# Introduction to NumPy Arrays¶

## Goals:¶

• Learn the basics of Python Numpy Arrays

References:

• http://www.numpy.org/

• https://docs.scipy.org/doc/numpy/user/quickstart.html

• https://www.datacamp.com/community/tutorials/python-numpy-tutorial

• https://blog.thedataincubator.com/2018/02/numpy-and-pandas/

• https://medium.com/@ericvanrees/pandas-series-objects-and-numpy-arrays-15dfe05919d7

• https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/

• https://towardsdatascience.com/a-hitchhiker-guide-to-python-numpy-arrays-9358de570121

• McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media. Kindle Edition

## What is NumPy?¶

• NumPy is short for “Numerical Python” and it is a fundamental python package for scientific computing.

• It uses a high-performance data structure known as the n-dimensional array or ndarray, a multi-dimensional array object, for efficient computation of arrays and matrices.

## What is an Array?¶

• Python arrays are data structures that store data similar to a list, except the type of objects stored in them is constrained.

• Elements of an array are all of the same type and indexed by a tuple of positive integers.

• The python module array allows you to specify the type of array at object creation time by using a type code, which is a single character. You can read more about each type code here: https://docs.python.org/3/library/array.html?highlight=array#module-array

```import array
```
```array_one = array.array('i',[1,2,3,4])
type(array_one)
```
```array.array
```
```type(array_one)
```
```int
```

## What is a NumPy N-Dimensional Array (ndarray)?¶

• It is an efficient multidimensional array providing fast array-oriented arithmetic operations.

• An ndarray as any other array, it is a container for homogeneous data (Elements of the same type)

• In NumPy, data in an ndarray is simply referred to as an array.

• As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or slicing operations.

• For numerical data, NumPy arrays are more efficient for storing and manipulating data than the other built-in Python data structures.

```import numpy as np
np.__version__
```
```'1.19.2'
```
```list_one = [1,2,3,4,5]
```
```numpy_array = np.array(list_one)
type(numpy_array)
```
```numpy.ndarray
```
```numpy_array
```
```array([1, 2, 3, 4, 5])
```

### Vectorized Operations¶

• The key difference between an array and a list is, arrays are designed to handle vectorized operations while a python list is not.

• NumPy operations perform complex computations on entire arrays without the need for Python for loops.

• In other words, if you apply a function to an array, it is performed on every item in the array, rather than on the whole array object.

• In a python list, you will have to perform a loop over the elements of the list.

```list_two = [1,2,3,4,5]
# The following will throw an error:
list_two + 2
```
```---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-03923fe34c76> in <module>
1 list_two = [1,2,3,4,5]
2 # The following will throw an error:
----> 3 list_two + 2

TypeError: can only concatenate list (not "int") to list
```
• Performing a loop to add 2 to every integer in the list

```for index, item in enumerate(list_two):
list_two[index] = item + 2
list_two
```
```[3, 4, 5, 6, 7]
```
• With a NumPy array, you can do the same simply by doing the following:

```numpy_array
```
```array([1, 2, 3, 4, 5])
```
```numpy_array + 2
```
```array([3, 4, 5, 6, 7])
```
• Any arithmetic operations between equal-size arrays applies the operation element-wise:

```numpy_array_one = np.array([1,2])
numpy_array_two = np.array([4,6])
```
```numpy_array_one + numpy_array_two
```
```array([5, 8])
```
```numpy_array_one > numpy_array_two
```
```array([False, False])
```

### Memory.¶

• NumPy internally stores data in a contiguous block of memory, independent of other built-in Python objects.

• NumPy arrays takes significantly less amount of memory as compared to python lists.

```import numpy as np
import sys
```
```python_list = [1,2,3,4,5,6]
python_list_size = sys.getsizeof(1) * len(python_list)
python_list_size
```
```168
```
```python_numpy_array = np.array([1,2,3,4,5,6])
python_numpy_array_size = python_numpy_array.itemsize * python_numpy_array.size
python_numpy_array_size
```
```48
```

## Basic Indexing and Slicing¶

### One Dimensional Array¶

• When it comes down to slicing and indexing, one-dimensional arrays are the same as python lists

```numpy_array
```
```array([1, 2, 3, 4, 5])
```
```numpy_array
```
```2
```
```numpy_array[1:4]
```
```array([2, 3, 4])
```
• You can slice the array and pass it to a variable. Remember that variables just reference objects.

• Any change that you make to the array slice, it will be technnically done on the original array object. Once again, variables just reference objects.

```numpy_array_slice = numpy_array[1:4]
numpy_array_slice
```
```array([2, 3, 4])
```
```numpy_array_slice = 10
numpy_array_slice
```
```array([ 2, 10,  4])
```
```numpy_array
```
```array([ 1,  2, 10,  4,  5])
```

### Two-Dimensional Array¶

• In a two-dimensional array, elements of the array are one-dimensional arrays

```numpy_two_dimensional_array = np.array([[1,2,3],[4,5,6],[7,8,9]])
```
```numpy_two_dimensional_array
```
```array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
```
```numpy_two_dimensional_array
```
```array([4, 5, 6])
```
• Instead of looping to the one-dimensional arrays to access specific elements, you can just pass a second index value

```numpy_two_dimensional_array
```
```6
```
```numpy_two_dimensional_array[1,2]
```
```6
```
• Slicing two-dimensional arrays is a little different than one-dimensional ones.

```numpy_two_dimensional_array
```
```array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
```
```numpy_two_dimensional_array[:1]
```
```array([[1, 2, 3]])
```
```numpy_two_dimensional_array[:2]
```
```array([[1, 2, 3],
[4, 5, 6]])
```
```numpy_two_dimensional_array[:3]
```
```array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
```
```numpy_two_dimensional_array[:2,1:]
```
```array([[2, 3],
[5, 6]])
```
```numpy_two_dimensional_array[:2,:1]
```
```array([,
])
```
```numpy_two_dimensional_array[1:]
```
```array([8, 9])
```