Devan  Johnson

Devan Johnson


Getting Started With Ruby the Easy Way Pt7

Have you ever heard about scope, recursion or OOP? If not, don’t worry. You will learn about these topics in this part of Getting started with Ruby series. Today, you will learn about what recursion is and how to use it. Next, you will learn about scope and its types. Lastly, you will learn about classes and objects. Without further ado, let’s begin!

Getting Started With Ruby the Easy Way Part 1 (Comments, Variables, Strings).

Getting Started With Ruby the Easy Way Part 2 (Data Types Pt1).

Getting Started With Ruby the Easy Way Part 3 (Data Types Pt2, Control Flow Pt1).

Getting Started With Ruby the Easy Way Part 4 (Control Flow Pt2).

Getting Started With Ruby the Easy Way Part 5 (Control Flow Pt3).

Getting Started With Ruby the Easy Way Part 6 (Methods).

Getting Started With Ruby the Easy Way Part 8 (OOP Pt2).

Getting Started With Ruby the Easy Way Part 9 (OOP Pt3).

Getting Started With Ruby the Easy Way Part 10 (OOP Pt4 and Beyond).


You know how to use loops. However, loops are not the only way to loop over some object. Remember, in Ruby, everything is an object. Another way to do that is with recursion. Recursions is basically about methods that call themselves. This approach is often used by programmers to solve problems that can be broken into easier sub-problems of the same type.

One simple and often used example of recursion is the factorial method.

This goal of this method is to find the product of all positive integers below a specified number. For example, let’s say you want to find factorial of 5 (5!). Then, the math equation is 5 * 4 * 3 * 2 * 1 which is equal to 120. How can you solve this with recursion?

Take a look at the equation. Notice that 5! is the same as 5 * 4!. The 4! is the same as 4 * 3!. The 3! is the same as 3 * 2! and so on. This gives us a simple equation n! = n * (n-1)!.

Furthermore, 1! = 1. This is also known as the base case. Meaning, it can be calculated without performing any more factorials.

Let’s now create a factorial method and implement what we discussed above. The factorial method will contain if block that will check if n is smaller or equal to 1 (n <= 1). This is the base case. If this condition is true the it will stop factorial method. Otherwise, it will run factorial method again with n decreased by 1 and multiple n by the result. This will repeat as long as the base case is false.

## Example of recursion
def factorial(n)
  if n <= 1
    n * factorial(n - 1)

puts factorial(5)
## outputs 120

As with any loops, recursive methods too can become infinite. These situations will happen when you forget to implement the base case. So, make sure to check your code before you execute it. And, any time you work with recursion, define and include the base case that makes the recursion stop. Below is example of the factorial method done wrong-it has no base case.

## Example of recursion done wrong
def factorial(n)
  n * fact(n - 1)

puts factorial(5)
## outputs "stack level too deep (SystemStackError)"

Variable scope

Scope defines where in a program a variable is accessible. In Ruby, there are types of variable scope. These are local, global, instance and class. Each type is handy in different situations, as you will learn in a moment. Don’t worry about what classes and instances are. Also, don’t worry that you may not understand all code in examples. You will learn about classes and instances soon.

#ruby #programming #design development

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Getting Started With Ruby the Easy Way Pt7
Shubham Ankit

Shubham Ankit


How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation


Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl


We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws =
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook = "example.xlsx")


We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")




#getting sheet names
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
result = 'sheet2'

#Getting the active sheet
sheetactive =
result = 'sheet1'




#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)




#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)

#getting the highest row number

#getting the highest column number

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.


#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


for row in ws.values:
  for value in row:



Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.




#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook"new.xlsx")




#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']




#checking the sheet value

#adding value to cell
ws['B2'] = 367

#checking value




We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'"new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.




Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.




#unmerge cells B2 to C9

The above code will unmerge cells from B2 to C9.


To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.


import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')"new2.xlsx")




Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:


import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws =

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
ws.add_chart(chart, "E3")"MyChart.xlsx")


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: 
Code Written in This Tutorial: 


Monty  Boehm

Monty Boehm


Twitter.jl: Julia Package to Access Twitter API


A Julia package for interacting with the Twitter API.

Twitter.jl is a Julia package to work with the Twitter API v1.1. Currently, only the REST API methods are supported; streaming API endpoints aren't implemented at this time.

All functions have required arguments for those parameters required by Twitter and an options keyword argument to provide a Dict{String, String} of optional parameters Twitter API documentation. Most function calls will return either a Dict or an Array <: TwitterType. Bad requests will return the response code from the API (403, 404, etc).

