MAINT: updated readme with conda installation guide

README.md

@@ -42,42 +42,63 @@ for func in d:
 It is recommended to first refer the [NetworkX's INSTALL.rst](https://github.com/networkx/networkx/blob/main/INSTALL.rst).
 nx-parallel requires Python >=3.10. Right now, the only dependencies of nx-parallel are networkx and joblib.
 
-### Install the released version
+### Installing nx-parallel using `pip`
 
 You can install the stable version of nx-parallel using pip:
 
-```sh
+```.sh
 $ pip install nx-parallel
 ```
 
 The above command also installs the two main dependencies of nx-parallel i.e. networkx
 and joblib. To upgrade to a newer release use the `--upgrade` flag:
 
-```sh
+```.sh
 $ pip install --upgrade nx-parallel
 ```
 
-### Install the development version
+### Installing the development version
 
 Before installing the development version, you may need to uninstall the
 standard version of `nx-parallel` and other two dependencies using `pip`:
 
-```sh
+```.sh
 $ pip uninstall nx-parallel networkx joblib
 ```
 
 Then do:
 
-```sh
+```.sh
 $ pip install git+https://github.com/networkx/nx-parallel.git@main
 ```
 
+### Installing nx-parallel with conda
+
+Installing `nx-parallel` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
+
+```.sh
+conda config --add channels conda-forge
+conda config --set channel_priority strict
+```
+
+Once the `conda-forge` channel has been enabled, `nx-parallel` can be installed with `conda`:
+
+```.sh
+conda install nx-parallel
+```
+
+or with `mamba`:
+
+```.sh
+mamba install nx-parallel
+```
+
 ## Backend usage
 
 You can run your networkx code by just setting the `NETWORKX_AUTOMATIC_BACKENDS` environment variable to `parallel`:
 
-```sh
-$ export NETWORKX_AUTOMATIC_BACKENDS=parallel && python nx_code.py
+```.sh
+export NETWORKX_AUTOMATIC_BACKENDS=parallel && python nx_code.py
 ```
 
 Note that for all functions inside `nx_code.py` that do not have an nx-parallel implementation their original networkx implementation will be executed. You can also use the nx-parallel backend in your code for only some specific function calls in the following ways: