diff --git a/.DS_Store b/.DS_Store
index 3991f7c..9a887ea 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/streamlit_football_demo/.DS_Store b/streamlit_football_demo/.DS_Store
index 5ac1c44..da6f27e 100644
Binary files a/streamlit_football_demo/.DS_Store and b/streamlit_football_demo/.DS_Store differ
diff --git a/streamlit_football_demo/README.md b/streamlit_football_demo/README.md
index f697638..6468010 100644
--- a/streamlit_football_demo/README.md
+++ b/streamlit_football_demo/README.md
@@ -6,6 +6,11 @@ While in the past, a well-made visualization or a small PPT used to be enough fo
 
 This code is part of my post [How to write Web apps using simple Python for Data Scientists?](https://towardsdatascience.com/how-to-write-web-apps-using-simple-python-for-data-scientists-a227a1a01582)
 
+
+Pre Requisite
+```
+pip install plotly-express
+```
 To Run app:
 
 ```
diff --git a/streamlit_football_demo/helloworld.py b/streamlit_football_demo/helloworld.py
index 9b8bc89..abf5eba 100644
--- a/streamlit_football_demo/helloworld.py
+++ b/streamlit_football_demo/helloworld.py
@@ -8,14 +8,23 @@
 
 This very simple webapp allows you to select and visualize players from certain clubs and certain nationalities.
 '''
+
+
 df = st.cache(pd.read_csv)("football_data.csv")
 
-clubs = st.sidebar.multiselect('Show Player for clubs?', df['Club'].unique())
-nationalities = st.sidebar.multiselect('Show Player from Nationalities?', df['Nationality'].unique())
+"Dataset Preview (fotoball_data.csv)",df
+
+#In the following part the isin function is used however 
+clubs = st.sidebar.multiselect('Show Player for clubs?', df['Club'].unique()) #give the multiselect dialogue box in the sidebar to select clubs
+nationalities = st.sidebar.multiselect('Show Player from Nationalities?', df['Nationality'].unique())#give the multiselect dialogue box in the sidebar to select nationalities
 
 new_df = df[(df['Club'].isin(clubs)) & (df['Nationality'].isin(nationalities))]
+
+"Dataset Preview with the respective filter of clubs and nationalities"
 st.write(new_df)
 
+
+
 # Create distplot with custom bin_size
 fig = px.scatter(new_df, x ='Overall',y='Age',color='Name')