diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d9e13dc --- /dev/null +++ b/.gitignore @@ -0,0 +1,165 @@ +# arquivos +projeto_completo.txt +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# VS CODE +.vscode/ \ No newline at end of file diff --git a/dump.md b/dump.md new file mode 100644 index 0000000..8f3f225 --- /dev/null +++ b/dump.md @@ -0,0 +1,7976 @@ + +## ./src/__init__.py + +```py + +``` + +## ./src/models/config/connection.py + +```py +""" +Tem a função de se conectar com o banco de dados +foi construida seguindo um padrão de acesso + +IA - confirmação da variavel de conexão +""" + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker, Session +from dotenv import load_dotenv +from pathlib import Path +import os + + +# Isso garante que as variáveis carreguem assim que o arquivo for lido +env_path = Path(__file__).parent.parent.parent.parent / '.env' +load_dotenv(dotenv_path=env_path) + + +class DBConnectionHandler: + def __init__(self) -> None: + """Configuração de conexão com o banco do desafio Looqbox""" + self.__connection_string = os.getenv("DATABASE_URL") + #self.__connection_string = "mysql+pymysql://looqbox-challenge:looq-challenge@35.199.115.174/looqbox-challenge" + # "tipo do banco://usuario:senha@ip de conexão/database" + + self.__engine = self.__create_database_engine() + self.session = None + + def __create_database_engine(self): + engine = create_engine(self.__connection_string, + pool_pre_ping=True) + return engine + + def get_engine(self): + return self.__engine + + def __enter__(self): + session_make = sessionmaker(bind=self.__engine) + self.session = session_make() + return self.session + + def __exit__(self, exc_type, exc_val, exc_tb): + self.session.close() + + +``` + +## ./src/models/__init__.py + +```py + +``` + +## ./src/models/repositories/imdb_repository.py + +```py +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy import select, column, table, and_, between +from sqlalchemy.orm import Session +from src.models.schemas.product_sales_schema import ProductSalesSchema + + + +class ImdbRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + self.__table = table("IMDB_movies") + + def read_db(self) -> pd.DataFrame: + + with self.__db.get_engine().connect() as conn: + try: + query = select("*").select_from(self.__table) + response = pd.read_sql(query, conn) + + return response + + except Exception as exception: + raise Exception(f"erro na query:{exception}") + + +``` + +## ./src/models/repositories/__init__.py + +```py + +``` + +## ./src/models/repositories/sales_repository.py + +```py +""" +Acesso ao banco de dados +faz a contrução da query, ela mantem a segurança de conexão +""" +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy import select, column, table, and_, between +from sqlalchemy.orm import Session +from src.models.schemas.product_sales_schema import ProductSalesSchema + +class SalesRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + self.__table = table("data_product_sales") + self.__col_map = ProductSalesSchema.COLUMN_MAP + + def read_db(self,product_code: int, store_code: int, data_range: list) -> pd.DataFrame: + + with self.__db.get_engine().connect() as conn: + try: + query = select("*").select_from(self.__table) + + if product_code is not None: + query = query.where(column(self.__col_map["product_code"]) == product_code) + + if store_code is not None: + query = query.where(column(self.__col_map["store_code"]) == store_code) + + if data_range is not None: + + data_start = data_range[0] + data_end = data_range[1] + + if data_start and data_end: + query = query.where(column(self.__col_map["data_range"]).between(data_start,data_end)) + + elif data_start: + query = query.where(column(self.__col_map["data_range"]) >= data_start) + + elif data_end: + query = query.where(column(self.__col_map["data_range"]) <= data_end) + + print(query) + response = pd.read_sql(query, conn) + + return response + + except Exception as exception: + raise Exception(f"erro na query:{exception}") + +``` + +## ./src/models/repositories/store_repository.py + +```py +""" +Como não são enviados dados nessa query, +não foi necessario a contrução de schemas +""" +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy.orm import Session +from sqlalchemy import text + + + +class StoreRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + + + def get_store_cad(self) -> pd.DataFrame: + + try: + query = text(""" + SELECT + STORE_CODE, + STORE_NAME, + START_DATE, + END_DATE, + BUSINESS_NAME, + BUSINESS_CODE + FROM data_store_cad + """) + with self.__db.get_engine().connect() as conn: + return pd.read_sql(query, conn) + + except Exception as exception: + raise Exception(f"erro na query {exception}") + + def get_store_sales(self) -> pd.DataFrame: + + try: + query = text(""" + SELECT + STORE_CODE, + DATE, + SALES_VALUE, + SALES_QTY + FROM data_store_sales + WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31' + """) + with self.__db.get_engine().connect() as conn: + return pd.read_sql(query, conn) + + except Exception as exception: + raise Exception(f"erro na query {exception}") +``` + +## ./src/models/schemas/__init__.py + +```py + +``` + +## ./src/models/schemas/product_sales_schema.py + +```py +""" +Shemas de dados - +Como vamos enviar dados para o banco de dados, +a camada de schema evita que seja enviado o tipo errado e ataques externos + +IA - Ajuda com o formato e tratamento com datas +""" +from pydantic import BaseModel +from typing import Optional, List, ClassVar, Dict +from datetime import datetime + + + +class ProductSalesSchema(BaseModel): + + def product_validator(self, product_code: Optional[int] = None) -> int: + + if product_code is None: + return None + + if type(product_code) == int: + return product_code + else: + raise ValueError("valores em números inteiros") + + + def store_validator(self, store_code: Optional[str] = None) -> str: + + if store_code is None: + return None + + elif type(store_code) == int: + store_code = str(store_code) + return store_code + else: + raise ValueError("valores em números inteiros") + + + def data_validator(self, data_range: Optional[List[str]] = None) -> Optional[List]: + + if data_range is None: + return None + + if not isinstance(data_range, list): + raise ValueError("data_range precisa ser uma lista") + + if len(data_range) != 2: + raise ValueError("data_range pracisa ter duas posiçoes") + + validate_data = [] + for data_inf in data_range: + + if data_inf is None: + validate_data.append(None) + continue + + try: + data_obj = datetime.strptime(data_inf, "%Y-%m-%d").date() + validate_data.append(data_obj) + except ValueError: + raise ValueError(f"Data {data_inf} fora do formato") + + if validate_data[0] and validate_data[1]: + if validate_data[0] > validate_data[1]: + raise ValueError(f"Data inicial - {validate_data[0]} maior que Data final - {validate_data[1]}") + + if validate_data[0] is None and validate_data[1] is None: + return None + + + return validate_data + + COLUMN_MAP: ClassVar[Dict[str, str]] = { + "product_code": "PRODUCT_CODE", + "store_code": "STORE_CODE", + "data_range": "DATE" + } +``` + +## ./src/notebooks/nt_case1.ipynb + +```ipynb +{ + "cells": [ + { + "cell_type": "markdown", + "id": "66dc8a40", + "metadata": {}, + "source": [ + "## Caso 1 - Função dinâmica para consultar a tabela data_product_sales" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ce951cfd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5db6c08f", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "\n", + "from src.models.schemas.product_sales_schema import ProductSalesSchema\n", + "from src.models.repositories.sales_repository import SalesRepository\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = SalesRepository()\n", + "schema = ProductSalesSchema()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "97f1c77d", + "metadata": {}, + "outputs": [], + "source": [ + "# função retrieve_data \n", + "\n", + "def retrieve_data(product_code:int = None, store_code:int = None, data_range:List = None) -> pd.DataFrame:\n", + " \"\"\"\n", + " Recupera dados da tabela data_product_sales com filtros opcionais.\n", + "\n", + " Parâmetros\n", + "\n", + " product_code: int -> código do produto, se NONE retorna todos\n", + "\n", + " store_code: int -> código da loja, se NONE rotorna todos\n", + "\n", + " data_range: list -> intervalo de datas, aceita NONE para intervalos abertos\n", + " \n", + " \"\"\"\n", + "\n", + " product_code = schema.product_validator(product_code)\n", + " store_code = schema.store_validator(store_code)\n", + " data_range = schema.data_validator(data_range)\n", + "\n", + " response = repo.read_db(product_code=product_code, store_code= store_code, data_range= data_range)\n", + "\n", + " return response \n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "888d8af7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales\n" + ] + }, + { + "data": { + "text/plain": [ + "( PRODUCT_CODE SALES_VALUE SALES_QTY\n", + " count 2.173133e+06 2.173133e+06 2.173133e+06\n", + " mean 2.641163e+04 2.428775e+03 1.565161e+02\n", + " std 4.611153e+04 2.731317e+03 4.527131e+01\n", + " min 1.000000e+01 6.279000e+01 3.900000e+01\n", + " 25% 1.111000e+03 7.490800e+02 1.200000e+02\n", + " 50% 5.065000e+03 1.405800e+03 1.490000e+02\n", + " 75% 2.314100e+04 3.122580e+03 1.820000e+02\n", + " max 2.414040e+05 4.783863e+04 3.850000e+02,\n", + " )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# uso da função - sem filtros\n", + "\n", + "base = retrieve_data()\n", + "base.describe(), base.info" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "52d26401", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"PRODUCT_CODE\" = :PRODUCT_CODE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 5840.0 5840.000000 5840.000000\n", + "mean 18.0 948.090959 86.980822\n", + "std 0.0 233.068771 21.382456\n", + "min 18.0 632.200000 58.000000\n", + "25% 18.0 741.200000 68.000000\n", + "50% 18.0 882.900000 81.000000\n", + "75% 18.0 1100.900000 101.000000\n", + "max 18.0 1591.400000 146.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - só o produto\n", + "\n", + "base = retrieve_data(product_code= 18)\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b66e255d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"STORE_CODE\" = :STORE_CODE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 136729.000000 136729.000000 136729.000000\n", + "mean 28427.114840 2443.597482 158.605219\n", + "std 47090.707043 2652.085917 45.859105\n", + "min 18.000000 62.790000 58.000000\n", + "25% 2120.000000 724.790000 121.000000\n", + "50% 6479.000000 1404.000000 152.000000\n", + "75% 26793.000000 3319.770000 184.000000\n", + "max 241404.000000 24202.800000 332.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - só a loja \n", + "base = retrieve_data(store_code=1)\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b4454c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"DATE\" BETWEEN :DATE_1 AND :DATE_2\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 39401.000000 39401.000000 39401.000000\n", + "mean 26658.305271 3132.077588 184.287049\n", + "std 46321.780252 3590.919739 55.651229\n", + "min 10.000000 75.000000 48.000000\n", + "25% 1223.000000 880.200000 152.000000\n", + "50% 6019.000000 1763.050000 179.000000\n", + "75% 23141.000000 4195.800000 223.000000\n", + "max 241404.000000 41877.430000 337.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - datas - intervalo fechado\n", + "base = retrieve_data(data_range=[\"2019-01-01\",\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "fd0bc8af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"DATE\" <= :DATE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 1.748619e+06 1.748619e+06 1.748619e+06\n", + "mean 2.635175e+04 2.253023e+03 1.495118e+02\n", + "std 4.606029e+04 2.436324e+03 3.907265e+01\n", + "min 1.000000e+01 6.279000e+01 3.900000e+01\n", + "25% 1.111000e+03 7.183800e+02 1.170000e+02\n", + "50% 5.065000e+03 1.332840e+03 1.390000e+02\n", + "75% 2.314100e+04 2.864000e+03 1.750000e+02\n", + "max 2.414040e+05 4.187743e+04 3.370000e+02\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - datas - intervalo aberto\n", + "base = retrieve_data(data_range=[ None,\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "dbe44ab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"PRODUCT_CODE\" = :PRODUCT_CODE_1 AND \"STORE_CODE\" = :STORE_CODE_1 AND \"DATE\" BETWEEN :DATE_1 AND :DATE_2\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 31.0 31.000000 31.000000\n", + "mean 18.0 939.509677 86.193548\n", + "std 0.0 236.899229 21.733874\n", + "min 18.0 654.000000 60.000000\n", + "25% 18.0 735.750000 67.500000\n", + "50% 18.0 850.200000 78.000000\n", + "75% 18.0 1079.100000 99.000000\n", + "max 18.0 1395.200000 128.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - todos os filtros\n", + "base = retrieve_data(product_code= 18, store_code=1, data_range=[\"2019-01-01\",\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "markdown", + "id": "2a9482e2", + "metadata": {}, + "source": [ + "## Decisões técnicas\n", + "\n", + "- Parâmetros opcionais com default `None` — qualquer combinação de filtros é válida\n", + "- Validação isolada no Schema antes de chegar ao Repository\n", + "- Datas entram como string ISO e são convertidas para `datetime.date` internamente\n", + "- Filtro de data suporta intervalo aberto (só início ou só fim)\n", + "- Queries montadas com SQLAlchemy Core — sem concatenação de string, sem risco de SQL Injection\n", + "- IA utilizada para: revisão de bugs nos validators e estrutura dos testes pytest" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} + +``` + +## ./src/notebooks/nt_case2.ipynb + +```ipynb +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0769bdcd", + "metadata": {}, + "source": [ + "## Caso2 - Ticket Medio por Loja" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8c175878", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "\n", + "root = \"/home/santanna/develope/projetos/data-challenge_luiz_bernardo\"\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "aa53ae46", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "from src.models.repositories.store_repository import StoreRepository\n", + "import matplotlib.pyplot as plt\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = StoreRepository()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ca11dcf6", + "metadata": {}, + "outputs": [], + "source": [ + "# bases de dados \n", + "\n", + "base_cad = repo.get_store_cad()\n", + "base_sales = repo.get_store_sales()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0869a461", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 7300 entries, 0 to 7299\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STORE_CODE 7300 non-null int64 \n", + " 1 DATE 7300 non-null object \n", + " 2 SALES_VALUE 7300 non-null float64\n", + " 3 SALES_QTY 7300 non-null int64 \n", + "dtypes: float64(1), int64(2), object(1)\n", + "memory usage: 228.3+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "(,\n", + " None)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + 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5464132019-10-01187601.5412160
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" + ], + "text/plain": [ + " STORE_CODE DATE SALES_VALUE SALES_QTY\n", + "5460 1 2019-10-01 187601.54 12160\n", + "5461 10 2019-10-01 139038.86 5223\n", + "5462 11 2019-10-01 252687.35 8481\n", + "5463 12 2019-10-01 223973.64 7659\n", + "5464 13 2019-10-01 187601.54 12160" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# formato da data para o filtro reconhecer\n", + "base_sales[\"DATE\"] = pd.to_datetime(base_sales['DATE'])\n", + "\n", + "# filtro \n", + "data_inicio = \"2019-10-01\"\n", + "data_fim = \"2019-12-31\"\n", + "\n", + "base_sales_filtrado = base_sales[base_sales['DATE'].between(data_inicio,data_fim)]\n", + "\n", + "base_sales_filtrado.describe, base_sales_filtrado.info()\n", + "base_sales_filtrado.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "46f0729f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODEDATESALES_VALUESALES_QTYSTORE_NAMESTART_DATEEND_DATEBUSINESS_NAMEBUSINESS_CODE
012019-10-01187601.5412160Sao Paulo2006-10-01Varejo1
1102019-10-01139038.865223Hong Kong2019-01-01Farma4
2112019-10-01252687.358481Rio de Janeiro2019-01-01Farma4
3122019-10-01223973.647659Madri2019-01-01Farma4
4132019-10-01187601.5412160Dubai2019-01-01Atacado5
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" + ], + "text/plain": [ + " STORE_CODE DATE SALES_VALUE SALES_QTY STORE_NAME START_DATE \\\n", + "0 1 2019-10-01 187601.54 12160 Sao Paulo 2006-10-01 \n", + "1 10 2019-10-01 139038.86 5223 Hong Kong 2019-01-01 \n", + "2 11 2019-10-01 252687.35 8481 Rio de Janeiro 2019-01-01 \n", + "3 12 2019-10-01 223973.64 7659 Madri 2019-01-01 \n", + "4 13 2019-10-01 187601.54 12160 Dubai 2019-01-01 \n", + "\n", + " END_DATE BUSINESS_NAME BUSINESS_CODE \n", + "0 Varejo 1 \n", + "1 Farma 4 \n", + "2 Farma 4 \n", + "3 Farma 4 \n", + "4 Atacado 5 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# união de tabelas \n", + "base_merge = base_sales_filtrado.merge(base_cad, on = \"STORE_CODE\", how = \"left\")\n", + "base_merge.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6a2342dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DATESALES_VALUE...SALES_QTYBUSINESS_CODE
countmeanmin25%50%75%maxstdcountmean...maxstdcountmeanmin25%50%75%maxstd
STORE_CODESTORE_NAMEBUSINESS_NAME
1Sao PauloVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
2ChicagoVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0238352.405217...23496.03756.44014492.01.01.01.01.01.01.00.0
3RomaVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
4TokioVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
5ParisProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
6BerlinProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
7New YorkProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
8BelemProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0228147.319239...22634.03625.28738992.02.02.02.02.02.02.00.0
9LondonFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0211649.871196...11614.01816.17662792.04.04.04.04.04.04.00.0
10Hong KongFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0163477.299348...9468.01512.57803192.04.04.04.04.04.04.00.0
11Rio de JaneiroFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0295348.721413...15480.02447.90879492.04.04.04.04.04.04.00.0
12MadriFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0262276.077174...13977.02214.47931292.04.04.04.04.04.04.00.0
13DubaiAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
14BahiaAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
15Buenos AiresAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
16SalvadorAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
17SidneyPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
18BangkokPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
19MiamiPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
20VancouverPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
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20 rows × 32 columns

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" + ], + "text/plain": [ + " DATE \\\n", + " count mean \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 92 2019-11-15 12:00:00 \n", + "2 Chicago Varejo 92 2019-11-15 12:00:00 \n", + "3 Roma Varejo 92 2019-11-15 12:00:00 \n", + "4 Tokio Varejo 92 2019-11-15 12:00:00 \n", + "5 Paris Proximidade 92 2019-11-15 12:00:00 \n", + "6 Berlin Proximidade 92 2019-11-15 12:00:00 \n", + "7 New York Proximidade 92 2019-11-15 12:00:00 \n", + "8 Belem Proximidade 92 2019-11-15 12:00:00 \n", + "9 London Farma 92 2019-11-15 12:00:00 \n", + "10 Hong Kong Farma 92 2019-11-15 12:00:00 \n", + "11 Rio de Janeiro Farma 92 2019-11-15 12:00:00 \n", + "12 Madri Farma 92 2019-11-15 12:00:00 \n", + "13 Dubai Atacado 92 2019-11-15 12:00:00 \n", + "14 Bahia Atacado 92 2019-11-15 12:00:00 \n", + "15 Buenos Aires Atacado 92 2019-11-15 12:00:00 \n", + "16 Salvador Atacado 92 2019-11-15 12:00:00 \n", + "17 Sidney Posto 92 2019-11-15 12:00:00 \n", + "18 Bangkok Posto 92 2019-11-15 12:00:00 \n", + "19 Miami Posto 92 2019-11-15 12:00:00 \n", + "20 Vancouver Posto 92 2019-11-15 12:00:00 \n", + "\n", + " \\\n", + " min \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-10-01 00:00:00 \n", + "2 Chicago Varejo 2019-10-01 00:00:00 \n", + "3 Roma Varejo 2019-10-01 00:00:00 \n", + "4 Tokio Varejo 2019-10-01 00:00:00 \n", + "5 Paris Proximidade 2019-10-01 00:00:00 \n", + "6 Berlin Proximidade 2019-10-01 00:00:00 \n", + "7 New York Proximidade 2019-10-01 00:00:00 \n", + "8 Belem Proximidade 2019-10-01 00:00:00 \n", + "9 London Farma 2019-10-01 00:00:00 \n", + "10 Hong Kong Farma 2019-10-01 00:00:00 \n", + "11 Rio de Janeiro Farma 2019-10-01 00:00:00 \n", + "12 Madri Farma 2019-10-01 00:00:00 \n", + "13 Dubai Atacado 2019-10-01 00:00:00 \n", + "14 Bahia Atacado 2019-10-01 00:00:00 \n", + "15 Buenos Aires Atacado 2019-10-01 00:00:00 \n", + "16 Salvador Atacado 2019-10-01 00:00:00 \n", + "17 Sidney Posto 2019-10-01 00:00:00 \n", + "18 Bangkok Posto 2019-10-01 00:00:00 \n", + "19 Miami Posto 2019-10-01 00:00:00 \n", + "20 Vancouver Posto 2019-10-01 00:00:00 \n", + "\n", + " \\\n", + " 25% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-10-23 18:00:00 \n", + "2 Chicago Varejo 2019-10-23 18:00:00 \n", + "3 Roma Varejo 2019-10-23 18:00:00 \n", + "4 Tokio Varejo 2019-10-23 18:00:00 \n", + "5 Paris Proximidade 2019-10-23 18:00:00 \n", + "6 Berlin Proximidade 2019-10-23 18:00:00 \n", + "7 New York Proximidade 2019-10-23 18:00:00 \n", + "8 Belem Proximidade 2019-10-23 18:00:00 \n", + "9 London Farma 2019-10-23 18:00:00 \n", + "10 Hong Kong Farma 2019-10-23 18:00:00 \n", + "11 Rio de Janeiro Farma 2019-10-23 18:00:00 \n", + "12 Madri Farma 2019-10-23 18:00:00 \n", + "13 Dubai Atacado 2019-10-23 18:00:00 \n", + "14 Bahia Atacado 2019-10-23 18:00:00 \n", + "15 Buenos Aires Atacado 2019-10-23 18:00:00 \n", + "16 Salvador Atacado 2019-10-23 18:00:00 \n", + "17 Sidney Posto 2019-10-23 18:00:00 \n", + "18 Bangkok Posto 2019-10-23 18:00:00 \n", + "19 Miami Posto 2019-10-23 18:00:00 \n", + "20 Vancouver Posto 2019-10-23 18:00:00 \n", + "\n", + " \\\n", + " 50% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-11-15 12:00:00 \n", + "2 Chicago Varejo 2019-11-15 12:00:00 \n", + "3 Roma Varejo 2019-11-15 12:00:00 \n", + "4 Tokio Varejo 2019-11-15 12:00:00 \n", + "5 Paris Proximidade 2019-11-15 12:00:00 \n", + "6 Berlin Proximidade 2019-11-15 12:00:00 \n", + "7 New York Proximidade 2019-11-15 12:00:00 \n", + "8 Belem Proximidade 2019-11-15 12:00:00 \n", + "9 London Farma 2019-11-15 12:00:00 \n", + "10 Hong Kong Farma 2019-11-15 12:00:00 \n", + "11 Rio de Janeiro Farma 2019-11-15 12:00:00 \n", + "12 Madri Farma 2019-11-15 12:00:00 \n", + "13 Dubai Atacado 2019-11-15 12:00:00 \n", + "14 Bahia Atacado 2019-11-15 12:00:00 \n", + "15 Buenos Aires Atacado 2019-11-15 12:00:00 \n", + "16 Salvador Atacado 2019-11-15 12:00:00 \n", + "17 Sidney Posto 2019-11-15 12:00:00 \n", + "18 Bangkok Posto 2019-11-15 12:00:00 \n", + "19 Miami Posto 2019-11-15 12:00:00 \n", + "20 Vancouver Posto 2019-11-15 12:00:00 \n", + "\n", + " \\\n", + " 75% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-12-08 06:00:00 \n", + "2 Chicago Varejo 2019-12-08 06:00:00 \n", + "3 Roma Varejo 2019-12-08 06:00:00 \n", + "4 Tokio Varejo 2019-12-08 06:00:00 \n", + "5 Paris Proximidade 2019-12-08 06:00:00 \n", + "6 Berlin Proximidade 2019-12-08 06:00:00 \n", + "7 New York Proximidade 2019-12-08 06:00:00 \n", + "8 Belem Proximidade 2019-12-08 06:00:00 \n", + "9 London Farma 2019-12-08 06:00:00 \n", + "10 Hong Kong Farma 2019-12-08 06:00:00 \n", + "11 Rio de Janeiro Farma 2019-12-08 06:00:00 \n", + "12 Madri Farma 2019-12-08 06:00:00 \n", + "13 Dubai Atacado 2019-12-08 06:00:00 \n", + "14 Bahia Atacado 2019-12-08 06:00:00 \n", + "15 Buenos Aires Atacado 2019-12-08 06:00:00 \n", + "16 Salvador Atacado 2019-12-08 06:00:00 \n", + "17 Sidney Posto 2019-12-08 06:00:00 \n", + "18 Bangkok Posto 2019-12-08 06:00:00 \n", + "19 Miami Posto 2019-12-08 06:00:00 \n", + "20 Vancouver Posto 2019-12-08 06:00:00 \n", + "\n", + " SALES_VALUE \\\n", + " max std count \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "2 Chicago Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "3 Roma Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "4 Tokio Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "5 Paris Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "6 Berlin Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "7 New York Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "8 Belem Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "9 London Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "10 Hong Kong Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "11 Rio de Janeiro Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "12 Madri Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "13 Dubai Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "14 Bahia Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "15 Buenos Aires Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "16 Salvador Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "17 Sidney Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "18 Bangkok Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "19 Miami Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "20 Vancouver Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "\n", + " ... SALES_QTY \\\n", + " mean ... max \n", + "STORE_CODE STORE_NAME BUSINESS_NAME ... \n", + "1 Sao Paulo Varejo 230577.049674 ... 