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app.py

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+import time  # to simulate a real time data, time loop
+
+import numpy as np  # np mean, np random
+import pandas as pd  # read csv, df manipulation
+import plotly.express as px  # interactive charts
+import streamlit as st  # 🎈 data web app development
+dataset_url = "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv"
+
+# read csv from a URL
+@st.experimental_memo
+def get_data() -> pd.DataFrame:
+    return pd.read_csv(dataset_url)
+
+df = get_data()
+st.set_page_config(
+    page_title="Real-Time Data Science Dashboard",
+    page_icon="✅",
+    layout="wide",
+)
+# dashboard title
+st.title("Real-Time / Live Data Science Dashboard")
+# top-level filters
+job_filter = st.selectbox("Select the Job", pd.unique(df["job"]))
+# dataframe filter
+df = df[df["job"] == job_filter]
+# create three columns
+kpi1, kpi2, kpi3 = st.columns(3)
+
+# fill in those three columns with respective metrics or KPIs
+kpi1.metric(
+    label="Age ⏳",
+    value=round(avg_age),
+    delta=round(avg_age) - 10,
+)
+
+kpi2.metric(
+    label="Married Count 💍",
+    value=int(count_married),
+    delta=-10 + count_married,
+)
+
+kpi3.metric(
+    label="A/C Balance ＄",
+    value=f"$ {round(balance,2)} ",
+    delta=-round(balance / count_married) * 100,
+)
+# create two columns for charts
+fig_col1, fig_col2 = st.columns(2)
+
+with fig_col1:
+    st.markdown("### First Chart")
+    fig = px.density_heatmap(
+        data_frame=df, y="age_new", x="marital"
+    )
+    st.write(fig)
+   
+with fig_col2:
+    st.markdown("### Second Chart")
+    fig2 = px.histogram(data_frame=df, x="age_new")
+    st.write(fig2)
+    st.markdown("### Detailed Data View")
+st.dataframe(df)
+for seconds in range(200):
+
+    df["age_new"] = df["age"] * np.random.choice(range(1, 5))
+    df["balance_new"] = df["balance"] * np.random.choice(range(1, 5))
+    time.sleep(1)
+    # creating a single-element container.
+placeholder = st.empty()
+with placeholder.container():
+
+    # create three columns
+    kpi1, kpi2, kpi3 = st.columns(3)
+
+    # fill in those three columns with respective metrics or KPIs
+    kpi1.metric(
+        label="Age ⏳",
+        value=round(avg_age),
+        delta=round(avg_age) - 10,
+    )
+    
+    kpi2.metric(
+        label="Married Count 💍",
+        value=int(count_married),
+        delta=-10 + count_married,
+    )
+    
+    kpi3.metric(
+        label="A/C Balance ＄",
+        value=f"$ {round(balance,2)} ",
+        delta=-round(balance / count_married) * 100,
+    )
+
+    # create two columns for charts
+    fig_col1, fig_col2 = st.columns(2)
+    
+    with fig_col1:
+        st.markdown("### First Chart")
+        fig = px.density_heatmap(
+            data_frame=df, y="age_new", x="marital"
+        )
+        st.write(fig)
+        
+    with fig_col2:
+        st.markdown("### Second Chart")
+        fig2 = px.histogram(data_frame=df, x="age_new")
+        st.write(fig2)
+
+    st.markdown("### Detailed Data View")
+    st.dataframe(df)
+    time.sleep(1)
+