Numpy
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.show_config()
3. Create a null vector of size 10 (★☆☆)
Z = np.zeros(10)
print(Z)
4. How to find the memory size of any array (★☆☆)
Z = np.zeros((10,10))
print("%d bytes" % (Z.size * Z.itemsize))
5. How to get the documentation of the numpy add function from the command line? (★☆☆)
%run `python -c "import numpy; numpy.info(numpy.add)"`
6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)
Z = np.zeros(10)
Z[4] = 1
print(Z)
7. Create a vector with values ranging from 10 to 49 (★☆☆)
Z = np.arange(10,50)
print(Z)
8. Reverse a vector (first element becomes last) (★☆☆)
Z = np.arange(50)
Z = Z[::-1]
print(Z)
9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)
Z = np.arange(9).reshape(3,3)
print(Z)
10. Find indices of non-zero elements from [1,2,0,0,4,0] (★☆☆)
nz = np.nonzero([1,2,0,0,4,0])
print(nz)
11. Create a 3x3 identity matrix (★☆☆)
Z = np.eye(3)
print(Z)
12. Create a 3x3x3 array with random values (★☆☆)
Z = np.random.random((3,3,3))
print(Z)
13. 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)
14. Create a random vector of size 30 and find the mean value (★☆☆)
Z = np.random.random(30)
m = Z.mean()
print(m)
15. Create a 2d array with 1 on the border and 0 inside (★☆☆)
Z = np.ones((10,10))
Z[1:-1,1:-1] = 0
print(Z)
16. 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)
print(Z)
17. What is the result of the following expression? (★☆☆)
print(0 * np.nan)
print(np.nan == np.nan)
print(np.inf > np.nan)
print(np.nan - np.nan)
print(np.nan in set([np.nan]))
print(0.3 == 3 * 0.1)
18. 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)
19. 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)
20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?
print(np.unravel_index(99,(6,7,8)))
21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)
Z = np.tile( np.array([[0,1],[1,0]]), (4,4))
print(Z)
22. Normalize a 5x5 random matrix (★☆☆)
Z = np.random.random((5,5))
Z = (Z - np.mean (Z)) / (np.std (Z))
print(Z)
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)])
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))
print(Z)
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)
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))
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
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))
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))
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))
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
32. Is the following expressions true? (★☆☆)
np.sqrt(-1) == np.emath.sqrt(-1)
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')