22832.0 \n", + "2 Chicago Varejo 238352.405217 ... 23496.0 \n", + "3 Roma Varejo 230577.049674 ... 22832.0 \n", + "4 Tokio Varejo 230577.049674 ... 22832.0 \n", + "5 Paris Proximidade 230577.049674 ... 22832.0 \n", + "6 Berlin Proximidade 230577.049674 ... 22832.0 \n", + "7 New York Proximidade 230577.049674 ... 22832.0 \n", + "8 Belem Proximidade 228147.319239 ... 22634.0 \n", + "9 London Farma 211649.871196 ... 11614.0 \n", + "10 Hong Kong Farma 163477.299348 ... 9468.0 \n", + "11 Rio de Janeiro Farma 295348.721413 ... 15480.0 \n", + "12 Madri Farma 262276.077174 ... 13977.0 \n", + "13 Dubai Atacado 230577.049674 ... 22832.0 \n", + "14 Bahia Atacado 230577.049674 ... 22832.0 \n", + "15 Buenos Aires Atacado 230577.049674 ... 22832.0 \n", + "16 Salvador Atacado 230577.049674 ... 22832.0 \n", + "17 Sidney Posto 91046.423913 ... 10597.0 \n", + "18 Bangkok Posto 91046.423913 ... 10597.0 \n", + "19 Miami Posto 91046.423913 ... 10597.0 \n", + "20 Vancouver Posto 91046.423913 ... 10597.0 \n", + "\n", + " BUSINESS_CODE \\\n", + " std count mean min \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 3657.795031 92.0 1.0 1.0 \n", + "2 Chicago Varejo 3756.440144 92.0 1.0 1.0 \n", + "3 Roma Varejo 3657.795031 92.0 1.0 1.0 \n", + "4 Tokio Varejo 3657.795031 92.0 1.0 1.0 \n", + "5 Paris Proximidade 3657.795031 92.0 2.0 2.0 \n", + "6 Berlin Proximidade 3657.795031 92.0 2.0 2.0 \n", + "7 New York Proximidade 3657.795031 92.0 2.0 2.0 \n", + "8 Belem Proximidade 3625.287389 92.0 2.0 2.0 \n", + "9 London Farma 1816.176627 92.0 4.0 4.0 \n", + "10 Hong Kong Farma 1512.578031 92.0 4.0 4.0 \n", + "11 Rio de Janeiro Farma 2447.908794 92.0 4.0 4.0 \n", + "12 Madri Farma 2214.479312 92.0 4.0 4.0 \n", + "13 Dubai Atacado 3657.795031 92.0 5.0 5.0 \n", + "14 Bahia Atacado 3657.795031 92.0 5.0 5.0 \n", + "15 Buenos Aires Atacado 3657.795031 92.0 5.0 5.0 \n", + "16 Salvador Atacado 3657.795031 92.0 5.0 5.0 \n", + "17 Sidney Posto 1656.537410 92.0 3.0 3.0 \n", + "18 Bangkok Posto 1656.537410 92.0 3.0 3.0 \n", + "19 Miami Posto 1656.537410 92.0 3.0 3.0 \n", + "20 Vancouver Posto 1656.537410 92.0 3.0 3.0 \n", + "\n", + " \n", + " 25% 50% 75% max std \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "2 Chicago Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "3 Roma Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "4 Tokio Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "5 Paris Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "6 Berlin Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "7 New York Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "8 Belem Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "9 London Farma 4.0 4.0 4.0 4.0 0.0 \n", + "10 Hong Kong Farma 4.0 4.0 4.0 4.0 0.0 \n", + "11 Rio de Janeiro Farma 4.0 4.0 4.0 4.0 0.0 \n", + "12 Madri Farma 4.0 4.0 4.0 4.0 0.0 \n", + "13 Dubai Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "14 Bahia Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "15 Buenos Aires Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "16 Salvador Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "17 Sidney Posto 3.0 3.0 3.0 3.0 0.0 \n", + "18 Bangkok Posto 3.0 3.0 3.0 3.0 0.0 \n", + "19 Miami Posto 3.0 3.0 3.0 3.0 0.0 \n", + "20 Vancouver Posto 3.0 3.0 3.0 3.0 0.0 \n", + "\n", + "[20 rows x 32 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base ticket\n", + "\n", + "base_tk = (\n", + " base_merge\n", + " .groupby(['STORE_CODE', 'STORE_NAME','BUSINESS_NAME'])\n", + ")\n", + "\n", + "base_tk.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5bd6f58f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODESTORE_NAMEBUSINESS_NAMESALES_VALUESALES_QTY
01Sao PauloVarejo21213088.571378476
12ChicagoVarejo21928421.281412372
23RomaVarejo21213088.571378476
34TokioVarejo21213088.571378476
45ParisProximidade21213088.571378476
56BerlinProximidade21213088.571378476
67New YorkProximidade21213088.571378476
78BelemProximidade20989553.371365988
89LondonFarma19471788.15671638
910Hong KongFarma15039911.54570745
1011Rio de JaneiroFarma27172082.37918336
1112MadriFarma24129399.10831168
1213DubaiAtacado21213088.571378476
1314BahiaAtacado21213088.571378476
1415Buenos AiresAtacado21213088.571378476
1516SalvadorAtacado21213088.571378476
1617SidneyPosto8376271.00612968
1718BangkokPosto8376271.00612968
1819MiamiPosto8376271.00612968
1920VancouverPosto8376271.00612968
\n", + "
" + ], + "text/plain": [ + " STORE_CODE STORE_NAME BUSINESS_NAME SALES_VALUE SALES_QTY\n", + "0 1 Sao Paulo Varejo 21213088.57 1378476\n", + "1 2 Chicago Varejo 21928421.28 1412372\n", + "2 3 Roma Varejo 21213088.57 1378476\n", + "3 4 Tokio Varejo 21213088.57 1378476\n", + "4 5 Paris Proximidade 21213088.57 1378476\n", + "5 6 Berlin Proximidade 21213088.57 1378476\n", + "6 7 New York Proximidade 21213088.57 1378476\n", + "7 8 Belem Proximidade 20989553.37 1365988\n", + "8 9 London Farma 19471788.15 671638\n", + "9 10 Hong Kong Farma 15039911.54 570745\n", + "10 11 Rio de Janeiro Farma 27172082.37 918336\n", + "11 12 Madri Farma 24129399.10 831168\n", + "12 13 Dubai Atacado 21213088.57 1378476\n", + "13 14 Bahia Atacado 21213088.57 1378476\n", + "14 15 Buenos Aires Atacado 21213088.57 1378476\n", + "15 16 Salvador Atacado 21213088.57 1378476\n", + "16 17 Sidney Posto 8376271.00 612968\n", + "17 18 Bangkok Posto 8376271.00 612968\n", + "18 19 Miami Posto 8376271.00 612968\n", + "19 20 Vancouver Posto 8376271.00 612968" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# requisitos para o ticket médio\n", + "\n", + "base_tk = (\n", + " base_tk\n", + " .agg(\n", + " SALES_VALUE=(\"SALES_VALUE\", \"sum\"),\n", + " SALES_QTY=(\"SALES_QTY\", \"sum\")\n", + " )\n", + " .reset_index()\n", + " )\n", + "base_tk" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a32de4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODESTORE_NAMEBUSINESS_NAMESALES_VALUESALES_QTYTM
01Sao PauloVarejo21213088.57137847615.39
12ChicagoVarejo21928421.28141237215.53
23RomaVarejo21213088.57137847615.39
34TokioVarejo21213088.57137847615.39
45ParisProximidade21213088.57137847615.39
56BerlinProximidade21213088.57137847615.39
67New YorkProximidade21213088.57137847615.39
78BelemProximidade20989553.37136598815.37
89LondonFarma19471788.1567163828.99
910Hong KongFarma15039911.5457074526.35
1011Rio de JaneiroFarma27172082.3791833629.59
1112MadriFarma24129399.1083116829.03
1213DubaiAtacado21213088.57137847615.39
1314BahiaAtacado21213088.57137847615.39
1415Buenos AiresAtacado21213088.57137847615.39
1516SalvadorAtacado21213088.57137847615.39
1617SidneyPosto8376271.0061296813.67
1718BangkokPosto8376271.0061296813.67
1819MiamiPosto8376271.0061296813.67
1920VancouverPosto8376271.0061296813.67
\n", + "
" + ], + "text/plain": [ + " STORE_CODE STORE_NAME BUSINESS_NAME SALES_VALUE SALES_QTY TM\n", + "0 1 Sao Paulo Varejo 21213088.57 1378476 15.39\n", + "1 2 Chicago Varejo 21928421.28 1412372 15.53\n", + "2 3 Roma Varejo 21213088.57 1378476 15.39\n", + "3 4 Tokio Varejo 21213088.57 1378476 15.39\n", + "4 5 Paris Proximidade 21213088.57 1378476 15.39\n", + "5 6 Berlin Proximidade 21213088.57 1378476 15.39\n", + "6 7 New York Proximidade 21213088.57 1378476 15.39\n", + "7 8 Belem Proximidade 20989553.37 1365988 15.37\n", + "8 9 London Farma 19471788.15 671638 28.99\n", + "9 10 Hong Kong Farma 15039911.54 570745 26.35\n", + "10 11 Rio de Janeiro Farma 27172082.37 918336 29.59\n", + "11 12 Madri Farma 24129399.10 831168 29.03\n", + "12 13 Dubai Atacado 21213088.57 1378476 15.39\n", + "13 14 Bahia Atacado 21213088.57 1378476 15.39\n", + "14 15 Buenos Aires Atacado 21213088.57 1378476 15.39\n", + "15 16 Salvador Atacado 21213088.57 1378476 15.39\n", + "16 17 Sidney Posto 8376271.00 612968 13.67\n", + "17 18 Bangkok Posto 8376271.00 612968 13.67\n", + "18 19 Miami Posto 8376271.00 612968 13.67\n", + "19 20 Vancouver Posto 8376271.00 612968 13.67" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculo do ticket medio\n", + "\n", + "base_tk[\"TM\"] = (base_tk[\"SALES_VALUE\"] / base_tk[\"SALES_QTY\"]).round(2)\n", + "\n", + "base_tk" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a0d5d58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_NAMEBUSINESS_NAMETM
13BahiaAtacado15.39
17BangkokPosto13.67
7BelemProximidade15.37
5BerlinProximidade15.39
14Buenos AiresAtacado15.39
1ChicagoVarejo15.53
12DubaiAtacado15.39
9Hong KongFarma26.35
8LondonFarma28.99
11MadriFarma29.03
18MiamiPosto13.67
6New YorkProximidade15.39
4ParisProximidade15.39
10Rio de JaneiroFarma29.59
2RomaVarejo15.39
15SalvadorAtacado15.39
0Sao PauloVarejo15.39
16SidneyPosto13.67
3TokioVarejo15.39
19VancouverPosto13.67
\n", + "
" + ], + "text/plain": [ + " STORE_NAME BUSINESS_NAME TM\n", + "13 Bahia Atacado 15.39\n", + "17 Bangkok Posto 13.67\n", + "7 Belem Proximidade 15.37\n", + "5 Berlin Proximidade 15.39\n", + "14 Buenos Aires Atacado 15.39\n", + "1 Chicago Varejo 15.53\n", + "12 Dubai Atacado 15.39\n", + "9 Hong Kong Farma 26.35\n", + "8 London Farma 28.99\n", + "11 Madri Farma 29.03\n", + "18 Miami Posto 13.67\n", + "6 New York Proximidade 15.39\n", + "4 Paris Proximidade 15.39\n", + "10 Rio de Janeiro Farma 29.59\n", + "2 Roma Varejo 15.39\n", + "15 Salvador Atacado 15.39\n", + "0 Sao Paulo Varejo 15.39\n", + "16 Sidney Posto 13.67\n", + "3 Tokio Varejo 15.39\n", + "19 Vancouver Posto 13.67" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# BASE DE RESPOSTA TM \n", + "\n", + "base = base_tk[[\"STORE_NAME\", \"BUSINESS_NAME\", \"TM\"]].sort_values(\"STORE_NAME\")\n", + "\n", + "base" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9e44954b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RESPOSTA FINAL \n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, len(base) * 0.4 + 1))\n", + "ax.axis(\"off\")\n", + "\n", + "table = ax.table(\n", + " cellText=base[[\"STORE_NAME\", \"BUSINESS_NAME\", \"TM\"]].values,\n", + " colLabels=[\"Loja\", \"Categoria\", \"TM\"],\n", + " cellLoc=\"left\",\n", + " loc=\"center\"\n", + ")\n", + "\n", + "table.auto_set_font_size(False)\n", + "table.set_fontsize(10)\n", + "table.auto_set_column_width([0, 1, 2])\n", + "\n", + "for j in range(3):\n", + " table[0, j].set_facecolor(\"#264653\")\n", + " table[0, j].set_text_props(color=\"white\", fontweight=\"bold\")\n", + "\n", + "for i in range(1, len(base) + 1):\n", + " cor = \"#f0f4f8\" if i % 2 == 0 else \"white\"\n", + " for j in range(3):\n", + " table[i, j].set_facecolor(cor)\n", + "\n", + "plt.title(\"Ticket Médio por Loja — Q4 2019\", fontsize=13, pad=20)\n", + "plt.tight_layout()\n", + "plt.savefig(\"tabela_tm.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} + +``` + +## ./src/notebooks/nt_case3.ipynb + +```ipynb +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9308855", + "metadata": {}, + "source": [ + "## Caso 3 - Visalização Livre\n", + "\n", + "> Eu sempre fui um amante da sétima arte, e sempre na conversar com os amigos, a gente nota, a volta de uma onda já vivida, como a volta do \"TODO MUNDO EM PÂNICO\", o que deve acarretar em outros lançamntos do tema comedia pastelão.\n", + "\n", + "> Como temos um histórico de filmes, vou tentar encontrar alguma relação.\n", + "> Acredito que dados com contextos, contam historias. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "790149de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "\n", + "root = \"/home/santanna/develope/projetos/data-challenge_luiz_bernardo\"\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c8e7249b", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "from src.models.repositories.imdb_repository import ImdbRepository\n", + "import matplotlib.pyplot as plt\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = ImdbRepository()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4ddf36d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 1000 non-null int64 \n", + " 1 Title 1000 non-null str \n", + " 2 Genre 1000 non-null str \n", + " 3 Director 1000 non-null str \n", + " 4 Actors 1000 non-null str \n", + " 5 Year 1000 non-null int64 \n", + " 6 Runtime 1000 non-null int64 \n", + " 7 Rating 1000 non-null float64\n", + " 8 Votes 1000 non-null int64 \n", + " 9 RevenueMillions 872 non-null float64\n", + " 10 Metascore 936 non-null float64\n", + "dtypes: float64(3), int64(4), str(4)\n", + "memory usage: 86.1 KB\n" + ] + }, + { + "data": { + "text/plain": [ + "(,\n", + " None)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base de dados \n", + "base = repo.read_db()\n", + "base.describe, base.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da2985d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdTitleGenreDirectorActorsYearRuntimeRatingVotesRevenueMillionsMetascore
01Guardians of the GalaxyAction,Adventure,Sci-FiJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0
12PrometheusAdventure,Mystery,Sci-FiRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0
23SplitHorror,ThrillerM. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar...20161177.0157606138.062.0
34SingAnimation,Comedy,FamilyChristophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma...20161087.060545270.059.0
45Suicide SquadAction,Adventure,FantasyDavid AyerWill Smith, Jared Leto, Margot Robbie, Viola D...20161236.0393727325.040.0
....................................
991992Taare Zameen ParDrama,Family,MusicAamir KhanDarsheel Safary, Aamir Khan, Tanay Chheda, Sac...20071659.01026971.042.0
993994Resident Evil: AfterlifeAction,Adventure,HorrorPaul W.S. AndersonMilla Jovovich, Ali Larter, Wentworth Miller,K...2010976.014090060.037.0
997998Step Up 2: The StreetsDrama,Music,RomanceJon M. ChuRobert Hoffman, Briana Evigan, Cassie Ventura,...2008986.07069958.050.0
998999Search PartyAdventure,ComedyScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0
9991000Nine LivesComedy,Family,FantasyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.0
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541 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Id Title ... RevenueMillions Metascore\n", + "0 1 Guardians of the Galaxy ... 333.0 76.0\n", + "1 2 Prometheus ... 126.0 65.0\n", + "2 3 Split ... 138.0 62.0\n", + "3 4 Sing ... 270.0 59.0\n", + "4 5 Suicide Squad ... 325.0 40.0\n", + ".. ... ... ... ... ...\n", + "991 992 Taare Zameen Par ... 1.0 42.0\n", + "993 994 Resident Evil: Afterlife ... 60.0 37.0\n", + "997 998 Step Up 2: The Streets ... 58.0 50.0\n", + "998 999 Search Party ... NaN 22.0\n", + "999 1000 Nine Lives ... 20.0 11.0\n", + "\n", + "[541 rows x 11 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base explorando\n", + "\"\"\"\n", + "generos são listas de sub temas, podemos usar isso.\n", + "\"\"\"\n", + "\n", + "base_genre = (\n", + " base\n", + " .groupby(['Genre'])\n", + ")\n", + "\n", + "base_genre" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "03cdf12a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Id Title Genre ... Votes RevenueMillions Metascore\n", + "0 1 Guardians of the Galaxy Action ... 757074 333.0 76.0\n", + "0 1 Guardians of the Galaxy Adventure ... 757074 333.0 76.0\n", + "0 1 Guardians of the Galaxy Sci-Fi ... 757074 333.0 76.0\n", + "1 2 Prometheus Adventure ... 485820 126.0 65.0\n", + "1 2 Prometheus Mystery ... 485820 126.0 65.0\n", + "\n", + "[5 rows x 11 columns]\n", + "Genre\n", + "Drama 513\n", + "Action 303\n", + "Comedy 279\n", + "Adventure 259\n", + "Thriller 195\n", + "Crime 150\n", + "Romance 141\n", + "Sci-Fi 120\n", + "Horror 119\n", + "Mystery 106\n", + "Fantasy 101\n", + "Biography 81\n", + "Family 51\n", + "Animation 49\n", + "History 29\n", + "Name: count, dtype: int64\n", + "\n", + "Total de linhas após explode: 2555\n" + ] + } + ], + "source": [ + "# separa os gêneros e explode — uma linha por gênero\n", + "base_imdb = (\n", + " base\n", + " .assign(Genre=base[\"Genre\"].str.split(\",\"))\n", + " .explode(\"Genre\")\n", + ")\n", + "\n", + "base_imdb[\"Genre\"] = base_imdb[\"Genre\"].str.strip()\n", + "\n", + "print(base_imdb.head())\n", + "\n", + "print(base_imdb[\"Genre\"].value_counts().head(15))\n", + "print(f\"\\nTotal de linhas após explode: {len(base_imdb)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "df09b163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearGenrecountavg_ratingavg_revenue
02006Action117.000000166.300000
12006Adventure137.153846122.846154
22006Animation27.000000221.000000
32006Biography37.00000061.333333
42006Comedy127.00000076.250000
..................
1902016Sci-Fi206.250000130.800000
1912016Sport56.8000004.666667
1922016Thriller606.18333330.387097
1932016War36.6666671.500000
1942016Western27.00000093.000000
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" + ], + "text/plain": [ + " Year Genre count avg_rating avg_revenue\n", + "0 2006 Action 11 7.000000 166.300000\n", + "1 2006 Adventure 13 7.153846 122.846154\n", + "2 2006 Animation 2 7.000000 221.000000\n", + "3 2006 Biography 3 7.000000 61.333333\n", + "4 2006 Comedy 12 7.000000 76.250000\n", + ".. ... ... ... ... ...\n", + "190 2016 Sci-Fi 20 6.250000 130.800000\n", + "191 2016 Sport 5 6.800000 4.666667\n", + "192 2016 Thriller 60 6.183333 30.387097\n", + "193 2016 War 3 6.666667 1.500000\n", + "194 2016 Western 2 7.000000 93.000000\n", + "\n", + "[195 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_agr = (\n", + " base_imdb\n", + " .groupby([\"Year\", \"Genre\"])\n", + " .agg(\n", + " count = (\"Id\", \"count\"),\n", + " avg_rating = (\"Rating\", \"mean\"),\n", + " avg_revenue = (\"RevenueMillions\", \"mean\")\n", + " )\n", + " .reset_index()\n", + ")\n", + "base_agr" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7650ff46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearGenrecountavg_ratingavg_revenue
102006Horror46.75000024.750000
282007Horror116.81818258.000000
452008Horror46.50000044.333333
632009Horror76.28571429.857143
792010Horror86.12500027.285714
952011Horror35.66666740.500000
1122012Horror56.20000054.800000
1302013Horror136.38461556.384615
1482014Horror86.50000019.571429
1672015Horror116.09090920.000000
1862016Horror455.80000032.038462
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" + ], + "text/plain": [ + " Year Genre count avg_rating avg_revenue\n", + "10 2006 Horror 4 6.750000 24.750000\n", + "28 2007 Horror 11 6.818182 58.000000\n", + "45 2008 Horror 4 6.500000 44.333333\n", + "63 2009 Horror 7 6.285714 29.857143\n", + "79 2010 Horror 8 6.125000 27.285714\n", + "95 2011 Horror 3 5.666667 40.500000\n", + "112 2012 Horror 5 6.200000 54.800000\n", + "130 2013 Horror 13 6.384615 56.384615\n", + "148 2014 Horror 8 6.500000 19.571429\n", + "167 2015 Horror 11 6.090909 20.000000\n", + "186 2016 Horror 45 5.800000 32.038462" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_terror = base_agr.loc[base_agr[\"Genre\"] == \"Horror\"]\n", + "\n", + "base_terror" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4b909b18", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Ano=%{x}
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+ } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 16 + }, + "text": "Volume de Filmes de Comédia por Ano" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Ano" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Quantidade de Filmes" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "# linha de comédia por ano\n", + "fig = px.line(\n", + " base_terror,\n", + " x=\"Year\",\n", + " y=\"count\",\n", + " markers=True,\n", + " title=\"Volume de Filmes de Comédia por Ano\",\n", + " labels={\"count\": \"Quantidade de Filmes\", \"Year\": \"Ano\"}\n", + ")\n", + "\n", + "fig.update_traces(line_color=\"#e76f51\", line_width=3, marker_size=8)\n", + "fig.update_layout(\n", + " template=\"plotly_white\",\n", + " title_font_size=16\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f0e15d81", + "metadata": {}, + "source": [ + "## Análise Parcial \n", + "\n", + "> Não podemos prosseguir, pois temos um espaço temporal muito curto para fazer uma análise periodica de interesse sobre os temas dos filmes.\n", + "\n", + "> Mas análisando os dados podemos fazer uma analise interessante.\n", + "> podemos fazer uma analise a partir do faturamento junto com as quantidades de votos e o valor final dos votos, para buscar filmes que fizeram sucesso, vendo sucesso como filmes que tiveram faturamento, total de votos e nota acima da média \n", + "\n", + "* Já podemos ver nos anos finais um pico, reflexo dos Streammes?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "eaea8859", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yearcountavg_ratingavg_revenueavg_votes
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12007537.287.9244331.0
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62012646.9108.0285226.1
72013916.987.1219049.6
82014986.985.1203930.2
920151276.678.3115726.2
1020162976.554.748591.8
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" + ], + "text/plain": [ + " Year count avg_rating avg_revenue avg_votes\n", + "0 2006 44 7.3 86.2 269290.0\n", + "1 2007 53 7.2 87.9 244331.0\n", + "2 2008 52 6.8 99.1 275505.4\n", + "3 2009 51 7.1 112.6 255780.6\n", + "4 2010 60 6.9 105.1 252782.3\n", + "5 2011 63 6.9 87.6 240790.3\n", + "6 2012 64 6.9 108.0 285226.1\n", + "7 2013 91 6.9 87.1 219049.6\n", + "8 2014 98 6.9 85.1 203930.2\n", + "9 2015 127 6.6 78.3 115726.2\n", + "10 2016 297 6.5 54.7 48591.8" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Buscando as medias base\n", + "\n", + "base_means = (\n", + " base\n", + " .groupby([\"Year\"])\n", + " .agg(\n", + " count = (\"Id\", \"count\"),\n", + " avg_rating = (\"Rating\", \"mean\"),\n", + " avg_revenue = (\"RevenueMillions\", \"mean\"),\n", + " avg_votes = (\"Votes\", \"mean\")\n", + " )\n", + " .reset_index()\n", + " .round(1)\n", + ")\n", + "base_means" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36a22445", + "metadata": {}, + "outputs": [ + { + "ename": "_IncompleteInputError", + "evalue": "incomplete input (2738720405.py, line 2)", + "output_type": "error", + "traceback": [ + " \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[63]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m \u001b[39m\u001b[31m\"\"\"\u001b[39m\n ^\n\u001b[31m_IncompleteInputError\u001b[39m\u001b[31m:\u001b[39m incomplete input\n" + ] + } + ], + "source": [ + "# buscando os filmes de sucesso \n", + "\"\"\"\n", + "deixei pois vocês queriam ver a linha de raciocinio \n", + "\n", + "for x in base_means:\n", + " print(\"isso é x\",x)\n", + " for y in base_means:\n", + " print(\"isso é y\",base_means[y])\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d39cd7bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdTitleGenreDirectorActorsYearRuntimeRatingVotesRevenueMillionsMetascorecountavg_ratingavg_revenueavg_votes
01Guardians of the GalaxyActionJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
11Guardians of the GalaxyAdventureJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
21Guardians of the GalaxySci-FiJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
32PrometheusAdventureRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0646.9108.0285226.1
42PrometheusMysteryRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0646.9108.0285226.1
................................................
