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

Views: 44856
codebasics

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

Views: 129595
APMonitor.com

Learn how to do array index slicing in Numpy Python.

Views: 3817
DevNami

'''
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: 220
MachineLearning with Python

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: 4201
LucidProgramming

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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🐦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: 347
Rylan Fowers

In this video we will look at how to slice lists and strings in Python. Slicing allows us to extract certain elements from these lists and strings. This can be extremely useful for stripping out certain values from lists or getting a substring of a characters from a string. Let's take a look at a few code examples.
The code from this video can be found at:
https://github.com/CoreyMSchafer/code_snippets/tree/master/Slicing
If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account:
https://www.patreon.com/coreyms
Or a one-time contribution through PayPal:
https://goo.gl/649HFY
If you would like to see additional ways in which you can support the channel, you can check out my support page:
http://coreyms.com/support/
Equipment I use and books I recommend:
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You can find me on:
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#Python

Views: 78401
Corey Schafer

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:
- Why Array?
- Advantage of Array in Python
- When to use Array
- Importing module
- Creating Array
- Type of array
- Type code
- Storing value in array
- Printing array values
- buffer_info function
- Printing type code
- reverse function
- Printing value of specific index
- Printing values using loop
- Printing length of array
- Creating array of unicode value
- Creating new array by copying existing array
- Copying type from old array
Editing Monitors :
https://amzn.to/2RfKWgL
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https://amzn.to/2OUP21a.
Check out our website: http://www.telusko.com
Follow Telusko on Twitter: https://twitter.com/navinreddy20
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Follow Navin Reddy on Instagram: https://www.instagram.com/navinreddy20
Subscribe to our other channel:
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Views: 86706
Telusko

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
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Subscribe to our other channel:
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Views: 54453
Telusko

In this Python 3 programming tutorial, we cover how to manipulate lists in Python. We are able to add things to lists by appending, we are able to remove them with del, we are able to order lists, reverse them, and more.
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: 171859
sentdex

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: 600
Yong Wang

One of the most powerful features in pandas is multi-level indexing (or "hierarchical indexing"), which allows you to add extra dimensions to your Series or DataFrame objects. But when should you use a MultiIndex, and how do you create, slice, and merge MultiIndexed objects?
In this video, I'll demonstrate:
- How to create a Series with a MultiIndex, and how to convert it to a DataFrame
- How to select from a Series with a MultiIndex
- How to create a DataFrame with a MultiIndex
- How to select from a DataFrame with a MultiIndex
- How to merge two DataFrames with MultiIndexes
WANT TO JOIN MY NEXT WEBCAST? Become a member ($5/month):
https://www.patreon.com/dataschool
=== RELATED RESOURCES ===
Download the lesson notebook: http://nbviewer.jupyter.org/github/justmarkham/pandas-videos/blob/master/pandas_multiindex.ipynb
Using the pandas index (Part 1): https://www.youtube.com/watch?v=OYZNk7Z9s6I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=17
Using the pandas index (Part 2): https://www.youtube.com/watch?v=15q-is8P_H4&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=18
Analyzing groups with groupby: https://www.youtube.com/watch?v=qy0fDqoMJx8&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=14
Selection and slicing with loc: https://www.youtube.com/watch?v=xvpNA7bC8cs&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=19
My full pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y
DataCamp course on MultiIndex: https://www.datacamp.com/courses/manipulating-dataframes-with-pandas?tap_a=5644-dce66f&tap_s=280411-a25fc8
DataCamp course on merging: https://www.datacamp.com/courses/merging-dataframes-with-pandas?tap_a=5644-dce66f&tap_s=280411-a25fc8
Tidy data: http://r4ds.had.co.nz/tidy-data.html
== LET'S CONNECT! ==
Newsletter: https://www.dataschool.io/subscribe/
Twitter: https://twitter.com/justmarkham
Facebook: https://www.facebook.com/DataScienceSchool/
LinkedIn: https://www.linkedin.com/in/justmarkham/

