PYTHON NUMPY Lesson 2 – Creating Array - Dimension - Indexing
PYTHON NUMPY Lesson 2 – Creating Array - Dimension - Indexing
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.
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:
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Dimensions in Arrays:
-- 0-D Arrays:
0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array
-- 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.
-- 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.
-- 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.
-- Check Number of Dimensions?
NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have.
-- 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
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NumPy Array Indexing:
-- Access Array Elements:
Array indexing is the same as accessing an array element.
You can access an array element by referring to its index number.
The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
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