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app.py

@@ -26,80 +26,34 @@ def on_btn_click():
 
 
 def main():
-    st.title(" All Graphs")
+    st.title(" US Real Estate Data and Market Trends")
     (
         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(" Monthly / Weekly", [" Monthly ", " Weekly"])
     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,
-    )
-    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,
-    )
-    st.bar_chart(
-        pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
-    )
+        option = st.selectbox(" Current / Historical", [" Current ", " Historical"])
+    (
+        col1,
+        col2,
+    ) = st.columns(2)
+    with col1:
+        option = st.selectbox(" Median / Mean", [" Median ", " Mean"])
+    with col2:
+        option = st.selectbox(" San Francisco", [" San Francisco"])
+    (
+        col1,
+        col2,
+    ) = st.columns(2)
+    with col1:
+        selected_color = st.color_picker(" Choose a palate", "#FF0000")
+    with col2:
+        value = st.slider(" No of colors", min_value=0, max_value=100, value=50, key=48)
+    if st.checkbox(" Show raw data"):
+        st.write("Checkbox checked!")
+    st.subheader(" Global 3D Visualization")
     st.pydeck_chart(
         pdk.Deck(
             map_style=None,
@@ -133,52 +87,29 @@ def main():
             ],
         )
     )
-    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],
+    st.subheader(" 2D Visualization")
+    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),
+                }
             )
         )
-        fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
-        st.plotly_chart(fig)
+        .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,
+    )
 
 
 if __name__ == "__main__":