Views: 10103
Data School

This tutorial has been update to Python 3.7 at https://www.mastercode.online/courses/tutorial/get-a-character-via-the-index-in-python-37/
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 and Slicing in Python
Strings are an ordered collection of letters, numbers and symbols which we can actually access each of these by their position(indexing) in a string. We can access a group of letters by slicing. In this tutorial, we will cover indexing and slicing a string in Python as one since they go hand in hand in Python.
Indexing in Python
Indexing provides each character contained in a string an identity so we can access that character or characters via slicing. The index always starts at the number 0 and counts up till the end of the string. Each character, space, symbol, and the number is automatically assigned an index number.
Example of a String Index
In the above example, we see how a string is indexed. You may have noticed the count start at 0 so this means that the letter T is at the 0 index. Then the index counts up from there including spaces and symbols.
Example How To Access a character via Indexing
#Indexing from the left side
a = 'This is a string'
a[0]
'T'
#Indexing from the right side
a = 'This is a string'
a[-1]
'g'
In the above example, we created a string and assign it the name(variable) of a. On the next line we index from the left to obtain the first letter in the string. Notice that we use zero to get the first letter in the string. If we just wanted to get the second letter, then it would look like a[1] to get the h. In the second example, we index from the right using a negative index number. We get the g at the end of the string. Negative numbers count backwards. When we index from the right or the end we do not begin with 0 we actually start with -1.
Here Are a Couple More Examples of Indexing
b = 'A new string for us to use'
b[1]
' '
b[4]
'w'
b[6]
's'
b[-4]
' '
b[-7]
' '
b[-6]
't'
Slicing in Python
In above section, we looked at indexing where we can only access one character at a time. Slicing gives us the ability to access a group of characters or a group of characters by skipping(stepping). Same rules apply positive number starts from the left and a negative number starts from the right. Let's look at some examples of slicing.
Our String For Theses Examples
#Our String for Slicing Examples
a = '0123456789'
Example a[1:5]
a = '0123456789'
a[1:5]
'1234'
In the above example, we slice from the first index which in this case is the number one and slice up to but not including index of 5. Let's break it down more "a" is name(variable) for the object then we have the opening square bracket which either indicates indexing or slicing then we have the number 1 which indicates index of 1 which is the starting point then a colon(:) which indicates we are slicing then we have the number 5 which instructs Python to stop at the index before the number 5 which is the fourth index position. Some new programmers get confused with the ending point in a slice which stops one spot before the number I give when writing the code. Hope that does not confuse you.
Example a[2:]
a = '0123456789'
a[2:]
'23456789'
In this example, we slice but we do not give an ending index so what happens. Python will start at the beginning index and count up to the end of the string. In this case, Python starts at the second index and continues to count up to the end of the string.
Example a[:7]
a = '0123456789'
a[:7]
'0123456'
In this example, we slice from an index of zero up to an index of 6 because the ending index always finishes one index position before we stated in the code. If we do not provide a starting index position like in above example Python will start index 0 and count up to the ending index.
Example a[0:-1]
a = '0123456789'
a[0:-1]
'012345678'
In this example, we slice from the start of the string up to but not including the last number. Remember negative numbers count from the right starting at -1. We could have also wrote this like this a[:-1] and we would have got the same result. Try it.
Example a[:]
a = '0123456789'
a[:]
'0123456789'
In this example, we basically just make a copy of the object that we just sliced. We provide no starting and ending index so we just get a copy of the string.
Stepping Through a String
We are going to add a third limit to our slice this limit allows us to step or skip through our string. Let's take a look at some examples.
a = '0123456789'
a[0:9:2]
'02468'
What happened in this example? Well we slice from the 0 index and then end at 8 index(9 is what we coded but always one before) and then we step through string by 2 this is the third limit we have added.

Views: 20499
Master Code Online

I easily split up the Khan's Quote into a list.
I del entries from the List, insert new entries into the List.
Thereby improving on the quote. :-)

Views: 418
george boole

Hope u like the Video. PLZZZ do like and subscribe for more video on python.

Views: 66
Chanel OVO

Using indexes to modify our Tic Tac Toe game board
Playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDeAams7fkdcwOGBpGdHpXln
#python #programming #tutorial

Views: 22837
sentdex

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: 1856
TheEngineeringWorld

Hi guys! This is my first go at uploading some of my work! I just got done with a really difficult project and was excited to share this with you since their doesn't seem to be a lot of stuff on this topic (for Python 3, anyway) I hope you guys enjoy it!

Views: 438
Aaron Roach

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

Views: 35634
DataCamp

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

Views: 12586
Professor Craven

This short tutorial shows hot to unroll an array using NumPy library and Python programming language.
More information about used functions:
flatten https://goo.gl/8LXwkt
ravel https://goo.gl/h7I8ED

Views: 801
buggedcomputers

An array is "cyclically sorted" if it is possible to cyclically shift
its entries so that it becomes sorted.
The following list is an example of a cyclically sorted array:
A = [4, 5, 6, 7, 1, 2, 3]
Write a function that determines the index of the smallest element
of the cyclically sorted array.
This video is one part of the Binary Search playlist on my channel. For more videos on binary search and how to apply it to various problems, check out the other videos:
https://www.youtube.com/playlist?list=PL5tcWHG-UPH1kjiE-Fqt1xCSkcwyfn2Jb
The software written in this video is available at:
https://github.com/vprusso/youtube_tutorials/blob/master/algorithms/search_algorithms/binary_search/cyclic_sorted_array.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:
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Views: 290
LucidProgramming

