The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.
Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).
Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types. The following diagram shows a relationship between ndarray, data type object (dtype) and array scalar type −
An instance of ndarray class can be constructed by different array creation routines described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −
It creates an ndarray from any object exposing array interface, or from any method that returns an
array.numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
|Sr.No.||Parameter & Description|
|1||object: Any object exposing the array interface method returns an array, or any (nested) sequence.|
|2||dtype: Desired data type of array, optional|
|3||copy: Optional. By default (true), the object is copied|
|4||order: C (row major) or F (column major) or A (any) (default)|
|5||subok: By default, returned array forced to be a base class array. If true, sub-classes passed through|
|6||ndmin: Specifies minimum dimensions of resultant array|
import numpy as np a = np.array([1,2,3]) print (a)
[1 2 3]
# more than one dimensions import numpy as np a = np.array([[1, 2], [3, 4]]) print (a)
[[1 2] [3 4]]
# minimum dimensions import numpy as np a = np.array([1, 2, 3,4,5], ndmin = 2) print (a)
[[1 2 3 4 5]]
The ndarray object consists of contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block.
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