Intro To Numpy - Numpy For Machine Learning 1

Summary

In this video, John Elder from CodingMe.com introduces the basics of NumPy, a fundamental library for machine learning and data science. He explains the limitations of Python lists for handling large datasets and how NumPy arrays solve these issues by offering a more efficient, homogeneous data structure. John demonstrates how to create and manipulate NumPy arrays, covering basic functions such as `shape`, `arange`, `zeros`, and `full`. He also outlines the importance of learning these foundational skills for progressing into more advanced topics like pandas and machine learning algorithms.

Takeaways

• 😀 Numpy is fundamental to machine learning and data science, helping manage large data sets efficiently.
• 💡 Python lists are versatile but become inefficient with large data sets due to varying data types and memory usage.
• 🚀 Numpy arrays are like Python lists but optimized for performance, especially with large, numerical datasets.
• 📦 Numpy arrays require all elements to be of the same data type, making them more efficient for machine learning tasks.
• 🔢 Numpy stands for 'Numeric Python,' and its arrays are called 'ndarrays,' representing n-dimensional arrays.
• 💻 To use Numpy, you must install it via pip (`pip install numpy`) and import it as `import numpy as np`.
• 🔧 You can create arrays with functions like `np.array`, `np.arange`, `np.zeros`, and `np.full` for different types of data initialization.
• 🧮 Numpy arrays support multi-dimensional data structures, useful for more complex data analysis in machine learning.
• ⚡ Numpy arrays offer various mathematical operations and functions for data manipulation, like `shape` to get array size.
• 📘 Converting Python lists to Numpy arrays is straightforward using `np.array()`.

Q & A

What is NumPy and why is it important for machine learning?

NumPy is a powerful library in Python used for working with arrays, which is fundamental for handling large datasets in machine learning. It's designed for numerical operations and is optimized for efficiency, making it essential when dealing with large amounts of data.

Why are Python lists not ideal for machine learning tasks?

Python lists can store different data types, which can lead to inefficiency when working with large datasets. Lists are computationally expensive in terms of memory and processing speed, especially when handling millions or billions of data points, which is common in machine learning.

How does a NumPy array differ from a Python list?

Unlike Python lists, NumPy arrays require all elements to be of the same data type, which allows for more efficient processing. NumPy arrays are optimized for numerical operations and can handle multi-dimensional data.

How can you create a NumPy array from a Python list?

You can convert a Python list to a NumPy array by using the `numpy.array()` function and passing the list as an argument. For example: `np.array([1, 2, 3])` converts a Python list to a NumPy array.

What is the function of the `np.shape` method in NumPy?

The `np.shape` method in NumPy is used to return the dimensions of the array. It provides information on how many elements exist in each dimension of the array.

How can you generate an array of zeros in NumPy?

To generate an array filled with zeros in NumPy, you can use the `np.zeros()` function, passing the number of elements or dimensions as an argument. For example, `np.zeros(10)` creates a one-dimensional array with 10 zeros.

What does the `np.arange()` function do?

The `np.arange()` function creates a NumPy array with a sequence of numbers within a specified range. You can also define the step size for the range. For example, `np.arange(0, 10, 2)` creates an array with values `[0, 2, 4, 6, 8]`.

What is an N-dimensional array in NumPy?

An N-dimensional array (ndarray) in NumPy refers to an array that can have any number of dimensions, not limited to one or two. This is particularly useful for machine learning and data science, where multi-dimensional data is common.

How can you create multi-dimensional arrays in NumPy?

You can create multi-dimensional arrays in NumPy by passing a tuple representing the dimensions to functions like `np.zeros()` or `np.full()`. For example, `np.zeros((2, 10))` creates a 2D array with two rows and 10 columns filled with zeros.

What are some basic functions provided by NumPy for array manipulation?

NumPy provides many functions for array manipulation, such as `np.zeros()` to create arrays of zeros, `np.full()` to create arrays filled with a specific value, `np.shape` to get the dimensions, and `np.arange()` to create sequences of numbers.

