commit name

dash.py

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+import time
+import plotly.express as px
+import pandas as pd
+import numpy as np
+import streamlit as st
+
+df = pd.read_csv('bank.csv')
+
+
+st.set_page_config(page_title="Bank Data", page_icon="", layout="wide")
+
+st.title("Bank Data Analysis")
+
+job_filter = st.selectbox('Select Job', pd.unique(df['job']))
+
+df_filtered = df[df['job'] == job_filter]
+
+avg_age = np.mean(df_filtered['age'])
+count_married = int(df_filtered['marital'].value_counts()['married'])
+
+kp1, kp2, kp3 = st.columns(3)
+
+kp1.metric(label="Average Age", value=round(avg_age), delta=round(avg_age) - 10)
+kp2.metric(label="Married Count", value=count_married, delta=None)
+
+st.subheader("Age vs Marital Status")
+fig = px.density_heatmap(df_filtered, x="age", y="marital", nbinsx=20, nbinsy=5, color_continuous_scale="Blues")
+st.plotly_chart(fig, use_container_width=True)
+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('### first chart')
+    fig1 = px.histogram(data_frame = df,x='age')
+    st.write(fig2)
+st.dataframe(df)
+st.markdown('### charts')
+
+def main():
+    st.header("welcome")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+pandas
+streamlit
+scikit-learn==1.0.2