DataFrame methods are defined for functions returning composite types: Tweets, Places, Lists, and Users.


Before one can make use of this package, you must create an application on the Twitter's Developer Platform.

Once your application is approved, you can access your dashboard/portal to grab your authentication credentials from the "Details" tab of the application.

Note that you will also want to ensure that your App has Read / Write OAuth access in order to post tweets. You can find out more about this on Stack Overflow.


To install this package, enter ] on the REPL to bring up Julia's package manager. Then add the package:

julia> ]
(v1.7) pkg> add Twitter

Tip: Press Ctrl+C to return to the julia> prompt.


To run Twitter.jl, enter the following command in your Julia REPL

julia> using Twitter

Then the a global variable has to be declared with the twitterauth function. This function holds the consumer_key(API Key), consumer_secret(API Key Secret), oauth_token(Access Token), and oauth_secret(Access Token Secret) respectively.

twitterauth("6nOtpXmf...", # API Key
            "sES5Zlj096S...", # API Key Secret
            "98689850-Hj...", # Access Token
            "UroqCVpWKIt...") # Access Token Secret
  • Ensure you put your credentials in an env file to avoid pushing your secrets to the public 🙀.

Note: This package does not currently support OAuth authentication.

Code examples

See runtests.jl for example function calls.

using Twitter, Test
using JSON, OAuth

# set debugging


mentions_timeline_default = get_mentions_timeline()
tw = mentions_timeline_default[1]
tw_df = DataFrame(mentions_timeline_default)
@test 0 <= length(mentions_timeline_default) <= 20
@test typeof(mentions_timeline_default) == Vector{Tweets}
@test typeof(tw) == Tweets
@test size(tw_df)[2] == 30

user_timeline_default = get_user_timeline(screen_name = "randyzwitch")
@test typeof(user_timeline_default) == Vector{Tweets}

home_timeline_default = get_home_timeline()
@test typeof(home_timeline_default) == Vector{Tweets}

get_tweet_by_id = get_single_tweet_id(id = "434685122671939584")
@test typeof(get_tweet_by_id) == Tweets

duke_tweets = get_search_tweets(q = "#Duke", count = 200)
@test typeof(duke_tweets) <: Dict

#test sending/deleting direct messages
#commenting out because Twitter API changed. Come back to fix
# send_dm = post_direct_messages_send(text = "Testing from Julia, this might disappear later $(time())", screen_name = "randyzwitch")
# get_single_dm = get_direct_messages_show(id =
# destroy = post_direct_messages_destroy(id =
# @test typeof(send_dm) == Tweets
# @test typeof(get_single_dm) == Tweets
# @test typeof(destroy) == Tweets

#creating/destroying friendships
add_friend = post_friendships_create(screen_name = "kyrieirving")

unfollow = post_friendships_destroy(screen_name = "kyrieirving")
unfollow_df = DataFrame(unfollow)
@test typeof(add_friend) == Users
@test typeof(unfollow) == Users
@test size(unfollow_df)[2] == 40

# create a cursor for follower ids
follow_cursor_test = get_followers_ids(screen_name = "twitter", count = 10_000)
@test length(follow_cursor_test["ids"]) == 10_000

# create a cursor for friend ids - use barackobama because he follows a lot of accounts!
friend_cursor_test = get_friends_ids(screen_name = "BarackObama", count = 10_000)
@test length(friend_cursor_test["ids"]) == 10_000

# create a test for home timelines
home_t = get_home_timeline(count = 2)
@test length(home_t) > 1

# TEST of cursoring functionality on user timelines
user_t = get_user_timeline(screen_name = "stefanjwojcik", count = 400)
@test length(user_t) == 400
# get the minimum ID of the tweets returned (the earliest)
minid = minimum( for x in user_t);

# now iterate until you hit that tweet: should return 399
# WARNING: current versions of julia cannot use keywords in macros? read here:
# eventually replace since_id = minid
tweets_since = get_user_timeline(screen_name = "stefanjwojcik", count = 400, since_id = 1001808621053898752, include_rts=1)

@test length(tweets_since)>=399

# testing get_mentions_timeline
mentions = get_mentions_timeline(screen_name = "stefanjwojcik", count = 300) 
@test length(mentions) >= 50 #sometimes API doesn't return number requested (twitter API specifies count is the max returned, may be much lower)
@test Tweets<:typeof(mentions[1])

# testing retweets_of_me
my_rts = get_retweets_of_me(count = 300)
@test Tweets<:typeof(my_rts[1])

Want to contribute?

