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Some additions for better understanding #2

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5 changes: 5 additions & 0 deletions streamlit_football_demo/README.md
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Expand Up @@ -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:

```
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13 changes: 11 additions & 2 deletions streamlit_football_demo/helloworld.py
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Expand Up @@ -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')

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