You might have encountered a piece of software asking you for permission to modify your
PATH variable, or another program's installation instructions cryptically telling you that you have to "set your
LD_LIBRARY_PATH variable correctly".
As a data scientist, you might encounter other environment variable issues when interacting with your compute stack (particularly if you don't have full control over it, like I do). This post is meant to demystify what an environment variable is, and how it gets used in a data science context.
What Is An Environment Variable?
First off, let me explain what an environment variable is, by going in-depth into the
PATH environment variable. I'd encourage you to execute the commands here inside your bash terminal (with appropriate modifications -- read the text to figure out what I'm doing!).
When you log into your computer system, say, your local computer’s terminal or your remote server via SSH, your bash interpreter needs to know where to look for particular programs, such as
nano (the text editor), or
git (your version control software), or your Python executable. This is controlled by your PATH variable. It specifies the paths to folders where your executable programs are found.
By historical convention, command line programs, such as
top, are found in the directory
/usr/bin. (By historical convention, the
/bin folder is for software binaries, which is why they are named
/bin.) These are the ones that are bundled with your operating system, and as such, need special permissions to upgrade.
Try it out in your terminal:
$ which which /usr/bin/which $ which top /usr/bin/top
Other programs are installed (for whatever reason) into
ls is one example:
$ which ls /bin/ls
Yet other programs might be installed in other special directories:
$ which nano /usr/local/bin/nano
How does your Bash terminal figure out where to go to look for stuff? It uses the
PATH environment variable. It looks something like this:
$ echo $PATH /usr/bin:/bin:/usr/local/bin
The most important thing to remember about the
PATH variable is that it is "colon-delimited". That is, each directory path is separated by the next using a "colon" (
:) character. The order in which your bash terminal is looking for programs goes from left to right:
On my particular computer, when I type in
ls, my bash interpreter will look inside the
/usr/bin directory first. It'll find that
ls doesn't exist in
/usr/bin, and so it'll move to the next directory,
/bin. Since my
ls exists under
/bin, it'll execute the
ls program from there.
You can see, then, that this is simultaneously super flexible for customizing your compute environment, yet also potentially super frustrating if a program modified your
PATH variable without you knowing.
Wait, you can actually modify your
PATH variable? Yep, and there's a few ways to do this.
How To Modify the
Using a Bash Session
The first way is transient, or temporary, and only occurs for your particular bash session. You can make a folder have higher priority than the existing paths by "pre-pending" it to the
$ export PATH=/path/to/my/folder:$PATH $ echo $PATH /path/to/my/folder:/usr/bin:/bin:/usr/local/bin
Or I can make it have a lower priority than existing paths by "appending" it to the
$ export PATH=$PATH:/path/to/my/folder $ echo $PATH /usr/bin:/bin:/usr/local/bin:/path/to/my/folder
The reason this is temporary is because I only export it during my current bash session.
If I wanted to make my changes somewhat more permanent, then I would include inside my
.bash_profile file. (I recommend using the
.bashrc file.) The
.bash_profile file lives inside your home directory (your
$HOME environment variable specifies this), and is a file that your bash interpreter will execute first load. It will execute all of the commands inside there. This means, you can change your PATH variable by simply putting inside your
...other stuff above... # Make /path/to/folder have higher priority export PATH=/path/to/folder:$PATH # Make /path/to/other/folder have lower priority export PATH=$PATH:/path/to/folder ...other stuff below...
Data Science and the
PATH environment variable
Now, how is this relevant to data scientists? Well, if you're a data scientist, chances are that you use Python, and that your Python interpreter comes from the Anaconda Python distribution (a seriously awesome thing, go get it!). What the Anaconda Python installer does is prioritize the
/path/to/anaconda/bin folder in the
PATH environment variable. You might have other Python interpreters installed on your system (that is, Apple ships its own). However, this
PATH modification ensures that each time you type
python into your Bash terminal, you execute the Python interpreter shipped with the Anaconda Python distribution. In my case, after installing the Anaconda Python distribution, my
PATH looks like:
$ echo $PATH /Users/ericmjl/anaconda/bin:/usr/bin:/bin:/usr/local/bin
Even better, what conda environments do is prepend the path to the conda environment binaries folder while the environment is activated. For example, with my blog, I keep it in an environment named
$ echo $PATH /Users/ericmjl/anaconda/bin:/usr/bin:/bin:/usr/local/bin $ which python /Users/ericmjl/anaconda/bin/python $ source activate lektor $ echo $PATH /Users/ericmjl/anaconda/envs/lektor/bin:/Users/ericmjl/anaconda/bin:/usr/bin:/bin:/usr/local/bin $ which python /Users/ericmjl/anaconda/envs/lektor/bin/python
Notice how the bash terminal now preferentially picks the Python inside the higher-priority
If you've gotten to this point, then you'll hopefully realize there's a few important concepts listed here. Let's recap them:
PATHis an environment variable stored as a plain text string used by the bash interpreter to figure out where to find executable programs.
