Plotly Choropleth地图：缺失值区域处理、多列数据提示及年份动画实现技术问询

Hey, let's work through each of your questions with practical code snippets and straightforward explanations:

1. Show Country Names on Hover for Missing Value Regions #

The problem here is that Plotly's Choropleth only generates hover data for countries present in your dataset. To fix this, we can add a secondary Scattergeo trace that covers every country (including those with missing data) — we'll just hide the visible elements of this trace so it only contributes hover text.

Here's the adjusted code:

import plotly.graph_objects as go
import geopandas as gpd

# Get a full list of ISO3 country codes and names using GeoPandas' built-in dataset
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
full_countries = world[['name', 'iso_a3']].rename(columns={'name':'Zone', 'iso_a3':'Code zone (ISO3)'})
full_countries = full_countries.dropna(subset=['Code zone (ISO3)'])

# Your original choropleth trace
choropleth_trace = go.Choropleth(
    locations = world_map_df_sous_nutrition["Code zone (ISO3)"],
    z = round((world_map_df_sous_nutrition["Proportion pop en sous-nutrition"]),2),
    text = world_map_df_sous_nutrition["Zone"],
    colorscale = "earth",
    autocolorscale = False,
    reversescale = True,
    marker_line_color = "white",
    marker_line_width = .2,
    colorbar_tickprefix = "%",
    colorbar_title = "Proportion de personnes en sous nutrition"
)

# Scattergeo trace to add hover text for all countries
scatter_trace = go.Scattergeo(
    locations = full_countries["Code zone (ISO3)"],
    text = full_countries["Zone"],
    mode = 'text',
    textfont = {'color':'rgba(0,0,0,0)'},  # Make text invisible
    hoverinfo = 'text',
    marker = {'opacity':0}  # Hide markers entirely
)

# Combine both traces in the figure
fig = go.Figure(data=[choropleth_trace, scatter_trace])

# Your existing layout code remains the same
fig.update_layout(
    title_text="L'état de la sous-nutrition dans le monde en 2017",
    geo=dict(
        landcolor = 'lightgray',
        showland = True,
        showcountries = True,
        countrycolor = 'gray',
        countrywidth = 0.5,
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: FAO',
        showarrow = False
    )],
    margin={"r":0,"t":0,"l":0,"b":0},
)
fig.show()

Now when you hover over any country (even missing data regions), you'll see the country name pop up.

2. Replicate GeoPandas Missing Value Styles in Plotly #

Plotly doesn't support hatch patterns directly in Choropleth traces, but we can combine multiple traces to mimic most of the GeoPandas style:

1. A base Choropleth trace for countries with valid data
2. A second Choropleth trace for missing countries, styled with light gray fill and red borders
3. A custom legend entry to label missing regions

Here's the code:

import plotly.graph_objects as go
import geopandas as gpd

# Get full country list
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
full_countries = world[['name', 'iso_a3']].rename(columns={'name':'Zone', 'iso_a3':'Code zone (ISO3)'})
full_countries = full_countries.dropna(subset=['Code zone (ISO3)'])

# Split your data into present and missing values
present_df = world_map_df_sous_nutrition.dropna(subset=["Proportion pop en sous-nutrition"])
missing_df = full_countries[~full_countries["Code zone (ISO3)"].isin(present_df["Code zone (ISO3)"])]

# Trace for countries with valid data
present_trace = go.Choropleth(
    locations = present_df["Code zone (ISO3)"],
    z = round(present_df["Proportion pop en sous-nutrition"],2),
    text = present_df["Zone"],
    colorscale = "earth",
    autocolorscale = False,
    reversescale = True,
    marker_line_color = "white",
    marker_line_width = .2,
    colorbar_tickprefix = "%",
    colorbar_title = "Proportion de personnes en sous nutrition",
    name = "Data Available"
)

