01 - EDA produccion hidrocaburos 2022.ipynb

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   "source": [
    "# Análisis exploratorio de la producción de petróleo y gas en la Cuenca Neuquina en el año 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42bd196-0184-41fe-9287-7201bffaed33",
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   "source": [
    "## INTRODUCCIÓN\n",
    "\n",
    "Como inspiración de proyecto final del \"Curso para Crear tus Proyectos de Ciencia de Datos\" de Platzi es que realizo este análisis sobre los datos de producción de petróleo y gas en el año 2022.\n",
    "\n",
    "La idea es practicar la exploración y análisis de datos reales con el objetivo de responder algunas preguntas interesantes como:\n",
    "\n",
    "* ¿Cuáles son las provincias que abarca a la cuenca neuquina?\n",
    "* ¿Cómo es la evolución de la producción de gas y petróleo en la cuenca neuquina?\n",
    "* ¿Cuáles son las empresas que mayor producción tuvieron?\n",
    "* ¿Cuales son las áreas de concesión que mas producción registraron?\n",
    "* ¿Cuál es la formación geológica desde donde se obtiene la mayor producción?\n",
    "* ¿Cuál es la tecnología obtiene la mayor producción de gas y petróleo?\n",
    "\n",
    "Los datos fueron extraídos de https://datos.gob.ar/dataset/energia-produccion-petroleo-gas-por-pozo-capitulo-iv/archivo/energia_876b3746-85e2-4039-adeb-b1354436159f, propocionados por la Secretaría de Energía de la Nación Argentina.\n",
    "\n",
    "La idea es empezar con el análisis de 2022, para luego continuar con otros años de análisis que se encuentran disponibles en la misma fuente citada."
   ]
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  {
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   "metadata": {},
   "source": [
    "## LIBRERÍAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8439e375-5fac-43af-9502-d60da2cc717a",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "source": [
    "## EXPLORACIÓN Y LIMPIEZA\n",
    "\n",
    "### Carga de los datos y exploración inicial\n",
    "\n",
    "Vamos a cargar el conjunto de datos completos, vamos a ver de que se trata y luego vamos a hacer una selección de los datos que necesitamos para el análisis."
   ]
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    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\Marco\\AppData\\Local\\Temp\\ipykernel_13756\\3827011130.py:1: DtypeWarning: Columns (16,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  prodCompleto_22 = pd.read_csv('./Datasets/produccin-de-pozos-de-gas-y-petrleo-2022.csv')\n"
     ]
    }
   ],
   "source": [
    "prodCompleto_22 = pd.read_csv('./Datasets/produccin-de-pozos-de-gas-y-petrleo-2022.csv')"
   ]
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   "source": [
    "Aparece un Warning porque las columnas 16 y 36 tienen dos tipos de datos y pandas no sabe cómo definirlas"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idempresa</th>\n",
       "      <th>anio</th>\n",
       "      <th>mes</th>\n",
       "      <th>idpozo</th>\n",
       "      <th>prod_pet</th>\n",
       "      <th>prod_gas</th>\n",
       "      <th>prod_agua</th>\n",
       "      <th>iny_agua</th>\n",
       "      <th>iny_gas</th>\n",
       "      <th>iny_co2</th>\n",
       "      <th>...</th>\n",
       "      <th>idareayacimiento</th>\n",
       "      <th>areayacimiento</th>\n",
       "      <th>cuenca</th>\n",
       "      <th>provincia</th>\n",
       "      <th>tipo_de_recurso</th>\n",
       "      <th>proyecto</th>\n",
       "      <th>clasificacion</th>\n",
       "      <th>subclasificacion</th>\n",
       "      <th>sub_tipo_recurso</th>\n",
       "      <th>fecha_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Z001</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>145622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>AGR</td>\n",
       "      <td>GENERAL ROCA</td>\n",
       "      <td>NEUQUINA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "      <td>Sin Proyecto</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Z001</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>32186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>PUSU</td>\n",
       "      <td>PUESTO SURVELIN</td>\n",
       "      <td>NEUQUINA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "      <td>Sin Proyecto</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Z001</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>145612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Z122</td>\n",
       "      <td>DON JOSE</td>\n",
       "      <td>NEUQUINA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "      <td>Sin Proyecto</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Z001</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>145623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>AGR</td>\n",
       "      <td>GENERAL ROCA</td>\n",
       "      <td>NEUQUINA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "      <td>Sin Proyecto</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>EXPLORACION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z001</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>145615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>FDRO</td>\n",
       "      <td>FLOR DE ROCA</td>\n",
       "      <td>NEUQUINA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "      <td>Sin Proyecto</td>\n",
       "      <td>SERVICIO</td>\n",
       "      <td>SUMIDERO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  idempresa  anio  mes  idpozo  prod_pet  prod_gas  prod_agua  iny_agua  \\\n",
       "0      Z001  2022    1  145622       0.0       0.0        0.0       0.0   \n",
       "1      Z001  2022    1   32186       0.0       0.0        0.0       0.0   \n",
       "2      Z001  2022    1  145612       0.0       0.0        0.0       0.0   \n",
       "3      Z001  2022    1  145623       0.0       0.0        0.0       0.0   \n",
       "4      Z001  2022    1  145615       0.0       0.0        0.0       0.0   \n",
       "\n",
       "   iny_gas  iny_co2  ...  idareayacimiento   areayacimiento    cuenca  \\\n",
       "0      0.0      0.0  ...               AGR     GENERAL ROCA  NEUQUINA   \n",
       "1      0.0      0.0  ...              PUSU  PUESTO SURVELIN  NEUQUINA   \n",
       "2      0.0      0.0  ...              Z122         DON JOSE  NEUQUINA   \n",
       "3      0.0      0.0  ...               AGR     GENERAL ROCA  NEUQUINA   \n",
       "4      0.0      0.0  ...              FDRO     FLOR DE ROCA  NEUQUINA   \n",
       "\n",
       "   provincia tipo_de_recurso      proyecto clasificacion subclasificacion  \\\n",
       "0  Rio Negro    CONVENCIONAL  Sin Proyecto   EXPLORACION      EXPLORACION   \n",
       "1  Rio Negro    CONVENCIONAL  Sin Proyecto   EXPLORACION      EXPLORACION   \n",
       "2  Rio Negro    CONVENCIONAL  Sin Proyecto   EXPLORACION      EXPLORACION   \n",
       "3  Rio Negro    CONVENCIONAL  Sin Proyecto   EXPLORACION      EXPLORACION   \n",
       "4  Rio Negro    CONVENCIONAL  Sin Proyecto      SERVICIO         SUMIDERO   \n",
       "\n",
       "  sub_tipo_recurso  fecha_data  \n",
       "0              NaN  2022-01-31  \n",
       "1              NaN  2022-01-31  \n",
       "2              NaN  2022-01-31  \n",
       "3              NaN  2022-01-31  \n",
       "4              NaN  2022-01-31  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vemos cómo se carga el conjunto de datos\n",
    "prodCompleto_22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4630b46-c551-4fda-ab8b-a9dd6404c638",
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    {
     "data": {
      "text/plain": [
       "Index(['idempresa', 'anio', 'mes', 'idpozo', 'prod_pet', 'prod_gas',\n",
       "       'prod_agua', 'iny_agua', 'iny_gas', 'iny_co2', 'iny_otro', 'tef',\n",
       "       'vida_util', 'tipoextraccion', 'tipoestado', 'tipopozo',\n",
       "       'observaciones', 'fechaingreso', 'rectificado', 'habilitado',\n",
       "       'idusuario', 'empresa', 'sigla', 'formprod', 'profundidad', 'formacion',\n",
       "       'idareapermisoconcesion', 'areapermisoconcesion', 'idareayacimiento',\n",
       "       'areayacimiento', 'cuenca', 'provincia', 'tipo_de_recurso', 'proyecto',\n",
       "       'clasificacion', 'subclasificacion', 'sub_tipo_recurso', 'fecha_data'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veamos que los nombres cada columnas\n",
    "prodCompleto_22.columns"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 971882 entries, 0 to 971881\n",
      "Data columns (total 38 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   idempresa               971882 non-null  object \n",
