Merge branch 'main' of https://github.com/ibehave-ibots/course-attend…

…ance-service into nickdelgrosso/issue18

learning_projects/webapp/Home.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import seaborn as sns
+
+st.title(':red[Home Page]')
+
+st.header(":violet[Text elements]")
+
+st.markdown(body='**:orange[Markdown support] with st.markdown**',help='this is a tooltip. :green[Look!] you can use _italics_ and **bold** here as well!')
+st.markdown(body='Here is latex formula $a+b^2=c$')
+
+st.markdown(body='**:orange[Code] with st.code**')
+st.code(body='print("Hello World")',language='python')
+
+st.divider()
+
+code = '''
+a = 20
+b = 25
+c = a + b
+'''
+st.code(body=code, language='python',line_numbers=True)
+
+code = '''def get_name(name):
+    return name'''
+st.code(body=code,language='python')
+
+st.code(body='c=complex(a,b) #this is in matlab',language='matlab')
+st.code(body='ls *.csv',language='bash')
+
+st.write("---")
+
+st.markdown(body='**:orange[Text] with st.text**')
+st.text(body='This is a text with tooltip', help='here you go')
+st.text(body='**bold** :green[color] **:green[bold color]** _:green[italicized color]_ **_:green[bold italicized color]_**',help='Notice text styling does not work')
+
+"---"
+
+st.markdown(body='**:orange[Text] with st.text**')
+
+st.markdown(body='**:orange[Emoji support]** :sunglasses::+1::100:')
+
+st.write('iBOTS team')
+st.write(pd.DataFrame({'names':['Nicholas Del Grosso','Mohammad Bashiri','Sangeetha Nandakumar']}))
+
+# magic command
+df = pd.DataFrame({'names':['Nicholas Del Grosso','Mohammad Bashiri','Sangeetha Nandakumar']})
+df
+
+"---"
+
+st.header(":violet[Data elements]")
+st.markdown(body='**:orange[Dataframe] with st.dataframe**')
+
+st.dataframe(data=df, width=None, height=None, use_container_width=False, hide_index=True, column_order=None, column_config=None)
+
+st.markdown(body='**:orange[Data Editor] with st.data_editor**', help='Edit your data with this')
+st.data_editor(data=df)
+
+
+st.data_editor(data=df,column_config={
+    "names":st.column_config.Column("iBOTS")
+})