Outlines

00:00

💡 Introduction to NumPy for Machine Learning

In this video, John Alderhood from Codingman.com introduces NumPy, an essential library for machine learning and data science. He emphasizes the importance of understanding NumPy for handling large datasets efficiently, as lists in Python can be slow and memory-intensive. John also promotes Codingman.com courses and offers a 50% discount with a special coupon code.

05:01

🔢 Introduction to Python Lists and Their Limitations

John demonstrates how Python lists work, creating a simple list and explaining how they can contain different data types such as strings, numbers, and booleans. He explains the inefficiency of using lists for large datasets in machine learning due to their slow speed and high memory usage, which makes NumPy a better alternative.

10:03

⚡ Transitioning to NumPy Arrays for Efficient Data Handling

John introduces NumPy arrays as a powerful alternative to Python lists, explaining that all elements in a NumPy array must be of the same data type. He shows how to install NumPy and create an array. He emphasizes that NumPy arrays are more efficient for handling large datasets, especially in machine learning, as they reduce computational overhead.

📏 Exploring Basic NumPy Functions

In this section, John demonstrates basic functions in NumPy such as checking the shape of an array and creating arrays using the `arange()` and `zeros()` functions. He explains the utility of these functions in creating arrays with specific values or ranges, as well as the ability to generate multi-dimensional arrays.

🔄 Creating Multi-Dimensional Arrays in NumPy

John expands on multi-dimensional arrays, showing how to create arrays with multiple dimensions using the `zeros()` and `full()` functions. He demonstrates how to customize the dimensions and fill the arrays with specific values, illustrating the flexibility and power of NumPy for handling complex data structures.

🔄 Converting Python Lists to NumPy Arrays

John explains how to convert regular Python lists into NumPy arrays, making it easier to work with data in machine learning projects. He highlights that accessing elements from NumPy arrays works just like accessing elements from Python lists, making the transition between the two simple and intuitive.

🚀 Preview of Upcoming NumPy Tutorials

John wraps up by encouraging viewers to stick with the series, promising more advanced topics such as using Pandas and Scikit-learn. He emphasizes the importance of mastering NumPy for machine learning and encourages viewers to stay tuned for future videos that will build on these foundational concepts.

Keywords

💡NumPy

NumPy is a library in Python used for numerical computations. In the video, the speaker emphasizes its importance for machine learning and data science because of its ability to handle large datasets efficiently. NumPy arrays are the core data structure, and the speaker explains how they are more powerful than Python lists due to their homogeneous nature and optimized performance for numerical tasks.

💡Array

An array in NumPy is a collection of elements, all of the same type, arranged in a grid-like format. The speaker compares arrays with Python lists, highlighting that arrays are more efficient for handling large datasets because they require less memory and perform computations faster. Arrays are fundamental in NumPy and machine learning, particularly when working with large amounts of data.

💡Python List

A Python list is a built-in data structure that can hold multiple data types, including strings, numbers, and other lists. The video contrasts Python lists with NumPy arrays, explaining that lists can be inefficient for large datasets because they allow mixed data types, which increases computational complexity and memory usage. The flexibility of lists becomes a disadvantage when dealing with massive amounts of data in machine learning.

💡Machine Learning

Machine learning is a field of study that focuses on using algorithms to allow computers to learn from and make predictions on data. The video introduces NumPy as a foundational tool for machine learning, as it is used to process and manipulate the vast amounts of numerical data that machine learning models rely on. Efficient data handling through NumPy arrays is critical in this domain.

💡Computational Efficiency

Computational efficiency refers to how quickly and effectively a program or algorithm performs a task. The speaker highlights how NumPy arrays are much more computationally efficient than Python lists, particularly when working with large datasets in machine learning. This efficiency is vital because machine learning models often involve processing millions or billions of data points.

