Numpy

Question 1

convert list to numpy array

Answer 1

np.array(list)

Question 2

arange

Answer 2

np.arange(10,30)

Return evenly spaced values within a given interval.

Question 3

zeros

Answer 3

np.zeros(count)

Question 4

ones

Answer 4

np.ones(count)

Question 5

linspace

Answer 5

np.linspace(0,10,3)

np.linspace(start,stop,count)

Return evenly spaced numbers over a specified interval.

Question 6

eye

Answer 6

np.eye(4)

Creates an 4x4 identity matrix
array([[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., 1., 0.],
[ 0., 0., 0., 1.]])

Question 7

rand

Answer 7

np.random.rand(2)
Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1) .

np.random,rand(5,5)

creates 5x5 matrix with random float values 0-1

Question 8

randn

Answer 8

np.random.randn(2) –> 2 values

np.random.randn(5,5)
creates 5x5 matrix with random float values.

Return a sample (or samples) from the “standard normal” distribution. Unlike rand which is uniform:

Question 9

randint

Answer 9

np.random.randint(1,10)

Return random integers from low (inclusive) to high (exclusive). similar to random module

Question 10

Reshape

Answer 10

array.reshape(5,5)

Returns an array containing the same data with a new shape.

Question 11

argmax

Answer 11

arr = np.array([[3, 2, 1], [6, 5, 4], [9, 8, 7]])
np.argmax(arr)
# Find the indices of the maximum element along the flattened array

np.argmax(arr,axis = 0)
# Find the indices of the maximum element along the first axis (rows)

np.argmxax(arr, axis = 1)
# Find the indices of the maximum element along the second axis (columns)

argmax is a function that returns the indices of the maximum values along a given axis.

Question 12

Shape

Answer 12

Shape is an attribute that arrays have (not a method) returns the shape of an array

Question 13

dtype

Answer 13

You can also grab the data type of the object in the array:

Question 14

Fetching the values from numpy array

Answer 14

arr[index]

arr[start : stop]

Question 15

Broadcasting

Answer 15

arr[0:5]=100

Question 16

copy

Answer 16

To get a copy, need to be explicit

arr.copy()

Question 17

Indexing a 2D array (matrices) in numpy

Answer 17

The general format is arr_2d[row][col] or arr_2d[row,col].
arr_2d[1][0] == arr_2d[1,0]

Question 18

2D array slicing in numpy

Answer 18

arr_2d[row,col]

arr_2d[:2,1:]

arr_2d[2,:]

Question 19

Fancy Indexing in Numpy

Answer 19

arr2d = arr[[0., 0., 0., 0.],
[1., 1., 1., 1.],
[2., 2., 2., 2.],
[3., 3., 3., 3.]]
print(arr2d[[1,3]]) –> arr2d[[row1,row2,…]]
o/p
——
array([[1., 1., 1., 1.],
[3., 3., 3., 3.]])

Question 20

Selection based on comparison operators

Answer 20

arr > 4

returns an array of true or false
boll_arr = array([False, True, False, False, True]

arr[boll_arr] == arr[arr>2]
returns values greater than 4
array([ 5, 6, 7, 8])

Question 21

Arithematic operators on array

Answer 21

arr + arr
arr * arr
arr - arr
arr / arr
arr ** arr

Question 22

Array functions

Answer 22

np.sqrt(arr)
np.exp(arr)
np.max(arr)
np.sin(arr)
np.log(arr)