Numpy练习
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1. Import the numpy package under the name np
import numpy as np2. Print the numpy version and the configuration
print np.__version__
# np.show_config()1.10.43. Create a null vector of size 10
E = np.empty(3) # not zero acturally
Z = np.zeros(10)
print(Z)[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]4. How to get the documentation of the numpy add function from the command line ?
!python -c"import numpy; numpy.info(numpy.add)"add(x1, x2[, out])
Add arguments element-wise.
Parameters
----------
x1, x2 : array_like
The arrays to be added. If ``x1.shape != x2.shape``, they must be
broadcastable to a common shape (which may be the shape of one or
the other).
Returns
-------
add : ndarray or scalar
The sum of `x1` and `x2`, element-wise. Returns a scalar if
both `x1` and `x2` are scalars.
Notes
-----
Equivalent to `x1` + `x2` in terms of array broadcasting.
Examples
--------
>>> np.add(1.0, 4.0)
5.0
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.add(x1, x2)
array([[ 0., 2., 4.],
[ 3., 5., 7.],
[ 6., 8., 10.]])5. Create a null vector of size 10 but the fifth value which is 1
Z = np.zeros(10)
Z[4] = 1 # index just like list
print(Z)[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]6. Create a vector with values ranging from 10 to 49
V = np.arange(10,50) # np.arange not np.range
print(V)[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]7. Reverse a vector (first element becomes last)
V = np.arange(5)
V = V[::-1]
print(V)[4 3 2 1 0]8. Create a 3x3 matrix with values ranging from 0 to 8
A = np.arange(9).reshape(3,3)
print(A)[[0 1 2]
[3 4 5]
[6 7 8]]9. Find indices of non-zero elements from [1,2,0,0,4,0]
arr = np.array([1,2,0,0,4,0])
# list comprehension is not consise VS nonzero
nz1 = [i for i in range(len(arr)) if arr[i]==0] # a list
nz = np.nonzero(arr) # return a tuple
print nz
print nz1(array([0, 1, 4]),)
[2, 3, 5]10. Create a 3x3 identity matrix
A = np.eye(3) # for indentity matrix
B = np.identity(3) # or identity
print A
print B == A[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 0. 0. 1.]]
[[ True True True]
[ True True True]
[ True True True]]11. Create a 3x3x3 array with random values
Z = np.random.random((3,3,3))
print Z[[[ 0.37802182 0.51185549 0.09273136]
[ 0.35946865 0.44674969 0.76084106]
[ 0.95776962 0.35601145 0.8915905 ]]
[[ 0.39016786 0.63052983 0.20385571]
[ 0.04379682 0.32062423 0.97007016]
[ 0.4026562 0.76746884 0.84974329]]
[[ 0.85230695 0.6368344 0.42200517]
[ 0.98098412 0.24666028 0.86381806]
[ 0.71310323 0.89115971 0.85823333]]]12. Create a 10x10 array with random values and find the minimum and maximum values
Z = np.random.random((10,10))
z_max, z_min = Z.max(), Z.min()
# z_max, z_min = np.max(Z), np.min(Z)
print z_max
print z_min0.996975591901
0.014812377168913. Create a random vector of size 30 and find the mean value
Z = np.random.random(10)
m = Z.mean()
# m = np.mean(Z)
print m0.49904817199814. Create a 2d array with 1 on the border and 0 inside
Z = np.ones((5,5))
Z[1:-1, 1:-1] = 0 # indexing
print Z[[ 1. 1. 1. 1. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 1. 1. 1. 1.]]15. What is the result of the following expression ?
