How to Setup Anaconda For Data Science?

To start any data science project it’s important to set up your computer with the right tools. Anaconda is one of the most widely used platforms for data science with Python because it consist of many useful libraries and tools which are pre-installed. Please make sure your laptop or PC has at least 4 GB RAM to run Anaconda without issues. In this guide you’ll learn how to set up Anaconda step-by-step.

Step 1: Download Anaconda

Visit Anaconda's official website. Select the version for your operating system Windows, macOS or Linux. Download the Anaconda Distribution. Choose the Python 3.x version as it’s the most commonly used in data science. To go in more detail refer to:

• How to install Anaconda on Windows?
• How to install Anaconda on Linux?

After successfully completing the installation process you are here now the Anaconda Navigator.

Anaconda Navigator contains lots of stuff inside it. So let's understand which we need for our next data science project.

Step 2: Launch Jupyter Notebook

Jupyter Notebook is like a digital notebook where you can write code, notes and charts. To use it:

1. Open Anaconda Navigator from your Start Menu
2. Click Launch under Jupyter Notebook

This will open a new tab in your browser. From there click New > Python 3 Notebook to start writing code.  Below is a demo image to demonstrate how Jupyter Notebook UI looks like:

Once you’ve set up Anaconda and started using Jupyter Notebook you want to explore other helpful tools that come with it. These tools make coding and data analysis even easier.

2. JupyterLab

JupyterLab is an upgraded version of Jupyter Notebook. It allow to open multiple notebooks, text files, terminals and more all in one place. It’s great if you like working with many files at once in a clean and flexible layout. Below is a demo image to demonstrate how JupyterLab UI looks like:

3. Spyder

One of the most important and powerful Python IDE is Spyder. It’s especially helpful for data scientists and includes features like variable explorer, plots and integration with libraries like NumPy, Pandas, and Matplotlib and other open-source software. Below is a demo image to demonstrate how Spyder UI looks like:

4. RStudio

When it comes to the data science world then Python and R are the two most programming languages that come into our minds. R Studio is an integrated development environment(IDE) for the R programming language. It provides literate programming tools which basically allow the use of R scripts, outputs, text and images in reports, Word documents and even an HTML files. Below is a demo image to demonstrate how RStudio UI looks like:

Step 3: Using Anaconda Prompt

If you like using the command line Anaconda also gives you a tool called Anaconda Prompt. Here you can type commands to manage your packages and environments. To open Anaconda Prompt in Windows: Click Start, search or select Anaconda Prompt from the menu.

Once you launch the prompt you will notice that the terminal now has (base) written in front of the computer name. It means that your base conda environment is set.

Now let's discuss some useful commands. To view all the installed packages please type the command as follows:

conda list

Let’s say the user wants to install pandas but he/she does not know the version. The user can use the following command to search for its versions:

conda search pandas

To install a package type the following command:

conda install pandas

And the user wants to install pandas with version 1.2.4 then use the following command to do so:

conda install pandas==1.2.4

To remove the package please type the command as follows: "conda remove pandas"

To set up avirtual environment for Python using Anaconda you may refer to this article How to Set up Virtual Environment for Python using Anaconda.