NumPy ndarray: Difference between revisions

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* [[Numpy_Concepts#numpy|Numpy Concepts]]
* [[Numpy_Concepts#numpy|Numpy Concepts]]
=Overview=
=Overview=
<code>ndarray</code> is an N-dimensional array object. It is a fast, flexible container for large  datasets in Python. It is used to implement a Pandas [[Pandas_Series#Overview|Series]]. It allows performing mathematical operations on whole blocks of data using similar syntax to the equivalent operation between scalar elements. <code>ndarray</code>s are homogeneous, all elements of an <code>ndarray</code> instance have the same [[#Data_Type|data type]]. <code>ndarray</code>s can be created by [[#Convert_Python_Data_Structures|converting Python data structures]], using [[NumPy_ndarray#With_Generators|generators]], or [[#By_Specifying_Shape_and_Value|initializing blocks of memory of specified shape with specified values]].
<code>ndarray</code> is an N-dimensional array object. It is a fast, flexible container for large  datasets in Python. It is used to implement a Pandas [[Pandas_Series#Overview|Series]]. It allows performing mathematical operations on whole blocks of data using similar syntax to the equivalent operation between scalar elements. It also allows applying same mathematical operation, or function, to all array elements without the need to write loops. Examples are provided in [[#Array_Arithmetic|Array Arithmetic]] section. <code>ndarray</code>s are homogeneous, all elements of an <code>ndarray</code> instance have the same [[#Data_Type|data type]]. <code>ndarray</code>s can be created by [[#Convert_Python_Data_Structures|converting Python data structures]], using [[NumPy_ndarray#With_Generators|generators]], or [[#By_Specifying_Shape_and_Value|initializing blocks of memory of specified shape with specified values]].


=<span id='Geometry'></span><tt>ndarray</tt> Geometry=
=<span id='Geometry'></span><tt>ndarray</tt> Geometry=

Revision as of 19:46, 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. It also allows applying same mathematical operation, or function, to all array elements without the need to write loops. Examples are provided in Array Arithmetic section. 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 Geometry

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]])

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

The Python data structures provided as arguments to np.array() are interpreting according to the array's geometry.

With Generators

By Specifying Shape and Value

Element Data Type