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

Practice answering questions related to Numpy

Numpy Practice Problems

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

NumPy Documentation

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

University of Michigan - Ann Arbor

Data Processing and Modeling

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Python Libraries for Data Science

In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. It is primarily used for data cleaning and preparation through the use of dataframes and series. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is a free software anyone can download.

Pandas

NumPy

The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines, such as routines for numerical integration, interpolation, optimization, linear algebra, and statistics. SciPy is NumPy extension of Python.

-Interacting with NumPy
-Linear Algebra with SciPy

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