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 Title
+st.title('Real Time/ Live Data Sceince Dashboard')
+#Selection sur le type de job
+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)
+