PATHis colon-delimited; higher priority directories are to the left of the string, while lower priority directories are to the right of the string.
PATHcan be modified by prepending or appending directories to the environment variable. It can be done transiently inside a bash session by running the
exportcommand at the command prompt, or it can be done permanently across bash sessions by adding an
exportline inside your
Other Environment Variables of Interest
Now, what other environment variables might a data scientist encounter? These are a sampling of them that you might see, and might have to fix, especially in contexts where your system administrators are off on vacation (or taking too long to respond).
For general use, you'll definitely want to know where your
HOME folder is -- on Linux systems, it's often
/home/username, while on macOS systems, it's often
/Users/username. You can figure out what
HOME is by doing:
$ echo $HOME /Users/ericmjl
If you're a Python user, then the
PYTHONPATH is one variable that might be useful. It is used by the Python interpreter, and specifies where to find Python modules/packages.
If you have to deal with C++ libraries, then knowing your
LD_LIBRARY_PATH environment variable is going to be very important. I'm not well-versed enough in this to espouse on it intelligently, so I would defer to this website for more information on best practices for using the
If you're working with Spark, then the
PYSPARK_PYTHON environment variable would be of interest. This essentially tells Spark which Python to use for both its driver and its workers; you can also set the
PYSPARK_DRIVER_PYTHON to be separate from the
PYSPARK_PYTHON environment variable, if needed.
Hack Your Environment Variables
This is where the most fun happens! Follow along for some stuff you might be able to do by hacking your environment variables.
Hack #1: Enable access to PyPy. I occasionally keep up with the development of PyPy, but because PyPy is not yet the default Python interpreter, and is not yet
conda install-able, I have to put it in its own
$HOME/pypy/bin directory. To enable access to the PyPy interpreter, I have to make sure that my
/path/to/pypy is present in the
PATH environment variable, but at a lower priority than my regular CPython interpreter.
Hack #2: Enable access to other language interpreters/compilers. This is analogous to PyPy. I once was trying out Lua's JIT interpreter to use Torch for deep learning, and needed to add a path to there in my
Hack #3: Install Python packages to your home directory. On shared Linux compute systems that use the
modules system rather than
conda environments, a
modulefile that you load might be configured with a virtual environment that you don't have permissions to modify. If you need to install a Python package, you might want to
pip install --user my_pkg_name. This will install it to
$HOME/.local/lib/python-[version]/site-packages/. Ensuring that your
$HOME/.local/lib/python-[version]/site-packages at a high enough priority is going to be important in this case.
Hack 4: Debugging when things go wrong. In case something throws an error, or you have unexpected behaviour -- something I encountered before was my Python interpreter not being found correctly after loading all of my Linux modules -- then a way to debug is to temporarily set your PATH environment variable to some sensible "defaults" and sourcing that, effectively "resetting" your PATH variable, so that you can manually prepend/append while debugging.
To do this, place the following line inside a file named
.path_default, inside your home directory:
export PATH="" # resets PATH to an empty string. export PATH=/usr/bin:/bin:/usr/local/bin:$PATH # this is a sensible default; customize as needed.
After something goes wrong, you can reset your PATH environment variable by using the "source" command:
$ echo $PATH /some/complicated/path:/more/complicated/paths:/really/complicated/paths $ source ~/.path_default $ echo $PATH /usr/bin:/bin:/usr/local/bin
Note - you can also execute the exact same commands inside your bash session; the interactivity may also be helpful.
I hope you enjoyed this article, and that it'll give you a, ahem, path forward whenever you encounter these environment variables!
How to Become a Data Scientist in 8 StepsFind out everything you need to know about becoming a data scientist, and find out whether it’s the right career for you!
YOLO Object Detection ExplainedUnderstand YOLO object detection, its benefits, how it has evolved over the last couple of years and some real-life applications.
5 Ways to Use Data Science in MarketingDiscover five ways you can use data science in marketing. Get ahead of the game, improve your data skills, and work on a data science marketing project.
How Data Science is Changing SoccerWith the Fifa 2022 World Cup upon us, learn about the most widely used data science use-cases in soccer.
The Deep Learning Revolution in Space Science
Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions.
Regular Expressions Cheat SheetRegular expressions (regex or regexp) are a pattern of characters that describe an amount of text. Regular expressions are one of the most widely used tools in natural language processing and allow you to supercharge common text data manipulation tasks. Use this cheat sheet as a handy reminder when working with regular expressions.
DataCamp Team •