{"id":181014,"date":"2024-03-01T09:58:00","date_gmt":"2024-03-01T08:58:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=181014"},"modified":"2026-08-09T19:40:58","modified_gmt":"2026-08-09T18:40:58","slug":"project-environment-preparation-essential-steps-for-smooth-workflow","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/project-environment-preparation-essential-steps-for-smooth-workflow","title":{"rendered":"Project Environment Preparation: Essential Steps for Smooth Workflow"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n

If you are getting started on a Data Science project,<\/a> this article will be of interest to you!<\/strong><\/p>\n\n\n

Before starting a Machine Learning project<\/a>, it’s crucial to prepare and organise your development environment properly!<\/p>\n\n\n

In this article, we’ll show you how to set up and prepare your environment in several stages.<\/p>\n\n\n

a) Settings<\/h3>\n\n\n

Before getting started, it’s important to make a few adjustments to your working environment. This step is an essential prerequisite before tackling the root of the problem.<\/p>\n\n\n

The first question is which code support you are going to use: there are several solutions for installing Python.<\/a><\/p>\n\n\n

You can use Anaconda, Spyder or any other suitable IDE<\/strong> (or development environment).<\/p>\n\n\n

Generally speaking, we recommend using Anaconda for several reasons:<\/p>\n\n\n

  1. Firstly, Anaconda’s features integrate well into your environment and are very easy to use.<\/li>
  2. Secondly, the Anaconda Desktop user interface is relatively easy to learn.<\/li>
  3. Finally, Anaconda has a number of packages and libraries that are essential for carrying out the most basic tasks.<\/li><\/ol>\n\n\n
    \n\n\n\n\n\n
    <\/th>\nOn Windows<\/th>\nOn Mac<\/th>\nOn Linux<\/th>\n<\/tr>\n<\/thead>\n
    Python<\/td>\nhttps:\/\/www.python.org\/downloads\/<\/td>\nsudo apt-get install python<\/td>\n<\/tr>\n
    Spyder<\/td>\nhttps:\/\/docs.spyder-ide.org\/3\/installation.html<\/td>\nsudo apt-get install spyder<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n
    \n
    Python and Spyder installation commands<\/figcaption>\n<\/center>\n\n\n

    b) Virtual environment<\/h3>\n\n\n

    Once python has been installed<\/a>, it is important to create a virtual environment.<\/p>\n\n\n

    What for?<\/h4>\n\n\n

    It is important to ensure that only the packages you are using are installed in your environment. The virtual environment allows the Python interpreter<\/strong> to integrate installed libraries and scripts in isolation.<\/p>\n\n\n

    Virtualenv (venv)<\/strong> is one of the most widely used tools for this. In most versions of Anaconda, it is pre-integrated. If not, you will need to download it before creating a new environment in the project folder.<\/p>\n\n\n

    The benefits<\/h4>\n\n\n

    You can create and change virtual environments<\/strong> as often as you like. Each of them can have different libraries and associated packages.<\/p>\n\n\n

    You can also create environments in Anaconda<\/strong> quite easily and import the different packages you need directly by searching and installing them.<\/p>\n\n\n

    \"Illustration<\/figure>\n\n\n

    How do we do it?<\/h4>\n\n\n

    There are specific commands for using, activating and deactivating a virtual environment from the terminal on your machine:<\/p>\n\n\n

    \n\n\n\n\n\n\n
    <\/th>\nWindows<\/th>\nMac OS<\/th>\nLinux<\/th>\n<\/tr>\n<\/thead>\n
    Creation<\/td>\n$ conda create -n nom_env python=3.6<\/td>\n$ mkvirtualenv nom_env<\/td>\n<\/tr>\n
    Activation<\/td>\n$ conda activate nom_env<\/td>\n$ source activate nom_env<\/td>\n<\/tr>\n
    Deactivation<\/td>\n$ deactivate<\/td>\n(nom_env)$ source deactivate<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n

    c) Text editors<\/h3>\n\n\n

    If you’re working directly with .py python files<\/strong> for script import, for example, there are various text editors available, such as Spyder, VSCode, PyCharm, Eclipse with PyDev, and so on.<\/strong><\/p>\n\n\n

    We recommend using Spyder or VSCode because they have a built-in Python kernel and a library browser, which makes it easier to check code and manipulate installed modules.<\/p>\n\n\n

    It is also possible to integrate extensions quite easily, for example for formatting code. There are Black and Flake8 for example.<\/p>\n\n\n

    If, on the other hand, you only want to use Jupyter Notebook, you can install the following formatting extensions: AutoPep8, isort, Flake8_nb…<\/p>\n\n\n

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    Related articles<\/p>\n\n\n