[environment variables, configuration, password, secret, credentials, hiding credentials, security, anonymous, anonymity, python, r, windows, mac, linux, API, cloud]

Safely Store Your Credentials By Configuring Environment Variables

2 mins


Manage your personal credentials and secret keys with efficiency and privacy, whether working with cloud computing tools, API integrations, or other tools that may require them. In this building block, you will learn how to configure environment variables on both Mac/Linux and Windows systems to safely store and use your credentials. Additionally, you will understand how to access these variables in R and Python working environments.

Environment variables

When working with APIs or cloud services, you usually need to access some personal credentials or secret keys. With environment variables, we can access such variables without literally writing them down in a notebook or script (e.g., password = "..."). The basic idea is that these global variables are stored permanently and are attached to your operating system. Therefore, you can access these variables regardless of whether you are working in RStudio or a Jupyter Notebook.

As long as you never print its value, you can safely push a script that references an environment variable to a public Github repository without having to worry about disclosing any login credentials. However, you do need to inform others with whom you’re collaborating about its value because it’s not contained in the script itself.


Never upload a script that contains privacy-sensitive information to Github. Even if you’d remove it afterward, it remains visible through the repository history.

Configure Environment Variables


  1. Go to the terminal and type printenv to list all environment variables stored on your machine.
  2. Open the terminal, go to your user directory (shortcut: cd ~), and type nano .bash_profile to open a text editor in the terminal.
  3. Within this window you can create new variables as follows: export VARIABLE_NAME="the string value you want to store";. Note that there is no space between the variable name and its value and that the string is enclosed in double quotes.
  4. Exit the editor by pressing Ctrl + X, choose Y (to save changes), and finally press Enter.
  5. You can check whether everything worked out correctly by restarting your terminal and typing printenv (VARIABLE_NAME should be listed there now!). If the new environment variables didn’t show up, you may need to use nano .zshrc instead of nano .bash_profile (see step 2).


  1. Open up “Control Panel” > “System and Security” > “System”.
  2. In the left sidebar click on “Advanced system settings”.
  3. Click on “Environment Variables” in the bottom right.
  4. Create a new “User Variable” (top list) and fill out the “Variable name” and “Variable value”.
  5. Double-click “OK” twice.

Access Environment Variables

Use the code snippets below to easily assign the value of the environment variable (VARIABLE_NAME) to a variable to be used in Python or R. Subsequently, you can re-use that variable throughout the script, for example for API authentication purposes.

import os
# VARIABLE_NAME is the name of the environment variable you defined in the terminal
api_password = os.environ['VARIABLE_NAME']   
# VARIABLE_NAME is the name of the environment variable you defined in the terminal
api_password = Sys.getenv("VARIABLE_NAME")

See also

  • Environment variables provide a secure way to store credentials and secret keys for API or cloud service access.

  • They are stored permanently in the operating system and can be accessed in different coding environments.

  • Uploading scripts with environment variable references to public repositories is safe, as the variable values are not disclosed.

  • Mac/Linux users can configure environment variables using the terminal and modifying the .bash_profile or .zshrc files.

  • Windows users can access environment variables through the “Control Panel” settings.

  • You can assign environment variable values to variables within (e.g.) Python and R scripts through the appropriate code snippets.