# Virtual Environments

Data Science sometimes requires the ability to be able to handle the overlapping of package dependencies in python which can disturb your working environment. Sometimes you might be experimenting with an earlier version of a package (or library) which requires earlier versions of other packages which can cause conflict as legacy code may raise errors or warnigns accordingly due to conflict.

To circumvent these troubling issues that recur and to become good at the craft, let us take a look at using virtual environments. Virtual environments can be characterized as empty places that are isolated from the local source and a place you can install new packages (different versions or whatnot).


```{tip}
It is recommended that for you create new virtual environments for each project to isolate their functionality and dependencies from each other. This will save you a lot of headache in the future. Past batches of EPA have always regretted not doing this.
```

## Virtual Environments with Anaconda

This part of the tutorial assumes that you have installed Anaconda as your primary installation and that you are using jupyter lab / notebook from it. Anaconda really simplifies package management.

To begin we will first create a virtual environment by relying on the conda command. Remember, you apply these steps either on the Anaconda Prompt or the console!

```shell
conda create -n myenv python=3.8
```

On the code above, myenv is the name of the virtual environment and python- is the version of python you want inside the virtual environment. Now we need to activate the created environment.

```shell
conda activate myenv
```

With this, you activate the virtual environment and any packets / dependencies that you wish can be installed in this virtual environment. If you wish to get out of the virtual environment just type the code below:

```shell
conda deactivate
```

You can also check all environments that you have already created in Anaconda by using:

```shell
conda env list
```

and if you would like to remove any of them, you can run the code:

```shell
conda env remove -n myenv
```

For adding new packages you use the following code below **after activating your environment**:

```shell
conda install -n myenv numpy
```

`numpy` is an example here. The syntax is

```shell
conda install -n [environment name] [package name]
```

Also for a specific package & version you use:

```shell
conda install -n myenv numpy==2.18.4
```

Great! You are done!

## Adding the Virtual Environment in Jupyter Notebook/Lab

When you have created your virtual environment and installed Jupyter Lab or Notebook within the environment, at first Jupyter Notebook (being a standalone application) will not recognise the virtual environment. To set up the connection between the two, after activating your virtual environment run this code:

```shell
pip install --user ipykernel
```
​
This command has added a package called `ipykernel`. Then we can manually add the virtual environment in the Jupyter Notebook. We can use this code:

```shell
python -m ipykernel install --user --name=myenv
```
​
Where `myenv` is the name of the environment we have created. Now all is ready to be used! You can check in the Jupyter Lab interface or when you create a new Jupyter Notebook. You can see it as in below in the launcher now:


![Jupyter Venv](figs/jupyter_venv.png)

Lastly you can remove the virtual environment from Jupyter with:

```shell
jupyter kernelspec uninstall myenv
```
​