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app.py

@@ -26,79 +26,109 @@ def on_btn_click():
 
 
 def main():
-    st.title(" 3D Visualisation")
-    z_data = pd.read_csv(
-        "https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv"
+    st.title(" Corona Dashboard")
+    (
+        col1,
+        col2,
+    ) = st.columns(2)
+    with col1:
+        option = st.selectbox(" San Francisco", [" San Francisco"])
+    with col2:
+        option = st.selectbox(" Monthly / Weekly", [" Monthly ", " Weekly"])
+    if st.checkbox(" Show raw data"):
+        st.write("Checkbox checked!")
+    if st.button(" Visualize"):
+        st.write("Button clicked!")
+    st.subheader(" Global Data")
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/plotly/datasets/master/volcano_db.csv",
+        encoding="iso-8859-1",
     )
-    fig = go.Figure(data=go.Surface(z=z_data, showscale=False))
-    fig.update_layout(
-        title="Mt Bruno Elevation",
-        width=400,
-        height=400,
-        margin=dict(t=40, r=0, l=20, b=20),
-    )
-    name = "default"
-    camera = dict(
-        up=dict(x=0, y=0, z=1),
-        center=dict(x=0, y=0, z=0),
-        eye=dict(x=1.25, y=1.25, z=1.25),
-    )
-    fig.update_layout(scene_camera=camera, title=name)
-    st.plotly_chart(fig)
-    df = px.data.election()
-    geojson = px.data.election_geojson()
-    fig = px.choropleth_mapbox(
-        df,
-        geojson=geojson,
-        color="Bergeron",
-        locations="district",
-        featureidkey="properties.district",
-        center={"lat": 45.5517, "lon": -73.7073},
-        mapbox_style="carto-positron",
-        zoom=9,
+    freq = df
+    freq = freq.Country.value_counts().reset_index().rename(columns={"count": "x"})
+    df_v = pd.read_csv(
+        "https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv"
     )
-    st.plotly_chart(fig)
     fig = make_subplots(
         rows=2,
         cols=2,
+        column_widths=[0.6, 0.4],
+        row_heights=[0.4, 0.6],
         specs=[
-            [{"type": "surface"}, {"type": "surface"}],
-            [{"type": "surface"}, {"type": "surface"}],
+            [{"type": "scattergeo", "rowspan": 2}, {"type": "bar"}],
+            [None, {"type": "surface"}],
         ],
     )
-    x = np.linspace(-5, 80, 10)
-    y = np.linspace(-5, 60, 10)
-    xGrid, yGrid = np.meshgrid(y, x)
-    z = xGrid ** 3 + yGrid ** 3
     fig.add_trace(
-        go.Surface(x=x, y=y, z=z, colorscale="Viridis", showscale=False), row=1, col=1
+        go.Scattergeo(
+            lat=df["Latitude"],
+            lon=df["Longitude"],
+            mode="markers",
+            hoverinfo="text",
+            showlegend=False,
+            marker=dict(color="crimson", size=4, opacity=0.8),
+        ),
+        row=1,
+        col=1,
     )
     fig.add_trace(
-        go.Surface(x=x, y=y, z=z, colorscale="RdBu", showscale=False), row=1, col=2
+        go.Bar(
+            x=freq["x"][0:10],
+            y=freq["Country"][0:10],
+            marker=dict(color="crimson"),
+            showlegend=False,
+        ),
+        row=1,
+        col=2,
     )
-    fig.add_trace(
-        go.Surface(x=x, y=y, z=z, colorscale="YlOrRd", showscale=False), row=2, col=1
-    )
-    fig.add_trace(
-        go.Surface(x=x, y=y, z=z, colorscale="YlGnBu", showscale=False), row=2, col=2
+    fig.add_trace(go.Surface(z=df_v.values.tolist(), showscale=False), row=2, col=2)
+    fig.update_geos(
+        projection_type="orthographic",
+        landcolor="white",
+        oceancolor="MidnightBlue",
+        showocean=True,
+        lakecolor="LightBlue",
     )
+    fig.update_xaxes(tickangle=45)
     fig.update_layout(
-        title_text="3D subplots with different colorscales", height=800, width=800
-    )
-    st.plotly_chart(fig)
-    fig = px.scatter_3d(
-        px.data.iris(),
-        x="sepal_length",
-        y="sepal_width",
-        z="petal_width",
-        color="petal_length",
-        size="petal_length",
-        size_max=18,
-        symbol="species",
-        opacity=0.7,
+        template="plotly_dark",
+        margin=dict(r=10, t=25, b=40, l=60),
+        annotations=[
+            dict(
+                text="Source: NOAA",
+                showarrow=False,
+                xref="paper",
+                yref="paper",
+                x=0,
+                y=0,
+            )
+        ],
     )
-    fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
     st.plotly_chart(fig)
+    (
+        col1,
+        col2,
+    ) = st.columns(2)
+    with col1:
+        st.table(
+            {
+                "Country": ["USA", "Canada", "UK", "Australia"],
+                "Population (millions)": [331, 38, 66, 25],
+                "GDP (trillion USD)": [22.675, 1.843, 2.855, 1.488],
+            }
+        )
+    with col2:
+        df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
+        fig = px.pie(
+            df,
+            values="pop",
+            names="country",
+            title="Population of American continent",
+            hover_data=["lifeExp"],
+            labels={"lifeExp": "life expectancy"},
+        )
+        fig.update_traces(textposition="inside", textinfo="percent+label")
+        st.plotly_chart(fig)
 
 
 if __name__ == "__main__":