diff --git a/ElM2D/ElM2D.py b/ElM2D/ElM2D.py
index 00385e6..4edf5e3 100644
--- a/ElM2D/ElM2D.py
+++ b/ElM2D/ElM2D.py
@@ -52,8 +52,6 @@
 import plotly.express as px
 import plotly.io as pio
 
-def main():
-    df = 
 
 class ElM2D():
     '''
diff --git a/README.md b/README.md
index 7f66efd..1cc5970 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,44 @@
 # ElM2D
-A mapping class to construct ElM2D plots from lists of compositions in a high performance manner
+A high performance mapping class to construct ElM2D plots from larger datasets of ionic compositions.
+
+Recommended installation through `pip` 
+
+```
+pip install ElM2D
+```
+
+## Usage 
+
+### Sorting
+
+To sort a list of compositions into an ordering of chemical similarity call
+
+```python
+from ElM2D import ElM2D
+...
+
+comps = df["formula"].to_numpy()
+sorted_indices = ElM2D().sort_compositions(comps)
+sorted_comps = comps[sorted_indices]
+```
+
+### Embedding
+
+Embeddings can be constructed through either the UMAP or PCA methods of dimensionality reduction. The embedded points are accessible via the `embedding` property. Higher dimensional embeddings can be created with the `n_components` parameter. 
+
+```python
+mapper = ElMD()
+embedding = mapper.fit_transform(df["formula"])
+embedding = mapper.fit_transform(df["formula"], how="PCA", n_components=7)
+```
+
+These embeddings may be visualized within a jupyter notebook, or exported to HTML to view in the web browser.
+
+```python
+mapper.fit_transform(df["formula"])
+mapper.plot()  # Returns a figure for viewing in notebooks
+...
+mapper.plot("ElM2D_Plot_UMAP.html")  # Returns a figure and saves as ElM2D_Plot_UMAP.html
+```
+
+
diff --git a/setup.py b/setup.py
index 5a1f709..a3ea6a9 100644
--- a/setup.py
+++ b/setup.py
@@ -2,9 +2,9 @@
 setup(
   name = 'ElM2D',        
   packages = ['ElM2D'],  
-  version = '0.1.0',      
+  version = '0.1.1',      
   license='GPL3',       
-  description = 'A mapping class to perform the ElMD metric on datasets on ionic compositions',  
+  description = 'A mapping class to embed large datasets of ionic compositions with respect to the ElMD metric.',  
   author = 'Cameron Hagreaves',              
   author_email = 'cameron.h@rgreaves.me.uk', 
   url = 'https://github.com/lrcfmd/ElM2D/',