💡Data Science

Data science involves analyzing and interpreting complex data to extract meaningful insights. NumPy is a key tool in data science because it provides fast, efficient array manipulations, which are essential when working with large datasets. In the video, the speaker emphasizes that understanding NumPy is crucial for anyone interested in pursuing data science.

💡N-dimensional Array (ndarray)

An N-dimensional array, or ndarray, is the core data structure of NumPy, allowing data to be stored in multiple dimensions (e.g., 1D, 2D, 3D, etc.). The speaker introduces this concept as being central to NumPy and explains that these arrays are powerful for machine learning and data science applications that require handling multi-dimensional datasets.

💡Pip Install

Pip is a package manager for Python that allows users to install and manage software packages. The video demonstrates how to install NumPy using the command 'pip install numpy,' which is necessary because NumPy does not come pre-installed with Python. This step ensures that users have the tools needed to work with large datasets in machine learning.

💡Memory Usage

Memory usage refers to the amount of RAM a program consumes while processing data. The speaker notes that NumPy arrays are more memory-efficient than Python lists because they store data in a more compact form. This efficiency is particularly important in machine learning, where datasets can be massive, and reducing memory usage can significantly speed up computations.

💡Shape

In NumPy, the shape of an array refers to its dimensions (e.g., the number of rows and columns in a 2D array). The video explains that using the 'shape' attribute allows users to understand the structure of an array. This is useful for machine learning, where data often comes in multi-dimensional forms, and understanding the shape is essential for properly processing the data.

Highlights

Introduction to using NumPy for machine learning, essential for handling large datasets.

Explanation of the importance of understanding NumPy for data science and machine learning.

Lists in Python can hold different data types, which becomes computationally expensive with large datasets.

NumPy arrays are more efficient for handling massive datasets as they enforce uniform data types.

NumPy arrays are faster and more memory-efficient compared to Python lists when dealing with large-scale data.

NumPy stands for Numeric Python and deals primarily with arrays.

The basic data type in NumPy is called 'ndarray,' which can have multiple dimensions.

Demonstration of creating basic NumPy arrays and how they differ from Python lists.

Explanation of how NumPy arrays only allow one data type (e.g., all numbers, booleans).

Introduction to NumPy's 'shape' function, which returns the number of elements in an array.

Using the 'arange' function in NumPy to create arrays with custom step sizes (e.g., 0, 2, 4...).

Creation of arrays filled with zeros using the 'zeros' function.

Demonstration of multi-dimensional arrays with NumPy.

Conversion of Python lists to NumPy arrays for optimized data handling.

Explanation of accessing elements from NumPy arrays, similar to Python lists.

Transcripts

what's going on guys john alderhood from

codingman.com and in this video we're

gonna look at numpy for machine learning

all right guys like i said in this video

we're gonna start to look at numpy for

machine learning before we get started

if you like this video want to see more

like it be sure to smash like button

below subscribe to the channel give me a

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check out codingme.com we have dozens of

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which is insanely cheap okay like i said

