markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) | Z = np.tile( np.array([[0,1],[1,0]]), (4,4))
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
22. Normalize a 5x5 random matrix (★☆☆) | Z = np.random.random((5,5))
Zmax, Zmin = Z.max(), Z.min()
Z = (Z - Zmin)/(Zmax - Zmin)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) | color = np.dtype([("r", np.ubyte, 1),
("g", np.ubyte, 1),
("b", np.ubyte, 1),
("a", np.ubyte, 1)]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) | Z = np.dot(np.ones((5,3)), np.ones((3,2)))
print(Z)
# Alternative solution, in Python 3.5 and above
Z = np.ones((5,3)) @ np.ones((3,2)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
25. 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
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
26. What is the output of the following script? (★☆☆) | # Author: Jake VanderPlas
print(sum(range(5),-1))
from numpy import *
print(sum(range(5),-1)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
27. 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 | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
28. What are the result of the following expressions? | print(np.array(0) / np.array(0))
print(np.array(0) // np.array(0))
print(np.array([np.nan]).astype(int).astype(float)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
29. How to round away from zero a float array ? (★☆☆) | # Author: Charles R Harris
Z = np.random.uniform(-10,+10,10)
print (np.copysign(np.ceil(np.abs(Z)), Z)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
30. 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)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
31. 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)
An equivalent way, with a context manager:
with np.errstate(divide='ignore'):
Z = np.ones(1) / 0 | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
32. Is the following expressions true? (★☆☆) | np.sqrt(-1) == np.emath.sqrt(-1) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
33. 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') | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
34. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
35. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
36. 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)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) | Z = np.zeros((5,5))
Z += np.arange(5)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) | def generate():
for x in range(10):
yield x
Z = np.fromiter(generate(),dtype=float,count=-1)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) | Z = np.linspace(0,1,11,endpoint=False)[1:]
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
40. Create a random vector of size 10 and sort it (★★☆) | Z = np.random.random(10)
Z.sort()
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
41. How to sum a small array faster than np.sum? (★★☆) | # Author: Evgeni Burovski
Z = np.arange(10)
np.add.reduce(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
42. Consider two random array A and B, check if they are equal (★★☆) | A = np.random.randint(0,2,5)
B = np.random.randint(0,2,5)
# Assuming identical shape of the arrays and a tolerance for the comparison of values
equal = np.allclose(A,B)
print(equal)
# Checking both the shape and the element values, no tolerance (values have to be exactly equal)
equal = np.array_equal(A,B)
print(equal... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
43. Make an array immutable (read-only) (★★☆) | Z = np.zeros(10)
Z.flags.writeable = False
Z[0] = 1 | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
44. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
45. Create random vector of size 10 and replace the maximum value by 0 (★★☆) | Z = np.random.random(10)
Z[Z.argmax()] = 0
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
46. Create a structured array with `x` and `y` coordinates covering the \[0,1\]x\[0,1\] area (★★☆) | Z = np.zeros((5,5), [('x',float),('y',float)])
Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),
np.linspace(0,1,5))
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
47. 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)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
48. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
49. How to print all the values of an array? (★★☆) | np.set_printoptions(threshold=np.nan)
Z = np.zeros((16,16))
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
50. How to find the closest value (to a given scalar) in a vector? (★★☆) | Z = np.arange(100)
v = np.random.uniform(0,100)
index = (np.abs(Z-v)).argmin()
print(Z[index]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
51. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
52. 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], 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
53. 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)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
54. How to read the following file? (★★☆) | from io import StringIO
# Fake file
s = StringIO("""1, 2, 3, 4, 5\n
6, , , 7, 8\n
, , 9,10,11\n""")
Z = np.genfromtxt(s, delimiter=",", dtype=np.int)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
55. 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]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
56. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
57. 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)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
58. 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)
print(Y) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
59. How to 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()]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
60. 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()) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
61. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
62. 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
print(it.operands[2]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
63. 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_... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
64. 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)
# Another solution
# Author: Bartosz Telenczuk
np.add.at(Z, I, 1)
print(Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
65. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
66. 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)
#Note that we should compute 256*256 first.
