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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) print(x[-1]) print(x[-3]) ############################################################################### # 4. Updating Numpy array print(x) x = 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 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)) ###############################################################################
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. ======================================= Channel - https://goo.gl/pnKLqE Playlist For This Tutorial - https://goo.gl/EyZFti Latest Video - https://goo.gl/atWRkF Facebook - https://www.facebook.com/mastercodeonline/ Twitter - https://twitter.com/mastercodeonlin?lang=en Website - http://mastercode.online ====================================== 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 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 - 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
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 📺Channel: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow? ▶️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 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
Learn how to split array using Python numpy.
Views: 2167 DevNami
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. ======================================= Channel - https://goo.gl/pnKLqE Playlist For This Tutorial - https://goo.gl/EyZFti Latest Video - https://goo.gl/atWRkF Facebook - https://www.facebook.com/mastercodeonline/ Twitter - https://twitter.com/mastercodeonlin?lang=en Website - http://mastercode.online ====================================== 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))
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 Share, Support, Subscribe!!! Subscribe: http://bit.ly/wscubechannel Google Reviews : https://goo.gl/LxpEsU Facebook : https://www.facebook.com/wscubetech.india Twitter : https://twitter.com/wscube Linkedin : https://www.linkedin.com/company/wscube-tech Google + : https://plus.google.com/+wscubetechjodhpur Youtube : https://www.youtube.com/c/wscubetechjodhpur Website : http://wscubetech.com
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 Editing Monitors : https://amzn.to/2RfKWgL https://amzn.to/2Q665JW https://amzn.to/2OUP21a. Check out our website: http://www.telusko.com Follow Telusko on Twitter: https://twitter.com/navinreddy20 Follow on Facebook: Telusko : https://www.facebook.com/teluskolearn... Navin Reddy : https://www.facebook.com/navintelusko Follow Navin Reddy on Instagram: https://www.instagram.com/navinreddy20 Subscribe to our other channel: Navin Reddy : https://www.youtube.com/channel/UCxmk... Telusko Hindi : https://www.youtube.com/channel/UCitz... Donation: PayPal Id : navinreddy20 Patreon : navinreddy20 http://www.telusko.com/contactus
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. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - *** 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: 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! - - - - - - - - - - - - - - - - - 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 - - - - - - - - - - - - - - - - - - - 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/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
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/ Follow us on Facebook https://www.facebook.com/AllTech-1089946481026048/
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.