representing coordinates, find -point by point distances (★★☆)
--Z = np.random.random((10,2)) -X,Y = np.atleast_2d(Z[:,0]), np.atleast_2d(Z[:,1]) -D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2) -print(D) - -# Much faster with scipy -import scipy -# Thanks Gavin Heverly-Coulson (#issue 1) -import scipy.spatial - -Z = np.random.random((10,2)) -D = scipy.spatial.distance.cdist(Z,Z) -print(D) --
How to convert a float (32 bits) array into an integer (32 bits) in place?
--Z = np.arange(10, dtype=np.int32) -Z = Z.astype(np.float32, copy=False) --
How to read the following file? (★★☆)
--# File content: -# ------------- -1,2,3,4,5 -6,,,7,8 -,,9,10,11 -# ------------- - -Z = np.genfromtxt("missing.dat", delimiter=",") --
What is the equivalent of enumerate for numpy arrays? (★★☆)
--Z = np.arange(9).reshape(3,3) -for index, value in np.ndenumerate(Z): - print(index, value) -for index in np.ndindex(Z.shape): - print(index, Z[index]) --
Generate a generic 2D Gaussian-like array (★★☆)
--X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10)) -D = np.sqrt(X*X+Y*Y) -sigma, mu = 1.0, 0.0 -G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) ) -print(G) --
How to randomly place p elements in a 2D array? (★★☆)
--# Author: Divakar - -n = 10 -p = 3 -Z = np.zeros((n,n)) -np.put(Z, np.random.choice(range(n*n), p, replace=False),1) --
Subtract the mean of each row of a matrix (★★☆)
--# Author: Warren Weckesser - -X = np.random.rand(5, 10) - -# Recent versions of numpy -Y = X - X.mean(axis=1, keepdims=True) - -# Older versions of numpy -Y = X - X.mean(axis=1).reshape(-1, 1) --
How to I sort an array by the nth column? (★★☆)
--# Author: Steve Tjoa - -Z = np.random.randint(0,10,(3,3)) -print(Z) -print(Z[Z[:,1].argsort()]) --
How to tell if a given 2D array has null columns? (★★☆)
--# Author: Warren Weckesser - -Z = np.random.randint(0,3,(3,10)) -print((~Z.any(axis=0)).any()) --
Find the nearest value from a given value in an array (★★☆)
--Z = np.random.uniform(0,1,10) -z = 0.5 -m = Z.flat[np.abs(Z - z).argmin()] -print(m) --
Considering two arrays with shape (1,3) and (3,1), how to compute their sum -using an iterator? (★★☆)
--A = np.arange(3).reshape(3,1) -B = np.arange(3).reshape(1,3) -it = np.nditer([A,B,None]): -for x,y,z in it: z[...] = x + y -C = it.operands[2] --
Create an array class that has a name attribute (★★☆)
--class NamedArray(np.ndarray): - def __new__(cls, array, name="no name"): - obj = np.asarray(array).view(cls) - obj.name = name - return obj - def __array_finalize__(self, obj): - if obj is None: return - self.info = getattr(obj, 'name', "no name") - -Z = NamedArray(np.arange(10), "range_10") -print (Z.name) --
Consider a given vector, how to add 1 to each element indexed by a second -vector (be careful with repeated indices)? (★★★)
--# Author: Brett Olsen - -Z = np.ones(10) -I = np.random.randint(0,len(Z),20) -Z += np.bincount(I, minlength=len(Z)) -print(Z) --
How to accumulate elements of a vector (X) to an array (F) based on an index -list (I)? (★★★)
--# Author: Alan G Isaac - -X = [1,2,3,4,5,6] -I = [1,3,9,3,4,1] -F = np.bincount(I,X) -print(F) --
Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique -colors (★★★)
--# Author: Nadav Horesh - -w,h = 16,16 -I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte) -F = I[...,0]*256*256 + I[...,1]*256 +I[...,2] -n = len(np.unique(F)) -print(np.unique(I)) --
Considering a four dimensions array, how to get sum over the last two axis -at once? (★★★)
--A = np.random.randint(0,10,(3,4,3,4)) -sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1) -print(sum) --
Considering a one-dimensional vector D, how to compute means of subsets of D -using a vector S of same size describing subset indices? (★★★)