      " 1   anio                    971882 non-null  int64  \n",
      " 2   mes                     971882 non-null  int64  \n",
      " 3   idpozo                  971882 non-null  int64  \n",
      " 4   prod_pet                971882 non-null  float64\n",
      " 5   prod_gas                971882 non-null  float64\n",
      " 6   prod_agua               971882 non-null  float64\n",
      " 7   iny_agua                971882 non-null  float64\n",
      " 8   iny_gas                 971882 non-null  float64\n",
      " 9   iny_co2                 971882 non-null  float64\n",
      " 10  iny_otro                971882 non-null  float64\n",
      " 11  tef                     971882 non-null  float64\n",
      " 12  vida_util               27502 non-null   float64\n",
      " 13  tipoextraccion          971746 non-null  object \n",
      " 14  tipoestado              971746 non-null  object \n",
      " 15  tipopozo                971746 non-null  object \n",
      " 16  observaciones           61621 non-null   object \n",
      " 17  fechaingreso            971882 non-null  object \n",
      " 18  rectificado             971882 non-null  object \n",
      " 19  habilitado              971882 non-null  object \n",
      " 20  idusuario               971882 non-null  int64  \n",
      " 21  empresa                 971882 non-null  object \n",
      " 22  sigla                   971882 non-null  object \n",
      " 23  formprod                939722 non-null  object \n",
      " 24  profundidad             971882 non-null  float64\n",
      " 25  formacion               938366 non-null  object \n",
      " 26  idareapermisoconcesion  971866 non-null  object \n",
      " 27  areapermisoconcesion    971866 non-null  object \n",
      " 28  idareayacimiento        971882 non-null  object \n",
      " 29  areayacimiento          971882 non-null  object \n",
      " 30  cuenca                  971846 non-null  object \n",
      " 31  provincia               971882 non-null  object \n",
      " 32  tipo_de_recurso         971882 non-null  object \n",
      " 33  proyecto                971882 non-null  object \n",
      " 34  clasificacion           759236 non-null  object \n",
      " 35  subclasificacion        759236 non-null  object \n",
      " 36  sub_tipo_recurso        38874 non-null   object \n",
      " 37  fecha_data              971882 non-null  object \n",
      "dtypes: float64(10), int64(4), object(24)\n",
      "memory usage: 281.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Veamos los tipo de datos en cada columna\n",
    "prodCompleto_22.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37cd21fd-0e7a-4182-9fc9-d67c8d1b1117",
   "metadata": {},
   "source": [
    "La advertencia para las columnas 16 y 36 es sobre columnas que no vamos a usar, así que no nos vamos a preocupar por transformar o corregir.\n",
    "\n",
    "Por otra parte vemos que tenemos 971882 registros para el conjunto de datos completo, que incluye la producción de toda la Argentina.\n",
    "\n",
    "Las columnas que nos van a servir para nuestro análisis son:\n",
    "\n",
    "    * mes: que vemos están indicados con el número del mes con un entero.\n",
    "    * prod_pet: nos da la producción de petróleo como flotante, medidos como m3.\n",
    "    * prod_gas: nos da la producción de gas como flotante, medido en m3.\n",
    "    * empresa: es la empresa que realiza la producción. Es objeto porque es un string.\n",
    "    * formacion: es la formación geológica desde donde se realiza la extracción. Es objeto porque es un string.\n",
    "    * areapermisoconcesion: es el área de concesión donde se encuentra el pozo que extrae la producción. Es objeto porque es un string.\n",
    "    * cuenca: es el datos que vamos a usar para filtrar nuestros datos, porque sólo queremos la cuenca neuquina. Es objeto porque es un string.\n",
    "    * provincia: porque la cuenca neuquina esta en varias provincias argentinas. Es objeto porque es un string.\n",
    "    * tipo_de_recurso: nos va a decir si es mediante la tecnología convencional o no convencional. Es objeto porque es un string.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "939cf89b-531c-4f95-a73e-668688e13803",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos a generar un DataFrame de la producción unicamente de la cuenca neuquina\n",
    "prod_2022 = prodCompleto_22.loc[prodCompleto_22['cuenca'] == 'NEUQUINA',\n",
    "                                ['mes', 'prod_pet', 'prod_gas', 'empresa', \n",
    "                            'formacion', 'areapermisoconcesion', \n",
    "                            'provincia', 'tipo_de_recurso']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b1d5e8f-3c3a-4682-9f55-756b5db7ba98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(369327, 8)"
      ]
     },
     "execution_count": 28,
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   ],
   "source": [
    "# Veamos con cuantos datos nos quedamos\n",
    "prod_2022.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f11314f1-1e0f-4363-87c1-d6282f33fae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3800121825489103"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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    }
   ],
   "source": [
    "#Veamos la relación de registros pertenecientes a la cuenca neuquina\n",
    "cant_registros_Neuquina = prod_2022.shape[0]\n",
    "cant_registros_original = prodCompleto_22.shape[0]\n",
    "cant_registros_Neuquina/cant_registros_original"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a4cce7-f237-4047-9e84-ac0ec6f159cb",
   "metadata": {},
   "source": [
    "Es decir, el 38% de los registros del conjunto de datos son de la cuenca neuquina."
   ]
  },
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       "    }\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>mes</th>\n",
       "      <th>prod_pet</th>\n",
       "      <th>prod_gas</th>\n",
       "      <th>empresa</th>\n",
       "      <th>formacion</th>\n",
       "      <th>areapermisoconcesion</th>\n",
       "      <th>provincia</th>\n",
       "      <th>tipo_de_recurso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PETROLERA DEL COMAHUE S.A.</td>\n",
       "      <td>punta rosada</td>\n",
       "      <td>GENERAL ROCA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PETROLERA DEL COMAHUE S.A.</td>\n",
       "      <td>formación improductiva</td>\n",
       "      <td>PUESTO SURVELIN</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PETROLERA DEL COMAHUE S.A.</td>\n",
       "      <td>punta rosada</td>\n",
       "      <td>DON JOSE</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PETROLERA DEL COMAHUE S.A.</td>\n",
       "      <td>formación improductiva</td>\n",
       "      <td>GENERAL ROCA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PETROLERA DEL COMAHUE S.A.</td>\n",
       "      <td>punta rosada</td>\n",
       "      <td>FLOR DE ROCA</td>\n",
       "      <td>Rio Negro</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mes  prod_pet  prod_gas                     empresa  \\\n",
       "0    1       0.0       0.0  PETROLERA DEL COMAHUE S.A.   \n",
       "1    1       0.0       0.0  PETROLERA DEL COMAHUE S.A.   \n",
       "2    1       0.0       0.0  PETROLERA DEL COMAHUE S.A.   \n",
       "3    1       0.0       0.0  PETROLERA DEL COMAHUE S.A.   \n",
       "4    1       0.0       0.0  PETROLERA DEL COMAHUE S.A.   \n",
       "\n",
       "                formacion areapermisoconcesion  provincia tipo_de_recurso  \n",
       "0            punta rosada        GENERAL ROCA   Rio Negro    CONVENCIONAL  \n",
       "1  formación improductiva      PUESTO SURVELIN  Rio Negro    CONVENCIONAL  \n",
       "2            punta rosada             DON JOSE  Rio Negro    CONVENCIONAL  \n",
       "3  formación improductiva        GENERAL ROCA   Rio Negro    CONVENCIONAL  \n",
       "4            punta rosada         FLOR DE ROCA  Rio Negro    CONVENCIONAL  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Veamos como se ve la tabla\n",
    "prod_2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2ff6c1c-440d-4146-8222-4b311ab3ad2e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 369327 entries, 0 to 971881\n",
      "Data columns (total 8 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   mes                   369327 non-null  int64  \n",
      " 1   prod_pet              369327 non-null  float64\n",
      " 2   prod_gas              369327 non-null  float64\n",
      " 3   empresa               369327 non-null  object \n",
      " 4   formacion             368283 non-null  object \n",
      " 5   areapermisoconcesion  369327 non-null  object \n",
