Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 17461
Noureddin Sadawi

Textbooks:
https://amzn.to/2VmpDwK
https://amzn.to/2GQSV3D
https://amzn.to/2SvTOQx
Welcome to Engineering Python. This is a Python programming course for engineers.
In this video, I'll talk about NumPy array indexing and slicing.
The course materials are available on YouTube and GitHub.
http://youtube.com/yongtwang
http://github.com/yongtwang
----------------------------------------
Smart Energy Operations Research Lab (SEORL): http://binghamton.edu/seorl

Views: 597
Yong Wang

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 4039
Noureddin Sadawi

This video walks through array indexing examples. Array[rowstart:rowend, columnstart:columnend] It also shows how to get the diagonal using np.diag().
This is a Python anaconda tutorial for help with coding, programming, or computer science. These are short python videos dedicated to troubleshooting python problems and learning Python syntax. For more videos see Python Help playlist by Rylan Fowers.
✅Subscribe: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow?sub_confirmation=1
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▶️Watch Latest Python Content: https://www.youtube.com/watch?v=myCPgAO9BgQ&list=PLL3Qv26_SCsGWTF5PRaWUY0yhURFvco7L
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The creator studies Applied and Computational Mathematics at BYU (BYU ACME or BYU Applied Math) and does work for the BYU Chemical Engineering Department
ARRAY INDEXING
Array indexing is very important to know. I will introduce it here.
We import numpy as np, since we will be creating arrays
For this example I will make a random matrix A with numbers between -5, and 5.we don’t need to import random. We will make it (3,3)
And we will change it to ints really quick
So here is A
Let’s bring it up again so we can have it for reference.
First if you want any entry in the array simply type its corresponding row and column index location with a comma separating.
Don’t forget that when coding, the first number is always 0.
So we follow row position 2, and column position 1 which gives us our -1
Now we type 1 colon. This starts from the 1 position row, and the colon tells it to go to the end. So this will be the 1 position row to the last position row.
Let’s compare this to colon 1. This does all the rows up to but not including the row in position one. So it will just print out the row in position 0.
Next let’s bring up A again for reference
1 colon, comma 1. After the comma it references columns. So this is the 1 position row to the end towards the bottom and taken specifically from the 1 position column
Next we have 1 comma 1 colon. This will be the row in the second position, and then the column from the first position to the end.
Now, we do 0 colon comma 1 colon 2. This will take the row in the 0th position to the end, but limit it to only the row in column position 1 up to but not including column position 2.
So that will give the middle column, as we see here.
Something good to remember for this video when indexing arrays is that rows (or the first numbers in the index) move you up and down and columns (the second numbers in the index) move you left and right
lastly I will quickly show you an easy way to get the diagonal of the matrix.
np.diag(A) will return an array with the diagonal
You can change the index with a keyword argument if you want above or below.
For here we have one above
Now we will do a negative to go below the diagonal.
There you have it, that is an introduction of python numpy array indexing

Views: 345
Rylan Fowers

In this Python NumPy Tutorial on Data Science, We discuss Numpy Indexing and Slicing Arrays. We Learn Numpy Boolean Indexing. NumPy is the ultimate package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, tools for integrating C/C++ and Fortran code, sophisticated (broadcasting) functions, useful linear algebra, random number capabilities and Fourier transform.
Basic slicing ( 0:32 ) extends Python’s basic concept of slicing to N dimensions. Basic slicing occurs when obj is a slice object (constructed by start:stop:step notation inside of brackets) .
NumPy Boolean arrays ( 8:12 ) used as indices are treated in a different manner entirely than index arrays. Boolean arrays must be of the same shape as the initial dimensions of the array being indexed. In the most straightforward case, the boolean array has the same shape.
****************************************************************
$$ What is Jupyter Notebook ? Introduction to Markdowns https://youtu.be/IdakPcu75ho
$$ Create Arrays Using NumPy Methods & Python Structures https://youtu.be/YNIwYUbL4qo
****************************************************************
*** Complete Python Programming Playlists ***
* Complete Playlist of Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Complete Play list of Python Smart Programming in Jupyter Notebook:
https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2
* Complete Playlist of Python Data Science
https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK
* Complete Play List of Python Coding Interview:
https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
****************************************************************
NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.
Topics include:
1. Using Jupyter Notebook
2. Creating NumPy arrays from Python structures
3. Slicing arrays
4. Using Boolean masking and broadcasting techniques
5. Plotting in Jupyter notebooks
6. Joining and splitting arrays
7. Rearranging array elements
8. Creating universal functions
9. Finding patterns
10. Building magic squares and magic cubes with NumPy and Python