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

Views: 8896
DevNami

#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
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Views: 7378
The Semicolon

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.
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Views: 67159
codebasics

In this video I am going to show How to use Slice function or slicing with Python Collections. Also I am going to show how to use Negative index with Python Collections. So What is Python Slice? A slize is a span of items that are taken from a sequence
List slicing format: list[start : end: step]. Span is a list containing copies of elements from start up to, but not including, end
If start not specified, 0 is used for start index. If end not specified, len(list) is used for end index. Slicing expressions can include a step value and negative indexes relative to end of list.
And What is Negative Indexing In Python: I a Python Collection such as Lists, Strings, Tuples, Bytes .. we can refer to an element by a negative index representing how far it is from the end.
example
# +---+---+---+---+---+---+
# | P | y | t | h | o | n |
# +---+---+---+---+---+---+
# 0 1 2 3 4 5 ---- Positive Index
# -6 -5 -4 -3 -2 -1 ---- Negative Index
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Views: 6585
ProgrammingKnowledge

Python - Arrays Implementation
Watch more Videos at https://www.tutorialspoint.com/videotutorials/index.htm
Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited

Views: 589
Tutorials Point (India) Pvt. Ltd.

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: 331
Python para Data Science e Machine Learning

Join the Family: https://discord.gg/Kgtnfw4
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Views: 1815
John Hammond

How to use Index function with Array in Python by Pearl Institute - Part 60.
All the tutorials are in Hindi Language.
Join us to Learn more
Pearl Institute Batala
(an ISO 9001:2015 Certified Institute)
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Views: 20
Pearl Institute

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 :
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For All other visit my udemy profile at :
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Views: 936
MyStudy

Python 3.7 Version - https://youtu.be/QHPEP2ztiPE
Check out Python 3.7 tutorial series at https://www.mastercode.online/courses/python-37-complete-tutorial-series
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In this Python instructional exercise, we are going to figure out how to iterate through a Python list. Iterate means looping through a procedure in programming. We will figure out how experience a list and print every item contained in a list in Python. We will utilize a for loop in Python. We quickly took a gander at for loops in the string segment of our instructional exercises. The for loop will experience every article that shows up in a list and print that question or give back that protest one time. On the off chance that we have to give back every item contained in a list of the for loop is our best alternative. We could likewise fulfill this errand by indexing every article, except that would take forever and take a great deal of code. Our objective when programing is to restrain the measure of code composed to perform an assignment.
For Loop Syntax
for variable in arrangement:
print(variable)
For Loop Syntax Explained
for - Indicates to Python that we need to iterate through or loop through our sequence(list, dictionary(keys), tuples, strings, and so on.)
variable - This variable can be anything that meets the Python variable rules. I think about this variable as only a placeholder for every item contained in a list.
in - In is an administrator in Python that check for the enrollment. I think about this as is it in that list, word reference, tuple, string, and so on. This implies is it a player in that question.
: - Colon demonstrates the begin of a piece of code. We will see a considerable measure of colons later on instructional exercises
print() - Print is still some portion of the for loop which is alluded to as a square of code. The print articulation will print every item contained in the arrangement to us.
variable - This is the same variable in the for loop line this equitable calls the placeholder variable so Python knows which questions print.
Cases Of Iterating in Python Using The For Loop
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for var in a:
... print(var)
...
1
2
3
4
5
6
7
8
9
10
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] - We make a list protest that contains the numbers 1 through 10. We allocate the variable of "a" to speak to the list object.
for var in a: - We then perform a for loop on our list object. In the first place we incorporate the for which demonstrates we need to iterate through the list object. We then appoint every article contained in the list question a provisional variable which we allude to as var in this case. At that point we utilize the in administrator to advise our project to look in the list item speak to by the variable 'a'.
print(var) - Important we have to include two spaces before this line of code. We incorporate our print explanation to show we might want to print every item that is spoken to by the transitory variable of var.
Given back our articles - We are given back every one of items contained in our list object. Notice every one is imprinted on another line.
Essential Infomation About The For Loop
The principal line must have a colon( : ) toward the end.
The second line must be indented( two spaces)
We can utilize else discretionary statement in a for loop.
The interim variable can be anything you need the length of it meets Pythons variable rules.
Conclusion
In this Python instructional exercise, we figure out how to iterate through a list utilizing the Python for loop. On the off chance that you have any inquiries concerning iterating please leave a remark beneath.