2550999Search PartyAdventureScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0986.985.1203930.2
2551999Search PartyComedyScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0986.985.1203930.2
25521000Nine LivesComedyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
25531000Nine LivesFamilyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
25541000Nine LivesFantasyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
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2555 rows × 15 columns

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" + ], + "text/plain": [ + " Id Title ... avg_revenue avg_votes\n", + "0 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "1 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "2 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "3 2 Prometheus ... 108.0 285226.1\n", + "4 2 Prometheus ... 108.0 285226.1\n", + "... ... ... ... ... ...\n", + "2550 999 Search Party ... 85.1 203930.2\n", + "2551 999 Search Party ... 85.1 203930.2\n", + "2552 1000 Nine Lives ... 54.7 48591.8\n", + "2553 1000 Nine Lives ... 54.7 48591.8\n", + "2554 1000 Nine Lives ... 54.7 48591.8\n", + "\n", + "[2555 rows x 15 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# montando tabela de sucessos\n", + "\n", + "base_greate = pd.merge(base_imdb, base_means, on= \"Year\", how= \"left\")\n", + "\n", + "base_greate" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "8410ad6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleGenreDirectorYearRatingavg_ratingVotesavg_votesRevenueMillionsavg_revenue
0300ActionZack Snyder20068.07.3637104269290.0211.086.2
1Casino RoyaleActionMartin Campbell20068.07.3495106269290.0167.086.2
2The Pursuit of HappynessBiographyGabriele Muccino20068.07.3361105269290.0163.086.2
3The DepartedCrimeMartin Scorsese20069.07.3937414269290.0132.086.2
4Inside ManCrimeSpike Lee20068.07.3285441269290.089.086.2
.................................
165Hacksaw RidgeBiographyMel Gibson20168.06.521176048591.867.054.7
166Now You See Me 2ActionJon M. Chu20167.06.515656748591.865.054.7
167Deepwater HorizonActionPeter Berg20167.06.58984948591.861.054.7
168FencesDramaDenzel Washington20167.06.55095348591.858.054.7
169Me Before YouDramaThea Sharrock20167.06.511332248591.856.054.7
\n", + "

170 rows × 10 columns

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RevenueMillions avg_revenue\n", + "0 300 Action ... 211.0 86.2\n", + "1 Casino Royale Action ... 167.0 86.2\n", + "2 The Pursuit of Happyness Biography ... 163.0 86.2\n", + "3 The Departed Crime ... 132.0 86.2\n", + "4 Inside Man Crime ... 89.0 86.2\n", + ".. ... ... ... ... ...\n", + "165 Hacksaw Ridge Biography ... 67.0 54.7\n", + "166 Now You See Me 2 Action ... 65.0 54.7\n", + "167 Deepwater Horizon Action ... 61.0 54.7\n", + "168 Fences Drama ... 58.0 54.7\n", + "169 Me Before You Drama ... 56.0 54.7\n", + "\n", + "[170 rows x 10 columns]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filtro de selecionados\n", + "\n", + "movies_top = (\n", + " (base_greate[\"Rating\"] > base_greate[\"avg_rating\"]) &\n", + " (base_greate[\"RevenueMillions\"] > base_greate[\"avg_revenue\"]) &\n", + " (base_greate[\"Votes\"] > base_greate[\"avg_votes\"])\n", + ")\n", + "\n", + "base_movie = base_greate.loc[\n", + " movies_top, \n", + " [\"Title\", \"Genre\", \"Director\", \"Year\", \"Rating\",\"avg_rating\", \"Votes\",\"avg_votes\", \"RevenueMillions\",\"avg_revenue\"]\n", + "].sort_values(by=[\"Year\", \"RevenueMillions\"], ascending=[True, False]).reset_index(drop=True)\n", + "\n", + "base_movie = base_movie.drop_duplicates(subset=[\"Title\"]).reset_index(drop=True)\n", + "\n", + "# 4. 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+ }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Filmes acima da média — Votos por Ano com Nota Média" + }, + "xaxis": { + "dtick": 1, + "tickmode": "linear", + "title": { + "text": "Ano" + } + }, + "yaxis": { + "title": { + "text": "Média de Votos" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "# média de votos e rating por ano\n", + "df_plot = (\n", + " base_movie\n", + " .groupby(\"Year\")\n", + " .agg(\n", + " avg_votes=(\"Votes\", \"mean\"),\n", + " avg_rating=(\"Rating\", \"mean\")\n", + " )\n", + " .reset_index()\n", + " .round(1)\n", + ")\n", + "\n", + "fig = go.Figure()\n", + "\n", + "# barras — votos por ano\n", + "fig.add_trace(go.Bar(\n", + " x=df_plot[\"Year\"],\n", + " y=df_plot[\"avg_votes\"],\n", + " name=\"Média de Votos\",\n", + " marker_color=\"steelblue\",\n", + " text=df_plot[\"avg_rating\"],\n", + " textposition=\"inside\",\n", + " texttemplate=\"⭐ %{text}\",\n", + "))\n", + "\n", + "fig.update_layout(\n", + " title=\"Filmes acima da média — Votos por Ano com Nota Média\",\n", + " xaxis_title=\"Ano\",\n", + " yaxis_title=\"Média de Votos\",\n", + " xaxis=dict(tickmode=\"linear\", dtick=1),\n", + " plot_bgcolor=\"white\",\n", + " height=500,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "748bf1a2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "steelblue" + }, + "name": "Média de Votos", + "opacity": 0.7, + "type": "bar", + "x": { + "bdata": "1gfXB9gH2QfaB9sH3AfdB94H3wfgBw==", + "dtype": "i2" + }, + "y": { + "bdata": "AAAAAASUIEEAAAAATLoaQQAAAABYXSJBAAAAACazI0EAAAAAIM8gQQAAAADUtBlBAAAAAC7QIkEAAAAAyOgaQQAAAABslxlBAAAAAKzXFEEAAAAAqC0IQQ==", + 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⭐ 9.0", + "The Bourne Ultimatum
⭐ 8.0", + "The Dark Knight
⭐ 9.0", + "Inglourious Basterds
⭐ 8.0", + "Inception
⭐ 9.0", + "Harry Potter and the Deathly Hallows: Part 2
⭐ 8.0", + "The Dark Knight Rises
⭐ 9.0", + "The Wolf of Wall Street
⭐ 8.0", + "Interstellar
⭐ 9.0", + "Star Wars: Episode VII - The Force Awakens
⭐ 8.0", + "Deadpool
⭐ 8.0" + ], + "textfont": { + "size": 9 + }, + "textposition": "top center", + "type": "scatter", + "x": { + "bdata": "1gfXB9gH2QfaB9sH3AfdB94H3wfgBw==", + "dtype": "i2" + }, + "y": { + "bdata": "xk0OAIQFCACsVxsAWaIOAAkqGAADAwkA9acSAG4zDQDD/A8AaBgKAFWUCQA=", + "dtype": "i4" + } + }, + { + "marker": { + "color": "tomato", + "line": { + "color": "black", + "width": 1 + }, + "size": 18, + "symbol": "star" + }, + "mode": "markers+text", + "name": "Pior", + "text": [ + "Inside Man
⭐ 8.0", + "American Gangster
⭐ 8.0", + "Wanted
⭐ 7.0", + "Harry Potter and the Half-Blood Prince
⭐ 8.0", + "True Grit
⭐ 8.0", + "Super 8
⭐ 7.0", + "Wreck-It Ralph
⭐ 8.0", + "Monsters University
⭐ 7.0", + "How to Train Your Dragon 2
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"\n", + "# média de votos por ano (barras)\n", + "df_bar = (\n", + " base_movie\n", + " .groupby(\"Year\")\n", + " .agg(avg_votes=(\"Votes\", \"mean\"))\n", + " .reset_index()\n", + " .round(0)\n", + ")\n", + "\n", + "# melhor e pior por ano (pontos)\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Melhor\")\n", + ")\n", + "\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Pior\")\n", + ")\n", + "\n", + "df_points = pd.concat([df_max, df_min]).sort_values(\"Year\")\n", + "\n", + "fig = go.Figure()\n", + "\n", + "# barras\n", + "fig.add_trace(go.Bar(\n", + " x=df_bar[\"Year\"],\n", + " y=df_bar[\"avg_votes\"],\n", + " name=\"Média de Votos\",\n", + " marker_color=\"steelblue\",\n", + " opacity=0.7,\n", + "))\n", + "\n", + "# pontos — melhor e pior\n", + "cores = {\"Melhor\": \"gold\", \"Pior\": \"tomato\"}\n", + "\n", + "for tipo, grupo in df_points.groupby(\"tipo\"):\n", + " fig.add_trace(go.Scatter(\n", + " x=grupo[\"Year\"],\n", + " y=grupo[\"Votes\"],\n", + " mode=\"markers+text\",\n", + " name=tipo,\n", + " marker=dict(\n", + " symbol=\"star\",\n", + " size=18,\n", + " color=cores[tipo],\n", + " line=dict(color=\"black\", width=1)\n", + " ),\n", + " text=grupo[\"Title\"] + \"
⭐ \" + grupo[\"Rating\"].astype(str),\n", + " textposition=\"top center\",\n", + " textfont=dict(size=9),\n", + " ))\n", + "\n", + "fig.update_layout(\n", + " title=\"Filmes acima da média — Votos por Ano com Melhor e Pior\",\n", + " xaxis_title=\"Ano\",\n", + " yaxis_title=\"Votos\",\n", + " xaxis=dict(tickmode=\"linear\", dtick=1),\n", + " plot_bgcolor=\"white\",\n", + " height=600,\n", + " legend=dict(orientation=\"h\", y=-0.2),\n", + ")\n", + "\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "c84ec2fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import pandas as pd\n", + "\n", + "# ============================================================\n", + "# USE SUA BASE REAL AQUI (base_movie já filtrada)\n", + "# ============================================================\n", + "# base_movie = sua DataFrame já filtrada com os filmes acima da média\n", + "\n", + "# 1. Média de votos por ano (barras)\n", + "df_bar = (\n", + " base_movie\n", + " .groupby(\"Year\")\n", + " .agg(avg_votes=(\"Votes\", \"mean\"))\n", + " .reset_index()\n", + ")\n", + "\n", + "# 2. Melhor filme por ano (mais votos)\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Melhor\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# 3. Pior filme por ano (menos votos)\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Pior\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# ============================================================\n", + "# CONFIGURAÇÕES VISUAIS\n", + "# ============================================================\n", + "COR_BARRA = \"#2E86AB\" # Azul petróleo\n", + "COR_MELHOR = \"#F6AE2D\" # Dourado\n", + "COR_PIOR = \"#E94F37\" # Vermelho coral\n", + "COR_FUNDO = \"#F8F9FA\" # Cinza claro\n", + "\n", + "TAMANHO_CIRCULO = 350 # Tamanho dos círculos (em pontos)\n", + "OFFSET_NOME = 120000 # Distância do nome acima do círculo\n", + "\n", + "# ============================================================\n", + "# CRIAR FIGURA\n", + "# ============================================================\n", + "fig, ax = plt.subplots(figsize=(18, 11))\n", + "\n", + "fig.patch.set_facecolor(COR_FUNDO)\n", + "ax.set_facecolor(COR_FUNDO)\n", + "\n", + "# --- BARRAS: Média de votos por ano ---\n", + "bars = ax.bar(\n", + " df_bar[\"Year\"],\n", + " df_bar[\"avg_votes\"],\n", + " color=COR_BARRA,\n", + " width=0.55,\n", + " edgecolor=\"white\",\n", + " linewidth=2,\n", + " zorder=2,\n", + " alpha=0.88,\n", + " label=\"Média de Votos\"\n", + ")\n", + "\n", + "# Média de Rating por ano (para escrever dentro das barras)\n", + "avg_rating_por_ano = base_movie.groupby(\"Year\")[\"Rating\"].mean()\n", + "# depois você faz:\n", + "rating_medio = avg_rating_por_ano.get(ano, 0)\n", + "\n", + "for bar, ano in zip(bars, df_bar[\"Year\"]):\n", + " altura = bar.get_height()\n", + " \n", + " # Valor da média de votos no TOPO da barra\n", + " # ax.text(\n", + " # bar.get_x() + bar.get_width() / 2,\n", + " # altura + 15000,\n", + " # f\"{altura:,.0f}\",\n", + " # ha=\"center\",\n", + " # va=\"bottom\",\n", + " # fontsize=10,\n", + " # fontweight=\"bold\",\n", + " # color=COR_BARRA\n", + " # )\n", + " \n", + " # Nota média DENTRO da barra (embaixo)\n", + " rating_medio = avg_rating_por_ano.get(ano, 0)\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura * 0.12,\n", + " f\"{rating_medio:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=12,\n", + " fontweight=\"bold\",\n", + " color=\"white\",\n", + " zorder=5\n", + " )\n", + "\n", + "# --- CÍRCULOS: Melhor filme do ano ---\n", + "ax.scatter(\n", + " df_max[\"Year\"],\n", + " df_max[\"Votes\"],\n", + " s=TAMANHO_CIRCULO,\n", + " c=COR_MELHOR,\n", + " edgecolors=\"black\",\n", + " linewidths=2,\n", + " zorder=10,\n", + " alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_max.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " color=\"black\",\n", + " zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:20] + \"...\" if len(row[\"Title\"]) > 20 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"] + OFFSET_NOME,\n", + " nome,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=9,\n", + " fontweight=\"bold\",\n", + " color=\"#333333\",\n", + " zorder=11\n", + " )\n", + "\n", + "# --- CÍRCULOS: Pior filme do ano ---\n", + "ax.scatter(\n", + " df_min[\"Year\"],\n", + " df_min[\"Votes\"],\n", + " s=TAMANHO_CIRCULO,\n", + " c=COR_PIOR,\n", + " edgecolors=\"black\",\n", + " linewidths=2,\n", + " zorder=10,\n", + " alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_min.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " color=\"black\",\n", + " zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:20] + \"...\" if len(row[\"Title\"]) > 20 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"] + OFFSET_NOME,\n", + " nome,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=9,\n", + " fontweight=\"bold\",\n", + " color=\"#333333\",\n", + " zorder=5\n", + " )\n", + "\n", + "# ============================================================\n", + "# ESTILIZAÇÃO FINAL\n", + "# ============================================================\n", + "ax.set_title(\n", + " \"Filmes Acima da Média por Ano\\nMédia de Votos (barras) | Melhor e Pior Filme (círculos)\",\n", + " fontsize=20,\n", + " fontweight=\"bold\",\n", + " color=\"#1a1a1a\",\n", + " pad=25\n", + ")\n", + "\n", + "ax.set_xlabel(\"Ano\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_ylabel(\"Quantidade de Votos\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "\n", + "ax.set_xticks(range(2006, 2017))\n", + "ax.set_xticklabels([str(a) for a in range(2006, 2017)], fontsize=12, fontweight=\"bold\")\n", + "\n", + "ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\", color=\"gray\", zorder=0)\n", + "ax.set_axisbelow(True)\n", + "\n", + "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f\"{int(x):,}\"))\n", + "\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "ax.spines[\"left\"].set_color(\"#cccccc\")\n", + "ax.spines[\"bottom\"].set_color(\"#cccccc\")\n", + "\n", + "ymax = max(df_bar[\"avg_votes\"].max(), df_max[\"Votes\"].max()) * 1.22\n", + "ax.set_ylim(0, ymax)\n", + "\n", + "# Legenda\n", + "legend_elements = [\n", + " mpatches.Patch(facecolor=COR_BARRA, edgecolor=\"white\", label=\"Média de Votos do Ano\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_MELHOR,\n", + " markeredgecolor='black', markersize=15, label=\"Melhor Filme do Ano\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_PIOR,\n", + " markeredgecolor='black', markersize=15, label=\"Pior Filme do Ano\"),\n", + "]\n", + "ax.legend(\n", + " handles=legend_elements,\n", + " loc=\"upper right\",\n", + " fontsize=12,\n", + " frameon=True,\n", + " fancybox=True,\n", + " shadow=True,\n", + " facecolor=\"white\",\n", + " edgecolor=\"#cccccc\"\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "a071791b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+98mmzH0mPvdsb+3bt8+q3MnJSX+tW68KFSqkum9cXJxaPfywbt0Ks9rm5eWl/QcCMxXbvdj7HuC9cn+ldk3dYTAY5ObmpoIFC6pipUpq9FAjde3WTRUrVkx1n7x4Du91nFLzzjvvatDgwRZlvBcyz97vXkfasGGDJk+aJEkqW7asFiz4NU8m/aTcee3dj+9zAMCDhak+AQB4gJn+P1Fk62fNmj909erVVPc1Go2aPWtmqvunJ2lmMpn06thXdfHiRTk7O+ubb79zUNIP2a1bt+5KTk62uDb27t2jf/755577Xrp0Ubt377a6trp1c/y1kWxMztQ1nBvt2bNHmzdtMr8uUKCABg4alH0BIUO6deumkiVLml8fPXpUq1evysaIUpeUyvdFYmKiZs+alea+S5cuUUjITZv754abusgaqV1Td18b0dHRunr1qnbu2KGffvpRbZ9oo/EffKCEhITsDv++uddxSu0ns8l+WMuO796LFy/qf6+9KpPJpHLlymnBrwvzbNIvN8tN3+cAgJyPxB8AALApMTFRc+bMTnX7X3/+qcuXLzukr4kTJ2rjxg3/n/TL+09ZPUjKli2rJk2aWJX//vvSe+677PdlMplMFmWNGjVO86kg2O+HH763eN21Wzf5+PhkTzDIMFdXV/Xt18+i7Kcff7R67+R0S5YsVkREhM1tJpNJs2bOvM8RIa9KTk7WnDmzNXrUS9kdCh5A9/u7Nz4+Ti+NHKHw8HCVL1/+dtKvdOks6w8Zl1e+zwEAOQOJPwAAkKqFv/6q2NhYm9tmzpzhsH6GDx+uk6dOK/jEST39dPYk/YYPH6GTp05b/PQfMCBbYslrevbqZVW2fJl1Ui+lZcuW2dUW0u/4sWPauWOHRVmXbHrvIfOefvppi9enT5/Wli2bsyeYDIqOjtai336zuW3Lls06c+bMfY4Ied369eu1cuUKq/LgEyctfhfYuWt3NkSXc3F8Mi47vnvd3PLr92XLdfLUaW3ctFmlSpXK0v6QOXnh+xwAkDOQ+AMAAKm6deuWzSezDh08qP379zusH2dnZ7m4uMjZ2dlhbaaXwWCQi4uLxQ9rajhGhw4drdauuXjxotXafXc7cGC/zp8/Z1Hm7u6ujh07ZkmMD5oFvy6weF2yZEk1tvFkJnKHMmXKqmHDhhZlCxcuzKZoMm727Fk21yWdMd1xf2iCvO+VsWPNSaljx4O1PiDAap26OxYvWmxVdud3kjs/2fm7SVa6+zil9jN48BCr/R6U45MVsuu7l99rc4+88n0OAMh+LtkdAAAAyFmKFSum69evm1/PmjlTffr0tbhZMH3G9DT3sceFCxe0dMkS7d27R+fPn1d4eLjy5cunokWLqkHDhurcqbMead36nu1ERkZqxozpWvfXX7p46ZLyu7mpevUa6te/n558sr3d8UyZPFlff/2VRdn7H4xX//79LcpMJpNOnz6t/fv2ad++vTp56pSu/vuvoqOjZTKZ5OHhoRIlSqhGjRp6vM0Tatu2rVxcHPMrl6PGejdHnYe0uLu7q2OnTlq8aJFF+bLff7c5Dahk+2m/Dh06yNPT06o8Pj5ea/74Q5u3bFbQkSCFhYUqLi5OXl5eKl26tBo2fEjdu3dXzVq1rPatU7uW4uPjlWxjPb///e81vf76/yzKPvroYz3Xp4/D+r/bvn37tGLFch0+fFiXL11SdHS0ChQooCJFiqho0aKqWrWa6tevrxYtW6pEiRJptpWW5ORk/bF6tUVZ27btMnRD0Gg0avGiRVq6dInOnj2r5ORkVapUSZ06d1a/fv3l6upqc7+IiAgdOLBf+/ft16FDh3Tx4gWFhoYqPj5ebm5u8vb2VqVKldSoUWM93aWLypUrl2oMNapXs0gWFS1aVDt27lJ8fLzmz5+nVStX6sKFC4qMjNSECV+oe48emdrPkbFLjjvv7dt30IEDB8yvN2/apIiICHl5eaXZf3a7+/vj8uXL+uuvvywS/CdPntTff2+zWd9eiYmJ+vPPtdq0cZOOHDms0NBQxcbGms9Vy4cf1nPP9VHhwoUdM6hUrF69Sr8uWKATJ04oISFBpUuX0eNtHtfzzw+7Z9+O+JzJ6DWfXtl9vJ0MTubvXRcXF1WuXEXvv/+Bbt68qdWrLNfLOnz4kNX+qR2n1OSmc3O3u49TeqT3+KTFaDSqRvVqFmV32ouLi9PsWbO0bPkyXbl8WUV8ffVIq0f04vDhFk+t7d27VzNnzNChQwcVHh6uokWLqnnzFhr2wguqWLFimv3fz2s1M9+9ycnJ2rJliwLWr9Phw4d17do1RUVFydvbW8WLl1DdenXVvn0HNW3a1Oqc2nu+MnsNZjTGs2fO6Mkn21m01ahRI/260PoJ8FdeGWN1DH9d+JsaNWp0z2OYlpz2e1xu/T4HAOQsJP4AAICFKlWrqmLFitq9+/bUTWfOnNGWLZv16KOPSZL+/fdf/bl2rbm+j4+POnbqZPf6S7Gxsfrss0/164IFVsmW2NhYRURE6MyZM1qyeLGaNm2q73/4UcWLF7fZ1vHjxzVk8CBdu3bNXBYVGambN//W9u1/65lneuutt9+2K65kU7LVkyYmG8mgbVu3atCggam2ExcXp5CQEB09elRLly5V1apVNWnSZFWqXNmuOFLjyLFKjj0P9ujZs5dV4m/Nmj80/sPxcnPLb1GekJBgdWPnThspbd2yRW+++YbFcbkjLi5O169fV2BgoKZPn6YOHTrq088+U6FChcx1kpKSbD5hJMlmMjDZZFmW2f7v9PP2229ZHR/p9s2oW7du6cyZM9q1a5fmzpXc3Nx0PPiEzZjtcezYMd26dcuirEGKvy63R1RUlEaOGGGRlJGkwMBABQYGatFvv2nW7Dk2b271799PRw4fttluQkKCIiMjdenSJW3dulU//fSjXho1Si+/PMZmfaPRaHEOk5KSFBYWpqFDBuvgwYMWde8+fxndz1GxO/q8+zVoYBXL33//neOfku3Xr7++/fYb8+uZM6ZbxDxjuuUfmvTvP0DffPO13e3v3LlDr//vdV25Yr0mbVxcnK5du6adO3dq4i+/6L33P1Dv3r0zMIq0xcfH69WxY7V27RqL8hMngnXiRLBWLF+h3377TaXLlLG5vyM+Z6SMX/PpkROOd2rq169vlfiLjIxUcnKynJz+mwzJ1nFKTW46N46SnuNzLyaTyeo7OCkpSbdu3dKQwYMsxh4VFaW558/rjz9Wa8bMWapXr55+/uknfffdtxZTh//zzz/6559/tHLlCk2bPl3NmjW32ff9vlYz+t0bGHhAb7z+us3pju/EefjwIc2fN09fff21evToaVHH3vOVmWswMzGaZH0NGI22r/HkFDFKyvT6dznx97jc+n0OAMhZmOoTAABYGTzEcmqnmTP+m2Zt9uxZFjcN+vbtJzc3N7vajYuL08ABAzR/3jybSZWUdu/ere7du+nGDeunO27cuK4B/fvZ/If6HYsW/ZauG8T2MCl9NxhOnTqlAQMGpLpWoj0cPVZHngd7NW7cWOUrVLAoi4yM1Pr1663qbty40ermWNmyZdWkaVOLspUrV2hwimRoWtauXaNnez+jiIjwdMWeGkf1v3DhrzZvFqUmsze5du+2/kt/Pz+/dLVhMt1+IjJl0u9uJ0+e1MAB/RUXF2djf/vHkJiYqO+/+07z589LV2wpb1Teq19793NU7I4+73Xq1LF60mP3row9hXM/tXy4papVr25+vX//7ScpJSkkJEQrViw3b/MsWFC9nnnG7rbXrl2r/v362byxn1JMTIzefutNTZ48yf7g7fTGG69bJf3uduXKZb377js2t2Xl51xG3yup950zjndqgo4EWZUVLFjQIumXHrnp3OQmJpP02muv2hy7JIWGhuqVMWM0Z84cffvtN6kej5iYGI195RXFx8dbbcuOazUj370bNmxQ72eesXuNU0deG/Zeg1kR4/2akjSn/h6XW7/PAQA5C4k/AABg5Ykn2qp8+fLm19u2bdPJkycVExOj3+5aZ8LV1VX9+vezu90vv/xC+/ZZrutWrXp1zZw1SwcCD2rL1m16cfhwi+3/XrmiV8e+atXWp59+qpCQEIuy8uXLa87ceQo6ekxbtm7Tc889p/nz7EsW2MvJ4KT69evr1Vdf06+/LtS69QHafyBQx4NPaM/efZo9e47VjZwrVy5r0W/WUxbZy9FjdeR5SI+eKf4KXZJ+//13q7JlNtaV7NGzp8WNoEuXLuqN11+3unkyZOhQbd22TUFHj2nBr7+qSpUqFttPnjyp9957z/z66LHjOnnqtIYOfd6qz6+//sZqvaM+ffo6tH9JWrF8hcXrWrVqaenvv+vgocM6EnRUmzZv0eIlS/XxJ5+o81NPZerJS0kKPBBo8dqzYEGL97s9QkJuav26dXp5zBjt3LVbh48EaeKkySpSxNei3qlTpzRp4kSr/Yv4+Kh79+766aeftWLlKv29fYeOHQ/WkaCjWrc+QGPHvqp8+fJZ7PPjDz/YdcMwJOSmNm3caHNbWrvbu5+jYnf0ec+fP78qV7a83oKCjqS5T06Rch2xGf8/nfT8+fMsbto/88wzVuuFpubKlSt67dWxFn/c4OzsrJEjX9LGTZt18NAhzZo1WxUqWE4F+NWXXyow8EDK5jLs5s2bWrVypYYNe0Hbd+zUwUOHNeaVV6zqbdmyRVeuXLEoc+TnjC0Zfa/YklOOd0pGo1GXLl3Ul19+YZFEvqNOnToZajc3nZvUfP/9d6papXKqPy2aN3NMR+kUEnJTf2/bpk8+/VQHDx3W+oAA1UoxxeL58+c0/oP35VmwoH7++RcdPhKkxUuWqmjRohb1rl27po0pjmN2Xavp/e69evWqxrw82uoJvWrVqunnXyZq77795u+dSZMnq2vXbqlOr50R9lyDWRXj/cj75eTf43Lz9zkAIOdgqk8AAGDFyclJAwcO0kcffWgumzVzpmrUrKnw8P/+yrVz584qWrSYXW3euHHDKjFVpEgRzZ07z3yjplChQnrzzbd08+ZNLV2yxFxv+/a/tW/fPvMaHjduXNeaP/6waMvZ2VlT/aepatWqkm6vK/fpZ5/rypUr2rJlSzpGn7ZWjzyiVo88YnObm5ubWj3yiMqWK6fHH3vUYtvff/+tgYMGpbs/R4/Vkechvbr36KHvvvvW4mbb39u26caNG+a+w8LCtHnzZov9DAaDenS3XEtm0sSJSkhIsCjr0aOH3n33v5sxzZo118xZs9X2iTYWT52tXrVKY8a8okqVKsnZ2dncR0pOTqmvf+So/iXp6tV/Ldpp/eijatDgv+m/PDw8VL58eT300EPq29f+RHtqrl27avG6iI9PhtoZOHCgXnllrPl1+/btVbBgQfXv19ei3ty5czT65ZctjuXMWbNTbbdKlSoa/fLLOnXqpFbfNeXrjRs3FBwcrJo1a9oVX6tWrTTmlVdUvXoNXbt2TQHr18vdvUCm93NU7Flx3n2KWJ7LCxcu2LVfduvatYu+/upL8x84/