Contributions are welcome! Kindly refer to the contribution guidelines.

Linux: Build Status 

CodeCov: codecov

Author: Randyzwitch
Source Code: 
License: View license

#julia #api #twitter 

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:  
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
        - 10:33  Finding the length of an array
        - 11:44  Adding Elements
        - 15:06  Removing elements
        - 18:32  Array concatenation
       - 20:59  Slicing
       - 23:26  Looping  

Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  • By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
  • Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
  • Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])



#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])



#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])



#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:


Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element



Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item


How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10



If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 

numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10



How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])



#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):



How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position


#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 20 and 30 only
print(numbers[1:3]) #second to third position


#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40



#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers



#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers



#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets




#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0




#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])




#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])




#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10



#array('i', [20, 30, 10, 20])


And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming 

Roar: Resource-Oriented Architectures in Ruby


Resource-Oriented Architectures in Ruby.


Roar is a framework for parsing and rendering REST documents. Nothing more.

Representers let you define your API document structure and semantics. They allow both rendering representations from your models and parsing documents to update your Ruby objects. The bi-directional nature of representers make them interesting for both server and client usage.

Roar comes with built-in JSON, JSON-HAL and XML support. JSON API support is available via the JSON API gem. Its highly modular architecture provides features like coercion, hypermedia, HTTP transport, client caching and more.

Roar is completely framework-agnostic and loves being used in web kits like Rails, Hanami, Sinatra, Roda, etc. If you use Rails, consider roar-rails for an enjoyable integration.


Roar is just a thin layer on top of the representable gem. While Roar gives you a DSL and behaviour for creating hypermedia APIs, representable implements all the mapping functionality.

If in need for a feature, make sure to check the representable API docs first.


The roar gem runs with all Ruby versions >= 1.9.3.

gem 'roar'

To use roar with Ruby versions < 2.2.0, add a version pin to your Gemfile:

gem 'sinatra', '~> 1.4'


Roar does not bundle dependencies for JSON and XML.

If you want to use JSON, add the following to your Gemfile:

gem 'multi_json'

If you want to use XML, add the following to your Gemfile:

gem 'nokogiri'

Defining Representers

Let's see how representers work. They're fun to use.

require 'roar/decorator'
require 'roar/json'

class SongRepresenter < Roar::Decorator
  include Roar::JSON

  property :title

API documents are defined using a decorator class. You can define plain attributes using the ::property method.

Now let's assume we'd have Song which is an ActiveRecord class. Please note that Roar is not limited to ActiveRecord. In fact, it doesn't really care whether it's representing ActiveRecord, Sequel::Model or just an OpenStruct instance.

class Song < ActiveRecord::Base


To render a document, you apply the representer to your model.

song = "Medicine Balls") #=> {"title":"Medicine Balls"}

Here, the song objects gets wrapped (or "decorated") by the decorator. It is treated as immutable - Roar won't mix in any behaviour.


The cool thing about representers is: they can be used for rendering and parsing. See how easy updating your model from a document is.

song = "Medicine Balls")'{"title":"Linoleum"}')
song.title #=> Linoleum

Unknown attributes in the parsed document are simply ignored, making half-baked solutions like strong_parameters redundant.

Module Representers

Module Representers are deprecated in Roar 1.1 and will be removed in Roar 2.0.

In place of inheriting from Roar::Decorator, you can also extend a singleton object with a representer module. Decorators and module representers actually have identical features. You can parse, render, nest, go nuts with both of them.

song = "Fate")

song.to_json #=> {"title":"Fate"}

Here, the representer is injected into the actual model and gives us a new #to_json method.

This also works both ways.

song =

song #=> {"title":"Fate"}

It's worth noting though that many people dislike #extend due to well-known performance issues and object pollution. As such this approach is no longer recommended. In this README we'll use decorators to illustrate this library.


Roar (or rather representable) also allows mapping collections in documents.

class SongRepresenter < Roar::Decorator
  include Roar::JSON

  property :title
  collection :composers

Where ::property knows how to handle plain attributes, ::collection does lists.

song = "Roxanne", composers: ["Sting", "Stu Copeland"]) #=> {"title":"Roxanne","composers":["Sting","Stu Copeland"]}

And, yes, this also works for parsing: from_json will create and populate the array of the composers attribute.


Now what if we need to tackle with collections of Songs? We need to implement an Album class.

class Album < ActiveRecord::Base
  has_many :songs

Another representer to represent.

class AlbumRepresenter < Roar::Decorator
  include Roar::JSON

  property :title
  collection :songs, extend: SongRepresenter, class: Song

Both ::property and ::collection accept options for nesting representers into representers.

