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app.py

@@ -26,159 +26,79 @@ def on_btn_click():
 
 
 def main():
-    st.title(" All Graphs")
-    (
-        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"],
-                }
-            )
-        )
-    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],
-        ),
-        use_container_width=True,
+    st.title(" 3D Visualisation")
+    z_data = pd.read_csv(
+        "https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv"
     )
-    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 = 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),
     )
-    st.bar_chart(
-        pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
+    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),
     )
-    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_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,
     )
-    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:
-        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)
-    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)
+    fig = make_subplots(
+        rows=2,
+        cols=2,
+        specs=[
+            [{"type": "surface"}, {"type": "surface"}],
+            [{"type": "surface"}, {"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
+    )
+    fig.add_trace(
+        go.Surface(x=x, y=y, z=z, colorscale="RdBu", showscale=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.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,
+    )
+    fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+    st.plotly_chart(fig)
 
 
 if __name__ == "__main__":