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

The corresponding IPython notebook is available from the github repo, thanks to the rst2ipynb conversion tool by Valentin Haenel

Thanks to Michiaki Ariga, there is now a Julia version.

1. Import the numpy package under the name np

   import numpy as np

2. Print the numpy version and the configuration.

   print np.__version__
   np.__config__.show()

3. Create a null vector of size 10

   Z = np.zeros(10)
   print Z

4. Create a null vector of size 10 but the fifth value which is 1

   Z = np.zeros(10)
   Z[4] = 1
   print Z

5. Create a vector with values ranging from 10 to 49

   Z = np.arange(10,50)
   print Z

6. Create a 3x3 matrix with values ranging from 0 to 8

   Z = np.arange(9).reshape(3,3)
   print Z

7. Find indices of non-zero elements from [1,2,0,0,4,0]

   nz = np.nonzero([1,2,0,0,4,0])
   print nz

8. Create a 3x3 identity matrix

   Z = np.eye(3)
   print Z

9. 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

10. Create a 3x3x3 array with random values

    Z = np.random.random((3,3,3))
    print Z

1. 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

2. 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

3. Create a checkerboard 8x8 matrix using the tile function

   Z = np.tile( np.array([[0,1],[1,0]]), (4,4))
   print Z

4. Normalize a 5x5 random matrix (between 0 and 1)

   Z = np.random.random((5,5))
   Zmax,Zmin = Z.max(), Z.min()
   Z = (Z - Zmin)/(Zmax - Zmin)
   print Z

5. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)

   Z = np.dot(np.ones((5,3)), np.ones((3,2)))
   print Z

6. Create a 5x5 matrix with row values ranging from 0 to 4

   Z = np.zeros((5,5))
   Z += np.arange(5)
   print Z

7. 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

8. Create a random vector of size 10 and sort it

   Z = np.random.random(10)
   Z.sort()
   print Z

9. 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

10. Create a random vector of size 30 and find the mean value

    Z = np.random.random(30)
    m = Z.mean()
    print m

1. Make an array immutable (read-only)

   Z = np.zeros(10)
   Z.flags.writeable = False
   Z[0] = 1

2. 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

3. Create random vector of size 10 and replace the maximum value by 0

   Z = np.random.random(10)
   Z[Z.argmax()] = 0
   print Z

4. 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

5. 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

6. 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

7. 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
   Z = np.random.random((10,2))
   D = scipy.spatial.distance.cdist(Z,Z)
   print D

8. 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

9. 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()

10. 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

1. Consider the following file:

   1,2,3,4,5
   6,,,7,8
   ,,9,10,11
   

   How to read it ?

   Z = np.genfromtxt("missing.dat", delimiter=",")

2. 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

3. 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

4. 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

5. 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)

6. 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

7. 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

8. 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

9. 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]

10. 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

1. 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

2. 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

3. 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

4. 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)

5. How to get the documentation of the numpy add function from the command line ?

   python -c "import numpy; numpy.info(numpy.add)"

6. 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)

1. 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

2. 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)

3. 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)

4. 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]) ?

   Answer needed actually

1. 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

2. 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: Stéfan van der Walt
   Z = np.arange(1,15,dtype=uint32)
   R = stride_tricks.as_strided(Z,(11,4),(4,4))
   print R

1. 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

2. 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

3. 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

4. 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: Stéfan 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.

1. Given a two dimensional array, how to extract unique rows ?

   Note

   See stackoverflow for explanations.

   # 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

2. 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)