10 International Expansion Mistakes That Will Sink Your Business or Startup

All businesses want to grow and expand. And for many, the lure of finding overseas markets is strong. After all, look how many Chinese businesses have expanded into the U.S. and are making a “killing” selling their products. Surely you can do the same thing. Just set up a website in a foreign language, feature your products, and voila – orders come rolling in.

Not so fast.

Expanding overseas demands a lot of research, time, energy, commitment on the part of your team, and, yes, money. Many businesses have jumped into overseas markets and have failed miserably. You don’t have to be one of them if you avoid these common mistakes.

IMAGE: PIXABAY

1. Expansion For The Wrong Reasons

Exactly what does this mean? Precisely this: if you are looking to expand overseas to boost a business that is flailing at home, and you think you can make some fast money somewhere else, think again. Businesses that are successful with their overseas expansions understand that they are in it for the long-haul. It takes time to spread a brand in a foreign market and to develop a customer base. Profitability is a long-term process.

2. Failure To Research The Market Thoroughly

There may be a sound market for your product in your home country. What is the comparable demographic in a foreign market you are considering? There are certainly a number of countries with a rapidly expanding middle class – people who are actively seeking consumer goods. But maybe not yours. Conducting enough market research in a country you are considering may involve using some local pros, but it will be well worth it in the end. Time, energy and money spent expanding into a bad market will be a “killer.”

3. Mistakes In Localization Marketing

Websites, blogs, social media – all are tools for marketing in our native land. But when we attempt to move those same efforts into a foreign market, there are some critical cultural considerations. Localizing a website and translating content correctly requires consultation with natives of that target market.

One of the things that many business owners do is contract with companies like The Word Point online translation service that employs natives in multiple countries to provide website localization and content translation. Because they have the cultural awareness that you do not, they will be able to advise you and help to ensure that all of your site and your content is appropriate for the target audience. And many businesses employ website designers in the target country.

4. Failure To Research Local Laws And Regulations

More than one business has failed to meet the legal requirements to do business in a foreign country. And those laws and regulations are continually changing. And those laws and regulations can relate to online businesses too. For example: recently, the General Data Protection Regulation went into effect in the EU.

Any online business operating within that area must conform to its requirements about consumer privacy and must have posted policies regarding privacy protection. And there are some countries that block access of their citizens to certain businesses selling certain products or services. If you don’t do your homework here, you are in for trouble.

5. Failure To Configure Shipping Arrangements

Believe it or not, many a company has moved into overseas markets without a plan for shipping, returns, and exchanges, etc. Do not open your doors anywhere without having this fully set up in advance. If you are scrambling at the last minute to get shipments out, you will have unhappy customers who will not return.

6. Not Running A Lead Generation Campaign

All of the research in the world is not equal to an actual litmus test. If you do not test your ability to get leads, you will not know how much interest there really is in your product. You should craft a landing age and purchase some local advertising. See what the response is over a period of time, not just days. Remember, expanding into a foreign market takes time and patience.

7. Not Sizing Up The Competition

You will have both local and other international competition. Again, this takes some research and probably some local expertise in the target country. Finding marketing pros in the target country will be invaluable in getting the information you need. Just be prepared to spend a little money.

8. Scrimping On Local Talent

No business can be successful without hiring some local talent – especially marketers and networkers. They do not come cheap, and trying to cut costs in this area is a huge mistake. Locate the best you can find – it will be worth it in the long run.

9. Not Physically Going There

You do need to go the cost of at least one trip to the target country, even if your business is solely online. You will need to meet face-to-face with the local talent you intend to use, to network, and to develop a better understanding of the culture.

10. Not Having A Comprehensive Plan

This is a combination of all of the previous mistakes. The only way to make a success of a foreign expansion is to spend lots of time in the pre-planning phase, this means that there are short-term, medium, and long-term goals; it means that the local team is on board; it means that the right due diligence takes place in terms of research and finding the right local talent to face the competition and spread your brand.