#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur
F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]
n = len(np.unique(F))
print(n) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
67. 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))
# solution by passing a tuple of axes (introduced in numpy 1.7.0)
sum = A.sum(axis=(-2,-1))
print(sum)
# solution by flattening the last two dimensions into one
# (useful for functions that don't accept tuples for axis argument)
sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)
pr... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
68. 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)
# Pandas solution as a reference due to more intuitive code
import pandas as pd
print(pd.Series(D).groupby(S).mean()) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
69. How to get the diagonal of a dot product? (★★★) | # Author: Mathieu Blondel
A = np.random.uniform(0,1,(5,5))
B = np.random.uniform(0,1,(5,5))
# 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
70. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
71. 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]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
72. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
73. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
74. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
75. 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)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
76. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
77. 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(Z, out=Z)
Z = np.random.uniform(-1.0,1.0,100)
np.negative(Z, out=Z) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
78. 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.uni... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
79. 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])) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
80. 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_sto... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
81. 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=np.uint32)
R = stride_tricks.as_strided(Z,(11,4),(4,4))
print(R) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
82. 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)
print(rank) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
83. How to find the most frequent value in an array? | Z = np.random.randint(0,10,50)
print(np.bincount(Z).argmax()) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
84. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
85. 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, index, value):
i,j = index
super(Symetric, self).__setitem__((i,j), value)
super(Symetric, self).__setitem__((j,i), value)
def symetric(Z):
return... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
86. 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. | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
87. 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)
print(S) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
88. 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... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
89. 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]]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
90. 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 (cartes... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
91. 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')
print(R) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
92. 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)
%timeit x*x*x
%timeit np.einsum('i,i,i->i',x,x,x) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
93. 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 = np.where(C.any((3,1)).all(1))[0]
print(rows) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
94. 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))
print(Z)
# solution for arrays of all dtypes (including string arrays and record arrays)
E = np.all(Z[:,1:] == Z[:,:-1], axis=1)
U = Z[~E]
print(U)
# soluiton for numerical arrays only, will work for any number of columns in Z
U = Z[Z.max(axis=1) != Z.min(axis=1)... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
95. 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)) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
96. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
97. 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/
A = np.random.uniform(0,1,10)
B = np.random.uniform(0,1,10)
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->ij', A, B) # np.outer(A, B) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
98. 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... | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
99. 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]) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
