What will be output for the following code?
import numpy as npa=np.array([[1,2,3],[0,1,4]])print (a.size)
1
5
6
4
Indexed
Sliced
Iterated
All of the mentioned above
Numbering Python
Number In Python
Numerical Python
None of the above
numpy.array(list)
numpy.array(list, dtype=float)
Both a and b
What will be the output of the following ?
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]
Number of Rows and Column in array
Size of each items in array
Number of elements in array
Largest element of an array
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 npa = np.array([1, 5, 4, 7, 8])a = a + 1print(a[1])
7
Mathematical and logical operations on arrays.
Fourier transforms and routines for shape manipulation.
Operations related to linear algebra.
All of the above
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.arange(5,1)print(a)
[ ]
[1 2 3 4 5]
[5 4 3 2 1]
[1 2 3 4]
import numpy as npa=np.array([2,4,1])b=aa[1]=3print(b)
NumPy arrays have contiguous memory location
They are more speedy to work with
They are more convenient to deal with
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
List
Array
Matrix
Set
Size, shape
memory consumption
data type of array
All of these
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
What will be the output of following Python code?
import numpy as npa = np.array([(10,20,30)])print(a.itemsize)
10
9
import numpy as npary = np.array([1,2,3,5,8])ary = ary + 1print (ary[1])
0
2
3
what will be output for the following code?
import numpy as npa=np.array([1,2,3,5,8])print(a.ndim)
Indexing
Slicing
Reshaping
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])
import numpy as nparr=np.array([1,2,3])print(arr.shape)
(3,)
(4,)
Web development
Machine learning and scientific computing
Game development
Database management
Tuple
To make a Matrix with all diagonal element 0
To make a Matrix with first row 0
To make a Matrix with all elements 0
arr=np.array([1,2,3,4],dtype='f')
arr=np.array([1,2,3,4],dtype=float)
It creates a new Python list.
It creates a NumPy array.
It performs element-wise addition.
It calculates the mean of an array.
Guido van Rossum
Travis Oliphant
Wes McKinney
Jim Hugunin
from numpy import *
import numpy
import numpy as my_numpy
All of above
rank
dtype
shape
None of these
np.array([4,5,6])
np.create_array([4,5,6])
np.createArray([4,5,6])
np.numpyArray([4,5,6])
Shape
both a) and b)
None of the above.
float
integer
string
boolean
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
np.array()
np.zeros()
np.empty()
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
change in shape of array
reshaping of array
get the shape of the array
np.ndim(array_name)
array_name.ndim()
np.dim(array_name)
array_name.dim
array_split()
split()
split_array()
hstack() and vstack()
What will be output for the following code ?
import numpy as npa=np.array([2,3,4,5])print(a.dtype)
int32
int
none of these
ndarray
narray
nd_array
darray
import numpy as npprint(np.maximum([2, 3, 4], [1, 5, 2]))
[1 5 2]
[1 5 4]
[2 3 4]
[2 5 4]
Filled with Zero
Filled with Blank space
Filled with random garbage value
Filled with One
import numpy as np
ary=np.array([1,2,3,5,8])
ary=ary+1
print(ary[1])
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)
None of These
make a matrix with first column 0
make a matrix with all elements 0
make a matrix with diagonal elements 0
Which syntax would print the last 3 numbers from the array below:
print(arr[3:])
print(arr[3])
print(arr[:3])
print(arr[4:])
What is the output of following code ?
a = np.array([[1,2,3],[4,5,6]])print(a.shape)
(3,2)
(1,1)