app.py

import streamlit as st
import pandas as pd


sales = pd.read_csv('superstore.csv', parse_dates=['Ship Date', 'Order Date'])

st.markdown('# Suivi des ventes')



orders = sales['Order ID'].nunique()
customers = sales['Customer Name'].nunique()
total_sales = sales['Sales'].sum()
benefits = sales['Profit'].sum()

st.markdown('### KPIs principaux 📈')

col1, col2, col3, col4 = st.columns(4)

col1.metric("Nombre de commandes", orders)
col2.metric("Nombre de clients", customers)
col3.metric("Chiffre d'affaires", str(round(total_sales))+ "$")
col4.metric("Bénéfices", str(round(benefits))+ "$")


with st.expander("### Formulaire de recherche 🔎"):
    categories = list(sales['Category'].unique())
    category_selector = st.multiselect(
        "Choisis ta catégorie de Produit",
        categories,
        ["Furniture"],
    )
    
    segments = list(sales['Segment'].unique())
    segment_selector = st.multiselect(
        "Choisis ton segment de clientèle",
        segments,
        ["Consumer"],
    )

def query_builder(filters):
    query = ''
    i = 0
    for filter in filters:
        i = i + 1
        if(i == 1):
            query = query + f"""{filter['column']} in {filter['value']}"""
        else:
            query = query + " " + f"""or {filter['column']} in {filter['value']}"""
    
    print(query)
    return query 

filters = [
    {"column": "Category", "value": category_selector},
    {"column": "Segment", "value": segment_selector}
]

daily_sales = sales[['Sales', 'Order Date', 'Category', 'Segment']].query(query_builder(filters)).groupby(by = ['Order Date']).sum().sort_values(by='Order Date').reset_index()
daily_profit = sales[['Profit', 'Order Date', 'Category', 'Segment']].query(query_builder(filters)).groupby(by = ['Order Date']).sum().sort_values(by='Order Date').reset_index()

st.markdown('### Suivi quotidien 📅')

st.markdown('#### Ventes 🛒')
st.line_chart(daily_sales, x = 'Order Date', y = 'Sales', color = "#e248db", x_label= 'Date', y_label= 'Ventes ($)')

st.markdown('#### Profit 💰')
st.line_chart(daily_profit, x = 'Order Date', y = 'Profit', color = "#39b2ca", x_label= 'Date', y_label= 'Profits ($)')


# utilisation de Plotly pour faire un camembert
import plotly.express as px

st.markdown('#### Nombre de ventes par categorie 📚')
fig = px.pie(sales[['Sales', 'Order Date', 'Category', 'Segment']].query(query_builder(filters)), values='Sales', names='Category')

st.plotly_chart(fig, use_container_width=True)