Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import time
+import plotly.express as px
+
+df = pd.read_csv('bank.csv')
+
+st.set_page_config(
+    page_title= 'Real Time Data Science Dashboard',
+    page_icon = '✅',
+    layout = 'wide'
+)
+#Dashboard
+st.title('Real Time/ Live Data Science Dashboard')
+
+#Selection sur le 
+job_filter = st.selectbox('Select the job',pd.unique(df['job']))
+
+#Filtrage du job
+df = df[df["job"] == job_filter]
+
+#Creer des KPI
+avg_age = np.mean(df.age)
+count_married = int(df[(df.marital == 'married')]['marital'].count())
+balance = np.mean(df.balance)
+
+kp1,kp2,kp3 = st.columns(3)
+kp1.metric(label='Age ⏳ ',value=round(avg_age), delta = round(avg_age)-10)
+kp2.metric(label = "Married Count 💍", value = int(count_married),delta = -10+count_married)
+kp3.metric(label = "A/C Balanc $", value = f"$ {round(balance,2)}",delta = -round(balance/count_married)*100)
+
+fig_col1,fig_col2 = st.columns(2)
+with fig_col1:
+    st.markdown("### First Chart")
+    fig1 = px.density_heatmap(data_frame=df, y ='age', x= 'marital')
+    st.write(fig1)
+with fig_col2:
+    st.markdown("### Second Chart")
+    fig2 = px.histogram(data_frame=df, x= 'age')
+    st.write(fig2)
+st.markdown("###Detailed Data view")
+st.dataframe(df)
+#time.sleep(1)