B_RFM  Analisis  [Tradicional 1010078].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa7a547e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!jt -l\n",
    "# !jt -t oceans16 -T\n",
    "# !jt -r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f92b530",
   "metadata": {},
   "source": [
    "Notas:\n",
    "--------\n",
    "- [x] Estudiar Recency.__\n",
    "    - [x] Histograma Row simple\n",
    "    - [x] Probar esta función\n",
    "    - [x] Implementar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7aaa87",
   "metadata": {},
   "source": [
    "##### Tarea anterior "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35683e4",
   "metadata": {},
   "source": [
    "- [x] __Construir la variable Recency.__\n",
    "    - [x] Sección para caso pequeño\n",
    "    - [x] Función para estadnarizar\n",
    "    - [x] Probar esta función\n",
    "    - [x] Implementar\n",
    "- [x] __Contruir Frecuency__\n",
    "    - [x] Sección para caso pequeño\n",
    "    - [x] Función para estadnarizar\n",
    "    - [x] Probar esta función\n",
    "    - [x] Implementar\n",
    "- [x] __Construir Monetary__\n",
    "    - [x] Sección para caso pequeño\n",
    "    - [x] Función para estadnarizar\n",
    "    - [x] Probar esta función\n",
    "    - [x] Implementar   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cc505f",
   "metadata": {},
   "source": [
    "# Inicio\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2a8234",
   "metadata": {},
   "source": [
    "\n",
    "__Rename__\n",
    "Recordar que la columna llamada\n",
    "\n",
    "- `Precio promedio` ahora es `Precio`\n",
    "- `Centro.1` ahora es `Centro`\n",
    "- `Centro` ahora es `Centro_Cod`\n",
    "\n",
    "Presenta los parametros del modelo:\n",
    "\n",
    "- Dirección datos\n",
    "- Dirección Graficos\n",
    "- _Material a analizar_\n",
    "- _Canal a analizar_\n",
    "- _Zona de los Datos_\n",
    "- _Año de inicio de analizis_\n",
    "\n",
    "Base de datos\n",
    "\n",
    "\n",
    "`Tipo_de_Cliente`\n",
    "`Y_producto`\n",
    "`Año_inicio`\n",
    "\n",
    "`file_direct_Graphic` "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bad43fc",
   "metadata": {},
   "source": [
    "## Parametros \n",
    "data frame `data_i`\n",
    "\n",
    "\n",
    "> - Producto `i_producto = Y_producto`\n",
    "> -Para el Canal `Tipo_de_Cliente`\n",
    "> -Variable a predecir/Grafico `Y_Variable`\n",
    "\n",
    "Columnas de interes para predecir `X_col`\n",
    "\n",
    "Diccionario del grupo de columnas `Dic_col`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0737e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#===================================\n",
    "# Archivo\n",
    "#===================================\n",
    "Zona_df='Z001'\n",
    "\n",
    "#===================================\n",
    "# Clientes\n",
    "#===================================\n",
    "Tipo_de_Cliente='Tradicional'\n",
    "\n",
    "#===================================\n",
    "# Producto de interes\n",
    "#===================================\n",
    "Y_producto='1010078'\n",
    "\n",
    "\n",
    "#===================================\n",
    "# Año de Partida\n",
    "#===================================\n",
    "Año_inicio='2018'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Ordenador de escritorio\n",
    "\n",
    "file_direct_O=\"C:/Users/user/Documents/Data/Data Clear/\"+Zona_df+\"/\"\n",
    "file_direct_Clear=\"C:/Users/user/Documents/Data/Data Clear/\"\n",
    "file_direct_Graphic='C:/Users/user/Documents/Graficos'\n",
    "Png_file_direct_Graphic=\"C:\\\\Users\\\\user\\\\Documents\\\\Graficos\"\n",
    "\"\"\"\n",
    "\n",
    "#Notebook\n",
    "file_direct_O=\"C:/Users/rolft/Documents/Repositorios/Data/Data Clear/\"+Zona_df+\"/\"\n",
    "file_direct_Clear=\"C:/Users/rolft/Documents/Repositorios/Data/Data Clear/\"\n",
    "file_direct_Graphic='C:/Users/rolft/Documents/Repositorios/Graficos'\n",
    "Png_file_direct_Graphic='C:\\\\Users\\\\rolft\\\\Documents\\\\Repositorios\\\\Graficos'\n",
    "\"\"\"\n",
    "#Graficos\n",
    "file_direct_Graphic_Canal=file_direct_Graphic+\"/\"+Tipo_de_Cliente\n",
    "file_direct_Graphic_Canal_Material=file_direct_Graphic_Canal+'/'+Y_producto\n",
    "\n",
    "Png_file_direct_Graphic_Canal=Png_file_direct_Graphic+'\\\\'+'Canal '+Tipo_de_Cliente\n",
    "Png_file_direct_Graphic_Canal_Material=Png_file_direct_Graphic_Canal+'\\\\'+Y_producto\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80e374d",
   "metadata": {},
   "source": [
    "## Intalar paquetes, Abrir data y ajustarla "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7c4706",
   "metadata": {},
   "source": [
    "### import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faaa036d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from adjustText import adjust_text\n",
    "# from adjustText import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ea91ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "                # BASE \n",
    "#-----------------------------------------------------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#from geopandas import *\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "#para ajustar las fechas\n",
    "import datetime\n",
    "\n",
    "#para calcular el skew de un array() o list()\n",
    "from scipy.stats import skew "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8916d4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "                # sklearn \n",
    "#-----------------------------------------------------\n",
    "\n",
    "#Separador de datos en train/test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Prediccion Ridge Regression\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d82be85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "                # statsmodels \n",
    "#-----------------------------------------------------\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
    "\n",
    "\n",
    "#from statsmodels.regression.mixed_linear_model import MixedLMResults\n",
    "# No se para qué sirve este"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "734ff701",
   "metadata": {},
   "outputs": [],
   "source": [
    "#====================================================\n",
    "#librerias para crear division de series temporales\n",
    "#====================================================\n",
    "\n",
    "from mizani.breaks import date_breaks\n",
    "from mizani.formatters import date_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34160d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stopwatch\n",
    "import time\n",
    "def time_convert(sec):\n",
    "  mins = sec // 60\n",
    "  sec = sec % 60\n",
    "  hours = mins // 60\n",
    "  mins = mins % 60\n",
    "  print(\"Time Lapsed = {0}:{1}:{2}\".format(int(hours),int(mins),sec),'\\n H:m:s')\n",
    "#==================================\n",
    "#          Example\n",
    "#    input(\"Press Enter to start\")\n",
    "start_time = time.time()\n",
    "#    input(\"Press Enter to stop\")\n",
    "#    end_time = time.time()\n",
    "#    time_lapsed = end_time - start_time\n",
    "#    time_convert(time_lapsed)\n",
    "#================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b400675d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Histograma con porcentajes\n",
    "from mizani.formatters import percent_format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e0194b5",
   "metadata": {},
   "source": [
    "### Funciones \n",
    "```python\n",
    "#Mes_name\n",
    "data_i.Mes.apply(Mes_name)\n",
    "```\n",
    ">Transforma la columna mes numerica en el nombre del mes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bc8afd",
   "metadata": {},
   "source": [
    "__Forma de crear una etiqueta para un valor especifico__\n",
    "```python\n",
    "#[Y_Graph]\n",
    "dfz=df.copy()\n",
    "dfz['Año select']=dfz.apply( lambda x: x['Año'] if x['Año']==2020 else '',axis=1)\n",
    "dfz\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad8627c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#===================================\n",
    "# Dicionario MES\n",
    "#===================================\n",
    "\n",
    "#===================================\n",
    "# Fecha\n",
    "D=['Enero','Febrero','Marzo','Abril',\n",
    " 'Mayo','Junio','Julio','Agosto',\n",
    " 'Septiembre','Octubre',\n",
    " 'Noviembre','Diciembre']\n",
    "Month_dic={i:D[i-1]  for i in range(1,13)}\n",
    "#===========================================\n",
    "\n",
    "\n",
    "def Mes_name(x):\n",
    "    D=['Enero','Febrero','Marzo','Abril',\n",
    "    'Mayo','Junio','Julio','Agosto',\n",
    "    'Septiembre','Octubre',\n",
    "    'Noviembre','Diciembre']\n",
    "    return(D[int(x)-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b646c867",
   "metadata": {},
   "source": [
    "## Dicionario \n",
    "rename aplicado a `Precio promedio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66d853bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dic_col={'Base':\n",
    "                             ['Material (Nombre)', 'Kilos Venta KG', 'Precio','Cliente'],\n",
    "        'Fecha':\n",
    "                             ['Día natural','Día Semana','Semana', 'Mes', 'Año'],\n",
    "        'Ubicacion':\n",
    "                             ['Centro','Población'],\n",
    "        'Cliente Categoria':\n",
    "                             ['Cluster','Segmento.1', 'Categoría Cliente'],\n",
    "        'Cliente Atributos':\n",
    "                             ['Año Creación', 'Material Favorito','Frecuencia Historica'] \n",
    "         ,'RFM Row':\n",
    "                 ['Recency días','Frecuency_Row','Monetary']\n",
    "        ,'RFM Customers':\n",
    "                 ['Recency Actual','Frecuency Actual del Cliente','Monetary Actual']\n",
    "\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a726445",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dic_col={'Base':\n",
    "                             ['Material (Nombre)', 'Kilos Venta KG', 'Precio','Cliente'],\n",
    "        'Fecha':\n",
    "                             ['Día natural','Día Semana','Semana', 'Mes', 'Año'],\n",
    "        'Ubicacion':\n",
    "                             ['Centro','Población'],\n",
    "        'Cliente Categoria':\n",
