upload child mortality & life expectancy file

pages/mortality_life.py

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+import pandas as pd
+import streamlit as st
+import altair as alt
+
+child_mortality = pd.read_csv("../child_mortality_0_5_year_olds_dying_per_1000_born.csv")
+life_expectancy = pd.read_csv("../life_expectancy.csv")
+
+st.title("Interactive Observatory: Child Mortality & Life Expectancy")
+st.write("""
+For this analysis, I selected Argentina, Australia, China, India, South Africa, the UK,
+and the USA as representative countries for different global regions. These countries 
+highlight diverse economic, social, and historical contexts, making them ideal for 
+observing trends in child mortality and life expectancy. The time period of 1900 to
+2024 was chosen as it encompasses rapid global industrialization, the rise of modern 
+medicine, and recent advancements in healthcare and living standards, while also including 
+years with limited historical data to provide a broader context.
+""")
+
+countries = list(child_mortality['country'].unique())
+selected_countries = st.sidebar.multiselect("Select Countries", countries, default=["Argentina", "Australia", "China", "India", "South Africa", "UK", "USA"])
+year_range = st.sidebar.slider("Select Year Range", 1900, 2024, (1900, 2024))
+
+filtered_mortality = child_mortality[child_mortality['country'].isin(selected_countries)]
+filtered_expectancy = life_expectancy[life_expectancy['country'].isin(selected_countries)]
+years = [str(year) for year in range(year_range[0], year_range[1] + 1)]
+filtered_mortality = filtered_mortality[['country'] + years]
+filtered_expectancy = filtered_expectancy[['country'] + years]
+
+def melt_dataframe(df, value_name):
+    df_melted = df.melt(id_vars=["country"], var_name="year", value_name=value_name)
+    df_melted["year"] = df_melted["year"].astype(int)
+    return df_melted
+
+mortality_melted = melt_dataframe(filtered_mortality, "Child Mortality")
+expectancy_melted = melt_dataframe(filtered_expectancy, "Life Expectancy")
+
+# Chart 1: Child Mortality Trends
+st.subheader("Chart 1: Child Mortality Trends")
+st.write("""
+This chart reveals significant regional disparities in child mortality rates across the 
+selected countries. For instance, India and South Africa initially exhibit much higher 
+mortality rates compared to developed nations like the USA and the UK, reflecting disparities 
+in healthcare access. Notably, the chart captures periods of global crises such as pandemics 
+or wars, where temporary spikes in child mortality rates can be observed, such as in South 
+Africa during the mid-20th century. Over time, all countries demonstrate a marked decline, 
+indicating progress in global health and development.
+""")
+
+mortality_chart = alt.Chart(mortality_melted).mark_line().encode(
+    x=alt.X("year:O", title="Year"),
+    y=alt.Y("Child Mortality:Q", title="Child Mortality (0–5 years per 1000 births)"),
+    color="country:N"
+).properties(width=700, height=400)
+
+st.altair_chart(mortality_chart)
+
+# Chart 2: Life Expectancy Trends
+st.subheader("Chart 2: Life Expectancy Trends")
+st.write("""
+This visualization shows how life expectancy has improved across all regions, with 
+countries like the UK and the USA maintaining consistently higher life expectancy, 
+while developing nations such as India and South Africa only recently catching up. 
+However, fluctuations are visible, notably during the 1918 influenza pandemic and 
+the global conflicts of the 20th century, where life expectancy briefly plummeted. 
+Interestingly, the sharp rise in life expectancy in countries like China during the
+mid-20th century reflects public health reforms and economic growth.
+""")
+
+expectancy_chart = alt.Chart(expectancy_melted).mark_line().encode(
+    x=alt.X("year:O", title="Year"),
+    y=alt.Y("Life Expectancy:Q", title="Life Expectancy at Birth"),
+    color="country:N"
+).properties(width=700, height=400)
+
+st.altair_chart(expectancy_chart)
+
+# Chart 3: Child Mortality vs. Life Expectancy
+st.subheader("Chart 3: Child Mortality vs. Life Expectancy")
+st.write("""
+This scatter plot illustrates the strong inverse relationship between child mortality 
+rates and life expectancy at birth, underscoring how advancements in healthcare, 
+nutrition, and living conditions improve both indicators simultaneously. A particularly 
+striking observation is how the data for countries like India and South Africa forms a 
+steep curve, signifying rapid improvements in life expectancy as child mortality declines. 
+Developed nations such as Australia and the USA show a plateau in the later stages, where 
+child mortality rates are already low, and life expectancy improvements are incremental. 
+The trajectory of China’s data during the mid-20th century highlights a rapid transition, 
+likely due to systemic public health efforts.
+""")
+
+merged_data = pd.merge(mortality_melted, expectancy_melted, on=["country", "year"])
+scatter_chart = alt.Chart(merged_data).mark_circle(size=60).encode(
+    x=alt.X("Life Expectancy:Q", title="Life Expectancy at Birth"),
+    y=alt.Y("Child Mortality:Q", title="Child Mortality (0–5 years per 1000 births)"),
+    color="country:N",
+    tooltip=["country", "year", "Child Mortality", "Life Expectancy"]
+).properties(width=700, height=400)
+
+st.altair_chart(scatter_chart)