100. 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) | _____no_output_____ | MIT | 100_Numpy_exercises.ipynb | ShuoGH/numpy-100 |
1. Fa il drop delle due tabelle dalla base di dati se sono già presenti | cursor.execute('DROP TABLE IF EXISTS "Boat";')
connection.commit()
cursor.execute('DROP TABLE IF EXISTS "Sailor";')
connection.commit() | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
2. Crea le due tabelle come descritto sopra. | cursor.execute('CREATE TABLE "Sailor" ("id" INT PRIMARY KEY, "name" CHAR(50) NOT NULL, "address" CHAR(50) NOT NULL, "age" INT NOT NULL, "level" FLOAT NOT NULL);')
connection.commit()
cursor.execute('CREATE TABLE "Boat" ("bid" CHAR(25) PRIMARY KEY, "bname" CHAR(50) NOT NULL, "size" CHAR(30) NOT NULL, "captain" INT NOT N... | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
3. Genera 1 milione di tuple (casuali1 ), in modo tale che ogni tupla abbia un valore diverso per l’attributo level, e le inserisce nella tabella S ailor. Assicurarsi inoltre che l’ultima tupla inserita, e solo quella, abbia come valore dell’attributo level, il valore 185. | start_time = time_ns() #inizioe conteggio tempo
id = range(186, 3000000)
level = random.sample(range(18600, 3000000), TABLE_LENGTH-1)
for i in range(0,len(level),1):
level[i] = level[i]/100
level.append(185.00)
tabella = []
for i in range(0,TABLE_LENGTH,1):
riga = {"id":id[i], "name": get_random_string(12), "... | Load data in "Sailor"
[-]
| MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
4. Genera 1 ulteriore milione di tuple (casuali) e le inserisce nella tabella B oat. | start_time = time_ns() #inizioe conteggio tempo
bid = get_random_bid()
size = ["large", "medium", "small"]
size_list = []
for i in range(0,TABLE_LENGTH,1):
size_list.append(size[random.randint(0,2)])
captain = []
for i in range(0,TABLE_LENGTH,1):
captain.append(id[random.randint(0,TABLE_LENGTH-1)])
tabella = [... | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
5. Ottiene dal database tutti gli id del milione di tuple della tabella Sailor e li stampa su stderr . | cursor.execute(""" SELECT id FROM "Sailor" """)
lista = cursor.fetchall()
for i in lista:
print(i[0],file=sys.stderr) | id
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| MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
6. Tutte le tuple con valore di level pari a 185 vengono modificate, cambiando il valore di level a 200 (la vostra query dovrà funzionare anche se la base di dati contiene più di una tupla con valore di level pari a 185). | cursor.execute('''UPDATE "Sailor" SET "level" = 200 WHERE "level" = 185''')
connection.commit() | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
7. Seleziona l’id e l’address di tutte le tuple della tabella Sailor che hanno valore di level pari a 200, e li stampa su stderr. | cursor.execute('''SELECT id, address FROM "Sailor" as sl WHERE "level" = 200''')
lista = cursor.fetchall()
for i in lista:
print(f"{i[0]},{i[1]}",file=sys.stderr)
| _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
8. Crea un indice B+tree sull’attributo level. | cursor.execute('DROP INDEX IF EXISTS "index_level";')
connection.commit()
cursor.execute('CREATE INDEX index_level ON "Sailor" ("level");')
connection.commit() | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
9. Ottiene dal database tutti gli id del milione di tuple della tabella Sailor e li stampa su stderr . | cursor.execute(""" SELECT id FROM "Sailor" """)
lista = cursor.fetchall()
for i in lista:
print(i[0],file=sys.stderr) | 0
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| MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
10. Tutte le tuple con valore di level pari a 200 vengono modificate, cambiando il valore di level a 210 (la vostra query dovrà funzionare anche se la base di dati contiene più di una tupla con valore di level pari a 200). | cursor.execute('''UPDATE "Sailor" SET "level" = 210 WHERE "level" = 200''')
connection.commit() | _____no_output_____ | MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
11. Seleziona l’id e l’address di tutte le tuple della tabella Sailor che hanno valore di level pari a 210, e li stampa su stderr. | cursor.execute('''SELECT id, address FROM "Sailor" as sl WHERE "level" = 210''')
lista = cursor.fetchall()
for i in lista:
print(f"{i[0]},{i[1]}",file=sys.stderr)
pd.read_sql('''SELECT * FROM "Sailor"''', connection)
pd.read_sql('''SELECT * FROM "Boat"''', connection)
def product(*args, repeat=1):
# product('AB... | 11
1771561
| MIT | A3_notebook.ipynb | MrFizban/SQLAssignment3 |
Laboratorio 7 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import altair as alt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
alt.themes.enable('opaque')
%matplotlib inline | _____no_output_____ | MIT | labs/lab07.ipynb | ClaudioFigueroa/mat281_portfolio |
En este laboratorio utilizaremos los mismos datos de diabetes vistos en la clase | diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True, as_frame=True)