      " 6   provincia             369327 non-null  object \n",
      " 7   tipo_de_recurso       369327 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 25.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Veamos el tipo de datos y si están todos completos o si nos faltan datos\n",
    "prod_2022.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c23acc-6508-4294-a921-9e6aaa743230",
   "metadata": {},
   "source": [
    "No nos faltan datos pero vemos que hay filas que tienen datos de producción de gas y petróleo en cero, esto es porque seguramente estaban registrando producción de agua o inyección de otros tipos.\n",
    "\n",
    "Vamos a borrar las filas que tienen producción de gas y petróleo de cero, para que no sean muchos registros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c8f7bf38-08c8-4e98-b7ad-3f589abce689",
   "metadata": {},
   "outputs": [],
   "source": [
    "# con ~ nos quedamos con las filas que no cumplen con esta condición\n",
    "prod_2022 = prod_2022.loc[~(prod_2022['prod_pet'] == 0) & ~(prod_2022['prod_gas'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ea4718ee-c537-44a6-98d0-30b6502f994f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mes</th>\n",
       "      <th>prod_pet</th>\n",
       "      <th>prod_gas</th>\n",
       "      <th>empresa</th>\n",
       "      <th>formacion</th>\n",
       "      <th>areapermisoconcesion</th>\n",
       "      <th>provincia</th>\n",
       "      <th>tipo_de_recurso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1</td>\n",
       "      <td>48.04</td>\n",
       "      <td>1.03</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>centenario</td>\n",
       "      <td>SEÑAL CERRO BAYO</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1</td>\n",
       "      <td>21.51</td>\n",
       "      <td>0.35</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>rayoso</td>\n",
       "      <td>CHACHAHUEN SUR</td>\n",
       "      <td>Mendoza</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1</td>\n",
       "      <td>35.81</td>\n",
       "      <td>21.78</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>quintuco</td>\n",
       "      <td>LOMA LA LATA - SIERRA BARROSA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1</td>\n",
       "      <td>69.70</td>\n",
       "      <td>53.28</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>vaca muerta</td>\n",
       "      <td>LOMA CAMPANA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>NO CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1</td>\n",
       "      <td>73.90</td>\n",
       "      <td>605.01</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>sierras blancas</td>\n",
       "      <td>LOMA CAMPANA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mes  prod_pet  prod_gas   empresa        formacion  \\\n",
       "301    1     48.04      1.03  YPF S.A.       centenario   \n",
       "316    1     21.51      0.35  YPF S.A.           rayoso   \n",
       "318    1     35.81     21.78  YPF S.A.         quintuco   \n",
       "319    1     69.70     53.28  YPF S.A.      vaca muerta   \n",
       "326    1     73.90    605.01  YPF S.A.  sierras blancas   \n",
       "\n",
       "              areapermisoconcesion provincia  tipo_de_recurso  \n",
       "301               SEÑAL CERRO BAYO   Neuquén     CONVENCIONAL  \n",
       "316                 CHACHAHUEN SUR   Mendoza     CONVENCIONAL  \n",
       "318  LOMA LA LATA - SIERRA BARROSA   Neuquén     CONVENCIONAL  \n",
       "319                   LOMA CAMPANA   Neuquén  NO CONVENCIONAL  \n",
       "326                   LOMA CAMPANA   Neuquén     CONVENCIONAL  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5a78d235-eaf0-4e16-90df-d694e1d7d3c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMNA:  mes\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "--> Cantidad de valores únicos:  12\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  empresa\n",
      "['YPF S.A.' 'VISTA ENERGY ARGENTINA SAU' 'VENOIL S.A.'\n",
      " 'VISTA OIL & GAS ARGENTINA SAU' 'TECPETROL S.A.' 'TOTAL AUSTRAL S.A.'\n",
      " 'SHELL ARGENTINA S.A.' 'ROCH S.A.' 'PETROLERA EL TREBOL S.A.'\n",
      " 'PETROLEOS SUDAMERICANOS S.A.' 'PRESIDENT PETROLEUM S.A.'\n",
      " 'PLUSPETROL S.A.' 'PATAGONIA ENERGY S.A.'\n",
      " 'PETROQUIMICA COMODORO RIVADAVIA S.A.' 'PAMPETROL S.A.P.E.M'\n",
      " 'PAMPA ENERGIA S.A.' 'PAN AMERICAN ENERGY SL' 'OILSTONE ENERGIA S.A.'\n",
      " 'MEDANITO S.A.' 'KILWER S.A.' 'MADALENA ENERGY ARGENTINA SRL '\n",
      " 'GRECOIL y CIA. S.R.L.' 'GEOPARK ARGENTINA LTD. (SUCURSAL ARGENTINA)'\n",
      " 'EXXONMOBIL EXPLORATION ARGENTINA S.R.L.'\n",
      " 'ENERGIA COMPAÑIA PETROLERA S.A.' 'CAPETROL ARGENTINA S.A.'\n",
      " 'CHEVRON ARGENTINA S.R.L.' 'CAPEX S.A.'\n",
      " 'ALIANZA PETROLERA ARGENTINA S.A.' 'Petrolera Aconcagua Energia S.A.']\n",
      "--> Cantidad de valores únicos:  30\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  formacion\n",
      "['centenario' 'rayoso' 'quintuco' 'vaca muerta' 'sierras blancas' 'lajas'\n",
      " 'grupo neuquén' 'huitrín' 'mulichinco' 'agrio' 'precuyo' 'lotena'\n",
      " 'challacó' 'loma montosa' 'chachao' 'basamento' 'formación improductiva'\n",
      " 'punta rosada' 'tordillo' 'barda negra' 'los molles' 'choiyoi' 'catriel'\n",
      " 'loncoche' 'pircala' 'quebrada del sapo']\n",
      "--> Cantidad de valores únicos:  26\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  areapermisoconcesion\n",
      "['SEÑAL CERRO BAYO' 'CHACHAHUEN SUR' 'LOMA LA LATA - SIERRA BARROSA'\n",
      " 'LOMA CAMPANA' 'ESTACION FERNANDEZ ORO' 'CERRO FORTUNOSO'\n",
      " 'CHIHUIDO DE LA SALINA SUR' 'VOLCAN AUCA MAHUIDA' 'CAÑADON AMARILLO'\n",
      " 'SEÑAL PICADA - PUNTA BARDA' 'VALLE DEL RIO GRANDE' 'EL PORTON'\n",
      " 'CHIHUIDO DE LA SIERRA NEGRA' 'RINCON DEL MANGRULLO'\n",
      " 'CHIHUIDO DE LA SALINA' 'FILO MORADO' 'AL NORTE DE LA DORSAL'\n",
      " 'PASO DE LAS BARDAS NORTE' 'OCTOGONO' 'PUESTO HERNANDEZ' 'DADIN'\n",
      " 'RIO NEUQUEN' 'AGUADA DE LA ARENA' 'LA AMARGA CHICA' 'LLANCANELO'\n",
      " 'CERRO HAMACA' 'LOS CALDENES' 'EL MANZANO ESTE' 'LAS MANADAS'\n",
      " 'EL OREJANO' 'LA RIBERA BLOQUE I ' 'CERRO MORADO ESTE' 'BANDURRIA SUR'\n",
      " 'AGUADA DEL CHAÑAR' 'EL MANZANO OESTE (RESTO)' 'ALTIPLANICIE DEL PAYUN'\n",
      " 'BAJO DEL TORO NORTE' 'ENTRE LOMAS' 'BAJADA DEL PALO OESTE'\n",
      " 'JAGÜEL DE LOS MACHOS RN' 'CHARCO DEL PALENQUE' 'BAJADA DEL PALO ESTE'\n",
      " '25 DE MAYO - MEDANITO SUD ESTE RN' 'JARILLA QUEMADA'\n",
      " 'COIRON AMARGO NORTE' 'AGUADA FEDERAL' 'VEGA GRANDE'\n",
      " 'EL MANZANO OESTE (AGRIO)' 'PUNTA SENILLOSA' 'AGUA SALADA'\n",
      " 'FORTIN DE PIEDRA ' 'PUESTO PARADA' 'LOS BASTOS' 'LOS TOLDOS II ESTE'\n",
      " 'RINCON LA CENIZA' 'AGUADA PICHANA ESTE  ' 'SAN ROQUE' 'LA ESCALONADA'\n",
      " 'CRUZ DE LORENA' 'SIERRAS BLANCAS' 'COIRON AMARGO SUR OESTE'\n",
      " 'BAJADA DE AÑELO' 'CAJON DE LOS CABALLOS' 'PUESTO ROJAS'\n",
      " 'CERRO MOLLAR NORTE' 'CERRO MOLLAR OESTE' 'EL MEDANITO' 'BAJO DEL PICHE'\n",
      " 'BARRANCA DE LOS LOROS' 'CENTRO ESTE' 'LOMA MONTOSA OESTE'\n",
      " 'EL SANTIAGUEÑO' 'PUESTO FLORES - ESTANCIA VIEJA' 'PUESTO PRADO'\n",
      " 'LAS BASES' 'ANGOSTURA' 'GOBERNADOR AYALA III' 'GOBERNADOR AYALA'\n",
      " 'CNQ 7/A' 'JAGÜEL CASA DE PIEDRA' 'LA CALERA' 'CENTENARIO CENTRO'\n",
      " 'AGUADA DEL CHIVATO - AGUADA BOCAREY' 'JAGÜEL DE LOS MACHOS LP'\n",
      " 'MEDANITO' 'EL SOSNEADO' '25 DE MAYO - MEDANITO SUD ESTE LP'\n",
      " 'GOBERNADOR AYALA V' 'MEDANITO SUR' 'RINCONADA - PUESTO MORALES'\n",
      " 'SIERRA CHATA' 'EL MANGRULLO' 'LINDERO ATRAVESADO' 'AGUADA CÁNEPA'\n",
      " 'BANDURRIA CENTRO' 'COIRON AMARGO SUR ESTE' 'AL SUR DE LA DORSAL'\n",
      " 'CERRO BANDERA' 'ANTICLINAL CAMPAMENTO' 'BAJO BAGUALES' 'LOMA NEGRA NI'\n",
      " 'PUESTO CORTADERA' 'CUTRAL CO SUR' 'COLLON CURA' 'AGUADA BAGUALES'\n",
      " 'PUESTO TOUQUET' 'EL PORVENIR' 'PORTEZUELO MINAS' 'MEDIANERA'\n",
      " 'CORRALERA NORESTE' 'MATA MORA NORTE' 'CORRALERA SUR'\n",
      " 'RINCONADA PUESTO MORALES RN' 'PUESTO MORALES ESTE' 'LA BREA'\n",
      " 'BAJO DEL CHOIQUE - LA INVERNADA' 'LOS TOLDOS I SUR'\n",
      " 'PAMPA DE LAS YEGUAS I' 'EL SAUCE' 'EL TRAPIAL ESTE' 'AGUA DEL CAJON'\n",
      " 'LOMA NEGRA' 'LA YESERA' 'PUESTO ZUÑIGA' 'LOMA DE LA MINA'\n",
      " 'CONFLUENCIA SUR' 'ATUEL NORTE EXPLOTACION' 'CATRIEL OESTE'\n",
      " 'CATRIEL VIEJO']\n",
      "--> Cantidad de valores únicos:  129\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  provincia\n",
      "['Neuquén' 'Mendoza' 'Rio Negro' 'La Pampa']\n",
      "--> Cantidad de valores únicos:  4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  tipo_de_recurso\n",
