Page 225 - AI Ver 1.0 Class 10
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>>> from numpy import array as ary # this will import ONLY
# arrays and referred as ary
Arrays are a collection of values of the same data types that can be arranged in one or more dimensions. They
can be numbers, characters, Booleans, etc. An array of one dimension is called a Vector, an array having two
dimensions is called a Matrix and an array with multiple dimensions is called as n-dimensional array.
In NumPy we can create n-dimensional arrays and are considered as an alternative to Python lists because they
allow faster access in reading and writing items effectively and efficiently.
So, if we compare NumPy-Arrays and Python-List then:
• Array is a collection of homogeneous values whereas list is a collection of heterogeneous values.
• In arrays data of one type does not support data of another type whereas in list it works perfectly by using data
of one type by converting into another data type.
• Arrays can be accessed only through package NumPy and occupies less memory space whereas list occupies
more memory space and can be accessed directly in Python without any package support.
• In arrays the mathematical operators can be directly used whereas in list the mathematical operators cannot be
used directly on it instead the operator needs to be used separately on individual elements.
• Arrays are mainly used for mathematical operations where lists are mainly used for data management.
• Syntax of creating an array is:
import numpy
marks = numpy.array([34,23,41,42])
Syntax of creating a list is:
marks = [34,23,41,42]
Creating an Array using NumPy
We can create different arrays using NumPy. Let us discuss about some of them.
• Creating a one-dimensional array:
import numpy
rollno = numpy.array([1, 2, 3])
print(rollno)
Output will be:
[1 2 3]
• Create a sequential 1 D array with values as multiples of 10 from 10 to 100:
import numpy as np
a = np.arange(10,101,10)
print(a)
Output will be:
[10 20 30 40 50 60 70 80 90 100]
Data Science 223