diabetes = pd.concat([diabetes_X, diabetes_y], axis=1)
diabetes.head() | _____no_output_____ | MIT | labs/lab07.ipynb | ClaudioFigueroa/mat281_portfolio |
Pregunta 1(1 pto)* ¿Por qué la columna de sexo tiene esos valores?* ¿Cuál es la columna a predecir?* ¿Crees que es necesario escalar o transformar los datos antes de comenzar el modelamiento? __Respuesta:__1.Tiene esos valores por que se cuantificaron los sexos, atribuyendoles la misma distancia desde el origen, y deb... | d_x=diabetes.drop("target",axis=1)
d_y=diabetes["target"]
regr_with_incerpet = LinearRegression(fit_intercept=True)# FIX ME PLEASE #
regr_with_incerpet.fit(d_x, d_y)
diabetes_y_pred_with_intercept = regr_with_incerpet.predict(d_x)
# Coeficientes
print(f"Coefficients: \n{regr_with_incerpet.coef_}\n")
# Intercepto
print(... | Coefficients:
[ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163
476.74583782 101.04457032 177.06417623 751.27932109 67.62538639]
Mean squared error: 26004.29
Coefficient of determination: -3.39
| MIT | labs/lab07.ipynb | ClaudioFigueroa/mat281_portfolio |
**Pregunta: ¿Qué tan bueno fue el ajuste del modelo?** __Respuesta:__ El ajuste se ve muy malo debido a que entrega un gran error. Pregunta 3(1 pto)Realizar multiples regresiones lineales utilizando una sola _feature_ a la vez. En cada iteración:- Crea un arreglo `X`con solo una feature filtrando `X`.- Crea un modelo ... | for col in ["age","sex","bmi","bp","s1","s2","s3","s4","s5","s6"]:
X_i = np.array([np.ones(diabetes[col].shape), diabetes[col]]).T
regr_i = LinearRegression(fit_intercept=True)
regr_i.fit(X_i,diabetes['target'])
diabetes_y_pred_i = regr_i.predict(X_i) # FIX ME PLEASE #
print(f"Feature: {col}")
p... | Feature: age
Coefficients: 304.1830745282946
Intercept: 152.13348416289605
Mean squared error: 5720.55
Coefficient of determination: 0.04
Feature: sex
Coefficients: 69.71535567841468
Intercept: 152.13348416289594
Mean squared error: 5918.89
Coefficient of determination: 0.00
Feature: bmi
Coefficients: 949.43... | MIT | labs/lab07.ipynb | ClaudioFigueroa/mat281_portfolio |
**Pregunta: Si tuvieras que escoger una sola _feauture_, ¿Cuál sería? ¿Por qué?** **Respuesta: Sería el bmi de debido a que posee el error medio cuadratico mas bajo, y el coeficiente de determinacion mas alto. Ejercicio 4(1 pto)Con la feature escogida en el ejercicio 3 realiza el siguiente gráfico:- Scatter Plot- Eje ... | regr = linear_model.LinearRegression(fit_intercept=True).fit(np.array([np.ones(diabetes["bmi"].shape), diabetes["bmi"]]).T, diabetes["target"])
xp=np.arange(-0.2,0.3,0.02)
yp=regr.coef_[1]*xp+regr.intercept_
df=pd.DataFrame({'xp': xp, 'yp': yp})
alt.Chart(diabetes).mark_circle(size=60).encode(
x='bmi',
y='targe... | _____no_output_____ | MIT | labs/lab07.ipynb | ClaudioFigueroa/mat281_portfolio |
MNIST in Keras with TensorboardThis sample trains an "MNIST" handwritten digit recognition model on a GPU or TPU backend using a Kerasmodel. Data are handled using the tf.data.Datset API. This isa very simple sample provided for educational purposes. Donot expect outstanding TPU performance on a dataset assmall as MNI... | BATCH_SIZE = 64
LEARNING_RATE = 0.002
# GCS bucket for training logs and for saving the trained model
# You can leave this empty for local saving, unless you are using a TPU.
# TPUs do not have access to your local instance and can only write to GCS.
BUCKET="gs://ml1-demo-martin/mnist" # a valid bucket name must start ... | _____no_output_____ | Apache-2.0 | courses/fast-and-lean-data-science/05K_MNIST_TF20Keras_Tensorboard_solution.ipynb | Glairly/introduction_to_tensorflow |
Imports | import os, re, math, json, time
import PIL.Image, PIL.ImageFont, PIL.ImageDraw
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.python.platform import tf_logging
print("Tensorflow version " + tf.__version__) | Tensorflow version 2.2.0-dlenv
| Apache-2.0 | courses/fast-and-lean-data-science/05K_MNIST_TF20Keras_Tensorboard_solution.ipynb | Glairly/introduction_to_tensorflow |
TPU/GPU detection | try: # detect TPUs
tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except ValueError: # detect GPUs
strategy = tf.distribute.MirroredSt... | _____no_output_____ | Apache-2.0 | courses/fast-and-lean-data-science/05K_MNIST_TF20Keras_Tensorboard_solution.ipynb | Glairly/introduction_to_tensorflow |
Colab-only auth for this notebook and the TPU | #IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ # this is always set on Colab, the value is 0 or 1 depending on GPU presence
#if IS_COLAB_BACKEND:
# from google.colab import auth
# auth.authenticate_user() # Authenticates the backend and also the TPU using your credentials so that they can access your private GCS buck... | _____no_output_____ | Apache-2.0 | courses/fast-and-lean-data-science/05K_MNIST_TF20Keras_Tensorboard_solution.ipynb | Glairly/introduction_to_tensorflow |
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