      "['CONVENCIONAL' 'NO CONVENCIONAL']\n",
      "--> Cantidad de valores únicos:  2\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Veamos cuales son todas las variables categóricas que tenemos\n",
    "\n",
    "lista_columnas = ['mes', 'empresa', 'formacion', \n",
    "          'areapermisoconcesion', 'provincia', 'tipo_de_recurso']\n",
    "\n",
    "for i in lista_columnas:\n",
    "    print('COLUMNA: ', i)\n",
    "    print(prod_2022[i].unique())\n",
    "    print('--> Cantidad de valores únicos: ', prod_2022[i].nunique())\n",
    "    print('-'*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e9037d-e108-415b-9778-8efa6627b161",
   "metadata": {},
   "source": [
    "Vemos que:\n",
    "    \n",
    "    * Todos los meses de 2022\n",
    "    * Tenemos 30 empresas trabajando en la cuenca Neuquina\n",
    "    * La explotación se esta haciendo en 26 formaciones georlógicas\n",
    "    * Hay 129 áreas de concesión relacionadas con la producción informada para el 2022\n",
    "    * La Cuenca Neuquina abarca 4 provincias\n",
    "    * Hay 2 tecnologías de extracción del recurso"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2bd363-aa9c-4df0-93b6-41847f23c6aa",
   "metadata": {},
   "source": [
    "## ANÁLISIS PARA EL 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f450f60-279f-44d0-b127-8f739048d441",
   "metadata": {},
   "source": [
    "### Producción por mes\n",
    "\n",
    "Veamos cómo fue la producción de gas y petróleo por mes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5fd7d515-4944-4d60-abc7-3a31f87895d7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>prod_pet</th>\n",
       "      <th>prod_gas</th>\n",
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       "      <th>1</th>\n",
       "      <td>1.540488</td>\n",
       "      <td>1.734963</td>\n",
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       "      <td>1.379174</td>\n",
       "      <td>1.478008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.555330</td>\n",
       "      <td>1.798837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.533533</td>\n",
       "      <td>1.718073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.619046</td>\n",
       "      <td>1.918188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.553299</td>\n",
       "      <td>1.866313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.643823</td>\n",
       "      <td>1.890473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.669801</td>\n",
       "      <td>1.890856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.670189</td>\n",
       "      <td>1.787671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.784203</td>\n",
       "      <td>1.736189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.730085</td>\n",
       "      <td>1.583874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.808584</td>\n",
       "      <td>1.726943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     prod_pet  prod_gas\n",
       "mes                    \n",
       "1    1.540488  1.734963\n",
       "2    1.379174  1.478008\n",
       "3    1.555330  1.798837\n",
       "4    1.533533  1.718073\n",
       "5    1.619046  1.918188\n",
       "6    1.553299  1.866313\n",
       "7    1.643823  1.890473\n",
       "8    1.669801  1.890856\n",
       "9    1.670189  1.787671\n",
       "10   1.784203  1.736189\n",
       "11   1.730085  1.583874\n",
       "12   1.808584  1.726943"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculamos la producción de gas y petroleo en Millones de m3\n",
    "prod_2022_mes = prod_2022.groupby('mes').sum()/1000000\n",
    "prod_2022_mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "65a06e70-5c7e-4dbf-b39e-44745896e52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graficamos la producción por mes\n",
    "plt.plot(prod_2022_mes.index, prod_2022_mes['prod_gas'], color='yellow', label='Producción Gas [Mm3]')\n",
    "plt.plot(prod_2022_mes.index, prod_2022_mes['prod_pet'], color='black', label='Producción Petróleo [Mm3]')\n",
    "\n",
    "# agregamos etiquetas y leyenda a la gráfica\n",
    "plt.xlabel('Mes')\n",
    "plt.ylabel('Producción')\n",
    "plt.title('Producción de Gas y Petróleo por mes')\n",
    "plt.legend()\n",
    "\n",
    "# muestramos la gráfica\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f00f650-e6e5-494a-af15-9883f38ac52e",
   "metadata": {},
   "source": [
    "En principio, se ve una tendendencia similar entre la producción de Gas y Petróleo hasta marzo/abril del 2022 y luego un aumento continuo de la producción de petróleo, mostrando mayores volumenes de producción a partir de octubre que la producción de gas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b452201-e1c7-47df-8436-529873b33c7f",
   "metadata": {},
   "source": [
    "### Producción por empresas\n",
    "\n",
    "Veamos cuál es el ranking de empresas con mayor producción de gas y de petróleo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e5c0eed9-7865-4a08-9a9c-4d9d035a58b9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>prod_gas</th>\n",
       "      <th>prod_pet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empresa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALIANZA PETROLERA ARGENTINA S.A.</th>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.010667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAPETROL ARGENTINA S.A.</th>\n",
       "      <td>0.009163</td>\n",
       "      <td>0.022164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAPEX S.A.</th>\n",
       "      <td>0.601226</td>\n",
       "      <td>0.143231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVRON ARGENTINA S.R.L.</th>\n",
       "      <td>0.094215</td>\n",
       "      <td>0.288055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENERGIA COMPAÑIA PETROLERA S.A.</th>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.003059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXXONMOBIL EXPLORATION ARGENTINA S.R.L.</th>\n",
       "      <td>0.219374</td>\n",
       "      <td>0.438729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOPARK ARGENTINA LTD. (SUCURSAL ARGENTINA)</th>\n",
       "      <td>0.003911</td>\n",
       "      <td>0.002583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRECOIL y CIA. S.R.L.</th>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KILWER S.A.</th>\n",
       "      <td>0.009604</td>\n",
       "      <td>0.124965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MADALENA ENERGY ARGENTINA SRL</th>\n",
       "      <td>0.008693</td>\n",
       "      <td>0.014767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDANITO S.A.</th>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.005790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OILSTONE ENERGIA S.A.</th>\n",
       "      <td>0.176871</td>\n",
       "      <td>0.055941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAMPA ENERGIA S.A.</th>\n",
       "      <td>0.481067</td>\n",
       "      <td>0.004307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAMPETROL S.A.P.E.M</th>\n",
       "      <td>0.002181</td>\n",
       "      <td>0.018455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAN AMERICAN ENERGY SL</th>\n",
       "      <td>0.463323</td>\n",
       "      <td>0.937154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATAGONIA ENERGY S.A.</th>\n",
       "      <td>0.003251</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETROLEOS SUDAMERICANOS S.A.</th>\n",
       "      <td>0.048147</td>\n",
       "      <td>0.174048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETROLERA EL TREBOL S.A.</th>\n",
       "      <td>0.010232</td>\n",
       "      <td>0.013196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETROQUIMICA COMODORO RIVADAVIA S.A.</th>\n",
       "      <td>0.232610</td>\n",
       "      <td>0.455564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLUSPETROL S.A.</th>\n",
       "      <td>1.786380</td>\n",
       "      <td>0.942742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT PETROLEUM S.A.</th>\n",
       "      <td>0.016604</td>\n",
       "      <td>0.053333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petrolera Aconcagua Energia S.A.</th>\n",
       "      <td>0.005322</td>\n",
       "      <td>0.049418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROCH S.A.</th>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.014546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHELL ARGENTINA S.A.</th>\n",
       "      <td>0.285632</td>\n",
       "      <td>1.675370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECPETROL S.A.</th>\n",
       "      <td>5.967515</td>\n",
       "      <td>0.468570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL AUSTRAL S.A.</th>\n",