--# Author: Jaime Fernández del Río - -D = np.random.uniform(0,1,100) -S = np.random.randint(0,10,100) -D_sums = np.bincount(S, weights=D) -D_counts = np.bincount(S) -D_means = D_sums / D_counts -print(D_means) --
How to get the diagonal of a dot product? (★★★)
--# Author: Mathieu Blondel - -# Slow version -np.diag(np.dot(A, B)) - -# Fast version -np.sum(A * B.T, axis=1) - -# Faster version -np.einsum("ij,ji->i", A, B). --
Consider the vector [1, 2, 3, 4, 5], how to build a new vector with 3 -consecutive zeros interleaved between each value? (★★★)
--# Author: Warren Weckesser - -Z = np.array([1,2,3,4,5]) -nz = 3 -Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz)) -Z0[::nz+1] = Z -print(Z0) --
Consider an array of dimension (5,5,3), how to mulitply it by an array with -dimensions (5,5)? (★★★)
--A = np.ones((5,5,3)) -B = 2*np.ones((5,5)) -print(A * B[:,:,None]) --
How to swap two rows of an array? (★★★)
--# Author: Eelco Hoogendoorn - -A = np.arange(25).reshape(5,5) -A[[0,1]] = A[[1,0]] -print(A) --
Consider a set of 10 triplets describing 10 triangles (with shared -vertices), find the set of unique line segments composing all the triangles (★★★)
--# Author: Nicolas P. Rougier - -faces = np.random.randint(0,100,(10,3)) -F = np.roll(faces.repeat(2,axis=1),-1,axis=1) -F = F.reshape(len(F)*3,2) -F = np.sort(F,axis=1) -G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] ) -G = np.unique(G) -print(G) --
Given an array C that is a bincount, how to produce an array A such that -np.bincount(A) == C? (★★★)
--# Author: Jaime Fernández del Río - -C = np.bincount([1,1,2,3,4,4,6]) -A = np.repeat(np.arange(len(C)), C) -print(A) --
How to compute averages using a sliding window over an array? (★★★)
--# Author: Jaime Fernández del Río - -def moving_average(a, n=3) : - ret = np.cumsum(a, dtype=float) - ret[n:] = ret[n:] - ret[:-n] - return ret[n - 1:] / n -Z = np.arange(20) -print(moving_average(Z, n=3)) --
Consider a one-dimensional array Z, build a two-dimensional array whose -first row is (Z[0],Z[1],Z[2]) and each subsequent row is shifted by 1 (last -row should be (Z[-3],Z[-2],Z[-1]) (★★★)
--# Author: Joe Kington / Erik Rigtorp -from numpy.lib import stride_tricks - -def rolling(a, window): - shape = (a.size - window + 1, window) - strides = (a.itemsize, a.itemsize) - return stride_tricks.as_strided(a, shape=shape, strides=strides) -Z = rolling(np.arange(10), 3) -print(Z) --
How to negate a boolean, or to change the sign of a float inplace? (★★★)
--# Author: Nathaniel J. Smith - -Z = np.random.randint(0,2,100) -np.logical_not(arr, out=arr) - -Z = np.random.uniform(-1.0,1.0,100) -np.negative(arr, out=arr) --
Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to -compute distance from p to each line i (P0[i],P1[i])? (★★★)
--def distance(P0, P1, p): - T = P1 - P0 - L = (T**2).sum(axis=1) - U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L - U = U.reshape(len(U),1) - D = P0 + U*T - p - return np.sqrt((D**2).sum(axis=1)) - -P0 = np.random.uniform(-10,10,(10,2)) -P1 = np.random.uniform(-10,10,(10,2)) -p = np.random.uniform(-10,10,( 1,2)) -print(distance(P0, P1, p)) --
Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, -how to compute distance from each point j (P[j]) to each line i (P0[i],P1[i])? (★★★)
--# Author: Italmassov Kuanysh -# based on distance function from previous question -P0 = np.random.uniform(-10, 10, (10,2)) -P1 = np.random.uniform(-10,10,(10,2)) -p = np.random.uniform(-10, 10, (10,2)) -print np.array([distance(P0,P1,p_i) for p_i in p]) --
Consider an arbitrary array, write a function that extract a subpart with a -fixed shape and centered on a given element (pad with a fill value when -necessary) (★★★)
