2820 lines
1.0 MiB
2820 lines
1.0 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "742175e1",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"import pyodbc\n",
|
||
"import configparser\n",
|
||
"\n",
|
||
"\n",
|
||
"config = configparser.ConfigParser()\n",
|
||
"config.read(r\"C:\\Users\\joao.herculano\\Documents\\Enviador de email\\credenciais.ini\")\n",
|
||
"\n",
|
||
"conn = pyodbc.connect(\n",
|
||
" f\"DRIVER={{SQL Server}};\"\n",
|
||
" f\"SERVER={config['banco']['host']},1433;\"\n",
|
||
" f\"DATABASE=GINSENG;\"\n",
|
||
" f\"UID={config['banco']['user']};\"\n",
|
||
" f\"PWD={config['banco']['password']}\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "79da977e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
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||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_39028\\3030190625.py:16: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
|
||
" df_vendas = pd.read_sql(query, conn)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"query = '''\n",
|
||
"select\n",
|
||
"\tbvb.[DATA],\n",
|
||
"\tbvb.pdv,\n",
|
||
"\tbvb.SKU ,\n",
|
||
"\tcast(replace(bvb.VENDAS,'.','') as int) as Vendas,\n",
|
||
"\tem.ORIGEM\n",
|
||
"from base_vendas_bi bvb \n",
|
||
"left join (\n",
|
||
"select *\n",
|
||
"from\n",
|
||
"estoque_mar\n",
|
||
"where origem is not null) em on cast(em.SKU as int) = cast(replace(bvb.SKU,'.','') as int) and cast( em.pdv as int) = cast(bvb.PDV as int)\n",
|
||
"WHERE EM.CATEGORIA not in ('SUPORTE À VENDA','EMBALAGENS') AND bvb.[DATA] >'2024-01-01'\n",
|
||
"'''\n",
|
||
"df_vendas = pd.read_sql(query, conn)\n",
|
||
"conn.close()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "6df600e1",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df_vendas = pd.read_csv(r\"C:\\Users\\joao.herculano\\OneDrive - GRUPO GINSENG\\Documentos\\CONSULTAS BANCO DE DADOS\\VENDAS 2025.csv\",decimal=',',sep=';')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "f4625575",
|
||
"metadata": {},
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||
"outputs": [
|
||
{
|
||
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>DATA</th>\n",
|
||
" <th>pdv</th>\n",
|
||
" <th>SKU</th>\n",
|
||
" <th>Vendas</th>\n",
|
||
" <th>ORIGEM</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2025-07-08</td>\n",
|
||
" <td>23706</td>\n",
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||
" <td>51231</td>\n",
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||
" <td>1</td>\n",
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" </tr>\n",
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||
" <tr>\n",
|
||
" <th>1</th>\n",
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||
" <td>2025-07-07</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>51231</td>\n",
|
||
" <td>0</td>\n",
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||
" <td>BOT</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2025-07-05</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>87321</td>\n",
|
||
" <td>1</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>3</th>\n",
|
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" <td>2025-07-09</td>\n",
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||
" <td>23706</td>\n",
|
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" <td>87321</td>\n",
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||
" <td>1</td>\n",
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" <td>BOT</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>4</th>\n",
|
||
" <td>2025-07-04</td>\n",
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" <td>23706</td>\n",
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" <td>87321</td>\n",
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" <td>2</td>\n",
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" <td>BOT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>4607343</th>\n",
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||
" <td>2024-04-19</td>\n",
|
||
" <td>23701</td>\n",
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" <td>48271</td>\n",
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||
" <td>1</td>\n",
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||
" <td>BOT</td>\n",
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||
" </tr>\n",
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" <th>4607344</th>\n",
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" <td>2024-04-19</td>\n",
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" <td>23701</td>\n",
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" <td>53792</td>\n",
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||
" <td>1</td>\n",
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" <td>BOT</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4607345</th>\n",
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||
" <td>2024-04-19</td>\n",
|
||
" <td>23701</td>\n",
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" <td>57488</td>\n",
|
||
" <td>1</td>\n",
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" <td>BOT</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>4607346</th>\n",
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||
" <td>2024-04-20</td>\n",
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||
" <td>23701</td>\n",
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" <td>56140</td>\n",
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" <td>1</td>\n",
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|
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" <td>2024-04-20</td>\n",
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" <td>23701</td>\n",
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
||
"<p>4607348 rows × 5 columns</p>\n",
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"</div>"
|
||
],
|
||
"text/plain": [
|
||
" DATA pdv SKU Vendas ORIGEM\n",
|
||
"0 2025-07-08 23706 51231 1 BOT\n",
|
||
"1 2025-07-07 23706 51231 0 BOT\n",
|
||
"2 2025-07-05 23706 87321 1 BOT\n",
|
||
"3 2025-07-09 23706 87321 1 BOT\n",
|
||
"4 2025-07-04 23706 87321 2 BOT\n",
|
||
"... ... ... ... ... ...\n",
|
||
"4607343 2024-04-19 23701 48271 1 BOT\n",
|
||
"4607344 2024-04-19 23701 53792 1 BOT\n",
|
||
"4607345 2024-04-19 23701 57488 1 BOT\n",
|
||
"4607346 2024-04-20 23701 56140 1 BOT\n",
|
||
"4607347 2024-04-20 23701 57488 2 BOT\n",
|
||
"\n",
|
||
"[4607348 rows x 5 columns]"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "08e5b701",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas = df_vendas.drop_duplicates()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9f1ce0f3",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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"<div>\n",
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"<style scoped>\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>DATA</th>\n",
|
||
" <th>pdv</th>\n",
|
||
" <th>SKU</th>\n",
|
||
" <th>Vendas</th>\n",
|
||
" <th>ORIGEM</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2025-07-08</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>51231</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2025-07-07</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>51231</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2025-07-05</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>87321</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2025-07-09</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>87321</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2025-07-04</td>\n",
|
||
" <td>23706</td>\n",
|
||
