https://numpy.org/doc/stable/numpy-user.pdf

University of Michigan - Ann Arbor

NumPy, which stands for Numerical Python, is a Python library that contains n-dimensional arrays and tools for processing those arrays.

NumPy

The n-dimensional array, or ndarray, is the main data structure enabled by the NumPy library. It can be one- or multi-dimensional depending on the parameters passed to np.array(), but it must consist of homogenous (same-type) data. To create a 1D array, pass in a single list of objects, e.g. 

```np.array([1, 2, 3])```. 

To create a 2D array, pass in a list of lists, e.g. 

```np.array([[1, 2, 3], [4, 5, 6]]).```

NumPy n-dimensional array

* np.array() convert python array to ndarray
```python
 li = [1,2,3,4,5,6]
arr = np.array(li)     # convert to nd array
 display(arr)
```

* np.arange() Similar to python range, but returns an ndarray type.
```python
 np.arange(1,7)  
array([1, 2, 3, 4, 5, 6])
```
* np.zeros(), np.full(), np.eye() basic matrix creation methods.
```python
> np.zeros((2,3))
array([[0., 0.],
       [0., 0.],
       [0., 0.]])

> np.full((2,3), 55)  # 2 and 3 represents the dimensions, 55 representing the element.
array([[55, 55, 55],
       [55, 55, 55]])

> np.eye(2)   # diagonally filling the inputted parameter
array([[1., 0.],
        0., 1.])
```

Common Initializing methods in Numpy

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