Views: 1827
TheEngineeringWorld

Slicing, bool arrays, and logical indexing

Views: 956
Rich Colburn

'''
Python Basics - Session # 6
Topic to be covered - Numpy in Python
1. What is Numpy
2. Creating Numpy
3. Accessing Numpy elements
4. Updating Numpy
5. Indexing / Slicing in Numpy
6. Basic Operations in Numpy
7. Functions using Numpy
mean, max, min, sort, var, std, argmin, argmax, nonzero, where, extract,
8. Broadcasting in Numpy
9. Numpy String Functions
10. Storage Comparision between List and Numpy
11. Processing time comparision between LiSst and Numpy
12. Matrix / Linear Algebra using Numpy
13. Iterations with Numpy
14. Numpy - converting to hexadecimal
15. I/O with Numpy
16. Matplotlib with Numpy
Various options to be explored
Barplot
'''
###############################################################################
# 1. What is Numpy ?
'''
1. Numpy is a library for scientific computing.
2. Numpys stands for Numerical Python.
3. Numpy consists of Multidimensional array objects and it has collection of
functions/routines to process those arrays.
4. There are advantages of using Numpy
a. Takes less memory as compared to List
b. Processing speed of numpy array is much higher.
'''
###############################################################################
# 2. How do we create numpy array?
import numpy as np
x = np.array([1,2,3])
print(x)
print(x.dtype)
x = np.array([1,2,3.0])
print(x.dtype)
print(x)
x = np.array([10,20,30,40,50], ndmin = 3)
print(x)
print(x.size)
print(x.shape)
###############################################################################
# 3. Accessing Numpy Elements
x = np.array([10,20,30,40,50])
print(x[2])
print(x[-1])
print(x[-3])
###############################################################################
# 4. Updating Numpy array
print(x)
x[2] = 80
print(x)
###############################################################################
# 5. Indexing / Slicing in Numpy
# Type 1
x = np.arange(10)
s = slice(2,9,2)
print(x[s])
print(x[slice(0,8,2)])
print(x[slice(1,8,3)])
print(x[0:8:2])
print(x[1:8:3])
x = np.arange(20)
y = x[10]
print(y)
y = x[:10]
print(y)
y = x[10:]
print(y)
print(y[2:8])
print(y[2:10:2])
print(y[2:10:3])
#
x = np.array([[10,20,30], [40,50,60], [70,80,90]])
print(x)
'''
[[10 20 30] ----- 0
[40 50 60] ----- 1
[70 80 90]] ----- 2
'''
######
print(x[1:])
print(x[2:])
print(x[0:])
print(x[3:])
print(x[:,0])
print(x[:,1])
print(x[:,2])
###############################################################################
# 6. Basic Operations in Numpy
x = [10,20,30]
y = [30,60,70]
print(x + y)
print(y / 10)
x = np.array([10,20,30])
y = np.array([30,60,70])
print(x+y)
print( y / 10)
print ( x * 10)
###############################################################################
#7. Functions using Numpy
# mean, max, min, sort, var, std, argmin, argmax, nonzero, where, extract,
Sachin_runs = np.array([110,105,155,0,191,174,0])
print(np.mean(Sachin_runs))
print(np.min(Sachin_runs))
print(np.max(Sachin_runs))
print(np.var(Sachin_runs))
print(np.std(Sachin_runs))
print(np.argmax(Sachin_runs))
print(np.argmin(Sachin_runs))
print(np.nonzero(Sachin_runs))
print(np.where(Sachin_runs GT 120))
condition = (Sachin_runs GT 100) & (Sachin_runs LT 160)
print(np.extract(condition, Sachin_runs))
###############################################################################

Views: 215
MachineLearning with Python

Learn how to do array index slicing in Numpy Python.