    "                             ['Cluster','Segmento.1', 'Categoría Cliente'],\n",
    "        'Cliente Atributos':\n",
    "                             ['Año Creación', 'Material Favorito','Frecuencia Historica'] \n",
    "         ,'RFM Row':\n",
    "                 ['Recency días','Frecuency_Row','Monetary']\n",
    "        ,'RFM Customers':\n",
    "                 ['Recency Actual','Frecuency Actual del Cliente','Monetary Actual']\n",
    "        ,'Ticket Row':\n",
    "                 ['Ticket Promedio','Ticket CV']\n",
    "        ,'Ticket Customers':\n",
    "                 ['Ticket Promedio Actual','Ticket CV Actual']\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e12a83",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Colores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbbf1e78",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'57DB5F'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "('{:X}{:X}{:X}').format(31, 119, 180)\n",
    "('{:X}{:X}{:X}').format(219, 96, 97)\n",
    "('{:X}{:X}{:X}').format(87, 219, 95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e71982",
   "metadata": {},
   "source": [
    "# Uso de F file\n",
    "\n",
    "- `data_i_M`: Monetary \n",
    "- `Clientes_i_RFM`: RFM Clientes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c853d2",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Abrir Archivo   Facturaciones\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25619431",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Se debe agregar el parametro `,index_col='Unnamed: 0'` de esta froma tal vez se pueda hacer un merge en base al indice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2bc23d3",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### R"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3526e76a",
   "metadata": {
    "hidden": true
   },
   "source": [
    "###### abrir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aea237e7",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Material (Nombre)</th>\n",
       "      <th>Kilos Venta KG</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Día natural</th>\n",
       "      <th>Día Semana</th>\n",
       "      <th>Semana</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Población</th>\n",
       "      <th>Centro</th>\n",
       "      <th>Cliente</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Segmento.1</th>\n",
       "      <th>Categoría Cliente</th>\n",
       "      <th>Año Creación</th>\n",
       "      <th>Material Favorito</th>\n",
       "      <th>Frecuencia Historica</th>\n",
       "      <th>Mes_num</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61455</th>\n",
       "      <td>1010078(Trutro Entero Selección)</td>\n",
       "      <td>22</td>\n",
       "      <td>1300</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>21</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>2020</td>\n",
       "      <td>La Pintana</td>\n",
       "      <td>Sucursal Lo Espejo</td>\n",
       "      <td>10003742</td>\n",
       "      <td>Clásico</td>\n",
       "      <td>Cobertura Pequeno SD</td>\n",
       "      <td>Especialista en Proc</td>\n",
       "      <td>2007</td>\n",
       "      <td>1010077</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Material (Nombre) Kilos Venta KG Precio Día natural  \\\n",
       "61455  1010078(Trutro Entero Selección)             22   1300  2020-05-23   \n",
       "\n",
       "      Día Semana Semana   Mes   Año   Población              Centro   Cliente  \\\n",
       "61455     Sábado     21  Mayo  2020  La Pintana  Sucursal Lo Espejo  10003742   \n",
       "\n",
       "       Cluster            Segmento.1     Categoría Cliente Año Creación  \\\n",
       "61455  Clásico  Cobertura Pequeno SD  Especialista en Proc         2007   \n",
       "\n",
       "      Material Favorito Frecuencia Historica Mes_num Recency  \n",
       "61455           1010077                   19       5  0 days  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#======================================================================\n",
    "# Se Cargan los datos de ventas con las caracteristicas de los clientes\n",
    "#=====================================================================\n",
    "\n",
    "file_direct=file_direct_O+'data_1010078_CT R'+'.csv'\n",
    "col_names=pd.read_csv(file_direct,\n",
    "                      encoding=\"utf-8\",sep=\";\",nrows=0).columns\n",
    "\n",
    "types_dict = {col: str for col in list(col_names)}\n",
    "\n",
    "data_i_R = pd.read_csv(file_direct,\n",
    "                   encoding=\"utf-8\",sep=\";\",dtype=types_dict\n",
    "                      ,index_col='Unnamed: 0'\n",
    "                      )\n",
    "\n",
    "# Se debe agregar el parametro ,index_col='Unnamed: 0' \n",
    "# de esta froma tal vez se pueda hacer un merge en base al indice\n",
    "\n",
    "#elimina una columna en especifico\n",
    "#DF_FULL.drop('Año natural', inplace=True, axis=1)\n",
    "data_i_R.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3921eaa8",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### F "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284f1d53",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "###### abrir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31a3c856",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Material (Nombre)</th>\n",
       "      <th>Kilos Venta KG</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Día natural</th>\n",
       "      <th>Día Semana</th>\n",
       "      <th>Semana</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Población</th>\n",
       "      <th>Centro</th>\n",
       "      <th>Cliente</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Segmento.1</th>\n",
       "      <th>Categoría Cliente</th>\n",
       "      <th>Año Creación</th>\n",
       "      <th>Material Favorito</th>\n",
       "      <th>Frecuencia Historica</th>\n",
       "      <th>Mes_num</th>\n",
       "      <th>Frecuency_Row</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61455</th>\n",
       "      <td>1010078(Trutro Entero Selección)</td>\n",
       "      <td>22</td>\n",
       "      <td>1300</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>21</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>2020</td>\n",
       "      <td>La Pintana</td>\n",
       "      <td>Sucursal Lo Espejo</td>\n",
       "      <td>10003742</td>\n",
       "      <td>Clásico</td>\n",
       "      <td>Cobertura Pequeno SD</td>\n",
       "      <td>Especialista en Proc</td>\n",
       "      <td>2007</td>\n",
       "      <td>1010077</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Material (Nombre) Kilos Venta KG Precio Día natural  \\\n",
       "61455  1010078(Trutro Entero Selección)             22   1300  2020-05-23   \n",
       "\n",
       "      Día Semana Semana   Mes   Año   Población              Centro   Cliente  \\\n",
       "61455     Sábado     21  Mayo  2020  La Pintana  Sucursal Lo Espejo  10003742   \n",
       "\n",
       "       Cluster            Segmento.1     Categoría Cliente Año Creación  \\\n",
       "61455  Clásico  Cobertura Pequeno SD  Especialista en Proc         2007   \n",
       "\n",
       "      Material Favorito Frecuencia Historica Mes_num Frecuency_Row  \n",
       "61455           1010077                   19       5           1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#======================================================================\n",
    "# Se Cargan los datos de ventas con las caracteristicas de los clientes\n",
    "#=====================================================================\n",
    "\n",
    "file_direct=file_direct_O+'data_1010078_CT F'+'.csv'\n",
    "col_names=pd.read_csv(file_direct,\n",
    "                      encoding=\"utf-8\",sep=\";\",nrows=0).columns\n",
    "\n",
    "types_dict = {col: str for col in list(col_names)}\n",
    "\n",
    "data_i_F = pd.read_csv(file_direct,\n",
    "                   encoding=\"utf-8\",sep=\";\",dtype=types_dict\n",
    "                      ,index_col='Unnamed: 0'\n",
    "                      )\n",
    "\n",
    "# Se debe agregar el parametro ,index_col='Unnamed: 0' \n",
    "# de esta froma tal vez se pueda hacer un merge en base al indice\n",
    "\n",
    "#elimina una columna en especifico\n",
    "#DF_FULL.drop('Año natural', inplace=True, axis=1)\n",
    "data_i_F.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f422fd",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### M "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228d2aa3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "###### abrir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f14ea23",
   "metadata": {
    "hidden": true
   },
   "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>Material (Nombre)</th>\n",
       "      <th>Kilos Venta KG</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Día natural</th>\n",
       "      <th>Día Semana</th>\n",
       "      <th>Semana</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Población</th>\n",
       "      <th>Centro</th>\n",
       "      <th>Cliente</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Segmento.1</th>\n",
       "      <th>Categoría Cliente</th>\n",
       "      <th>Año Creación</th>\n",
       "      <th>Material Favorito</th>\n",
       "      <th>Frecuencia Historica</th>\n",
       "      <th>Mes_num</th>\n",
       "      <th>Ingreso de Venta CLP</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61455</th>\n",
       "      <td>1010078(Trutro Entero Selección)</td>\n",
       "      <td>22</td>\n",
       "      <td>1300</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>21</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>2020</td>\n",
       "      <td>La Pintana</td>\n",
       "      <td>Sucursal Lo Espejo</td>\n",
       "      <td>10003742</td>\n",
       "      <td>Clásico</td>\n",
       "      <td>Cobertura Pequeno SD</td>\n",
       "      <td>Especialista en Proc</td>\n",
       "      <td>2007</td>\n",
       "      <td>1010077</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>28600</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Material (Nombre) Kilos Venta KG Precio Día natural  \\\n",
       "61455  1010078(Trutro Entero Selección)             22   1300  2020-05-23   \n",
