NumPy ndarray: Difference between revisions
| Line 6: | Line 6: | ||
=<tt>ndarray</tt> Creation= | =<tt>ndarray</tt> Creation= | ||
==Convert Python Data Structures== | ==Convert Python Data Structures== | ||
The <code>np.array()</code> function takes Python data structures, such as lists, lists of list, tuples, etc. and generates the corresponding shape <code>ndarray</code>. For example, a bi-dimensional 3 x 3 <code>ndarray</code> can be created by providing a list of 3 lists, each of the enclosed lists containing 3 elements: | |||
<syntaxhighlight lang='py'> | |||
import numpy as np | |||
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) | |||
</syntaxhighlight> | |||
==With Generators== | ==With Generators== | ||
==By Specifying Shape and Value== | ==By Specifying Shape and Value== | ||
=<span id='Data_Type'></span>Element Data Type= | =<span id='Data_Type'></span>Element Data Type= | ||
Revision as of 19:38, 20 May 2024
Internal
Overview
ndarray is an N-dimensional array object. It is a fast, flexible container for large datasets in Python. It is used to implement a Pandas Series. It allows performing mathematical operations on whole blocks of data using similar syntax to the equivalent operation between scalar elements. ndarrays are homogeneous, all elements of an ndarray instance have the same data type. ndarrays can be created by converting Python data structures, using generators, or initializing blocks of memory of specified shape with specified values.
ndarray Creation
Convert Python Data Structures
The np.array() function takes Python data structures, such as lists, lists of list, tuples, etc. and generates the corresponding shape ndarray. For example, a bi-dimensional 3 x 3 ndarray can be created by providing a list of 3 lists, each of the enclosed lists containing 3 elements:
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])