Global expansion is a given. We live in a much smaller world than we have ever lived in before. Businesses are reaching far and wide to foreign markets, and many are achieving success. Getting into these markets can be lucrative, but achieving that profit and ROI means doing it right, being patient, and letting the growth occur gradually. Gradual growth is sustainable.

Getting the chance to diversify, enter new markets, and drive more potential revenue are just some of the advantages of international expansion. However, there are still risks to expanding internationally such as issues with political stability and a few legal hurdles. A business becomes more vulnerable to these issues when they make mistakes. Here, we are going to talk about those mistakes to help you avoid them.

Sign up for free to get instant access to top talent across geographies

1. Failing to adapt your sales and marketing strategies

Expanding to another country means that you will encounter culture and consumer habits different from what you are used to. Hence, you would want to be more particular and intentional as you approach this unless you want to risk your message and marketing budget falling on deaf ears.

To avoid sales and marketing mishaps, take the time to research the current market competition, their local language, cultural nuances, and local consumer behaviour. By having a clear understanding of what works and what doesn’t, you can better adapt your sales and marketing strategies to the local context.

2. Not listening to your local team

One of the most disappointing mistakes of startups when scaling overseas is pushing aside valuable customer insights from their local teams. In reality, your local leaders are your best resource for real and practical data. Remember that your local team is probably far more familiar with the local market than you are. Let them take active participation in discussions regarding strategies and operations.

3. Not having a solid plan or strategies for expansion

Before you venture out to a country or state, you want to make sure that you take a methodological and data-backed approach with every decision. Being fast doesn’t always equate to success if it’s haphazardly done.

Make sure that you have ready resources, research, and workforce to carry out this expansion properly. Otherwise, you may lose a substantial amount of time and money.

4. Not hiring top talent

As Red Adair said before, “If you think it’s expensive to hire a professional, wait until you hire an amateur.”

As a startup, your team is going to be your best asset thus, skimping on talent to reduce salaries and wages, is not going to make things easier for you.

Unfortunately, this is extremely popular with startups due to their limited capital, but hiring incompetent individuals would just increase your employee turnover and eventually, you might even find yourself spending more on unnecessary hiring-related costs.

To deter this, always prioritise the quality of your candidates, allocate a sufficient budget to attract top talents, and set up a structured hiring process in place.

5. Not be aware of local employment, payroll laws and compliance

Failing to comply with local laws and tax regulations commonly stems from inadequate research. And in fact, there have been several startups that fell prey to this and weren’t able to recover the costs at all.

Bear in mind that there are regulations and laws relating to payment methods, taxes, and payroll forms that are specific to each country or state. You don’t want to run the risk of incurring tax penalties because of your lack of awareness. Consult with local legal experts before entering a new market.

6. Taking cross-cultural communications for granted

Failing to accurately translate marketing messages is a common mistake that even renowned brands weren’t able to steer clear of.

When Schweppes Tonic water launched in Italy, they unknowingly translated their name to “Schweppes Toilet Water.” And of course, that wasn’t received well by the Italians!

That being said, you would want to consider how your offerings and messages translate to other countries both literally and figuratively first before a massive rollout.

7. Lowballing international hires

A lot of companies see expansion as a way to maximize growth at a low cost; however, this usually doesn’t translate well. The best talent in the market comes with a price, and they know exactly what they’re worth. That’s why if you want the best talent, you have to be ready to provide a competitive salary as well.

8. Neglecting your international teams

Without having an established working structure, standards, and practices, your team’s operations and company culture will crumble. This is why besides working with a local pool of talented individuals, you need to appoint a well-rounded business leader from your international team who can personify your company culture and maintain employee engagement while aligning local teams with business goals and objectives; and at the same time, liaise with upper management in your headquarters.

Your visibility will mean a lot to your employees. Making a meaningful appearance now and then, whether by physical visits or having a virtual touch base with your remote team, will help you establish proper leadership.

9. Not seeking advice from business experts and legal consultants

Many startups try to cut corners by not having legal counsel or hiring friends and relatives for a minimum fee to act as their business and legal consultants. It may seem like an unnecessary expense at first, but in reality, having well-established lawyers and business experts on your side can guide you to take the right steps when establishing foreign subsidiaries.