Ll2rS68dkHz5s4113F2dtbAgYPsbnPSpIlWT5u+/PIYjX75ZfPrR1q3lv80f3Vo395849pkMunHH35I8zyn11NPP623x40zvx4z5hXt3rVLu1I8wREUdMRi7TJHfs6kJaPvlbvlpOP9/fff6Ycfvpd0ewq+tP5gwNYU0vbITecmNfd64j8zU3hm1qBBg81/aOPl5aXnh72gV8e+YlXv3XffU8dOnSTp9mdlv376/rvvLOocOXJYHTp0ML/Orms1vd+9kyZOVExMjEVZtWrVtPT3ZRZ/AFGlShVVqVIlw2s930ta12BWxXg/nvjL6b/H5dbvcwBAzkHiDwAA2NTrmWf03XffKjIyUpK0bNnvKvq35V9Sp5wSNC2bN29SYmKiRVmnTp2t/jpbkp5s96RFwkmSAtavNyecdu3aZXVDqnHjxuZE2N2e69PHoYk/6fb0fX/+uVZbNm/WyZMn9e+//yomJkaJiYmp3mD8998rNsvvxdFjdeR5SK+SJUuqZcuW2rbtv+khk5KStHLlCvMTd6tWrbKKr3mLFlZrX23YsMGq/X79B1iVlS5dWo8/3kZr1vyXPDWZTNq0aaNdN11T48j+vQsV0sWLF83bp06ZouPHj6tO7TqqVKmSKlaqpMqVK8vT0zPD8d4tJCTU4rW3d6EMtdPHxs2rli1bqnz58vrnn3/MZbdu3dLRoCDVT/Ek7Jkzp7Vq1Srt37df//xzXuHh4YqLizPfjLZ1U/rff/+1K/HXvHlzzZg5y5zYrVSpkl548UWH7eeI2LPivBdKcS5DQ0NlMpnu27RpGeXmll99+vTVTz/9KOn2Z+zzQ4fo5s2b5jpPtG2rsmXLKjo62q42NwQEWLx2dnZWv/79repVrlxFlStX0YkTweayHTt2KDo62u6nC+/l+eeHWZVVqVLVKvEXFnbL4vX9+JzL6HslpZx0vO2Zwlq6fXO+a7duGeojN52b3CjlenoVbDwZ5+rqqqefftqirHIl6/WUw0LDLF5n17Wa3u/e9evXWZW99tr/HPY+sce9rsGsijGj0++mR07/PS63fp8DAHIOEn8AAMAmDw8PPfNMb02fPk3S7cXpL126ZN7etGlT1a5t/xRZZ05br/0xb95cu9ftuvvGy7lz56y210glGVCzZi2b5RkVHBys4S++kO6/vI1O8RfR9nL0WB15HjKiZ69eFok/SVr2++/mxJ+taT5TPpERERGh69ct1xs0GAxWU4HdUbNWLYsbNpJ01s61aGxxdP+Ptn5UQUf+m8LJaDRq86ZN2rxpk7nMyclJNWrUUIcOHdV/QH95eXlnOP7IyAiL154F059QdHNzU+XK1jdYpdvX592JP+n2dXwn8ZecnKzPPvtUs2bOtPsG/R0xMfYlfV5+eYz5RmV63Gs/R8aeFec95bk0mUxKSEiwex3W7NSvf39NmTLZ/ATG6dOnLbYPGTLU7rYiIyN19arl0zVGo1FNGtv+o4WU5zIxMVFnz55V3bp17e4zNQaDQdXvWsPwDg9P6xvjSUn//dHD/fqcy+h75W456Xjbw2Aw6Jlnemv8h+MzdBM9N52b3MjJyclqTeAC7u5W9cqXL6/8+fNblLnbSDjd/b7Kzms1Pd+9tuKUpBYtW6a738xI6xrMyhgNWZz4yw2/x+Xm73MAQM7AGn8AACBVgwYNSvUf/Om5CSvd/kd2SsnJyTIajTZ/UgoN++8vtqOjrG/+e3rYvoHiqKekJCkqKkpDhwzO2HQ7GVyUx9FjdeR5yIh27drJy8vLouzYsWMKDg7W2bNndejQIYttngULqn17y6mhIm2MwdXV1WpdtTsKFixoVRYebt2GvRzd/wsvvqh69eqn2WdycrKOHTumb775Wk+2a2fxl+XplfI6iYqMSncbHh4eqd4wt3UdRvz/k8OSNM3fXzOmT0934kySXWv8ubi4qEHDhvesl5H9HBl7Vpz3lOcyf/78ueYmYdGiRfXUU0/Z3Fa3Xj01btzY7rZsfc5JSvVzztZ1FRYaaqOF9CtcuLDNNa1cnNP+G9z78TmX0fdKSjnpeNvi4uKiwoULq2HDhho27AWtWfunPp8wQW5u+e+9sw256dyk5ZWxY63Wsb37Z+eu3Vnaf2oKFy5s9buni4v176Ipf5eQ7v20Z3Zeq+n57rUVp6ur63192u9e12B2xJic7JgFLnPD73G5+fscAJAzkPgDAACpKl2mjNq2bWdVXr58ebV54ol0tWXrH83pkXTX9I+2npSITuVJoOjo9Cc1UrN27Rr9+6/lOh5eXl765ptvtXPXbp04eUonT53W7j17Hdano8fqyPOQEW5u+dXZxs39Zb//rmW//25V3rlTZ6u/6C9o42ZfQkKC1RShd9g6LrZuGNrL0f17enpq8ZIl+vzzCWrWrJnNJMHdrl27pq+++jKdUf+nSJEiFq/Dw2+lu42Ua/rczVay2uv/rzuTyaRZs2Zabe/cubNWrV6tw0eCzDedn3vuuXTHJd0+tvc6hhnZz9GxZ8V5v5XiXPr4FLFdMYdK7Q9KBg+2f1ppKfOfc5KU6KD1zTI6Zd39+JzL6HslpZx0vCXbCa39BwK1ZOnvenvcOJtPYKZHbjo3aXEy3F7HNrWf7Hra0O6nMDPwtGZ2Xqvp+e61FWdCQoJiY2Mz1HdG3OsazMoYjakc42vXr2W6bSl3/B6X27/PAQDZj6k+AQBAmoYMHao//1xrUTZw4KB038y0tZbNqFGj9fKYMXbtf/eNoPLlK1htDw62PQVl8PHMTU15t2NHj1mVvTxmjLp1725RlqEnAlPh6LE68jxkVK+evbRg/nyLshUrltv8S+uevXpalXl5ecnX19di/S+TyaTg4GCb028dP2Z93jKzvl9W9J8vXz71fvZZ9X72WSUmJuqff/7RP//8o/Pnz2n/vv1W78GdO3ZkOP5ixYpbvA7JwNMLcXFxOnv2rM3jaOv6LF/h9vpMISEhVlODFSniq+9/+NHqM+X8+fPpjkvKeKLlXvtlReyOPu+hKdaQKlbMeu3OnKxmrVpq1qyZxdp3xYsXV6dOndLVjpeXl4oWLaobN26YywoUKKA9e/fZ/cREdk+xeD8+5xy1jlZOO953ElpZJTedG1jKzms1Pd+9Xl5eKlasmNV0lDt27FCbNm0y1H963esadFSMzjaefrY1PX5iYqLO25j+PiNyw+9xuf37HACQ/fhtEgAApKlRo0YW09cULFhQPXv1SmMP2x597DGrmyUrVixXdHR0mn9xbusvz5s1a2bV1t49e3T27Fmrfn/77bd0x5qaxCTrvwRO+TSaJE2fNs1hfTp6rI48DxlV389PVatWtSi7fv26Ll++bFFWqVIlNWz4kM02Hn/c+qZSymSiJF29elUbNmywKn/0sccsXtv66+zUnqzMiv7vli9fPlWpUkVt2rTR0KHPa+KkSVZr5kTeNXVmejVo0MDidVRkZIamDl34669WZXt279b585Y35ry8vFSnzu0baUk2/orf1c3V6gbjkSNHtGfPnnTHlJWyOvbMnvf4+DidPWu5blhWTxWYFVI+9dd/wIBUp19LS8r3aGxsrJYuWWLX55yLi4tD/sghs7Lyc8bR8sLxTo/cdG5gKbuu1fR+9z7Rtq1V2bfffH1fn/q7F0fEWKhQIauy8+fOmdd7vWPFiuUKCQlJd4ypycm/x+WV73MAQPYi8QcAAO7p92XLzNNlHQg8mKF184oXL65neve2KLt48aKe7d1bf6xerWvXrsloNCoyMlIXLlzQwcBALVgwX6//7zU9/thjCrtrbbnixYurXbsnLdpKSkrSC8Oe1969e2U0GnXjxnV99NGH2rAhIGODtqFK5SpWZZMnTdL+/fuVkJCgc+fO6fX/vWb1V72Z4eixOvI8ZEbPnvdOHqeVYB4+YoRVMuC33xbq66++0s2bN2U0GhUYeEBDBg+yugHVvn0HValieS6LFStm1ceqlSt19epVm2v8OLL/d999Ry8Me17Tp0/Tvn37dP78eUVERMhkMikiIlxr/vhDhw8ftmijTJkyqR6be2narJlV2eEUayvaY8aM6Zo8eZLCw8OVmJiozZs3acyYl63qPdenj/lYFS1aVN7e3hbb/71yRV9++YXCwsIUGRmpNWvWaOiQwTbXmMxOjo7d0ec9KOio1TRlzZpan+uc7om2bS2maBwxYmSG2hkxcqRVQv/jjz/SZ59+quDgYMXExCgxMVE3blxXcHCwNmzYoG++/lp9+zynb77+2hFDyTRHf85lpbxwvNMjN50bWMquazW9370jRoy0+uOy48eP65lePbVx40ZFRUUpMTFRly9d0ratW/Xq2LFavXpVhuPLCEfEWKhQIavvtqioKL3xxuu6evWq4uPjtHLlCn300UcOjT0n/x6XV77PAQDZi6k+AQDAPTk5OTlk2qlx497R0aCjOnz4vxsdJ04Ea/ToUffcN2Xy5d333tOOHdsVHh5uLjt79qx6P2OZLOrZq5eWLF6cychve+rpp/Xtd98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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import pandas as pd\n", + "\n", + "# 1. Média de votos por ano (barras)\n", + "df_bar = (\n", + " base_movie\n", + " .groupby(\"Year\")\n", + " .agg(avg_votes=(\"Votes\", \"mean\"))\n", + " .reset_index()\n", + " .sort_values(\"Year\") # garante ordem\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# 2. Melhor filme por ano (mais votos)\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .sort_values(\"Year\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# 3. Pior filme por ano (menos votos)\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .sort_values(\"Year\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# média de rating por ano — calculada antes do loop\n", + "avg_rating_por_ano = base_movie.groupby(\"Year\")[\"Rating\"].mean()\n", + "\n", + "# ============================================================\n", + "# CONFIGURAÇÕES VISUAIS\n", + "# ============================================================\n", + "COR_BARRA = \"#2E86AB\"\n", + "COR_MELHOR = \"#F6AE2D\"\n", + "COR_PIOR = \"#E94F37\"\n", + "COR_FUNDO = \"#F8F9FA\"\n", + "TAMANHO_CIRCULO = 350\n", + "\n", + "fig, ax = plt.subplots(figsize=(18, 11))\n", + "fig.patch.set_facecolor(COR_FUNDO)\n", + "ax.set_facecolor(COR_FUNDO)\n", + "\n", + "# --- BARRAS ---\n", + "bars = ax.bar(\n", + " df_bar[\"Year\"],\n", + " df_bar[\"avg_votes\"],\n", + " color=COR_BARRA,\n", + " width=0.55,\n", + " edgecolor=\"white\",\n", + " linewidth=2,\n", + " zorder=2,\n", + " alpha=0.88,\n", + ")\n", + "\n", + "# rating médio dentro de cada barra\n", + "for bar, ano in zip(bars, df_bar[\"Year\"]):\n", + " altura = bar.get_height()\n", + " rating_medio = avg_rating_por_ano.get(ano, 0) # ← BUG CORRIGIDO: estava fora do loop\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura * 0.12,\n", + " f\"{rating_medio:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=12, fontweight=\"bold\",\n", + " color=\"white\", zorder=5\n", + " )\n", + "\n", + "# --- CÍRCULOS ---\n", + "for df, cor in [(df_max, COR_MELHOR), (df_min, COR_PIOR)]:\n", + " ax.scatter(\n", + " df[\"Year\"], df[\"Votes\"],\n", + " s=TAMANHO_CIRCULO, c=cor,\n", + " edgecolors=\"black\", linewidths=2,\n", + " zorder=10, alpha=0.95\n", + " )\n", + " for _, row in df.iterrows():\n", + " # nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=10, fontweight=\"bold\",\n", + " color=\"black\", zorder=11\n", + " )\n", + " # nome com seta — resolve sobreposição\n", + " nome = row[\"Title\"][:18] + \"...\" if len(row[\"Title\"]) > 18 else row[\"Title\"]\n", + " media_ano = df_bar.loc[df_bar[\"Year\"] == row[\"Year\"], \"avg_votes\"].values[0]\n", + " offset = 130000 if row[\"Votes\"] >= media_ano else -130000\n", + " va = \"bottom\" if offset > 0 else \"top\"\n", + " ax.annotate(\n", + " nome,\n", + " xy=(row[\"Year\"], row[\"Votes\"]),\n", + " xytext=(row[\"Year\"], row[\"Votes\"] + offset),\n", + " ha=\"center\", va=va,\n", + " fontsize=8, fontweight=\"bold\", color=\"#333333\",\n", + " arrowprops=dict(arrowstyle=\"-\", color=\"#aaaaaa\", lw=0.8),\n", + " zorder=11\n", + " )\n", + "\n", + "# --- ESTILIZAÇÃO ---\n", + "ax.set_title(\n", + " \"Filmes Acima da Média por Ano\\nMédia de Votos (barras) | Melhor e Pior Filme (círculos)\",\n", + " fontsize=20, fontweight=\"bold\", color=\"#1a1a1a\", pad=25\n", + ")\n", + "ax.set_xlabel(\"Ano\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_ylabel(\"Quantidade de Votos\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_xticks(range(2006, 2017))\n", + "ax.set_xticklabels([str(a) for a in range(2006, 2017)], fontsize=12, fontweight=\"bold\")\n", + "ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\", color=\"gray\", zorder=0)\n", + "ax.set_axisbelow(True)\n", + "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f\"{int(x):,}\"))\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "ax.spines[\"left\"].set_color(\"#cccccc\")\n", + "ax.spines[\"bottom\"].set_color(\"#cccccc\")\n", + "\n", + "ymax = max(df_bar[\"avg_votes\"].max(), df_max[\"Votes\"].max()) * 1.25\n", + "ax.set_ylim(0, ymax)\n", + "\n", + "legend_elements = [\n", + " mpatches.Patch(facecolor=COR_BARRA, edgecolor=\"white\", label=\"Média de Votos do Ano\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_MELHOR,\n", + " markeredgecolor='black', markersize=15, label=\"Melhor Filme do Ano\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_PIOR,\n", + " markeredgecolor='black', markersize=15, label=\"Pior Filme do Ano\"),\n", + "]\n", + "ax.legend(handles=legend_elements, loc=\"upper right\", fontsize=12,\n", + " frameon=True, fancybox=True, shadow=True,\n", + " facecolor=\"white\", edgecolor=\"#cccccc\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"grafico_case3.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "39a77b4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import pandas as pd\n", + "\n", + "# ============================================================\n", + "# SUPONDO QUE VOCÊ JÁ TEM NO SEU NOTEBOOK:\n", + "# base_means -> média de TODOS os filmes por ano\n", + "# base_movie -> filmes ACIMA da média (já filtrados)\n", + "# ============================================================\n", + "\n", + "# ============================================================\n", + "# PREPARAÇÃO DOS DADOS\n", + "# ============================================================\n", + "\n", + "# Barras: média de TODOS os filmes (base_means)\n", + "df_bar = base_means[[\"Year\", \"avg_votes\", \"avg_rating\"]].copy()\n", + "\n", + "# Melhor filme por ano (mais votos) entre os ACIMA DA MÉDIA\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Melhor\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# Pior filme por ano (menos votos) entre os ACIMA DA MÉDIA\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Pior\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# ============================================================\n", + "# CONFIGURAÇÕES VISUAIS\n", + "# ============================================================\n", + "COR_BARRA = \"#2E86AB\" # Azul petróleo\n", + "COR_MELHOR = \"#F6AE2D\" # Dourado\n", + "COR_PIOR = \"#E94F37\" # Vermelho coral\n", + "COR_FUNDO = \"#F8F9FA\" # Cinza claro\n", + "\n", + "TAMANHO_CIRCULO = 400 # Tamanho dos círculos (em pontos)\n", + "OFFSET_NOME = 130000 # Distância do nome acima do círculo\n", + "\n", + "# ============================================================\n", + "# CRIAR FIGURA\n", + "# ============================================================\n", + "fig, ax = plt.subplots(figsize=(18, 12))\n", + "\n", + "fig.patch.set_facecolor(COR_FUNDO)\n", + "ax.set_facecolor(COR_FUNDO)\n", + "\n", + "# --- BARRAS: Média de votos de TODOS os filmes por ano ---\n", + "bars = ax.bar(\n", + " df_bar[\"Year\"],\n", + " df_bar[\"avg_votes\"],\n", + " color=COR_BARRA,\n", + " width=0.55,\n", + " edgecolor=\"white\",\n", + " linewidth=2,\n", + " zorder=2,\n", + " alpha=0.88,\n", + " label=\"Media de Votos (todos os filmes)\"\n", + ")\n", + "\n", + "# Escrever dados nas barras\n", + "for bar, row in zip(bars, df_bar.itertuples()):\n", + " altura = bar.get_height()\n", + " rating_medio = row.avg_rating\n", + " \n", + " # Valor da média de votos no TOPO da barra\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura + 8000,\n", + " f\"{altura:,.0f}\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " color=COR_BARRA\n", + " )\n", + " \n", + " # Nota média DENTRO da barra (embaixo)\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura * 0.15,\n", + " f\"{rating_medio:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=12,\n", + " fontweight=\"bold\",\n", + " color=\"white\",\n", + " zorder=5\n", + " )\n", + "\n", + "# --- CÍRCULOS: Melhor filme do ano ---\n", + "ax.scatter(\n", + " df_max[\"Year\"],\n", + " df_max[\"Votes\"],\n", + " s=TAMANHO_CIRCULO,\n", + " c=COR_MELHOR,\n", + " edgecolors=\"black\",\n", + " linewidths=2,\n", + " zorder=10,\n", + " alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_max.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " color=\"black\",\n", + " zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"] + OFFSET_NOME,\n", + " nome,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=9,\n", + " fontweight=\"bold\",\n", + " color=\"#333333\",\n", + " zorder=11\n", + " )\n", + "\n", + "# --- CÍRCULOS: Pior filme do ano ---\n", + "ax.scatter(\n", + " df_min[\"Year\"],\n", + " df_min[\"Votes\"],\n", + " s=TAMANHO_CIRCULO,\n", + " c=COR_PIOR,\n", + " edgecolors=\"black\",\n", + " linewidths=2,\n", + " zorder=10,\n", + " alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_min.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " color=\"black\",\n", + " zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"],\n", + " row[\"Votes\"] + OFFSET_NOME,\n", + " nome,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=9,\n", + " fontweight=\"bold\",\n", + " color=\"#333333\",\n", + " zorder=11\n", + " )\n", + "\n", + "# ============================================================\n", + "# ESTILIZAÇÃO FINAL\n", + "# ============================================================\n", + "ax.set_title(\n", + " \"Filmes Acima da Media por Ano\\n\"\n", + " \"Media de Votos de TODOS os filmes (barras) | \"\n", + " \"Melhor e Pior filme acima da media (circulos)\",\n", + " fontsize=18,\n", + " fontweight=\"bold\",\n", + " color=\"#1a1a1a\",\n", + " pad=25\n", + ")\n", + "\n", + "ax.set_xlabel(\"Ano\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_ylabel(\"Quantidade de Votos\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "\n", + "ax.set_xticks(range(2006, 2017))\n", + "ax.set_xticklabels([str(a) for a in range(2006, 2017)], fontsize=12, fontweight=\"bold\")\n", + "\n", + "ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\", color=\"gray\", zorder=0)\n", + "ax.set_axisbelow(True)\n", + "\n", + "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f\"{int(x):,}\"))\n", + "\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "ax.spines[\"left\"].set_color(\"#cccccc\")\n", + "ax.spines[\"bottom\"].set_color(\"#cccccc\")\n", + "\n", + "ymax = max(df_bar[\"avg_votes\"].max(), df_max[\"Votes\"].max()) * 1.18\n", + "ax.set_ylim(0, ymax)\n", + "\n", + "# Legenda\n", + "legend_elements = [\n", + " mpatches.Patch(facecolor=COR_BARRA, edgecolor=\"white\", \n", + " label=\"Media de Votos (todos os filmes)\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_MELHOR,\n", + " markeredgecolor='black', markersize=15, label=\"Melhor Filme (acima da media)\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_PIOR,\n", + " markeredgecolor='black', markersize=15, label=\"Pior Filme (acima da media)\"),\n", + "]\n", + "ax.legend(\n", + " handles=legend_elements,\n", + " loc=\"upper right\",\n", + " fontsize=11,\n", + " frameon=True,\n", + " fancybox=True,\n", + " shadow=True,\n", + " facecolor=\"white\",\n", + " edgecolor=\"#cccccc\"\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "f0035b72", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import pandas as pd\n", + "\n", + "# ============================================================\n", + "# SUPONDO QUE VOCÊ JÁ TEM NO SEU NOTEBOOK:\n", + "# base_means -> média de TODOS os filmes por ano\n", + "# base_movie -> filmes ACIMA da média (já filtrados)\n", + "# ============================================================\n", + "\n", + "# ============================================================\n", + "# PREPARAÇÃO DOS DADOS\n", + "# ============================================================\n", + "\n", + "# Barras: média de TODOS os filmes (base_means)\n", + "df_bar = base_means[[\"Year\", \"avg_votes\", \"avg_rating\"]].copy()\n", + "\n", + "# Melhor filme por ano (mais votos) entre os ACIMA DA MÉDIA\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Melhor\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# Pior filme por ano (menos votos) entre os ACIMA DA MÉDIA\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Pior\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# ============================================================\n", + "# CONFIGURAÇÕES VISUAIS\n", + "# ============================================================\n", + "COR_BARRA = \"#2E86AB\"\n", + "COR_MELHOR = \"#F6AE2D\"\n", + "COR_PIOR = \"#E94F37\"\n", + "COR_FUNDO = \"#F8F9FA\"\n", + "\n", + "TAMANHO_CIRCULO = 650 # AUMENTADO: círculos maiores\n", + "OFFSET_NOME = 70000 # DIMINUÍDO: nome mais próximo do círculo\n", + "\n", + "# ============================================================\n", + "# CRIAR FIGURA\n", + "# ============================================================\n", + "fig, ax = plt.subplots(figsize=(18, 12))\n", + "\n", + "fig.patch.set_facecolor(COR_FUNDO)\n", + "ax.set_facecolor(COR_FUNDO)\n", + "\n", + "# --- BARRAS: Média de votos de TODOS os filmes por ano ---\n", + "bars = ax.bar(\n", + " df_bar[\"Year\"],\n", + " df_bar[\"avg_votes\"],\n", + " color=COR_BARRA,\n", + " width=0.55,\n", + " edgecolor=\"white\",\n", + " linewidth=2,\n", + " zorder=2,\n", + " alpha=0.88,\n", + ")\n", + "\n", + "# Escrever dados nas barras\n", + "for bar, row in zip(bars, df_bar.itertuples()):\n", + " altura = bar.get_height()\n", + " rating_medio = row.avg_rating\n", + " \n", + " # Valor da média de votos no TOPO da barra\n", + " # ax.text(\n", + " # bar.get_x() + bar.get_width() / 2,\n", + " # altura + 8000,\n", + " # f\"{altura:,.0f}\",\n", + " # ha=\"center\", va=\"bottom\",\n", + " # fontsize=10, fontweight=\"bold\", color=COR_BARRA\n", + " # )\n", + " \n", + " # Nota média DENTRO da barra (embaixo)\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura * 0.15,\n", + " f\"{rating_medio:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=12, fontweight=\"bold\", color=\"white\", zorder=5\n", + " )\n", + "\n", + "# --- CÍRCULOS: Melhor filme do ano ---\n", + "ax.scatter(\n", + " df_max[\"Year\"], df_max[\"Votes\"],\n", + " s=TAMANHO_CIRCULO, c=COR_MELHOR,\n", + " edgecolors=\"black\", linewidths=2, zorder=10, alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_max.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=10, fontweight=\"bold\", color=\"black\", zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"] + OFFSET_NOME, nome,\n", + " ha=\"center\", va=\"bottom\",\n", + " fontsize=9, fontweight=\"bold\", color=\"#333333\", zorder=11\n", + " )\n", + "\n", + "# --- CÍRCULOS: Pior filme do ano ---\n", + "ax.scatter(\n", + " df_min[\"Year\"], df_min[\"Votes\"],\n", + " s=TAMANHO_CIRCULO, c=COR_PIOR,\n", + " edgecolors=\"black\", linewidths=2, zorder=10, alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_min.