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/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub

Views: 49172
codebasics

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

Views: 19385
Noureddin Sadawi

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

Views: 135324
APMonitor.com

'''
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: 343
MachineLearning with Python

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

Views: 4399
Noureddin Sadawi

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

Views: 37920
DataCamp

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: 851
Yong Wang

Be sure to like, share and comment to show your support for our tutorials.
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Indexing List In Python
In this Python tutorial, we will teach you all about indexing list in Python. Indexing list in Python is a very important concept which gives us the ability to access our objects that appear in a list. List in Python can contain a lot of information that is important in order to run our programs and having a way to access the content within a list allows us to use the content as it is needed when our program runs. Indexing allows us to access one object and slicing allows us to access numerous objects at one time.
Indexing List In Python Explained
[table id=9 /]
In the above table, you can see the indexing of a list is very similar to indexing strings. The only difference is that in the list each object holds an index position where in strings each character holds an index position. As always the index always starts at 0 and counts up for each object contained in a list. If we want to access an object going from the end of a list(right to left), we use a negative index number. The last index position when going right to left always starts at -1.
Examples Of Indexing List in Python
Access Index From Left To Right
a = ['List', 12345, [123, 456]]
a[1]
12345
a = ['List', 12345, [123, 456]] - We create a list object that contains a string object, number object and another list object. We assign our list object a variable named 'a' to represent the list.
a[1] - We call our list object via the variable 'a' then we request the index position of 1.
12345 - We are returned the 1 index positions object which happens to 12345.
Access Index From Right To Left
a = ['List', 12345, [123, 456]]
a[-1]
[123, 456]
a = ['List', 12345, [123, 456]] - We create a list object and assign the list a variable of 'a'.
a[-1] - We then call our list via the variable of 'a' and we then index from the right using a negative index position. Remember when indexing from the right we need to use negative numbers and the starting index position from the right is -1.
[123, 456] - We are returned a list that was contained in our list object. The list object is the last object contained in the list and we used -1 to access this list object.
Conclusion
In this Python tutorial, we looked at accessing list using indexing which is vital in programming when using list. If we can not access our content stored in list then list would be useless. We can pull one object out of a list using indexing if we need to pull more we could index multiple times or we can use slicing which we will cover in the next tutorial. If you have any questions about indexing in Python leave a comment below.
In this tutorial we use Python 3.5.0

Views: 13919
Master Code Online

Enjoyed my video? Leave a like!
GitHub Link: https://github.com/maxg203/Python-for-Beginners
Personal Website: http://maxgoodridge.com

Views: 6462
Max Goodridge

Views: 264
Data Science for All

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: 485
Rylan Fowers

Some of the common steps needed to prepare a dataset to be given into a machine learning model. (selecting the data, processing it, and transforming it, visualizing it etc...). It starts with the common transformation techniques using various functions and methods in numpy and panda.

Views: 1235
Data Science for All

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: 1232
TheEngineeringWorld

Be sure to like, share and comment to show your support for our tutorials.
=======================================
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======================================
In this Python tutorial, we will explain how to use index and slicing assignment in Python list to change the objects in a Python list.

Views: 2634
Master Code Online

Learn how to do array index slicing in Numpy Python.

Views: 3918
DevNami

Hey guys, so recently i've had a lot of questions regarding indexing in python. I'm sure it can be a little bit confusing. I hope that this video gives it a better explanation.

Views: 4609
ZigZaggo

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

Views: 3749
Udacity

In this Python 3 programming tutorial, we cover the multi-dimensional list. Up until now, we have focused on single dimensional lists, but this is limiting. In programming, we are able to create lists with infinite depth. Here's how!
Sample code for this basics series: http://pythonprogramming.net/beginner-python-programming-tutorials/
Python 3 Programming tutorial Playlist: http://www.youtube.com/watch?v=oVp1vrfL_w4&feature=share&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M
http://seaofbtc.com
http://sentdex.com
http://hkinsley.com
https://twitter.com/sentdex
Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6