For instance, they can help you with the following areas:

Employment Laws

Contract Laws

Corporate and Securities Law

Tax Regulations

Intellectual Property

Real Estate

Data Security

Foreign Government Considerations

10. Thinking that there’s no such thing as transfer pricing laws

Some entrepreneurs think they’ve found the loophole to pay fewer taxes by offering their goods or services in a country with low tax laws. But transfer pricing laws exist and some penalties apply to companies who fail to comply. Study this carefully to avoid getting penalized.

Everyone wants nothing but the best for their companies. Therefore, by being able to identify when you are making these common mistakes will empower you to make the most sound decisions for your international expansion.

What is GEEK

Buddha Community

Best Electric Bikes and Scooters for Rental Business or Campus Facility

The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.

Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.

Electric Scooters Trends and Statistics

In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.

A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.

And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.

Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.

List of Best Electric Bikes for Rental Business or Campus Facility 2020:

It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.

We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.

Billy eBike

 https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides

To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.

Price: $2490

Available countries

Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.

Features

• Control – Ride with confidence with our ultra-wide BMX bars and a hyper-responsive twist throttle.
• Stealth- Ride like a ninja with our Gates carbon drive that’s as smooth as butter and maintenance-free.
• Drive – Ride further with our high torque fat bike motor, giving a better climbing performance.
• Accelerate – Ride quicker with our 20-inch lightweight cutout rims for improved acceleration.
• Customize – Ride your own way with 5 levels of power control. Each level determines power and speed.
• Flickable – Ride harder with our BMX /MotoX inspired geometry and lightweight aluminum package

Specifications

• Maximum speed: 20 mph (32 km/h)
• Range per charge: 41 miles (66 km)
• Maximum Power: 500W
• Motor type: Fat Bike Motor: Bafang RM G060.500.DC
• Load capacity: 300lbs (136kg)
• Battery type: 13.6Ah Samsung lithium-ion,
• Battery capacity: On/off-bike charging available
• Weight: w/o batt. 48.5lbs (22kg), w/ batt. 54lbs (24.5kg)
• Front Suspension: Fully adjustable air shock, preload/compression damping /lockout
• Rear Suspension: spring, preload adjustment
• Built-in GPS

Why Should You Buy This?

• Riding fun and excitement
• Better climbing ability and faster acceleration.
• Ride with confidence
• Billy folds for convenient storage and transportation.
• Shorty levers connect to disc brakes ensuring you stop on a dime
• belt drives are maintenance-free and clean (no oil or lubrication needed)

**Who Should Ride Billy? **

Both new and experienced riders

**Where to Buy? **Local distributors or ships from the USA.

Genze 200 series e-Bike

 https://www.genze.com/fleet/

Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.

Price: $2099.00

Available countries

The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.

Features

• 2 Frame Options
• 2 Sizes
• Integrated/Removable Battery
• Throttle and Pedal Assist Ride Modes
• Integrated LCD Display
• Connected App
• 24 month warranty
• GPS navigation
• Bluetooth connectivity

Specifications

• Maximum speed: 20 mph with throttle
• Range per charge: 15-18 miles w/ throttle and 30-50 miles w/ pedal assist
• Charging time: 3.5 hours
• Motor type: Brushless Rear Hub Motor
• Gears: Microshift Thumb Shifter
• Battery type: Removable Samsung 36V, 9.6AH Li-Ion battery pack
• Battery capacity: 36V and 350 Wh
• Weight: 46 pounds
• Derailleur: 8-speed Shimano
• Brakes: Dual classic
• Wheels: 26 x 20 inches
• Frame: 16, and 18 inches
• Operating Mode: Analog mode 5 levels of Pedal Assist Thrott­le Mode

Norco from eBikestore

 https://ebikestore.com/shop/norco-vlt-s2/

The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.

Price: $2,699.00

Available countries

This item is available via the various Norco bikes international distributors.