       "      <td>2.053680</td>\n",
       "      <td>0.122310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VENOIL S.A.</th>\n",
       "      <td>0.003749</td>\n",
       "      <td>0.005290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISTA ENERGY ARGENTINA SAU</th>\n",
       "      <td>0.343039</td>\n",
       "      <td>1.756746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISTA OIL &amp; GAS ARGENTINA SAU</th>\n",
       "      <td>0.155771</td>\n",
       "      <td>0.720182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YPF S.A.</th>\n",
       "      <td>8.143867</td>\n",
       "      <td>10.956465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             prod_gas   prod_pet\n",
       "empresa                                                         \n",
       "ALIANZA PETROLERA ARGENTINA S.A.             0.001034   0.010667\n",
       "CAPETROL ARGENTINA S.A.                      0.009163   0.022164\n",
       "CAPEX S.A.                                   0.601226   0.143231\n",
       "CHEVRON ARGENTINA S.R.L.                     0.094215   0.288055\n",
       "ENERGIA COMPAÑIA PETROLERA S.A.              0.000128   0.003059\n",
       "EXXONMOBIL EXPLORATION ARGENTINA S.R.L.      0.219374   0.438729\n",
       "GEOPARK ARGENTINA LTD. (SUCURSAL ARGENTINA)  0.003911   0.002583\n",
       "GRECOIL y CIA. S.R.L.                        0.001216   0.002701\n",
       "KILWER S.A.                                  0.009604   0.124965\n",
       "MADALENA ENERGY ARGENTINA SRL                0.008693   0.014767\n",
       "MEDANITO S.A.                                0.001687   0.005790\n",
       "OILSTONE ENERGIA S.A.                        0.176871   0.055941\n",
       "PAMPA ENERGIA S.A.                           0.481067   0.004307\n",
       "PAMPETROL S.A.P.E.M                          0.002181   0.018455\n",
       "PAN AMERICAN ENERGY SL                       0.463323   0.937154\n",
       "PATAGONIA ENERGY S.A.                        0.003251   0.007207\n",
       "PETROLEOS SUDAMERICANOS S.A.                 0.048147   0.174048\n",
       "PETROLERA EL TREBOL S.A.                     0.010232   0.013196\n",
       "PETROQUIMICA COMODORO RIVADAVIA S.A.         0.232610   0.455564\n",
       "PLUSPETROL S.A.                              1.786380   0.942742\n",
       "PRESIDENT PETROLEUM S.A.                     0.016604   0.053333\n",
       "Petrolera Aconcagua Energia S.A.             0.005322   0.049418\n",
       "ROCH S.A.                                    0.000896   0.014546\n",
       "SHELL ARGENTINA S.A.                         0.285632   1.675370\n",
       "TECPETROL S.A.                               5.967515   0.468570\n",
       "TOTAL AUSTRAL S.A.                           2.053680   0.122310\n",
       "VENOIL S.A.                                  0.003749   0.005290\n",
       "VISTA ENERGY ARGENTINA SAU                   0.343039   1.756746\n",
       "VISTA OIL & GAS ARGENTINA SAU                0.155771   0.720182\n",
       "YPF S.A.                                     8.143867  10.956465"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## con groupby agrupamos por empresa\n",
    "## con agg sumamos la información de producción por empresa\n",
    "## con div aplicamos la división de las sumaspor 1 millón, para dar en Mm3\n",
    "prod_2022_empresa = prod_2022.groupby('empresa').agg({'prod_gas': 'sum', 'prod_pet': 'sum'}).div(1000000)\n",
    "prod_2022_empresa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3404eaec-5486-4c8c-afc9-b75e75e746a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creamos fataframe para el caso de producción de gas y petróleo\n",
    "# para poder ordenarlos para graficar los rankings\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de gas\n",
    "## con head(10) hacemos que se muestre solo las 10 empresas con mas producción\n",
    "prod_2022_empresa_gas = prod_2022_empresa.sort_values(by='prod_gas', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de petróleo\n",
    "prod_2022_empresa_pet = prod_2022_empresa.sort_values(by='prod_pet', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Grafiquemos las producciones de gas y petróleo para ver los ranking de empresa\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))\n",
    "\n",
    "# Subgráfico de producción de gas\n",
    "axs[0].barh(prod_2022_empresa_gas['empresa'], prod_2022_empresa_gas['prod_gas'], color='yellow')\n",
    "axs[0].set_xlabel('Producción de gas [Mm3]')\n",
    "axs[0].invert_yaxis()\n",
    "\n",
    "# Subgráfico de producción de petróleo\n",
    "axs[1].barh(prod_2022_empresa_pet['empresa'], prod_2022_empresa_pet['prod_pet'], color='black')\n",
    "axs[1].set_xlabel('Producción de petróleo [Mm3]')\n",
    "axs[1].invert_yaxis()\n",
    "\n",
    "# Ajustamos el espacio entre subgráficos\n",
    "plt.tight_layout()\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4909f8c9-2bfd-49d5-865b-c1cd9d3fe410",
   "metadata": {},
   "source": [
    "Se puede ver que YPF S.A. lidera la producción de gas y petróleo en la cuenca Neuquina."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feca8783-2aa9-40a4-a5e6-31eaba7d8e34",
   "metadata": {},
   "source": [
    "### Producción por Área de Concesión\n",
    "\n",
    "Queremos ver cuáles son las Áreas de Concesión desde donde sale mas producción de gas y petróleo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7844a81e-5ad0-474d-85ca-330a3b091752",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>prod_gas</th>\n",
       "      <th>prod_pet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>areapermisoconcesion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25 DE MAYO - MEDANITO SUD ESTE LP</th>\n",
       "      <td>0.051844</td>\n",
       "      <td>0.109256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25 DE MAYO - MEDANITO SUD ESTE RN</th>\n",
       "      <td>0.033398</td>\n",
       "      <td>0.129903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGUA DEL CAJON</th>\n",
       "      <td>0.282289</td>\n",
       "      <td>0.022706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGUA SALADA</th>\n",
       "      <td>0.285290</td>\n",
       "      <td>0.090595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGUADA BAGUALES</th>\n",
       "      <td>0.004579</td>\n",
       "      <td>0.032632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIERRA CHATA</th>\n",
       "      <td>0.127446</td>\n",
       "      <td>0.001012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIERRAS BLANCAS</th>\n",
       "      <td>0.033400</td>\n",
       "      <td>0.328012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALLE DEL RIO GRANDE</th>\n",
       "      <td>0.055212</td>\n",
       "      <td>0.145995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEGA GRANDE</th>\n",
       "      <td>0.002582</td>\n",
       "      <td>0.002890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLCAN AUCA MAHUIDA</th>\n",
       "      <td>0.014417</td>\n",
       "      <td>0.084837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   prod_gas  prod_pet\n",
       "areapermisoconcesion                                 \n",
       "25 DE MAYO - MEDANITO SUD ESTE LP  0.051844  0.109256\n",
       "25 DE MAYO - MEDANITO SUD ESTE RN  0.033398  0.129903\n",
       "AGUA DEL CAJON                     0.282289  0.022706\n",
       "AGUA SALADA                        0.285290  0.090595\n",
       "AGUADA BAGUALES                    0.004579  0.032632\n",
       "...                                     ...       ...\n",
       "SIERRA CHATA                       0.127446  0.001012\n",
       "SIERRAS BLANCAS                    0.033400  0.328012\n",
       "VALLE DEL RIO GRANDE               0.055212  0.145995\n",
       "VEGA GRANDE                        0.002582  0.002890\n",
       "VOLCAN AUCA MAHUIDA                0.014417  0.084837\n",
       "\n",
       "[129 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## con groupby agrupamos por areapermisoconcesion\n",
    "## con agg sumamos la información de producción por areapermisoconcesion\n",
    "## con div aplicamos la división de las sumaspor 1 millón, para dar en Mm3\n",
    "prod_2022_area = prod_2022.groupby('areapermisoconcesion').agg({'prod_gas': 'sum', 'prod_pet': 'sum'}).div(1000000)\n",
    "prod_2022_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "affe82df-c190-46bd-bd71-f5ccc66f3bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creamos fataframe para el caso de producción de gas y petróleo\n",
    "# para poder ordenarlos para graficar los rankings de áreas\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de gas\n",
