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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 | |
| st.set_page_config( | |
| page_title="Real-Time Data Science Dashboard", | |
| page_icon="✅", | |
| layout="wide", | |
| ) | |
| # read csv from a github repo | |
| dataset_url = "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv" | |
| # read csv from a URL | |
| def get_data() -> pd.DataFrame: | |
| return pd.read_csv(dataset_url) | |
| df = get_data() | |
| # 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): | |
| 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) |