learning_projects/webapp/pages/1_Analysis.py

@@ -0,0 +1,192 @@
+import streamlit as st
+from sklearn.datasets import load_iris
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import time
+import matplotlib.pylab as plt
+
+def prep_page():
+    with st.sidebar:
+        with st.spinner('Loading'):
+            for i in range(10000):
+                pass
+
+@st.cache_data
+def load_dataset():
+    i = 0
+    my_bar = st.progress(0, text='')
+    with st.sidebar:
+        for i in range(100):
+            df = sns.load_dataset('titanic')
+            my_bar.progress(i + 1, text='#%d'%(i+1))
+    st.warning(f'You loaded same dataset for {i+1} times')
+    st.info('Done loading titanic dataset')
+    return df
+
+def call_titanic():
+    st.title(':blue[Titanic database Analysis]')
+    st.markdown(body='Loading titanic dataset 100 times')
+    df = load_dataset()
+    # st.balloons()
+
+    with st.expander('Display data'):
+        st.write(df)
+
+    st.success('Successfully setup the analysis project')    
+    "---"
+
+    st.markdown('Removing null values')
+    len_original = len(df)
+    df = df.dropna()
+    delta = len(df) - len_original
+    st.metric("Number of rows",value=len(df),delta=delta)
+
+    '''---'''
+    edit_true = st.checkbox(label='Do you want to edit data?')
+
+    if edit_true:
+        col_config = {
+            "sex":st.column_config.SelectboxColumn(label='Sex',options=['female','male']),
+            "age":st.column_config.NumberColumn(label='Age',min_value=0.,max_value=120),
+            "pclass":st.column_config.SelectboxColumn(label='Passenger class',options=[1,2,3]),
+            "alone":st.column_config.CheckboxColumn(label='Survived')
+        }
+
+
+        df_edited = st.data_editor(
+            df,
+            column_config=col_config,
+            use_container_width=True,
+            hide_index=True,
+            num_rows="fixed",
+        )
+
+    else:
+        df_edited = df
+
+    '''---'''
+
+    option = st.radio(label='Analysis options',options=['Sex','Survived','Class','Age','Clear'])
+
+    if option == 'Sex':
+        n_male = len(df[df.sex == 'male'])
+        n_female = len(df[df.sex == 'female'])
+        col1,col2=st.columns(2)
+        col1.metric('Number of male passengers',value=n_male)
+        col2.metric('Number of female passengers',value=n_female)
+    elif option == 'Survived':
+        survived = df[df.survived == 1]
+        n_survived = len(survived)
+        n_male_survived =len(survived[survived.sex == 'male'])
+        n_female_survived =len(survived[survived.sex == 'female'])
+        col1,col2,col3 = st.columns(3)
+        col1.metric("Survived",value=n_survived)
+        col2.metric("Number of male survivors",value=n_male_survived)
+        col3.metric("Number of female survivors",value=n_female_survived)
+    elif option == 'Class':
+        st.bar_chart(data=df,x='class',y='survived')
+    elif option == 'Age':
+        st.line_chart(data=df,x='age',y='fare')
+    else:
+        pass
+
+def call_general():
+    with st.form("Run analysis"):
+        st.title(':blue[Iris database Analysis]')
+        iris = load_iris()
+        df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
+        with st.expander('Display data'):
+            st.write(df)
+        option = st.radio(label='Statistics',options=['Sepal length','Sepal width','Petal length','Petal width','Clear'])
+
+        col1,col2 = st.columns(2)
+
+        if option == 'Sepal length':
+            col1.metric("Mean (cm)",np.round(df['sepal length (cm)'].mean(),1))
+            col2.metric("std (cm)",np.round(df['sepal length (cm)'].std(),1))
+            fig, ax = plt.subplots()
+            plt.hist(df['sepal length (cm)'], bins=10, edgecolor='red',histtype='step')
+            plt.hist(df['sepal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.xlabel('Value')
+            plt.ylabel('Frequency')
+            plt.title('Sepal length')            
+            st.pyplot(fig)
+
+
+        elif option == 'Sepal width':
+            col1.metric("Mean (cm)",np.round(df['sepal width (cm)'].mean(),1))
+            col2.metric("std (cm)",np.round(df['sepal width (cm)'].std(),1))
+            fig, ax = plt.subplots()
+            plt.hist(df['sepal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['sepal width (cm)'], bins=10, edgecolor='red',histtype='step')
+            plt.hist(df['petal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.xlabel('Value')
+            plt.ylabel('Frequency')
+            plt.title('Sepal length')            
+            st.pyplot(fig)
+
+        elif option == 'Petal length':
+            col1.metric("Mean (cm)",np.round(df['petal length (cm)'].mean(),1))
+            col2.metric("std (cm)",np.round(df['petal length (cm)'].std(),1))
+            fig, ax = plt.subplots()
+            plt.hist(df['sepal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['sepal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal length (cm)'], bins=10, edgecolor='red',histtype='step')
+            plt.hist(df['petal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.xlabel('Value')
+            plt.ylabel('Frequency')
+            plt.title('Sepal length')            
+            st.pyplot(fig)
+
+
+        elif option == 'Petal width':
+            col1.metric("Mean (cm)",np.round(df['petal width (cm)'].mean(),1))
+            col2.metric("std (cm)",np.round(df['petal width (cm)'].std(),1))
+            fig, ax = plt.subplots()
+            plt.hist(df['sepal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['sepal width (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal length (cm)'], bins=10, edgecolor='grey',histtype='step')
+            plt.hist(df['petal width (cm)'], bins=10, edgecolor='red',histtype='step')
+            plt.xlabel('Value')
+            plt.ylabel('Frequency')
+            plt.title('Sepal length')            
+            st.pyplot(fig)
+
+
+        else:
+            pass
+
+
+
+        st.form_submit_button()    
+
+def main():
+    home,titanic, general = st.tabs(tabs=['Home','Titanic','Iris'])
+    prep_page()
+
+    with home:
+        st.title(':blue[Analysis]')
+        st.write('Here are two other tabs')
+        st.write('Go to :green[Titanic] to checkout some analysis on Titanic dataset')
+        st.write('Go to :green[Iris] tab for analysis of Iris dataset')
+
+    with titanic:
+        call_titanic()
+
+    with general:
+        call_general()
+
+    # st.snow()    
+
+    # st.markdown(body='Feedback')
+    # with st.form("Feedback form"):
+    #     st.write('Your rating')
+    #     rating = st.slider("0 (bad) to 10 (excellent)",min_value=0,max_value=10,step=1)
+    #     submitted = st.form_submit_button("Submit")
+
+if __name__ == '__main__':
+    main()        

learning_projects/webapp/pages/2_Without Session State.py

@@ -0,0 +1,38 @@
+import streamlit as st
+
+st.title(':blue[Visualizations]')
+
+import streamlit as st
+import seaborn as sns
+import pandas as pd
+
+def load_data():
+    iris = sns.load_dataset('iris')
+    return iris
+
+def without_using_session_state():
+    data = load_data()
+
+    st.title('Interactive Data Visualization')
+
+    col1, col2 = st.columns(2,gap='large')
+
+    with col1:
+        st.subheader("Select Features to Plot")
+        available_features = data.columns[:-1]
+        selected_features = st.multiselect("Select features:", available_features)
+
+    with col2:
+        if selected_features:
+            st.subheader("Scatter Plot")
+            sns_plot = sns.pairplot(data=data, hue='species', vars=selected_features)
+            st.pyplot(sns_plot)
+
+
+
+def main():
+    without_using_session_state()
+
+
+if __name__ == '__main__':
+    main()

learning_projects/webapp/pages/3_With Session State.py

@@ -0,0 +1,43 @@
+import streamlit as st
+
+st.title(':blue[Visualizations]')
+
+import streamlit as st
+import seaborn as sns
+import pandas as pd
+
+def load_data():
+    iris = sns.load_dataset('iris')
+    return iris
+
+def using_session_state():
+    data = load_data()
+
+    # Initialize session state if it's the first run
+    if 'selected_features' not in st.session_state:
+        st.session_state.selected_features = []
+
+    st.title("Interactive Data Visualization")
+
+    col1, col2 = st.columns(2,gap='large')
+
+    with col1:
+        st.subheader("Select Features to Plot")
+        available_features = data.columns[:-1]
+        selected_features = st.multiselect("Select features:", available_features, default=st.session_state.selected_features,key='manage')
+
+        st.session_state.selected_features = selected_features
+
+    with col2:
+        if selected_features:
+            st.subheader("Scatter Plot")
+            sns_plot = sns.pairplot(data=data, hue='species', vars=selected_features)
+            st.pyplot(sns_plot)
+
+
+def main():
+    using_session_state()
+
+
+if __name__ == '__main__':
+    main()