Install different python versions with virtualenvwrapper

Sometimes we need to install different version of virtual environments in same machine.

Check this post for create virtual environment in Ubuntu 16.04.

First you need to install different python versions in your machine.

Ubuntu 14.04 and 16.04

If you are using Ubuntu 14.04 or 16.04, you can use J Fernyhough’s PPA:

sudo add-apt-repository ppa:jonathonf/python-3.6
sudo apt-get update
sudo apt-get install python3.6

Alternatively, you use Felix Krull’s deadsnakes PPA:

sudo add-apt-repository ppa:fkrull/deadsnakes
sudo apt-get update
sudo apt-get install python3.6
Ubuntu 16.10 and 17.04

If you are using Ubuntu 16.10 or 17.04, then Python 3.6 is in the universe repository, so you can just run

sudo apt-get update
sudo apt-get install python3.6

Then you need to create virtual environment with specific python environment.

mkvirtualenv -p /usr/bin/python3.6 python_3.6

This will install python 3.6 in your machine.

Happy Coding ūüôā

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Create virtual environment with virtualenvwrapper in windows

Suppose you need to work on three different projects project A, project B and project C. project A and project B need python 3 and some required libraries. But for project C you need python 2.7 and dependent libraries.

So best practice for this is to separate those project environments. For creating separate python virtual environment need to follow below steps:

Step 1: Install pip with this command:
python -m pip install -U pip

Step 2: Then install “virtualenvwrapper-win” package by using command (command can be executed windows power shell):

pip install virtualenvwrapper-win

Step 3: Create a new virtualenv environment by using command: mkvirtualenv python_3.5

Step 4: Activate the environment by using command:

workon < environment name> Continue reading Create virtual environment with virtualenvwrapper in windows

Create virtual environment with virtualenvwrapper in Ubuntu 16.04

Suppose you need to work on three different projects project A, project B and project C. project A and project B need python 3 and some required libraries. But for project C you need python 2.7 and dependent libraries.

So best practice for this is to seperate those project environemtns. To create virtual environment you can use below technique:

  1. Virtualenv: Virtualenv is a tool to create isolated Python environments.
  2. Virtualenvwrapper: While virtual environments certainly solve some big problems with package management, they’re not perfect. After creating a few environments, you’ll start to see that they create some problems of their own, most of which revolve around managing the environments themselves. To help with this, the virtualenvwrapper tool was created, which is just some wrapper scripts around the main virtualenv tool.A few of the more useful features of virtualenvwrapper are that it:- Organizes all of your virtual environments in one location;
    – Provides methods to help you easily create, delete, and copy environments; and,
    – Provides a single command to switch between environments
  3. Conda: Conda is a package manager application that quickly installs, runs, and updates packages and their dependencies. Conda is also an environment manager application. A conda environment is a directory that contains a specific collection of conda packages that you have installed.

Continue reading Create virtual environment with virtualenvwrapper in Ubuntu 16.04

Adding a radius search to your Ruby on Rails 5 application and PostgreSQL

Recently one of my project need to create a functionality for location based search. I choose PostgreSQL as database because I want to use some power of PostgreSQL.

So I want to create a functionality where I can send three param in a PostgreSQL and this database will return me location within this radius.  So I want to search what locations are within X meters of these coordinates.

Using PostgreSQL is lot easier.

First you need to enable some plugin for PostgreSQL.

  CREATE EXTENSION cube;
  CREATE EXTENSION earthdistance;

Now you can run query like:

SELECT \"franchises\".* FROM \"franchises\" WHERE (earth_box(ll_to_earth(40.810649, -73.598605), 2000) @> ll_to_earth(latitude, longitude))

You can also create a scope in rails model.

scope :within_radius, lambda {|latitude, longitude, metres| where("earth_box(ll_to_earth(?, ?), ?) @> ll_to_earth(latitude, longitude)", latitude, longitude, metres) }

And that’s it. You can call

  Franchise.within_radius(43.6422125, -79.3744256, 5000) 

and you’ll have a list of franchises within 5 km of NY.

Happy coding ūüôā

Custom seed files in Ruby on Rails 5 project

For project purpose sometimes we need to create multiple seed files. We can do this by simple creating rake tasks in ruby on rails.

To create a rake task for multiple seed file you can use below code:

# lib/tasks/custom_seed.rake
namespace :db do
  namespace :seed do
    Dir[File.join(Rails.root, 'db', 'seeds', '*.rb')].each do |filename|
      task_name = File.basename(filename, '.rb').intern    
      task task_name => :environment do
        load(filename) if File.exist?(filename)
      end
    end
  end
end

Please save this code to lib/tasks/custom_seed.rake file

Now create a folder called seeds inside your db folder: db/seeds

Now you can run any seed file from this folder by simple running below command:

# Name of the file without the .rb extension 
rake db:seed:seed_file_name 

Happy Coding ūüôā

zsh: command not found: bundle error

I have installed zsh in my ubuntu 16.04, but after install rails when trying to bundle install I found following error in my console:

zsh: command not found: bundle (after gem install bundle)

After searching on google I found following solution from stackoverflow.com:

  • echo ‘export PATH=”$HOME/.rbenv/bin:$PATH”‘ >> ~/.zshenv
  • echo ‘eval “$(rbenv init -)”‘ >> ~/.zshenv
  • echo ‘source $HOME/.zshenv’ >> ~/.zshrc
  • exec $SHELL

Running all those commands one by one will solve this problem.

Rename columns in pandas data-frame

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

We know for selecting a …¬†in a pandas data-frame we need to use bracket notation with full name of a column. Sometimes our column name is very long with space. So we need to rename this with another name. We can do this with following pandas commands.

import pandas as pd
ufo = pd.read_csv('http://bit.ly/uforeports')
ufo.head()
City Colors Reported Shape Reported State Time
0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00
1 Willingboro NaN OTHER NJ 6/30/1930 20:00
2 Holyoke NaN OVAL CO 2/15/1931 14:00
3 Abilene NaN DISK KS 6/1/1931 13:00
4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00
ufo.columns
Index(['City', 'Colors Reported', 'Shape Reported', 'State', 'Time',
       'Location'],
      dtype='object')
Index(['City', 'Colors Reported', 'Shape Reported', 'State', 'Time',
       'Location'],
      dtype='object')
ufo.rename(columns={'Colors Reported' : 'colors_reported',
'Shape Reported' : 'shape_reported'}, inplace=True)

This will rename the old column with new column names.

We can also rename column names without specifying old names. To do so we need to create a python list and replace the old column names.

ufo_cols = ['city', 'colors reported', 'shape reported', 'state', 'time']
ufo.columns = ufo_cols

This will replace all old columns with new columns.

If we have too many columns in a data-frame, we can simply use python replace method replace columns.

Following command will lower case the word and replace spaces with underscore:

ufo.columns = ufo.columns.str.lower().str.replace(' ', '_')