From 08c4c80099a9dc0d303f42a326022f4ce17f955e Mon Sep 17 00:00:00 2001
From: narenkhatwani <48763124+narenkhatwani@users.noreply.github.com>
Date: Thu, 1 Oct 2020 23:32:22 +0530
Subject: [PATCH] Some additions for better understanding

Added some dataset previews to give a beginner an idea about how the filter is applied and what is the difference
---
 .DS_Store                             | Bin 14340 -> 14340 bytes
 streamlit_football_demo/.DS_Store     | Bin 6148 -> 6148 bytes
 streamlit_football_demo/README.md     |   5 +++++
 streamlit_football_demo/helloworld.py |  13 +++++++++++--
 4 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/.DS_Store b/.DS_Store
index 3991f7c6f540d14ef986c6d97224ed1782b6e07d..9a887ea7388fccfb5c063afbec4f14d0d31a69f4 100644
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diff --git a/streamlit_football_demo/.DS_Store b/streamlit_football_demo/.DS_Store
index 5ac1c4494fb3de20633a90090810d555f68f31ab..da6f27efb4cb20cd3086eb358745e789f9d72717 100644
GIT binary patch
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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')