Search results “Indices in array python”

This tutorial covers array operations such as slicing, indexing, stacking. We will also go over how to index one array with another boolean array.
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Views: 34528
codebasics

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

Views: 13398
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: 115078
APMonitor.com

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
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Views: 1731
LucidProgramming

Learn how to do array index slicing in Numpy Python.

Views: 3599
DevNami

Python Tutorial to learn Python programming with examples
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In this video we will see:
- Accepting values from user and store them in Array in python
- Creating blank array
- Asking length of array from user and accepting the values
- Printing index of array value manually
- Printing index value of user entered value
- Printing index of array value by function
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Views: 17324
Telusko

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: 210
Tutorials Point (India) Pvt. Ltd.

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

Views: 195
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
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#Python

Views: 61154
Corey Schafer

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 ***
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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: 510
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: 424
Aaron Roach

Learn how to do Scalar Array Operation in Numpy Python.

Views: 445
DevNami

Get The Learn to Code Course Bundle!
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Enroll in The Complete Python Course on Udemy!
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In this video we are going to discuss lists and what they are in Python. So, if you have experience with programming in other languages, let's say PHP, you know to create an array, and what an array is. For those who do not have experience programming, an array is a way of keeping data organized and within a single construct. For single-dimensional arrays, we can implement it as a list in Python. To create a list in Python we use square brackets:
["Movies", "Games", "Python"]
This becomes a list that has 3 indexes. To call the first item in the list you write the following:
["Movies", "Games", "Python"][0]
Remember, when programming the first ID of the item in a list or an array will be 0. We can also concatenate a list item with a string.
print("I Like" + ["Movies", "Games", "Python"][0])
If we were to change the index to number to 1 it would print out "I Like Games". So, that is what a list is in Python. It's just a way to create a collection under one variable. In the next video we will cover dictionaries.
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Views: 3231
Joseph Delgadillo

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
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Views: 28131
Telusko

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

Views: 559
TheEngineeringWorld

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: 278
TheEngineeringWorld

'''
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: 49
MachineLearning with Python

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: 402
george boole

Learn to make and index arrays and lists in python.

Views: 145
Noah Tatko

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

Views: 71
Parwiz Forogh

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
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Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6

Views: 152980
sentdex

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/
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======================================
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: 17789
Master Code Online

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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OSPY

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

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DataCamp

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
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Yong Wang

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

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Chanel OVO

In this Python Beginner Tutorial, we will begin learning about dictionaries. Dictionaries allow us to work with key-value pairs in Python. We will go over dictionary methods, how to add and remove values, and also how to loop through the key-value pairs. Let's get started.
The code from this video can be found at:
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Corey Schafer

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!
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Python para Data Science e Machine Learning

Python Training Course - Python List Index. This python tutorial covers the index() method for a python list. The index() method will give the index of an object in a python list. One thing to remember about the index() method for a python list is that it will only return one value: the first instance of the list object. It will not give the index of later occurrences.
My Python Course - https://www.youtube.com/watch?v=Aah3TmR-dHc&list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J

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Python Programmer

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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ProgrammingKnowledge

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

Views: 6636
sentdex

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

Views: 10749
Professor Craven

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

Views: 1971
Noureddin Sadawi

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: 252
Prof Hoekstra

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

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

This video is a part of the following Data Science Playlist - https://www.youtube.com/playlist?list=PL47S5PRS_XOe8dLheR53ichylMMMMX_-T

Views: 720
हिंदी कोडिंग जोन

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

Views: 7803
DevNami

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.
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Python Programmer

Python Tutorial

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abdellah dz

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: 305
Ashman Malhotra

Eric Jones, co-author of SciPy and CEO of Enthought, Inc. shows techniques to access elements out of one-dimensional elements out of NumPy arrays in Python.

Views: 2557
Enthought

Problem:
Given an array of size n, find a peak element in the array.
For example:
In Array [1,4,3,6,7,5] 4 and 7 are Peak Elements. Return any one Peak Element.
Solution:
1: Initialize start = 0, end = array.length - 1
2: Repeat following steps till peak element is found:
(a) Find mid = (start+end)/2
(b) If mid is peak element, return array[mid]
(c) If array[mid-1] is greater than array[mid], find peak in left half of array set end = mid - 1
(d) Else find peak in right half of array set start = mid + 1
Time Complexity: O(log n)
Space Complexity: O(1)
Code: http://www.ideserve.co.in/learn/find-a-peak-element-in-an-array
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Views: 50721
IDeserve

I take a space delimited string and split it into a List
then I join the list back into a space delimited,
then comma delimited string

Views: 4425
george boole

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.
Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt
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Paul Programming

Learn how to find factorial of array in python numpy.

Views: 905
DevNami

Nyu poly admissions essay for catholic high school

Heading for apa annotated bibliography

Personal care assistant cover letter

Creating a cover letter for a resume

© 2018 Fixed rate of exchange definition

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