Basic Array Creation

A NumPy array is an efficient, multidimensional container for storing and manipulating large datasets of the same data types. Although they are similar to Python lists, they are more memory-efficient and allow for high-performance mathematical and numerical operations.

Now, it’s time to create your first NumPy arrays. The most straightforward way to do this is by using the array() function, passing either a list or a tuple as its argument, and only them.

Note

You should create NumPy arrays only from lists in all the tasks throughout our course.


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import numpy as np
# Creating an array from list
array_from_list = np.array([1, 2, 3, 2, 6, 1])
# Creating an array from tuple
array_from_tuple = np.array((1, 2, 3, 2, 6, 1))
print(f'Array from list: {array_from_list}')
print(f'Array from tuple: {array_from_tuple}')

Specifying Data Type

The data type of the array elements is defined implicitly; however, you can specify it explicitly with the dtype parameter:


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import numpy as np
# Creating an integer array without specifying dtype
array_1 = np.array([1, 2, 3])
# Creating an integer array with setting dtype to 1-byte integer
array_2 = np.array([1, 2, 3], dtype=np.int8)
print(f'First array dtype: {array_1.dtype}')
print(f'Second array dtype: {array_2.dtype}')

The first integer array uses the default int64 data type, which is an 8-byte integer. The second array uses int8, a 1-byte integer.

The most common NumPy data types include numpy.float16, numpy.float32, and numpy.float64, which store 2-byte, 4-byte, and 8-byte floating point numbers, respectively.

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Section 1. Chapter 2