numpy basic 1

Question 1

int8, uint8

Answer 1

i1, u1

Question 2

int16, uint16,

Answer 2

i2, u2

Question 3

int32, uint32

Answer 3

i4 , u4

Question 4

int64, uint64

Answer 4

i8, u8

Question 5

float16

Answer 5

f2

Question 6

float32

Answer 6

f4 or f

Question 7

float64

Answer 7

f8 or d

Question 8

float128

Answer 8

f16 or g

Question 9

complex64

Answer 9

c8 c16

Question 10

complex128

Answer 10

c32

Question 11

bool

Answer 11

?

Question 12

object

Answer 12

0

Question 13

string

Answer 13

S

Question 14

unicode

Answer 14

U

Question 15

array

Convert input data (list, tuple, array, or other sequence type) to an ndarray either by inferring a dtype
or explicitly specifying a dtype; copies the input data by default

Answer 15

In [19]: data1 = [6, 7.5, 8, 0, 1]
In [20]: arr1 = np.array(data1)
In [21]: arr1
Out[21]: array([ 6. , 7.5, 8. , 0. , 1. ])

Question 16

asarray

Convert input to ndarray, but do not copy if the input is already an ndarray

Answer 16

no shit seen look into the future

Question 17

arange Like the built-in range but returns an ndarray instead of a list

Answer 17

In [32]: np.arange(15) Out[32]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

Question 18

arange Like the built-in range but returns an ndarray instead of a list

Answer 18

In [32]: np.arange(15)

Out[32]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

Question 19

ones, zeros, empty ,full

Answer 19

quite the same hope u didn’t forget

Question 20

ones_like, zeros_like, empty_like , full like

Answer 20

makes a arr full of value like the arr given as arg

Question 21

np.astype

Answer 21

In [44]: numeric_strings = np.array([‘1.25’, ‘-9.6’, ‘42’], dtype=np.string_)
In [45]: numeric_strings.astype(float)
Out[45]: array([ 1.25, -9.6 , 42. ])

Question 22

arr 1d index and slicing prblm
arr = np.arange(10)
arr[5:8] = ?

Answer 22

Out[63]: array([5, 6, 7])

Question 23

arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

In [73]: arr2d[2]
= ?

Answer 23

Out[73]: array([7, 8, 9])

Question 24

arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

In [74]: arr2d[0][2] and arr2d[0, 2]
output?

Answer 24

3

Question 25

arr = [1,2,3,4] | what are the index of each value in arr

Answer 25

1=0 2=1 3=2 4=3

Question 26

.copy()

Answer 26

to copy an arr

Question 27

if arr_1 = [1,2,3] arr_2 =[ [5,6,7], [8,9,0] ] how to transform the first value of arr_2 to arr_1 expected value = [[1,2,3], [8,9,0]]

Answer 27

arr_2[0] = arr_1 | arr_2

Question 28

how to have dot product of arr1 and arr2

Answer 28

np.dot(arr1, arr2)

Question 29

how to create transpose of an arr

Answer 29

``` arr.T Simple transposing with .T is a special case of swapping axes. ndarray has the method swapaxes, which takes a pair of axis numbers and switches the indicated axes to rear‐ range the data: In [135]: arr Out[135]: array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7]], [[ 8, 9, 10, 11], [12, 13, 14, 15]]]) In [136]: arr.swapaxes(1, 2) Out[136]: array([[[ 0, 4], [ 1, 5], [ 2, 6], [ 3, 7]], [[ 8, 12], [ 9, 13], [10, 14], [11, 15]]]) ```