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
None of the above
What will be the output of the following ?
import numpy as npa = np.array([1, 5, 4, 7, 8])a = a + 1print(a[1])
4
5
6
7
unlimited length
all private members must have leading and trailing underscores
Preferred Installer Program
np.array([4,5,6])
np.create_array([4,5,6])
np.createArray([4,5,6])
np.numpyArray([4,5,6])
arr=np.array([1,2,3,4],dtype='float')
arr=np.array([1,2,3,4],dtype='f')
arr=np.array([1,2,3,4],dtype=float)
All of the above
full
empty
init
None of these
import numpy as npa = np.arange(1,5,2)print(a)
[1 3 5]
[1 3]
[1,3]
[1,2,3,4,5]
List
Array
Matrix
Set
np.concatenate()
np.join()
np.array_join()
np.join_array()
import numpy as npa = np.array([[ 1,2,3,4], [5,6,7,8], [9,10,11,12]])print(a[2,2])
11
10
8
array_split()
split()
split_array()
hstack() and vstack()
Indexed
Sliced
Iterated
All of the mentioned above
ndarray
narray
nd_array
darray
Size, shape
memory consumption
data type of array
All of these
Guido van Rossum
Travis Oliphant
Wes McKinney
Jim Hugunin
What is the datatype of x ?
import numpy as npa=np.array([1,2,3,4])x=a.tolist()
int
array
tuple
list
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])
21
12
28
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
Numbering Python
Number In Python
Numerical Python
make a matrix with first column 0
make a matrix with all elements 0
make a matrix with diagonal elements 0
It creates a new Python list.
It creates a NumPy array.
It performs element-wise addition.
It calculates the mean of an array.
arr=np.float([1,2,3,4])
arr=np.array([1,2,3,4]).toFloat()
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]
What will be output for the following code ?
import numpy as np
ary=np.array([1,2,3,5,8])
ary=ary+1
print(ary[1])
0
1
2
3
np.ndim(array_name)
array_name.ndim()
np.dim(array_name)
array_name.dim
import numpy as npa = np.array([[1, 2, 3],[0,1,4],[11,22,33]])print (a.size)
9
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]
numpy.array(list)
numpy.array(list, dtype=float)
Both a and b
What will be the output of following Python code?
import numpy as npa = np.array([(10,20,30)])print(a.itemsize)
To make a Matrix with all elements 0
What will be output for the following code?
import numpy as npary = np.array([1,2,3,5,8])ary = ary + 1print (ary[1])
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?
import numpy as npaa=np.array([1,2,3])print(a*2)
[2 4 6]
[1 2 3 1 2 3]
[3 4 5]
Error
import numpy as npa=np.array([2,4,1])b=a.copy()a[1]=3print(b)
import numpy as nparr=np.array([1,2,3])print(arr.shape)
(3,)
(4,)
NumPy arrays have contiguous memory location
They are more speedy to work with
They are more convenient to deal with
Indexing
Slicing
Reshaping
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
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]
[2 4 6 8]
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:])
all_like
ones_like
one_alike
all of the mentioned
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
import numpy as npy = np.array([[11, 12, 13, 14], [32, 33, 34, 35]])print(y.ndim)
What will be the output of the following code?
import numpy as npa=np.array([1,2,3])print(a.ndim)
numpy.linspace()
numpy.range()
numpy.arrange()
numpy.spaceline()
print(arr[1])
print(arr,0)
print(arr,1)
axes
degree
cordinate
points
What is the output of following code ?
a = np.array([[1,2,3],[4,5,6]])print(a.shape)
(3,2)
(1,1)
none of these
We can find the dimension of the array
Size of array
Operational activities on Matrix
None of the mentioned above
Filled with Zero
Filled with Blank space
Filled with random garbage value
Filled with One