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NumPy Arrays vs Python Lists
The difference between NumPy Arrays vs Python Lists is shown in the below table:
Feature NumPy Array Python List
Data Type Homogeneous (stores elements of the Heterogeneous (can store elements of
same type) different types)
Data Type Does not support mixed types within the Supports mixed types and allows implicit
Conversion same array conversion
Memory Efficiency More memory-efficient Occupies more memory
Accessibility Requires importing NumPy Built-in Python feature (does not require
an additional package)
Mathematical Supports direct mathematical operations Requires looping or applying operations to
Operations on all elements individual elements
Usage Mostly used for scientific computing, Mainly used for data storage and
numerical operations, and data analysis management
Syntax import numpy marks=[34,23,41,42]
marks=numpy.array([34,23,41,42])
Creating an Array using NumPy
Different ways to create an array using NumPy are as follows:
• Creating one dimensional array:
[1]: import numpy
rollno = numpy.array([1, 2, 3])
print(rollno)
[1 2 3]
• Create a sequential 1 D array with values as multiples of 10 from 10 to 100:
[1]: import numpy as np
a = np.arange(10,101,10)
print(a)
[ 10 20 30 40 50 60 70 80 90 100]
• Creating one-dimensional array with 4 random values:
[1]: import numpy as np
a = np.random.random(4)
print(a)
[0.4141628 0.1035279 0.05137008 0.98002355]
• Creating two-dimensional array of 3 rows and 4 columns with random integer values less than 10:
[1]: import numpy as np
a = np.random.randint(10, size=(3,4))
print(a)
[[3 1 4 1]
[7 6 0 1]
[4 2 2 9]]
Advance Python (Practical) 287