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=10, fontweight=\"bold\", color=\"black\", zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"] + OFFSET_NOME, nome,\n", + " ha=\"center\", va=\"bottom\",\n", + " fontsize=9, fontweight=\"bold\", color=\"#333333\", zorder=11\n", + " )\n", + "\n", + "# ============================================================\n", + "# ESTILIZAÇÃO FINAL\n", + "# ============================================================\n", + "ax.set_title(\n", + " \"Filmes Acima da Media por Ano\\n\"\n", + " \"Media de Votos de TODOS os filmes (barras) | \"\n", + " \"Melhor e Pior filme acima da media (circulos)\",\n", + " fontsize=18, fontweight=\"bold\", color=\"#1a1a1a\", pad=25\n", + ")\n", + "\n", + "ax.set_xlabel(\"Ano\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_ylabel(\"Quantidade de Votos\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "\n", + "ax.set_xticks(range(2006, 2017))\n", + "ax.set_xticklabels([str(a) for a in range(2006, 2017)], fontsize=12, fontweight=\"bold\")\n", + "\n", + "ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\", color=\"gray\", zorder=0)\n", + "ax.set_axisbelow(True)\n", + "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f\"{int(x):,}\"))\n", + "\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "ax.spines[\"left\"].set_color(\"#cccccc\")\n", + "ax.spines[\"bottom\"].set_color(\"#cccccc\")\n", + "\n", + "ymax = max(df_bar[\"avg_votes\"].max(), df_max[\"Votes\"].max()) * 1.18\n", + "ax.set_ylim(0, ymax)\n", + "\n", + "# Legenda\n", + "legend_elements = [\n", + " mpatches.Patch(facecolor=COR_BARRA, edgecolor=\"white\", label=\"Media de Votos (todos os filmes)\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_MELHOR,\n", + " markeredgecolor='black', markersize=18, label=\"Melhor Filme (acima da media)\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_PIOR,\n", + " markeredgecolor='black', markersize=18, label=\"Pior Filme (acima da media)\"),\n", + "]\n", + "ax.legend(handles=legend_elements, loc=\"upper right\", fontsize=11,\n", + " frameon=True, fancybox=True, shadow=True, facecolor=\"white\", edgecolor=\"#cccccc\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dc08596", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} + +``` + +## ./src/run.py + +```py + + + +``` + +## ./tests/__init__.py + +```py + +``` + +## ./tests/test_connection.py + +```py +""" +teste para verificar a conexçao com o banco - connection.py +IA - teste padrão utilizado a muito tempo, refinado com IA +""" +from src.models.config.connection import DBConnectionHandler +from sqlalchemy import text + + +def test_create_engine_database(): + db_connection = DBConnectionHandler() + engine = db_connection.get_engine() + print(engine) + + + +def test_work_engine_database(): + db_connection = DBConnectionHandler() + engine = db_connection.get_engine() + + try: + with engine.connect() as connection: + result = connection.execute(text("SELECT 1")) + assert result.scalar() == 1 + print("✅ Conexão com o banco realizada com sucesso.") + except Exception as e: + print("❌ Erro na conexão com o banco:", e) + assert False +``` + +## ./tests/test_repository_sales.py + +```py +""" +teste de variação dos filtro - repositores + +IA - teste feito com IA + +eu gosto de deixar a contrução de testes +pois ela abrange uma variedade maior dos erros que podem acontecer no processo +""" + +import pytest +import pandas as pd +from src.models.repositories.sales_repository import SalesRepository + + +@pytest.fixture(scope="module") +def repo(): + return SalesRepository() + + +# ── sem filtros ───────────────────────────────────────────────────────────── + +def test_sem_filtros_retorna_dataframe(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=None) + print("\n=== sem filtros ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert list(result.columns) == ["STORE_CODE", "PRODUCT_CODE", "DATE", "SALES_VALUE", "SALES_QTY"] + + +# ── filtro só por produto ──────────────────────────────────────────────────── + +def test_so_product_code(repo): + result = repo.read_db(product_code=18, store_code=None, data_range=None) + print("\n=== só product_code=18 ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + + +# ── filtro só por loja ─────────────────────────────────────────────────────── + +def test_so_store_code(repo): + result = repo.read_db(product_code=None, store_code=1, data_range=None) + print("\n=== só store_code=1 ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + # Convertido para string na comparação para bater com o retorno do banco + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + + +# ── filtro só por data — início e fim ─────────────────────────────────────── + +def test_data_inicio_e_fim(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== data início e fim ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + # Converte o objeto datetime.date para string para poder comparar com '2019-01-01' + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +# ── filtro só por data — só início ────────────────────────────────────────── + +def test_data_so_inicio(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=["2019-06-01", None]) + print("\n=== só data início ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-06-01" + + +# ── filtro só por data — só fim ───────────────────────────────────────────── + +def test_data_so_fim(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=[None, "2019-03-31"]) + print("\n=== só data fim ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-03-31" + + +# ── combinações ───────────────────────────────────────────────────────────── + +def test_produto_e_loja(repo): + result = repo.read_db(product_code=18, store_code=1, data_range=None) + print("\n=== produto + loja ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + + +def test_produto_e_data(repo): + result = repo.read_db(product_code=18, store_code=None, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== produto + data ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +def test_loja_e_data(repo): + result = repo.read_db(product_code=None, store_code=1, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== loja + data ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +def test_todos_os_filtros(repo): + result = repo.read_db(product_code=18, store_code=1, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== todos os filtros ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" +``` diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4302c91 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,121 @@ +annotated-types==0.7.0 +anyio==4.14.0 +argon2-cffi==25.1.0 +argon2-cffi-bindings==25.1.0 +arrow==1.4.0 +asttokens==3.0.1 +async-lru==2.3.0 +attrs==26.1.0 +babel==2.18.0 +beautifulsoup4==4.15.0 +bleach==6.4.0 +certifi==2026.6.17 +cffi==2.0.0 +charset-normalizer==3.4.7 +comm==0.2.3 +contourpy==1.3.3 +cycler==0.12.1 +debugpy==1.8.21 +decorator==5.3.1 +defusedxml==0.7.1 +dotenv==0.9.9 +executing==2.2.1 +fastjsonschema==2.21.2 +fonttools==4.63.0 +fqdn==1.5.1 +greenlet==3.5.2 +h11==0.16.0 +httpcore==1.0.9 +httpx==0.28.1 +idna==3.18 +iniconfig==2.3.0 +ipykernel==7.3.0 +ipython==9.14.1 +ipython_pygments_lexers==1.1.1 +ipywidgets==8.1.8 +isoduration==20.11.0 +jedi==0.20.0 +Jinja2==3.1.6 +json5==0.15.0 +jsonpointer==3.1.1 +jsonschema==4.26.0 +jsonschema-specifications==2025.9.1 +jupyter==1.1.1 +jupyter-console==6.6.3 +jupyter-events==0.12.1 +jupyter-lsp==2.3.1 +jupyter_builder==1.0.2 +jupyter_client==8.9.1 +jupyter_core==5.9.1 +jupyter_server==2.20.0 +jupyter_server_terminals==0.5.4 +jupyterlab==4.6.0 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.28.0 +jupyterlab_widgets==3.0.16 +kiwisolver==1.5.0 +lark==1.3.1 +MarkupSafe==3.0.3 +matplotlib==3.11.0 +matplotlib-inline==0.2.2 +mistune==3.3.2 +narwhals==2.22.1 +nbclient==0.11.0 +nbconvert==7.17.1 +nbformat==5.10.4 +nest-asyncio2==1.7.2 +notebook==7.6.0 +notebook_shim==0.2.4 +numpy==2.4.6 +packaging==26.2 +pandas==3.0.3 +pandocfilters==1.5.1 +parso==0.8.7 +path==17.1.1 +pexpect==4.9.0 +pillow==12.2.0 +platformdirs==4.10.0 +plotly==6.8.0 +pluggy==1.6.0 +prometheus_client==0.25.0 +prompt_toolkit==3.0.52 +psutil==7.2.2 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pycparser==3.0 +pydantic==2.13.4 +pydantic_core==2.46.4 +Pygments==2.20.0 +PyMySQL==1.2.0 +pyparsing==3.3.2 +pytest==9.1.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.2 +python-json-logger==4.1.0 +PyYAML==6.0.3 +pyzmq==27.1.0 +referencing==0.37.0 +requests==2.34.2 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rfc3987-syntax==1.1.0 +rpds-py==2026.5.1 +Send2Trash==2.1.0 +six==1.17.0 +soupsieve==2.8.4 +SQLAlchemy==2.0.51 +stack-data==0.6.3 +terminado==0.18.1 +tinycss2==1.5.1 +tornado==6.5.7 +traitlets==5.15.1 +typing-inspection==0.4.2 +typing_extensions==4.15.0 +tzdata==2026.2 +uri-template==1.3.0 +urllib3==2.7.0 +wcwidth==0.8.1 +webcolors==25.10.0 +webencodings==0.5.1 +websocket-client==1.9.0 +widgetsnbextension==4.0.15 diff --git a/sql_comentado.txt b/sql_comentado.txt new file mode 100644 index 0000000..098711a --- /dev/null +++ b/sql_comentado.txt @@ -0,0 +1,135 @@ +# testando a base +/*select * from data_product;*/ + +# apelidando +WITH product AS ( + SELECT * FROM `looqbox-challenge`.data_product +) + +# 1 - Quais são os 10 produtos mais caros da empresa? + +# teste de duplicatas pelo nome- tem duplicatas :( +/*SELECT + product.PRODUCT_NAME, + COUNT(*) as qtd +FROM data_product as product +GROUP BY + product.PRODUCT_NAME +ORDER BY + qtd DESC; +*/ + +# teste de duplicatas pelo codigo - sem duplicatas +/*SELECT + product.PRODUCT_COD, + COUNT(*) as qtd +FROM data_product as product +GROUP BY + product.PRODUCT_COD +ORDER BY + qtd DESC; +*/ + +# resposta +SELECT + product.PRODUCT_COD, + product.PRODUCT_NAME, + product.PRODUCT_VAL +FROM data_product AS product +ORDER BY + product.PRODUCT_VAL DESC +LIMIT + 10 +; + + +# 2 - Quais seções os departamentos 'BEBIDAS' e 'PADARIA' têm? + +# teste +/*SELECT + product.DEP_NAME, + product.SECTION_COD, + product.SECTION_NAME, + COUNT(*) as qtd_product +FROM data_product as product +WHERE + product.DEP_NAME = "BEBIDAS" OR product.DEP_NAME = "PADARIA" +GROUP BY + product.DEP_NAME, + product.SECTION_COD, + product.SECTION_NAME +ORDER BY + product.DEP_NAME +; +*/ + +# doble check - gestantes me pareceu suspeito +/*SELECT * +from data_product as product +WHERE product.SECTION_NAME = "GESTANTE" +*/ + +# resposta +SELECT DISTINCT + product.DEP_NAME, + product.SECTION_NAME +FROM data_product as product +WHERE + product.DEP_NAME IN ("BEBIDAS", "PADARIA") +ORDER BY + product.DEP_NAME desc +; + + + +# 3 - Qual foi a venda total de produtos (em $) de cada Área de Negócio no primeiro trimestre de 2019? + +#apelidando +/* +WITH + product AS ( + SELECT * FROM `looqbox-challenge`.data_product dp), + sales AS( + SELECT * FROM `looqbox-challenge`.data_product_sales), + store AS( + SELECT * FROM `looqbox-challenge`.data_store_cad dsc) +# dando uma olhada nas vendas + SELECT + sales.STORE_CODE, + SUM(sales.SALES_VALUE) +FROM + sales +GROUP BY + sales.STORE_CODE +ORDER BY + sales.STORE_CODE ASC; +*/ +#dando uma olhada nos negocios +/*SELECT DISTINCT + store.BUSINESS_CODE, + store.BUSINESS_NAME +FROM store;*/ +# resposta + +WITH + product AS ( + SELECT * FROM `looqbox-challenge`.data_product dp), + sales AS( + SELECT * FROM `looqbox-challenge`.data_product_sales), + store AS( + SELECT * FROM `looqbox-challenge`.data_store_cad dsc) +SELECT + store.BUSINESS_NAME, + COALESCE(SUM(sales.SALES_VALUE), 0) as FIRST_TRI_SALES +FROM store +Left JOIN sales # left tras todas as áres, até as zeradas +ON + store.STORE_CODE = sales.STORE_CODE +WHERE + sales.`DATE` + BETWEEN + '2019-01-01' AND '2019-03-31' +GROUP BY + store.BUSINESS_NAME +; + diff --git a/sql_query.txt b/sql_query.txt new file mode 100644 index 0000000..ad035ac --- /dev/null +++ b/sql_query.txt @@ -0,0 +1,95 @@ +# 1 - Quais são os 10 produtos mais caros da empresa? + +# apelidando +WITH product AS ( + SELECT * FROM `looqbox-challenge`.data_product +) +# resposta +SELECT + product.PRODUCT_COD, + product.PRODUCT_NAME, + product.PRODUCT_VAL +FROM data_product AS product +ORDER BY + product.PRODUCT_VAL DESC +LIMIT + 10 +; + + + +PRODUCT_COD|PRODUCT_NAME |PRODUCT_VAL| +-----------+--------------------------------------------------------------------------------+-----------+ + 301409|Whisky Escoces THE MACALLAN Ruby Garrafa 700ml com Caixa | 741.99| + 176185|Whisky Escoces JOHNNIE WALKER Blue Label Garrafa 750ml | 735.90| + 315481|Cafeteira Expresso 3 CORACOES Tres Modo Vermelho | 499.00| + 100280|Vinho Portugues Tinto Vintage QUINTA DO CRASTO Garrafa 750ml | 445.90| + 320046|Escova Dental Eletrica ORAL B D34 Professional Care 5000 110v | 399.90| + 190817|Champagne Rose VEUVE CLICQUOT PONSARDIM Garrafa 750ml | 366.90| + 153795|Champagne Frances Brut Imperial MOET Rose Garrafa 750ml | 359.90| + 311397|Conjunto de Panelas Allegra em Inox TRAMONTINA 5 Pecas Gratis Utensilios 5 Pecas| 359.00| + 147706|Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml | 329.90| + 154431|Champagne Frances Brut Imperial MOET & CHANDON Garrafa 750ml | 315.90| + + + + + +# 2 - Quais seções os departamentos 'BEBIDAS' e 'PADARIA' têm? + +# resposta +SELECT DISTINCT + product.DEP_NAME, + product.SECTION_NAME +FROM data_product as product +WHERE + product.DEP_NAME IN ("BEBIDAS", "PADARIA") +ORDER BY + product.DEP_NAME desc +; + + +DEP_NAME|SECTION_NAME | +--------+------------------+ +PADARIA |DOCES-E-SOBREMESAS| +PADARIA |GESTANTE | +PADARIA |PADARIA | +PADARIA |QUEIJOS-E-FRIOS | +BEBIDAS |BEBIDAS | +BEBIDAS |CERVEJAS | +BEBIDAS |REFRESCOS | +BEBIDAS |VINHOS | + + + + +# 3 - Qual foi a venda total de produtos (em $) de cada Área de Negócio no primeiro trimestre de 2019? + +WITH + sales AS( + SELECT * FROM `looqbox-challenge`.data_product_sales), + store AS( + SELECT * FROM `looqbox-challenge`.data_store_cad dsc) +SELECT + store.BUSINESS_NAME, + COALESCE(SUM(sales.SALES_VALUE), 0) as FIRST_TRI_SALES +FROM store +Left JOIN sales # left tras todas as áres, até as zeradas +ON + store.STORE_CODE = sales.STORE_CODE +WHERE + sales.`DATE` + BETWEEN + '2019-01-01' AND '2019-03-31' +GROUP BY + store.BUSINESS_NAME +; + + +BUSINESS_NAME|FIRST_TRI_SALES| +-------------+---------------+ +Varejo | 81032347.65| +Farma | 81776691.73| +Atacado | 80384884.60| +Posto | 32072326.40| +Proximidade | 80171122.80| \ No newline at end of file diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/__pycache__/__init__.cpython-314.pyc b/src/__pycache__/__init__.cpython-314.pyc new file mode 100644 index 0000000..7d9a649 Binary files /dev/null and b/src/__pycache__/__init__.cpython-314.pyc differ diff --git a/src/models/__init__.py b/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/models/__pycache__/__init__.cpython-314.pyc 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dotenv import load_dotenv +from pathlib import Path +import os + + +# Isso garante que as variáveis carreguem assim que o arquivo for lido +env_path = Path(__file__).parent.parent.parent.parent / '.env' +load_dotenv(dotenv_path=env_path) + + +class DBConnectionHandler: + def __init__(self) -> None: + """Configuração de conexão com o banco do desafio Looqbox""" + self.__connection_string = os.getenv("DATABASE_URL") + #self.__connection_string = "mysql+pymysql://looqbox-challenge:looq-challenge@35.199.115.174/looqbox-challenge" + # "tipo do banco://usuario:senha@ip de conexão/database" + + self.__engine = self.__create_database_engine() + self.session = None + + def __create_database_engine(self): + engine = create_engine(self.__connection_string, + pool_pre_ping=True) + return engine + + def get_engine(self): + return self.__engine + + def __enter__(self): + session_make = sessionmaker(bind=self.__engine) + self.session = session_make() + return self.session + + def __exit__(self, exc_type, exc_val, exc_tb): + self.session.close() + + \ No newline at end of file diff --git a/src/models/repositories/__init__.py b/src/models/repositories/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/models/repositories/__pycache__/__init__.cpython-314.pyc b/src/models/repositories/__pycache__/__init__.cpython-314.pyc new file mode 100644 index 0000000..107e978 Binary files /dev/null and b/src/models/repositories/__pycache__/__init__.cpython-314.pyc differ diff --git a/src/models/repositories/__pycache__/sales_repository.cpython-314.pyc b/src/models/repositories/__pycache__/sales_repository.cpython-314.pyc new file mode 100644 index 0000000..dad698b Binary files /dev/null and b/src/models/repositories/__pycache__/sales_repository.cpython-314.pyc differ diff --git a/src/models/repositories/imdb_repository.py b/src/models/repositories/imdb_repository.py new file mode 100644 index 0000000..24742f5 --- /dev/null +++ b/src/models/repositories/imdb_repository.py @@ -0,0 +1,26 @@ +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy import select, column, table, and_, between +from sqlalchemy.orm import Session +from src.models.schemas.product_sales_schema import ProductSalesSchema + + + +class ImdbRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + self.__table = table("IMDB_movies") + + def read_db(self) -> pd.DataFrame: + + with self.__db.get_engine().connect() as conn: + try: + query = select("*").select_from(self.__table) + response = pd.read_sql(query, conn) + + return response + + except Exception as exception: + raise Exception(f"erro na query:{exception}") + diff --git a/src/models/repositories/sales_repository.py b/src/models/repositories/sales_repository.py new file mode 100644 index 0000000..eeb48fd --- /dev/null +++ b/src/models/repositories/sales_repository.py @@ -0,0 +1,51 @@ +""" +Acesso ao banco de dados +faz a contrução da query, ela mantem a segurança de conexão +""" +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy import select, column, table, and_, between +from sqlalchemy.orm import Session +from src.models.schemas.product_sales_schema import ProductSalesSchema + +class SalesRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + self.__table = table("data_product_sales") + self.__col_map = ProductSalesSchema.COLUMN_MAP + + def read_db(self,product_code: int, store_code: int, data_range: list) -> pd.DataFrame: + + with self.__db.get_engine().connect() as conn: + try: + query = select("*").select_from(self.__table) + + if product_code is not None: + query = query.where(column(self.__col_map["product_code"]) == product_code) + + if store_code is not None: + query = query.where(column(self.__col_map["store_code"]) == store_code) + + if data_range is not None: + + data_start = data_range[0] + data_end = data_range[1] + + if data_start and data_end: + query = query.where(column(self.__col_map["data_range"]).between(data_start,data_end)) + + elif data_start: + query = query.where(column(self.__col_map["data_range"]) >= data_start) + + elif data_end: + query = query.where(column(self.__col_map["data_range"]) <= data_end) + + print(query) + response = pd.read_sql(query, conn) + + return response + + except Exception as exception: + raise Exception(f"erro na query:{exception}") + \ No newline at end of file diff --git a/src/models/repositories/store_repository.py b/src/models/repositories/store_repository.py new file mode 100644 index 0000000..4f178a2 --- /dev/null +++ b/src/models/repositories/store_repository.py @@ -0,0 +1,53 @@ +""" +Como não são enviados dados nessa query, +não foi necessario a contrução de schemas +""" +from src.models.config.connection import DBConnectionHandler +import pandas as pd +from sqlalchemy.orm import Session +from sqlalchemy import text + + + +class StoreRepository: + + def __init__(self): + self.__db = DBConnectionHandler() + + + def get_store_cad(self) -> pd.DataFrame: + + try: + query = text(""" + SELECT + STORE_CODE, + STORE_NAME, + START_DATE, + END_DATE, + BUSINESS_NAME, + BUSINESS_CODE + FROM data_store_cad + """) + with self.__db.get_engine().connect() as conn: + return pd.read_sql(query, conn) + + except Exception as exception: + raise Exception(f"erro na query {exception}") + + def get_store_sales(self) -> pd.DataFrame: + + try: + query = text(""" + SELECT + STORE_CODE, + DATE, + SALES_VALUE, + SALES_QTY + FROM data_store_sales + WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31' + """) + with self.