Views: 3817
DevNami

This tutorial covers various operations around array object in numpy such as array properties (ndim,shape,itemsize,size etc.), math operations (min,max,sqrt,std etc.), arange, reshape etc. Please give thumbs up/subscribe/comment if you like this tutorial.
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Google +: https://plus.google.com/106698781833798756600

Views: 66877
codebasics

Arrays are collections of strings, numbers, or other objects. This tutorial demonstrates how to create and manipulate arrays in Python with Numpy.

Views: 129284
APMonitor.com

Learn the various ways of numpy array indexing in this video.
https://github.com/nikhilkumarsingh/IntroToNumpy
More awesome topics covered here:
WhatsApp Bot using Twilio and Python: http://bit.ly/2JmZaNG
Discovering Hidden APIs: http://bit.ly/2umeMHb
RegEx in Python: http://bit.ly/2Hhtd6L
Introduction to Numpy: http://bit.ly/2RZMxvO
Introduction to Matplotlib: http://bit.ly/2UzwfqH
Introduction to Pandas: http://bit.ly/2GkDvma
Intermediate Python: http://bit.ly/2sdlEFs
Functional Programming in Python: http://bit.ly/2FaEFB7
Python Package Publishing: http://bit.ly/2SCLkaj
Multithreading in Python: http://bit.ly/2RzB1GD
Multiprocessing in Python: http://bit.ly/2Fc9Xrp
Parallel Programming in Python: http://bit.ly/2C4U81k
Concurrent Programming in Python: http://bit.ly/2BYiREw
Dataclasses in Python: http://bit.ly/2SDYQub
Exploring YouTube Data API: http://bit.ly/2AvToSW
Jupyter Notebook (Tips, Tricks and Hacks): http://bit.ly/2At7x3h
Decorators in Python: http://bit.ly/2sdloX0
Inside Python: http://bit.ly/2Qr9gLG
Exploring datetime: http://bit.ly/2VyGZGN
Computer Vision for noobs: http://bit.ly/2RadooB
Python for web: http://bit.ly/2SEZFmo
Awesome Linux Terminal: http://bit.ly/2VwdTYH
Tips, tricks, hacks and APIs: http://bit.ly/2Rajllx
Optical Character Recognition: http://bit.ly/2LZ8IfL
Facebook Messenger Bot Tutorial: http://bit.ly/2BYjON6
#numpy #array #indexing#numpy #array #indexing

Views: 63
Indian Pythonista

Learn to work with the Numpy array, a faster and more powerful alternative to the list

Views: 35522
DataCamp

This Is Our 13 th Video In Numpy Array Python For Data Science Or Data Manipulating, In This Video We Are Going To Cover Numpy Array Fancy Indexing
Python Data Science Playlist
https://www.youtube.com/watch?v=k9A5oxTTLeE&list=PL1FgJUcJJ03vXmv0nUOxJd1TL7C1JBHNV

Views: 169
Parwiz Forogh

www.Stats-Lab.com | Data Analysis with Python | Numerical Computing with Python

Views: 3927
Dragonfly Statistics

In this tutorial, we learn to reshape NumPy arrays using the reshape( ) function. We use it to convert one dimensional arrays to two/multi dimensional arrays.

