supermarket_dashboard.py

import dash
from dash import html, dcc
from dash.dependencies import Input, Output

import pandas as pd
import numpy as np

import plotly.graph_objs as go
import plotly.express as px
import dash_bootstrap_components as dbc
from dash_bootstrap_templates import load_figure_template

load_figure_template('minty')
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.MINTY])
server = app.server

# ========== Data Preparation ========== #
df_data = pd.read_csv("./data/supermarket_sales.csv")
df_data["Date"] = pd.to_datetime(df_data["Date"])

# ========== Layout ========== #
app.layout = html.Div([
    dbc.Row([
        dbc.Col([
            dbc.Card([
                html.Img(src='./assets/logo.png',height=50*2,width=30*3,
                style={"margin-left": "auto", "margin-right": "auto","margin-bottom":"20px", "display": "block"}),
                
                html.H5("Cidades:"),
                dcc.Checklist(df_data["City"].value_counts().index,
                            df_data["City"].value_counts().index, id="check_city",
                            inputStyle={"margin-right": "5px", "margin-left": "20px"}),

                html.H5("Variável de análise:"),
                dcc.RadioItems(["gross income", "Rating"],"gross income", id="main_variable",
                                inputStyle={"margin-right": "5px", "margin-left": "20px"}),
            ], style={"height": "90vh", "margin": "20px", "padding": "20px"}),
        ], sm=2),
        dbc.Col([
                dbc.Row([
                    dbc.Col([dcc.Graph(id="city_fig"), ],style={"padding":"0px"}, sm=4),

                    dbc.Col([dcc.Graph(id="gender_fig"), ],style={"padding":"0px"}, sm=4),

                    dbc.Col([dcc.Graph(id="pay_fig"), ],style={"padding":"0px"}, sm=4),

                ]),
                dbc.Row([
                    dbc.Col([dcc.Graph(id="income_per_date_fig")],style={"padding":"0px"},)
                ]),
                dbc.Row([
                    dbc.Col([dcc.Graph(id="income_per_product_fig")],style={"padding":"0px"},)
                ]),
        ], sm=10),
    ]),
])


# ========== Callbacks ========== #
@ app.callback([
    Output("city_fig", "figure"),
    Output("pay_fig", "figure"),
    Output("gender_fig", "figure"),
    Output("income_per_date_fig", "figure"),
    Output("income_per_product_fig", "figure"),
],
    [
    Input("check_city", "value"),
    Input("main_variable", "value")
])
def render_graphs(city_list, main_variable):
    df_city, df_gender,df_payment,df_income_time,df_product_income = get_dfs_filtered(df_data,city_list,main_variable)

    fig_city = px.bar(df_city, x="City", y=main_variable)
    fig_payment = px.bar(df_payment, y="Payment",x=main_variable, orientation="h")
    fig_gender = px.bar(df_gender, y="Gender", x=main_variable, color="City", barmode="group")
    fig_product_income = px.bar(df_product_income, x=main_variable, y="Product line", color="City", orientation="h", barmode="group")
    fig_income_date = px.bar(df_income_time, x="Date",y=main_variable,)

    for fig in [fig_city, fig_payment, fig_gender, fig_income_date, fig_product_income]:
        fig.update_layout(margin=dict(l=0, r=0, t=20, b=0), height=200)

    fig_product_income.update_layout(margin=dict(l=0, r=0, t=20, b=20), height=500)

    return fig_city, fig_payment, fig_gender, fig_product_income, fig_income_date

def get_dfs_filtered(df_data,city_list,main_variable):
    operation = np.sum if main_variable == "gross income" else np.mean

    df_filtered = df_data[df_data["City"].isin(city_list)]
    df_city = df_filtered.groupby("City")[main_variable].apply(
        operation).to_frame().reset_index()
    df_gender = df_filtered.groupby(["Gender", "City"])[main_variable].apply(
        operation).to_frame().reset_index()
    df_payment = df_filtered.groupby("Payment")[main_variable].apply(
        operation).to_frame().reset_index()

    df_income_time = df_filtered.groupby("Date")[main_variable].apply(
        operation).to_frame().reset_index()
    df_product_income = df_filtered.groupby(["Product line", "City"])[
        main_variable].apply(operation).to_frame().reset_index()

    return df_city, df_gender,df_payment,df_income_time,df_product_income


# ========== Run Server ========== #
if __name__ == '__main__':
    app.run_server(port=8050, debug=True)