__db.get_engine().connect() as conn: + return pd.read_sql(query, conn) + + except Exception as exception: + raise Exception(f"erro na query {exception}") \ No newline at end of file diff --git a/src/models/schemas/__init__.py b/src/models/schemas/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/models/schemas/__pycache__/__init__.cpython-314.pyc b/src/models/schemas/__pycache__/__init__.cpython-314.pyc new file mode 100644 index 0000000..18c211b Binary files /dev/null and b/src/models/schemas/__pycache__/__init__.cpython-314.pyc differ diff --git a/src/models/schemas/__pycache__/product_sales_schema.cpython-314.pyc b/src/models/schemas/__pycache__/product_sales_schema.cpython-314.pyc new file mode 100644 index 0000000..2be6df6 Binary files /dev/null and b/src/models/schemas/__pycache__/product_sales_schema.cpython-314.pyc differ diff --git a/src/models/schemas/product_sales_schema.py b/src/models/schemas/product_sales_schema.py new file mode 100644 index 0000000..7a4dc22 --- /dev/null +++ b/src/models/schemas/product_sales_schema.py @@ -0,0 +1,77 @@ +""" +Shemas de dados - +Como vamos enviar dados para o banco de dados, +a camada de schema evita que seja enviado o tipo errado e ataques externos + +IA - Ajuda com o formato e tratamento com datas +""" +from pydantic import BaseModel +from typing import Optional, List, ClassVar, Dict +from datetime import datetime + + + +class ProductSalesSchema(BaseModel): + + def product_validator(self, product_code: Optional[int] = None) -> int: + + if product_code is None: + return None + + if type(product_code) == int: + return product_code + else: + raise ValueError("valores em números inteiros") + + + def store_validator(self, store_code: Optional[str] = None) -> str: + + if store_code is None: + return None + + elif type(store_code) == int: + store_code = str(store_code) + return store_code + else: + raise ValueError("valores em números inteiros") + + + def data_validator(self, data_range: Optional[List[str]] = None) -> Optional[List]: + + if data_range is None: + return None + + if not isinstance(data_range, list): + raise ValueError("data_range precisa ser uma lista") + + if len(data_range) != 2: + raise ValueError("data_range pracisa ter duas posiçoes") + + validate_data = [] + for data_inf in data_range: + + if data_inf is None: + validate_data.append(None) + continue + + try: + data_obj = datetime.strptime(data_inf, "%Y-%m-%d").date() + validate_data.append(data_obj) + except ValueError: + raise ValueError(f"Data {data_inf} fora do formato") + + if validate_data[0] and validate_data[1]: + if validate_data[0] > validate_data[1]: + raise ValueError(f"Data inicial - {validate_data[0]} maior que Data final - {validate_data[1]}") + + if validate_data[0] is None and validate_data[1] is None: + return None + + + return validate_data + + COLUMN_MAP: ClassVar[Dict[str, str]] = { + "product_code": "PRODUCT_CODE", + "store_code": "STORE_CODE", + "data_range": "DATE" + } \ No newline at end of file diff --git a/src/notebooks/__init__py b/src/notebooks/__init__py new file mode 100644 index 0000000..e69de29 diff --git a/src/notebooks/grafico_case3.png b/src/notebooks/grafico_case3.png new file mode 100644 index 0000000..30f8842 Binary files /dev/null and b/src/notebooks/grafico_case3.png differ diff --git a/src/notebooks/nt_case1.ipynb b/src/notebooks/nt_case1.ipynb new file mode 100644 index 0000000..294c35d --- /dev/null +++ b/src/notebooks/nt_case1.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "66dc8a40", + "metadata": {}, + "source": [ + "## Caso 1 - Função dinâmica para consultar a tabela data_product_sales" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ce951cfd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5db6c08f", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "\n", + "from src.models.schemas.product_sales_schema import ProductSalesSchema\n", + "from src.models.repositories.sales_repository import SalesRepository\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = SalesRepository()\n", + "schema = ProductSalesSchema()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "97f1c77d", + "metadata": {}, + "outputs": [], + "source": [ + "# função retrieve_data \n", + "\n", + "def retrieve_data(product_code:int = None, store_code:int = None, data_range:List = None) -> pd.DataFrame:\n", + " \"\"\"\n", + " Recupera dados da tabela data_product_sales com filtros opcionais.\n", + "\n", + " Parâmetros\n", + "\n", + " product_code: int -> código do produto, se NONE retorna todos\n", + "\n", + " store_code: int -> código da loja, se NONE rotorna todos\n", + "\n", + " data_range: list -> intervalo de datas, aceita NONE para intervalos abertos\n", + " \n", + " \"\"\"\n", + "\n", + " product_code = schema.product_validator(product_code)\n", + " store_code = schema.store_validator(store_code)\n", + " data_range = schema.data_validator(data_range)\n", + "\n", + " response = repo.read_db(product_code=product_code, store_code= store_code, data_range= data_range)\n", + "\n", + " return response \n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "888d8af7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales\n" + ] + }, + { + "data": { + "text/plain": [ + "( PRODUCT_CODE SALES_VALUE SALES_QTY\n", + " count 2.173133e+06 2.173133e+06 2.173133e+06\n", + " mean 2.641163e+04 2.428775e+03 1.565161e+02\n", + " std 4.611153e+04 2.731317e+03 4.527131e+01\n", + " min 1.000000e+01 6.279000e+01 3.900000e+01\n", + " 25% 1.111000e+03 7.490800e+02 1.200000e+02\n", + " 50% 5.065000e+03 1.405800e+03 1.490000e+02\n", + " 75% 2.314100e+04 3.122580e+03 1.820000e+02\n", + " max 2.414040e+05 4.783863e+04 3.850000e+02,\n", + " )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# uso da função - sem filtros\n", + "\n", + "base = retrieve_data()\n", + "base.describe(), base.info" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "52d26401", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"PRODUCT_CODE\" = :PRODUCT_CODE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 5840.0 5840.000000 5840.000000\n", + "mean 18.0 948.090959 86.980822\n", + "std 0.0 233.068771 21.382456\n", + "min 18.0 632.200000 58.000000\n", + "25% 18.0 741.200000 68.000000\n", + "50% 18.0 882.900000 81.000000\n", + "75% 18.0 1100.900000 101.000000\n", + "max 18.0 1591.400000 146.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - só o produto\n", + "\n", + "base = retrieve_data(product_code= 18)\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b66e255d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"STORE_CODE\" = :STORE_CODE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 136729.000000 136729.000000 136729.000000\n", + "mean 28427.114840 2443.597482 158.605219\n", + "std 47090.707043 2652.085917 45.859105\n", + "min 18.000000 62.790000 58.000000\n", + "25% 2120.000000 724.790000 121.000000\n", + "50% 6479.000000 1404.000000 152.000000\n", + "75% 26793.000000 3319.770000 184.000000\n", + "max 241404.000000 24202.800000 332.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - só a loja \n", + "base = retrieve_data(store_code=1)\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b4454c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"DATE\" BETWEEN :DATE_1 AND :DATE_2\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 39401.000000 39401.000000 39401.000000\n", + "mean 26658.305271 3132.077588 184.287049\n", + "std 46321.780252 3590.919739 55.651229\n", + "min 10.000000 75.000000 48.000000\n", + "25% 1223.000000 880.200000 152.000000\n", + "50% 6019.000000 1763.050000 179.000000\n", + "75% 23141.000000 4195.800000 223.000000\n", + "max 241404.000000 41877.430000 337.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - datas - intervalo fechado\n", + "base = retrieve_data(data_range=[\"2019-01-01\",\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "fd0bc8af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"DATE\" <= :DATE_1\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 1.748619e+06 1.748619e+06 1.748619e+06\n", + "mean 2.635175e+04 2.253023e+03 1.495118e+02\n", + "std 4.606029e+04 2.436324e+03 3.907265e+01\n", + "min 1.000000e+01 6.279000e+01 3.900000e+01\n", + "25% 1.111000e+03 7.183800e+02 1.170000e+02\n", + "50% 5.065000e+03 1.332840e+03 1.390000e+02\n", + "75% 2.314100e+04 2.864000e+03 1.750000e+02\n", + "max 2.414040e+05 4.187743e+04 3.370000e+02\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - datas - intervalo aberto\n", + "base = retrieve_data(data_range=[ None,\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "dbe44ab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT * \n", + "FROM data_product_sales \n", + "WHERE \"PRODUCT_CODE\" = :PRODUCT_CODE_1 AND \"STORE_CODE\" = :STORE_CODE_1 AND \"DATE\" BETWEEN :DATE_1 AND :DATE_2\n", + " PRODUCT_CODE SALES_VALUE SALES_QTY\n", + "count 31.0 31.000000 31.000000\n", + "mean 18.0 939.509677 86.193548\n", + "std 0.0 236.899229 21.733874\n", + "min 18.0 654.000000 60.000000\n", + "25% 18.0 735.750000 67.500000\n", + "50% 18.0 850.200000 78.000000\n", + "75% 18.0 1079.100000 99.000000\n", + "max 18.0 1395.200000 128.000000\n", + "\n" + ] + } + ], + "source": [ + "# uso da função - todos os filtros\n", + "base = retrieve_data(product_code= 18, store_code=1, data_range=[\"2019-01-01\",\"2019-01-31\"])\n", + "print(base.describe())\n", + "print(base.info)" + ] + }, + { + "cell_type": "markdown", + "id": "2a9482e2", + "metadata": {}, + "source": [ + "## Decisões técnicas\n", + "\n", + "- Parâmetros opcionais com default `None` — qualquer combinação de filtros é válida\n", + "- Validação isolada no Schema antes de chegar ao Repository\n", + "- Datas entram como string ISO e são convertidas para `datetime.date` internamente\n", + "- Filtro de data suporta intervalo aberto (só início ou só fim)\n", + "- Queries montadas com SQLAlchemy Core — sem concatenação de string, sem risco de SQL Injection\n", + "- IA utilizada para: revisão de bugs nos validators e estrutura dos testes pytest" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/nt_case2.ipynb b/src/notebooks/nt_case2.ipynb new file mode 100644 index 0000000..cec7400 --- /dev/null +++ b/src/notebooks/nt_case2.ipynb @@ -0,0 +1,2003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0769bdcd", + "metadata": {}, + "source": [ + "## Caso2 - Ticket Medio por Loja" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8c175878", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "\n", + "root = \"/home/santanna/develope/projetos/data-challenge_luiz_bernardo\"\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "aa53ae46", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "from src.models.repositories.store_repository import StoreRepository\n", + "import matplotlib.pyplot as plt\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = StoreRepository()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ca11dcf6", + "metadata": {}, + "outputs": [], + "source": [ + "# bases de dados \n", + "\n", + "base_cad = repo.get_store_cad()\n", + "base_sales = repo.get_store_sales()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0869a461", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 7300 entries, 0 to 7299\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STORE_CODE 7300 non-null int64 \n", + " 1 DATE 7300 non-null object \n", + " 2 SALES_VALUE 7300 non-null float64\n", + " 3 SALES_QTY 7300 non-null int64 \n", + "dtypes: float64(1), int64(2), object(1)\n", + "memory usage: 228.3+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "(,\n", + " None)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_sales.describe, base_sales.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b66ea522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1840 entries, 5460 to 7299\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STORE_CODE 1840 non-null int64 \n", + " 1 DATE 1840 non-null datetime64[s]\n", + " 2 SALES_VALUE 1840 non-null float64 \n", + " 3 SALES_QTY 1840 non-null int64 \n", + "dtypes: datetime64[s](1), float64(1), int64(2)\n", + "memory usage: 57.6 KB\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " STORE_CODE DATE SALES_VALUE SALES_QTY\n", + "5460 1 2019-10-01 187601.54 12160\n", + "5461 10 2019-10-01 139038.86 5223\n", + "5462 11 2019-10-01 252687.35 8481\n", + "5463 12 2019-10-01 223973.64 7659\n", + "5464 13 2019-10-01 187601.54 12160" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# formato da data para o filtro reconhecer\n", + "base_sales[\"DATE\"] = pd.to_datetime(base_sales['DATE'])\n", + "\n", + "# filtro \n", + "data_inicio = \"2019-10-01\"\n", + "data_fim = \"2019-12-31\"\n", + "\n", + "base_sales_filtrado = base_sales[base_sales['DATE'].between(data_inicio,data_fim)]\n", + "\n", + "base_sales_filtrado.describe, base_sales_filtrado.info()\n", + "base_sales_filtrado.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "46f0729f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODEDATESALES_VALUESALES_QTYSTORE_NAMESTART_DATEEND_DATEBUSINESS_NAMEBUSINESS_CODE
012019-10-01187601.5412160Sao Paulo2006-10-01Varejo1
1102019-10-01139038.865223Hong Kong2019-01-01Farma4
2112019-10-01252687.358481Rio de Janeiro2019-01-01Farma4
3122019-10-01223973.647659Madri2019-01-01Farma4
4132019-10-01187601.5412160Dubai2019-01-01Atacado5
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" + ], + "text/plain": [ + " STORE_CODE DATE SALES_VALUE SALES_QTY STORE_NAME START_DATE \\\n", + "0 1 2019-10-01 187601.54 12160 Sao Paulo 2006-10-01 \n", + "1 10 2019-10-01 139038.86 5223 Hong Kong 2019-01-01 \n", + "2 11 2019-10-01 252687.35 8481 Rio de Janeiro 2019-01-01 \n", + "3 12 2019-10-01 223973.64 7659 Madri 2019-01-01 \n", + "4 13 2019-10-01 187601.54 12160 Dubai 2019-01-01 \n", + "\n", + " END_DATE BUSINESS_NAME BUSINESS_CODE \n", + "0 Varejo 1 \n", + "1 Farma 4 \n", + "2 Farma 4 \n", + "3 Farma 4 \n", + "4 Atacado 5 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# união de tabelas \n", + "base_merge = base_sales_filtrado.merge(base_cad, on = \"STORE_CODE\", how = \"left\")\n", + "base_merge.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6a2342dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DATESALES_VALUE...SALES_QTYBUSINESS_CODE
countmeanmin25%50%75%maxstdcountmean...maxstdcountmeanmin25%50%75%maxstd
STORE_CODESTORE_NAMEBUSINESS_NAME
1Sao PauloVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
2ChicagoVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0238352.405217...23496.03756.44014492.01.01.01.01.01.01.00.0
3RomaVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
4TokioVarejo922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.01.01.01.01.01.01.00.0
5ParisProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
6BerlinProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
7New YorkProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.02.02.02.02.02.02.00.0
8BelemProximidade922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0228147.319239...22634.03625.28738992.02.02.02.02.02.02.00.0
9LondonFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0211649.871196...11614.01816.17662792.04.04.04.04.04.04.00.0
10Hong KongFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0163477.299348...9468.01512.57803192.04.04.04.04.04.04.00.0
11Rio de JaneiroFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0295348.721413...15480.02447.90879492.04.04.04.04.04.04.00.0
12MadriFarma922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0262276.077174...13977.02214.47931292.04.04.04.04.04.04.00.0
13DubaiAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
14BahiaAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
15Buenos AiresAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
16SalvadorAtacado922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.0230577.049674...22832.03657.79503192.05.05.05.05.05.05.00.0
17SidneyPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
18BangkokPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
19MiamiPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
20VancouverPosto922019-11-15 12:00:002019-10-01 00:00:002019-10-23 18:00:002019-11-15 12:00:002019-12-08 06:00:002019-12-31 00:00:00NaN92.091046.423913...10597.01656.53741092.03.03.03.03.03.03.00.0
\n", + "

20 rows × 32 columns

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" + ], + "text/plain": [ + " DATE \\\n", + " count mean \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 92 2019-11-15 12:00:00 \n", + "2 Chicago Varejo 92 2019-11-15 12:00:00 \n", + "3 Roma Varejo 92 2019-11-15 12:00:00 \n", + "4 Tokio Varejo 92 2019-11-15 12:00:00 \n", + "5 Paris Proximidade 92 2019-11-15 12:00:00 \n", + "6 Berlin Proximidade 92 2019-11-15 12:00:00 \n", + "7 New York Proximidade 92 2019-11-15 12:00:00 \n", + "8 Belem Proximidade 92 2019-11-15 12:00:00 \n", + "9 London Farma 92 2019-11-15 12:00:00 \n", + "10 Hong Kong Farma 92 2019-11-15 12:00:00 \n", + "11 Rio de Janeiro Farma 92 2019-11-15 12:00:00 \n", + "12 Madri Farma 92 2019-11-15 12:00:00 \n", + "13 Dubai Atacado 92 2019-11-15 12:00:00 \n", + "14 Bahia Atacado 92 2019-11-15 12:00:00 \n", + "15 Buenos Aires Atacado 92 2019-11-15 12:00:00 \n", + "16 Salvador Atacado 92 2019-11-15 12:00:00 \n", + "17 Sidney Posto 92 2019-11-15 12:00:00 \n", + "18 Bangkok Posto 92 2019-11-15 12:00:00 \n", + "19 Miami Posto 92 2019-11-15 12:00:00 \n", + "20 Vancouver Posto 92 2019-11-15 12:00:00 \n", + "\n", + " \\\n", + " min \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-10-01 00:00:00 \n", + "2 Chicago Varejo 2019-10-01 00:00:00 \n", + "3 Roma Varejo 2019-10-01 00:00:00 \n", + "4 Tokio Varejo 2019-10-01 00:00:00 \n", + "5 Paris Proximidade 2019-10-01 00:00:00 \n", + "6 Berlin Proximidade 2019-10-01 00:00:00 \n", + "7 New York Proximidade 2019-10-01 00:00:00 \n", + "8 Belem Proximidade 2019-10-01 00:00:00 \n", + "9 London Farma 2019-10-01 00:00:00 \n", + "10 Hong Kong Farma 2019-10-01 00:00:00 \n", + "11 Rio de Janeiro Farma 2019-10-01 00:00:00 \n", + "12 Madri Farma 2019-10-01 00:00:00 \n", + "13 Dubai Atacado 2019-10-01 00:00:00 \n", + "14 Bahia Atacado 2019-10-01 00:00:00 \n", + "15 Buenos Aires Atacado 2019-10-01 00:00:00 \n", + "16 Salvador Atacado 2019-10-01 00:00:00 \n", + "17 Sidney Posto 2019-10-01 00:00:00 \n", + "18 Bangkok Posto 2019-10-01 00:00:00 \n", + "19 Miami Posto 2019-10-01 00:00:00 \n", + "20 Vancouver Posto 2019-10-01 00:00:00 \n", + "\n", + " \\\n", + " 25% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-10-23 18:00:00 \n", + "2 Chicago Varejo 2019-10-23 18:00:00 \n", + "3 Roma Varejo 2019-10-23 18:00:00 \n", + "4 Tokio Varejo 2019-10-23 18:00:00 \n", + "5 Paris Proximidade 2019-10-23 18:00:00 \n", + "6 Berlin Proximidade 2019-10-23 18:00:00 \n", + "7 New York Proximidade 2019-10-23 18:00:00 \n", + "8 Belem Proximidade 2019-10-23 18:00:00 \n", + "9 London Farma 2019-10-23 18:00:00 \n", + "10 Hong Kong Farma 2019-10-23 18:00:00 \n", + "11 Rio de Janeiro Farma 2019-10-23 18:00:00 \n", + "12 Madri Farma 2019-10-23 18:00:00 \n", + "13 Dubai Atacado 2019-10-23 18:00:00 \n", + "14 Bahia Atacado 2019-10-23 18:00:00 \n", + "15 Buenos Aires Atacado 2019-10-23 18:00:00 \n", + "16 Salvador Atacado 2019-10-23 18:00:00 \n", + "17 Sidney Posto 2019-10-23 18:00:00 \n", + "18 Bangkok Posto 2019-10-23 18:00:00 \n", + "19 Miami Posto 2019-10-23 18:00:00 \n", + "20 Vancouver Posto 2019-10-23 18:00:00 \n", + "\n", + " \\\n", + " 50% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-11-15 12:00:00 \n", + "2 Chicago Varejo 2019-11-15 12:00:00 \n", + "3 Roma Varejo 2019-11-15 12:00:00 \n", + "4 Tokio Varejo 2019-11-15 12:00:00 \n", + "5 Paris Proximidade 2019-11-15 12:00:00 \n", + "6 Berlin Proximidade 2019-11-15 12:00:00 \n", + "7 New York Proximidade 2019-11-15 12:00:00 \n", + "8 Belem Proximidade 2019-11-15 12:00:00 \n", + "9 London Farma 2019-11-15 12:00:00 \n", + "10 Hong Kong Farma 2019-11-15 12:00:00 \n", + "11 Rio de Janeiro Farma 2019-11-15 12:00:00 \n", + "12 Madri Farma 2019-11-15 12:00:00 \n", + "13 Dubai Atacado 2019-11-15 12:00:00 \n", + "14 Bahia Atacado 2019-11-15 12:00:00 \n", + "15 Buenos Aires Atacado 2019-11-15 12:00:00 \n", + "16 Salvador Atacado 2019-11-15 12:00:00 \n", + "17 Sidney Posto 2019-11-15 12:00:00 \n", + "18 Bangkok Posto 2019-11-15 12:00:00 \n", + "19 Miami Posto 2019-11-15 12:00:00 \n", + "20 Vancouver Posto 2019-11-15 12:00:00 \n", + "\n", + " \\\n", + " 75% \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-12-08 06:00:00 \n", + "2 Chicago Varejo 2019-12-08 06:00:00 \n", + "3 Roma Varejo 2019-12-08 06:00:00 \n", + "4 Tokio Varejo 2019-12-08 06:00:00 \n", + "5 Paris Proximidade 2019-12-08 06:00:00 \n", + "6 Berlin Proximidade 2019-12-08 06:00:00 \n", + "7 New York Proximidade 2019-12-08 06:00:00 \n", + "8 Belem Proximidade 2019-12-08 06:00:00 \n", + "9 London Farma 2019-12-08 06:00:00 \n", + "10 Hong Kong Farma 2019-12-08 06:00:00 \n", + "11 Rio de Janeiro Farma 2019-12-08 06:00:00 \n", + "12 Madri Farma 2019-12-08 06:00:00 \n", + "13 Dubai Atacado 2019-12-08 06:00:00 \n", + "14 Bahia Atacado 2019-12-08 06:00:00 \n", + "15 Buenos Aires Atacado 2019-12-08 06:00:00 \n", + "16 Salvador Atacado 2019-12-08 06:00:00 \n", + "17 Sidney Posto 2019-12-08 06:00:00 \n", + "18 Bangkok Posto 2019-12-08 06:00:00 \n", + "19 Miami Posto 2019-12-08 06:00:00 \n", + "20 Vancouver Posto 2019-12-08 06:00:00 \n", + "\n", + " SALES_VALUE \\\n", + " max std count \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "2 Chicago Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "3 Roma Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "4 Tokio Varejo 2019-12-31 00:00:00 NaN 92.0 \n", + "5 Paris Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "6 Berlin Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "7 New York Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "8 Belem Proximidade 2019-12-31 00:00:00 NaN 92.0 \n", + "9 London Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "10 Hong Kong Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "11 Rio de Janeiro Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "12 Madri Farma 2019-12-31 00:00:00 NaN 92.0 \n", + "13 Dubai Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "14 Bahia Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "15 Buenos Aires Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "16 Salvador Atacado 2019-12-31 00:00:00 NaN 92.0 \n", + "17 Sidney Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "18 Bangkok Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "19 Miami Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "20 Vancouver Posto 2019-12-31 00:00:00 NaN 92.0 \n", + "\n", + " ... SALES_QTY \\\n", + " mean ... max \n", + "STORE_CODE STORE_NAME BUSINESS_NAME ... \n", + "1 Sao Paulo Varejo 230577.049674 ... 