Search results “Np array indices”

Visit my personal web-page for the Python code:
www.imperial.ac.uk/people/n.sadawi

Views: 13541
Noureddin Sadawi

This tutorial covers array operations such as slicing, indexing, stacking. We will also go over how to index one array with another boolean array.
Website: http://codebasicshub.com/
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Views: 34904
codebasics

Learn how to do array index slicing in Numpy Python.

Views: 3609
DevNami

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: 274
Yong Wang

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

Views: 115655
APMonitor.com

Visit my personal web-page for the Python code:
www.imperial.ac.uk/people/n.sadawi

Views: 3275
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.
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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: 199
Rylan Fowers

Slicing, bool arrays, and logical indexing

Views: 782
Rich Colburn

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:
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* Complete Playlist of Python Data Science
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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. 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: 591
TheEngineeringWorld

Python Tutorial to learn Python programming with examples
Complete Python Tutorial for Beginners Playlist : https://www.youtube.com/watch?v=hEgO047GxaQ&t=0s&index=2&list=PLsyeobzWxl7poL9JTVyndKe62ieoN-MZ3
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In this video we will see:
- Accepting values from user and store them in Array in python
- Creating blank array
- Asking length of array from user and accepting the values
- Printing index of array value manually
- Printing index value of user entered value
- Printing index of array value by function
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Views: 18077
Telusko

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

Views: 27949
DataCamp

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.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
*** 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. 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
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Views: 521
TheEngineeringWorld

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
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"Python Numpy Tutorials" | "Python Numpy" | "Numpy" | "Data Science" | "Data Science Using Python" | "Python Numpy – Array of Zeros"

Views: 308
Ashman Malhotra

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

Views: 3879
Dragonfly Statistics

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/
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Google +: https://plus.google.com/106698781833798756600

Views: 53680
codebasics

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

Views: 3230
Udacity

In this tutorial, we create NumPy arrays using the array function. We also look at various methods of specifying the data elements of the array.

Views: 774
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: 3327
Udacity

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: 4155
Enthought

'''
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: 54
MachineLearning with Python

Views: 153
Data Science for All

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

Views: 4916
PyCursos

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

Views: 7842
DevNami

Visit my personal web-page for the Python code:
www.imperial.ac.uk/people/n.sadawi

Views: 1987
Noureddin Sadawi

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: 72
Parwiz Forogh

In this tutorial, we learn to extract data elements from two dimensional NumPy arrays.

Views: 1822
Rsquared Academy

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: 33003
Enthought

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: 67
Deep Hobbying

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: 148
The IO Show

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: 383
Benjamin Wilson

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: 4936
APMonitor.com

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/ )
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Views: 1082
Python Programmer

Visit my personal web-page for the Python code:
www.imperial.ac.uk/people/n.sadawi

Views: 4346
Noureddin Sadawi

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
Website: http://codebasicshub.com/
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Views: 12435
codebasics

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: 6727
Rsquared Academy

Welcome to the Software Carpentry lecture on matrix programming. In this episode, we'll have a look at some of the ways you can index arrays.
Visit the Software Carpentry page for this episode at:
http://software-carpentry.org/4_0/matrix/indexing

Views: 4751
softwarecarpentry

Numpy Arrays #4: Views, Copy e Fancy Indexing

Views: 688
PyCursos

This video will teach different operation on array in numpy.
Indexing
Reshaping
Max, min, argmax, argmin, sort
+, - , *, /,Power
Mean, std
Cross, Dot
Visit complete course on Data Science with Python :
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090
For All other visit my udemy profile at :
https://www.udemy.com/user/ankitmistry/

Views: 825
MyStudy

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: 436
IT Connected

In this tutorial, we learn to change the dimensions of an array using the shape and resize functions. These functions are useful for in place reshaping as we can change the dimensions of the array without creating a new one.

Views: 1862
Rsquared Academy

#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
For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon
Facebook : https://www.facebook.com/thesemicolon.code
Support us on Patreon : https://www.patreon.com/thesemicolon
Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R

Views: 5847
The SemiColon

Com mais de 100 aulas de vídeo em HD e notebooks de códigos detalhados para cada vídeo, Python para Data Science e Machine Learning é um dos cursos mais abrangentes para ciência de dados e Machine Learning da Udemy!
Com esse material, você será capaz de usar NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning e muito mais.
Use o cupom a seguir para um desconto especial no curso!
https://www.udemy.com/python-para-data-science-e-machine-learning/?couponCode=_YOUTUBE

Views: 179
Python para Data Science e Machine Learning

PyData Madrid 2016
Most of the talks and workshop tutorials can be found here: https://github.com/PyDataMadrid2016/Conference-Info
Advanced (a.k.a. fancy) indexing is one of NumPy's greatest features. Once past the rather steep learning curve, it enables a very expressive and powerful syntax, and makes coding a wide range of complex operations a breeze. But this versatility comes with a dark side of surprising results for some seemingly simple cases, and conflicts with the design choices of more recent data analysis packages. This has led to a viewpoint with growing support among the community that fancy indexing may be too fancy for its own good. This talk will review the workings of advanced indexing, highlighting where it excels, and where it falls short, and give some context on the logic behind some design decisions. It will also cover the existing NumPy Enhancement Proposal (NEP) to "implement an intuitive and fully featured advanced indexing."

Views: 1073
PyData

Numpy Tutorial 1. Introduction to the Numpy ND array object. Basic math, array shapes, data types and broadcasting.

Views: 947
Rich Colburn

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© 2018 Articles on language teaching and learning

Pool Accessary Options. Types of Pool Water Sanitation. There are a variety of pool water treatment options beyond the traditional chlorine, although it remains the most popular option. Chlorine is added to a pool to combat algae or other bacteria that can gather in the water. Chlorinated water relies on a proper pH balance to prevent an overly chemical-smelling pool. While saline pools, also known as saltwater pools, are not chlorine-free, they consist of a salt-chlorine generator that produces lower levels of chlorine. Mineral water pools are chlorine-free and use disinfecting minerals to prevent bacteria and algae. Pool Maintenance.