Numpy Flashcards
create identity matrix of dimension n
a=np.eye(n)
convert a 1D array to a 3D array
a=np.array([i for i in range(27)]
y=a.reshape((3,3,3))
convert all elements of array from float to int, from binary to boolean
y=a.astype(‘int’)
y=a.astype(‘bool’)
From 2 numpy arrays, extract the indexes in which the elements in the 2 arrays match
print(np.where(a == b))
Output a sequence of equally gapped 5 numbers in the range 0 to 100
o = np.linspace(0, 100, 5)
Output a matrix (numpy array) of dimension 2-by-3 with each and every value equal to 5
o = np.full((2, 3), 5)
o=5*np.ones((2,3))
Output an array by repeating a smaller array of 2 dimensions, 10 times
o = np.tile(a, 10)
Output a 5-by-5 array of random integers between 0 (inclusive) and 10 (exclusive)
o=np.random.randint(0,10,(5,5))
Output a 3-by-3 array of random numbers following normal distribution
o = np.random.normal(size = (3,3)) #normal takes (mu,sigma,size)
Given 2 numpy arrays as matrices, output the result of multiplying the 2 matrices
o = a@b
also possible:
o = np.matmul(a, b)
Create a 1D array of numbers from 0 to 9
a=np.arange(10)
Create a 3×3 numpy array of all True’s
a=np.full((3,3), True, dtype=bool)
np.ones((3,3), dtype=bool)
Extract all odd numbers from arr
arr[arr % 2 == 1]
Replace all odd numbers in arr with -1
arr[arr % 2 == 1] = -1
Replace all odd numbers in arr with -1 without changing arr
out = np.where(arr % 2 == 1, -1, arr)
Convert a 1D array to a 2D array with 2 rows
arr.reshape(2, -1) # Setting to -1 automatically decides the number of rows or cols !!!!!!
Stack arrays a and b vertically
np. concatenate([a, b], axis=0)
np. vstack([a, b])
np. r_[a,b]
Stack arrays a and b horizontally
np. concatenate([a, b], axis=1)
np. hstack([a, b])
np. c_[a,b]
a = np.array([1,2,3])
Desired output:
array([1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])
np.r_[np.repeat(a,3),np.tile(a,3)]
Get the common items between a and b
np.intersect1d(a,b)
From array a remove all items present in array b
np.setdiff1d(a,b)
Get the positions where elements of a and b match
np.where(a == b)
a[a==b]
Convert the function maxx that works on two scalars, to work on two arrays.
func=np.vectorize(scalar_func, otypes=[float]) func(a,b)
Swap columns 1 and 2 in the array arr.
arr[:, [1,0,2]]
Swap rows 1 and 2 in the array arr:
arr[[1,0,2], :]
Reverse the rows of a 2D array arr
arr[::-1]
Reverse the columns of a 2D array arr.
arr[:, ::-1]
Create a 2D array of shape 5x3 to contain random decimal numbers between 5 and 10.
# Solution Method 1: rand_arr = np.random.randint(low=5, high=10, size=(5,3)) + np.random.random((5,3))
# Solution Method 2: rand_arr = np.random.uniform(5,10, size=(5,3))
Print or show only 3 decimal places of the numpy array rand_arr
np.set_printoptions(precision=3)
Pretty print rand_arr by suppressing the scientific notation (like 1e10)
np.set_printoptions(suppress=True, precision=6) # precision is optional
Limit the number of items printed in python numpy array a to a maximum of 6 elements, show all
np. set_printoptions(threshold=6)
np. set_printoptions(threshold=np.nan)
Find the mean, median, standard deviation of iris’s sepallength
np. mean(sepallength)
np. median(sepallength)
np. std(sepallength)
Flatten array a into one D
a.ravel(), np.ravel(a)
Compute the maximum for each row in the given array.
np.amax(a, axis=1)
Compute the min-by-max for each row for given 2d numpy array.
np.apply_along_axis(lambda x: np.min(x)/np.max(x), arr=a, axis=1)