22832.0 \n", + "2 Chicago Varejo 238352.405217 ... 23496.0 \n", + "3 Roma Varejo 230577.049674 ... 22832.0 \n", + "4 Tokio Varejo 230577.049674 ... 22832.0 \n", + "5 Paris Proximidade 230577.049674 ... 22832.0 \n", + "6 Berlin Proximidade 230577.049674 ... 22832.0 \n", + "7 New York Proximidade 230577.049674 ... 22832.0 \n", + "8 Belem Proximidade 228147.319239 ... 22634.0 \n", + "9 London Farma 211649.871196 ... 11614.0 \n", + "10 Hong Kong Farma 163477.299348 ... 9468.0 \n", + "11 Rio de Janeiro Farma 295348.721413 ... 15480.0 \n", + "12 Madri Farma 262276.077174 ... 13977.0 \n", + "13 Dubai Atacado 230577.049674 ... 22832.0 \n", + "14 Bahia Atacado 230577.049674 ... 22832.0 \n", + "15 Buenos Aires Atacado 230577.049674 ... 22832.0 \n", + "16 Salvador Atacado 230577.049674 ... 22832.0 \n", + "17 Sidney Posto 91046.423913 ... 10597.0 \n", + "18 Bangkok Posto 91046.423913 ... 10597.0 \n", + "19 Miami Posto 91046.423913 ... 10597.0 \n", + "20 Vancouver Posto 91046.423913 ... 10597.0 \n", + "\n", + " BUSINESS_CODE \\\n", + " std count mean min \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 3657.795031 92.0 1.0 1.0 \n", + "2 Chicago Varejo 3756.440144 92.0 1.0 1.0 \n", + "3 Roma Varejo 3657.795031 92.0 1.0 1.0 \n", + "4 Tokio Varejo 3657.795031 92.0 1.0 1.0 \n", + "5 Paris Proximidade 3657.795031 92.0 2.0 2.0 \n", + "6 Berlin Proximidade 3657.795031 92.0 2.0 2.0 \n", + "7 New York Proximidade 3657.795031 92.0 2.0 2.0 \n", + "8 Belem Proximidade 3625.287389 92.0 2.0 2.0 \n", + "9 London Farma 1816.176627 92.0 4.0 4.0 \n", + "10 Hong Kong Farma 1512.578031 92.0 4.0 4.0 \n", + "11 Rio de Janeiro Farma 2447.908794 92.0 4.0 4.0 \n", + "12 Madri Farma 2214.479312 92.0 4.0 4.0 \n", + "13 Dubai Atacado 3657.795031 92.0 5.0 5.0 \n", + "14 Bahia Atacado 3657.795031 92.0 5.0 5.0 \n", + "15 Buenos Aires Atacado 3657.795031 92.0 5.0 5.0 \n", + "16 Salvador Atacado 3657.795031 92.0 5.0 5.0 \n", + "17 Sidney Posto 1656.537410 92.0 3.0 3.0 \n", + "18 Bangkok Posto 1656.537410 92.0 3.0 3.0 \n", + "19 Miami Posto 1656.537410 92.0 3.0 3.0 \n", + "20 Vancouver Posto 1656.537410 92.0 3.0 3.0 \n", + "\n", + " \n", + " 25% 50% 75% max std \n", + "STORE_CODE STORE_NAME BUSINESS_NAME \n", + "1 Sao Paulo Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "2 Chicago Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "3 Roma Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "4 Tokio Varejo 1.0 1.0 1.0 1.0 0.0 \n", + "5 Paris Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "6 Berlin Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "7 New York Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "8 Belem Proximidade 2.0 2.0 2.0 2.0 0.0 \n", + "9 London Farma 4.0 4.0 4.0 4.0 0.0 \n", + "10 Hong Kong Farma 4.0 4.0 4.0 4.0 0.0 \n", + "11 Rio de Janeiro Farma 4.0 4.0 4.0 4.0 0.0 \n", + "12 Madri Farma 4.0 4.0 4.0 4.0 0.0 \n", + "13 Dubai Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "14 Bahia Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "15 Buenos Aires Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "16 Salvador Atacado 5.0 5.0 5.0 5.0 0.0 \n", + "17 Sidney Posto 3.0 3.0 3.0 3.0 0.0 \n", + "18 Bangkok Posto 3.0 3.0 3.0 3.0 0.0 \n", + "19 Miami Posto 3.0 3.0 3.0 3.0 0.0 \n", + "20 Vancouver Posto 3.0 3.0 3.0 3.0 0.0 \n", + "\n", + "[20 rows x 32 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base ticket\n", + "\n", + "base_tk = (\n", + " base_merge\n", + " .groupby(['STORE_CODE', 'STORE_NAME','BUSINESS_NAME'])\n", + ")\n", + "\n", + "base_tk.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5bd6f58f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODESTORE_NAMEBUSINESS_NAMESALES_VALUESALES_QTY
01Sao PauloVarejo21213088.571378476
12ChicagoVarejo21928421.281412372
23RomaVarejo21213088.571378476
34TokioVarejo21213088.571378476
45ParisProximidade21213088.571378476
56BerlinProximidade21213088.571378476
67New YorkProximidade21213088.571378476
78BelemProximidade20989553.371365988
89LondonFarma19471788.15671638
910Hong KongFarma15039911.54570745
1011Rio de JaneiroFarma27172082.37918336
1112MadriFarma24129399.10831168
1213DubaiAtacado21213088.571378476
1314BahiaAtacado21213088.571378476
1415Buenos AiresAtacado21213088.571378476
1516SalvadorAtacado21213088.571378476
1617SidneyPosto8376271.00612968
1718BangkokPosto8376271.00612968
1819MiamiPosto8376271.00612968
1920VancouverPosto8376271.00612968
\n", + "
" + ], + "text/plain": [ + " STORE_CODE STORE_NAME BUSINESS_NAME SALES_VALUE SALES_QTY\n", + "0 1 Sao Paulo Varejo 21213088.57 1378476\n", + "1 2 Chicago Varejo 21928421.28 1412372\n", + "2 3 Roma Varejo 21213088.57 1378476\n", + "3 4 Tokio Varejo 21213088.57 1378476\n", + "4 5 Paris Proximidade 21213088.57 1378476\n", + "5 6 Berlin Proximidade 21213088.57 1378476\n", + "6 7 New York Proximidade 21213088.57 1378476\n", + "7 8 Belem Proximidade 20989553.37 1365988\n", + "8 9 London Farma 19471788.15 671638\n", + "9 10 Hong Kong Farma 15039911.54 570745\n", + "10 11 Rio de Janeiro Farma 27172082.37 918336\n", + "11 12 Madri Farma 24129399.10 831168\n", + "12 13 Dubai Atacado 21213088.57 1378476\n", + "13 14 Bahia Atacado 21213088.57 1378476\n", + "14 15 Buenos Aires Atacado 21213088.57 1378476\n", + "15 16 Salvador Atacado 21213088.57 1378476\n", + "16 17 Sidney Posto 8376271.00 612968\n", + "17 18 Bangkok Posto 8376271.00 612968\n", + "18 19 Miami Posto 8376271.00 612968\n", + "19 20 Vancouver Posto 8376271.00 612968" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# requisitos para o ticket médio\n", + "\n", + "base_tk = (\n", + " base_tk\n", + " .agg(\n", + " SALES_VALUE=(\"SALES_VALUE\", \"sum\"),\n", + " SALES_QTY=(\"SALES_QTY\", \"sum\")\n", + " )\n", + " .reset_index()\n", + " )\n", + "base_tk" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a32de4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODESTORE_NAMEBUSINESS_NAMESALES_VALUESALES_QTYTM
01Sao PauloVarejo21213088.57137847615.39
12ChicagoVarejo21928421.28141237215.53
23RomaVarejo21213088.57137847615.39
34TokioVarejo21213088.57137847615.39
45ParisProximidade21213088.57137847615.39
56BerlinProximidade21213088.57137847615.39
67New YorkProximidade21213088.57137847615.39
78BelemProximidade20989553.37136598815.37
89LondonFarma19471788.1567163828.99
910Hong KongFarma15039911.5457074526.35
1011Rio de JaneiroFarma27172082.3791833629.59
1112MadriFarma24129399.1083116829.03
1213DubaiAtacado21213088.57137847615.39
1314BahiaAtacado21213088.57137847615.39
1415Buenos AiresAtacado21213088.57137847615.39
1516SalvadorAtacado21213088.57137847615.39
1617SidneyPosto8376271.0061296813.67
1718BangkokPosto8376271.0061296813.67
1819MiamiPosto8376271.0061296813.67
1920VancouverPosto8376271.0061296813.67
\n", + "
" + ], + "text/plain": [ + " STORE_CODE STORE_NAME BUSINESS_NAME SALES_VALUE SALES_QTY TM\n", + "0 1 Sao Paulo Varejo 21213088.57 1378476 15.39\n", + "1 2 Chicago Varejo 21928421.28 1412372 15.53\n", + "2 3 Roma Varejo 21213088.57 1378476 15.39\n", + "3 4 Tokio Varejo 21213088.57 1378476 15.39\n", + "4 5 Paris Proximidade 21213088.57 1378476 15.39\n", + "5 6 Berlin Proximidade 21213088.57 1378476 15.39\n", + "6 7 New York Proximidade 21213088.57 1378476 15.39\n", + "7 8 Belem Proximidade 20989553.37 1365988 15.37\n", + "8 9 London Farma 19471788.15 671638 28.99\n", + "9 10 Hong Kong Farma 15039911.54 570745 26.35\n", + "10 11 Rio de Janeiro Farma 27172082.37 918336 29.59\n", + "11 12 Madri Farma 24129399.10 831168 29.03\n", + "12 13 Dubai Atacado 21213088.57 1378476 15.39\n", + "13 14 Bahia Atacado 21213088.57 1378476 15.39\n", + "14 15 Buenos Aires Atacado 21213088.57 1378476 15.39\n", + "15 16 Salvador Atacado 21213088.57 1378476 15.39\n", + "16 17 Sidney Posto 8376271.00 612968 13.67\n", + "17 18 Bangkok Posto 8376271.00 612968 13.67\n", + "18 19 Miami Posto 8376271.00 612968 13.67\n", + "19 20 Vancouver Posto 8376271.00 612968 13.67" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculo do ticket medio\n", + "\n", + "base_tk[\"TM\"] = (base_tk[\"SALES_VALUE\"] / base_tk[\"SALES_QTY\"]).round(2)\n", + "\n", + "base_tk" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a0d5d58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_NAMEBUSINESS_NAMETM
13BahiaAtacado15.39
17BangkokPosto13.67
7BelemProximidade15.37
5BerlinProximidade15.39
14Buenos AiresAtacado15.39
1ChicagoVarejo15.53
12DubaiAtacado15.39
9Hong KongFarma26.35
8LondonFarma28.99
11MadriFarma29.03
18MiamiPosto13.67
6New YorkProximidade15.39
4ParisProximidade15.39
10Rio de JaneiroFarma29.59
2RomaVarejo15.39
15SalvadorAtacado15.39
0Sao PauloVarejo15.39
16SidneyPosto13.67
3TokioVarejo15.39
19VancouverPosto13.67
\n", + "
" + ], + "text/plain": [ + " STORE_NAME BUSINESS_NAME TM\n", + "13 Bahia Atacado 15.39\n", + "17 Bangkok Posto 13.67\n", + "7 Belem Proximidade 15.37\n", + "5 Berlin Proximidade 15.39\n", + "14 Buenos Aires Atacado 15.39\n", + "1 Chicago Varejo 15.53\n", + "12 Dubai Atacado 15.39\n", + "9 Hong Kong Farma 26.35\n", + "8 London Farma 28.99\n", + "11 Madri Farma 29.03\n", + "18 Miami Posto 13.67\n", + "6 New York Proximidade 15.39\n", + "4 Paris Proximidade 15.39\n", + "10 Rio de Janeiro Farma 29.59\n", + "2 Roma Varejo 15.39\n", + "15 Salvador Atacado 15.39\n", + "0 Sao Paulo Varejo 15.39\n", + "16 Sidney Posto 13.67\n", + "3 Tokio Varejo 15.39\n", + "19 Vancouver Posto 13.67" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# BASE DE RESPOSTA TM \n", + "\n", + "base = base_tk[[\"STORE_NAME\", \"BUSINESS_NAME\", \"TM\"]].sort_values(\"STORE_NAME\")\n", + "\n", + "base" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9e44954b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# RESPOSTA FINAL \n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, len(base) * 0.4 + 1))\n", + "ax.axis(\"off\")\n", + "\n", + "table = ax.table(\n", + " cellText=base[[\"STORE_NAME\", \"BUSINESS_NAME\", \"TM\"]].values,\n", + " colLabels=[\"Loja\", \"Categoria\", \"TM\"],\n", + " cellLoc=\"left\",\n", + " loc=\"center\"\n", + ")\n", + "\n", + "table.auto_set_font_size(False)\n", + "table.set_fontsize(10)\n", + "table.auto_set_column_width([0, 1, 2])\n", + "\n", + "for j in range(3):\n", + " table[0, j].set_facecolor(\"#264653\")\n", + " table[0, j].set_text_props(color=\"white\", fontweight=\"bold\")\n", + "\n", + "for i in range(1, len(base) + 1):\n", + " cor = \"#f0f4f8\" if i % 2 == 0 else \"white\"\n", + " for j in range(3):\n", + " table[i, j].set_facecolor(cor)\n", + "\n", + "plt.title(\"Ticket Médio por Loja — Q4 2019\", fontsize=13, pad=20)\n", + "plt.tight_layout()\n", + "plt.savefig(\"tabela_tm.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a0ad3d9", + "metadata": {}, + "source": [ + "## Decisões técnicas\n", + "\n", + "- Separação entre etapa de preparação dos dados e cálculo dos indicadores — evita misturar transformação com regra de negócio\n", + "- Agregações feitas com operações vetorizadas do Pandas (`groupby`/`agg`) para reduzir custo de processamento em grandes volumes\n", + "- Ticket médio calculado após a consolidação dos dados — `SALES_VALUE / SALES_QTY` evita média incorreta por linha\n", + "- Datas normalizadas para `datetime` antes dos filtros e agrupamentos\n", + "- Agrupamento por dimensões de negócio (`STORE_CODE`, `STORE_NAME`, `BUSINESS_NAME`) para manter rastreabilidade do resultado\n", + "- Tratamento da base final focado em métricas de negócio, mantendo apenas colunas necessárias para resposta\n", + "- IA utilizada para: revisão da lógica de agregação e validação dos cálculos de indicadores" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/nt_case3.html b/src/notebooks/nt_case3.html new file mode 100644 index 0000000..60741db --- /dev/null +++ b/src/notebooks/nt_case3.html @@ -0,0 +1,9495 @@ + + + + + +nt_case3 + + + + + + + + + + + + +
+ + + + +
+ + diff --git a/src/notebooks/nt_case3.ipynb b/src/notebooks/nt_case3.ipynb new file mode 100644 index 0000000..3510170 --- /dev/null +++ b/src/notebooks/nt_case3.ipynb @@ -0,0 +1,2564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9308855", + "metadata": {}, + "source": [ + "## Caso 3 - Visalização Livre\n", + "\n", + "> Eu sempre fui um amante da sétima arte, e sempre na conversar com os amigos, a gente nota, a volta de uma onda já vivida, como a volta do \"TODO MUNDO EM PÂNICO\", o que deve acarretar em outros lançamntos do tema comedia pastelão.\n", + "\n", + "> Como temos um histórico de filmes, vou tentar encontrar alguma relação.\n", + "> Acredito que dados com contextos, contam historias. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "790149de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/santanna/develope/projetos/data-challenge_luiz_bernardo\n" + ] + } + ], + "source": [ + "# link do notebook com o sistema\n", + "import sys\n", + "import os\n", + "\n", + "\n", + "root = \"/home/santanna/develope/projetos/data-challenge_luiz_bernardo\"\n", + "# raiz do projeto\n", + "if root not in sys.path:\n", + " sys.path.insert(0, root)\n", + "\n", + "print(sys.path[0]) # confirma" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c8e7249b", + "metadata": {}, + "outputs": [], + "source": [ + "# importações\n", + "from src.models.repositories.imdb_repository import ImdbRepository\n", + "import matplotlib.pyplot as plt\n", + "from typing import List\n", + "import pandas as pd\n", + "\n", + "repo = ImdbRepository()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4ddf36d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 1000 non-null int64 \n", + " 1 Title 1000 non-null str \n", + " 2 Genre 1000 non-null str \n", + " 3 Director 1000 non-null str \n", + " 4 Actors 1000 non-null str \n", + " 5 Year 1000 non-null int64 \n", + " 6 Runtime 1000 non-null int64 \n", + " 7 Rating 1000 non-null float64\n", + " 8 Votes 1000 non-null int64 \n", + " 9 RevenueMillions 872 non-null float64\n", + " 10 Metascore 936 non-null float64\n", + "dtypes: float64(3), int64(4), str(4)\n", + "memory usage: 86.1 KB\n" + ] + }, + { + "data": { + "text/plain": [ + "(,\n", + " None)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base de dados \n", + "base = repo.read_db()\n", + "base.describe, base.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da2985d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdTitleGenreDirectorActorsYearRuntimeRatingVotesRevenueMillionsMetascore
01Guardians of the GalaxyAction,Adventure,Sci-FiJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0
12PrometheusAdventure,Mystery,Sci-FiRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0
23SplitHorror,ThrillerM. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar...20161177.0157606138.062.0
34SingAnimation,Comedy,FamilyChristophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma...20161087.060545270.059.0
45Suicide SquadAction,Adventure,FantasyDavid AyerWill Smith, Jared Leto, Margot Robbie, Viola D...20161236.0393727325.040.0
....................................
991992Taare Zameen ParDrama,Family,MusicAamir KhanDarsheel Safary, Aamir Khan, Tanay Chheda, Sac...20071659.01026971.042.0
993994Resident Evil: AfterlifeAction,Adventure,HorrorPaul W.S. AndersonMilla Jovovich, Ali Larter, Wentworth Miller,K...2010976.014090060.037.0
997998Step Up 2: The StreetsDrama,Music,RomanceJon M. ChuRobert Hoffman, Briana Evigan, Cassie Ventura,...2008986.07069958.050.0
998999Search PartyAdventure,ComedyScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0
9991000Nine LivesComedy,Family,FantasyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.0
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" + ], + "text/plain": [ + " Id Title ... RevenueMillions Metascore\n", + "0 1 Guardians of the Galaxy ... 333.0 76.0\n", + "1 2 Prometheus ... 126.0 65.0\n", + "2 3 Split ... 138.0 62.0\n", + "3 4 Sing ... 270.0 59.0\n", + "4 5 Suicide Squad ... 325.0 40.0\n", + ".. ... ... ... ... ...\n", + "991 992 Taare Zameen Par ... 1.0 42.0\n", + "993 994 Resident Evil: Afterlife ... 60.0 37.0\n", + "997 998 Step Up 2: The Streets ... 58.0 50.0\n", + "998 999 Search Party ... NaN 22.0\n", + "999 1000 Nine Lives ... 20.0 11.0\n", + "\n", + "[541 rows x 11 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# base explorando\n", + "\"\"\"\n", + "generos são listas de sub temas, podemos usar isso.\n", + "\"\"\"\n", + "\n", + "base_genre = (\n", + " base\n", + " .groupby(['Genre'])\n", + ")\n", + "\n", + "base_genre" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "03cdf12a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Id Title Genre ... Votes RevenueMillions Metascore\n", + "0 1 Guardians of the Galaxy Action ... 757074 333.0 76.0\n", + "0 1 Guardians of the Galaxy Adventure ... 757074 333.0 76.0\n", + "0 1 Guardians of the Galaxy Sci-Fi ... 757074 333.0 76.0\n", + "1 2 Prometheus Adventure ... 485820 126.0 65.0\n", + "1 2 Prometheus Mystery ... 485820 126.0 65.0\n", + "\n", + "[5 rows x 11 columns]\n", + "Genre\n", + "Drama 513\n", + "Action 303\n", + "Comedy 279\n", + "Adventure 259\n", + "Thriller 195\n", + "Crime 150\n", + "Romance 141\n", + "Sci-Fi 120\n", + "Horror 119\n", + "Mystery 106\n", + "Fantasy 101\n", + "Biography 81\n", + "Family 51\n", + "Animation 49\n", + "History 29\n", + "Name: count, dtype: int64\n", + "\n", + "Total de linhas após explode: 2555\n" + ] + } + ], + "source": [ + "# separa os gêneros e explode — uma linha por gênero\n", + "base_imdb = (\n", + " base\n", + " .assign(Genre=base[\"Genre\"].str.split(\",\"))\n", + " .explode(\"Genre\")\n", + ")\n", + "\n", + "base_imdb[\"Genre\"] = base_imdb[\"Genre\"].str.strip()\n", + "\n", + "print(base_imdb.head())\n", + "\n", + "print(base_imdb[\"Genre\"].value_counts().head(15))\n", + "print(f\"\\nTotal de linhas após explode: {len(base_imdb)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "df09b163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearGenrecountavg_ratingavg_revenue
02006Action117.000000166.300000
12006Adventure137.153846122.846154
22006Animation27.000000221.000000
32006Biography37.00000061.333333
42006Comedy127.00000076.250000
..................
