analisis deudores BCRA - parte 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis deudores BCRA (parte 1)\n",
    "## mayo de 2020\n",
    "\n",
    "Autor: Carlos A. Crespo (lvccrespo@gmail.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El Banco Central de la República Argentina actualiza mensualmente información respecto a la totalidad de deudores del sistema financiero. Esta información es pública y se conforma con los datos que mensualmente envían las entidades financieras en base a su contabilidad.\n",
    "\n",
    "Cabe aclarar, que los datos son publicados por dos años más desde la cancelación de cualquier deuda o por cinco años desde que la deuda entró en mora (artículo 26 inciso 4, de la Ley 25.326 de Protección de Datos Personales y su decreto reglamentario).\n",
    "\n",
    "Para más información: https://www.bcra.gob.ar/BCRAyVos/Situacion_Crediticia.asp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos descargado la información a febrero 2020, así que ¡manos a la obra!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procesamiento previo\n",
    "Contamos con un archivo _\"deudores.txt\"_ que pesa **4.57 GB**. ***Cargar directamente toda esa info en memoria haría explotar todo*** así que vamos a levantar un pequeño dataset de prueba para revisar la información.\n",
    "\n",
    "Repasando los metadatos de la base, hemos decidido:\n",
    "- quedarnos con las columnas __\"entidad\"__, __\"cuitcuil\"__, __\"totalprestamos\"__ (el resto de los campos se descartan)\n",
    "- el archivo contempla la deuda tanto de personas físicas como de __empresas__. Haremos foco en estás últimas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     entidad     cuitcuil totalprestamos\n",
      "0          7  20024515916           15,0\n",
      "1          7  20017364643           21,0\n",
      "2          7  20005717621          214,0\n",
      "3          7  20017290313            2,0\n",
      "4          7  20002534410            4,0\n",
      "..       ...          ...            ...\n",
      "195        7  20040603051          225,0\n",
      "196        7  20030273991          129,0\n",
      "197        7  20033912170           49,0\n",
      "198        7  20040283677           39,0\n",
      "199        7  20040247956           13,0\n",
      "\n",
      "[200 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "col_specification =[(0, 5), (13, 24), (29, 41)] # los valores corresponden al punto de inicio y corte de cada campo\n",
    "nombres_columnas = [\"entidad\", \"cuitcuil\", \"totalprestamos\"]\n",
    "test = pd.read_fwf('deudores.txt',  nrows = 200, colspecs=col_specification, \n",
    "                     header=None,index=False, names = nombres_columnas)\n",
    "\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder manejar el archivo y obtener los datos que requerimos vamos a usar una funcionalidad de pandas que se llama ***chunksize***.\n",
    "Básicamente lo que hace es partir el archivo original en porciones más chicas (y manipulables para nuestra vieja y querida PC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 10 ** 5 # tamaño del chunk. Esto es, cuántas filas va a contener cada \"porción\" del archivo original\n",
    "\n",
    "df_chunk = pd.read_fwf('deudores.txt', chunksize=size, colspecs=col_specification,\n",
    "                       header=None,index=False, names = nombres_columnas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya tenemos las columnas que necesitamos.\n",
    "\n",
    "Ahora vamos a quedarnos sólamente con las personas jurídicas.\n",
    "Sabemos que en Argentina el CUIT permite identificar inequívocamente si se trata de una persona física o jurídica: aquellos que empiezan con 30, 33 y 34 corresponden a empresas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_list = [] # en esta lista vamos a alojar cada chunk que vamos iterando\n",
    "\n",
    "for chunk in df_chunk:  \n",
    "    # Nos quedamos sólo con personas jurídicas\n",
    "    chunk_filter = chunk[chunk[\"cuitcuil\"].astype(str).str[:2].isin([\"30\",\"33\",\"34\"])]\n",
    "    chunk_list.append(chunk_filter)\n",
    "    \n",
    "# Concatenamos los datos filtrados en un DataFrame \n",
    "df = pd.concat(chunk_list)\n",
    "\n",
    "# Sabemos que los valores de totalprestamos corresponden a miles de pesos con un decimal. \n",
    "# Ajustamos para poder procesar adecuadamente y pasamos a $ MM.\n",
    "\n",
    "df.totalprestamos = df.totalprestamos.str.replace(\",\",\".\").astype(float) / 1000\n",
    "\n",
    "# Exportamos el dataset definitivo\n",
