NumPyCreating Arrays
Create a NumPy ndarray Object
NumPy is used to work with arrays. The array object in NumPy is calledndarray.
We can create a NumPyndarray object by using thearray() function.
type(): This built-in Python function tells us the type of the object passed to it. Like in above code it shows thatarr isnumpy.ndarray type.
To create anndarray,we can pass a list, tuple or any array-like object into thearray() method, and it will be converted into anndarray:
Example
Use a tuple to create a NumPy array:
arr = np.array((1, 2, 3, 4, 5))
print(arr)
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
arr = np.array(42)
print(arr)
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:
arr = np.array([1, 2, 3, 4, 5])
print(arr)
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 callednumpy.mat
Example
Create a 2-D array containing two arrays with the values 1,2,3 and 4,5,6:
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
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:
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)
Check Number of Dimensions?
NumPy Arrays provides thendim attribute that returns an integer that tells us how many dimensions the array have.
Example
Check how many dimensions the arrays have:
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)
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 thendmin argument.
Example
Create an array with 5 dimensions and verify that it has 5 dimensions:
arr = np.array([1, 2, 3, 4], ndmin=5)
print(arr)
print('number of dimensions :', arr.ndim)
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.