Views: 8287
Rsquared Academy

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

Views: 3591
Udacity

The basics of slicing 1- and 2-dimensional NumPy arrays. From the introductory Data Science with Python 3 course, available for $10 here: https://www.udemy.com/transition-to-data-science-in-python/?couponCode=YOUTUBE

Views: 752
Benjamin Wilson

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 2548
Noureddin Sadawi

Numpy Arrays #3: Numpy Arrays Dtypes, Indexing & Slicing

Views: 5245
PyCursos

Eric Jones, co-author of SciPy and CEO of Enthought, Inc. demonstrates the use of fancy indexing for the selection of values from a NumPy array. The PyLab environment used for the exercise is available in the Enthought Python Distribution at http://enthought.com/products/epd.php or from the matplotlib Sourceforge page at http://matplotlib.sourceforge.net/index.html.

Views: 4288
Enthought

Numpy Arrays #4: Views, Copy e Fancy Indexing

Views: 755
PyCursos

In this lesson, “Python Numpy – Creating Empty Array”, I discussed how you can create a Numpy Empty Array.
In Numpy, you will use empty() function to create empty array. It accepts the shape of the array as a parameter and generates required array for you with No value initialized at any index. It is the responsibility of the developer to use this function carefully and get all values initialised or updated before making use of the array otherwise it could be problematic also.
In this lesson, you will learn:
1. How to create single dimensional – Numpy Empty Array.
2. How to create two-dimensional – Numpy Empty Array.
3. Assigning Numpy Data Type (dtype) while creating Numpy Empty Array.
4. Checking Numpy Array Type (dtype)
https://youtu.be/TIRvp8gyZAA
*********************************************************************
Please subscribe to my channel:
https://www.youtube.com/c/ashmanmalhotra?sub_confirmation=1
*********************************************************************
Thank you for watching my video on "Python Numpy – Array of Ones"
*********************************************************************
Contact: [email protected] for training inquiries
*********************************************************************
"Python Numpy Tutorials" | "Python Numpy" | "Numpy" | "Data Science" | "Data Science Using Python" | "Python Numpy – Creating Empty Array"

Views: 1513
Ashman Malhotra

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 5827
Noureddin Sadawi

https://premium.mysirg.com
https://mysirg.com
http://saurabhshuklaclasses.com
Like, Comments, Share and SUBSCRIBE
visit www.mysirg.com for all FREE videos

Views: 5127
MySirG.com

Eric Jones, co-author of SciPy and CEO of Enthought, Inc. demonstrates basic math with 1D NumPy arrays along with array creation with arange and linspace. A speed comparison between operations done with NumPy arrays and those done with Python lists illustrates that NumPy arrays provide significant gains in processing speed.

Views: 34170
Enthought

In this lesson, “Numpy Array of Zeros”, I discussed how you can create array of zeros.
In Numpy, you will use zeros() function to create array of zeros. It accepts shape of the array as parameter and generates required array for you with zeros at each index.
In this lesson, you will learn:
1. How to create single dimensional – Numpy Array of Zeros
2. How to create two dimensional – Numpy Array of Zeros
3. Assigning Numpy Data Type (dtype) while creating Numpy Array of Zeros
4. Checking Numpy Array Type (dtype)
https://youtu.be/7pHBdm7nzFk
*********************************************************************
Please subscribe to my channel:
https://www.youtube.com/c/ashmanmalhotra?sub_confirmation=1
*********************************************************************
Thank you for watching my video on "Python Numpy – Array of Zeros"
*********************************************************************
Contact: [email protected] for training inquiries
*********************************************************************
"Python Numpy Tutorials" | "Python Numpy" | "Numpy" | "Data Science" | "Data Science Using Python" | "Python Numpy – Array of Zeros"

