full
empty
init
None of these
np.concatenate()
np.join()
np.array_join()
np.join_array()
what will be output for the following code?
import numpy as npa=np.array([1,2,3,5,8])print(a.ndim)
0
1
2
3
A machine learning library
A web development framework
A numerical computing library in Python
A data visualization tool
axes
degree
cordinate
points
List
Array
Matrix
Set
np.ndim(array_name)
array_name.ndim()
np.dim(array_name)
array_name.dim
What will be the output?
import numpy as npa = np.array([[1,2],[3,4]])print(a.shape)
(4,)
(2,2)
(2,1)
4
What will be output for the following code?
import numpy as npa = np.array([1,2,3,5,8])b = np.array([0,3,4,2,1])c = a + bc = c*aprint (c[2])
18
20
21
22
What will be the output of the following ?
import numpy as npa=np.array([2,4])b=np.array([3,5])c=a*bprint(c)
[ 2 4 3 5]
[ 6 20]
[ 6 12 10 20]
26
import numpy as npa = np.array([1, 5, 4, 7, 8])a = a + 1print(a[1])
5
6
7
change in shape of array
reshaping of array
get the shape of the array
All of above
import numpy as npa=np.array([2,4,1])b=aa[1]=3print(b)
[2 4 1]
[3 4 1]
[2 3 1]
[2 4 3]
What will be the output of the following Python code?
len(["hello",2, 4, 6])
Error
the shape is the number of rows
the shape is the number of columns
the shape is the number of element in each dimension
Total number of elements in array
size
dtype
ndim
shape
Numbering Python
Number In Python
Numerical Python
None of the above
import numpy as npa=np.array([2,4,1])b=a.copy()a[1]=3print(b)
NumPy arrays have contiguous memory location
They are more speedy to work with
They are more convenient to deal with
All of the above
ndarray
narray
nd_array
darray
Filled with Zero
Filled with Blank space
Filled with random garbage value
Filled with One
rank
What will be the output of the following code?
import numpy as npa=np.array([1,2,3])print(a.ndim)
89
[1,2,3,4]
[1,2,3],[3,4,5],[1,3,4]
[[2 3 5][ 4 5 6][4 5 6]]
print(arr[1])
print(arr,0)
print(arr,1)
None of These
What is the output of the following code ?
import numpy as npa = np.array([[1,2,3]])print(a.shape)
(2,3)
(3,1)
(1,3)
Size, shape
memory consumption
data type of array
All of these
What is a correct syntax to print the numbers [3, 4, 5] from the array below:
arr = np.array([1,2,3,4,5,6,7])
print(arr[2:4])
print(arr[2:5])
print(arr[2:6])
print(arr[3:6])
Which syntax would print the last 3 numbers from the array below:
print(arr[3:])
print(arr[3])
print(arr[:3])
print(arr[4:])
Indexing
Slicing
Reshaping
np.array()
np.zeros()
np.empty()
import numpy as npa = np.array([1,2,3])b = np.array([4,5,6])print(a+b)
[5 7 9]
[1 2 3 4 5 6]
import numpy as npy = np.array([[11, 12, 13, 14], [32, 33, 34, 35]])print(y.ndim)
import numpy as npa = np.arange(1,5,2)print(a)
[1 3 5]
[1 3]
[1,3]
[1,2,3,4,5]
import numpy as nparr=np.array([1,2,3])print(arr.shape)
(3,)
create()
list()
tuple()
array()
import numpy as npa=np.array([[1,2,3],[0,1,4]])print (a.size)
What is a correct syntax to print the number 8 from the array below:
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print(arr[3,0])
print(arr[1,2])
print(arr[7,2])
None of The Above
import numpy as npa=np.array([2,4,1])b=np.array([3,5])c=a+bprint(c)
[2 4 1 3 5 ]
[5 9 1]
15
ValueError
range()
space()
arange()
linspace()
float
integer
string
boolean
numpy.array(list)
numpy.array(list, dtype=float)
Both a and b
What will be output for the following code ?
import numpy as npa = np.array([[1, 2, 3],[0,1,4],[11,22,33]])print (a.size)
9
Web development
Machine learning and scientific computing
Game development
Database management
numpy.maximum()
numpy.arraymax()
numpy.amax()
numpy.big()
make a matrix with first column 0
make a matrix with all elements 0
make a matrix with diagonal elements 0
from numpy import *
import numpy
import numpy as my_numpy
What is the output of the following code?
import numpy as np a=np.array([1,2,3,5,8]) b=np.array([0,3,4,2,1]) c=a+b c=c*a print(c[2])
10
12
28
We can find the dimension of the array
Size of array
Operational activities on Matrix
None of the mentioned above
import numpy as npa=np.array([2,3,4,5])print(a.dtype)
int32
int
none of these