app.py

import streamlit as st  # web development
import numpy as np  # np mean, np random
import pandas as pd  # read csv, df manipulation
import time  # to simulate a real time data, time loop
import plotly.express as px  # interactive charts

# read csv from a github repo
df = pd.read_csv(
    "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv"
)

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"]))

# creating a single-element container.
placeholder = st.empty()

# dataframe filter

df = df[df["job"] == job_filter]

# near real-time / live feed simulation

for seconds in range(200):
    # while True:

    df["age_new"] = df["age"] * np.random.choice(range(1, 5))
    df["balance_new"] = df["balance"] * np.random.choice(range(1, 5))

    # creating KPIs
    avg_age = np.mean(df["age_new"])

    count_married = int(
        df[(df["marital"] == "married")]["marital"].count()
        + np.random.choice(range(1, 30))
    )

    balance = np.mean(df["balance_new"])

    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)
# placeholder.empty()