    "## con head(10) hacemos que se muestre solo las 10 empresas con mas producción\n",
    "prod_2022_area_gas = prod_2022_area.sort_values(by='prod_gas', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de petróleo\n",
    "prod_2022_area_pet = prod_2022_area.sort_values(by='prod_pet', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Grafiquemos las producciones de gas y petróleo para ver los ranking de empresa\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))\n",
    "\n",
    "# Subgráfico de producción de gas\n",
    "axs[0].barh(prod_2022_area_gas['areapermisoconcesion'], prod_2022_area_gas['prod_gas'], color='yellow')\n",
    "axs[0].set_xlabel('Producción de gas [Mm3]')\n",
    "axs[0].invert_yaxis()\n",
    "\n",
    "# Subgráfico de producción de petróleo\n",
    "axs[1].barh(prod_2022_area_pet['areapermisoconcesion'], prod_2022_area_pet['prod_pet'], color='black')\n",
    "axs[1].set_xlabel('Producción de petróleo [Mm3]')\n",
    "axs[1].invert_yaxis()\n",
    "\n",
    "# Ajustamos el espacio entre subgráficos\n",
    "plt.tight_layout()\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd685a4d-9ad7-4a5f-ba76-d6c96ce151a1",
   "metadata": {},
   "source": [
    "Se observa que el Área Fortín de Piedra, de Tecpetrol S.A., es la que saca mayor volumen de gas y el área Loma Campana, de YPF S.A., es de donde sale la mayor producción de petróleo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b281acd1-714f-491e-9094-5bbf20aa1fd6",
   "metadata": {},
   "source": [
    "### Prducción por formación geológica\n",
    "\n",
    "Veamos ahora cual es la formación geológica que mayor producción está generando."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "edf5bbcd-ce6c-45ff-ba0f-24107e9d5955",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>prod_gas</th>\n",
       "      <th>prod_pet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formacion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agrio</th>\n",
       "      <td>0.307364</td>\n",
       "      <td>0.460743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barda negra</th>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.003039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basamento</th>\n",
       "      <td>0.062009</td>\n",
       "      <td>0.038227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catriel</th>\n",
       "      <td>0.007422</td>\n",
       "      <td>0.006624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centenario</th>\n",
       "      <td>0.134889</td>\n",
       "      <td>0.809453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chachao</th>\n",
       "      <td>0.018202</td>\n",
       "      <td>0.017686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>challacó</th>\n",
       "      <td>0.002791</td>\n",
       "      <td>0.006137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>choiyoi</th>\n",
       "      <td>0.083077</td>\n",
       "      <td>0.228475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formación improductiva</th>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grupo neuquén</th>\n",
       "      <td>0.136573</td>\n",
       "      <td>0.363283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huitrín</th>\n",
       "      <td>0.587911</td>\n",
       "      <td>0.408554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lajas</th>\n",
       "      <td>1.533434</td>\n",
       "      <td>0.265138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loma montosa</th>\n",
       "      <td>0.028900</td>\n",
       "      <td>0.079019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loncoche</th>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.004374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>los molles</th>\n",
       "      <td>0.486585</td>\n",
       "      <td>0.076493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotena</th>\n",
       "      <td>0.157882</td>\n",
       "      <td>0.137162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mulichinco</th>\n",
       "      <td>1.610961</td>\n",
       "      <td>0.150876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pircala</th>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precuyo</th>\n",
       "      <td>0.177978</td>\n",
       "      <td>0.106822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>punta rosada</th>\n",
       "      <td>1.598335</td>\n",
       "      <td>0.210738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quebrada del sapo</th>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quintuco</th>\n",
       "      <td>1.012698</td>\n",
       "      <td>0.735878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rayoso</th>\n",
       "      <td>0.039261</td>\n",
       "      <td>0.581944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sierras blancas</th>\n",
       "      <td>2.031986</td>\n",
       "      <td>0.485248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tordillo</th>\n",
       "      <td>0.137996</td>\n",
       "      <td>0.210802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vaca muerta</th>\n",
       "      <td>10.957893</td>\n",
       "      <td>14.098045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         prod_gas   prod_pet\n",
       "formacion                                   \n",
       "agrio                    0.307364   0.460743\n",
       "barda negra              0.001031   0.003039\n",
       "basamento                0.062009   0.038227\n",
       "catriel                  0.007422   0.006624\n",
       "centenario               0.134889   0.809453\n",
       "chachao                  0.018202   0.017686\n",
       "challacó                 0.002791   0.006137\n",
       "choiyoi                  0.083077   0.228475\n",
       "formación improductiva   0.014782   0.001999\n",
       "grupo neuquén            0.136573   0.363283\n",
       "huitrín                  0.587911   0.408554\n",
       "lajas                    1.533434   0.265138\n",
       "loma montosa             0.028900   0.079019\n",
       "loncoche                 0.000309   0.004374\n",
       "los molles               0.486585   0.076493\n",
       "lotena                   0.157882   0.137162\n",
       "mulichinco               1.610961   0.150876\n",
       "pircala                  0.000056   0.000790\n",
       "precuyo                  0.177978   0.106822\n",
       "punta rosada             1.598335   0.210738\n",
       "quebrada del sapo        0.000062   0.000008\n",
       "quintuco                 1.012698   0.735878\n",
       "rayoso                   0.039261   0.581944\n",
       "sierras blancas          2.031986   0.485248\n",
       "tordillo                 0.137996   0.210802\n",
       "vaca muerta             10.957893  14.098045"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_2022_formacion = prod_2022.groupby('formacion').agg({'prod_gas': 'sum', 'prod_pet': 'sum'}).div(1000000)\n",
    "prod_2022_formacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "62cc3861-7e7d-4535-9d01-072ac7030064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafiquemos\n",
    "prod_2022_formacion_gas = prod_2022_formacion.sort_values(by='prod_gas', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de petróleo\n",
    "prod_2022_formacion_pet = prod_2022_formacion.sort_values(by='prod_pet', ascending=False).reset_index().head(10)\n",
    "\n",
    "# Grafiquemos las producciones de gas y petróleo para ver los ranking de empresa\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))\n",
    "\n",
    "# Subgráfico de producción de gas\n",
    "axs[0].barh(prod_2022_formacion_gas['formacion'], prod_2022_formacion_gas['prod_gas'], color='yellow')\n",
    "axs[0].set_xlabel('Producción de gas [Mm3]')\n",
    "axs[0].invert_yaxis()\n",
    "\n",
    "# Subgráfico de producción de petróleo\n",
    "axs[1].barh(prod_2022_formacion_pet['formacion'], prod_2022_formacion_pet['prod_pet'], color='black')\n",
    "axs[1].set_xlabel('Producción de petróleo [Mm3]')\n",
    "axs[1].invert_yaxis()\n",
    "\n",
    "# Ajustamos el espacio entre subgráficos\n",
    "plt.tight_layout()\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb85be41-8f01-49d4-9049-e3e15e339223",
   "metadata": {},
   "source": [
    "Por lejos, la mayor producción de gas y petróleo se está obteniendo de la formación Vaca Muerta."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45a0840-831f-46e3-a98f-df2337fc4af1",
   "metadata": {},
   "source": [
    "### Producción por tipo de tecnología\n",
    "\n",
    "Veamos cual es la tecnología que está aportando la mayor producción de gas y petróleo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "cebbf501-63d1-4cbd-98b8-16c43ad88792",
