Anaconda Installation Guide (feat. JupyterLab)

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1. Introducing Anaconda

Anaconda is a distribution for the Python and R programming languages, and provides an environment where you can easily install and manage various libraries and packages required for data science, machine learning, artificial intelligence (AI), and big data analysis. In particular, it includes development tools such as Jupyter Notebook and Spyder, allowing you to easily use functions essential for data analysis.

Anaconda has the following characteristics:

- Includes 1,500+ data science packages
- Supports multiple operating systems (Windows, macOS, Linux)
- Easily manage packages and Python versions through virtual environments
- Includes essential tools for data analysis

Anaconda homepage (Source: anaconda.com)

2. Download Anaconda

The Anaconda installation process is as follows:

1. Visit the Anaconda homepage: Go to [https://www.anaconda.com].

2. Click the Download menu: Click the "Free Download" button at the top of the page.

3. Email registration: If you do not wish to register, click the "Skip registration" button to proceed directly to download.

Skip registration (source: anaconda.com)

4. Select your operating system: Select and download the installation file that matches your operating system (Windows, macOS, Linux) and architecture.

Choosing an operating system and architecture (Source: anaconda.com)

5. Select Python version: You can select the version of Python to use when installing Anaconda. It is generally recommended to use the latest version of Python, but you can also select a lower version as needed.

3. Install Anaconda

Once the download is complete, you can install Anaconda by running the installation file according to your operating system.

Windows

1. Run the downloaded `Anaconda3-2024.06-1-Windows-x86_64.exe` file.

2. Click "Next >", read the license agreement, and click "I Agree".

3. Select the “Just Me” option, click “Next >”, choose a download location and click “Next >”.

4. It will ask you whether you want to add Anaconda to your environment variable. I recommend not selecting the "Add Anaconda to my PATH environment variable" option. Then click "Install" to start the installation.

5. Once the installation is complete, click “Next >” and then “Finish” to complete the installation.

macOS and Linux

1. Launch Terminal and run the downloaded `.sh` file:

bash Anaconda3-2024.06-1-Linux-x86_64.sh

2. Select the installation path and other options and proceed with the installation.

4. Verifying the Installation

Once the installation is complete, open the terminal (or command prompt) to verify that Anaconda has been installed properly.

conda --version

If the Anaconda version is displayed when you enter the command above, the installation was completed successfully.

5. Commands related to virtual environments

Anaconda can independently manage Python versions and packages across multiple projects through virtual environments. This allows you to work in different environments without conflicts.

Creating a virtual environment

conda create --name `Virtual environment name` python=`version`

Activate virtual environment

conda activate `Virtual environment name`

Disable virtual environment

conda deactivate

Delete virtual environment

conda remove --name `Virtual environment name` --all

Check the list of virtual environments

conda info --envs

6. How to install JupyterLab

Anaconda includes Jupyter Notebook by default, but JupyterLab is a more advanced version that provides a more powerful user interface and features. To install JupyterLab, use the following command:

Installing JupyterLab

conda install -c conda-forge jupyterlab

Run JupyterLab after installation

jupyter-lab

Entering the above command will open the JupyterLab interface in your browser, where you can manage various files and perform data analysis tasks.

JupyterLab Key Features

- Write and run code, check results
- Support various file formats (.csv, .json, .md, etc.)
- Support data visualization and interactive graphs

Using Anaconda and JupyterLab together can increase the productivity of your data science projects. JupyterLab allows you to efficiently manage multiple experiments and quickly switch between projects.

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