The extend: option tells Roar which representer to use for the nested objects (here, the array items of the album.songs field). When parsing a document class: defines the nested object type.

Consider the following object setup.

album = "True North")
album.songs << "The Island")
album.songs << "Changing Tide")

You apply the AlbumRepresenter and you get a nested document. #=> {"title":"True North","songs":[{"title":"The Island"},{"title":"Changing Tide"}]}

This works vice-versa.

album ='{"title":"Indestructible","songs":[{"title":"Tropical London"},{"title":"Roadblock"}]}')

puts album.songs[1] #=> #<Song title="Roadblock">

The nesting of two representers can map composed object as you find them in many many APIs.

In case you're after virtual nesting, where a nested block in your document still maps to the same outer object, check out the ::nested method.

Inline Representer

Sometimes you don't wanna create two separate representers - although it makes them reusable across your app. Use inline representers if you're not intending this.

class AlbumRepresenter < Roar::Decorator
  include Roar::JSON

  property :title

  collection :songs, class: Song do
    property :title

This will give you the same rendering and parsing behaviour as in the previous example with just one module.

Syncing Objects

Usually, when parsing, nested objects are created from scratch. If you want nested objects to be updated instead of being newly created, use parse_strategy:.

class AlbumRepresenter < Roar::Decorator
  include Roar::JSON

  property :title

  collection :songs, extend: SongRepresenter, parse_strategy: :sync

This will advise Roar to update existing songs.

album.songs[0].object_id #=> 81431220'{"title":"True North","songs":[{"title":"Secret Society"},{"title":"Changing Tide"}]}')

album.songs[0].title #=> Secret Society
album.songs[0].object_id #=> 81431220

Roar didn't create a new Song instance but updated its attributes, only.

We're currently working on better strategies to easily implement POST and PUT semantics in your APIs without having to worry about the nitty-gritties.


Roar provides coercion with the virtus gem.

require 'roar/coercion'
require 'roar/json'

class SongRepresenter < Roar::Decorator
  include Roar::JSON
  include Roar::Coercion

  property :title
  property :released_at, type: DateTime

The :type option allows to set a virtus-compatible type.

song ='{"released_at":"1981/03/31"}')

song.released_at #=> 1981-03-31T00:00:00+00:00

More Features

Roar/representable gives you many more mapping features like renaming attributes, wrapping, passing options, etc. See the representable documentation for a detailed explanation.


Roar comes with built-in support for embedding and processing hypermedia in your documents.

class SongRepresenter < Roar::Decorator
  include Roar::JSON
  include Roar::Hypermedia

  property :title

  link :self do

The Hypermedia feature allows declaring links using the ::link method. In the block, you have access to the represented model. When using representer modules, the block is executed in the model's context.

However, when using decorators, the context is the decorator instance, allowing you to access additional data. Use represented to retrieve model data.

class SongRepresenter < Roar::Decorator
  # ..
  link :self do

This will render links into your representation. #=> {"title":"Roxanne","links":[{"rel":"self","href":"http://songs/Roxanne"}]}

Per default, links are pushed into the hash using the links key. Link blocks are executed in represented context, allowing you to call any instance method of your model (here, we call #title).

Also, note that roar-rails allows using URL helpers in link blocks.

Passing Options

Sometimes you need more data in the link block. Data that's not available from the represented model.

class SongRepresenter < Roar::Decorator
  include Roar::JSON

  property :title

  link :self do |opts|

Pass this data to the rendering method.

representer =
representer.to_json(base_url: "localhost:3001/")

Any options passed to #to_json will be available as block arguments in the link blocks.

Specify Decorator

If you have a property that is a separate class or model, you can specify a decorator for that property. Suppose there is a separate Artist model for an album. When the album is eagerly loaded, the artist model could be represented along with it.

class ArtistRepresenter < Roar::Decorator
  property :name

class AlbumRepresenter < Roar::Decorator
  # ..
  property :artist, decorator: ArtistRepresenter

Consuming Hypermedia

Since we defined hypermedia attributes in the representer we can also consume this hypermedia when we parse documents.


representer.links[:self].href #=> "http://songs/Roxanne"

Reading link attributes works by using #links[] on the consuming instance.

This allows an easy way to discover hypermedia and build navigational logic on top.

Media Formats

While Roar comes with a built-in hypermedia format, there's official media types that are widely recognized. Roar currently supports HAL and JSON API.

Simply by including a module you make your representer understand the media type. This makes it easy to change formats during evaluation.


