Create Child Mortality vs Population

pages/Child Mortality vs Population

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+import streamlit as st
+import pandas as pd
+import altair as alt
+
+# Load data
+child_mortality_path = "child_mortality_0_5_year_olds_dying_per_1000_born.csv"
+population_path = "pop.csv"
+
+child_mortality = pd.read_csv(child_mortality_path)
+population = pd.read_csv(population_path)
+
+# Data Cleaning
+def convert_population(value):
+    if isinstance(value, str):
+        if 'B' in value:
+            return float(value.replace('B', '')) * 1_000_000_000
+        elif 'M' in value:
+            return float(value.replace('M', '')) * 1_000_000
+        elif 'k' in value:
+            return float(value.replace('k', '')) * 1_000
+        else:
+            return float(value)
+    return value
+
+population.iloc[:, 1:] = population.iloc[:, 1:].applymap(convert_population)
+
+# Title and Description
+st.title("Child Mortality Rate vs Population")
+
+st.write("""
+    This visualization explores the relationship between child mortality rates (per 1,000 live births) and population size for a selected country over time. 
+    By displaying both metrics side-by-side on a dual-y-axis chart, the visual aims to provide insights into how population trends and child mortality rates have evolved across decades.
+    The visualization is interactive, allowing users to select a country from the dropdown menu. This enables tailored exploration of trends specific to different countries.
+    Hovering over the chart provides tooltips with detailed information for each data point, including the year, population size, and child mortality rate.
+""")
+
+st.subheader("Select a Country")
+countries = sorted(child_mortality['country'].unique())
+selected_country = st.selectbox("Country", countries, index=0)
+
+if selected_country:
+   
+    mortality_country = child_mortality[child_mortality['country'] == selected_country].melt(
+        id_vars='country', var_name='year', value_name='child_mortality'
+    )
+    population_country = population[population['country'] == selected_country].melt(
+        id_vars='country', var_name='year', value_name='population'
+    )
+
+    merged_country_data = pd.merge(mortality_country, population_country, on=['country', 'year'], how='inner')
+    merged_country_data['year'] = merged_country_data['year'].astype(int)
+    merged_country_data = merged_country_data[merged_country_data['year'] % 20 == 0]
+
+    # Dual-Y Axis Chart
+    dual_axis_chart = alt.layer(
+        # First Layer: Child Mortality
+        alt.Chart(merged_country_data).mark_line(point=True).encode(
+            x=alt.X('year:O', title='Year (Every 20 Years)', axis=alt.Axis(labelAngle=0)),
+            y=alt.Y('child_mortality:Q', title='Child Mortality Rate (per 1,000 live births)', axis=alt.Axis(titleColor='lightblue')),
+            tooltip=['year', 'child_mortality']
+        ).properties(
+            width=800,
+            height=400,
+        ),
+        # Second Layer: Population
+        alt.Chart(merged_country_data).mark_line(color='orange', point=True).encode(
+            x=alt.X('year:O'),
+            y=alt.Y('population:Q', title='Population (in millions)', axis=alt.Axis(titleColor='orange')),
+            tooltip=['year', 'population']
+        )
+    ).resolve_scale(
+        y='independent'
+    ).properties(
+        title=f" Child Mortality and Population Trends in {selected_country}"
+    )
+
+    st.altair_chart(dual_axis_chart, use_container_width=True)