0*np.nan #nannannp.nan == np.nanFalsenp.inf > np.nanFalsenp.nan - np.nannan0.3 == 3 * 0.1False16. 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[[0 0 0 0 0]
[1 0 0 0 0]
[0 2 0 0 0]
[0 0 3 0 0]
[0 0 0 4 0]]17. Create a 8x8 matrix and fill it with a checkerboard pattern
Z = np.zeros((8,8),dtype=int)
Z[1::2,0::2]=1
Z[0::2,1::2]=1
print Z[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]18. 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))(1, 5, 4)19. Create a checkerboard 8x8 matrix using the tile function
Z = np.tile(np.array([[0,1],[1,0]]), (4,4))
print Z[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]20. Normalize a 5x5 random matrix
Z = np.random.random((5,5))
z_max, z_min = Z.max(), Z.min()
Z = (Z - z_min)/(z_max - z_min)
print Z[[ 0.35432088 0.9860153 0.73550363 0.30350038 0.10499184]
[ 0.22329659 0. 0.54464366 0.99324627 0.98878285]
[ 0.4801603 0.08399077 0.43971682 0.71831189 0.79786892]
[ 1. 0.12234266 0.99166839 0.64018204 0.27405883]
[ 0.68890375 0.26652723 0.97298099 0.94534027 0.58056662]]21. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)
A = np.ones((5,3))
B = np.ones((3,2))
print np.dot(A,B) #or A.dot(B)[[ 3. 3.]
[ 3. 3.]
[ 3. 3.]
[ 3. 3.]
[ 3. 3.]]22. Given a 1D array, negate all elements which are between 3 and 8, in place
Z = np.arange(11)
Z[(3 < Z) & (Z <= 8)] *= -1 # boolean index
print Z[ 0 1 2 3 -4 -5 -6 -7 -8 9 10]23. Create a 5x5 matrix with row values ranging from 0 to 4
Z = np.zeros((5,5))
Z += np.arange(5) # matrix + row
print Z[[ 0. 1. 2. 3. 4.]
[ 0. 1. 2. 3. 4.]
[ 0. 1. 2. 3. 4.]
[ 0. 1. 2. 3. 4.]
[ 0. 1. 2. 3. 4.]]24. 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 [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]25. Create a vector of size 10 with values ranging from 0 to 1, both excluded
Z = np.linspace(0,1,num=12,endpoint=True)[1:-1]
print Z[ 0.09090909 0.18181818 0.27272727 0.36363636 0.45454545 0.54545455
0.63636364 0.72727273 0.81818182 0.90909091]26. Create a random vector of size 10 and sort it
Z = np.random.random(10)
Z.sort()
print Z[ 0.02092486 0.10778371 0.1580741 0.17828872 0.28058869 0.63512671
0.70412522 0.84783555 0.93924023 0.98453489]27. How to sum a small array faster than np.sum ?
Z = np.arange(10)
%timeit np.sum(Z)
%timeit np.add.reduce(Z)The slowest run took 21.24 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 2.08 µs per loop
The slowest run took 10.39 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 1.15 µs per loop28. 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)
#Returns True if two arrays are element-wise equal within a tolerance.
print equalFalse29. Make an array immutable (read-only)
Z = np.zeros(10, dtype='int')
Z.flags.writeable = False
# Z[0] = 1 raise ValueError30. 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[ 0.97581795 0.59808053 0.4108556 0.53083869 0.27302014 0.36028763
0.88051885 0.89321379 1.17598494 0.95036096]
[ 0.49590473 1.55488672 1.42839068 0.06888012 0.22952511 0.71644146
0.48692754 0.42476661 0.85430172 1.30708871]31. Create random vector of size 10 and replace the maximum value by 0
Z = np.random.random(10)
Z[Z.argmax()] = 0 # Z.argmax()
print Z[ 0.79605583 0. 0.43405045 0.74944543 0.87654654 0.04885993
0.03266925 0.09662387 0.86090177 0.48594978]32. 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))33. 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)-128
127
-2147483648
2147483647
-9223372036854775808
9223372036854775807
-3.40282e+38
3.40282e+38
1.19209e-07
-1.79769313486e+308
1.79769313486e+308
2.22044604925e-1634. How to find the closest value (to a given scalar) in an array ?