Views: 415
Ashman Malhotra

Array indexing refers to any use of the square brackets [] to index array values. There are many options available for indexing, which give NumPy indexing a great power, but with power comes some complexity and potential room for some confusion. This section is just an overview of the various options and issues related to indexing.
detail explanation, please direct yourself to the below mentioned weblink:
https://deephobbying.com/numpy/indexing/
You could find this tutorial’s code in the below mentioned GitHub repo:
https://github.com/DeepHobbying/Getting-Started-with-Numpy/blob/master/LT2_Indexing.py

Views: 57
Deep Hobbying

Views: 311
Cognitive Class

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

Views: 3565
Udacity

Numpy and it's importance/value,Numpy Array, Numpy ndarray indexing, ndarray boolean indexing,ndarray data types,Arithmetic array operation, statistical operations using Numpy, Functions, sort,unique,union,intersection,subsets, broadcasting using Numpy in Urdu/Hindi

Views: 80
Saima Academy

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 9942
Noureddin Sadawi

In this video we cover a lot of the basic operations available in NumPy like array addition, subtraction, multiplication, finding the max, argmax, min, argmin etc. We also touch on indexing and how you access the specific values you want with slicing.
If you have any questions or would like to get involved in the community join the discord at https://discord.gg/bevYwcG

Views: 637
IT Connected

( Python Training : https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
Subscribe to our channel to get video updates. Hit the subscribe button above.
#Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonNumpy
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
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LinkedIn: https://www.linkedin.com/company/edureka

Views: 179770
edureka!

Numpy array slicing. Learn how to slice arrays in numpy. Numpy array slicing takes the form numpy_array[start:stop:step] in this short tutorial I show you how to use array slicing in numpy. This is part of my wider course on Data Science with Python.
If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer
More Python Learning resources:-
Learn Python - https://www.learnpython.org/
Google's Python Class - https://developers.google.com/edu/python/
My Python Course - https://www.youtube.com/watch?v=Aah3TmR-dHc&list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J
### Books (affiliate links)
1. Automate the Boring Stuff With Python - http://amzn.to/2kSPOtA
(or for free here https://automatetheboringstuff.com/ )
2. Python Crash Course -http://amzn.to/2BsorSq
3. Effective Computation in Physics - http://amzn.to/2BJxVFC
4. Learn Python the Hard Way - http://amzn.to/2p4TQVd

Views: 2001
Python Programmer

Download Code:
https://github.com/EnggQasim/UIT/blob/master/Numpy/Chapter%205%20(Index%2C%20Slice%20and%20Reshape%20NumPy%20Arrays).ipynb

Views: 103
IT Expert By Sir Qasim

This video goes through numpy array masking by showing you how to do it on a random matrix. A mask creates a matrix that has boolean values that match the mask statement. You can use the mask to examine specific parts of a matrix. Here is some syntax: np.random.random(()) and mask = A greater than 0.
Comment for any questions.
This is a Python anaconda tutorial for help with coding, programming, or computer science. These are short python videos dedicated to troubleshooting python problems and learning Python syntax. For more videos see Python Help playlist by Rylan Fowers.
✅Subscribe: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow?sub_confirmation=1
📺Channel: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow?
▶️Python Arrays: https://www.youtube.com/watch?v=y7GhAkAGv7c
▶️Array VstacK: https://www.youtube.com/watch?v=icRukKNvito
▶️Array Hstack: https://www.youtube.com/watch?v=kqXEewzHnQw
▶️Watch Latest Python Content: https://www.youtube.com/watch?v=myCPgAO9BgQ&list=PLL3Qv26_SCsGWTF5PRaWUY0yhURFvco7L
▶️Watch Latest Other Content: https://www.youtube.com/watch?v=2YfQsLd2Ups&list=PLL3Qv26_SCsFVXXdsxOSB00RSByLSJICj&index=1
🐦Follow Rylan on Twitter: https://twitter.com/rylanpfowers
The creator studies Applied and Computational Mathematics at BYU (BYU ACME or BYU Applied Math) and does work for the BYU Chemical Engineering Department
ARRAY MASKING
array masking is a powerful coding tool and I will show you how it works here.
First, so we can make matrices we import numpy as np
And today I’ll show you with a random matrix so we will import random.
First we will make a random 5x5 matrix with entries between -5 and 5.
To create a random matrix we type np.random.random(()) and insert a tuple with the size of the desired matrix
Normally np.random.random will automatically create random numbers between 0 and 1, so we will just multiply by 10 and subtract 5 to make this between -5, 5
So here is our random 5x5 matrix A
And now we will create the mask. Let’s call this mask, though you can name it anything.
We will do a mask that is anywhere A is greater than 0
When we print out the mask it will be a matrix the same size as A with boolean values in every spot. True where the value is greater than 0 and False where the value is less than 0
Once we have this mask we can type A[mask]
This will be all the values of A that satisfy the mask.
So here it is, all the positive numbers in A
And Let’s show you A again.
There you have it, that is how you do numpy array masking

