NumPy Creating Arrays
Create a NumPy ndarray Object
NumPy is used to work with arrays. The array object in NumPy is called
ndarray.
We can create a NumPy
ndarray object by using the array() function.
Example
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
print(type(arr))
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type(): This built-in Python function tells us the type of the object passed to it. Like in above code
it shows that arr is
numpy.ndarray type.
To create an ndarray,
we can pass a list, tuple or any array-like object into the array()
method, and it will be converted into an
ndarray:
Example
Use a tuple to create a NumPy array:
import numpy as np
arr = np.array((1, 2, 3, 4, 5))
print(arr)
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Dimensions in Arrays
A dimension in arrays is one level of array depth (nested arrays).
nested array: are arrays that have arrays as their elements.
0-D Arrays
0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array.
Example
Create a 0-D array with value 42
import numpy as np
arr = np.array(42)
print(arr)
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1-D Arrays
An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array.
These are the most common and basic arrays.
Example
Create a 1-D array containing the values 1,2,3,4,5:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
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2-D Arrays
An array that has 1-D arrays as its elements is called a 2-D array.
These are often used to represent matrix or 2nd order tensors.
NumPy has a whole sub module dedicated towards matrix operations called
numpy.mat
Example
Create a 2-D array containing two arrays with the values 1,2,3 and 4,5,6:
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
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3-D arrays
An array that has 2-D arrays (matrices) as its elements is called 3-D array.
These are often used to represent a 3rd order tensor.
Example
Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6:
import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)
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Check Number of Dimensions?
NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have.
Example
Check how many dimensions the arrays have:
import numpy as np
a = np.array(42)
b = np.array([1, 2, 3, 4, 5])
c = np.array([[1, 2, 3], [4, 5, 6]])
d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(a.ndim)
print(b.ndim)
print(c.ndim)
print(d.ndim)
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Higher Dimensional Arrays
An array can have any number of dimensions.
When the array is created, you can define the number of dimensions by using
the ndmin argument.
Example
Create an array with 5 dimensions and verify that it has 5 dimensions:
import numpy as np
arr = np.array([1, 2, 3, 4], ndmin=5)
print(arr)
print('number of dimensions :', arr.ndim)
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In this array the innermost dimension (5th dim) has 4 elements, the 4th dim has 1 element that is the vector, the 3rd dim has 1 element that is the matrix with the vector, the 2nd dim has 1 element that is 3D array and 1st dim has 1 element that is a 4D array.