Views: 155005
sentdex

#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: 7972
The Semicolon

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: 64
Deep Hobbying

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: 752
Ryan Chesler

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: 2457
TheEngineeringWorld

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
# 9. Numpy String Functions
''' add '''
u = 'hello'
v = ' world'
print(np.char.add(u,v))
print(np.char.multiply(u,3))
''' center '''
print(np.char.center(u,11,fillchar='#'))
''' Capitalize '''
print(np.char.capitalize(u + v))
''' title '''
print(np.char.title(u + v))
''' lower & uppper '''
print(np.char.lower('HELLO'))
print(np.char.upper('world'))
'''split '''
print(np.char.split('my name is khan'))
'''splitlines'''
print(np.char.splitlines('my name is \n khan'))
''' replace '''
print(np.char.replace('dd//mm//yy','//',':'))
print(np.char.replace('My name is Oly','Oly','Aly'))
'''encode and decode'''
enc = np.char.encode('alpha',encoding='cp424')
print(enc)
dec =np.char.decode(enc, 'cp424')
print(dec)
###############################################################################
# 10. Storage comparision between List and Numpy
''' Storage Comparision '''
import sys
Size = range(1000)
print(sys.getsizeof(Size) * len(Size))
Nump = np.arange(1000)
print(Nump.size * Nump.itemsize)
###############################################################################
###############################################################################
# 11. Speed Comparision between List and Numpy
''' Speed Comparision '''
import time
size = 10000
t1 = time.time()
X = range(size)
Y = range(size)
Z = [ X[i] + Y[i] for i in range(len(X))]
print('Time taken by List :', time.time() - t1)
t2 = time.time()
X = np.arange(size)
Y = np.arange(size)
Z = X + Y
print('Time taken by Numpy Array :', time.time() - t2)
############################################################################
# 12. Matrix / Linear Algebra using Numpy
'''
There is a separate Playlist for Matrices / Vector and Linear Algenbra,
So will not cover here
'''
###############################################################################
# 13. Iterations with Numpy
x = np.random.randint(0,9,(5,5))
print(x)
print(x.T)
for i in np.nditer(x):
print(i)
y = np.arange(0,100,5)
print(y.T)
y1 = np.random.randint(0,9,(4,4))
print(y1)
print(y1.reshape(2,8))
print(y1.reshape(8,2))

Views: 279
MachineLearning with Python

The List data type in Python allows you to store several values together. Lists are ordered and can hold duplicate values.
Lists are also known as Arrays or Vectors.
Learn how to define lists, access individual elements, alter them, and use list-builder notation to construct lists mathematically. We will use IDLE to define lists and see their elements.
Get Python for your computer at http://python.org

Views: 86902
Barry Brown

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

Views: 72563
codebasics

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: 461
Ashman Malhotra

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: 6084
APMonitor.com

Create an array from a list, use indices a[i,j] i.s.o. a[i][j], etc.
0:00 Numpy
1:00 Lists vs Numpy Arrays
2:59 Array-function: creating an array from a list
5:06 Using math fucntions from numpy
7:00 Spanning axes: arange and linspace
12:00 Using numpy i.s.o. math
13:40 Array creation with zeros() and ones(), 2D arrays
14:23 Array shape
16:29 Slicing arrays: slect rows & columns
18:44 Stacking columns or rows

Views: 296
Prof Hoekstra

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: 987
MyStudy

In this Machine Learning Tutorial, we will begin learning about Python NumPy & Machine Learning with Python. This video series python tutorials for beginners in Hindi for each beginner and intermediates. Let's get started.
So, In this tutorial we will see,
00:20 NumPy: Indexing
#NumPy #PythonMachineLearning #MachineLearning
Our Latest PYTHON Video Tutorial
Python Programming | What is Python :- https://youtu.be/7p_rO_1WVH8
Operators in Python Programming :- https://youtu.be/v2hl8XiKyCI
Python Data Types | Data Types in Python :- https://youtu.be/JH8uVgAZtIA
Python if-else Statement:- https://youtu.be/jYu0rwI1xRI
Python While loop & Types of loop :- https://youtu.be/TOV7LrseDr8
Python For loop | Python For Loop Syntax :- https://youtu.be/KBOlvpmtl-o
What is String in Python? :- https://youtu.be/J063UGG_4Ro
Python Break & Continue Statement :- https://youtu.be/xolw0uirD8I
20+ Python Built-in Functions :- https://youtu.be/dRPIFOxxVm4
Python String Functions | lower(), isalpha(), isdigit() :- https://youtu.be/dbzecCXo8XU
Python String Functions | Capitalize(), Count(), Index() :- https://youtu.be/sjqauSJueu0
List in Python:- https://youtu.be/Rbx82rIa50o
Python list Function :- https://youtu.be/e6znZGm1xCI
append(), extend(), insert() Functions in Python :- https://youtu.be/coxRG0edJ34
remove(), pop(), clear() Functions in Python :- https://youtu.be/ZuMwbhLgw0A
Python Dictionary :- https://youtu.be/T9Nu3UrNWew
Python Dictionary Method | Clear(), items(), popitem(), keys(), values() :- https://youtu.be/8nwZuZEkBzc
Python Polymorphism | Polymorphism Example in Python :- https://youtu.be/Hmg2NGQq2h8
Encapsulation in Python | Encapsulation Example in Python :-
https://youtu.be/0oaPEq5bfNk
Name Error & Key Error Exception in Python:- https://youtu.be/13Ok5L3vXvA
Python SQLite | SQLite Database :- https://youtu.be/_4_39jwjQfU
Pandas Library in Python | Data Cleaning in Machine Learning :- https://youtu.be/zpJ-fwjssG0
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Views: 92
WsCube Tech