Features

• VLT aluminum frame- for stiffness and wheel security.
• Bosch e-bike system – for their reliability and performance.
• E-bike components – for added durability.
• Hydraulic disc brakes – offer riders more stopping power for safety and control at higher speeds.
• Practical design features – to add convenience and versatility.

Specifications

• Maximum speed: KMC X9 9spd
• Motor type: Bosch Active Line
• Gears: Shimano Altus RD-M2000, SGS, 9 Speed
• Battery type: Power Pack 400
• Battery capacity: 396Wh
• Suspension: SR Suntour suspension fork
• Frame: Norco VLT, Aluminum, 12x142mm TA Dropouts

Bodo EV

http://www.bodoevs.com/bodoev/products_show.asp?product_id=13

Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.

Price: $799

Available countries

This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.

Features

• Reliable
• Environment friendly
• Comfortable riding
• Fashionable
• Economical
• Durable – long service life
• Braking stability
• LED lighting technology

Specifications

• Maximum speed: 45km/h
• Range per charge: 50km per person
• Charging time: 8 hours
• Maximum Power: 3000W
• Motor type: Brushless DC Motor
• Load capacity: 100kg
• Battery type: Lead-acid battery
• Battery capacity: 60V 20AH
• Weight: w/o battery 47kg

#android app #autorent #entrepreneurship #ios app #minimum viable product (mvp) #mobile app development #news #app like bird #app like bounce #app like lime #autorent #best electric bikes 2020 #best electric bikes for rental business #best electric kick scooters 2020 #best electric kickscooters for rental business #best electric scooters 2020 #best electric scooters for rental business #bird scooter business model #bird scooter rental #bird scooter rental cost #bird scooter rental price #clone app like bird #clone app like bounce #clone app like lime #electric rental scooters #electric scooter company #electric scooter rental business #how do you start a moped #how to start a moped #how to start a scooter rental business #how to start an electric company #how to start electric scooterrental business #lime scooter business model #scooter franchise #scooter rental business #scooter rental business for sale #scooter rental business insurance #scooters franchise cost #white label app like bird #white label app like bounce #white label app like lime

HAML Lint: Tool For Writing Clean and Consistent HAML

HAML-Lint

haml-lint is a tool to help keep your HAML files clean and readable. In addition to HAML-specific style and lint checks, it integrates with RuboCop to bring its powerful static analysis tools to your HAML documents.

You can run haml-lint manually from the command line, or integrate it into your SCM hooks.

Requirements

• Ruby 2.4+
• HAML 4.0+

Installation

gem install haml_lint

If you'd rather install haml-lint using bundler, don't require it in your Gemfile:

gem 'haml_lint', require: false

Then you can still use haml-lint from the command line, but its source code won't be auto-loaded inside your application.

Usage

Run haml-lint from the command line by passing in a directory (or multiple directories) to recursively scan:

haml-lint app/views/

You can also specify a list of files explicitly:

haml-lint app/**/*.html.haml

haml-lint will output any problems with your HAML, including the offending filename and line number.

File Encoding

haml-lint assumes all files are encoded in UTF-8.

Command Line Flags

Command Line Flag	Description
--auto-gen-config	Generate a configuration file acting as a TODO list
--auto-gen-exclude-limit	Number of failures to allow in the TODO list before the entire rule is excluded
-c/--config	Specify which configuration file to use
-e/--exclude	Exclude one or more files from being linted
-i/--include-linter	Specify which linters you specifically want to run
-x/--exclude-linter	Specify which linters you don't want to run
-r/--reporter	Specify which reporter you want to use to generate the output
-p/--parallel	Run linters in parallel using available CPUs
--fail-fast	Specify whether to fail after the first file with lint
--fail-level	Specify the minimum severity (warning or error) for which the lint should fail
--[no-]color	Whether to output in color
--[no-]summary	Whether to output a summary in the default reporter
--show-linters	Show all registered linters
--show-reporters	Display available reporters
-h/--help	Show command line flag documentation
-v/--version	Show haml-lint version
-V/--verbose-version	Show haml-lint, haml, and ruby version information

Configuration

haml-lint will automatically recognize and load any file with the name .haml-lint.yml as a configuration file. It loads the configuration based on the directory haml-lint is being run from, ascending until a configuration file is found. Any configuration loaded is automatically merged with the default configuration (see config/default.yml).

