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app.py

@@ -26,128 +26,98 @@ def on_btn_click():
 
 
 def main():
-    st.title(" All Graphs")
+    st.title(" Corona Dashboard")
     (
         col1,
         col2,
     ) = st.columns(2)
     with col1:
-        st.line_chart(
-            pd.DataFrame(
-                {
-                    "Apple": yf.download("AAPL", start="2023-01-01", end="2023-07-31")[
-                        "Adj Close"
-                    ],
-                    "Google": yf.download(
-                        "GOOGL", start="2023-01-01", end="2023-07-31"
-                    )["Adj Close"],
-                    "Microsoft": yf.download(
-                        "MSFT", start="2023-01-01", end="2023-07-31"
-                    )["Adj Close"],
-                }
-            )
-        )
+        option = st.selectbox(" San Francisco", [" San Francisco"])
     with col2:
-        data = pd.DataFrame(
-            {"X": [1, 2, 3, 4, 5], "Y1": [10, 16, 8, 14, 12], "Y2": [5, 8, 3, 6, 7]}
-        )
-        st.area_chart(data)
-    st.plotly_chart(
-        ff.create_distplot(
-            [np.random.randn(200) - 2, np.random.randn(200), np.random.randn(200) + 2],
-            ["Negative Shift", "Normal", "Positive Shift"],
-            bin_size=[0.1, 0.25, 0.5],
+        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",
+    )
+    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"
+    )
+    fig = make_subplots(
+        rows=2,
+        cols=2,
+        column_widths=[0.6, 0.4],
+        row_heights=[0.4, 0.6],
+        specs=[
+            [{"type": "scattergeo", "rowspan": 2}, {"type": "bar"}],
+            [None, {"type": "surface"}],
+        ],
+    )
+    fig.add_trace(
+        go.Scattergeo(
+            lat=df["Latitude"],
+            lon=df["Longitude"],
+            mode="markers",
+            hoverinfo="text",
+            showlegend=False,
+            marker=dict(color="crimson", size=4, opacity=0.8),
         ),
-        use_container_width=True,
+        row=1,
+        col=1,
     )
-    source = vds.cars()
-    chart = {
-        "mark": "point",
-        "encoding": {
-            "x": {"field": "Horsepower", "type": "quantitative"},
-            "y": {"field": "Miles_per_Gallon", "type": "quantitative"},
-            "color": {"field": "Origin", "type": "nominal"},
-            "shape": {"field": "Origin", "type": "nominal"},
-        },
-    }
-    tab1, tab2 = st.tabs(["Streamlit theme (default)", "Vega-Lite native theme"])
-    with tab1:
-        st.vega_lite_chart(source, chart, theme="streamlit", use_container_width=True)
-    with tab2:
-        st.vega_lite_chart(source, chart, theme=None, use_container_width=True)
-    st.altair_chart(
-        alt.Chart(
-            pd.DataFrame(
-                {
-                    "x": np.random.rand(50),
-                    "y": np.random.rand(50),
-                    "size": np.random.randint(10, 100, 50),
-                    "color": np.random.rand(50),
-                }
-            )
-        )
-        .mark_circle()
-        .encode(
-            x="x",
-            y="y",
-            size="size",
-            color="color",
-            tooltip=["x", "y", "size", "color"],
-        )
-        .properties(width=600, height=400),
-        use_container_width=True,
+    fig.add_trace(
+        go.Bar(
+            x=freq["x"][0:10],
+            y=freq["Country"][0:10],
+            marker=dict(color="crimson"),
+            showlegend=False,
+        ),
+        row=1,
+        col=2,
     )
-    st.bar_chart(
-        pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
+    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",
     )
-    st.pydeck_chart(
-        pdk.Deck(
-            map_style=None,
-            initial_view_state=pdk.ViewState(
-                latitude=37.76, longitude=-122.4, zoom=11, pitch=50
-            ),
-            layers=[
-                pdk.Layer(
-                    "HexagonLayer",
-                    data=pd.DataFrame(
-                        np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
-                        columns=["lat", "lon"],
-                    ),
-                    get_position="[lon, lat]",
-                    radius=200,
-                    elevation_scale=4,
-                    elevation_range=[0, 1000],
-                    pickable=True,
-                    extruded=True,
-                ),
-                pdk.Layer(
-                    "ScatterplotLayer",
-                    data=pd.DataFrame(
-                        np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
-                        columns=["lat", "lon"],
-                    ),
-                    get_position="[lon, lat]",
-                    get_color="[200, 30, 0, 160]",
-                    get_radius=200,
-                ),
-            ],
-        )
+    fig.update_xaxes(tickangle=45)
+    fig.update_layout(
+        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,
+            )
+        ],
     )
-    import datetime
-
-    np.random.seed(1)
-    programmers = ["Alex", "Nicole", "Sara", "Etienne", "Chelsea", "Jody", "Marianne"]
-    base = datetime.datetime.today()
-    dates = base - np.arange(180) * datetime.timedelta(days=1)
-    z = np.random.poisson(size=(len(programmers), len(dates)))
-    fig = go.Figure(data=go.Heatmap(z=z, x=dates, y=programmers, colorscale="Viridis"))
-    fig.update_layout(title="GitHub commits per day", xaxis_nticks=36)
     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,
@@ -159,26 +129,6 @@ def main():
         )
         fig.update_traces(textposition="inside", textinfo="percent+label")
         st.plotly_chart(fig)
-    with col2:
-        fig = go.Figure(
-            go.Sunburst(
-                labels=[
-                    "Eve",
-                    "Cain",
-                    "Seth",
-                    "Enos",
-                    "Noam",
-                    "Abel",
-                    "Awan",
-                    "Enoch",
-                    "Azura",
-                ],
-                parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"],
-                values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
-            )
-        )
-        fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
-        st.plotly_chart(fig)
 
 
 if __name__ == "__main__":