" <td>87321</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4607343</th>\n",
|
||
" <td>2024-04-19</td>\n",
|
||
" <td>23701</td>\n",
|
||
" <td>48271</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
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|
||
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|
||
" <th>4607344</th>\n",
|
||
" <td>2024-04-19</td>\n",
|
||
" <td>23701</td>\n",
|
||
" <td>53792</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4607345</th>\n",
|
||
" <td>2024-04-19</td>\n",
|
||
" <td>23701</td>\n",
|
||
" <td>57488</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4607346</th>\n",
|
||
" <td>2024-04-20</td>\n",
|
||
" <td>23701</td>\n",
|
||
" <td>56140</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4607347</th>\n",
|
||
" <td>2024-04-20</td>\n",
|
||
" <td>23701</td>\n",
|
||
" <td>57488</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>BOT</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>4606789 rows × 5 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" DATA pdv SKU Vendas ORIGEM\n",
|
||
"0 2025-07-08 23706 51231 1 BOT\n",
|
||
"1 2025-07-07 23706 51231 0 BOT\n",
|
||
"2 2025-07-05 23706 87321 1 BOT\n",
|
||
"3 2025-07-09 23706 87321 1 BOT\n",
|
||
"4 2025-07-04 23706 87321 2 BOT\n",
|
||
"... ... ... ... ... ...\n",
|
||
"4607343 2024-04-19 23701 48271 1 BOT\n",
|
||
"4607344 2024-04-19 23701 53792 1 BOT\n",
|
||
"4607345 2024-04-19 23701 57488 1 BOT\n",
|
||
"4607346 2024-04-20 23701 56140 1 BOT\n",
|
||
"4607347 2024-04-20 23701 57488 2 BOT\n",
|
||
"\n",
|
||
"[4606789 rows x 5 columns]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "afb4394f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_39028\\3226250567.py:1: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" df_vendas['DATA'] = pd.to_datetime(df_vendas['DATA'])\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas['DATA'] = pd.to_datetime(df_vendas['DATA'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "ec2ac338",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_39028\\2691959404.py:1: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" df_vendas['DATA_MES'] = pd.to_datetime(df_vendas['DATA'], dayfirst=True, errors='coerce').dt.to_period('M').astype(str)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas['DATA_MES'] = pd.to_datetime(df_vendas['DATA'], dayfirst=True, errors='coerce').dt.to_period('M').astype(str)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "c01ca1d0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_39028\\76099472.py:3: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" df_vendas['Vendas'] = df_vendas['Vendas'].astype('Int64')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#df_vendas['Vendas'] = df_vendas['Vendas'].str.replace('.','')\n",
|
||
"\n",
|
||
"df_vendas['Vendas'] = df_vendas['Vendas'].astype('Int64')"
|
||
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|
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|
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
"df_tabela = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\compilado_tab_pedido.xlsx\")"
|
||
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|
||
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|
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|
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|
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||
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|
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|
||
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|
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||
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||
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||
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|
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|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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||
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|
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
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|
||
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|
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"text/plain": [
|
||
" Nome da Origem SKU1 SKU2 \\\n",
|
||
"0 TABELA DE PREÇOS (1) 1.xlsx 1594 1594 \n",
|
||
"1 TABELA DE PREÇOS (1) 1.xlsx 1594 1594 \n",
|
||
"2 TABELA DE PREÇOS (1) 1.xlsx 1594 1594 \n",
|
||
"3 TABELA DE PREÇOS (1) 1.xlsx 17912 17912 \n",
|
||
"4 TABELA DE PREÇOS (1) 1.xlsx 17912 17912 \n",
|
||
"... ... ... ... \n",
|
||
"45678 TABELA DE PREÇOS (3).xlsx 9590 9590 \n",
|
||
"45679 TABELA DE PREÇOS (3).xlsx 9591 9591 \n",
|
||
"45680 TABELA DE PREÇOS (3).xlsx 9591 9591 \n",
|
||
"45681 TABELA DE PREÇOS (3).xlsx 9593 9593 \n",
|
||
"45682 TABELA DE PREÇOS (3).xlsx 9593 9593 \n",
|
||
"\n",
|
||
" Descrição MARCA CATEGORIA LINHA \\\n",
|
||
"0 QDB MASC CILIO COLEC ROSE 10g QDB MAQUIAGEM QDB \n",
|
||
"1 QDB MASC CILIO COLEC ROSE 10g QDB MAQUIAGEM QDB \n",
|
||
"2 QDB MASC CILIO COLEC ROSE 10g QDB MAQUIAGEM QDB \n",
|
||
"3 QDB BATOM CORALICE 3,8g QDB MAQUIAGEM QDB \n",
|
||
"4 QDB BATOM CORALICE 3,8g QDB MAQUIAGEM QDB \n",
|
||
"... ... ... ... ... \n",
|
||
"45678 NIINA SCR CORR LIQ PERF MATCH COR 0 10ml EUDORA MAQUIAGEM EUDORA \n",
|
||
"45679 NIINA SCR CORR LIQ PERF MATCH COR95 10ml EUDORA MAQUIAGEM EUDORA \n",
|
||
"45680 NIINA SCR CORR LIQ PERF MATCH COR95 10ml EUDORA MAQUIAGEM EUDORA \n",
|
||
"45681 NIINA SCR CORR LIQ PERF MATCH COR65 10ml EUDORA MAQUIAGEM EUDORA \n",
|
||
"45682 NIINA SCR CORR LIQ PERF MATCH COR65 10ml EUDORA MAQUIAGEM EUDORA \n",
|
||
"\n",
|
||
" UF Tipo Preço PC PV extracao_desc \n",
|
||
"0 AL COMPRA 13.55 49.90 QDB \n",
|
||
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|
||
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|
||
"3 AL COMPRA 8.12 33.90 QDB \n",
|
||
"4 BA COMPRA 9.14 33.90 QDB \n",
|
||
"... .. ... ... ... ... \n",
|
||
"45678 SE COMPRA 11.77 54.99 NIINA \n",
|
||
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|
||
"45680 SE COMPRA 11.77 54.99 NIINA \n",
|
||
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|
||
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|
||
"\n",
|
||
"[45683 rows x 12 columns]"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_tabela['extracao_desc'] = df_tabela['Descrição'].str.split(' ').str[0]\n",
|
||
"df_tabela"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "82f0833d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_tabela['LINHA'] = np.where(df_tabela['MARCA'] == 'EUDORA',df_tabela['extracao_desc'],df_tabela['LINHA'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "5a7e68c9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_tabela = df_tabela.groupby(['Nome da Origem','SKU1', 'SKU2','Descrição', 'MARCA','CATEGORIA', 'LINHA','Tipo Preço','extracao_desc'])[['PC','PV']].max().reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "84840a43",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2 = pd.merge(df_vendas,df_tabela[['SKU2','LINHA','CATEGORIA','Descrição','MARCA']],left_on='SKU',right_on='SKU2',how='left')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "bdba97bd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2 = df_vendas2.drop_duplicates()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "30a464df",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>2</th>\n",
|
||
" <td>2024-03</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>2024-05</td>\n",
|
||
" <td>464714</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>2024-06</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>2024-07</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tr>\n",