   "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>prod_gas</th>\n",
       "      <th>prod_pet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo_de_recurso</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CONVENCIONAL</th>\n",
       "      <td>6.306975</td>\n",
       "      <td>5.110532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO CONVENCIONAL</th>\n",
       "      <td>14.823412</td>\n",
       "      <td>14.377023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prod_gas   prod_pet\n",
       "tipo_de_recurso                      \n",
       "CONVENCIONAL      6.306975   5.110532\n",
       "NO CONVENCIONAL  14.823412  14.377023"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_2022_tecno = prod_2022.groupby('tipo_de_recurso').agg({'prod_gas': 'sum', 'prod_pet': 'sum'}).div(1000000)\n",
    "prod_2022_tecno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a178f1c9-1c31-4071-a6ce-4f10cb69f960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creamos una figura y dos ejes\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
    "\n",
    "# Creamos una lista con los valores de la columna de prod_gas\n",
    "prod_2022_tecno_gas = prod_2022_tecno['prod_gas']\n",
    "# Creamos una lista con los valores de la columna de prod_pet\n",
    "prod_2022_tecno_pet = prod_2022_tecno['prod_pet']\n",
    "\n",
    "# Colores para la dona de producción 1\n",
    "prod_2022_tecno_gas_colors = ['green', 'blue']\n",
    "# Colores para la dona de producción 2\n",
    "prod_2022_tecno_pet_colors = ['green', 'blue']\n",
    "\n",
    "# Etiquetas para la dona de producción 1\n",
    "prod_2022_tecno_gas_labels = ['Convencional', 'No Convencional']\n",
    "# Etiquetas para la dona de producción 2\n",
    "prod_2022_tecno_pet_labels = ['Convencional', 'No Convencional']\n",
    "\n",
    "# Creamos el gráfico de dona para la producción 1\n",
    "axs[0].pie(prod_2022_tecno_gas, \n",
    "           colors=prod_2022_tecno_gas_colors, \n",
    "           labels=prod_2022_tecno_gas_labels, \n",
    "           autopct='%1.1f%%', startangle=90)\n",
    "axs[0].set_title('Producción de Gas')\n",
    "\n",
    "# Creamos el gráfico de dona para la producción 2\n",
    "axs[1].pie(prod_2022_tecno_pet, \n",
    "           colors=prod_2022_tecno_pet_colors, \n",
    "           labels=prod_2022_tecno_pet_labels,\n",
    "           autopct='%1.1f%%', startangle=90)\n",
    "axs[1].set_title('Producción de Petróleo')\n",
    "\n",
    "# Agregamos un título a la figura\n",
    "fig.suptitle('Gráficos de producción según tecnología', fontsize=14)\n",
    "\n",
    "# Para que veamos fondo blanco\n",
    "fig.set_facecolor('white')\n",
    "# Mostramos la figura\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5a04d2-4776-48ef-9c8e-19baf5d7031c",
   "metadata": {},
   "source": [
    "### Producción por provincia\n",
    "\n",
    "Veamos cual es la provincia desde donde se saca la mayor producción de gas y petróleo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ca819d21-44b6-4fd6-9fa4-dcc87059f0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>prod_gas</th>\n",
       "      <th>prod_pet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>provincia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>La Pampa</th>\n",
       "      <td>0.230822</td>\n",
       "      <td>0.613344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mendoza</th>\n",
       "      <td>0.539477</td>\n",
       "      <td>1.671136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neuquén</th>\n",
       "      <td>18.952042</td>\n",
       "      <td>15.950096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rio Negro</th>\n",
       "      <td>1.408047</td>\n",
       "      <td>1.252980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prod_gas   prod_pet\n",
       "provincia                      \n",
       "La Pampa    0.230822   0.613344\n",
       "Mendoza     0.539477   1.671136\n",
       "Neuquén    18.952042  15.950096\n",
       "Rio Negro   1.408047   1.252980"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_2022_provincia = prod_2022.groupby('provincia').agg({'prod_gas': 'sum', 'prod_pet': 'sum'}).div(1000000)\n",
    "prod_2022_provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6e2f1eff-bc3c-4988-ae7c-7ebe406347b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafiquemos\n",
    "prod_2022_provincia_gas = prod_2022_provincia.sort_values(by='prod_gas', ascending=False).reset_index()\n",
    "\n",
    "# Ordenamos el DataFrame según la producción de petróleo\n",
    "prod_2022_provincia_pet = prod_2022_provincia.sort_values(by='prod_pet', ascending=False).reset_index()\n",
    "\n",
    "# Grafiquemos las producciones de gas y petróleo para ver los ranking de empresa\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))\n",
    "\n",
    "# Subgráfico de producción de gas\n",
    "axs[0].barh(prod_2022_provincia_gas['provincia'], prod_2022_provincia_gas['prod_gas'], color='yellow')\n",
    "axs[0].set_xlabel('Producción de gas [Mm3]')\n",
    "axs[0].invert_yaxis()\n",
    "\n",
    "# Subgráfico de producción de petróleo\n",
    "axs[1].barh(prod_2022_provincia_pet['provincia'], prod_2022_provincia_pet['prod_pet'], color='black')\n",
    "axs[1].set_xlabel('Producción de petróleo [Mm3]')\n",
    "axs[1].invert_yaxis()\n",
    "\n",
    "# Ajustamos el espacio entre subgráficos\n",
    "plt.tight_layout()\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c2cda7-acd0-4683-8a89-2eefd0a93130",
   "metadata": {},
   "source": [
    "Se observa que por mucho, las mayores producción de gas y petróleo se obtienen de los recursos hidrocarburíferos de la provincia de Neuquén."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b441500-7f9b-40b9-aac6-62414acd4064",
   "metadata": {},
   "source": [
    "## AUTOMATIZACIÓN PARA EXPLORACIÓN Y ANÁLISIS DE OTROS AÑOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2a35a9-e373-432d-8859-5d455acdc514",
   "metadata": {},
   "source": [
    "### EXPLORACIÓN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "82907dab-eee8-4130-8705-4939755a6bdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Marco\\AppData\\Local\\Temp\\ipykernel_13756\\1104313387.py:2: DtypeWarning: Columns (36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  prodCompleto_21 = pd.read_csv('./Datasets/produccin-de-pozos-de-gas-y-petrleo-2021.csv')\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>mes</th>\n",
       "      <th>prod_pet</th>\n",
       "      <th>prod_gas</th>\n",
       "      <th>empresa</th>\n",
       "      <th>formacion</th>\n",
       "      <th>areapermisoconcesion</th>\n",
       "      <th>provincia</th>\n",
       "      <th>tipo_de_recurso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1</td>\n",
       "      <td>25.26</td>\n",
       "      <td>842.65</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>mulichinco</td>\n",
       "      <td>AGUADA DE LA ARENA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1</td>\n",
       "      <td>49.32</td>\n",
       "      <td>1511.21</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>mulichinco</td>\n",
       "      <td>AGUADA DE LA ARENA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1</td>\n",
       "      <td>60.30</td>\n",
       "      <td>1.27</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>rayoso</td>\n",
       "      <td>CHIHUIDO DE LA SIERRA NEGRA</td>\n",
       "      <td>Mendoza</td>\n",
       "      <td>CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
       "      <td>2225.80</td>\n",
       "      <td>264.72</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>vaca muerta</td>\n",
       "      <td>LOMA CAMPANA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>NO CONVENCIONAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>1</td>\n",
       "      <td>2306.65</td>\n",
       "      <td>298.78</td>\n",
       "      <td>YPF S.A.</td>\n",
       "      <td>vaca muerta</td>\n",
       "      <td>LOMA CAMPANA</td>\n",
       "      <td>Neuquén</td>\n",
       "      <td>NO CONVENCIONAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mes  prod_pet  prod_gas   empresa    formacion  \\\n",
       "300    1     25.26    842.65  YPF S.A.   mulichinco   \n",
       "318    1     49.32   1511.21  YPF S.A.   mulichinco   \n",
       "372    1     60.30      1.27  YPF S.A.       rayoso   \n",
       "378    1   2225.80    264.72  YPF S.A.  vaca muerta   \n",
       "384    1   2306.65    298.78  YPF S.A.  vaca muerta   \n",
       "\n",
       "            areapermisoconcesion provincia  tipo_de_recurso  \n",
       "300           AGUADA DE LA ARENA   Neuquén     CONVENCIONAL  \n",