Views: 628
Rylan Fowers

In this episode of The IO Show, you will learn, how to create numpy arrays in python. Further we have also talked about the shape, size & dimension of the arrays.
Further in this tutorial, we have talked about indexing & slicing of the array.
I hope you will like this episode of The IO Show. Stay tuned for more interesting tutorials.
For any help & queries, Find us on Instagram: @theioshow

Views: 166
The IO Show

#Numpy #Matplotlib #MachineLearning #DataAnalytics #DataScience
This Tutorial is a part of the series Data Analytics with Python.
This video is a tutorial to learning Numpy and Matplotlib in Python.
What is Numpy used for ?
Numpy arrays are very fast and efficient for mathematical operations. The ndarrays for Numpy add functionality for multi dimentional arrays.
What is Matplotlib?
Matplotlib is an extension for Numpy with the ability of plotting graphs and Data Visualization.
The functions covered in this tutorial are:
Numpy :
- List to numpy array
- Multiplication
- np.arange (Generating numbers with specified gaps)
- Multidimentional Array
- ndim (checking the dimensions of array)
- np.shape()
- np.random.randn()
- Accessing via Index
Matplotlib:
- pyplot
- Adding labels
- Changing scale of Axis
- Different color and shape of plot points
- Plot more than one graph
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Views: 7355
The Semicolon

This video will teach different operation on array in numpy.
Indexing
Reshaping
Max, min, argmax, argmin, sort
+, - , *, /,Power
Mean, std
Cross, Dot
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Views: 932
MyStudy

This introductory homework assignment solution covers Numpy and loops (for and while) in Python. The example problems use simple vectors and matrices, reshaping, index referencing, initialization, dot product, cross product, matrix inverse, size, and range.

Views: 5645
APMonitor.com

https://github.com/codebasics/py/blob/master/numpy/nditer.ipynb
nditer can be used to iterate through numpy array in variety of ways. C style and F style iteration is possible using flags in nditer. You can also iterate two broadcastable arrays concurrently using nditer
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codebasics

In this NumPy Python Data Science Tutorial, i discuss NumPy Structured arrays and NumPy Record arrays. Structured arrays use structured data type.
NumPy Structured arrays ( 1:20 ) are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields.
NumPy Record Arrays ( 7:55 ) use a special datatype, numpy.record, that allows field access by attribute on the structured scalars obtained from the array.
NumPy is the ultimate package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, tools for integrating C/C++ and Fortran code, sophisticated (broadcasting) functions, useful linear algebra, random number capabilities and Fourier transform.
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*** Complete Python Programming Playlists ***
* Complete Playlist of Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Complete Play list of Python Smart Programming in Jupyter Notebook:
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* Complete Play List of Python Coding Interview:
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. You will learn how to work with NumPy and Python within Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.
Topics include:
• Using Jupyter Notebook
• Creating NumPy arrays from Python structures - https://youtu.be/69ComsKKRvA
• Slicing arrays - https://youtu.be/z4vDLNMDFE4
• Using Boolean masking and broadcasting techniques - https://youtu.be/QD6IBF0Hic4
• Plotting in Jupyter notebooks
• Joining and splitting arrays
• Rearranging array elements
• Creating universal functions
• Finding patterns
• Building magic squares and magic cubes with NumPy and Python
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Views: 1404
TheEngineeringWorld