1902016Sci-Fi206.250000130.800000
1912016Sport56.8000004.666667
1922016Thriller606.18333330.387097
1932016War36.6666671.500000
1942016Western27.00000093.000000
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YearGenrecountavg_ratingavg_revenue
102006Horror46.75000024.750000
282007Horror116.81818258.000000
452008Horror46.50000044.333333
632009Horror76.28571429.857143
792010Horror86.12500027.285714
952011Horror35.66666740.500000
1122012Horror56.20000054.800000
1302013Horror136.38461556.384615
1482014Horror86.50000019.571429
1672015Horror116.09090920.000000
1862016Horror455.80000032.038462
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+ } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 16 + }, + "text": "Volume de Filmes de Comédia por Ano" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Ano" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Quantidade de Filmes" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "# linha de comédia por ano\n", + "fig = px.line(\n", + " base_terror,\n", + " x=\"Year\",\n", + " y=\"count\",\n", + " markers=True,\n", + " title=\"Volume de Filmes de Comédia por Ano\",\n", + " labels={\"count\": \"Quantidade de Filmes\", \"Year\": \"Ano\"}\n", + ")\n", + "\n", + "fig.update_traces(line_color=\"#e76f51\", line_width=3, marker_size=8)\n", + "fig.update_layout(\n", + " template=\"plotly_white\",\n", + " title_font_size=16\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f0e15d81", + "metadata": {}, + "source": [ + "## Análise Parcial \n", + "\n", + "> Não podemos prosseguir, pois temos um espaço temporal muito curto para fazer uma análise periodica de interesse sobre os temas dos filmes.\n", + "\n", + "> Mas análisando os dados podemos fazer uma analise interessante.\n", + "> podemos fazer uma analise a partir do faturamento junto com as quantidades de votos e o valor final dos votos, para buscar filmes que fizeram sucesso, vendo sucesso como filmes que tiveram faturamento, total de votos e nota acima da média \n", + "\n", + "* Já podemos ver nos anos finais um pico, reflexo dos Streammes?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "eaea8859", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yearcountavg_ratingavg_revenueavg_votes
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62012646.9108.0285226.1
72013916.987.1219049.6
82014986.985.1203930.2
920151276.678.3115726.2
1020162976.554.748591.8
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" + ], + "text/plain": [ + " Year count avg_rating avg_revenue avg_votes\n", + "0 2006 44 7.3 86.2 269290.0\n", + "1 2007 53 7.2 87.9 244331.0\n", + "2 2008 52 6.8 99.1 275505.4\n", + "3 2009 51 7.1 112.6 255780.6\n", + "4 2010 60 6.9 105.1 252782.3\n", + "5 2011 63 6.9 87.6 240790.3\n", + "6 2012 64 6.9 108.0 285226.1\n", + "7 2013 91 6.9 87.1 219049.6\n", + "8 2014 98 6.9 85.1 203930.2\n", + "9 2015 127 6.6 78.3 115726.2\n", + "10 2016 297 6.5 54.7 48591.8" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Buscando as medias base\n", + "\n", + "base_means = (\n", + " base\n", + " .groupby([\"Year\"])\n", + " .agg(\n", + " count = (\"Id\", \"count\"),\n", + " avg_rating = (\"Rating\", \"mean\"),\n", + " avg_revenue = (\"RevenueMillions\", \"mean\"),\n", + " avg_votes = (\"Votes\", \"mean\")\n", + " )\n", + " .reset_index()\n", + " .round(1)\n", + ")\n", + "base_means" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36a22445", + "metadata": {}, + "outputs": [ + { + "ename": "_IncompleteInputError", + "evalue": "incomplete input (2738720405.py, line 2)", + "output_type": "error", + "traceback": [ + " \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[63]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m \u001b[39m\u001b[31m\"\"\"\u001b[39m\n ^\n\u001b[31m_IncompleteInputError\u001b[39m\u001b[31m:\u001b[39m incomplete input\n" + ] + } + ], + "source": [ + "# buscando os filmes de sucesso \n", + "\"\"\"\n", + "deixei pois vocês queriam ver a linha de raciocinio \n", + "\n", + "for x in base_means:\n", + " print(\"isso é x\",x)\n", + " for y in base_means:\n", + " print(\"isso é y\",base_means[y])\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d39cd7bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdTitleGenreDirectorActorsYearRuntimeRatingVotesRevenueMillionsMetascorecountavg_ratingavg_revenueavg_votes
01Guardians of the GalaxyActionJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
11Guardians of the GalaxyAdventureJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
21Guardians of the GalaxySci-FiJames GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.0757074333.076.0986.985.1203930.2
32PrometheusAdventureRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0646.9108.0285226.1
42PrometheusMysteryRidley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.065.0646.9108.0285226.1
................................................
2550999Search PartyAdventureScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0986.985.1203930.2
2551999Search PartyComedyScot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014936.04881NaN22.0986.985.1203930.2
25521000Nine LivesComedyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
25531000Nine LivesFamilyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
25541000Nine LivesFantasyBarry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.01243520.011.02976.554.748591.8
\n", + "

2555 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " Id Title ... avg_revenue avg_votes\n", + "0 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "1 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "2 1 Guardians of the Galaxy ... 85.1 203930.2\n", + "3 2 Prometheus ... 108.0 285226.1\n", + "4 2 Prometheus ... 108.0 285226.1\n", + "... ... ... ... ... ...\n", + "2550 999 Search Party ... 85.1 203930.2\n", + "2551 999 Search Party ... 85.1 203930.2\n", + "2552 1000 Nine Lives ... 54.7 48591.8\n", + "2553 1000 Nine Lives ... 54.7 48591.8\n", + "2554 1000 Nine Lives ... 54.7 48591.8\n", + "\n", + "[2555 rows x 15 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# montando tabela de sucessos\n", + "\n", + "base_greate = pd.merge(base_imdb, base_means, on= \"Year\", how= \"left\")\n", + "\n", + "base_greate" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "8410ad6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleGenreDirectorYearRatingavg_ratingVotesavg_votesRevenueMillionsavg_revenue
0300ActionZack Snyder20068.07.3637104269290.0211.086.2
1Casino RoyaleActionMartin Campbell20068.07.3495106269290.0167.086.2
2The Pursuit of HappynessBiographyGabriele Muccino20068.07.3361105269290.0163.086.2
3The DepartedCrimeMartin Scorsese20069.07.3937414269290.0132.086.2
4Inside ManCrimeSpike Lee20068.07.3285441269290.089.086.2
.................................
165Hacksaw RidgeBiographyMel Gibson20168.06.521176048591.867.054.7
166Now You See Me 2ActionJon M. Chu20167.06.515656748591.865.054.7
167Deepwater HorizonActionPeter Berg20167.06.58984948591.861.054.7
168FencesDramaDenzel Washington20167.06.55095348591.858.054.7
169Me Before YouDramaThea Sharrock20167.06.511332248591.856.054.7
\n", + "

170 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Title Genre ... RevenueMillions avg_revenue\n", + "0 300 Action ... 211.0 86.2\n", + "1 Casino Royale Action ... 167.0 86.2\n", + "2 The Pursuit of Happyness Biography ... 163.0 86.2\n", + "3 The Departed Crime ... 132.0 86.2\n", + "4 Inside Man Crime ... 89.0 86.2\n", + ".. ... ... ... ... ...\n", + "165 Hacksaw Ridge Biography ... 67.0 54.7\n", + "166 Now You See Me 2 Action ... 65.0 54.7\n", + "167 Deepwater Horizon Action ... 61.0 54.7\n", + "168 Fences Drama ... 58.0 54.7\n", + "169 Me Before You Drama ... 56.0 54.7\n", + "\n", + "[170 rows x 10 columns]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filtro de selecionados\n", + "\n", + "movies_top = (\n", + " (base_greate[\"Rating\"] > base_greate[\"avg_rating\"]) &\n", + " (base_greate[\"RevenueMillions\"] > base_greate[\"avg_revenue\"]) &\n", + " (base_greate[\"Votes\"] > base_greate[\"avg_votes\"])\n", + ")\n", + "\n", + "base_movie = base_greate.loc[\n", + " movies_top, \n", + " [\"Title\", \"Genre\", \"Director\", \"Year\", \"Rating\",\"avg_rating\", \"Votes\",\"avg_votes\", \"RevenueMillions\",\"avg_revenue\"]\n", + "].sort_values(by=[\"Year\", \"RevenueMillions\"], ascending=[True, False]).reset_index(drop=True)\n", + "\n", + "base_movie = base_movie.drop_duplicates(subset=[\"Title\"]).reset_index(drop=True)\n", + "\n", + "# 4. Visualiza o resultado\n", + "base_movie" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0035b72", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import pandas as pd\n", + "\n", + "\n", + "# ============================================================\n", + "# PREPARAÇÃO DOS DADOS\n", + "# ============================================================\n", + "\n", + "# Barras: média de TODOS os filmes (base_means)\n", + "df_bar = base_means[[\"Year\", \"avg_votes\", \"avg_rating\"]].copy()\n", + "\n", + "# Melhor filme por ano (mais votos) entre os ACIMA DA MÉDIA\n", + "df_max = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmax()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Melhor\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# Pior filme por ano (menos votos) entre os ACIMA DA MÉDIA\n", + "df_min = (\n", + " base_movie.loc[base_movie.groupby(\"Year\")[\"Votes\"].idxmin()]\n", + " [[\"Year\", \"Title\", \"Rating\", \"Votes\"]]\n", + " .assign(tipo=\"Pior\")\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "# ============================================================\n", + "# CONFIGURAÇÕES VISUAIS\n", + "# ============================================================\n", + "COR_BARRA = \"#2E86AB\"\n", + "COR_MELHOR = \"#F6AE2D\"\n", + "COR_PIOR = \"#E94F37\"\n", + "COR_FUNDO = \"#F8F9FA\"\n", + "\n", + "TAMANHO_CIRCULO = 650 # AUMENTADO: círculos maiores\n", + "OFFSET_NOME = 70000 # DIMINUÍDO: nome mais próximo do círculo\n", + "\n", + "# ============================================================\n", + "# CRIAR FIGURA\n", + "# ============================================================\n", + "fig, ax = plt.subplots(figsize=(18, 12))\n", + "\n", + "fig.patch.set_facecolor(COR_FUNDO)\n", + "ax.set_facecolor(COR_FUNDO)\n", + "\n", + "# --- BARRAS: Média de votos de TODOS os filmes por ano ---\n", + "bars = ax.bar(\n", + " df_bar[\"Year\"],\n", + " df_bar[\"avg_votes\"],\n", + " color=COR_BARRA,\n", + " width=0.55,\n", + " edgecolor=\"white\",\n", + " linewidth=2,\n", + " zorder=2,\n", + " alpha=0.88,\n", + ")\n", + "\n", + "# Escrever dados nas barras\n", + "for bar, row in zip(bars, df_bar.itertuples()):\n", + " altura = bar.get_height()\n", + " rating_medio = row.avg_rating\n", + " \n", + " # Valor da média de votos no TOPO da barra\n", + " # ax.text(\n", + " # bar.get_x() + bar.get_width() / 2,\n", + " # altura + 8000,\n", + " # f\"{altura:,.0f}\",\n", + " # ha=\"center\", va=\"bottom\",\n", + " # fontsize=10, fontweight=\"bold\", color=COR_BARRA\n", + " # )\n", + " \n", + " # Nota média DENTRO da barra (embaixo)\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " altura * 0.15,\n", + " f\"{rating_medio:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=12, fontweight=\"bold\", color=\"white\", zorder=5\n", + " )\n", + "\n", + "# --- CÍRCULOS: Melhor filme do ano ---\n", + "ax.scatter(\n", + " df_max[\"Year\"], df_max[\"Votes\"],\n", + " s=TAMANHO_CIRCULO, c=COR_MELHOR,\n", + " edgecolors=\"black\", linewidths=2, zorder=10, alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_max.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=10, fontweight=\"bold\", color=\"black\", zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"] + OFFSET_NOME, nome,\n", + " ha=\"center\", va=\"bottom\",\n", + " fontsize=9, fontweight=\"bold\", color=\"#333333\", zorder=11\n", + " )\n", + "\n", + "# --- CÍRCULOS: Pior filme do ano ---\n", + "ax.scatter(\n", + " df_min[\"Year\"], df_min[\"Votes\"],\n", + " s=TAMANHO_CIRCULO, c=COR_PIOR,\n", + " edgecolors=\"black\", linewidths=2, zorder=10, alpha=0.95\n", + ")\n", + "\n", + "for _, row in df_min.iterrows():\n", + " # Nota dentro do círculo\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"],\n", + " f\"{row['Rating']:.1f}\",\n", + " ha=\"center\", va=\"center\",\n", + " fontsize=10, fontweight=\"bold\", color=\"black\", zorder=11\n", + " )\n", + " # Nome do filme em cima do círculo\n", + " nome = row[\"Title\"][:22] + \"...\" if len(row[\"Title\"]) > 22 else row[\"Title\"]\n", + " ax.text(\n", + " row[\"Year\"], row[\"Votes\"] + OFFSET_NOME, nome,\n", + " ha=\"center\", va=\"bottom\",\n", + " fontsize=9, fontweight=\"bold\", color=\"#333333\", zorder=11\n", + " )\n", + "\n", + "# ============================================================\n", + "# ESTILIZAÇÃO FINAL\n", + "# ============================================================\n", + "ax.set_title(\"Filmes Acima da Média por Ano\\nMédia de Votos (barras) | Maior e Menor destaques por ano (círculos)\",\n", + " fontsize=18, fontweight=\"bold\", color=\"#1a1a1a\", pad=25\n", + ")\n", + "\n", + "ax.set_xlabel(\"Ano\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "ax.set_ylabel(\"Quantidade de Votos\", fontsize=14, fontweight=\"bold\", color=\"#333333\", labelpad=12)\n", + "\n", + "ax.set_xticks(range(2006, 2017))\n", + "ax.set_xticklabels([str(a) for a in range(2006, 2017)], fontsize=12, fontweight=\"bold\")\n", + "\n", + "ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\", color=\"gray\", zorder=0)\n", + "ax.set_axisbelow(True)\n", + "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f\"{int(x):,}\"))\n", + "\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "ax.spines[\"left\"].set_color(\"#cccccc\")\n", + "ax.spines[\"bottom\"].set_color(\"#cccccc\")\n", + "\n", + "ymax = max(df_bar[\"avg_votes\"].max(), df_max[\"Votes\"].max()) * 1.18\n", + "ax.set_ylim(0, ymax)\n", + "\n", + "# Legenda\n", + "legend_elements = [\n", + " mpatches.Patch(facecolor=COR_BARRA, edgecolor=\"white\", label=\"Media de Votos (todos os filmes)\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_MELHOR,\n", + " markeredgecolor='black', markersize=18, label=\"Maior Destaque do ano\"),\n", + " plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=COR_PIOR,\n", + " markeredgecolor='black', markersize=18, label=\"Menor Destaque do ano\"),\n", + "]\n", + "ax.legend(handles=legend_elements, loc=\"upper right\", fontsize=11,\n", + " frameon=True, fancybox=True, shadow=True, facecolor=\"white\", edgecolor=\"#cccccc\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9a567f9d", + "metadata": {}, + "source": [ + "## Análise Final\n", + "\n", + "A análise foi realizada explorando os dados de filmes com foco em métricas de avaliação do público, principalmente nota média e quantidade de votos recebidos ao longo dos anos.\n", + "\n", + "Para interpretação dos resultados, foi construída uma visualização comparando a quantidade média de votos por ano. As barras representam o volume médio de votos, enquanto os valores apresentados indicam a média anual. Também foram destacados pontos de maior e menor representatividade dentro de cada período para facilitar a identificação de comportamentos fora do padrão.\n", + "\n", + "Ao observar a evolução temporal, nota-se uma redução no volume médio de votos nos anos mais recentes, mesmo com o aumento da quantidade de filmes analisados. Esse comportamento pode indicar uma mudança na forma como o público interage e avalia filmes, porém não é possível afirmar uma relação direta com fatores externos, como crescimento das plataformas de streaming, pois não existem variáveis adicionais no conjunto de dados que comprovem essa hipótese.\n", + "\n", + "Também foram observados possíveis padrões entre determinados anos e grupos de filmes com características semelhantes. Entretanto, para validar uma relação entre esses agrupamentos e temas específicos, seriam necessárias análises adicionais envolvendo variáveis como gênero, orçamento, popularidade ou perfil do público.\n", + "\n", + "Como conclusão, o dado que pode ser confirmado pela análise é que existe uma diminuição na quantidade média de votos recebidos pelos filmes nos anos mais recentes, apesar do aumento no volume de produções disponíveis. Esse comportamento sugere uma mudança no engajamento do público ao longo do tempo, mas requer dados complementares para explicar as causas.\n" + ] + }, + { + "cell_type": "markdown", + "id": "588c35da", + "metadata": {}, + "source": [ + "## Decisões técnicas\n", + "\n", + "- Análise orientada a métricas de sucesso combinando faturamento, quantidade de votos e avaliação média do público\n", + "- Normalização das métricas utilizando valores médios da base para criar critérios comparáveis entre filmes\n", + "- Exploração dos dados por período para identificar tendências temporais e possíveis mudanças de comportamento\n", + "- Separação entre correlação observada e hipótese — evitando afirmar causas sem evidência suficiente nos dados\n", + "- Uso de visualizações para destacar padrões, outliers e filmes com maior relevância dentro das métricas analisadas\n", + "- Filtros e agregações aplicados antes da análise gráfica para reduzir ruído e melhorar interpretação\n", + "- IA utilizada para: revisão da interpretação dos gráficos e validação das conclusões extraídas dos dados" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/tabela_tm.png b/src/notebooks/tabela_tm.png new file mode 100644 index 0000000..490db8f Binary files /dev/null and b/src/notebooks/tabela_tm.png differ diff --git a/src/run.py b/src/run.py new file mode 100644 index 0000000..139597f --- /dev/null +++ b/src/run.py @@ -0,0 +1,2 @@ + + diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/__pycache__/__init__.cpython-314.pyc 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teste padrão utilizado a muito tempo, refinado com IA +""" +from src.models.config.connection import DBConnectionHandler +from sqlalchemy import text + + +def test_create_engine_database(): + db_connection = DBConnectionHandler() + engine = db_connection.get_engine() + print(engine) + + + +def test_work_engine_database(): + db_connection = DBConnectionHandler() + engine = db_connection.get_engine() + + try: + with engine.connect() as connection: + result = connection.execute(text("SELECT 1")) + assert result.scalar() == 1 + print("✅ Conexão com o banco realizada com sucesso.") + except Exception as e: + print("❌ Erro na conexão com o banco:", e) + assert False \ No newline at end of file diff --git a/tests/test_repository_sales.py b/tests/test_repository_sales.py new file mode 100644 index 0000000..88994c6 --- /dev/null +++ b/tests/test_repository_sales.py @@ -0,0 +1,132 @@ +""" +teste de variação dos filtro - repositores + +IA - teste feito com IA + +eu gosto de deixar a contrução de testes +pois ela abrange uma variedade maior dos erros que podem acontecer no processo +""" + +import pytest +import pandas as pd +from src.models.repositories.sales_repository import SalesRepository + + +@pytest.fixture(scope="module") +def repo(): + return SalesRepository() + + +# ── sem filtros ───────────────────────────────────────────────────────────── + +def test_sem_filtros_retorna_dataframe(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=None) + print("\n=== sem filtros ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert list(result.columns) == ["STORE_CODE", "PRODUCT_CODE", "DATE", "SALES_VALUE", "SALES_QTY"] + + +# ── filtro só por produto ──────────────────────────────────────────────────── + +def test_so_product_code(repo): + result = repo.read_db(product_code=18, store_code=None, data_range=None) + print("\n=== só product_code=18 ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + + +# ── filtro só por loja ─────────────────────────────────────────────────────── + +def test_so_store_code(repo): + result = repo.read_db(product_code=None, store_code=1, data_range=None) + print("\n=== só store_code=1 ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + # Convertido para string na comparação para bater com o retorno do banco + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + + +# ── filtro só por data — início e fim ─────────────────────────────────────── + +def test_data_inicio_e_fim(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== data início e fim ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + # Converte o objeto datetime.date para string para poder comparar com '2019-01-01' + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +# ── filtro só por data — só início ────────────────────────────────────────── + +def test_data_so_inicio(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=["2019-06-01", None]) + print("\n=== só data início ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-06-01" + + +# ── filtro só por data — só fim ───────────────────────────────────────────── + +def test_data_so_fim(repo): + result = repo.read_db(product_code=None, store_code=None, data_range=[None, "2019-03-31"]) + print("\n=== só data fim ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-03-31" + + +# ── combinações ───────────────────────────────────────────────────────────── + +def test_produto_e_loja(repo): + result = repo.read_db(product_code=18, store_code=1, data_range=None) + print("\n=== produto + loja ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + + +def test_produto_e_data(repo): + result = repo.read_db(product_code=18, store_code=None, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== produto + data ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +def test_loja_e_data(repo): + result = repo.read_db(product_code=None, store_code=1, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== loja + data ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" + + +def test_todos_os_filtros(repo): + result = repo.read_db(product_code=18, store_code=1, data_range=["2019-01-01", "2019-01-31"]) + print("\n=== todos os filtros ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + assert result["PRODUCT_CODE"].unique().tolist() == [18] + assert [str(x) for x in result["STORE_CODE"].unique().tolist()] == ["1"] + assert result["DATE"].min().strftime('%Y-%m-%d') >= "2019-01-01" + assert result["DATE"].max().strftime('%Y-%m-%d') <= "2019-01-31" \ No newline at end of file diff --git a/tests/test_repository_store b/tests/test_repository_store new file mode 100644 index 0000000..678411e --- /dev/null +++ b/tests/test_repository_store @@ -0,0 +1,56 @@ +""" +teste de variação dos filtro - repositores + +IA - teste feito com IA + +eu gosto de deixar a contrução de testes +pois ela abrange uma variedade maior dos erros que podem acontecer no processo +""" + +# tests/test_store_repository.py +import pytest +import pandas as pd +from src.models.repositories.store_repository import StoreRepository + + +@pytest.fixture(scope="module") +def repo(): + return StoreRepository() + + +# ── get_store_cad ─────────────────────────────────────────────────────────── + +def test_store_cad_retorna_dataframe(repo): + result = repo.get_store_cad() + print("\n=== store_cad ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + + +def test_store_cad_colunas(repo): + result = repo.get_store_cad() + esperadas = ["STORE_CODE", "STORE_NAME", "START_DATE", "END_DATE", "BUSINESS_NAME", "BUSINESS_CODE"] + assert list(result.columns) == esperadas + + +# ── get_store_sales ───────────────────────────────────────────────────────── + +def test_store_sales_retorna_dataframe(repo): + result = repo.get_store_sales() + print("\n=== store_sales ===") + print(result.head(5)) + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + + +def test_store_sales_colunas(repo): + result = repo.get_store_sales() + esperadas = ["STORE_CODE", "DATE", "SALES_VALUE", "SALES_QTY"] + assert list(result.columns) == esperadas + + +def test_store_sales_periodo(repo): + result = repo.get_store_sales() + assert result["DATE"].min() >= "2019-01-01" + assert result["DATE"].max() <= "2019-12-31" \ No newline at end of file