       "318           AGUADA DE LA ARENA   Neuquén     CONVENCIONAL  \n",
       "372  CHIHUIDO DE LA SIERRA NEGRA   Mendoza     CONVENCIONAL  \n",
       "378                 LOMA CAMPANA   Neuquén  NO CONVENCIONAL  \n",
       "384                 LOMA CAMPANA   Neuquén  NO CONVENCIONAL  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Guardamos el conjunto de datos\n",
    "prodCompleto_21 = pd.read_csv('./Datasets/produccin-de-pozos-de-gas-y-petrleo-2021.csv')\n",
    "#Armamos el dataset con la info que queremos\n",
    "prod_2021 = prodCompleto_21.loc[prodCompleto_21['cuenca'] == 'NEUQUINA',\n",
    "                                ['mes', 'prod_pet', 'prod_gas', 'empresa', \n",
    "                            'formacion', 'areapermisoconcesion', \n",
    "                            'provincia', 'tipo_de_recurso']]\n",
    "# Sacamos las filas con producción 0\n",
    "prod_2021 = prod_2021.loc[~(prod_2021['prod_pet'] == 0) & ~(prod_2021['prod_gas'] == 0)]\n",
    "prod_2021.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "01304e1b-1bd3-4c9a-b64a-5ea902e86b2a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMNA:  mes\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "--> Cantidad de valores únicos:  12\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  empresa\n",
      "['YPF S.A.' 'WINTERSHALL DEA ARGENTINA S.A' 'VENOIL S.A.'\n",
      " 'VISTA OIL & GAS ARGENTINA SAU' 'TECPETROL S.A.' 'TOTAL AUSTRAL S.A.'\n",
      " 'SAN JORGE PETROLEUM S.A.' 'SHELL ARGENTINA S.A.' 'ROCH S.A.'\n",
      " 'PETROLERA EL TREBOL S.A.' 'PETROLEOS SUDAMERICANOS S.A.'\n",
      " 'PRESIDENT PETROLEUM S.A.' 'PLUSPETROL S.A.'\n",
      " 'PETROQUIMICA COMODORO RIVADAVIA S.A.' 'PAMPETROL S.A.P.E.M'\n",
      " 'PAMPA ENERGIA S.A.' 'PAN AMERICAN ENERGY SL' 'OILSTONE ENERGIA S.A.'\n",
      " 'MEDANITO S.A.' 'KILWER S.A.' 'MADALENA ENERGY ARGENTINA SRL '\n",
      " 'GRECOIL y CIA. S.R.L.' 'GEOPARK ARGENTINA LTD. (SUCURSAL ARGENTINA)'\n",
      " 'EXXONMOBIL EXPLORATION ARGENTINA S.R.L.'\n",
      " 'ENERGIA COMPAÑIA PETROLERA S.A.' 'CAPETROL ARGENTINA S.A.'\n",
      " 'CHEVRON ARGENTINA S.R.L.' 'CAPEX S.A.'\n",
      " 'Petrolera Aconcagua Energia S.A.']\n",
      "--> Cantidad de valores únicos:  29\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  formacion\n",
      "['mulichinco' 'rayoso' 'vaca muerta' 'centenario' 'huitrín' 'lajas'\n",
      " 'punta rosada' 'sierras blancas' 'grupo neuquén' 'lotena' 'chachao'\n",
      " 'loma montosa' 'agrio' 'quintuco' 'los molles' 'precuyo' 'basamento'\n",
      " 'loncoche' 'tordillo' 'barda negra' 'challacó' 'formación improductiva'\n",
      " 'choiyoi' 'catriel' 'pircala' 'quebrada del sapo']\n",
      "--> Cantidad de valores únicos:  26\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  areapermisoconcesion\n",
      "['AGUADA DE LA ARENA' 'CHIHUIDO DE LA SIERRA NEGRA' 'LOMA CAMPANA'\n",
      " 'BANDURRIA SUR' 'LA AMARGA CHICA' 'CERRO MORADO ESTE'\n",
      " 'ESTACION FERNANDEZ ORO' 'RIO NEUQUEN' 'LA RIBERA BLOQUE I '\n",
      " 'RINCON DEL MANGRULLO' 'LOMA LA LATA - SIERRA BARROSA'\n",
      " 'EL MANZANO OESTE (RESTO)' 'AL NORTE DE LA DORSAL' 'CHACHAHUEN SUR'\n",
      " 'VALLE DEL RIO GRANDE' 'SEÑAL PICADA - PUNTA BARDA' 'PUESTO HERNANDEZ'\n",
      " 'OCTOGONO' 'LOS CALDENES' 'CERRO FORTUNOSO' 'DADIN' 'LLANCANELO'\n",
      " 'CHIHUIDO DE LA SALINA' 'VOLCAN AUCA MAHUIDA' 'LOMA DE LA MINA'\n",
      " 'CAÑADON AMARILLO' 'CERRO HAMACA' 'LAS MANADAS' 'SEÑAL CERRO BAYO'\n",
      " 'PASO DE LAS BARDAS NORTE' 'ALTIPLANICIE DEL PAYUN' 'EL MANZANO ESTE'\n",
      " 'CHIHUIDO DE LA SALINA SUR' 'EL PORTON' 'FILO MORADO'\n",
      " 'BAJO DEL TORO NORTE' 'EL OREJANO' 'LA RIBERA BLOQUE II  '\n",
      " 'AGUADA DEL CHAÑAR' 'AGUADA FEDERAL' 'VEGA GRANDE'\n",
      " 'JAGÜEL DE LOS MACHOS RN' 'BAJADA DEL PALO OESTE' 'ENTRE LOMAS'\n",
      " 'COIRON AMARGO NORTE' 'BAJADA DEL PALO ESTE' 'CHARCO DEL PALENQUE'\n",
      " '25 DE MAYO - MEDANITO SUD ESTE RN' 'JARILLA QUEMADA' 'AGUA SALADA'\n",
      " 'FORTIN DE PIEDRA ' 'PUNTA SENILLOSA' 'LOS BASTOS' 'PUESTO PARADA'\n",
      " 'RINCON LA CENIZA' 'LA ESCALONADA' 'AGUADA PICHANA ESTE  ' 'SAN ROQUE'\n",
      " 'CONFLUENCIA SUR' 'CRUZ DE LORENA' 'SIERRAS BLANCAS' 'BAJADA DE AÑELO'\n",
      " 'COIRON AMARGO SUR OESTE' 'CAJON DE LOS CABALLOS' 'PUESTO ROJAS'\n",
      " 'EL MEDANITO' 'BAJO DEL PICHE' 'EL SANTIAGUEÑO' 'CENTRO ESTE'\n",
      " 'BARRANCA DE LOS LOROS' 'LOMA MONTOSA OESTE'\n",
      " 'PUESTO FLORES - ESTANCIA VIEJA' 'LAS BASES' 'ANGOSTURA' 'PUESTO PRADO'\n",
      " 'CENTENARIO CENTRO' 'LA CALERA' 'CNQ 7/A' 'JAGÜEL CASA DE PIEDRA'\n",
      " 'GOBERNADOR AYALA III' 'GOBERNADOR AYALA' 'EL SOSNEADO' 'MEDANITO'\n",
      " '25 DE MAYO - MEDANITO SUD ESTE LP' 'JAGÜEL DE LOS MACHOS LP'\n",
      " 'GOBERNADOR AYALA V' 'MEDANITO SUR' 'RINCONADA - PUESTO MORALES'\n",
      " 'EL MANGRULLO' 'SIERRA CHATA' 'LINDERO ATRAVESADO'\n",
      " 'COIRON AMARGO SUR ESTE' 'BANDURRIA CENTRO' 'AGUADA CÁNEPA' 'COLLON CURA'\n",
      " 'AL SUR DE LA DORSAL' 'CERRO BANDERA' 'LOMA NEGRA NI' 'CUTRAL CO SUR'\n",
      " 'BAJO BAGUALES' 'PUESTO CORTADERA' 'ANTICLINAL CAMPAMENTO'\n",
      " 'AGUADA DEL CHIVATO - AGUADA BOCAREY' 'MEDIANERA' 'MATA MORA NORTE'\n",
      " 'CORRALERA NORESTE' 'RINCONADA PUESTO MORALES RN' 'PUESTO MORALES ESTE'\n",
      " 'LA BREA' 'PUESTO TOUQUET' 'AGUADA BAGUALES' 'EL PORVENIR'\n",
      " 'BAJO DEL CHOIQUE - LA INVERNADA' 'PAMPA DE LAS YEGUAS I'\n",
      " 'LOS TOLDOS I SUR' 'EL SAUCE' 'EL TRAPIAL ESTE' 'AGUA DEL CAJON'\n",
      " 'LOMA NEGRA' 'LA YESERA' 'PUESTO ZUÑIGA' 'CATRIEL OESTE' 'CATRIEL VIEJO'\n",
      " 'ATUEL NORTE EXPLOTACION']\n",
      "--> Cantidad de valores únicos:  124\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  provincia\n",
      "['Neuquén' 'Mendoza' 'Rio Negro' 'La Pampa']\n",
      "--> Cantidad de valores únicos:  4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "COLUMNA:  tipo_de_recurso\n",
      "['CONVENCIONAL' 'NO CONVENCIONAL' 'SIN RESERVORIO']\n",
      "--> Cantidad de valores únicos:  3\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Veamos si estan todos los meses del 2021\n",
    "\n",
    "lista_columnas = ['mes', 'empresa', 'formacion', \n",
    "          'areapermisoconcesion', 'provincia', 'tipo_de_recurso']\n",
    "\n",
    "for i in lista_columnas:\n",
    "    print('COLUMNA: ', i)\n",
    "    print(prod_2021[i].unique())\n",
    "    print('--> Cantidad de valores únicos: ', prod_2021[i].nunique())\n",
    "    print('-'*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ed4b97-6189-43e6-aae6-311b3f0f9b28",
   "metadata": {},
   "source": [
    "## CONCLUSIONES\n",
    "\n",
    "Luego del análsis realizado podemos responder a todas las preguntas:\n",
    "\n",
    "* **¿Cuáles son las provincias que abarca a la cuenca neuquina?**\n",
    "\n",
    "La cuenca Neuquina abarca las provincias de Neuquén, mendoza, Río Negro y La Pampa. Pudimos ver que Neuquén es por mucho la mayor productora de gas y petróleo de la cuenca.\n",
    "\n",
    "* **¿Cómo es la evolución de la producción de gas y petróleo en la cuenca neuquina?**\n",
    "\n",
    "Vimos que a lo largo del 2022 hubo una tendencia creciente en la producción de petróleo, donde superó a la producción de gas a partir de Octube aproximadamente. También se observa la clara relación de la producción de gas con la temporada de invierno.\n",
    "\n",
    "* **¿Cuáles son las empresas que mayor producción tuvieron?**\n",
    "\n",
    "Vimos que YPF S.A. es la empresa que lidera la producción de gas y de petróleo. En el caso de la producción de gas, le sigue de cerca la empresa Tecpetrol S.A.\n",
    "\n",
    "* **¿Cuales son las áreas de concesión que mas producción registraron?**\n",
    "\n",
    "Vimos que el área Fortín de Piedra, operada por Tecpetrol S.A., es el área que mayor producción de gas sacó en 2022. En cambio, en producción de petróleo, el área con mayor producción es Loma Campana, operado por YPF S.A.\n",
    "\n",
    "* **¿Cuál es la formación geológica desde donde se obtiene la mayor producción?**\n",
    "\n",
    "Como era de esperar, la formación geológica de la que mayor producción de gas y petróleo se obtiene es Vaca Muerta.\n",
    "\n",
    "* **¿Cuál es la tecnología obtiene la mayor producción de gas y petróleo?**\n",
    "\n",
    "la producción No Convencional es la tecnología que genera la obtención del 70,2% de la producción de Gas y el 73,8% de la producción de petróleo en 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8cad71-137f-4c74-861f-99f33358c7cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020dd00c-29da-41de-a1f1-1e39f3372be9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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