Create app.py

app.py

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+import streamlit as st 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+
+df = pd.read_csv('bank.csv')
+st.set_page_config(page_title='Real time dashboard',
+page_icon = '✅',layout="wide")
+
+#DASHBOARD TITLE
+st.title('Real time dashbord analysis')
+#filtre sur le type de job
+job_filter = st.selectbox('select a job',pd.unique(df['job']))
+df = df[df['job']== job_filter]
+
+#Creation d indicateurs
+avg_age = np.mean(df['age'])
+count_married = int(df[(df['marital'] == 'married')]['marital'].count())
+balance = np.mean(df['balance'])
+
+kpi1,kpi2,kpi3= st.columns(3)
+kpi1.metric(label='Age ⏳',value=round(avg_age),delta=round(avg_age))
+kpi2.metric(label='Married Count 💍', value=count_married,
+             delta= round(count_married))
+kpi3.metric(label='Balance ＄',value=f'＄ {round(balance,2)}',
+    delta = round(balance/count_married)*100)
+#Graphiques
+col1,col2 = st.columns(2)
+with col1:
+    st.markdown('### First chart')
+    fig1 = plt.figure()
+    sns.barplot(data=df,x='marital',y='age',palette='muted')
+    st.pyplot(fig1)
+with col2:
+    st.markdown('### Second chart')
+    fig2 = plt.figure()
+    sns.histplot(data=df,x='age',palette='muted')
+    st.pyplot(fig2)
+st.markdown('### Detailed data view')
+st.dataframe(df)
+
+
+