in this video we're going to start to

look at numpy and we're going to do a

series of videos on numpy here because

numpy is fundamental to machine learning

data science all that good stuff and a

lot of times people kind of just gloss

over it you kind of think you know how

to use it you use it for a few things

but you don't really know what you're

doing with it or whatever if you want to

get into machine learning or data

science or anything like that it's a

really good idea to have a solid

understanding of numpy and that's we're

going to look at in this series of

videos so in this video i just want to

start to talk about what is numpy what

do you use it for start to wrap our

brains around exactly what it's all

about so numpy very simply is just a

special type of array in python and we

have arrays or lists as python calls

them so let's head over to our code i'm

using the sublime text editor and the

git bash terminal as always and as

always you can find a link to the code

in the pin comment section below as well

as a link to the playlist with all the

other numpy videos in this series so

check that out if you haven't so far so

i've got a file i'm just calling it

intro.pi so let's come up here and let's

just create a basic python list i'm

going to call it list one right and we

know python lists we can go you know

something like that so if we wanted to

then print out this list very simply we

could do that save this head over to our

terminal in my c num directory i just

created a directory called num and let's

just run python intro.pi and it just

prints out the python list so we already

know all about this we wanted to print

out you know an item from this list

we could say the first item list start

at zero that would print out one if we

came back over here ran this guy again

it'd print out one right so that's just

your basic python list now that's cool

that's great for a lot of things

but we can run into some problems using

lists for large sets of data and the

reason why

is because lists can have lots of

different data types in them so we can

have you know john elder we can have a

number 41 we can have another list we

can have booleans true false whatever so

if we print this guy list two you know

we know what's going to happen here come

back over here

you know we've got strings we've got

numbers we've got other lists we've got

booleans and now this is really cool a

lot of programming languages won't allow

you to do this in an array but python

does with lists and that's great but

it's a problem when you start using

massive amounts of data and machine

learning is all about using massive

amounts of data so the reason why it's a

problem is because doing it like this is

very slow it's computationally expensive

right it costs a lot of memory and all

that stuff to deal with these lists now

when we're dealing with a little bitty

list like this it's no big deal but when

you're dealing with lists with millions

of records or billions of records

terabytes of records it becomes a lot

harder to do it comes a lot slower takes

a lot more time takes a lot more

computational juju and all of that stuff

so

instead of using a list for data science

and machine learning we use something

called a numpy array and a numpy array

is just a list

but it's different right it's more

powerful it has cooler things so we can

come over here and we can import numpy

so let's go import

numpy

and the convention is to import it as np

and you'll see why in just a second now

this does not come with python we need

to install it so let's head back over to

our terminal

and let's go pip

install numpy

and i've already got it so it's saying

hey you've already got it but it will

install on your computer and that's all

there is to it you're good to go

so now let's head back over here and let

me just kind of

comment out that stuff here and numpy if

you haven't figured it out yet stands

for numeric python

right we're creating numpy arrays which

are just like lists only they're

different couple of things to keep in

mind

anything in a numpy array has to be of

the same data type right so they have to

be all numbers or they have to be all

booleans or they have to be all whatever

they all have to be the same data type

which is usually okay because in machine

learning you're often dealing with

numbers right so the data type of a

numpy is an nd array and that stands for

in

dimensional

array so you know lists can have

multi-dimensions so can numpy arrays so

you might have one dimension you might

have two dimensions you might have three

dimensions you might have 100 dimensions

you have n dimensions like in math when

you don't know how many use n so n

dimensional arrays that's a numpy array

so let's just go ahead and create one of

these real quick let's go i'm just going

to call it mp1 short for numpy one i

guess we can set this equal to a n p dot

array and then inside of here we can

create our numpy array now notice this

np that's because we imported this as np

so we have to sort of slap an mp in

front of everything we want to create so

we want to create a numpy array

this is how we do it now inside of here

lots of different ways you can do this

we can go you know zero one two three

four five six seven

eight

nine

remember all arrays whether numpy or

list or whatever they always start at

zero so this is the zeroth item one two

three four we have 10 items in here so

if we want to print this out see what it

looks like in p1 we can head back over

here run this guy again and we can see

it looks just like a regular python list

so don't get weird about numpy arrays

they're really just basically lists only