    "df.to_csv(\"deudores_juridicas_202002.csv\") #ya no tenemos que repetir el proceso."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al momento de crear \"chunk_filter\" podríamos haber considerado la siguiente fórmula ```\"cuitcuil > 30000000000\"```\n",
    "\n",
    "**¿Por qué no filtrar los casos de esa manera?**\n",
    "\n",
    "- La información **nos llega directamente como \"string\"** y podemos manipularla como \"string\". **¿Qué ganancia hay en cambiar eso?**\n",
    "- **¿Y si hay cuitcuils que no son exclusivamente numéricos?** Cualquier paso en falso nos va a consumir mucho tiempo (no nos olvidemos que es un .txt de casi 5 GB). Tampoco tanto tiempo! pero regalarlo así... :P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trabajamos con los datos\n",
    "Como primer análisis exploratorio queremos visualizar los datos resumiéndolos por entidad.\n",
    "\n",
    "Nos interesa obtener deuda total, promedio de deuda, el coeficiente de variación ([CV](https://es.wikipedia.org/wiki/Coeficiente_de_variaci%C3%B3n)) y la cantidad de empresas por entidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculo de agregados\n",
    "df = df.groupby('entidad').agg({'totalprestamos':['sum', 'mean', 'std'] , 'cuitcuil': 'nunique'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajustamos \"etiquetas\" de columnas para visualizar mejor\n",
    "df = df.reset_index()\n",
    "df.columns = [\"entidad\", \"Prestamos Total en $ MM\", \"Promedio Deuda en $ MM\", \"sd\", \"Cantidad de empresas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cálculo Coeficiente de Variación\n",
    "df['CV'] = df[df.columns[3]]  / df[df.columns[2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para simplificar la visualización nos quedamos con las 15 instituciones con mayor deuda consolidada y añadimos etiquetas correspondientes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by = df.columns[1], ascending = False)[0:14]\n",
    "\n",
    "# pasar nombres de entidades\n",
    "df[\"entidad\"].replace({7: \"Galicia\",\n",
    "                   11: \"Nación\",\n",
    "                   14: \"Bapro\",\n",
    "                   15: \"ICBC\",\n",
    "                   16: \"Citi\",\n",
    "                   17: \"BBVA\",\n",
    "                   27: \"Supervielle\",\n",
    "                   29: \"Ciudad\",\n",
    "                   34: \"Patagonia\",\n",
    "                   72: \"Santander\",\n",
    "                   150: \"HSBC\",\n",
    "                   191: \"Credicoop\",\n",
    "                   259: \"Itaú\",\n",
    "                   285: \"Macro\",\n",
    "                   300: \"BICE\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualización preliminar y algunas preguntas\n",
    "Ya estamos en condiciones de poder ver algunos resultados preliminares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "g = sns.barplot(x='entidad', y='Prestamos Total en $ MM', data=df)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90); # el \";\" evita texto en el output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuál es el banco con mayor volumen de deuda? **Nación.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(x='entidad', y='Cantidad de empresas', data=df)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuál es el banco con mayor cantidad de empresas deudoras? **Galicia.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(x='entidad', y='Promedio Deuda en $ MM', data=df)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuál es el banco con el promedio más alto de deuda? **Citi.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(x='entidad', y='CV', data=df)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Hay algún banco que se destaque en términos de mayor variabilidad de montos? **Nación.**\n",
    "En este punto podríamos suponer que a mayor variabilidad, más \"universal\" es su oferta: realiza pequeños, medianos y grandes prestamos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interrogantes finales\n",
    "¿Hay datos que estén sesgando los resultados? Llama la atención los valores extremos de Citi y Galicia. \n",
    "\n",
    "¿Cómo podemos visualizar esta información de una manera más atractiva y directa? ¿podemos combinar los indicadores?\n",
    "\n",
    "¿Cuál será el comportamiento si agrupamos las empresas por actividad? ¿y por región?\n",
    "\n",
    "¿Podemos armar series históricas e identificar tendencias de mediano / largo plazo?\n",
    "\n",
    "Te invito a que sumes más preguntas... Muchas gracias!"
   ]
  }
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