# Trace for missing countries
missing_trace = go.Choropleth(
    locations = missing_df["Code zone (ISO3)"],
    z = [0]*len(missing_df),  # Dummy value to lock color
    text = missing_df["Zone"] + "<br>Missing values",
    colorscale = [[0, 'lightgray'], [1, 'lightgray']],  # Fixed light gray fill
    autocolorscale = False,
    marker_line_color = "red",
    marker_line_width = 1,
    showscale = False,  # Hide extra colorbar
    name = "Missing Values"
)

fig = go.Figure(data=[present_trace, missing_trace])

fig.update_layout(
    title_text="L'état de la sous-nutrition dans le monde en 2017",
    geo=dict(
        showland=False,  # Disable default land since we use custom traces
        showcountries=False,
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: FAO',
        showarrow = False
    )],
    margin={"r":0,"t":0,"l":0,"b":0},
    legend=dict(
        title="Status",
        x=0.1, y=0.1
    )
)
fig.show()

Note: Plotly doesn't support hatch patterns in choropleths yet, so this is the closest we can get without complex SVG overlays.

3. Add Multiple Column Values to Hover & Switch Coloring Modes #

Adding Multiple Columns to Hover #

Simply modify the text parameter in your Choropleth trace to include all columns you want to display. Use <br> to create line breaks for readability:

text = (
    world_map_df_sous_nutrition["Zone"]
    + "<br>Sous-nutrition: " 
    + round(world_map_df_sous_nutrition["Proportion pop en sous-nutrition"],2).astype(str) 
    + "%"
    + "<br>Population: " 
    + world_map_df_sous_nutrition["Population"].astype(str)
)

Plotly will automatically show this combined text when you hover over a country.

Switching Coloring Modes #

You can add a dropdown menu to let users switch between different columns for coloring. Here's how:

import plotly.graph_objects as go

# Define columns you want to switch between
color_modes = [
    {"label": "Sous-nutrition", "col": "Proportion pop en sous-nutrition", "unit": "%"},
    {"label": "Population", "col": "Population", "unit": ""}
]

# Initial trace with first column
initial_mode = color_modes[0]
fig = go.Figure(data=go.Choropleth(
    locations = world_map_df_sous_nutrition["Code zone (ISO3)"],
    z = round(world_map_df_sous_nutrition[initial_mode["col"]],2),
    text = (
        world_map_df_sous_nutrition["Zone"]
        + "<br>" + initial_mode["label"] + ": "
        + round(world_map_df_sous_nutrition[initial_mode["col"]],2).astype(str)
        + initial_mode["unit"]
    ),
    colorscale = "earth",
    autocolorscale = False,
    reversescale = True,
    marker_line_color = "white",
    marker_line_width = .2,
    colorbar_tickprefix = initial_mode["unit"],
    colorbar_title = initial_mode["label"]
))

# Create dropdown buttons
buttons = []
for mode in color_modes:
    button = dict(
        label=mode["label"],
        method="update",
        args=[
            {"z": [round(world_map_df_sous_nutrition[mode["col"]],2)]},
            {
                "colorbar": {"tickprefix": mode["unit"], "title": mode["label"]},
                "data[0].text": [
                    world_map_df_sous_nutrition["Zone"]
                    + "<br>" + mode["label"] + ": "
                    + round(world_map_df_sous_nutrition[mode["col"]],2).astype(str)
                    + mode["unit"]
                ]
            }
        ]
    )
    buttons.append(button)

fig.update_layout(
    title_text="L'état de la sous-nutrition dans le monde en 2017",
    geo=dict(
        landcolor = 'lightgray',
        showland = True,
        showcountries = True,
        countrycolor = 'gray',
        countrywidth = 0.5,
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: FAO',
        showarrow = False
    )],
    margin={"r":0,"t":0,"l":0,"b":0},
    updatemenus=[dict(
        buttons=buttons,
        direction="down",
        x=0.1,
        y=1.1
    )]
)
fig.show()

This adds a dropdown at the top of the figure that updates the coloring, hover text, and colorbar when users select a different metric.