--# Author: Nicolas Rougier - -Z = np.random.randint(0,10,(10,10)) -shape = (5,5) -fill = 0 -position = (1,1) - -R = np.ones(shape, dtype=Z.dtype)*fill -P = np.array(list(position)).astype(int) -Rs = np.array(list(R.shape)).astype(int) -Zs = np.array(list(Z.shape)).astype(int) - -R_start = np.zeros((len(shape),)).astype(int) -R_stop = np.array(list(shape)).astype(int) -Z_start = (P-Rs//2) -Z_stop = (P+Rs//2)+Rs%2 - -R_start = (R_start - np.minimum(Z_start,0)).tolist() -Z_start = (np.maximum(Z_start,0)).tolist() -R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist() -Z_stop = (np.minimum(Z_stop,Zs)).tolist() - -r = [slice(start,stop) for start,stop in zip(R_start,R_stop)] -z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)] -R[r] = Z[z] -print(Z) -print(R) --
Consider an array Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an -array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]? (★★★)
--# Author: Stefan van der Walt - -Z = np.arange(1,15,dtype=uint32) -R = stride_tricks.as_strided(Z,(11,4),(4,4)) -print(R) --
Compute a matrix rank (★★★)
--# Author: Stefan van der Walt - -Z = np.random.uniform(0,1,(10,10)) -U, S, V = np.linalg.svd(Z) # Singular Value Decomposition -rank = np.sum(S > 1e-10) --
How to find the most frequent value in an array?
--Z = np.random.randint(0,10,50) -print(np.bincount(Z).argmax()) --
Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)
--# Author: Chris Barker - -Z = np.random.randint(0,5,(10,10)) -n = 3 -i = 1 + (Z.shape[0]-3) -j = 1 + (Z.shape[1]-3) -C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides) -print(C) --
Create a 2D array subclass such that Z[i,j] == Z[j,i] (★★★)
--# Author: Eric O. Lebigot -# Note: only works for 2d array and value setting using indices - -class Symetric(np.ndarray): - def __setitem__(self, (i,j), value): - super(Symetric, self).__setitem__((i,j), value) - super(Symetric, self).__setitem__((j,i), value) - -def symetric(Z): - return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric) - -S = symetric(np.random.randint(0,10,(5,5))) -S[2,3] = 42 -print(S) --
Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). -How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)
--# Author: Stefan van der Walt - -p, n = 10, 20 -M = np.ones((p,n,n)) -V = np.ones((p,n,1)) -S = np.tensordot(M, V, axes=[[0, 2], [0, 1]]) -print(S) - -# It works, because: -# M is (p,n,n) -# V is (p,n,1) -# Thus, summing over the paired axes 0 and 0 (of M and V independently), -# and 2 and 1, to remain with a (n,1) vector. --
Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)
--# Author: Robert Kern - -Z = np.ones(16,16) -k = 4 -S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0), - np.arange(0, Z.shape[1], k), axis=1) --
How to implement the Game of Life using numpy arrays? (★★★)
--# Author: Nicolas Rougier - -def iterate(Z): - # Count neighbours - N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] + - Z[1:-1,0:-2] + Z[1:-1,2:] + - Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:]) - - # Apply rules - birth = (N==3) & (Z[1:-1,1:-1]==0) - survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1) - Z[...] = 0 - Z[1:-1,1:-1][birth | survive] = 1 - return Z - -Z = np.random.randint(0,2,(50,50)) -for i in range(100): Z = iterate(Z) --
How to get the n largest values of an array (★★★)
--Z = np.arange(10000) -np.random.shuffle(Z) -n = 5 - -# Slow -print (Z[np.argsort(Z)[-n:]]) - -# Fast -print (Z[np.argpartition(-Z,n)[:n]]) --
Given an arbitrary number of vectors, build the cartesian product (every -combinations of every item) (★★★)