The HAL format is a simple media type that defines embedded resources and hypermedia.

require 'roar/json/hal'

class SongRepresenter < Roar::Decorator
  include Roar::JSON::HAL

  property :title

  link :self do

Documentation for HAL can be found in the API docs.

Make sure you understand the different contexts for links when using decorators.


Including the Roar::JSON::HAL module adds some more DSL methods to your module. It still allows using ::link but treats them slightly different.

#=> {"title":"Roxanne","_links":{"self":{"href":"http://songs/Roxanne"}}}

According to the HAL specification, links are now key with their rel attribute under the _links key.

Parsing works like-wise: Roar will use the same setters as before but it knows how to read HAL.


Nested, or embedded, resources can be defined using the :embedded option.

class AlbumRepresenter < Roar::Decorator
  include Roar::JSON::HAL

  property :title

  collection :songs, class: Song, embedded: true do
    property :title

To embed a resource, you can use an inline representer or use :extend to specify the representer name.

#=> {"title":"True North","_embedded":{"songs":[{"title":"The Island"},{"title":"Changing Tide"}]}}

HAL keys nested resources under the _embedded key and then by their type.

All HAL features in Roar are discussed in the API docs, including array links.


Roar also supports JSON API via the Roar JSON API gem.

Client-Side Support

Being a bi-directional mapper that does rendering and parsing, Roar representers are perfectly suitable for use in clients, too. In many projects, representers are shared as gems between server and client.

Consider the following shared representer.

class SongRepresenter < Roar::Decorator
  include Roar::JSON
  include Roar::Hypermedia

  property :title
  property :id

  link :self do

In a client where you don't have access to the database it is common to use OpenStruct classes as domain objects.

require 'roar/client'
require 'roar/json'

class Song < OpenStruct
  include Roar::JSON
  include SongRepresenter
  include Roar::Client

HTTP Support

The Client module mixes all necessary methods into the client class, e.g. it provides HTTP support

song = "Roxanne") "http://localhost:4567/songs", as: "application/json") #=> 42

What happens here?

  • You're responsible for initializing the client object with attributes. This can happen with in the constructor or using accessors.
  • post will use the included SongRepresenter to compile the document using #to_json.
  • The document gets POSTed to the passed URL.
  • If a document is returned, it is deserialized and the client's attributes are updated.

This is a very simple but efficient mechanism for working with representations in a client application.

Roar works with all HTTP request types, check out GET.

song =
song.get(uri: "http://localhost:4567/songs/1", as: "application/json")

song.title #=> "Roxanne"
song.links[:self].href #=> http://localhost/songs/1

As GET is not supposed to send any data, you can use #get on an empty object to populate it with the server data.


Roar supports SSL connections - they are automatically detected via the protocol.

song.get(uri: "https://localhost:4567/songs/1")

Basic Authentication

The HTTP verbs allow you to specify credentials for HTTP basic auth.

song.get(uri: "http://localhost:4567/songs/1", basic_auth: ["username", "secret_password"])

Client SSL certificates

(Only currently supported with Net:Http)

song.get(uri: "http://localhost:4567/songs/1", pem_file: "/path/to/client/cert.pem", ssl_verify_mode: OpenSSL::SSL::VERIFY_PEER)

Note: ssl_verify_mode is not required and will default to OpenSSL::SSL::VERIFY_PEER)

Request customization

All verbs yield the request object before the request is sent, allowing to modify it. It is a Net::HTTP::Request instance (unless you use Faraday).

song.get(uri: "http://localhost:4567/songs/1") do |req|
 req.add_field("Cookie", "Yumyum")

Error handling

In case of a non-2xx response status, #get and friends raise a Roar::Transport::Error exception. The original response can be accessed as follows.

  song.get(uri: "http://localhost/songs1") # not-existing.
rescue Roar::Transport::Error => exception
  exception.response.code #=> 404


Roar also comes with XML support.

class SongRepresenter < Roar::Decorator
  include Roar::XML
  include Roar::Hypermedia

  property :title
  property :id

  link :self do

Include the Roar::XML engine and get bi-directional XML for your objects.

song = "Roxanne", id: 42)

Note that you now use #to_xml and #from_xml.

  <link rel="self" href="http://songs/Roxanne"/>

Please consult the representable XML documentation for all its great features.


Questions? Need help? Free 1st Level Support on ! We also have a mailing list, yiha!


Roar is released under the MIT License.

Author: trailblazer
Source code:
License: MIT license

#ruby  #ruby-on-rails 

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