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
Python Tutorial in Hindi : https://www.youtube.com/watch?v=JNbup20svwU&list=PLk_Jw3TebqxD7JYo0vnnFvVCEv5hON_ew
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: 68836
Telusko

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: 1033
Benjamin Wilson

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:
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
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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: 1935
TheEngineeringWorld

( 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).
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Views: 200693
edureka!

Slicing, bool arrays, and logical indexing

Views: 1082
Rich Colburn

Python How to Find the index of an item in a List or Array
mylist = [6,8,2,5]
print "Index of 5 is: ", mylist.index(5)
print "Index of 8 is: ", mylist.index(8)
for index, value in enumerate(mylist):
print(index, value)

Views: 1925
OSPY

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: 2489
Python Programmer

Describes the process to swap two values in a Python array.
From http://cs.simpson.edu/cmsc150/index.php?chapter=sorting

Views: 13257
Professor Craven

In this Python 3.7 tutorial we will look at the index() list method in Python. For more information please visit https://www.mastercode.online/courses/tutorial/index-list-method/

Views: 581
Master Code Online

Python: Lists, Loops, Append
Create an empty list, jump into a loop that continues to append it's iteration variable value into the empty list.

Views: 15196
george boole

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

Views: 2220
Rsquared Academy

This Is Our 12 th video in Numpy Array Python For Data Science
In This Video We Want To Cover Numpy Array Comparison
Python Data Science Playlist
https://www.youtube.com/watch?v=k9A5oxTTLeE&list=PL1FgJUcJJ03vXmv0nUOxJd1TL7C1JBHNV

Views: 170
Parwiz Forogh

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

Views: 3669
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: 4334
Enthought

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

Views: 5380
PyCursos

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: 2345
Rsquared Academy

Code to get indices of non zero elements.
Like and share. It's FREE too :)
Download source code at:
https://drive.google.com/file/d/1QOZNEWk10uTZAONaefYVWypMJnkNO-lG/
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Views: 159
AllTech

In this video, we are going to be solving the so-called "Two-Sum Problem":
Problem: Given an array of integers, return indices of the two numbers such that they add up to a specific target. You may assume that each input would have exactly one solution, and you may not use the same element twice.
We investigate three different approaches to solving this problem.
Method 1: A brute-force approach that takes O(n^2) time to solve with O(1) space. We loop through the array and create all possible pairings of elements.
Method 2: A slightly better approach time-wise, taking O(n) time, but worse from a space standpoint, with a space complexity of O(n). In this approach, we make use of an auxiliary hash table to keep track of whether it's possible to construct the target based on the elements we've processed thus far in the array.
Method 3: This approach has a time complexity of O(n) and a constant space complexity, O(1). Here, we have two indices that we keep track of, one at the front and one at the back. We move either the left or right indices based on whether the sum of the elements at these indices is either greater or lesser than the target element.
The software written in this video is available at:
https://github.com/vprusso/youtube_tutorials/blob/master/data_structures/arrays/two_sum.py
Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here:
http://bit.ly/lp_vim
If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe:
http://bit.ly/lp_subscribe

Views: 5300
LucidProgramming

How to loop through 2d lists in Python.

Views: 15952
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