This video teach different ways of creating numpy array.
Python List
arange
Linspace
Zeros, ones, empty
random
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Views: 1797
MyStudy

In this Python Numpy data Science Tutorial, We learn NumPy Functions numpy.append and numpy.hstack to Add and Remove Elements from NumPy Arrays as well as Horizontally and Vertically Stacking Arrays. Jupyter Notebook interactive environment is used for Coding.
Numpy Data Science Create Arrays Using NumPy Methods and Python Structures https://youtu.be/69ComsKKRvA
NumPy Indexing and Slicing Arrays, Boolean Mask Arrays , Numpy Python Data Science https://youtu.be/z4vDLNMDFE4
Computation On Arrays and NumPy Broadcasting Functionality In Python Data Science https://youtu.be/QD6IBF0Hic4
NumPy Arrays Tutorial, NumPy Structured Arrays vs Record Arrays in Python Data Science https://youtu.be/8y-o1zWSXR8
Create Plots and Figures in Python Using NumPy & Matplotlib Examples Tutorial Python Data Science 🐍 https://youtu.be/tC3qntC0hhU
NumPy Matplotlib Tutorial, Matplotlib Pie Charts, Bar charts, Box Plots In Python Data Science 🐍 https://youtu.be/tz1NuF7C0L0
NumPy Data Science, Learn Python Shallow Copy Vs Deep Copy, Data Science With Python Programming 🐍 https://youtu.be/qdAM-N1-Ajo
-----------------------------------------------------------------------------------------------------
*** Complete Python Programming Playlists ***
* Complete Playlist of Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Complete Play list of Python Smart Programming in Jupyter Notebook:
https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2
* Complete Playlist of Python Data Science
https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK
* Complete Play List of Python Coding Interview:
https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
* NumPy Data Science Essential Training with Python 3
https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b
-----------------------------------------------------------------------------------------------------

Views: 871
TheEngineeringWorld

Indexing can be done in numpy by using an array as an index. In case of slice, a view or shallow copy of the array is returned but in index array a copy of the original array is returned. Numpy arrays can be indexed with other arrays or any other sequence with the exception of tuples

Views: 21
SK TECH WARRIOR

In this video I am going to show How to use Slice function or slicing with Python Collections. Also I am going to show how to use Negative index with Python Collections. So What is Python Slice? A slize is a span of items that are taken from a sequence
List slicing format: list[start : end: step]. Span is a list containing copies of elements from start up to, but not including, end
If start not specified, 0 is used for start index. If end not specified, len(list) is used for end index. Slicing expressions can include a step value and negative indexes relative to end of list.
And What is Negative Indexing In Python: I a Python Collection such as Lists, Strings, Tuples, Bytes .. we can refer to an element by a negative index representing how far it is from the end.
example
# +---+---+---+---+---+---+
# | P | y | t | h | o | n |
# +---+---+---+---+---+---+
# 0 1 2 3 4 5 ---- Positive Index
# -6 -5 -4 -3 -2 -1 ---- Negative Index
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Views: 6551
ProgrammingKnowledge

A Moment with NumPy is a video series which explains the usage of individual functions of Numpy (A SciPy Library).
This video talks about how we can slice NumPy arrays for single as well as for multiple dimension. It clearly explains the syntax, its usage as well as how it can be extended to the 3rd dimension.
NumPy is a popular and widely used array for storing multi dimensional data and used in mathematical computations of multiple SciPy and Scikit-Learn Libraries
Hope it helps you in learning something new.. enjoy!
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Views: 37
CodesBay

Learn how to view the shape of an Array using Python Numpy.

Views: 8873
DevNami