SÉANCE 4 – Data Visualisation et Applications Interactives avec Streamlit

import plotly.express as px

# Scatter interactif
fig = px.scatter(df, x="total_bill", y="tip", color="day",
                 hover_data=["size"], title="Tips Dataset")
fig.show()

# Line chart
fig = px.line(df_time, x="date", y="value", 
              title="Évolution temporelle")
fig.show()

# Bar chart
fig = px.bar(df_cat, x="categorie", y="ventes", color="region")
fig.show()

# Pie chart
fig = px.pie(df_cat, values="ventes", names="categorie")
fig.show()

# Graphique à bulles
fig = px.scatter(df, x="prix", y="quantite", size="ca", 
                 color="categorie", hover_name="produit")

# Sunburst (hiérarchique)
fig = px.sunburst(df, path=["region", "ville", "magasin"], 
                  values="ventes")

# Treemap
fig = px.treemap(df, path=["categorie", "produit"], 
                 values="ventes", color="marge")

# app.py
import streamlit as st

st.title("Mon premier dashboard 📊")
st.write("Hello World!")

# Slider
age = st.slider("Votre âge", 0, 100, 25)
st.write(f"Vous avez {age} ans")

# Checkbox
if st.checkbox("Afficher les données"):
    st.write({"a": 1, "b": 2})

# Bouton
if st.button("Cliquez ici"):
    st.balloons()

import streamlit as st

# Page config (doit être en premier)
st.set_page_config(
    page_title="Mon Dashboard",
    page_icon="📊",
    layout="wide"
)

# Sidebar
st.sidebar.title("Navigation")
page = st.sidebar.radio("Aller à", ["Accueil", "Analyse", "Visualisation"])

# Colonnes
col1, col2, col3 = st.columns(3)
with col1:
    st.metric("CA", "150K €", "+12%")
with col2:
    st.metric("Clients", "1.2K", "+5%")
with col3:
    st.metric("Panier moyen", "125 €", "-2%")

# Tabs
tab1, tab2, tab3 = st.tabs(["Données", "Graphiques", "Stats"])

with tab1:
    st.write("Contenu tab 1")

with tab2:
    st.write("Contenu tab 2")

# Input text
nom = st.text_input("Nom")

# Number input
quantite = st.number_input("Quantité", min_value=1, max_value=100)

# Slider
prix_max = st.slider("Prix maximum", 0, 1000, 500)

# Select box
categorie = st.selectbox("Catégorie", ["A", "B", "C"])

# Multi select
produits = st.multiselect("Produits", 
                          ["Laptop", "Souris", "Clavier"])

# Date input
date = st.date_input("Date")

# File uploader
fichier = st.file_uploader("Choisir un fichier CSV")
if fichier:
    df = pd.read_csv(fichier)
    st.write(df)

import streamlit as st
import duckdb
import pandas as pd
import plotly.express as px

st.set_page_config(page_title="Dashboard Ventes", layout="wide")

# Connexion DuckDB
@st.cache_resource
def get_connection():
    return duckdb.connect("data/sales.db")

con = get_connection()

# Sidebar - Filtres
st.sidebar.title("Filtres")
date_debut = st.sidebar.date_input("Date début")
date_fin = st.sidebar.date_input("Date fin")

# Requête avec filtres
@st.cache_data
def load_data(date_debut, date_fin):
    query = f"""
        SELECT *
        FROM ventes
        WHERE date BETWEEN "{date_debut}" AND "{date_fin}"
    """
    return con.execute(query).fetchdf()

df = load_data(date_debut, date_fin)

# KPIs
col1, col2, col3 = st.columns(3)
col1.metric("CA Total", f"{df["montant"].sum():,.0f} €")
col2.metric("Nb Ventes", len(df))
col3.metric("Panier Moyen", f"{df["montant"].mean():,.0f} €")

# Graphiques
fig = px.bar(df.groupby("categorie")["montant"].sum().reset_index(),
             x="categorie", y="montant")
st.plotly_chart(fig, use_container_width=True)