Here's an example configuration file:

linters:
  ImplicitDiv:
    enabled: false
    severity: error

  LineLength:
    max: 100

All linters have an enabled option which can be true or false, which controls whether the linter is run, along with linter-specific options. The defaults are defined in config/default.yml.

Linter Options

Option	Description
enabled	If false, this linter will never be run. This takes precedence over any other option.
include	List of files or glob patterns to scope this linter to. This narrows down any files specified via the command line.
exclude	List of files or glob patterns to exclude from this linter. This excludes any files specified via the command line or already filtered via the include option.
severity	The severity of the linter. External tools consuming haml-lint output can use this to determine whether to warn or error based on the lints reported.

Global File Exclusion

The exclude global configuration option allows you to specify a list of files or glob patterns to exclude from all linters. This is useful for ignoring third-party code that you don't maintain or care to lint. You can specify a single string or a list of strings for this option.

Skipping Frontmatter

Some static blog generators such as Jekyll include leading frontmatter to the template for their own tracking purposes. haml-lint allows you to ignore these headers by specifying the skip_frontmatter option in your .haml-lint.yml configuration:

skip_frontmatter: true

Inheriting from Other Configuration Files

The inherits_from global configuration option allows you to specify an inheritance chain for a configuration file. It accepts either a scalar value of a single file name or a vector of multiple files to inherit from. The inherited files are resolved in a first in, first out order and with "last one wins" precedence. For example:

inherits_from:
  - .shared_haml-lint.yml
  - .personal_haml-lint.yml

First, the default configuration is loaded. Then the .shared_haml-lint.yml configuration is loaded, followed by .personal_haml-lint.yml. Each of these overwrite each other in the event of a collision in configuration value. Once the inheritance chain is resolved, the base configuration is loaded and applies its rules to overwrite any in the intermediate configuration.

Lastly, in order to match your RuboCop configuration style, you can also use the inherit_from directive, which is an alias for inherits_from.

Linters

» Linters Documentation

haml-lint is an opinionated tool that helps you enforce a consistent style in your HAML files. As an opinionated tool, we've had to make calls about what we think are the "best" style conventions, even when there are often reasonable arguments for more than one possible style. While all of our choices have a rational basis, we think that the opinions themselves are less important than the fact that haml-lint provides us with an automated and low-cost means of enforcing consistency.

Custom Linters

Add the following to your configuration file:

require:
  - './relative/path/to/my_first_linter.rb'
  - 'absolute/path/to/my_second_linter.rb'

The files that are referenced by this config should have the following structure:

module HamlLint
  # MyFirstLinter is the name of the linter in this example, but it can be anything
  class Linter::MyFirstLinter < Linter
    include LinterRegistry

    def visit_tag
      return unless node.tag_name == 'div'
      record_lint(node, "You're not allowed divs!")
    end
  end
end

For more information on the different types on HAML node, please look through the HAML parser code: https://github.com/haml/haml/blob/master/lib/haml/parser.rb

Keep in mind that by default your linter will be disabled by default. So you will need to enable it in your configuration file to have it run.

Disabling Linters within Source Code

One or more individual linters can be disabled locally in a file by adding a directive comment. These comments look like the following:

-# haml-lint:disable AltText, LineLength
[...]
-# haml-lint:enable AltText, LineLength

You can disable all linters for a section with the following:

-# haml-lint:disable all

Directive Scope

A directive will disable the given linters for the scope of the block. This scope is inherited by child elements and sibling elements that come after the comment. For example:

-# haml-lint:disable AltText
#content
  %img#will-not-show-lint-1{ src: "will-not-show-lint-1.png" }
  -# haml-lint:enable AltText
  %img#will-show-lint-1{ src: "will-show-lint-1.png" }
  .sidebar
    %img#will-show-lint-2{ src: "will-show-lint-2.png" }
%img#will-not-show-lint-2{ src: "will-not-show-lint-2.png" }

The #will-not-show-lint-1 image on line 2 will not raise an AltText lint because of the directive on line 1. Since that directive is at the top level of the tree, it applies everywhere.

