Ndarray is The n-dimensional array object defined in the numpy is ndarray which stores the collection of the similar type of elements.
An item in an ndarray takes the same size of block in the memory. The ndarray object can be accessed by using the 0 based indexing.
Creating a ndarray :
x = numpy.array
Before creating the ndarraay make sure that the numpy module is installed and imported into the program. If not install then check this article (Installation-of-numpy).
import numpy as np x = np.array() print(x)
The above code will create the empty n-dimensional array. We can also pass the parameters in the array to create the equivalent n-dimensional array, Use following syntax.
numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
Following are the in-details use of parameters.
|1||object||It represents the object. It can be a list, dictionary, set, tuple etc.|
|2||dtype||The dtype parameter is used for changing data type. e.g float, int, complex etc|
|3||copy||By default it is true and optional. Which means the object is copied.|
|4||order||Order can be C (column), R (row), or A (any)(default)|
|5||subok||It returned base class array by default. We can change it option to true so it will passes through subclasses.|
|6||ndmin||Ndmin means minimum dimensions of the resulting array.|
Following are the some example to creating ndarray.
- creating array using list
import numpy as np x = np.array([11,25,48]) print (x)
[11, 25, 48]
For in-depth opertaion of ndarray check this article ( Numpy-Ndarray-Operation)
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