TidyTuesday Week 36: Henley Passport Index Data

This week we are exploring data from the Henley Passport Index API. The Henley Passport Index is produced by Henley & Partners and captures the number of countries to which travelers in possession of each passport in the world may enter visa free.

FIgure of my passport app :::

1. Python code

import pandas as pd
import streamlit as st
import plotly.express as px
import pycountry
import ast

country_lists = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-09/country_lists.csv"
)
rank_by_year = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-09/rank_by_year.csv"
)


visa_columns = [
    "visa_required",
    "visa_online",
    "visa_on_arrival",
    "visa_free_access",
    "electronic_travel_authorisation",
]

visa_columns_map = {
    "Visa Required": "visa_required",
    "eVisa (Online Application)": "visa_online",
    "Visa on Arrival": "visa_on_arrival",
    "Visa Free Access": "visa_free_access",
    "Electronic Travel Authorisation (ETA)": "electronic_travel_authorisation",
}

for col in visa_columns:
    country_lists[col] = country_lists[col].apply(
        lambda x: ast.literal_eval(x) if pd.notnull(x) else []
    )


tab1, tab2 = st.tabs(
    ["📊 Change in Visa Ranking Graph", "🌍 Interactive Visa Requirements Map"]
)

with tab1:
    st.subheader("Change in Country Visa Ranking over time")
    default = ["France", "Germany", "Italy", "Spain", "United Kingdom", "United States"]
    countries = sorted(rank_by_year["country"].unique())

    # set up the containers
    with st.container(border=True):
        country = st.multiselect("Countries", countries, default=default)

    data = (
        rank_by_year[rank_by_year["country"].isin(country)]
        .pivot(index="year", columns="country", values="rank")
        .sort_index()
    )

    plotly_data = data.reset_index().melt(
        id_vars="year", var_name="country", value_name="rank"
    )

    rank_fig = px.line(
        plotly_data,
        x="year",
        y="rank",
        color="country",
        labels={
            "year": "Year",
            "rank": "Visa Free Access Rank",
            "country": "Country",
        },
        title="Country Visa Free Access Rank Over Time",
    )
    rank_fig.update_yaxes(autorange="reversed")

    # ✅ display inside tab1
    st.plotly_chart(rank_fig, use_container_width=True)

with tab2:
    st.subheader("Interactive Visa Map")

    # Sort the Data
    country_lists["country"] = sorted(country_lists["country"])

    # Dropdown for selecting home country
    home_country = st.selectbox(
        "Select your country of citizenship:", country_lists["country"]
    )

    visa_label = st.selectbox(
        "Select the Visa Requirement of Interest:", list(visa_columns_map.keys())
    )
    visa_type = visa_columns_map[visa_label]

    # Extract destinations for selected visa type
    destinations_raw = country_lists.loc[
        country_lists["country"] == home_country, visa_type
    ].values[0]

    # Flatten nested [[{...}]] → [{...}]
    if (
        isinstance(destinations_raw, list)
        and len(destinations_raw) == 1
        and isinstance(destinations_raw[0], list)
    ):
        destinations_raw = destinations_raw[0]

    if isinstance(destinations_raw, list) and len(destinations_raw) > 0:
        df_map = pd.DataFrame(destinations_raw)

        # Convert alpha-2 → alpha-3
        def alpha2_to_alpha3(alpha2):
            try:
                return pycountry.countries.get(alpha_2=alpha2).alpha_3
            except:
                return None

        df_map["iso_alpha3"] = df_map["code"].apply(alpha2_to_alpha3)
        df_map = df_map.dropna(subset=["iso_alpha3"])

        if not df_map.empty:
            fig = px.choropleth(
                df_map,
                locations="iso_alpha3",
                hover_name="name",
                color_discrete_sequence=["#0083B8"],
                title=f"{visa_type.replace('_',' ').title()} Destinations for {home_country}",
            )
            st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning(f"No {visa_type} destinations found for {home_country}.")
    else:
        st.warning(f"No {visa_type} data available for {home_country}.")

    st.write(
        f"**Countries with {visa_type.replace('_',' ').title()} for {home_country}:**"
    )
    df_map = df_map[["name", "code"]].rename(
        columns={"name": "Name", "code": "2 Digit Country Code"}
    )
    st.dataframe(df_map[["Name", "2 Digit Country Code"]])