|
||
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|
||
" <th>8</th>\n",
|
||
" <td>2024-09</td>\n",
|
||
" <td>459134</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2024-10</td>\n",
|
||
" <td>463509</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>2024-11</td>\n",
|
||
" <td>603101</td>\n",
|
||
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|
||
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|
||
" <th>11</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>423722</td>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>2025-03</td>\n",
|
||
" <td>473589</td>\n",
|
||
" </tr>\n",
|
||
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|
||
" <th>15</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>17</th>\n",
|
||
" <td>2025-06</td>\n",
|
||
" <td>530743</td>\n",
|
||
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|
||
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|
||
" <th>18</th>\n",
|
||
" <td>2025-07</td>\n",
|
||
" <td>539341</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
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|
||
" <td>596157</td>\n",
|
||
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|
||
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|
||
" <th>20</th>\n",
|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
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|
||
" <td>298299</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
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|
||
" DATA_MES Vendas\n",
|
||
"0 2024-01 324004\n",
|
||
"1 2024-02 275766\n",
|
||
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|
||
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|
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
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|
||
"id": "3434dd25",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2['vendas2024'] = np.where(df_vendas2['DATA_MES'].str[0:4] == '2024',df_vendas2['Vendas'],0)\n",
|
||
"\n",
|
||
"df_vendas2['vendas2025'] = np.where(df_vendas2['DATA_MES'].str[0:4] == '2025',df_vendas2['Vendas'],0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "86e82656",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2['MES_NUM'] = df_vendas2['DATA_MES'].str.split('-').str[1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "c8df8d89",
|
||
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|
||
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>603101</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"text/plain": [
|
||
" MES_NUM vendas2024 vendas2025\n",
|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "ba9077e2",
|
||
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|
||
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|
||
{
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>473589</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>143737</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>06</td>\n",
|
||
" <td>453788</td>\n",
|
||
" <td>530743</td>\n",
|
||
" <td>76955</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>99141</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>596157</td>\n",
|
||
" <td>174131</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>09</td>\n",
|
||
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|
||
" <td>659121</td>\n",
|
||
" <td>199987</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>463509</td>\n",
|
||
" <td>298299</td>\n",
|
||
" <td>-165210</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>11</td>\n",
|
||
" <td>603101</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-603101</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>534696</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-534696</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" MES_NUM vendas2024 vendas2025 diff\n",
|
||
"0 01 324004 423722 99718\n",
|
||
"1 02 275766 378668 102902\n",
|
||
"2 03 376775 473589 96814\n",
|
||
"3 04 370084 437396 67312\n",
|
||
"4 05 464714 608451 143737\n",
|
||
"5 06 453788 530743 76955\n",
|
||
"6 07 440200 539341 99141\n",
|
||
"7 08 422026 596157 174131\n",
|
||
"8 09 459134 659121 199987\n",
|
||
"9 10 463509 298299 -165210\n",
|
||
"10 11 603101 0 -603101\n",
|
||
"11 12 534696 0 -534696"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"comparacao = df_vendas2.groupby('MES_NUM')[['vendas2024','vendas2025']].sum().reset_index()\n",
|
||
"\n",
|
||
"comparacao['diff'] = comparacao['vendas2025'] - comparacao['vendas2024']\n",
|
||
"\n",
|
||
"comparacao"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "d1282957",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: >"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas2.groupby('MES_NUM')[['vendas2024','vendas2025']].sum().reset_index().plot(kind='line')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "cfc65e28",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2['MES_NUM'] = df_vendas2['MES_NUM'].astype('Int64')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "9fa52385",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Remove meses 11 e 12\n",
|
||
"df_plot = (\n",
|
||
" df_vendas2[df_vendas2['MES_NUM'] < 10]\n",
|
||
" .groupby('MES_NUM')[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Converte para formato longo para uso no seaborn\n",
|
||
"df_long = df_plot.melt(id_vars='MES_NUM', value_vars=['vendas2024', 'vendas2025'],\n",
|
||
" var_name='Ano', value_name='Vendas')\n",
|
||
"\n",
|
||
"# Gráfico com Seaborn\n",
|
||
"sns.set(style='whitegrid', context='talk')\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.lineplot(data=df_long, x='MES_NUM', y='Vendas', hue='Ano', marker='o', linewidth=2.5)\n",
|
||
"plt.title('Comparativo de Vendas Mensais (2024 x 2025)')\n",
|
||
"plt.xlabel('Mês')\n",
|
||
"plt.ylabel('Vendas')\n",
|
||
"plt.xticks(range(1, 11))\n",
|
||
"plt.ylim(bottom=0)\n",
|
||
"plt.legend(title='Ano')\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "1e913ce0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas2['TRIMESTRE'] = np.ceil(df_vendas2['MES_NUM'] / 3).astype(int)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "d8a34a04",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"CATEGORIA\n",
|
||
"PERFUMARIA 1457933\n",
|
||
"MAQUIAGEM 625165\n",
|
||
"PELE 592499\n",
|
||
"CABELO 369098\n",
|
||
"DESODORANTE 346924\n",
|
||
"SABONETE 236514\n",
|
||
"INFANTIL 203354\n",
|
||
"GIFT 123774\n",
|
||
"FACIAL 89401\n",
|
||
"ACESSORIO 64508\n",
|
||
"OLEO 52426\n",
|
||
"SOLAR 49937\n",
|
||
"BARBA 16810\n",
|
||
"PETS 16337\n",
|
||
"HOME CARE 11998\n",
|
||
"SUPORTE 5665\n",
|
||
"KIT INICIO 1585\n",
|
||
"EUDORA 578\n",
|
||
"UNHA 83\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_vendas2.CATEGORIA.value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "73c4074e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"top5_categorias = ['PERFUMARIA','MAQUIAGEM','PELE','DESODORANTE','CABELO']\n",
|
||
"df_vendas_categ = df_vendas2[df_vendas2['CATEGORIA'].isin(top5_categorias)]\n",
|
||
"\n",
|
||
"df_vendas_categ = df_vendas_categ[df_vendas_categ['MARCA']== 'BOTICARIO']\n",
|
||
"\n",
|
||
"df_vendas_categ_eud = df_vendas2[df_vendas2['MARCA'] == 'EUDORA']\n",
|
||
"\n",
|
||
"top_categorias_eud = df_vendas_categ_eud['CATEGORIA'].value_counts().head(5).index.tolist()\n",
|
||
"\n",
|
||
"df_vendas_categ_eud = df_vendas_categ_eud[df_vendas_categ_eud['CATEGORIA'].isin(top_categorias_eud)]\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "07e85399",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_vendas_categ com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped = (\n",
|
||
" df_vendas_categ\n",
|
||
" .groupby(['CATEGORIA', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped['CATEGORIA'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped.pivot(index='CATEGORIA', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=45, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.87, 0.02, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Trimestral BOT por Categoria (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "95e97053",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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m1WVTTT0dTz1dzGoInzKcdN7TEGgAsUN8mj6IT1MP8WnqIT5NTcSniAc6hBGWTtj77rtvtpNBJH5WX7V99dVXbUZLTRLgKUDbvn27nUQ0gYBOKsrAQHz4iyLNAKx6ajoZ6KTgqbbQ1KlTbQZSZdH4u/ya6bdcuXLu8ssvt8c01FIBhf5PwBZbCqQ1UYpqGKrGWqT3Oxh0666vgm4NdfV0ofvzzz/bttVEOdqXNXSWSQMKn7aXtoWGaGnYZKVKlXYp6NYwZ52edWwl2I4ddSJo2yngFnUQVa1a1eoXVqhQIeqgW0Odb775ZrswVmbbsccem5DXA6Q74tP0QXyaeohPUw/xaWoiPkW80CGMXO677z43ZswYOzGrjpMPqPPi2+jjtGHDBgu658yZ43766Sc7AOlulu7qlyxZ0u4GI/6ZM2PHjrW7hAq2dJLRScNvVwVkGrqlYSU6sSszwxenP++886ytTkD7778/xefjFGzrfe/QoUO2YDs4iYPf53IG3aqlp5psQbqT/9VXX9mszgQCsbFkyRLXsWNHO+6deuqp7v7777eMpp0JunUMVqaMhr7qIld1+FD4NGmKOonUGaRMJw2n+/XXX9369eut3po6KZSt5o95eQXdGuJ8zz332LBXZUypviWAwkV8mtqIT1Mb8WlqIj5NPcSniCt1CAPe+vXrs84///ysMmXKZDVo0CBr1apV9vi///6b77L//fdfHNYQedm+fbt937FjR9jn77jjjqyKFStmjRs3LtT++eeft+1du3btrJUrV9rjw4YNy3r44YftcX1VqFAha+nSpXF8JZnpiSeesPf71ltvzVq4cGG258Ltg/6xv/76K7S9tH0nTpwY2if/+eefOK09tI80atTItkOTJk2yZs6cGXFfDLcdg9v4gQceyJozZ05M1zeTPf7447admjdvnjVr1ix7bOvWrVmDBw/OOv3007Nq1aqVtWDBglznNm2T++67z46J5513XtbQoUNDz/Xr189+59y5cxPwioD0Rnya2ohPUxvxaWojPk0dxKeINzqEkcuvv/6adc0119iBo169ejsddOvn4DIE5LHVo0ePrNtuuy1r8+bN9v/gid6/9zo5aLvefPPNFmz36dMnFGz/9ttvuX7nt99+m9WtW7esxYsXx/GVZHYAkF+w3bFjR/vKKWfQPWnSJHuc/S52nn766axnnnkm2/tc0KA7uG11Ibxo0aI4rHlm8xe2Ol76oDp4YdqsWTN7fvTo0WGXzxl0v/vuu/a4jr2rV6+O06sAMg/xaWoiPk1txKeph/g0NRGfIhHoEIaZNm1a1rZt20L/10nj6quvLlDQHU1Ajtj4/vvv7eCv7dWpU6ewQbfo8QsvvNDaKuj2wfby5ctD29AHDv47d/Djc7GkbaFAev78+RH3q7vvvtva3XvvvZYtlVfQra8pU6bEZf0z+QLpzjvvzFq3bl22/S3aoDu4bbVN1X7AgAHscwm6sPXbyB8bJ0+eHPH3+KC7UqVKWTVq1MgaPnx4zNcdyETEp6mN+DS1EZ+mHuLT1ER8ikSJrngM0prq0KhQvCZu8FTHSbOHqsbQ8uXLbZIHTdqgmlCqDZVTsI6bahOp3hfiR5OgaKIA1Q167733rGaXJl7xddb8NtJELNqWqjmkOnpHHnmke/fdd205vw19PSL/Pb9JW7BrBg4caHWiVMdQ20b1uPw+Ftyv7r77bpsJvVGjRjaRx4EHHpjrd2mylQcffNBdcskl9n/V3ENsZtj2dfRUu1ATPYjf33T81MQN5cuXd7Nnz3bdu3e32l1+X/Q12fy2VU0v1WTTBEeqZck+F7uabNpu559/vtWrDNY/1L6m7ffHH39YLbbTTz/dZnTOSTNqi2a5b9GihdV203I6VwIoXMSnqY/4NHURn6Ye4tPURHyKhEpYVzSSamiC7jiFq8EVTSZG8OfOnTtb28qVK2dt2LAhTq8CsnHjxqx33nnHMiq0DVTjKVwmhuoRVatWzdp88skn9hjZM4mhYZEazqN6iP5uvh/S4+vtyV133WXP33///aFsGZ8hE+7OvmpNrVmzJm6vI5PkdQffyy8TIzhMUhlTel538SP9Puy6Z5991t7nSy65JGvJkiX2mM90CR7/tA+q3UsvvZQ1ZswYG9qqocn+WBouE8OfFwEUHuLT9EF8mnqIT1MP8WlqIj5FotEhnMH8iUN1avI60AeD7rp164YmdtBBKjh0xJ84zjjjjFDdG8Re8OSti5xIQXfwpOJPPhoKpm1IwJ042j4fffRRKOjW/rhixYoCB9sjR47M+vHHHxPwCjJHXsF2zmF0kWq2TZ8+PdSGYDs+dFx87LHHbCiyOoNee+210HPBC9sHH3zQtkfZsmWzypUrZz+XL1/evl988cVZ119/vU1y9PXXX2fbjgAKF/FpeiA+TW3Ep6mD+DQ1EZ8iGeymfxKbo4xE0JAtP6SkQ4cO2YYm6CPhh2P5IUHLli1z99xzjw0r0fCtt99+24ZzeRpSoqFgBxxwgD0X/H2IDQ0N0XbScBB9eRs3bnSffvqp69u3r1uzZo1r3LixDdPScDwtU7RoUTd58mR3yy232GMaknfMMcck9LVkGr+P+e9//vmn+/zzz22b/frrr+6iiy5y3bp1s+GyH374oQ3D0xAi7Xt+GQ3v8ttd+56GG2mf1M8a2uX3YcT+mBkcOjlmzBhXrVo1d/DBB4e2kY6fGkb5888/29At7Y9Dhw5177//PsfMONE20Put4a8a8qjjX5s2bULP6/ymIa9HHHGEq1u3rtu8ebMNz1u3bp1bsGCBLaNhlF6JEiXckCFDGPYKFDLi09RHfJq6iE9TD/FpaiM+RaLRIZyBoj1x5HwsZ9CtQEAnC9UGGzZsGCeOOPnmm2/cnDlz3Ndff20ndL3vNWvWdFdccYXbc889rc2mTZvcqFGjwgbdnmpLjR8/3l199dX2nII0JE7OoFv72IoVK9xVV13lWrdubXW/IgXbzz33nAUDb775pitTpkyiX0pa1mR7/fXX8z1mKqgePXq069Onj9VaC26rYNCtenzaL1Vn76233uKYGSeqN6og+bXXXrMAWttDddbuu+8+q5F32WWXufbt21vNS9H+pmPpwoULLfgeO3asfdfxl3MdUPiIT1Mb8Wl6Ij5NXsSn6YH4FIlEh3CGiTbYbtu2rT2nADsoGHTrDlSVKlXcJ598QrAdJ/3797c7iMqyCNIJQoFa8P3PKxNDvvrqK5tgRSf9QYMG2Z1HxJa2yeLFi913331nEz3ss88+ti/mDLr79evnlixZYvvY888/7ypWrGgBtYKEcMG2MmsUuJ188skJfHXpfcysV6+eTZxy4oknhp7LOamKjoUXX3yxu+uuu+yCKWe73377zXXs2NH98MMPBNtJEHSrg0LHTl0EqcNC5z1d2PrJVYKZbTn3Y53zABQe4tPURnya2ohPUw/xaXohPkWiMFVkBtGszDpxXHjhhTYcIVKwrbtRmuFXQ7c0LGG//fbLNbuzD7p18OLEER/PPPOMe+WVV2wbaBvpxL9161a7O6hALOf7rxOChnaJgu4PPvjAfvZBt4Iz3SFetGhRQl5PplHGkr6mTZuW7XHN8KvtpJO/AnDduRcfdCsw0CzruiDyQyqDwfbff/9tF7sE24VL90o1FEvHTNFx0Afbek7HTD/bsg+2NXTy9ttvd0cddVS2YZc6tiqI09BXDZ3s3LmzfXHMjJ3g0PIgXcQ2bdrUfta+pWD7lFNOsWwMZcYE97Gcv09fCsL333//uLwGIFMQn6Y24tPURnyaWohPUxvxKZJOYksYI1569eoVmonZF4cPN4OlnyBAxcv9BAGRCtH7SQYoNh97b7/9thWRb9WqVdZPP/0UsV24GX3DTeSxadMme+6zzz7Lmj9/fkzXHf83SYqK/5966qk2c/qAAQOyXnnlFdueU6dODbXz+6K2z/Dhw7NN5BGcKXbo0KFZtWrVyjrttNOy5s2bl5DXlClGjx5t20Bfjz76qM2QHZwoJ9pJVdauXRuaIOLvv/+O++vIFOvXr8/2/+C2Clq2bFlWz549s0455ZSsSpUqZZvII9xxFEBsEJ+mNuLT1EZ8mrqIT1ML8SmSFR3CGUAHdwVc1apVsxND586dQ89t27atwCcO77fffrPAG7G1ePFim0H0nHPOyfrhhx9Cj2ubRDqZSPA5nYR80K3AXQGcn90ZsfXCCy/YftWyZcus7777Lttz69atC/08fvx4C+4U0Im2TzDo7tChQ9Yff/yRNWLEiKzTTz+dYDtBQfcjjzxS4GPm4MGDs9q2bRu6WM5rv8WuXdi2bt06a9CgQbb/+E4l/37nfN8VdD/zzDMWdGt25/79+4eeI+gGYo/4NLURn6Y24tPUR3yaGohPkcwoGZEBNLygYcOGbq+99rJ6Q5q1V8MONEOsn6ghOKQkr9liP/vsM1erVi0b7qWhDYg9FYjXsDtNjqKhI16k+kGioZI//vijDb0788wzbdikhn1pmccff9xNmjTJhvMFJ/FA4dMQvIEDB7qzzjrLhlGedNJJtj9pv9KQLtVpE00YoPp7Grq1fv1621+1bfzwPA2pVL09TeKhYXpalmF4saGhxnqPVUtP20fbokGDBrYNNNHNO++8Y8dN1T3UxDj5HTM1c/CLL75oswFrOKwww3bh0xBV7UN6b1V/UkOQVUNUk3JoqJ2Gu/r33Q9B1xDJ4PA8DYMVze6s7RduEisAhYf4NLURn6Yu4tPUQ3yamohPkezoEE5jfr5AHWQUbJ9//vn2mGYk1cFI9YW6du3qHnjggaiCbc3U7IO+oUOH2vOcOGLHv/faNlKuXLk8aw95CqR18nnppZdsVlIF3KK6QgoctN2rVq1qJyHEztq1a0N18W699VYLtkXbVNvW1/fyE6ssXbrUtq0mZVGbSy+9NFvQ/fLLL7tZs2bZxa6CPv/7UHj03mu25tWrV4ceU8Cs454m7ejdu7cdIxWcaR/U/qXaXkceeWS+M2x//PHHVl8RhU/vvS6CtL9oEo6yZcu66dOnW+eSOokUeGt/ql27th0Hfc08bSd1HGkm+2DQredbtmxJsA3ECPFpaiM+TW3Ep6mH+DQ1EZ8iFdAhnMZyBmXFixd3F1xwgf2soFtB8zfffON+/fVX16RJE7vrFCnY9icO3clX5kZed/9ROPx7rMBNEzr4yVPyu8jZe++9XYUKFexnbV8FDwqutZy23+WXX872i4O5c+faSV8T5FSuXDnbhZJ//xVsazKWVatW2XdNIDB48GCbnEXtFdD5oFuTc2ifVQYNwXbh69Gjh81mruBZwZYmLJo6daplP6ljQpk0mnH7hRdesCDbT8ah9l7wjj2TqsSP9quaNWvaxagymOrUqWMXucp80YzZmoRKX9oPK1Wq5G644QYLvH0GmjIxdA4U7X+amEoXxGoHoPARn6Y24tPURnyaWohPUxfxKVIBHcJpaMaMGRZE626t7rSXKVMmdPc+Z9CtdiVLlrSgQMG2n8EyXLDNiSMxFCRrW2hIjwS3TTgKBCpWrGiBwMaNG22bBoN0gu3Y8kHXyJEj7f9+5t+cxo0bZ4Hcli1b7M6/AgZtt23btllgrQBPQYEypxQYaEilZmBnGGXh01BlzdasLAtlWPjjpS6EbrrpJpvpvE+fPnaxo+3hMzE05FLZbZplW8dWgu3E0DFRs5zffPPNNju2zn26ONL+tXLlSvf555/bxe38+fNtyOX48ePtvKjheOXLl3cHH3ywO+644+z/f/75pxs9erQNowVQuIhP0wvxaWohPk09xKepjfgUqYAzb5rRsB3dHezUqZOdtDXcTgch1azxdGLQSUPP6Y6VahGpto0CNQXbOkkQbCfPkErViVIQ9uabb7rt27fbtvHPhaPgWttY2zL4exAfPujSNhMNEcpJ+5PuFGsbDRgwwIJtDdvSSV+ZULoI1sWw7gL7iyXVmCLYLnzqeFCwrQDtrrvuCgXbulDVRWurVq1sm+ri1W8LtdXdfdFx8amnngr9Ph0zn332WY6ZceTPV9p2ykDT0LrvvvvOHj/qqKMsk0JZFX54smodKgjXttX5Ulk16tA49NBD3Z133mnDJ0uVKpXgVwWkF+LT9EF8mpqIT1ML8WnqIz5FKqBDOI10797d9ezZMzRsTgG17tiquLzuROkE7vmabT7oVi0b1SHSnSwN/1KQRrCdHK6//np3yCGHuG+//dbu6usOv0784QJpbT9RYK47+7q7SF2oxPDBmbZbcNuI9jHd7dc+pklw9JwP1BWE6+SvYJs6erHPvFBNtvr167sOHTpkC7L89jjssMNsn/MXUH47apiksjJEx8du3bpZ7TwdMxWsc8yMLW2TnJTBdO2119rFq4ZTBoNxZcuMHTvW9ikF2M2aNbPgfObMmVaX7ZJLLrFtqMd0vAVQeIhP0xPxaWoiPk1+xKepi/gUqYYO4TS6i6i7TnXr1rVaT1deeaVr3ry5BdqqyfXbb7+533//PdsyCro1iYMPujXBQJcuXey5ESNGcBcxSQI23QWuXr2627Bhg500pkyZEgq6g0FccKiehnjpDr+/40gWRvz4beJn3NadYPETBXgaBvT/2rsTeKvn/I/j3xljNElkEH/9R1q00a5Ni1KNlpkUqRRStKq0aSGZaLQhlchSQto3kiWhKWUpWijtaVFKJaU0lvN/vD8z3+N3b+fW5X/Pel/Px+Ny7znnns5yf+f3/n1/3+/nkzdv3jSNPGTjxo3u66+/dtWrV3clS5aMwzPIHrS8TjMv/GdgMGwHtyV1PBd9tkrwYFdB3c/EUG0vDXQQtqNr0qRJtlRS21MwdPvtR++T9mf6rPTv08iRI60xjsK2DnL1vg0YMMAaG/Xt29dmN4lmarBkGcha5NPUQz5NTuTT5EA+TU7kUyQraginUH0hLRPxZxH1QaMvLeFR58o5c+aEz8rr7JRmaYjONunDx4d2fRhppoYK1bPjiK1gwf8gBbPWrVu7devWWRMI+fbbb+1905l8vZ/6Pb+j0FlgddxWY4frr7/eLqPbduz496FKlSr2/qjek5ZTaiaND9bpd+rBGRjaljX7QjXZ/HWEgKy1evVqe09Ey1fVyVf0mRfclubOnWvbkroC16hRI8225JuwaCaGQnfnzp3td7X9FSpUKG7PLTvs67RkbtSoUXbA6j83/XumWRhqzKFl6KpzqOYrWnKusD1lyhT7He0L1fW5QoUK9qUmH/rZHwADyBrk09RAPk0N5NPERz5NTuRTJLUQktqQIUNCRYoUCXXt2jW0ZcuWNNf9+OOP9v9XX301VKJEiVCXLl1CdevWDVWtWjXUs2fP0LRp08K3PXLkSGj27NmhKlWq2P1VqlQptH79+pg/n+xmxowZoQkTJoR++OGH0E8//WSX/fzzz2n+7y1ZsiRUu3Zte39q1aoV6t+/f2jPnj2ho0ePhv7973+H9u3bF3rggQdCpUuXDlWoUCG0cePGuDwn/PLejRkzxt4vbXfaDtNvm+m/Hz9+vN2+TZs2of3798f4UWcfx48fD02ZMiVUvnx5e70HDRp0wm30fmk70/b0ySefRNwmgz+//fbboQ0bNsTg0WdPq1evtvfKfzVr1iz01VdfpdmG/Gfo8uXLQ2XLlg3VrFnTblu9evXQjh070tzWS/+eAsga5NPkRj5NTeTTxEY+TT7kUyS73+k/8R6Uxm+jM4haVqJ6MqqvFjxjq7fVn9Ht1KmTda3U2WAt79qxY4edOVQziA4dOtiZYTl27JgtxdPZKtWsYeZFdGn5jpaXiM7YagmW6gSprprnZ9L4s4uqJ6Szivq/mgioyLw6/eq91pJLLdvT2WLV6qPofPytXbvWlrYuWbLE3o/bbrvNlstGovdVTXd0JlhnmSM1+0DW0XJjzUzTzDN99qm2lzoAy7x586zWmpZ+TZgwwc7SZzQTxs/EQHRpibGWz2lb0jZy9OhRV6ZMGau3ptkVwRls6tzcsWNH9/nnn9tnpGbSaD+p2Wqa3QQgusinyY18mvrIp4mLfJpcyKdIdvzlJTEtJVG40vK5hQsXWrdKfQDpw9/vGNSVVGFbNb569eplO32FNS0z0eUqYt6gQQNb9qWabX//+9+t0cdZZ50V76eX0rTz9jXzVEdNQVl11VTnSaG7bNmyFsy0E9EOQkuF1MyhXLlytnxIdbxUk0+197Zs2WLXqZ6XloHp91haEl0zZsyw7UXbjg9ckQKZDp7UHEANH7Tdqbv6Z599Zr9XoEABu1xLYPVeLl682BqsaHkXYTv6NACh+pWi0K3lxwps5cuXt5pemQnbQtiODQ0QaT+2aNEiq8O2bds2q3+oZeh6v4KhW/tCDSRpIEqdnbUEXQdYes8BRB/5NHmRT5Mb+TT5kU+TC/kUyY4Zwklu/vz5NltCoVs12lR0XKHMh211/VWI7tKlS5puvsuXL7frv/rqK5vJoQ8yxJZqOf3jH/9wVatWdd27d3evvfaanSn0QbxixYrhrqTqJKtQHaQQoFpDur2vL0Q9r+i7//77rdaTP1jSQa527grgwR168Izwhx9+aNuiuqWLLvf19XQwpSYDOljq0aMHXbfjPBND74VqWSqAq9YX21T8BQ9qmzdv7r777jvruKyZZtqXaYDCh24/OKHBCO33Nm/ebIMZOpBitgwQO+TT5EU+TU7k09RCPk185FOkAgaEk1RwJ6AdubpRKnRfe+21tsxOHzAKbzrD2LVrV/c///M/Jyzv0uVvvvmmGz16tIV1xIZfFqIdvZpqHDhwwM66ly5d2pZwbdiwwTqOalaGmnNoKYkaA1SuXNn99a9/tR1G8Ix/8G+BHUp0KURrm9IMGBX612wKHfBoxoQOkDR7Sf/370dwCZBCnJpFKNzt2rXLOjXny5fP5c+f3zVp0sTuQ012EP/Qrc9DfS6KD3CIL7//0nLVIUOG2MGpljFrloW2w2Do9p+DvsmHPjt1nQ6mAEQX+TR5kU+TF/k0NZFPEx/5FMmOAeEUDN36wNHMCoU5hWqdmY+0bKhZs2buyy+/tDONnPWNPnVg9jNdfBDTUiztPHRWUWf2PYVtLeHSdaqp59WqVcuWYWqmjTpwEwRix29D2oFrR66Q3K5dO9uRr1y50pZwiQ6KtDyyVatWdnvNtAgeCCnc6X1TjT0tfY3UuRuxp/dl9uzZts2lr9mWUYd1xJ4OcLVv08Gq6hpu3brVQre63AdDt2g/eMstt9hMNdXc02wpANFHPk0u5NPkRj5NbeTT5EA+RbJinUGSCY7f+7PvoppPmnmhMKYPGdWoUR0bX6tLvxcM2/qgWrVqlatUqZKd4Ud0afmIlm2pvpD4s/J6/c8880yr+aWlJdqxi2oKaTaGlpXoPdTv6nf+9a9/2ewM7UR0MPXxxx/H9XllJz4wV6tWzc7kqsahzs4PGzbMlsVq+9PSSV0+fPhwq82m912NA1SLzVNw033pPQ7OnEF86cCocePGrl+/fra8UgMRaqzj3zO/bSK6/Lbi921BukzbWPv27e2z74033rA6iGpepf/rMtVs27t3r91eS5VLlChhB8M6mAIQPeTT5EQ+TX7k09RGPk0M5FOkKgaEk4B22ArRwZ2+/zBKH7rVJVZFyvUBo5pfKmrub+d37tOnT7cle2r+oNCmM/mIHi3zUTMOLavTWcMgHSDpPdOMjE8//dR27PpenUm1TE87F9Vy0xlGzdbwtfbWr1/vli5daqENsaNgfOmll9oBj2bJ6ABKQU3bnHb0qsGm91szK/R+Tps2zc7k6/3T9zrLH9yG/fcso0ysRh4+dKvLOqE7djSLUNuRlrzqYNbzB6R+H6Y6pP6zUftGH7oVrn3o1uX6fFQTJDWlatGiRdyeF5CqyKfJjXyaOsinqY18Gl/kU6Q0lYxA4howYECoSJEioRtuuCE0fvz40Pbt20M///zzCbf76aefwt/Pmzcv1LBhw1DRokVDXbp0Ca1YsSJ83dSpU0OVKlUKlStXLrRhw4aYPY/savDgwfb+3XnnnaFNmzZFvM3ixYvtNtdcc01ox44dobFjx9rP1apVC+3cudNu8+OPP4Zvf+DAgdCzzz5rfwuIrkjbmixdutTeowoVKoS2bNmS5ronnnjCrqtevXqoXbt29r3/uv3220M9evQI7d27N0bPAL/F8ePH7bOydOnS9r717ds33g8p5Q0fPjy8nVx55ZW273v77bfT3Cb4OThq1KhQsWLFQkuWLAnvA9euXRtq0qSJ3UerVq1Cu3fvtusOHjwY42cDpD7yaXIjnyY38mn2RD6NPfIpUh01hBPYmjVr7KyRzshr2ZY6V6r5hpo7tG3b1urQ6CyUFywuH6zZpppRbdq0sW6WWiqk2+nM4mWXXRbHZ5f6dCZ+4sSJ1gBAZwQLFiyY4W19AxWdWVRtNr236hSs9ztYH4paUbHjt6eMXvO+fftaowfV8dIsC82u0NI8fWm71BKg8847z7311lu2LWvWkz+rrPf6L3/5SxyeFTJL76caHw0YMMBmyCxevNjeT2Q91ThU3UnVUtPMF+3v1NRGmjZtasvL1aBDy5L90vIvvvjCZq/pPdHnrH5PVKtN9S615FzLZ7Uf5DMTyFrk0+RGPk1u5NPsjXwaO+RTZAcMCCcwLSm4/fbbbSet0Kbg/corr9gHkWpEaUmdPqTUIEDLhNILhm4tVVBxc+04VHuIsB2/sB0pwKmul5YBqZGDarLNnDnTdiQE7PjQDlvbzdNPP2078uD74Hf477zzjuvcubN1bVaYVtAeNWpUmoOlIIXuZcuW2QGwOjYjOUL3/Pnz3RVXXHHSA2b8/2kQSLVD1UhKy8u19FjLyvWzYoo6MWtgQtuV6opqmxw0aJAtg9XyZS159s1xtK1pcKl///627BlA1iKfJi/yaXIjn0LIp7FDPkWqY0A4weksroKYipR3797dmjio3pc+UHSmXrTz1k5fZ4EvvPDCNHW7FNBV2+uzzz6zs1oKAoULF47jM0p96vCrTr+1a9e29yyjsK1Afu2111rAVmdSnU1UyOvZs6e74447CNtxovfghhtusOYBOmBSU470oVuOHDlitdrUXKVKlSpWMy+jmTMe72nyCXbgRtbSNpQrV67wzxoMGjNmjDtw4IDr06ePK1KkiDt48KAd+KpWae7cuV2pUqWsIc7VV19tg0/aVjXopIPd9AdLqrkHIDrIp8mHfJrcyKcIIp9GD/kU2QkDwglOyw7atWvnduzYYTtyfbBoRoaWLmhZj7pY6qyuPlwUtrXDVzdZNYfwsyy0rERdggcOHOgKFSoU76eU0tS1VwdEdevWtQMlddOOFLYUqjVDRst9mjVrZktN9PPdd99tZxKHDh0aXmKC2NK2pG7Zeg+03dWqVcs9/PDDaUK3n4Xx+uuv29I8hXO91zqLnFHYBvCLRx991AaQ9JmnQQdPMyq0vX377bc20KRZiArOGnxauHChLY0UDVZUrVrVDpAV1HVgrMsAxAb5NLmQT5Mf+RSIPvIpspv/tEREwtLsCp0F1g5esykUtDWGr7NWTZo0sWCtgKAzU6ontXr1alsCpm6/OpOljpaNGjWypXmE7ejasmWLhW3xAUx0gBQMYL1797ZwrW6xCnMK26KZMartpQOpDz/8MI7PJHvTzl31oHQGWOFZyyV1gHTs2LFwJ1/fTVbblK/b1a1bN8I2kAnLly93EyZMsM867Zu0/NzTAIS2Ny07HzdunBs7dqxte82bN7eZGOqqraCt39WAhQaUtO/TfdJlG4gd8mnyIJ+mBvIpEF3kU2RHDAgnMIVs/wGkemyaaaHGHQoECtlagqAzvjp7paCnDzAVONdtNXNDgVs1bLS8QcvxEF0FChRwTz31lC2J1Bl81RMS7Qx8AOvVq5cdODVu3NiuVyj3k/Q1Y0YHUaIlfXrfEBuqDaXmGulDt2o8RQrdqpfoA/f1119v3+vMsM4as3wLODkNLmi5sgYYZs2aZaE6feju0aOHfZZqv6agrVpton2c6q/psjJlyti2qv3iggULbKADQPSRT5ML+TR5kU+B2CGfIltSyQjE388//5zhdUePHg3deeedoSJFioTGjRtnl40aNcp+rlatWmjnzp1pbr9u3brQxIkTQ/Xq1Qtt3Lgx6o8daS1evDhUvnx5e3/0vnk9evSwy/r27RvatWtXmvf9p59+Cr93et9KlSoVWrNmTZyeQfYye/Zse1/q1q0bWrRoUZrrjh8/HlqwYEGoZs2adpuOHTva9uiv8++Zrq9cuXJo06ZNp9yegezMbxvffvttaMKECaEaNWqESpYsGbrvvvtCe/bsSXPbKVOmhCpWrGjb3vDhw8Ofm97+/ftDS5cuDfXu3Tu0du3amD4PILsgn6YO8mlyIZ8CsUM+RXZFDeE4W7VqlduwYUN4OYGWIejMb40aNdIUjNdSOzUIUDdRnfWdPHnyCQ0CdDu/VEh0NipHjhxxfHbZ15IlS+wM4+HDh139+vVt2d3LL79syyP90q1IzQB0Zr9169b296AziupkiujSa60uzJploW7oqrnmt79gzTY1Y9FZ4GDNNs/X3FONKNV2O+OMM+L0bIDE5z/79PmojvVqLKXmHFqm3KlTpxNqtqme2zfffGP12lq1amX1SAFEF/k0NZFPkwf5FIgt8imyIwaE4+iZZ56xTr7q4BukxhzaofuwpbdIS320nMsXLNcHkkJ3pJpQ/sOM7qPxD91aVqL3TgdC2pmoQL2WmUR6b3wjiM2bN9v7mT9//rg99uxGB7SqB6WmABmFbm17gwcPjhi6161bZ0FB26JqThUvXjyOzwZI7dB98803p7keQNYin6Y28mnyIJ8CsUU+RXZDDeE4UY2ZESNGWLC69957bWevHfhtt91mZ+mDZ971oaSZGeoMLNrJjx49OsMGAT7IEbbjS4Xl9R6rPp7C9Ndff21hW3yNryA/e6ZgwYKE7Rjx58N0kKtu6eqgvXXrVjdkyBC3aNGi8O1UB6patWoRa7aJak3lzJnT7d271/35z3+O2/MBElGkZhp+UOiss86yOoeaeZYnTx43Z84cq7+mz8tgzTbNaNPnp4K5amEGa7oByDrk09RHPk185FMg+sinADOE40INNh588EHbgavjcokSJSLeLv1MCu3cO3To4D777DP7wClbtmz4rD0Sl5ZzKZzpTKMOmtRsxYdu38EZ8ROcDaMlstq2TjYTI/3yPB1UKWy/++671jBHDQkAOPfBBx+4ihUr2vcZdTdPPxNDTTrUhKNt27Y20yJXrlzh206fPt06N2sAav78+RzcAlmMfJq9kE8TG/kUiA7yKfALklqMrV+/3jr0aoaFzij5sK3g7Ls2ZzSTQjMv1LXyyJEj7pFHHnFHjx4lbCcB1dzTchKdaXzzzTfD3Z0VtiPNxED0RDr/5Q9qpVSpUifMxNDSypN1d27fvr1ti1dffTVhG/ivYcOGuVtvvdVmF4rCdmZmYrRs2dL2awrfn3/+efhA13dwfuihh6w2KWEbyFrk0+yHfJo4yKdAbJBPgbRIazGm2RPbt2+3nXSwjpM+YDIKz7t377blQVu2bLHGHfny5bOaUMuWLbPr0wd1JObyvIxCd6SdELKeb2wT6SAnfejW9lm7dm0L3SNHjnQrVqw4IXTrTLCWW3700Ufu0KFDMX0uQCLTAaifbaHPPc1qymzovuGGG2xm086dO90LL7wQ3ub8fq5x48Y2OwpA1iKfZk/k0/gjnwKxQT4FTsSAcIzow0I7ep1VUshSN2ZddqqKHWr4oA8dLd2bPXu2O/fcc+1Mr5YsqMuvMAsjOUO3lpxIpGUqyFpqltKwYUM7k5vRzJdg6FbNNjUH0HK8Tz/91JbR7tmzJ3xbBYCrrrrKZkK98cYb7qKLLorp8wESmZaoqt6oZhmKtpPMhm7t4zQgoRmH2vYOHDhg17OfA6KDfAryafyQT4HYIZ8CJ+IvOEb0YaEdvbpQ5s6d2z5UdNmpGmvotmoIoPpsmoWhoNCgQQO7bt68eXbmlzLQyRW6dUZf3nvvvTQhDtGh4v6quabZFG3atMl06C5durQtD9IyO9Vf03Ja8dcrdCuQX3LJJTF+RkBiGjRokDWUEu3j1GyjW7duvyp0a/u88MILbaahBpa+//579nFAFJFPIeTT2COfArFBPgUyxoBwjOXIkcM+aA4ePHjK5XT+Q0ZnelWkXOFaMzJUp01nt9TtUpfTrTm56P1UYXoVndeOBdGVN29e6/yqYLx8+XKrG5XZ0K0DJC0P0gHvtGnT4vDogeSgeoYvvfSSLTv3+zd1XW7RokWmQ7f2hzqQ1TaowalixYpZLUT2cUD0kU9BPo0t8ikQfeRT4OQYEI4R7cD1YeKDs995axZGRmeXgs06/GwNH9C1RK9QoUIxfAbISpUrV3YFChSI98PINrQEVt2W1Sznk08+yXTo1nanmlDqJOvDATt/IC11NddBrbrU9+7d2+XJkyd8nQ/d2mf50B2pkYf2bX7ZnZYu79u3zz4ntR0yAwOIHvIpgsinsUU+BaKHfAqcGgPCMaKdtD5M1HTj9NNPd++8844t8/HXRfpA8eFawUDLEhSwzzvvPLvsjDPOiPEzAJKXti+Fbu3sMxu6/TZ5/Phxa0IQDBEAfgnbzz//vKtTp47NtChYsOAJt1Hobt68eTh0K1CPHTs2TY1KH7YnT57spk6d6kqUKOEaNWpk+0cOcoHoIZ8C8UM+BaKDfApkDgPCMabldOXKlXO7du1ys2bNcqtXr7bLg7Mr0p+NUsMALQlS51jhbBQQ2almM50sdAeXBwW3v0mTJtnPNWvWPOm/AWTnsK0wHQzbfn/mtxcfunv06GE/jxo1yg0ePNjt3r3blt8dOXLEDR8+3GpYanscNmyYLacFEBvkUyB6yKdA7JBPgcz7XYi9R8xt3rzZ3XTTTbY0T0sYtFxBSw9EZ4IVDvxZKZ2N0gdP/vz53ZNPPskHEHASX3/9tduxY4fVifryyy+t8L/q4F155ZXWPdtbs2aN7fh1Wx0EP/vss9Z51m+D2uH7sK2zxaoV9dhjj1kjAgAnD9s6ePX7MHVhDm43CtavvPKK+8c//mE/qwab6rJpUEkNdooWLeoefvjhiDM5AEQX+RSIDvIpEBvkU+DXYUA4ThQI1HhDTTj0AVO7dm3Xrl07++ARda8cM2aM1XLTzl/F0PkAAjI2c+ZMa2SzcuVK98MPP5zQKKVixYq2jQVDd8+ePd327dtdyZIl3QMPPOAuvfTS8DaoA1wFCs3E0CwoHfQCcFbv8JlnnnH16tVzHTp0cEWKFIkYtvv162chWgE6/ZLW999/3+q6qbu6DpQvu+wym+WkZXgMLAHxQz4Fshb5FIgN8inw6zEgHEcbN2509913n+34dda3cOHCLnfu3PaBpWUK+qBSXTad+SVsA6cOAKpdWL9+fQvOmsm0f/9+C+E666ttrGHDhnbb4DbYpUsXt23bNputUbx4cXf++ee7tWvX2pI93Y8OfNn+gP/Q9qAv1QudOHGibRs6wFXt0WDYVvMOzbRo2rSp6969e5pZGIod2j5V+1DbpQK3lslqcIl6bED8kU+BrEE+BWKDfAr8NgwIx5kCgZp3jB8/3h08eNCWL8jll1/uKlWq5Fq1amVLigBEpuL/qvekZa0Kz2XLlk1zvWZkqEmOlrdqxtM111zjHn/88fD12uYGDBhgs6L27NkTXiakWRvt27e3IA7gP5YsWeJ69eplddWuvvpqm6kkaiyVI0cO+17Xz5s3zzqga5vU9gQguZBPgf8f8ikQO+RT4LdhQDhBHD582D6w9u7da0uCChQoYEXPdVYLQGQLFy60M71a1jpw4MDw0iB9rOlssK+1poPZBQsW2NIg1UbUctg+ffqE70fbng5+NSNDv6sDXtV08wECwC+WL19uddk0c6Jq1apu3Lhx4ZkXkcK2n3EBIPmQT4Ffj3wKxB75FPj1GBBOYHxIASffNrSkVd3Qn3766XDjm5Md1KrmoZYTaVbT0KFDrTZbsEkHgJPzHc4/+ugjW2qn0O1nYqgm2+zZswnbQIpjuwYiI58C8UE+BX6b3//G30MM8CEFZLxtqAOzakCpC7PC9qnObWlGhYKBOjKrUcCKFSvscsI2cHJqIqUvUdgWdUZXh3PVatOycjXcUNi+8cYbXadOnQjbQApjuwYiI58CsUM+Bf7/GBAGkJS0fPX48ePuL3/5S6Z/Rw0G/v73v9v3H3zwQfhyNQ/wZ5cB/MfcuXPdPffc45o0aeJat27tJk2aZA2lPB+61ZBDlytkq6uzGnD8+9//JmwDALId8ikQXeRTIOtw6hFAUlKXcwXkY8eOnfK2wTPB6paeM2dOa9CxatUqa9ah2RgPPfSQ1UcE8Eu3Zs248Aeia9ascRs2bLAZFnnz5rXtyoduLc/78ssv3aBBg9wTTzxh2xLLXQEA2Q35FIge8imQtZghDCApaTaFgvO+ffusQYcCdUbL8vx1y5Yts8YcOjv8+eefuzvuuMMCgpoQ+A7OQHang0+Fbc2kGDx4sHU979ixo103depU64ou/iC2YsWK7rHHHgsvz2vXrp1drrCt0A0AQHZBPgWig3wKZD2aygFIOvrY0tnem266yWZiDBs2LLzULlJdKN22b9++7sMPPwyfUa5UqZIrXbq01XdT3bbcuXPH6dkAiRW2J06c6K655hp355132rbhKXiPHj3aZl08++yz1rnZd2+WYCOPatWqWTMdYSYGACA7IJ8C0UE+BaKDGcIAko4C9cUXX+xatGhhP48dO9bCtL8u/Xmu/fv3u/z587u7777bOjj7WlJdu3a1s8eEbeCXsF2nTh3Xo0ePcNjWjCXRwam2rzx58tiSu2DYTt/IY/HixbaNCWEbAJAdkE+BrEc+BaKHAWEACelkixd8zSgFA4WAbdu2WV0oLa0Lhm596bZXXHGFu/fee232xZEjR9xll13mihYtGu5IC2R3wbB911132ZJXzwdmzWTSNnXBBReEr/PbWfrQrQNbLc/r1q1bjJ8JAADRQz4FYod8CkQXexsACWnXrl1pfg52WPZBWaHguuuucwUKFLD6awoNb731Vjh068vf9rnnnnPjxo1zZ555puvXr58755xzYvp8gEQ1dOhQC9u1atVyvXv3ThO2Vf9Q25D+P3v2bHfJJZe422+/PXy9387EB2+F7n/+858266l9+/ZxeEYAAEQH+RSIDfIpEH3UEAaQkAFgwoQJ7pZbbrHZE3/729/SBG9fZ03/10fYjBkz3MyZM93KlSvtNjfeeKMtu9NMi02bNrmlS5e6999/384KP/PMM65QoUJxfHZA4pgzZ47VL8yRI4ctUW3Tpo1dru1KIdvPvtBBqgJ38eLF3bnnnmuXa2lerly5rB7b+eef74oUKZJmVpM6rP/pT3+K23MDACArkU+B2CCfArHBgDCAhPLyyy9bLTXxwVrL7mrUqGHLhVSbzQs2A1ixYoV7/fXX3QsvvHDCfWoJke5DTQjUmRbALzOd1Kn57bfftpkXqqummRiaqeT5sK2abArQWtYapMsUritUqOBOP/1016hRI9ewYUOWvAIAUgb5FIgd8ikQGwwIA0gou3fvdk2bNrWzvK1bt7alQlu3brXGAWeffbbNrlD3ZXWZjUSzMBQi9H/NwtAZ4urVq1tjDs4GAydSJ/QHH3zQLViwwJbRdezY0bqia6ldr1693Lx582yZnZbiaRtUl2Y1wlm/fr1btWqVO3z4sNu+fXv4/nTgq/sBACBVkE+B2CKfAtHHgDCAhOHrQWlJnmqqqfi/grVmV0yePNn961//ct9//73dtl69eq5UqVJWo416a0DmjRgxwsJ0z549I4Zu1TxUF+dXX33Vvfbaa65x48auc+fOLl++fCfcl7bHb7/91g5w9+3bZzOd9PsAAKQK8ikQfeRTIPYYEAaQcN577z3Xtm1baxCgmmp+Gd2iRYvc5s2b3ejRo20JkCgENGjQwF111VW2JMj74YcfbHkQgF+omcbzzz9v24w6m6vOmq93GAzdOXPmdEePHrX6iFoiqxpswbigwO5/DwCA7IB8CkQH+RSID7YUAAlH4VlnfbW0bt26dXaZdvaq06amApp9IaojtXPnTuvOrAYf/fv3d1OmTHHffPMNYRtIR13OFbZV67BTp04WtoO1EPPmzWshvHbt2jYbSttXyZIlLWxH6tpM2AYAZCfkUyDrkU+B+GFrARA3WmK3bdu2NJf5s7zlypWzphxPPvmkBWjt5BUCHnvsMTdr1izryDx9+nR333332dlk3/Dj/vvvt6VGfukegP/MvFC9Q4Xtu+66yxp0BKUP3erM/N1339kBrLYrfe+DNgAAqYx8CsQG+RSIL0pGAIgLLf158cUXrRFAs2bNwsvu/DIfhetWrVpZUwB1ZlYAHzlypAVwdWWeNGmS/Y5fejd//ny3bNkyW873+OOPu2LFisX7KQIJtQwvUtgOdkIXRQIF6/SNPNQBXd2dtVQPAIBURT4FYoN8CsQfA8IAYk478S5dutj36qx80003uRYtWoSbAvgQMHXqVDdw4EBrzKFwrdpsCts6K6wOzQrbup0/M6xOz/pdQgFw6rCtg9rTTjvNvn/zzTdd+fLlrXt6pJpthG4AQKojnwKxQT4FEsMvp10AIEYuu+wyd/bZZ7tDhw7ZDl8dm7WTb9mypYVuf0b4yiuvdGeddZabM2eO/aywrW7OCtsKC+nrsP3xj3+0LwBpa7KdLGx369bNvfHGG27MmDHWNT398jxR6Nbsp+PHj7v69evbgTIAAKmEfApEH/kUSBzUEAYQU9qRqztz586dbYdfokQJV6BAAVt299JLL1kTDk+X33HHHTbD4pxzznGvvvqqu/jii22WhQ8LADKuyaYZE927d88wbPfq1cvCtuocFi9ePE1DjmDovvbaa92mTZvsgFe/DwBAKiGfAtFHPgUSCzOEAcSU7/x6+eWXu/POO8928OoaO3PmTDtbrJ+Dy/MUAjQLQ00D1LxD3wdrSgH4hapAbdy40bYl0faiA1d/ncKy334UtufNm2cd07t27eouuuiicI02/T8Yuvv06ePOOOMMq6mYK1euuD5HAACyGvkUiB7yKZCYmCEMIC7Kli3r6tata005qlat6u6++24LBzprrLO8O3bssNvpOp1FVj02zdBQHTZKnwORKSxryeuoUaPs57lz57pBgwa5Y8eO2c+RwrbqJQbDtkK2n4mhg1xte7peszoKFy4cx2cHAEB0kU+BrEc+BRITA8IAYk47dFGzDtV6euKJJ1zDhg1djx49bOmdQrcac3zxxRd2u0aNGtntli5dasHBnyEGEJkOZn3o1oHqsGHDwiE6fdhWzcNg2PazpNQp/Z577rEZHeIvBwAgFZFPgeginwKJhXUtAGIiuCP3/9eyO82wePnll93777/vmjZtajMs1BxAoVsh4JZbbnGlSpVyl156qVu7dq0bO3as69evXzg8AHBu5cqVbtu2bW7r1q0uT548dpCq0K3tpVOnTjarSUvq9u7d6+bPn3/KsD1jxgz3+OOPuwMHDrj+/fvbZWxzAIBUQz4Food8CiS234U4jQkgCh599FHb8ZcpU8YCs5d+566Q0KRJE1t2N2LEiPCZX4XugwcPulatWlkwUKBo27atq1Chgs3YOPPMM+P47IDEMX78eDtA/eqrr8KXFSpUyGZPVK5c2TowaxsSbXt/+9vfbLbThRdeGDFsT58+3bZfNcd58cUXbYkfAACpgHwKxAb5FEh8zBAGkOWGDBninnvuOduRq7ZT/fr1XfPmzd0FF1zg/vjHP4Z37mogoDPAun7WrFnWSbZmzZquZcuW9rsK1trh63eKFi3qypUr5+677z7CNvBfQ4cOdRMmTLDwrAPSw4cP22wmdVxWbbZnn33W1alTxz322GOuW7duFrDVwVm3j9TV2YdtzYTSUj7CNgAgVZBPgdggnwLJgRnCALLU6tWr3Y033mjf58yZ03bwqqt2ySWXuIoVK9ryIHWFDS7vUe01dYdV9+Z77703fJ2WET311FNu9+7drm/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|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_vendas_categ com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped = (\n",
|
||
" df_vendas_categ_eud\n",
|
||
" .groupby(['CATEGORIA', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped['CATEGORIA'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped.pivot(index='CATEGORIA', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=45, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.87, 0.02, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Trimestrais EUD por Categoria (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "c7d729c2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>LINHA</th>\n",
|
||
" <th>vendas2024</th>\n",
|
||
" <th>vendas2025</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>ARBO</td>\n",
|
||
" <td>176875</td>\n",
|
||
" <td>98918</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>BOTICOLLECTION</td>\n",
|
||
" <td>197291</td>\n",
|
||
" <td>121789</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>EGEO</td>\n",
|
||
" <td>251091</td>\n",
|
||
" <td>163082</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>FLORATTA</td>\n",
|
||
" <td>320346</td>\n",
|
||
" <td>277117</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>MALBEC</td>\n",
|
||
" <td>222658</td>\n",
|
||
" <td>233735</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" LINHA vendas2024 vendas2025\n",
|
||
"0 ARBO 176875 98918\n",
|
||
"1 BOTICOLLECTION 197291 121789\n",
|
||
"2 EGEO 251091 163082\n",
|
||
"3 FLORATTA 320346 277117\n",
|
||
"4 MALBEC 222658 233735"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"top5_linhas= ['ARBO','BOTICOLLECTION','EGEO','FLORATTA','MALBEC']\n",
|
||
"df_perfumaria = df_vendas2[df_vendas2['CATEGORIA']=='PERFUMARIA']\n",
|
||
"df_perfumaria = df_perfumaria[df_perfumaria['LINHA'].isin(top5_linhas)]\n",
|
||
"\n",
|
||
"df_perfumaria.groupby(['LINHA'])[['vendas2024', 'vendas2025']].sum().reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "13174572",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_perfumaria com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped2 = (\n",
|
||
" df_perfumaria\n",
|
||
" .groupby(['LINHA', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped2['LINHA'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped2.pivot(index='LINHA', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=45, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.92, 0.05, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Top 5 Linhas BOT — por Trimestre (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "2395b0cc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>LINHA</th>\n",
|
||
" <th>vendas2024</th>\n",
|
||
" <th>vendas2025</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>CLUB</td>\n",
|
||
" <td>14320</td>\n",
|
||
" <td>23091</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>DIVA</td>\n",
|
||
" <td>17556</td>\n",
|
||
" <td>23991</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>EUD</td>\n",
|
||
" <td>26609</td>\n",
|
||
" <td>30445</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>INSTANCE</td>\n",
|
||
" <td>8692</td>\n",
|
||
" <td>9031</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>PULSE</td>\n",
|
||
" <td>13408</td>\n",
|
||
" <td>12455</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" LINHA vendas2024 vendas2025\n",
|
||
"0 CLUB 14320 23091\n",
|
||
"1 DIVA 17556 23991\n",
|
||
"2 EUD 26609 30445\n",
|
||
"3 INSTANCE 8692 9031\n",
|
||
"4 PULSE 13408 12455"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"top5_linhas_EUD= ['DIVA','CLUB','PULSE','INSTANCE','EUD']\n",
|
||
"df_perfumaria_eud = df_vendas_categ_eud[df_vendas_categ_eud['CATEGORIA']=='PERFUMARIA']\n",
|
||
"df_perfumaria_eud = df_perfumaria_eud[df_perfumaria_eud['LINHA'].isin(top5_linhas_EUD)]\n",
|
||
"\n",
|
||
"df_perfumaria_eud.groupby(['LINHA'])[['vendas2024', 'vendas2025']].sum().reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "758fd053",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_perfumaria com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped3 = (\n",
|
||
" df_perfumaria_eud\n",
|
||
" .groupby(['LINHA', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped3['LINHA'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped3.pivot(index='LINHA', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=45, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.92, 0.05, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Top 5 Marcas EUD (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "d294dc9b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>vendas2024</th>\n",
|
||
" <th>vendas2025</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LINHA</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>DR BOTICA</th>\n",
|
||
" <td>14731</td>\n",
|
||
" <td>19288</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ELYSEE</th>\n",
|
||
" <td>14919</td>\n",
|
||
" <td>20577</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>INSENSATEZ</th>\n",
|
||
" <td>10471</td>\n",
|
||
" <td>6594</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>MEN</th>\n",
|
||
" <td>16412</td>\n",
|
||
" <td>9884</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>THE BLEND</th>\n",
|
||
" <td>16013</td>\n",
|
||
" <td>16249</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" vendas2024 vendas2025\n",
|
||
"LINHA \n",
|
||
"DR BOTICA 14731 19288\n",
|
||
"ELYSEE 14919 20577\n",
|
||
"INSENSATEZ 10471 6594\n",
|
||
"MEN 16412 9884\n",
|
||
"THE BLEND 16013 16249"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"bot5_linhas= ['DR BOTICA','INSENSATEZ','ELYSEE','MEN','THE BLEND']\n",
|
||
"df_perfumaria_b5 = df_vendas2[df_vendas2['CATEGORIA']=='PERFUMARIA']\n",
|
||
"\n",
|
||
"df_perfumaria_b5 = df_perfumaria_b5[df_perfumaria_b5['LINHA'].isin(bot5_linhas)]\n",
|
||
"\n",
|
||
"df_perfumaria_b5.groupby(['LINHA'])[['vendas2024', 'vendas2025']].sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "d5a424d5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_perfumaria com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped3 = (\n",
|
||
" df_perfumaria_b5\n",
|
||
" .groupby(['LINHA', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped3['LINHA'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped3.pivot(index='LINHA', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=45, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.92, 0.05, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"fig.suptitle('Bot 5 Vendas Trimestrais (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "54b32d61",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>PDV</th>\n",
|
||
" <th>CANAL</th>\n",
|
||
" <th>DESCRIÇÃO</th>\n",
|
||
" <th>PDV DESC</th>\n",
|
||
" <th>REGIÃO</th>\n",
|
||
" <th>ESTADO</th>\n",
|
||
" <th>CIDADE</th>\n",
|
||
" <th>UF</th>\n",
|
||
" <th>MARCA</th>\n",
|
||
" <th>ANALISTA</th>\n",
|
||
" <th>GESTÃO</th>\n",
|
||
" <th>SUPERVISOR</th>\n",
|
||
" <th>STATUS</th>\n",
|
||
" <th>ANALISTA EUD</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>23156</td>\n",
|
||
" <td>LJ</td>\n",
|
||
" <td>SHOPPING CENTRO SUL</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SERGIPE</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SE</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>INATIVO</td>\n",
|
||
" <td>LOJA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>4494</td>\n",
|
||
" <td>MTZ</td>\n",
|
||
" <td>ESCRITORIO</td>\n",
|
||
" <td>4494-COMERCIO-PONTA VERDE-MACEIO</td>\n",
|
||
" <td>ND</td>\n",
|
||
" <td>ALAGOAS</td>\n",
|
||
" <td>MACEIÓ</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>CP GINSENG</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>INATIVO</td>\n",
|
||
" <td>INATIVO</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1137</td>\n",
|
||
" <td>MTZ</td>\n",
|
||
" <td>AMG</td>\n",
|
||
" <td>1137-AMG SERRARIA</td>\n",
|
||
" <td>ND</td>\n",
|
||
" <td>ALAGOAS</td>\n",
|
||
" <td>MACEIÓ</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>Inativa</td>\n",
|
||
" <td>INATIVO</td>\n",
|
||
" <td>INATIVO</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>12522</td>\n",
|
||
" <td>LJ</td>\n",
|
||
" <td>MACEIO SHOP EXP</td>\n",
|
||
" <td>12522-COMERCIO -MACEIO SHOP EXPANSAO</td>\n",
|
||
" <td>MCZ BAIXA</td>\n",
|
||
" <td>ALAGOAS</td>\n",
|
||
" <td>MACEIÓ</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>LUAN</td>\n",
|
||
" <td>Betina Melo</td>\n",
|
||
" <td>Efigênia Herculano</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>LOJA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>12817</td>\n",
|
||
" <td>LJ</td>\n",
|
||
" <td>SHOPPING PATIO</td>\n",
|
||
" <td>12817-COMERCIO -SHOPPING PATIO</td>\n",
|
||
" <td>MCZ ALTA</td>\n",
|
||
" <td>ALAGOAS</td>\n",
|
||
" <td>MACEIÓ</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>JEFFERSON</td>\n",
|
||
" <td>Pamella Barbosa</td>\n",
|
||
" <td>Maxwell Vieira</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>LOJA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>81</th>\n",
|
||
" <td>24269</td>\n",
|
||
" <td>VD</td>\n",
|
||
" <td>COMERCIO-ER JACOBINA</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BA3</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>JEFFERSON</td>\n",
|
||
" <td>Caio Luna</td>\n",
|
||
" <td>FERNANDA</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>HARY</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>82</th>\n",
|
||
" <td>24293</td>\n",
|
||
" <td>HIB</td>\n",
|
||
" <td>COMERCIO-HIB MORRO DO CHAPEU</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BA3</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>MARCYARA</td>\n",
|
||
" <td>Alysson</td>\n",
|
||
" <td>FERNANDA</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>DIELLY</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>83</th>\n",
|
||
" <td>910173</td>\n",
|
||
" <td>LJ</td>\n",
|
||
" <td>QDB PARQUE SHOP</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>DIELLY</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>LOJA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>84</th>\n",
|
||
" <td>910291</td>\n",
|
||
" <td>LJ</td>\n",
|
||
" <td>QDB MACEIO SHOP</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AL</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>DIELLY</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>LOJA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>85</th>\n",
|
||
" <td>23813</td>\n",
|
||
" <td>HIB</td>\n",
|
||
" <td>VALENTE</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BA</td>\n",
|
||
" <td>O BOTICARIO</td>\n",
|
||
" <td>MARCYARA</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>CLÁUDIA</td>\n",
|
||
" <td>ATIVO</td>\n",
|
||
" <td>HARY</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>86 rows × 14 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" PDV CANAL DESCRIÇÃO \\\n",
|
||
"0 23156 LJ SHOPPING CENTRO SUL \n",
|
||
"1 4494 MTZ ESCRITORIO \n",
|
||
"2 1137 MTZ AMG \n",
|
||
"3 12522 LJ MACEIO SHOP EXP \n",
|
||
"4 12817 LJ SHOPPING PATIO \n",
|
||
".. ... ... ... \n",
|
||
"81 24269 VD COMERCIO-ER JACOBINA \n",
|
||
"82 24293 HIB COMERCIO-HIB MORRO DO CHAPEU \n",
|
||
"83 910173 LJ QDB PARQUE SHOP \n",
|
||
"84 910291 LJ QDB MACEIO SHOP \n",
|
||
"85 23813 HIB VALENTE \n",
|
||
"\n",
|
||
" PDV DESC REGIÃO ESTADO CIDADE UF \\\n",
|
||
"0 NaN NaN SERGIPE NaN SE \n",
|
||
"1 4494-COMERCIO-PONTA VERDE-MACEIO ND ALAGOAS MACEIÓ AL \n",
|
||
"2 1137-AMG SERRARIA ND ALAGOAS MACEIÓ AL \n",
|
||
"3 12522-COMERCIO -MACEIO SHOP EXPANSAO MCZ BAIXA ALAGOAS MACEIÓ AL \n",
|
||
"4 12817-COMERCIO -SHOPPING PATIO MCZ ALTA ALAGOAS MACEIÓ AL \n",
|
||
".. ... ... ... ... ... \n",
|
||
"81 NaN NaN NaN NaN BA3 \n",
|
||
"82 NaN NaN NaN NaN BA3 \n",
|
||
"83 NaN NaN NaN NaN AL \n",
|
||
"84 NaN NaN NaN NaN AL \n",
|
||
"85 NaN NaN NaN NaN BA \n",
|
||
"\n",
|
||
" MARCA ANALISTA GESTÃO SUPERVISOR STATUS \\\n",
|
||
"0 NaN Inativa Inativa Inativa INATIVO \n",
|
||
"1 CP GINSENG Inativa Inativa Inativa INATIVO \n",
|
||
"2 O BOTICARIO Inativa Inativa Inativa INATIVO \n",
|
||
"3 O BOTICARIO LUAN Betina Melo Efigênia Herculano ATIVO \n",
|
||
"4 O BOTICARIO JEFFERSON Pamella Barbosa Maxwell Vieira ATIVO \n",
|
||
".. ... ... ... ... ... \n",
|
||
"81 O BOTICARIO JEFFERSON Caio Luna FERNANDA ATIVO \n",
|
||
"82 O BOTICARIO MARCYARA Alysson FERNANDA ATIVO \n",
|
||
"83 O BOTICARIO DIELLY NaN NaN ATIVO \n",
|
||
"84 O BOTICARIO DIELLY NaN NaN ATIVO \n",
|
||
"85 O BOTICARIO MARCYARA NaN CLÁUDIA ATIVO \n",
|
||
"\n",
|
||
" ANALISTA EUD \n",
|
||
"0 LOJA \n",
|
||
"1 INATIVO \n",
|
||
"2 INATIVO \n",
|
||
"3 LOJA \n",
|
||
"4 LOJA \n",
|
||
".. ... \n",
|
||
"81 HARY \n",
|
||
"82 DIELLY \n",
|
||
"83 LOJA \n",
|
||
"84 LOJA \n",
|
||
"85 HARY \n",
|
||
"\n",
|
||
"[86 rows x 14 columns]"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_pdv = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")\n",
|
||
"\n",
|
||
"df_pdv['PDV'] = df_pdv['PDV'].astype('str')\n",
|
||
"df_pdv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"id": "775fd296",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_vendas3 = pd.merge(df_vendas2,df_pdv[['PDV','UF','CANAL']],left_on='pdv',right_on='PDV',how='left')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"id": "25150239",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_perfumaria com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped4 = (\n",
|
||
" df_vendas3\n",
|
||
" .groupby(['UF', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped4['UF'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped4.pivot(index='UF', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=0, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.2)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.92, 0.017, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Trimestrais por UF (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"id": "63894039",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1400x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# file: plot_trimestres_matplotlib.py\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Assumimos que você tem um DataFrame chamado df_perfumaria com colunas:\n",
|
||
"# 'CATEGORIA', 'TRIMESTRE', 'vendas2024', 'vendas2025'.\n",
|
||
"# Se já tiver o resultado do groupby use-o; aqui eu recomputo para garantir consistência.\n",
|
||
"df_grouped5 = (\n",
|
||
" df_vendas3\n",
|
||
" .groupby(['CANAL', 'TRIMESTRE'])[['vendas2024', 'vendas2025']]\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ordem consistente de categorias (preserva a ordem encontrada; pode ordenar alfabeticamente se preferir)\n",
|
||
"categorias = list(df_grouped5['CANAL'].unique())\n",
|
||
"\n",
|
||
"# Pivot para facilitar acesso por trimestre\n",
|
||
"pivot = df_grouped5.pivot(index='CANAL', columns='TRIMESTRE', values=['vendas2024', 'vendas2025'])\n",
|
||
"# Após pivot, pivot[('vendas2024', t)] retorna vendas2024 do trimestre t por categoria.\n",
|
||
"# Substitui NaN por 0 para evitar erros (caso faltem categorias em algum trimestre)\n",
|
||
"pivot = pivot.fillna(0)\n",
|
||
"\n",
|
||
"# Configuração do plot\n",
|
||
"n_cats = len(categorias)\n",
|
||
"x = np.arange(n_cats) # posições no eixo x para categorias\n",
|
||
"width = 0.35 # largura das barras\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # removido sharey para permitir ylim individual\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Calcular o máximo global para definir ylim\n",
|
||
"max_value = 0\n",
|
||
"\n",
|
||
"for i, trimestre in enumerate([1, 2, 3, 4]):\n",
|
||
" ax = axes[i]\n",
|
||
" # extrair valores por categoria — garantimos a mesma ordem de categorias\n",
|
||
" if ('vendas2024', trimestre) in pivot.columns:\n",
|
||
" vendas24 = pivot[('vendas2024', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas24 = np.zeros(n_cats)\n",
|
||
" \n",
|
||
" if ('vendas2025', trimestre) in pivot.columns:\n",
|
||
" vendas25 = pivot[('vendas2025', trimestre)].reindex(categorias).fillna(0).values\n",
|
||
" else:\n",
|
||
" vendas25 = np.zeros(n_cats)\n",
|
||
"\n",
|
||
" # posições das barras\n",
|
||
" ax.bar(x - width/2, vendas24, width, label='vendas2024')\n",
|
||
" ax.bar(x + width/2, vendas25, width, label='vendas2025')\n",
|
||
"\n",
|
||
" ax.set_title(f'Trimestre {trimestre}')\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(categorias, rotation=0, ha='right')\n",
|
||
"\n",
|
||
" # Calcular máximo local e atualizar ylim para 1.2x o maior valor\n",
|
||
" local_max = max(vendas24.max(), vendas25.max())\n",
|
||
" max_value = max(max_value, local_max)\n",
|
||
" ax.set_ylim(bottom=0, top=local_max * 1.1)\n",
|
||
" ax.grid(axis='y', linestyle='--', alpha=0.4)\n",
|
||
"\n",
|
||
"# Legenda única (coloca fora do último axes)\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc=(0.83, 0.92), ncol=1, frameon=True, fontsize='small')\n",
|
||
"fig.text(0.92, 0.017, '* Quantia em unidades vendidas',fontsize=10)\n",
|
||
"\n",
|
||
"fig.suptitle('Vendas Trimestrais por Canal (2024 vs 2025)', fontsize=16)\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96]) # espaço para o suptitle\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6ac0f2f1",
|
||
"metadata": {},
|
||
"source": [
|
||
"Adicionar a Ruptura Bruta média nos trimestres"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4cf95a26",
|
||
"metadata": {},
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|