Views: 28963
Master Code Online

In this programming tutorial, we cover Python lists and tuples.
Both data structures contain data, but are slightly different. Python lists are mutable, meaning they can be changed and manipulated. Tuples are immutable, meaning they cannot be changed. This is what sets them apart and why you would use a specific one. Lists are usually more popular, since people want to be able to change them, but tuples are also useful when you do not want or need to change the data.
It should also be noted that Tuples are faster to process and iterate through, so this gives them a bonus, again, if you are not needing to manipulate them.
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
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Views: 121650
sentdex

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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Views: 419
Ashman Malhotra

Find Complete Code at GeeksforGeeks Article: https://www.geeksforgeeks.org/program-cyclically-rotate-array-one-python-list-slicing/
This video is contributed by Afzal Ansari
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Views: 1549
GeeksforGeeks

This video is part of an online course, Intro to Parallel Programming. Check out the course here: https://www.udacity.com/course/cs344.

Views: 30753
Udacity

python list function
python list() function
python list append
python list slice
python list index
python list length
python tuple
dictionary in python

Views: 38
CSE TECH'S

In this video I am going to show How to use Slice function or slicing with Python Collections. Also I am going to show how to use Negative index with Python Collections. So What is Python Slice? A slize is a span of items that are taken from a sequence
List slicing format: list[start : end: step]. Span is a list containing copies of elements from start up to, but not including, end
If start not specified, 0 is used for start index. If end not specified, len(list) is used for end index. Slicing expressions can include a step value and negative indexes relative to end of list.
And What is Negative Indexing In Python: I a Python Collection such as Lists, Strings, Tuples, Bytes .. we can refer to an element by a negative index representing how far it is from the end.
example
# +---+---+---+---+---+---+
# | P | y | t | h | o | n |
# +---+---+---+---+---+---+
# 0 1 2 3 4 5 ---- Positive Index
# -6 -5 -4 -3 -2 -1 ---- Negative Index
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- Python Slices
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Verwandte Suchanfragen zu python slices
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Views: 189191
ProgrammingKnowledge

Lists are a way to store ordered data. In this Python tutorial, we show you how to create lists, access elements by index, slice lists, join two lists (concatenation), and more. We will talk about sets, dictionaries and tuples in separate videos.
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Python instructor: Ulka Simone Mohanty
Written & Produced by Michael Harrison
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Views: 99837
Socratica

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

Views: 3568
Udacity

Python: Array Slicing. Full course here:
http://www.damiantgordon.com/Videos/ProgrammingAndAlgorithms/MainMenu.html

Views: 1620
Damian T. Gordon

This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions.
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Views: 787020
Paul Programming

( 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:
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2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
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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
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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: 180771
edureka!

In this lesson, “Python Numpy – Creating Empty Array”, I discussed how you can create a Numpy Empty Array.
In Numpy, you will use empty() function to create empty array. It accepts the shape of the array as a parameter and generates required array for you with No value initialized at any index. It is the responsibility of the developer to use this function carefully and get all values initialised or updated before making use of the array otherwise it could be problematic also.
In this lesson, you will learn:
1. How to create single dimensional – Numpy Empty Array.
2. How to create two-dimensional – Numpy Empty Array.
3. Assigning Numpy Data Type (dtype) while creating Numpy Empty Array.
4. Checking Numpy Array Type (dtype)
https://youtu.be/TIRvp8gyZAA
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"Python Numpy Tutorials" | "Python Numpy" | "Numpy" | "Data Science" | "Data Science Using Python" | "Python Numpy – Creating Empty Array"

Views: 1519
Ashman Malhotra

Loop through list with both content and index
Python How to Access Index of List or Array in For Loop
mylist = [6,8,2,5]
for index, value in enumerate(mylist):
print(index, value)
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Views: 57
OSPY

Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well.
You can find the code from this video here:
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Books!
Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ
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Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

Views: 206031
Google Developers

Learn the concept of Index data structure in pandas in this video. Also learn how to do indexing of dataframe and series objects.
Code here: https://github.com/nikhilkumarsingh/IntroToPandas
More awesome topics covered here:
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Discovering Hidden APIs: http://bit.ly/2umeMHb
RegEx in Python: http://bit.ly/2Hhtd6L
Introduction to Numpy: http://bit.ly/2RZMxvO
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Functional Programming in Python: http://bit.ly/2FaEFB7
Python Package Publishing: http://bit.ly/2SCLkaj
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#pandas #python #tutorial

Views: 73
Indian Pythonista

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

Views: 80
Saima Academy

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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