However, on line 4, the directive enables the AltText linter for the remainder of the #content element's content. This means that the #will-show-lint-1 image on line 5 will raise an AltText lint because it is a sibling of the enabling directive that appears later in the #content element. Likewise, the #will-show-lint-2 image on line 7 will raise an AltText lint because it is a child of a sibling of the enabling directive.

Lastly, the #will-not-show-lint-2 image on line 8 will not raise an AltText lint because the enabling directive on line 4 exists in a separate element and is not a sibling of the it.

Directive Precedence

If there are multiple directives for the same linter in an element, the last directive wins. For example:

-# haml-lint:enable AltText
%p Hello, world!
-# haml-lint:disable AltText
%img#will-not-show-lint{ src: "will-not-show-lint.png" }

There are two conflicting directives for the AltText linter. The first one enables it, but the second one disables it. Since the disable directive came later, the #will-not-show-lint element will not raise an AltText lint.

You can use this functionality to selectively enable directives within a file by first using the haml-lint:disable all directive to disable all linters in the file, then selectively using haml-lint:enable to enable linters one at a time.

Onboarding Onto a Preexisting Project

Adding a new linter into a project that wasn't previously using one can be a daunting task. To help ease the pain of starting to use Haml-Lint, you can generate a configuration file that will exclude all linters from reporting lint in files that currently have lint. This gives you something similar to a to-do list where the violations that you had when you started using Haml-Lint are listed for you to whittle away, but ensuring that any views you create going forward are properly linted.

To use this functionality, call Haml-Lint like:

haml-lint --auto-gen-config

This will generate a .haml-lint_todo.yml file that contains all existing lint as exclusions. You can then add inherits_from: .haml-lint_todo.yml to your .haml-lint.yml configuration file to ensure these exclusions are used whenever you call haml-lint.

By default, any rules with more than 15 violations will be disabled in the todo-file. You can increase this limit with the auto-gen-exclude-limit option:

haml-lint --auto-gen-config --auto-gen-exclude-limit 100

Editor Integration

Vim

If you use vim, you can have haml-lint automatically run against your HAML files after saving by using the Syntastic plugin. If you already have the plugin, just add let g:syntastic_haml_checkers = ['haml_lint'] to your .vimrc.

Vim 8 / Neovim

If you use vim 8+ or Neovim, you can have haml-lint automatically run against your HAML files as you type by using the Asynchronous Lint Engine (ALE) plugin. ALE will automatically lint your HAML files if it detects haml-lint in your PATH.

Sublime Text 3

If you use SublimeLinter 3 with Sublime Text 3 you can install the SublimeLinter-haml-lint plugin using Package Control.

Atom

If you use atom, you can install the linter-haml plugin.

TextMate 2

If you use TextMate 2, you can install the Haml-Lint.tmbundle bundle.

Visual Studio Code

If you use Visual Studio Code, you can install the Haml Lint extension

Git Integration

If you'd like to integrate haml-lint into your Git workflow, check out our Git hook manager, overcommit.

Rake Integration

To execute haml-lint via a Rake task, make sure you have rake included in your gem path (e.g. via Gemfile) add the following to your Rakefile:

require 'haml_lint/rake_task'

HamlLint::RakeTask.new

By default, when you execute rake haml_lint, the above configuration is equivalent to running haml-lint ., which will lint all .haml files in the current directory and its descendants.

You can customize your task by writing:

require 'haml_lint/rake_task'

HamlLint::RakeTask.new do |t|
  t.config = 'custom/config.yml'
  t.files = ['app/views', 'custom/*.haml']
  t.quiet = true # Don't display output from haml-lint to STDOUT
end

You can also use this custom configuration with a set of files specified via the command line:

# Single quotes prevent shell glob expansion
rake 'haml_lint[app/views, custom/*.haml]'

Files specified in this manner take precedence over the task's files attribute.

