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, itβs 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.
1234567import 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:
1234567import 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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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, itβs 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.
1234567import 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:
1234567import 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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