Upload app.py

app.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+
+def load_data():
+    # Load the data
+    data = pd.read_csv(
+        "micro_world.csv"
+    )
+    return data
+
+
+def introduction():
+    # Write the title and the subheader
+    st.title(
+        "Overview of Financial Accessibility in the Philippines"
+    )
+    st.subheader(
+        """
+        In line with the National Strategy for Financial Inclusion (NSFI) 2022-2028 by Bangko Sentral ng Pilipinas (BSP), this sprint aims to:
+        1. Profile financial inclusion (FI) metrics in the Philippines using survey data from World Bank.
+        2. Formulate policy recommendations to further improve access to financial services particularly to vulnerable sectors.
+        """
+    )
+
+    # Load photo
+    st.image("streamlit-photo-1.jpeg")
+
+    # Load data
+    data = load_data()
+
+    # Display data
+    st.markdown("**The Data**")
+    st.dataframe(data)
+    st.markdown("Source: Global Findex 2017 from World Bank.")
+
+
+def fi_state_ph():
+    # Write the title
+    st.title(
+        "This is the current state of FI in the Philippines."
+    )
+
+    # Load data
+    data = load_data()
+
+    # Fetch Philippine data
+    philippine_data = data[
+        data['economy'] == 'Philippines'
+        ]
+
+    # Create another column for debit card ownership
+    philippine_data['has_debit_card'] = philippine_data['fin2'].apply(
+        lambda x: 1 if x == 1 else 0
+    )
+
+    # Compute overall debit card ownership
+    percent_debit_card_ownership = philippine_data['has_debit_card'].sum() * 100.0 / philippine_data[
+        'wpid_random'].count()
+
+    # Partition the page into 2
+    col1, col2 = st.columns(2)
+
+    # Display text in column 1
+    col1.markdown(
+        "In the Philippines, there is still an opportunity to expand access to financial services: "
+    )
+
+    # Display metric in column 2
+    col2.metric(
+        label='% of Population with Debit Card',
+        value=percent_debit_card_ownership
+    )
+
+    # Display text
+    st.markdown("In terms of gender breakdown:")
+
+    # Create another column for gender
+    philippine_data['gender'] = philippine_data['female'].apply(
+        lambda x: 'male' if x == 1 else 'female'
+    )
+
+    # Compute breakdown of access to debit card by gender
+    debit_by_gender = philippine_data.groupby('gender').agg(
+        total_debit_card_owners=('has_debit_card', 'sum'),
+        total_population=('wpid_random', 'count')
+    ).reset_index()
+
+    # Compute % debit card ownership
+    debit_by_gender['% debit card ownership'] = debit_by_gender['total_debit_card_owners'] * 100.0 / debit_by_gender[
+        'total_population']
+
+    # Plot the data
+    fig, ax = plt.subplots(figsize=(6, 3), dpi=200)
+    ax.bar(
+        debit_by_gender["gender"],
+        debit_by_gender["% debit card ownership"],
+    )
+    ax.set_xlabel("Gender")
+    ax.set_ylabel("% Debit Card Ownership")
+
+    # Show the data
+    st.pyplot(fig)
+
+
+def fi_state_worldwide():
+    # Write the title and the subheader
+    st.title(
+        "This is the current state of FI worldwide."
+    )
+    st.markdown(
+        "**Here is a bubble map presenting the % of debit card ownership per country:**"
+    )
+
+    # Load data
+    data = load_data()
+
+    # Create another column for debit card ownership
+    data['has_debit_card'] = data['fin2'].apply(
+        lambda x: 1 if x == 1 else 0
+    )
+
+    # Group the data and apply aggregations
+    grouped_data = data.groupby(['economy', 'economycode', 'regionwb']).agg(
+        total_debit_card_owners=('has_debit_card', 'sum'),
+        total_population=('wpid_random', 'count')
+    ).reset_index()
+
+    # Compute debit card ownership in %
+    grouped_data['% of population with debit card'] = grouped_data['total_debit_card_owners'] * 100.0 / grouped_data[
+        'total_population']
+
+    # Build the bubble map
+    fig = px.scatter_geo(
+        grouped_data,
+        locations="economycode",
+        color="regionwb",
+        hover_name="economy",
+        size="% of population with debit card",
+        projection="natural earth"
+    )
+
+    # Show the figure
+    st.plotly_chart(fig)
+
+
+def recommendations():
+    # Write the title
+    st.title(
+        "What We Can Do"
+    )
+
+
+def the_team():
+    # Write the title
+    st.title(
+        "The Team"
+    )
+
+
+list_of_pages = [
+    "Towards Financial Inclusion",
+    "FI Status of the Philippines",
+    "FI Status Worldwide",
+    "What We Can Do",
+    "The Team"
+]
+
+st.sidebar.title(':scroll: Main Pages')
+selection = st.sidebar.radio("Go to: ", list_of_pages)
+
+if selection == "Towards Financial Inclusion":
+    introduction()
+
+elif selection == "FI Status of the Philippines":
+    fi_state_ph()
+
+elif selection == "FI Status Worldwide":
+    fi_state_worldwide()
+
+elif selection == "What We Can Do":
+    recommendations()
+
+elif selection == "The Team":
+    the_team()
+