Z = np.arange(100)
v = np.random.uniform(0,100)
print v
index = (np.abs(Z -v)).argmin() #argmin()
print Z[index]56.5834847025
5735. 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[((0.0, 0.0), (0.0, 0.0, 0.0)) ((0.0, 0.0), (0.0, 0.0, 0.0))
((0.0, 0.0), (0.0, 0.0, 0.0)) ((0.0, 0.0), (0.0, 0.0, 0.0))
((0.0, 0.0), (0.0, 0.0, 0.0)) ((0.0, 0.0), (0.0, 0.0, 0.0))
((0.0, 0.0), (0.0, 0.0, 0.0)) ((0.0, 0.0), (0.0, 0.0, 0.0))
((0.0, 0.0), (0.0, 0.0, 0.0)) ((0.0, 0.0), (0.0, 0.0, 0.0))]36. 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)import scipy
import scipy.spatial
Z = np.random.random((10,2))
D = scipy.spatial.distance.cdist(Z,Z)
# print D37. How to convert a float (32 bits) array into an integer (32 bits) in place ?
Z = np.arange(10,dtype=np.float32)
Z = Z.astype(np.int32,copy=False) #astype
print(Z)[0 1 2 3 4 5 6 7 8 9]38. Consider the following file,How to read it ?
1,2,3,4,5 6,,,7,8 ,,9,10,11
Z = np.genfromtxt('missing.dat',delimiter=",")
print Z[[ 1. 2. 3. 4. 5.]
[ 6. nan nan 7. 8.]
[ nan nan 9. 10. 11.]]39. 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)((0, 0), 0)
((0, 1), 1)
((0, 2), 2)
((1, 0), 3)
((1, 1), 4)
((1, 2), 5)
((2, 0), 6)
((2, 1), 7)
((2, 2), 8)for index in np.ndindex(Z.shape):
print(index,Z[index])((0, 0), 0)
((0, 1), 1)
((0, 2), 2)
((1, 0), 3)
((1, 1), 4)
((1, 2), 5)
((2, 0), 6)
((2, 1), 7)
((2, 2), 8)40. 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, miu = 1.0, 0.0
G = np.exp((D-miu)**2/(2.0*sigma**2))41. How to randomly place p elements in a 2D array ?
n = 10
p = 3
Z = np.zeros((n,n))
index = np.random.choice(np.arange(n*n),p,replace=False)
np.put(Z,index,1)
Zarray([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]])41. How to I sort an array by the nth column ?
Z = np.random.randint(0,10,(3,3))
print Z
print Z[Z[:,1].argsort()][[8 0 9]
[0 6 6]
[4 4 1]]
[[8 0 9]
[4 4 1]
[0 6 6]]42. Subtract the mean of each row of a matrix
X = np.random.randint(4,size=(2,3))
print X
Y = X - X.mean(axis=1, keepdims=True)
print Y[[2 2 1]
[1 1 1]]
[[ 0.33333333 0.33333333 -0.66666667]
[ 0. 0. 0. ]]43. How to tell if a given 2D array has null columns ?
# numpy.any(a, axis=None, out=None, keepdims=False)
# Test whether any array element along a given axis evaluates to True.
Z = np.random.randint(0,3,(3,10))
print (~Z.any(axis=0)).any()True44. Find the nearest value from a given value in an array
# numpy.ndarray.flat
# A 1-D iterator over the array.
# This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.
Z = np.random.uniform(0,1,10)
z = 0.5
m = Z.flat[np.abs(Z-z).argmin()]
print m0.49465650779245. How to swap two rows of an array ?
A = np.arange(25).reshape(5,5)
A[[0,1]] = A[[1,0]]
print A[[ 5 6 7 8 9]
[ 0 1 2 3 4]
[10 11 12 13 14]
[15 16 17 18 19]
[20 21 22 23 24]]46. How to find the most frequent value in an array ?
# np.bincount()
# Count number of occurrences of each value in array of non-negative ints.
Z = np.random.randint(0,5,10)
print Z
print np.bincount(Z)
print np.bincount(Z).argmax()[1 0 4 1 1 2 0 1 2 3]
[2 4 2 1 1]
147. How to get the n largest values of an array?
# np.random.shuffle(x), Modify a sequence in-place
# np.argsort(x) Returns the indices that would sort an array.
# np.argpartition(x)
Z = np.arange(10)
np.random.shuffle(Z)
n = 2
print Z[np.argsort(Z)[-n:]] # slow
print Z[np.argpartition(-Z,n)[:n]] # fast[8 9]
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