4. Animated Choropleth Map by Year #

Plotly supports animated choropleths using the frame parameter. First, make sure your data is in long format (one row per country per year). Then use this code:

import plotly.graph_objects as go
import geopandas as gpd

# Assume your data is in long format: columns = ["Code zone (ISO3)", "Zone", "Year", "Proportion pop en sous-nutrition"]
# Get full country list (same as before)
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
full_countries = world[['name', 'iso_a3']].rename(columns={'name':'Zone', 'iso_a3':'Code zone (ISO3)'})
full_countries = full_countries.dropna(subset=['Code zone (ISO3)'])

# Get unique sorted years
years = sorted(world_map_df_long["Year"].unique())

# Create frames for each year
frames = []
for year in years:
    year_data = world_map_df_long[world_map_df_long["Year"] == year]
    # Merge with full countries to ensure all regions are covered
    year_data_merged = full_countries.merge(year_data, on="Code zone (ISO3)", how="left")
    frame = go.Frame(
        data=[go.Choropleth(
            locations = year_data_merged["Code zone (ISO3)"],
            z = round(year_data_merged["Proportion pop en sous-nutrition"],2),
            text = (
                year_data_merged["Zone_x"]
                + "<br>Année: " + str(year)
                + "<br>Sous-nutrition: " 
                + year_data_merged["Proportion pop en sous-nutrition"].fillna("Missing").astype(str)
                + "%"
            ),
            colorscale = "earth",
            autocolorscale = False,
            reversescale = True,
            marker_line_color = "white",
            marker_line_width = .2,
            colorbar_tickprefix = "%",
            colorbar_title = "Proportion de personnes en sous nutrition"
        )],
        name=str(year)
    )
    frames.append(frame)

# Initial figure (first year)
initial_year = years[0]
initial_data = world_map_df_long[world_map_df_long["Year"] == initial_year]
initial_data_merged = full_countries.merge(initial_data, on="Code zone (ISO3)", how="left")

fig = go.Figure(
    data=[go.Choropleth(
        locations = initial_data_merged["Code zone (ISO3)"],
        z = round(initial_data_merged["Proportion pop en sous-nutrition"],2),
        text = (
            initial_data_merged["Zone_x"]
            + "<br>Année: " + str(initial_year)
            + "<br>Sous-nutrition: " 
            + initial_data_merged["Proportion pop en sous-nutrition"].fillna("Missing").astype(str)
            + "%"
        ),
        colorscale = "earth",
        autocolorscale = False,
        reversescale = True,
        marker_line_color = "white",
        marker_line_width = .2,
        colorbar_tickprefix = "%",
        colorbar_title = "Proportion de personnes en sous nutrition"
    )],
    frames=frames,
    layout=go.Layout(
        title_text="Evolution de la sous-nutrition dans le monde",
        geo=dict(
            landcolor = 'lightgray',
            showland = True,
            showcountries = True,
            countrycolor = 'gray',
            countrywidth = 0.5,
            showframe=False,
            showcoastlines=False,
            projection_type='equirectangular'
        ),
        annotations = [dict(
            x=0.55,
            y=0.1,
            xref='paper',
            yref='paper',
            text='Source: FAO',
            showarrow = False
        )],
        margin={"r":0,"t":0,"l":0,"b":0},
        updatemenus=[dict(
            type="buttons",
            showactive=True,
            buttons=[dict(
                label="Play",
                method="animate",
                args=[None, {"frame": {"duration": 500, "redraw": True}, "fromcurrent": True}]
            ), dict(
                label="Pause",
                method="animate",
                args=[[None], {"frame": {"duration": 0, "redraw": False}, "mode": "immediate"}]
            )]
        )],
        sliders=[dict(
            steps=[dict(
                method="animate",
                args=[[str(year)], {"frame": {"duration": 500, "redraw": True}, "mode": "immediate"}],
                label=str(year)
            ) for year in years],
            active=0,
            currentvalue={"prefix": "Année: "}
        )]
    )
)

fig.show()

This creates an animated map with play/pause buttons and a year slider, letting users watch the data evolve over time.

内容的提问来源于stack exchange，提问作者ElMeTeOr