Documentation

Code documentation is generated with YARD and hosted by RubyDoc.info.

Contributing

We love getting feedback with or without pull requests. If you do add a new feature, please add tests so that we can avoid breaking it in the future.

Speaking of tests, we use Appraisal to test against both HAML 4 and 5. We use rspec to write our tests. To run the test suite, execute the following from the root directory of the repository:

appraisal bundle install
appraisal bundle exec rspec

Community

All major discussion surrounding HAML-Lint happens on the GitHub issues page.

Changelog

If you're interested in seeing the changes and bug fixes between each version of haml-lint, read the HAML-Lint Changelog.

Author: sds
Source Code: https://github.com/sds/haml-lint
License: MIT license

#haml #lint 

Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. 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:

1. 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
array.array()

1. 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
arr.array()

1. 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
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:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

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])


print(numbers)

#output

#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])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", 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])

print(numbers)

#output

#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])


print(len(numbers))

#output
# 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:

array_name[index_value_of_item]

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

#output

#10
#20
#30

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
 
#output

#30
#20
#10

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
print(numbers.index(10))

#output

#0

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
print(numbers.index(10))

#output

#0

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])

print(numbers)

#output

#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:
    print(number)
    
#output
#10
#20
#30

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)):
    print(values[value])

#output

#10
#20
#30

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

#output

#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

#output

#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

print(numbers)

#output

#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
numbers.append(40)

print(numbers)

#output

#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
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", 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

numbers.extend([40,50,60])

print(numbers)

#output

#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

numbers.insert(0,40)

print(numbers)

#output

#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])

numbers.remove(10)

print(numbers)

#output

#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])

numbers.remove(10)

print(numbers)

#output

#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
numbers.pop(0)

print(numbers)

#output

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

Conclusion

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.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python 

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
array.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
arr.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
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:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

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])


print(numbers)

#output

#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])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", 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])

print(numbers)

#output

#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])


print(len(numbers))

#output
# 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:

array_name[index_value_of_item]

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

#output

#10
#20
#30

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
 
#output

#30
#20
#10

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
print(numbers.index(10))

#output

#0

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
print(numbers.index(10))

#output

#0

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])

print(numbers)

#output

#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:
    print(number)
    
#output
#10
#20
#30

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)):
    print(values[value])

#output

#10
#20
#30

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

#output

#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

#output

#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

print(numbers)

#output

#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
numbers.append(40)

print(numbers)

#output

#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
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", 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

numbers.extend([40,50,60])

print(numbers)

#output

#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

numbers.insert(0,40)

print(numbers)

#output

#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])

numbers.remove(10)

print(numbers)

#output

#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])

numbers.remove(10)

print(numbers)

#output

#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
numbers.pop(0)

print(numbers)

#output

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

Conclusion

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 

How This Cybersecurity Startup Is Using Machine Learning

The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. While the conventional cybersecurity approach has benefited many, having cybersecurity without cyber-intelligence and necessary awareness can put the security professionals off-guarded to more complicated and novel threats.

Furthermore, with limited cybersecurity resources, businesses need to prioritise their efforts to strengthen cyber posture effectively; however, many organisations do not have an anchor point or a guiding principle, to begin with. With cyber-intelligence inputs missing from cybersecurity capabilities like incident management, vulnerability management, risk assessment and brand monitoring, businesses end up running their security practice in silos instead of an integrated approach.

And, thus, in an attempt to revolutionise the cyber threat visibility and intelligence market, CYFIRMA, a cyber analytics startup assists businesses to understand the relevance of the current threat landscape. Not only it provides insights on threat actors and indicators, emerging threats and digital risks, but also automatically applies intelligence into cyber posture management. To dig deeper, Analytics India Magazine got in touch with the chairman and CEO of the company, Kumar Ritesh, to understand how the company uses a predictive intelligence-driven approach to discover cyber threats.

#startups #cyber security startup india #cybersecurity startup #machine learning #startup #startups