--# Author: Stefan Van der Walt - -def cartesian(arrays): - arrays = [np.asarray(a) for a in arrays] - shape = (len(x) for x in arrays) - - ix = np.indices(shape, dtype=int) - ix = ix.reshape(len(arrays), -1).T - - for n, arr in enumerate(arrays): - ix[:, n] = arrays[n][ix[:, n]] - - return ix - -print (cartesian(([1, 2, 3], [4, 5], [6, 7]))) --
How to create a record array from a regular array? (★★★)
--Z = np.array([("Hello", 2.5, 3), - ("World", 3.6, 2)]) -R = np.core.records.fromarrays(Z.T, - names='col1, col2, col3', - formats = 'S8, f8, i8') --
Consider a large vector Z, compute Z to the power of 3 using 3 different -methods (★★★)
--Author: Ryan G. - -x = np.random.rand(5e7) - -%timeit np.power(x,3) -1 loops, best of 3: 574 ms per loop - -%timeit x*x*x -1 loops, best of 3: 429 ms per loop - -%timeit np.einsum('i,i,i->i',x,x,x) -1 loops, best of 3: 244 ms per loop --
Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A -that contain elements of each row of B regardless of the order of the elements -in B? (★★★)
--# Author: Gabe Schwartz - -A = np.random.randint(0,5,(8,3)) -B = np.random.randint(0,5,(2,2)) - -C = (A[..., np.newaxis, np.newaxis] == B) -rows = (C.sum(axis=(1,2,3)) >= B.shape[1]).nonzero()[0] -print(rows) --
Considering a 10x3 matrix, extract rows with unequal values (e.g. [2,2,3]) (★★★)
--# Author: Robert Kern - -Z = np.random.randint(0,5,(10,3)) -E = np.logical_and.reduce(Z[:,1:] == Z[:,:-1], axis=1) -U = Z[~E] -print(Z) -print(U) --
Convert a vector of ints into a matrix binary representation (★★★)
--# Author: Warren Weckesser - -I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128]) -B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int) -print(B[:,::-1]) - -# Author: Daniel T. McDonald - -I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8) -print(np.unpackbits(I[:, np.newaxis], axis=1)) --
Given a two dimensional array, how to extract unique rows? (★★★)
--# Author: Jaime Fernández del Río - -Z = np.random.randint(0,2,(6,3)) -T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1]))) -_, idx = np.unique(T, return_index=True) -uZ = Z[idx] -print(uZ) --
Considering 2 vectors A & B, write the einsum equivalent of inner, outer, -sum, and mul function (★★★)
--# Author: Alex Riley -# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/ - -np.einsum('i->', A) # np.sum(A) -np.einsum('i,i->i', A, B) # A * B -np.einsum('i,i', A, B) # np.inner(A, B) -np.einsum('i,j', A, B) # np.outer(A, B) --
Considering a path described by two vectors (X,Y), how to sample it using -equidistant samples (★★★)?
--# Author: Bas Swinckels - -phi = np.arange(0, 10*np.pi, 0.1) -a = 1 -x = a*phi*np.cos(phi) -y = a*phi*np.sin(phi) - -dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths -r = np.zeros_like(x) -r[1:] = np.cumsum(dr) # integrate path -r_int = np.linspace(0, r.max(), 200) # regular spaced path -x_int = np.interp(r_int, r, x) # integrate path -y_int = np.interp(r_int, r, y) --
Given an integer n and a 2D array X, select from X the rows which can be -interpreted as draws from a multinomial distribution with n degrees, i.e., -the rows which only contain integers and which sum to n. (★★★)
--# Author: Evgeni Burovski - -X = np.asarray([[1.0, 0.0, 3.0, 8.0], - [2.0, 0.0, 1.0, 1.0], - [1.5, 2.5, 1.0, 0.0]]) -n = 4 -M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1) -M &= (X.sum(axis=-1) == n) -print(X[M]) --
Compute bootstrapped 95% confidence intervals for the mean of a 1D array X -(i.e., resample the elements of an array with replacement N times, compute -the mean of each sample, and then compute percentiles over the means). (★★★)
--# Author: Jessica B. Hamrick - -X = np.random.randn(100) # random 1D array -N = 1000 # number of bootstrap samples -idx = np.random.randint(0, X.size, (N, X.size)) -means = X[idx].mean(axis=1) -confint = np.percentile(means, [2.5, 97.5]) -print(confint) --