they're more powerful now numpy itself

doesn't just have arrays it has a whole

sort of suite of math functions that you

can do stuff to those arrays as well as

other types of functions so let's spend

the rest of this video just kind of

familiarizing ourselves with some very

basic sort of functions and things that

come with numpy so first of all we can

see the shape of our array so we can

call mp1 dot shape right and this will

tell us basically the number of elements

in our array think of it as the lend

function in python sort of right so if

we do this print this guy real quick

do that come back over here we see

there's 10 items in this array and we

know that's true one two three four five

six seven eight nine

ten right ten items so that's always

interesting to be able to do so that's

cool so there's lots of different ways

you can create numpy arrays we can use

the a range function if we don't wanna

you know explicitly do something like

this we could go np

dot a range and we could say 10 right so

here if we print this guy and p2

save this run

it we get the same thing we get a numpy

array with 10 items in it just from zero

to nine and that's cool well this is

from zero to nine zero one two three we

can tell this to step instead of doing

every one like zero one two three we

could say hey do it every two so two

four six or every three three six nine

whatever we want so we could just sort

of modify this a little bit now let's

see

range here here let's say step so we go

mp3

equals an mp

dot a range we could say from zero to

ten but step

two right so if we want to print this

guy

we can do that back over here

run the sky again and here we see we've

created an umpire array with steps of

two so zero two four six eight right so

that's helpful

we can create an array of zeros right so

let's go np4

equals

mp.zeros use the zeros function

and again let's say we want 10 items

right so we can print this guy and p4

save this head back over here run this

guy again so now you see we have ten

zeros and they're actually zero point so

it's not strictly an integer it's

zero point this is not a separating

factor it's not like a comma just think

of these as zero point zero basically

right so that's cool we're dealing with

one-dimensional arrays here right you

can do multi-dimensional arrays just as

easily and numpy is great at

multi-dimensional so let's go

multi-dimensional

zeros all right stick with the zero

theme here so we go np dot zeros

and use double parenthesis here so we

start out how many dimensions do we want

let's say

two dimensions and how many items in

each dimension let's say 10 right stick

with the 10. and so let's print out this

guy

save this run it

you can see here we have two dimensions

right here's the first one there's ten

zeros in it here's the second one ten

zeros in it you can see they're

separated just by their brackets right

pretty cool pretty easy say we don't

want to use zeros right

we can use the full function so let's go

in p6 equals np.full

all right so let's say we want 10 items

and what do we want them to be well we

don't want them to be zeros let's have

them be sixes right or whatever right so

we can print np six save this guy run it

and you can see now we have

an array of sixes right

very cool we could do multi-dimensional

full so let's go mp7 equals mp.full

and let's say we want two dimensions

with 10 elements in each dimension and

again let's say we want to fill it with

sixes right so let's print

np7 save this guy head back over here

run it again

and here we have two dimensions of sixes

right there's the first one there's the

second one so just a few functions to

play around with just to sort of

familiarize ourselves with numpy we can

actually convert

python lists

to numpy

so let's create a list i don't know my

list

set that equal to a python list of one

two three four five whatever now if we

wanted to convert that into a numpy

array we could let's go mp8

equals and np dot array and all we have

to do is just pass in my list

numpy will convert it for us and if we

print out

np8

we can see

it's going to look just like a regular

list but rest sure that has been

converted to a numpy

array so accessing specific elements out

of a numpy array is exactly the same as

accessing any element out of any python

list so we could print np8 and let's say

we want the zeroth item we just pop in a

zero there and that will print out one

because that's the zeroth item we save

this and run it we see sure enough it

just prints out one so this is just a

very brief introduction to numpy i don't

want to overwhelm you in this first

video there's all kinds of cool things

you could do with numpy specifically for

machine learning and we're going to

start to learn all about all of those

things in the coming videos in this

playlist so if you guys are interested

in this let me know i'll continue on and

my goal is to start to learn more about

numpy and then sort of progress to other

machine learning things like pandas

scikit-learn all that stuff and then

slowly build up a skill set for you guys

for basic machine learning things and

then we can get into algorithms and all

that cool stuff but we need the basics

first we need to understand this

you know kind of boring stuff numpy

arrays this is not exciting it's a

little bit boring but it could be kind

of fun too so stick with me on this

we'll learn this stuff we'll knock this

stuff out and then we'll move into more

interesting things but uh should be fun

so that's all for this video if you

liked it be sure to smash like button

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