       "\n",
       "      Día Semana Semana   Mes   Año   Población              Centro   Cliente  \\\n",
       "61455     Sábado     21  Mayo  2020  La Pintana  Sucursal Lo Espejo  10003742   \n",
       "\n",
       "       Cluster            Segmento.1     Categoría Cliente Año Creación  \\\n",
       "61455  Clásico  Cobertura Pequeno SD  Especialista en Proc         2007   \n",
       "\n",
       "      Material Favorito Frecuencia Historica Mes_num Ingreso de Venta CLP  \\\n",
       "61455           1010077                   19       5                28600   \n",
       "\n",
       "      Monetary  \n",
       "61455      0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#======================================================================\n",
    "# Se Cargan los datos de ventas con las caracteristicas de los clientes\n",
    "#=====================================================================\n",
    "\n",
    "file_direct=file_direct_O+'data_1010078_CT M'+'.csv'\n",
    "col_names=pd.read_csv(file_direct,\n",
    "                      encoding=\"utf-8\",sep=\";\",nrows=0).columns\n",
    "\n",
    "types_dict = {col: str for col in list(col_names)}\n",
    "\n",
    "data_i_M = pd.read_csv(file_direct,\n",
    "                   encoding=\"utf-8\",sep=\";\",dtype=types_dict\n",
    "                      ,index_col='Unnamed: 0'\n",
    "                      )\n",
    "\n",
    "# Se debe agregar el parametro ,index_col='Unnamed: 0' \n",
    "# de esta froma tal vez se pueda hacer un merge en base al indice\n",
    "\n",
    "#elimina una columna en especifico\n",
    "#DF_FULL.drop('Año natural', inplace=True, axis=1)\n",
    "data_i_M.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8240fbd",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Merge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51e6f868",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R (198596, 19)\n",
      "F (198596, 19)\n",
      "M (198596, 20)\n",
      "RF (198596, 20)\n",
      "RFM (198596, 22)\n"
     ]
    }
   ],
   "source": [
    "print('R',data_i_R.shape)\n",
    "print('F',data_i_F.shape)\n",
    "print('M',data_i_M.shape)\n",
    "\n",
    "print('RF',(pd.merge(data_i_R,data_i_F )).shape)\n",
    "\n",
    "print('RFM',pd.merge(pd.merge(data_i_R,\n",
    "                              data_i_F ) ,\n",
    "                    data_i_M).shape)\n",
    "data_i_RFM = pd.merge(pd.merge(data_i_R,data_i_F ),data_i_M)\n",
    "# [Dic_col['Fecha']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c29b30",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Funcion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec713b7",
   "metadata": {
    "hidden": true
   },
   "source": [
    "###### def "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "015ac527",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# Ajustar la data\n",
    "# =============================================================================\n",
    "def Ajuste_data_RFM(data):\n",
    "    start_time = time.time()\n",
    "\n",
    "    df=data.copy()\n",
    "\n",
    "# =================================\n",
    "# ELiminare dodos los valores Nan\n",
    "# =================================\n",
    "    #for i in df.columns.to_list():\n",
    "    #    df=df[df[i].notna()]\n",
    "\n",
    "    \n",
    "    \n",
    "#=================================\n",
    "# Proceso Recency a Int \n",
    "#=================================\n",
    "        \n",
    "    df['Recency días']=df['Recency'].apply( lambda x:  x.split(' ')[0])\n",
    "    \n",
    "#=================================\n",
    "# Proceso iterativo Pasar a numero\n",
    "#=================================\n",
    "\n",
    "    for i in ['Kilos Venta KG'\n",
    "#               ,'Venta Neta'\n",
    "              ,'Precio'\n",
    "#               RFM\n",
    "              ,'Recency días'\n",
    "              ,'Frecuency_Row'\n",
    "              ,'Monetary'\n",
    "              ,'Ingreso de Venta CLP'\n",
    "             ]:\n",
    "        \n",
    "        df[i]=df[i].astype('float64').astype('int64')\n",
    "    for i in ['Kilos Venta KG'\n",
    "#               ,'Venta Neta'\n",
    "              ,'Precio'\n",
    "#               RFM\n",
    "              ,'Recency días'\n",
    "#               ,'Frecuency_Row'\n",
    "#               ,'Monetary'\n",
    "               ,'Ingreso de Venta CLP'\n",
    "             ]:\n",
    "        df[i]=df[i].astype('int64')     \n",
    "\n",
    "\n",
    "\n",
    "#==========================================================================================\n",
    "# Ajuste de las fechas\n",
    "#-----------------------------------------------------------------------------------------\n",
    "    df[\"Día natural\"] = df[\"Día natural\"].apply(lambda x: \n",
    "                                                datetime.datetime.strptime(str(x),\n",
    "                                                                           '%Y-%m-%d').date())\n",
    "    \n",
    "    #Separacion fecha\n",
    "    \n",
    "    #ELiminado de DF_FULL\n",
    "    #df['Semana']=df[\"Día natural\"].apply(lambda x: x.isocalendar()[1] )\n",
    "    #df['Mes']=df[\"Día natural\"].apply(lambda x: x.month)\n",
    "    #df['Año']=df[\"Día natural\"].apply(lambda x: x.year)\n",
    "    #df['Semana']=df['Semana'].astype('int64')\n",
    "    \n",
    "#     df['Año']=df['Año'].astype('int64')\n",
    "#     D_W=['Lunes','Martes','Miércoles','Jueves', 'Viernes','Sábado','Domingo']\n",
    "#     df['Día Semana']=df[\"Día natural\"].apply(lambda x: D_W[x.isocalendar()[2] -1])\n",
    "#     df=df[df['Día Semana']!='Domingo']\n",
    "    \n",
    "    print(df.dtypes)\n",
    "    end_time = time.time()\n",
    "    time_convert(end_time - start_time)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c8e6f0",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "###### aplicación "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "705fd67b",
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Material (Nombre)       object\n",
      "Kilos Venta KG           int64\n",
      "Precio                   int64\n",
      "Día natural             object\n",
      "Día Semana              object\n",
      "Semana                  object\n",
      "Mes                     object\n",
      "Año                     object\n",
      "Población               object\n",
      "Centro                  object\n",
      "Cliente                 object\n",
      "Cluster                 object\n",
      "Segmento.1              object\n",
      "Categoría Cliente       object\n",
      "Año Creación            object\n",
      "Material Favorito       object\n",
      "Frecuencia Historica    object\n",
      "Mes_num                 object\n",
      "Recency                 object\n",
      "Frecuency_Row            int64\n",
      "Ingreso de Venta CLP     int64\n",
      "Monetary                 int64\n",
      "Recency días             int64\n",
      "dtype: object\n",
      "Time Lapsed = 0:0:1.8961198329925537 \n",
      " H:m:s\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Material (Nombre)</th>\n",
       "      <th>Kilos Venta KG</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Día natural</th>\n",
       "      <th>Día Semana</th>\n",
       "      <th>Semana</th>\n",
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       "      <th>Año</th>\n",
       "      <th>Población</th>\n",
       "      <th>Centro</th>\n",
       "      <th>...</th>\n",
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       "      <th>Año Creación</th>\n",
       "      <th>Material Favorito</th>\n",
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       "      <th>Mes_num</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frecuency_Row</th>\n",
       "      <th>Ingreso de Venta CLP</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Recency días</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1010078(Trutro Entero Selección)</td>\n",
       "      <td>22</td>\n",
       "      <td>1300</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>21</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>2020</td>\n",
       "      <td>La Pintana</td>\n",
       "      <td>Sucursal Lo Espejo</td>\n",
       "      <td>...</td>\n",
       "      <td>Especialista en Proc</td>\n",
       "      <td>2007</td>\n",
       "      <td>1010077</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0 days</td>\n",
       "      <td>1</td>\n",
       "      <td>28600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1010078(Trutro Entero Selección)</td>\n",
       "      <td>110</td>\n",
       "      <td>1174</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>9</td>\n",
       "      <td>Marzo</td>\n",
       "      <td>2018</td>\n",
       "      <td>Puente Alto</td>\n",
       "      <td>Sucursal Lo Espejo</td>\n",
       "      <td>...</td>\n",
       "      <td>Especialista en Crud</td>\n",
       "      <td>2007</td>\n",
       "      <td>1020253</td>\n",
       "      <td>464</td>\n",
       "      <td>3</td>\n",
       "      <td>0 days</td>\n",
       "      <td>1</td>\n",
       "      <td>129140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Material (Nombre)  Kilos Venta KG  Precio Día natural  \\\n",
       "0  1010078(Trutro Entero Selección)              22    1300  2020-05-23   \n",
       "1  1010078(Trutro Entero Selección)             110    1174  2018-03-01   \n",
       "\n",
       "  Día Semana Semana    Mes   Año    Población              Centro  ...  \\\n",
       "0     Sábado     21   Mayo  2020   La Pintana  Sucursal Lo Espejo  ...   \n",
       "1     Jueves      9  Marzo  2018  Puente Alto  Sucursal Lo Espejo  ...   \n",
       "\n",
       "      Categoría Cliente Año Creación Material Favorito Frecuencia Historica  \\\n",
       "0  Especialista en Proc         2007           1010077                   19   \n",
       "1  Especialista en Crud         2007           1020253                  464   \n",
       "\n",
       "  Mes_num Recency Frecuency_Row Ingreso de Venta CLP Monetary  Recency días  \n",
       "0       5  0 days             1                28600        0             0  \n",
       "1       3  0 days             1               129140        0             0  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_i_RFM=Ajuste_data_RFM(data_i_RFM)\n",
    "data_i_RFM.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0fb8648",
   "metadata": {},
   "source": [
    "## Data Customers\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d42145",
   "metadata": {},
   "source": [
    "### abrir "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d247c089",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Abrir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "509859b6",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>Fecha ultima Compra</th>\n",
       "      <th>Recency Actual</th>\n",
       "      <th>Fecha Creacion</th>\n",
       "      <th>Frecuency Actual del Cliente</th>\n",
       "      <th>Kilos Venta KG Promedio</th>\n",
       "      <th>Precio Promedio</th>\n",
       "      <th>Año Creación Promedio</th>\n",
       "      <th>Frecuencia Historica Promedio</th>\n",
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      ],
      "text/plain": [
       "    Cliente Fecha ultima Compra Recency Actual Fecha Creacion  \\\n",
       "0  10003742          2020-05-23            521     2020-05-23   \n",
       "\n",
       "  Frecuency Actual del Cliente Kilos Venta KG Promedio Precio Promedio  \\\n",
       "0        0.0033444816053511705                    22.0          1300.0   \n",
       "\n",
       "  Año Creación Promedio Frecuencia Historica Promedio  \\\n",
       "0                2007.0                          19.0   \n",
       "\n",
       "  Ingreso de Venta Promedio  \n",
       "0                   28600.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#======================================================================\n",
    "# Se Cargan los datos de ventas con las caracteristicas de los clientes\n",
    "#=====================================================================\n",
    "\n",
    "file_direct=file_direct_O+'Clientes_1010078_CT RFM'+'.csv'\n",
    "col_names=pd.read_csv(file_direct,\n",
    "                      encoding=\"utf-8\",sep=\";\",nrows=0).columns\n",
    "\n",
    "types_dict = {col: str for col in list(col_names)}\n",
    "\n",
    "Clientes_i_RFM= pd.read_csv(file_direct,\n",
    "                   encoding=\"utf-8\",sep=\";\",dtype=types_dict\n",
    "                      ,index_col='Unnamed: 0'\n",
    "                      )\n",
    "\n",
    "# Se debe agregar el parametro ,index_col='Unnamed: 0' \n",
    "# de esta froma tal vez se pueda hacer un merge en base al indice\n",
    "\n",
    "#elimina una columna en especifico\n",
    "#DF_FULL.drop('Año natural', inplace=True, axis=1)\n",
    "Clientes_i_RFM.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c58c0ab",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cliente                                    10007508\n",
       "Fecha ultima Compra                      2021-10-26\n",
       "Recency Actual                                    0\n",
       "Fecha Creacion                           2018-03-01\n",
       "Frecuency Actual del Cliente     0.2017524296094161\n",
       "Kilos Venta KG Promedio           93.15865384615384\n",
       "Precio Promedio                  1266.8942307692307\n",
       "Año Creación Promedio                        2007.0\n",
       "Frecuencia Historica Promedio                 464.0\n",
       "Ingreso de Venta Promedio        117683.47115384616\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i_RFM.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64c11a5",
   "metadata": {},
   "source": [
    "### Función Ajuste "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40b8aa1",
   "metadata": {},
   "source": [
    "###### def  `Ajuste_Clientes_RFM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f21b77fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# Ajustar la data\n",
    "# =============================================================================\n",
    "def Ajuste_Clientes_RFM(data):\n",
    "    start_time = time.time()\n",
    "    df=data.copy()\n",
    "\n",
    "# =============================================================================\n",
    "# Proceso iterativo Pasar a numero\n",
    "#------------------------------------------------------------------------------\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# Float\n",
    "    for i in [\n",
    "            #RFM\n",
    "            'Recency Actual'\n",
    "            ,'Frecuency Actual del Cliente'\n",
    "            ,'Ingreso de Venta Promedio'\n",
    "              \n",
    "            #Val Fact\n",
    "            ,'Kilos Venta KG Promedio'\n",
    "            ,'Precio Promedio'\n",
    "            \n",
    "            # Val historicos (ind del producto)\n",
    "            ,'Año Creación Promedio'\n",
    "            ,'Frecuencia Historica Promedio'\n",
    "             ]:\n",
    "        df[i]=df[i].astype('float64')\n",
    "        #.astype('int64')\n",
    "        \n",
    "        \n",
    "#------------------------------------------------------------------------ \n",
    "    # Int\n",
    "    for i in [\n",
    "                #RFM\n",
    "                'Recency Actual'\n",
    "#                 ,'Frecuency Actual del Cliente'\n",
    "#                 ,'Ingreso de Venta Promedio'\n",
    "\n",
    "                #Val Fact\n",
    "#                 ,'Kilos Venta KG Promedio'\n",
    "#                 ,'Precio Promedio'\n",
    "\n",
    "                # Val historicos (ind del producto)\n",
    "                ,'Año Creación Promedio'\n",
    "                ,'Frecuencia Historica Promedio'\n",
    "                ]:\n",
    "        df[i]=df[i].astype('int64')\n",
    "        \n",
    "\n",
    "\n",
    "#==========================================================================================\n",
    "# Ajuste de las fechas\n",
    "#-----------------------------------------------------------------------------------------\n",
    "    for date in ['Fecha ultima Compra','Fecha Creacion']:\n",
    "        df[date] = df[date].apply(lambda x: \n",
    "                                            datetime.datetime.strptime(str(x),\n",
    "                                                                           '%Y-%m-%d').date())\n",
    "    \n",
    "    print(df.dtypes)\n",
    "    end_time = time.time()\n",
    "    time_convert(end_time - start_time)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ad7552",
   "metadata": {},
   "source": [
    "##### Aplicación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "92341999",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cliente                           object\n",
      "Fecha ultima Compra               object\n",
      "Recency Actual                     int64\n",
      "Fecha Creacion                    object\n",
      "Frecuency Actual del Cliente     float64\n",
      "Kilos Venta KG Promedio          float64\n",
      "Precio Promedio                  float64\n",
      "Año Creación Promedio              int64\n",
      "Frecuencia Historica Promedio      int64\n",
      "Ingreso de Venta Promedio        float64\n",
      "dtype: object\n",
      "Time Lapsed = 0:0:0.09108257293701172 \n",
      " H:m:s\n"
     ]
    }
   ],
   "source": [
    "Clientes_i_RFM=Ajuste_Clientes_RFM(Clientes_i_RFM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a5fea17c",
   "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>Cliente</th>\n",
       "      <th>Fecha ultima Compra</th>\n",
       "      <th>Recency Actual</th>\n",
       "      <th>Fecha Creacion</th>\n",
       "      <th>Frecuency Actual del Cliente</th>\n",
       "      <th>Kilos Venta KG Promedio</th>\n",
       "      <th>Precio Promedio</th>\n",
       "      <th>Año Creación Promedio</th>\n",
       "      <th>Frecuencia Historica Promedio</th>\n",
       "      <th>Ingreso de Venta Promedio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>521</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>19</td>\n",
       "      <td>28600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.201752</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>1266.894231</td>\n",
       "      <td>2007</td>\n",
       "      <td>464</td>\n",
       "      <td>117683.471154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>2021-10-23</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>0.059365</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>1347.782051</td>\n",
       "      <td>2007</td>\n",
       "      <td>101</td>\n",
       "      <td>29699.448718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>47</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>0.039591</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>383</td>\n",
       "      <td>38390.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>2021-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1320.923077</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>29060.307692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cliente Fecha ultima Compra  Recency Actual Fecha Creacion  \\\n",
       "0  10003742          2020-05-23             521     2020-05-23   \n",
       "1  10007508          2021-10-26               0     2018-03-01   \n",
       "2  10017269          2021-10-23               3     2018-01-27   \n",
       "3  10023880          2021-09-09              47     2018-12-28   \n",
       "4  10028999          2021-09-24              32     2019-11-05   \n",
       "\n",
       "   Frecuency Actual del Cliente  Kilos Venta KG Promedio  Precio Promedio  \\\n",
       "0                      0.003344                22.000000      1300.000000   \n",
       "1                      0.201752                93.158654      1266.894231   \n",
       "2                      0.059365                22.038462      1347.782051   \n",
       "3                      0.039591                27.500000      1400.000000   \n",
       "4                      0.017541                22.000000      1320.923077   \n",
       "\n",
       "   Año Creación Promedio  Frecuencia Historica Promedio  \\\n",
       "0                   2007                             19   \n",
       "1                   2007                            464   \n",
       "2                   2007                            101   \n",
       "3                   2007                            383   \n",
       "4                   2007                             62   \n",
       "\n",
       "   Ingreso de Venta Promedio  \n",
       "0               28600.000000  \n",
       "1              117683.471154  \n",
       "2               29699.448718  \n",
       "3               38390.000000  \n",
       "4               29060.307692  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i_RFM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206de433",
   "metadata": {},
   "source": [
    "### Atributos de volumen "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc3bb9d",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "###### Kilo promedio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf3f31e6",
   "metadata": {
    "hidden": true
   },
   "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>Cliente</th>\n",
       "      <th>Ticket Promedio Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>93.158654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>22.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068</th>\n",
       "      <td>9992154</td>\n",
       "      <td>36.171429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>9993925</td>\n",
       "      <td>160.517495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5070</th>\n",
       "      <td>9994662</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5071</th>\n",
       "      <td>9994800</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5072</th>\n",
       "      <td>9997223</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5073 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cliente  Ticket Promedio Actual\n",
       "0     10003742               22.000000\n",
       "1     10007508               93.158654\n",
       "2     10017269               22.038462\n",
       "3     10023880               27.500000\n",
       "4     10028999               22.000000\n",
       "...        ...                     ...\n",
       "5068   9992154               36.171429\n",
       "5069   9993925              160.517495\n",
       "5070   9994662               22.000000\n",
       "5071   9994800               22.000000\n",
       "5072   9997223               22.000000\n",
       "\n",
       "[5073 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= data_i_RFM\n",
    "df_g=df[['Cliente','Kilos Venta KG']].groupby('Cliente')\\\n",
    "        .mean().reset_index()\\\n",
    "        .rename(columns={'Kilos Venta KG':Dic_col['Ticket Customers'][0]})\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d64991e",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### CV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ba2b7d",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "######  Std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85a19633",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cliente                0\n",
      "Ticket std Actual    367\n",
      "dtype: int64\n"
     ]
    },
    {
     "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>Cliente</th>\n",
       "      <th>Ticket std Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>36.215256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>0.612474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>12.102631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cliente  Ticket std Actual\n",
       "0  10003742           0.000000\n",
       "1  10007508          36.215256\n",
       "2  10017269           0.612474\n",
       "3  10023880          12.102631\n",
       "4  10028999           0.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_G=df[['Cliente','Kilos Venta KG']].groupby('Cliente')\\\n",
    "        .std().reset_index()\\\n",
    "        .rename(columns={'Kilos Venta KG':'Ticket std Actual'})\n",
    "print(\n",
    "df_G.isnull().sum()\n",
    ")\n",
    "df_G=df_G.fillna(0)\n",
    "df_G.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c569db0",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "###### Coeficiente de variación "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d5cdd548",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Cliente</th>\n",
       "      <th>Ticket Promedio Actual</th>\n",
       "      <th>Ticket std Actual</th>\n",
       "      <th>Ticket CV Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>36.215256</td>\n",
       "      <td>0.388748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>0.612474</td>\n",
       "      <td>0.027791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>12.102631</td>\n",
       "      <td>0.440096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068</th>\n",
       "      <td>9992154</td>\n",
       "      <td>36.171429</td>\n",
       "      <td>18.376080</td>\n",
       "      <td>0.508027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>9993925</td>\n",
       "      <td>160.517495</td>\n",
       "      <td>81.963154</td>\n",
       "      <td>0.510618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5070</th>\n",
       "      <td>9994662</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5071</th>\n",
       "      <td>9994800</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5072</th>\n",
       "      <td>9997223</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5073 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cliente  Ticket Promedio Actual  Ticket std Actual  Ticket CV Actual\n",
       "0     10003742               22.000000           0.000000          0.000000\n",
       "1     10007508               93.158654          36.215256          0.388748\n",
       "2     10017269               22.038462           0.612474          0.027791\n",
       "3     10023880               27.500000          12.102631          0.440096\n",
       "4     10028999               22.000000           0.000000          0.000000\n",
       "...        ...                     ...                ...               ...\n",
       "5068   9992154               36.171429          18.376080          0.508027\n",
       "5069   9993925              160.517495          81.963154          0.510618\n",
       "5070   9994662               22.000000           0.000000          0.000000\n",
       "5071   9994800               22.000000           0.000000          0.000000\n",
       "5072   9997223               22.000000           0.000000          0.000000\n",
       "\n",
       "[5073 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group=pd.merge(df_g,df_G,how='right')\n",
    "\n",
    "df_group[Dic_col['Ticket Customers'][1]]=\\\n",
    "df_group.apply(lambda x: \n",
    "               x['Ticket std Actual']/\n",
    "               x[Dic_col['Ticket Customers'][0]] ,axis=1)\n",
    "df_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dc48c941",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Frecuency Actual del Cliente'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dic_col['RFM Customers'][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd90ebd8",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Merge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37777a34",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5073, 14)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i=pd.merge(Clientes_i_RFM,df_group)\n",
    "Clientes_i['$F^{-1}$ Actual']=Clientes_i[Dic_col['RFM Customers'][1]].apply( lambda x: x**(-1))\n",
    "Clientes_i.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7e6aa518",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.956580356080733"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.201752**-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d57979da",
   "metadata": {
    "hidden": true
   },
   "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>Cliente</th>\n",
       "      <th>Fecha ultima Compra</th>\n",
       "      <th>Recency Actual</th>\n",
       "      <th>Fecha Creacion</th>\n",
       "      <th>Frecuency Actual del Cliente</th>\n",
       "      <th>Kilos Venta KG Promedio</th>\n",
       "      <th>Precio Promedio</th>\n",
       "      <th>Año Creación Promedio</th>\n",
       "      <th>Frecuencia Historica Promedio</th>\n",
       "      <th>Ingreso de Venta Promedio</th>\n",
       "      <th>Ticket Promedio Actual</th>\n",
       "      <th>Ticket std Actual</th>\n",
       "      <th>Ticket CV Actual</th>\n",
       "      <th>$F^{-1}$ Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>521</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>19</td>\n",
       "      <td>28600.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.201752</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>1266.894231</td>\n",
       "      <td>2007</td>\n",
       "      <td>464</td>\n",
       "      <td>117683.471154</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>36.215256</td>\n",
       "      <td>0.388748</td>\n",
       "      <td>4.956570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>2021-10-23</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>0.059365</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>1347.782051</td>\n",
       "      <td>2007</td>\n",
       "      <td>101</td>\n",
       "      <td>29699.448718</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>0.612474</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>16.845022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>47</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>0.039591</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>383</td>\n",
       "      <td>38390.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>12.102631</td>\n",
       "      <td>0.440096</td>\n",
       "      <td>25.258465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>2021-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1320.923077</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>29060.307692</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.008334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cliente Fecha ultima Compra  Recency Actual Fecha Creacion  \\\n",
       "0  10003742          2020-05-23             521     2020-05-23   \n",
       "1  10007508          2021-10-26               0     2018-03-01   \n",
       "2  10017269          2021-10-23               3     2018-01-27   \n",
       "3  10023880          2021-09-09              47     2018-12-28   \n",
       "4  10028999          2021-09-24              32     2019-11-05   \n",
       "\n",
       "   Frecuency Actual del Cliente  Kilos Venta KG Promedio  Precio Promedio  \\\n",
       "0                      0.003344                22.000000      1300.000000   \n",
       "1                      0.201752                93.158654      1266.894231   \n",
       "2                      0.059365                22.038462      1347.782051   \n",
       "3                      0.039591                27.500000      1400.000000   \n",
       "4                      0.017541                22.000000      1320.923077   \n",
       "\n",
       "   Año Creación Promedio  Frecuencia Historica Promedio  \\\n",
       "0                   2007                             19   \n",
       "1                   2007                            464   \n",
       "2                   2007                            101   \n",
       "3                   2007                            383   \n",
       "4                   2007                             62   \n",
       "\n",
       "   Ingreso de Venta Promedio  Ticket Promedio Actual  Ticket std Actual  \\\n",
       "0               28600.000000               22.000000           0.000000   \n",
       "1              117683.471154               93.158654          36.215256   \n",
       "2               29699.448718               22.038462           0.612474   \n",
       "3               38390.000000               27.500000          12.102631   \n",
       "4               29060.307692               22.000000           0.000000   \n",
       "\n",
       "   Ticket CV Actual  $F^{-1}$ Actual  \n",
       "0          0.000000       299.000000  \n",
       "1          0.388748         4.956570  \n",
       "2          0.027791        16.845022  \n",
       "3          0.440096        25.258465  \n",
       "4          0.000000        57.008334  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f002f74",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dc038559",
   "metadata": {},
   "source": [
    "# Analisis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d693db",
   "metadata": {},
   "source": [
    "##### CV Lote "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d691b621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156732887902)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_Graph=Dic_col['Ticket Customers'][0]\n",
    "X_Graph=Dic_col['Ticket Customers'][1]\n",
    "(ggplot(Clientes_i,\n",
    "      aes(x=X_Graph,\n",
    "         y=Y_Graph\n",
    "#          ,color='Cliente'\n",
    "         )\n",
    "      )\n",
    "+geom_point(alpha=0.2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d68ae6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156735051002)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph=Dic_col['Ticket Customers'][0]\n",
    "X_Graph=Dic_col['Ticket Customers'][1]\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "+scale_y_continuous(limits=(0,2550)\n",
    "                    ,breaks=[i for i in range(0,2700,100)]\n",
    "                   )\n",
    "+scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "                    ,limits=(0,3)\n",
    "                    ,breaks=[i/100 for i in range(0,300,25)]\n",
    "                   )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f820fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'$F^{-1}$ Actual'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Clientes_i.columns)[13]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73b0179",
   "metadata": {},
   "source": [
    "###### F.-1 CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "879bd434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156735240837)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph=(Clientes_i.columns)[13]\n",
    "# Dic_col['RFM Customers'][1]\n",
    "X_Graph=Dic_col['Ticket Customers'][1]\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "+scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "                    ,limits=(0,3)\n",
    "                    ,breaks=[i/100 for i in range(0,300,25)]\n",
    "                   )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94712395",
   "metadata": {},
   "source": [
    "##### RFM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bd756e",
   "metadata": {},
   "source": [
    "###### f-1 Rece "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a6c771e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156735236868)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph=(Clientes_i.columns)[13]\n",
    "# Dic_col['RFM Customers'][1]\n",
    "X_Graph= Dic_col['RFM Customers'][0]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4559e6e",
   "metadata": {},
   "source": [
    "###### F-1 Mon "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e41fb1c2",
   "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>Cliente</th>\n",
       "      <th>Fecha ultima Compra</th>\n",
       "      <th>Recency Actual</th>\n",
       "      <th>Fecha Creacion</th>\n",
       "      <th>Frecuency Actual del Cliente</th>\n",
       "      <th>Kilos Venta KG Promedio</th>\n",
       "      <th>Precio Promedio</th>\n",
       "      <th>Año Creación Promedio</th>\n",
       "      <th>Frecuencia Historica Promedio</th>\n",
       "      <th>Ingreso de Venta Promedio</th>\n",
       "      <th>Ticket Promedio Actual</th>\n",
       "      <th>Ticket std Actual</th>\n",
       "      <th>Ticket CV Actual</th>\n",
       "      <th>$F^{-1}$ Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003742</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>521</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>19</td>\n",
       "      <td>28600.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10007508</td>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.201752</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>1266.894231</td>\n",
       "      <td>2007</td>\n",
       "      <td>464</td>\n",
       "      <td>117683.471154</td>\n",
       "      <td>93.158654</td>\n",
       "      <td>36.215256</td>\n",
       "      <td>0.388748</td>\n",
       "      <td>4.956570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10017269</td>\n",
       "      <td>2021-10-23</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>0.059365</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>1347.782051</td>\n",
       "      <td>2007</td>\n",
       "      <td>101</td>\n",
       "      <td>29699.448718</td>\n",
       "      <td>22.038462</td>\n",
       "      <td>0.612474</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>16.845022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10023880</td>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>47</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>0.039591</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>2007</td>\n",
       "      <td>383</td>\n",
       "      <td>38390.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>12.102631</td>\n",
       "      <td>0.440096</td>\n",
       "      <td>25.258465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10028999</td>\n",
       "      <td>2021-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1320.923077</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>29060.307692</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.008334</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>5068</th>\n",
       "      <td>9992154</td>\n",
       "      <td>2021-08-18</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>0.056885</td>\n",
       "      <td>36.171429</td>\n",
       "      <td>1294.657143</td>\n",
       "      <td>2007</td>\n",
       "      <td>113</td>\n",
       "      <td>46135.600000</td>\n",
       "      <td>36.171429</td>\n",
       "      <td>18.376080</td>\n",
       "      <td>0.508027</td>\n",
       "      <td>17.579468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>9993925</td>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.408158</td>\n",
       "      <td>160.517495</td>\n",
       "      <td>1257.278085</td>\n",
       "      <td>2012</td>\n",
       "      <td>594</td>\n",
       "      <td>200374.351750</td>\n",
       "      <td>160.517495</td>\n",
       "      <td>81.963154</td>\n",
       "      <td>0.510618</td>\n",
       "      <td>2.450031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5070</th>\n",
       "      <td>9994662</td>\n",
       "      <td>2021-10-14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>0.015996</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1304.545455</td>\n",
       "      <td>2007</td>\n",
       "      <td>37</td>\n",
       "      <td>28700.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.515629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5071</th>\n",
       "      <td>9994800</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>118</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>0.025931</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1230.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>27060.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.563750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5072</th>\n",
       "      <td>9997223</td>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>465</td>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>2019</td>\n",
       "      <td>15</td>\n",
       "      <td>23980.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5073 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cliente Fecha ultima Compra  Recency Actual Fecha Creacion  \\\n",
       "0     10003742          2020-05-23             521     2020-05-23   \n",
       "1     10007508          2021-10-26               0     2018-03-01   \n",
       "2     10017269          2021-10-23               3     2018-01-27   \n",
       "3     10023880          2021-09-09              47     2018-12-28   \n",
       "4     10028999          2021-09-24              32     2019-11-05   \n",
       "...        ...                 ...             ...            ...   \n",
       "5068   9992154          2021-08-18              69     2018-10-03   \n",
       "5069   9993925          2021-10-26               0     2018-01-02   \n",
       "5070   9994662          2021-10-14              12     2020-02-10   \n",
       "5071   9994800          2021-06-30             118     2020-12-16   \n",
       "5072   9997223          2020-07-18             465     2020-07-18   \n",
       "\n",
       "      Frecuency Actual del Cliente  Kilos Venta KG Promedio  Precio Promedio  \\\n",
       "0                         0.003344                22.000000      1300.000000   \n",
       "1                         0.201752                93.158654      1266.894231   \n",
       "2                         0.059365                22.038462      1347.782051   \n",
       "3                         0.039591                27.500000      1400.000000   \n",
       "4                         0.017541                22.000000      1320.923077   \n",
       "...                            ...                      ...              ...   \n",
       "5068                      0.056885                36.171429      1294.657143   \n",
       "5069                      0.408158               160.517495      1257.278085   \n",
       "5070                      0.015996                22.000000      1304.545455   \n",
       "5071                      0.025931                22.000000      1230.000000   \n",
       "5072                      0.003344                22.000000      1090.000000   \n",
       "\n",
       "      Año Creación Promedio  Frecuencia Historica Promedio  \\\n",
       "0                      2007                             19   \n",
       "1                      2007                            464   \n",
       "2                      2007                            101   \n",
       "3                      2007                            383   \n",
       "4                      2007                             62   \n",
       "...                     ...                            ...   \n",
       "5068                   2007                            113   \n",
       "5069                   2012                            594   \n",
       "5070                   2007                             37   \n",
       "5071                   2020                              7   \n",
       "5072                   2019                             15   \n",
       "\n",
       "      Ingreso de Venta Promedio  Ticket Promedio Actual  Ticket std Actual  \\\n",
       "0                  28600.000000               22.000000           0.000000   \n",
       "1                 117683.471154               93.158654          36.215256   \n",
       "2                  29699.448718               22.038462           0.612474   \n",
       "3                  38390.000000               27.500000          12.102631   \n",
       "4                  29060.307692               22.000000           0.000000   \n",
       "...                         ...                     ...                ...   \n",
       "5068               46135.600000               36.171429          18.376080   \n",
       "5069              200374.351750              160.517495          81.963154   \n",
       "5070               28700.000000               22.000000           0.000000   \n",
       "5071               27060.000000               22.000000           0.000000   \n",
       "5072               23980.000000               22.000000           0.000000   \n",
       "\n",
       "      Ticket CV Actual  $F^{-1}$ Actual  \n",
       "0             0.000000       299.000000  \n",
       "1             0.388748         4.956570  \n",
       "2             0.027791        16.845022  \n",
       "3             0.440096        25.258465  \n",
       "4             0.000000        57.008334  \n",
       "...                ...              ...  \n",
       "5068          0.508027        17.579468  \n",
       "5069          0.510618         2.450031  \n",
       "5070          0.000000        62.515629  \n",
       "5071          0.000000        38.563750  \n",
       "5072          0.000000       299.000000  \n",
       "\n",
       "[5073 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f0c9c175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monetary Actual'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Ingreso de Venta Promedio'\n",
    "Dic_col['RFM Customers'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b00ee95d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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8PFz9+vVTbGysMjIyNGnSJL355ps6ceJEvfvX1NQ0c8JfLiwsTH369HF1DAAAcAOgCAUAwM988803MplMys3N1ZNPPqk2bdooJCREzzzzjM6fP++w78aNG9W5c2d5e3urb9++2rdvn8xms9LS0uz7xMbGaujQoXr99dfVuXNneXl56fPPP5ckbd26VXfeead8fHwUFBSk6dOnOzzqVVdXp2effVYWi0VeXl4KCQnRsGHD9NNPP9n3KS0tVWJiolq3bi1fX1/de++92rdv31Xf5+nTpzVx4kT5+/srKChIs2fPdnp/knTq1CnNmDFDISEh8vLyUu/evbV9+/Yrjn3PPfcoPDxcb7zxhlPfhg0b1LVrV91+++2SpEOHDmnEiBEKCAiQn5+fEhIS9PXXXzscYzKZ9OKLLyo1NVXBwcG6+eabNXnyZPtntX79ek2ePFmSFBQUJJPJpIiICEnSsWPHNGXKFHXo0EE+Pj76/e9/r3nz5uns2bNX/Yzq07t3b0lScXGx/VpZv369HnvsMQUGBtqLOSdPntTUqVMVFBQkHx8f9e3b1+lzu3Rt5ObmqmPHjvL19dXQoUN18uRJlZSUaPDgwTKbzerWrVu9dyqtX79ePXr0kLe3t0JDQ5WSkuJ0Dnfv3q3evXvL29tb3bt3r7cwWN/jeE29rgAAAK6kpasDAADgjlJSUjRixAjl5+frk08+UXp6ujp27KgnnnhCkrR//34lJiZq2LBhWrFihUpKSvTwww/Xe5fTvn37VFpaqsWLF6t169a65ZZbVFBQoDFjxmjy5MlKT0/XsWPH9Nxzz6myslJ//etfJUlLlixRTk6Oli5dqm7duunEiRPavn27vYBy5swZDRgwQIZhKCsrS2azWS+++KJiY2O1b98+denSpcH3N2XKFL3//vt64YUXFBkZqaysLP3nP/9x2OfcuXOKj4/X8ePHlZmZqdDQUOXm5iohIUGfffaZoqOj6x3bZDJp7NixWr16taqqquwFjoqKCn3wwQf2Il1RUZFiYmLUvXt3rV+/Xh4eHsrMzFRcXJwOHz4sLy8v+5hr1qzRPffco9dff12HDx/W7NmzFRwcrBdeeEEJCQmaP3++MjIy9N577ykgIMB+7IkTJ9S2bVstX75cbdq00ZEjR5SWlqby8nKtXbu2MZeCg+LiYklS+/bt7ed67ty5GjZsmN544w3ZbDbZbDbdf//9Onr0qJYsWaKwsDBlZ2frgQce0I4dOzRw4ED7ePv379fJkye1fPlynTp1Sn/84x81depUffvtt5o4caJmzZqlJUuWaPTo0SotLbV/lsuXL9fs2bP1pz/9ScuWLdOhQ4eUkpIim82mF154QZJUXl6uwYMHKzo6Wvn5+aqsrNT06dN15syZK77HX3JdAQAAXJEBAMANLDU11fDz87NvFxcXG5KMxMREh/3uvvtuIy4uzr6dmJhodOzY0bDZbPa2devWGZKM1NRUe9uAAQOMVq1aGd9++6297cKFC4bFYjHGjh3r8Bpbt241TCaTceDAAcMwDCMhIcF48MEHG8z+8ssvO+xvGIZx5swZo23btsakSZMaPO7gwYOGyWQyXnvtNXtbXV2dER4ebvz8T4O1a9caLVu2NL766iuH4/v27ev0+Vzuiy++MCQZubm59rasrCxDklFUVGQYhmFMnDjRiIyMNGpqauz7/PDDD4afn5+RlZVlb5Nk9OnTx2H88ePHG7feeqt9+9JnX1FRccVcdXV1Rl5entGyZUvDarVecV9JxtKlS426ujrjzJkzxsaNGw2z2WzExMQYhvH/18oDDzzgcNymTZsMScbWrVvtbTabzejatasxYMAAe9uAAQMMPz8/48SJE/a2WbNmGZKM7Oxse9uXX35pSDLeeecdwzAM4/Tp04bZbDbmzp3r8LpZWVmGj4+Pfbw5c+YY/v7+RmVlpX2f999/35DkcH1c/m+gqdcVAADA1fA4HgAA9Rg0aJDDdlRUlMrKyuzbe/fu1dChQx0W1x4xYkS9Y/Xo0UNhYWH27SNHjqikpEQPPfSQzp8/b/8ZMGCATCaT/bGnXr16adu2bUpLS9PevXudvk3u448/Vrdu3dStWzd7m9ls1rBhw/Txxx83+N4+/fRTGYahUaNG2dtatmzplH/79u2Kjo5Wp06dHHLGxcVp7969DY4vSdHR0erevbvDI3kbNmzQXXfdpcjISPv4I0aMUMuWLe1jt2nTRrfddpvT+Fc7Hw0xDEMrV65UVFSUfHx85OnpqfHjx+v8+fMqKiq66vFz5syRp6en/P39NWrUKN11113Ky8tz2OeBBx5w2P7444/l7+/v0O7h4aGHHnpIu3fvls1ms7fffvvtCgwMtG936tRJknTfffc5tX377beSLj5iV1VVpcTERIfzcu+996qmpkYHDhyQJP373//WwIED1bp1a/tYgwYN0k033XTF99zU6woAAOBqeBwPAIB6/HziLkmtWrVSbW2tffvYsWMKCgpy2KdNmzby9PR0Gut3v/udw/alRa1/XgT6uUvFhpSUFHl4eOj1119Xenq6goKCNHPmTC1cuFAmk0mVlZVq166d0/Ht2rXTyZMnG3xvx44dk6enp9q0aePQHhwc7JRz//799b6nFi1aNDj+JePHj9fChQv1448/qqqqSrt379bq1asdxl+5cqVWrlzpdKyPj4/Ddn3nozHrOq1cuVLPPPOMZs+erYEDB6pNmzbau3evZs6c6XA+G/LUU08pKSlJXl5eioiIkL+/v9M+l5/fyspKp89Sunhe6urqVFVVpYCAgAbf1+Xtl9ou5b10/fTq1avezJeun2PHjqljx45XzXu5pl5XAAAAV0MRCgCAJggJCVFFRYVDW2VlZb1rQplMJofttm3bSrq4ztGdd97ptH/79u0lSV5eXkpLS1NaWpqOHj2qtWvXKi0tTR06dNCECRPUtm1bFRYWOh1fXl5uf42GstfV1amystKhEHX8+HGnnD169NBrr73W4FhXMm7cOM2bN08FBQWqrKxUixYt9NBDDzmMn5CQoBkzZjgdW1+xpyneeustDR8+XEuWLLG3HTx4sNHHh4WF6Y477rjiPvWd38s/S+niefH09HRaBPxaXTq3f/vb33TLLbc49V+60ywkJEQ//PCDU399bZeP35TrCgAA4GooQgEA0AR9+vTRli1btGzZMvsjee+8806jju3SpYvCwsJUVFSkmTNnNuqYjh076vnnn9crr7yiQ4cOSZL69++vgoICHTx4UFFRUZIkq9WqLVu2aOjQoVfMbjKZtHHjRk2ZMkWSdP78eW3atMlhv/vuu0/btm1T+/bt7YWxaxEeHq67775bGzZsUGVlpeLj4x3uHrvvvvt04MAB9ezZs1F3Vl3J5XcLXVJTU2Pvu+Tyx+l+bf3799ef//xnvffeexoyZIgk6cKFC3rrrbcUExPzi99rTEyMfH19VVZW1uDddJLUt29fZWdn66effrLfebV9+3adPn36qvmbcl0BAABcDUUoAACaYO7cuerTp49Gjx6txx9/XN98842WLVumVq1aOawTVR+TyaTly5dr3LhxslqtSkhIkJ+fn0pKSrR161Y9//zz6tSpk0aOHKnevXurZ8+e8vPz0+bNm3Xy5Ende++9kqTJkydrxYoVGjp0qDIyMuzfYlZTU6PnnnuuwdePiorSyJEj9fTTT6u2tlYRERHKyspyWKtIkiZOnKhXXnlFsbGxeuaZZ9SpUyedOnVK+/fv17lz5xzuLmrI+PHjNWPGDBmGoTlz5jj0paenq0+fPho8eLAef/xxBQcHq7y8XLt27dI999yjsWPHXnX8S7p27SpJysrK0siRI+Xr66vo6GjFx8fr5Zdf1po1a9SpUyfl5eXp6NGjjR63KRISEtS3b19NmDBBzz//vMLCwpSTk6PDhw8rKyvrF48fEBCgRYsWafbs2SorK9PAgQPl4eGhoqIibdq0SW+//bZ8fX319NNPKysrS/fff7/9mxdTU1OvejdTU68rAACAq2FhcgAAmqBnz57Kz8/XwYMHNWrUKL322mtav369bDab/a6TK0lMTNS2bdtUWFiosWPHavjw4Vq2bJkiIiLs6wndfffd+vvf/66kpCQNGzZMu3bt0oYNG+yLVvv7+2vXrl3q3bu3pk+frocfflienp7auXOnunTpcsXXX7t2rYYPH67Zs2dr4sSJ6tKli5588kmHfby8vPSPf/xDQ4cOVWZmpgYNGqQZM2Zo37596t+/f6M+p8TERLVs2VK+vr4aOXKkQ1/Hjh316aefKjAwUDNmzNDgwYP13HPPyWq1qkePHo0a/5KePXsqLS1Nubm5iomJ0bBhwyRJCxcu1Lhx47Rw4UI9/PDD8vLy0qpVq65p7GvVokULvfvuuxo+fLjmzp2rUaNG6dixY9q6datiY2N/ldeYNWuW1q1bp48++kgPPvigEhMT9Ze//EV9+vSx3/kVEhKid999VzU1NUpMTNTSpUuVlZWlkJCQK479S64rAACAKzEZhmG4OgQAAL8FH3zwgeLj47Vz504NGDDA1XEAAAAAt0IRCgCAJpoxY4bi4uIUGBior776SosXL1b79u21b9++qz6SBwAAANxoWBMKAIAmOnXqlJKTk3XixAkFBARoyJAheumllyhAAQAAAPXgTigAAAAAAABcd/xXLQAAAAAAAK47ilAAAAAAAAC47ihCAQAAAAAA4LqjCAUAAAAAAIDrjiIUAAAAAAAArjuKUAAAAAAAALjuKEIBAAAAAADguqMIBQAAAAAAgOuOIhQAAAAAAACuu/8DfH/3dghruM4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156735708148)>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph=(Clientes_i.columns)[13]\n",
    "# Dic_col['RFM Customers'][1]\n",
    "X_Graph= 'Ingreso de Venta Promedio'\n",
    "# Dic_col['RFM Customers'][2]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56ad19f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cliente', 'Fecha ultima Compra', 'Recency Actual', 'Fecha Creacion',\n",
       "       'Frecuency Actual del Cliente', 'Kilos Venta KG Promedio',\n",
       "       'Precio Promedio', 'Año Creación Promedio',\n",
       "       'Frecuencia Historica Promedio', 'Ingreso de Venta Promedio',\n",
       "       'Ticket Promedio Actual', 'Ticket std Actual', 'Ticket CV Actual',\n",
       "       '$F^{-1}$ Actual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clientes_i.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02f925a",
   "metadata": {},
   "source": [
    "##### Precio "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba4c982",
   "metadata": {},
   "source": [
    "###### F-1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a2165a3e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156736164756)>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph=(Clientes_i.columns)[13]\n",
    "# Dic_col['RFM Customers'][1]\n",
    "X_Graph= 'Precio Promedio'\n",
    "# 'Ingreso de Venta Promedio'\n",
    "# Dic_col['RFM Customers'][2]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b7e9e75f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156736197572)>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph= Dic_col['RFM Customers'][1]\n",
    "# (Clientes_i.columns)[13]\n",
    "X_Graph= 'Precio Promedio'\n",
    "# 'Ingreso de Venta Promedio'\n",
    "# Dic_col['RFM Customers'][2]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d14c98",
   "metadata": {},
   "source": [
    "###### R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cb70d281",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gnE6n9u/fr/nz5ystLU09evTQ8uXLtXPnTlVVVd102wEBAfrb3/6mRYsW6emnn1ZVVZWGDBmi3bt3Kzk52Sf5AQAAWivDNE3T7hAAAAAAAABoX5hTCgAAAAAAAJajKQUAAAAAAADL0ZQCAAAAAACA5WhKAQAAAAAAwHI0pQAAAAAAAGA5mlIAAAAAAACwHE0pAAAAAAAAWI6mFAAAAAAAACxHUwoAAAAAAACWoykFAAAAAAAAy9GUAgAAAAAAgOVoSgEAAAAAAMBy/weFZay1jxhEXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (156736246842)>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Clientes_i.copy()\n",
    "Y_Graph= Dic_col['RFM Customers'][0]\n",
    "# (Clientes_i.columns)[13]\n",
    "X_Graph= 'Precio Promedio'\n",
    "# 'Ingreso de Venta Promedio'\n",
    "# Dic_col['RFM Customers'][2]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b1ed5f",
   "metadata": {},
   "source": [
    "###### Fact "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0c898662",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\mizani\\utils.py:50: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "arange: cannot compute length",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    700\u001b[0m                 \u001b[0mtype_pprinters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m                 deferred_pprinters=self.deferred_printers)\n\u001b[1;32m--> 702\u001b[1;33m             \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m             \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\lib\\pretty.py\u001b[0m in \u001b[0;36mpretty\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    392\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m                                 \u001b[1;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'__repr__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 394\u001b[1;33m                             \u001b[1;32mreturn\u001b[0m \u001b[0m_repr_pprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_default_pprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\lib\\pretty.py\u001b[0m in \u001b[0;36m_repr_pprint\u001b[1;34m(obj, p, cycle)\u001b[0m\n\u001b[0;32m    698\u001b[0m     \u001b[1;34m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    699\u001b[0m     \u001b[1;31m# Find newlines and replace them with p.break_()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 700\u001b[1;33m     \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    701\u001b[0m     \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    702\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[0mPrint\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mshow\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m         \"\"\"\n\u001b[1;32m---> 97\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__str__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m'<ggplot: (%d)>'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m__str__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     86\u001b[0m         \u001b[0mPrint\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mshow\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m         \"\"\"\n\u001b[1;32m---> 88\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[1;31m# Return and empty string so that print(p) is \"pretty\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, return_ggplot, show)\u001b[0m\n\u001b[0;32m    203\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mplot_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_build\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m             \u001b[1;31m# setup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m_build\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m         \u001b[1;31m# Apply and map statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m         \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    301\u001b[0m         \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\layer.py\u001b[0m in \u001b[0;36mcompute_statistic\u001b[1;34m(self, layout)\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m             \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmap_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\layer.py\u001b[0m in \u001b[0;36mcompute_statistic\u001b[1;34m(self, layout)\u001b[0m\n\u001b[0;32m    322\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_layer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\stats\\stat.py\u001b[0m in \u001b[0;36mcompute_layer\u001b[1;34m(cls, data, params, layout)\u001b[0m\n\u001b[0;32m    274\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_panel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpscales\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mgroupby_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'PANEL'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\utils.py\u001b[0m in \u001b[0;36mgroupby_apply\u001b[1;34m(df, cols, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m         \u001b[1;31m# function fn should be free to modify dataframe d, therefore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    633\u001b[0m         \u001b[1;31m# do not mark d as a slice of df i.e no SettingWithCopyWarning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 634\u001b[1;33m         \u001b[0mlst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    635\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\stats\\stat.py\u001b[0m in \u001b[0;36mfn\u001b[1;34m(pdata)\u001b[0m\n\u001b[0;32m    272\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mpdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m             \u001b[0mpscales\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_scales\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'PANEL'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_panel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpscales\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mgroupby_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'PANEL'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\stats\\stat.py\u001b[0m in \u001b[0;36mcompute_panel\u001b[1;34m(cls, data, scales, **params)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[0mstats\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mold\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'group'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m             \u001b[0mnew\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscales\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m             \u001b[0munique\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muniquecols\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m             \u001b[0mmissing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\stats\\stat_bin_2d.py\u001b[0m in \u001b[0;36mcompute_group\u001b[1;34m(cls, data, scales, **params)\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[1;31m# create the cutting parameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m         xbreaks = fuzzybreaks(scales.x, breaks=breaks.x,\n\u001b[0m\u001b[0;32m     90\u001b[0m                               binwidth=binwidth.x, bins=bins.x)\n\u001b[0;32m     91\u001b[0m         ybreaks = fuzzybreaks(scales.y, breaks.y,\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\plotnine\\stats\\binning.py\u001b[0m in \u001b[0;36mfuzzybreaks\u001b[1;34m(scale, breaks, boundary, binwidth, bins, right)\u001b[0m\n\u001b[0;32m    269\u001b[0m     \u001b[1;31m# can then be adjusted with finer(epsilon based rather than\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m     \u001b[1;31m# some arbitrary small number) precision.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m     \u001b[0mbreaks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboundary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msrange\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbinwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbinwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    272\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_adjust_breaks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbreaks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: arange: cannot compute length"
     ]
    }
   ],
   "source": [
    "df=data_i_RFM.copy()\n",
    "Y_Graph= Dic_col['RFM Row'][0]\n",
    "# (Clientes_i.columns)[13]\n",
    "X_Graph=  Dic_col['RFM Row'][1]\n",
    "# 'Precio'\n",
    "# 'Ingreso de Venta Promedio'\n",
    "# Dic_col['RFM Customers'][2]\n",
    "# Dic_col['Ticket Customers'][1]\n",
    "\n",
    "df=df[df[Dic_col['RFM Row'][1]]<1]\n",
    "\n",
    "# Color_Graph=Dic_col['Ticket Customers'][0]\n",
    "\n",
    "#int\n",
    "# df[X_Graph]=df[X_Graph].astype('int64')\n",
    "\n",
    "# df[X_Graph]=df[X_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Y_Graph]=df[Y_Graph].apply(lambda x:  np.log(x))\n",
    "# df[Color_Graph]=df[Color_Graph].apply(lambda x:  np.log(x))\n",
    "\n",
    "\n",
    "# df_mean[X_Graph]=df_mean[X_Graph].astype('int64')\n",
    "\n",
    "G_calor_densidad_Dips=(ggplot(df#[df[Y_Graph]>0]\n",
    "            , aes(x=X_Graph#'Día numero'#'Fecha',\n",
    "            ,y=Y_Graph\n",
    "#             ,color=Color_Graph\n",
    "                 ))\n",
    "+geom_bin2d(alpha=0.5)\n",
    " \n",
    "# +scale_y_continuous(limits=(0,2550)\n",
    "#                     ,breaks=[i for i in range(0,2700,100)]\n",
    "#                    )\n",
    "# +scale_x_continuous(labels=lambda l: [\"%d%%\" % (v * 100) for v in l]\n",
    "#                     ,limits=(0,3)\n",
    "#                     ,breaks=[i/100 for i in range(0,300,25)]\n",
    "#                    )\n",
    "+theme(axis_text_x = element_text(angle=90, hjust=1),figure_size=(13,8)) \n",
    "+ggtitle('Dispersión de los Clientes  del Canal '+ Tipo_de_Cliente+ '\\n sobre el producto '+ Y_producto)\n",
    ")   \n",
    "G_calor_densidad_Dips"
   ]
  }
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