np.concatenate()
np.join()
np.array_join()
np.join_array()
numpy.array(list)
numpy.array(list, dtype=float)
Both a and b
None of the above
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
21
12
28
Filled with Zero
Filled with Blank space
Filled with random garbage value
Filled with One
To make a Matrix with all element 0
To make a Matrix with all diagonal element 0
To make a Matrix with first row 0
What will be the output of the following ?
import numpy as npa = np.array( [2, 3, 4, 5] )b = np.arange(4)print(a+b)
[2 3 4 5]
[3 4 5 6]
[1 2 3 4]
[2 4 6 8]
import numpy as npa = np.arange(5,1)print(a)
[ ]
[1 2 3 4 5]
[5 4 3 2 1]
arr=np.float([1,2,3,4])
arr=np.array([1,2,3,4]).toFloat()
arr=np.array([1,2,3,4],dtype='float')
arr=np.farray([1,2,3,4])
import numpy as npa = np.array([[ 1,2,3,4], [5,6,7,8], [9,10,11,12]])print(a[2,2])
7
11
8
List
Array
Matrix
Set
Size, shape
memory consumption
data type of array
All of these
What will be the output?
import numpy as npa = np.array([[1,2],[3,4]])print(a.shape)
(4,)
(2,2)
(2,1)
4
NumPy arrays have contiguous memory location
They are more speedy to work with
They are more convenient to deal with
All of the above
A machine learning library
A web development framework
A numerical computing library in Python
A data visualization tool
Web development
Machine learning and scientific computing
Game development
Database management
make a matrix with first column 0
make a matrix with all elements 0
make a matrix with diagonal elements 0
create()
list()
tuple()
array()
Shape
both a) and b)
None of the above.
import numpy as npa=np.array([2,4,1])b=a.copy()a[1]=3print(b)
[2 4 1]
[2 3 1]
[3 4 1]
[2 4 3]
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()
What will be the output of the following Python code?
len(["hello",2, 4, 6])
Error
6
3
What is the output of the following code ?
import numpy as npy = np.array([[11, 12, 13, 14], [32, 33, 34, 35]])print(y.ndim)
1
2
0
np.array()
np.zeros()
np.empty()
ndarray
narray
nd_array
darray
all_like
ones_like
one_alike
all of the mentioned
array_split()
split()
split_array()
hstack() and vstack()
Indexing
Slicing
Reshaping
Mathematical and logical operations on arrays.
Fourier transforms and routines for shape manipulation.
Operations related to linear algebra.
print(arr[1])
print(arr,0)
print(arr,1)
None of These
shape, dtype, ndim
objects, type, list
objects, non vectorization
Unicode and shape
Number of Rows and Column in array
Size of each items in array
Number of elements in array
Largest element of an array
It creates a new Python list.
It creates a NumPy array.
It performs element-wise addition.
It calculates the mean of an array.
np.ndim(array_name)
array_name.ndim()
np.dim(array_name)
array_name.dim
import numpy as npa = np.array([1,2,3,5,8])b = np.array([0,1,5,4,2])c = a + bc = c*aprint (c[2])
24
None of these
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
To make a Matrix with all elements 0
We can find the dimension of the array
Size of array
Operational activities on Matrix
None of the mentioned above
np.array([4,5,6])
np.create_array([4,5,6])
np.createArray([4,5,6])
np.numpyArray([4,5,6])
rank
dtype
shape
Which syntax would print the last 3 numbers from the array below:
arr = np.array([1,2,3,4,5,6,7])
print(arr[3:])
print(arr[3])
print(arr[:3])
print(arr[4:])
What is a correct syntax to print the numbers [3, 4, 5] from the array below:
print(arr[2:4])
print(arr[2:5])
print(arr[2:6])
print(arr[3:6])
What will be output for the following code?
import numpy as npa=np.array([[1,2,3],[0,1,4]])print (a.size)
5
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
numpy.maximum()
numpy.arraymax()
numpy.amax()
numpy.big()
89
[1,2,3,4]
[1,2,3],[3,4,5],[1,3,4]
[[2 3 5][ 4 5 6][4 5 6]]
import numpy as nparr=np.array([1,2,3])print(arr.shape)
(3,)
Indexed
Sliced
Iterated
All of the mentioned above
import numpy as npa = np.array([[1,2,3]])print(a.shape)
(2,3)
(3,1)
(1,3)