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app.py

@@ -26,98 +26,128 @@ def on_btn_click():
 
 
 def main():
-    st.title(" Corona Dashboard")
+    st.title(" All Graphs")
     (
         col1,
         col2,
     ) = st.columns(2)
     with col1:
-        option = st.selectbox(" San Francisco", [" San Francisco"])
+        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:
-        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),
+        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],
         ),
-        row=1,
-        col=1,
+        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,
+    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.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.bar_chart(
+        pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
     )
-    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,
-            )
-        ],
+    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,
+                ),
+            ],
+        )
     )
+    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,
@@ -129,6 +159,26 @@ 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__":