python-csv-gastos-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis del Presupuesto Funcional Gastos 2022 con pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalar las librerías con las que vamos a trabajar \n",
    "\n",
    "Pandas es una librería estándar de python para el manejo de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de Pandas\n",
    "\n",
    "Importamos la librería pandas y la renombramos como \"pd\" para utilizarla en el resto del notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de variables\n",
    "\n",
    "Creamos la variable que contiene los datos que vamos a usar mediante ```=``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_movilidad = \"http://datosabiertos.torrent.es/dataset/52cdc35c-6515-4aa8-953f-aeec7e131b16/resource/b16a1948-eb1a-47cf-ac16-1540f7bc13f5/download/ODTRPuntosMovilidadReducida.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que la variable se ha creado "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://datosabiertos.torrent.es/dataset/52cdc35c-6515-4aa8-953f-aeec7e131b16/resource/b16a1948-eb1a-47cf-ac16-1540f7bc13f5/download/ODTRPuntosMovilidadReducida.csv'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_movilidad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación del Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un dataframe de pandas leyendo el csv de la base de datos \n",
    "\n",
    "Según la [documentación oficial de pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), un dataframe es una estructura de datos de dos dimensiones \"de tamaño variable y potencialmente heterogéneos\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xd3 in position 13: invalid continuation byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-8e853d455511>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_movilidad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_dtype_objs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xd3 in position 13: invalid continuation byte"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(url_movilidad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como muestra este error, no ha sido posible mostrar el dataframe porque no se ha podido decodificar un byte, \"el byte de continuación no es válido\"\n",
    "\n",
    "Para solucionar este error, según [stack overflow](https://stackoverflow.com/questions/5552555/unicodedecodeerror-invalid-continuation-byte) -un foro de programación en el que se resuelven dudas- era un problema de decodificación en utf-8 \n",
    "\n",
    "UTF-8 según [wikipedia](https://es.wikipedia.org/wiki/UTF-8) \"es un formato de codificación de caracteres\"\n",
    "\n",
    "Para resolver el error, era necesario añadir ````encoding='latin-1'````\n",
    "\n",
    "Según [wikipedia](https://ca.wikipedia.org/wiki/Latin-1), latin-1 \"es una norma de la ISO que define la codificación del alfabeto latino, incluyendo los diacríticos (como letras acentuadas, ñ, ç), y letras especiales (como ß, Ø), necesarios para la escritura de las lenguas originarias de Europa occidental\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url_movilidad, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Añadiendo ````encoding='latin-1'```` ya es posible mostrar el dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>PMR;TOLL LA ALBERCA;;;</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>PMR;TOLL LA ALBERCA;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>PMR;VALENCIA;70;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>PMR;VIRGEN DEL OLIVAR;24;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>PMR;XIRIVELLA;25;;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           TIPUS;DIRECCIÓ;NÚMERO;;\n",
       "0         PMR;RAMIRO DE MAEZTU;7;;\n",
       "1            PMR;6 DE DICIEMBRE;;;\n",
       "2           PMR;AVDA AL VEDAT;29;;\n",
       "3    PMR;VICENTE BLASCO IBAÑEZ;1;;\n",
       "4        PMR;VIRGEN DE LA PAZ;11;;\n",
       "..                             ...\n",
       "177         PMR;TOLL LA ALBERCA;;;\n",
       "178         PMR;TOLL LA ALBERCA;;;\n",
       "179              PMR;VALENCIA;70;;\n",
       "180     PMR;VIRGEN DEL OLIVAR;24;;\n",
       "181             PMR;XIRIVELLA;25;;\n",
       "\n",
       "[182 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se puede observar, el csv no ha sido leído correctamente, ya que se muestra una única columna\n",
    "\n",
    "Para separar los datos en columnas, añadimos ```delimiter``` -que sirve para delimitar los datos a partir de ```;```- como argumento "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url_movilidad, encoding='latin-1', delimiter = ';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que ha funcionado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>TIPUS</th>\n",
       "      <th>DIRECCIÓ</th>\n",
       "      <th>NÚMERO</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMR</td>\n",
       "      <td>RAMIRO DE MAEZTU</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMR</td>\n",
       "      <td>6 DE DICIEMBRE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMR</td>\n",
       "      <td>AVDA AL VEDAT</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PMR</td>\n",
       "      <td>VICENTE BLASCO IBAÑEZ</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PMR</td>\n",
       "      <td>VIRGEN DE LA PAZ</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>PMR</td>\n",
       "      <td>TOLL LA ALBERCA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>PMR</td>\n",
       "      <td>TOLL LA ALBERCA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>PMR</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>PMR</td>\n",
       "      <td>VIRGEN DEL OLIVAR</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>PMR</td>\n",
       "      <td>XIRIVELLA</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TIPUS               DIRECCIÓ NÚMERO  Unnamed: 3  Unnamed: 4\n",
       "0     PMR       RAMIRO DE MAEZTU      7         NaN         NaN\n",
       "1     PMR         6 DE DICIEMBRE    NaN         NaN         NaN\n",
       "2     PMR          AVDA AL VEDAT     29         NaN         NaN\n",
       "3     PMR  VICENTE BLASCO IBAÑEZ      1         NaN         NaN\n",
       "4     PMR       VIRGEN DE LA PAZ     11         NaN         NaN\n",
       "..    ...                    ...    ...         ...         ...\n",
       "177   PMR        TOLL LA ALBERCA    NaN         NaN         NaN\n",
       "178   PMR        TOLL LA ALBERCA    NaN         NaN         NaN\n",
       "179   PMR               VALENCIA     70         NaN         NaN\n",
       "180   PMR      VIRGEN DEL OLIVAR     24         NaN         NaN\n",
       "181   PMR              XIRIVELLA     25         NaN         NaN\n",
       "\n",
       "[182 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambio de base de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pesar de que ahora los datos se dividan en columnas, parece que el csv no se termina de leer correctamente, por lo que no se puede analizar la tabla debidamente\n",
    "\n",
    "Por ello, he decidido elegir otra base de datos, definiendo una nueva variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_prep = \"https://datos.alcobendas.org/dataset/f4e12b31-2f87-4f37-877d-2ad7d7088e68/resource/e40b6eb5-a00f-4541-b8dd-74e36a493593/download/presupuesto-funcional-gastos-2022.-normalizado.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que la variable se ha creado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://datos.alcobendas.org/dataset/f4e12b31-2f87-4f37-877d-2ad7d7088e68/resource/e40b6eb5-a00f-4541-b8dd-74e36a493593/download/presupuesto-funcional-gastos-2022.-normalizado.csv'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de un nuevo dataframe\n",
    "\n",
    "Una vez definida la nueva variable, creamos un nuevo dataframe de pandas leyendo la nueva base de datos \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url_prep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación mostramos el nuevo dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Código</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>1: GASTOS DE PERSONAL</th>\n",
       "      <th>2: GASTOS CORRIENTES EN BIENES Y SERVICIOS</th>\n",
       "      <th>3: GASTOS FINANCIEROS</th>\n",
       "      <th>4: TRANSFERENCIAS CORRIENTES</th>\n",
       "      <th>5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS</th>\n",
       "      <th>6: INVERSIONES REALES</th>\n",
       "      <th>7: TRANSFERENCIAS DE CAPITAL</th>\n",
       "      <th>8: ACTIVOS FINANCIEROS</th>\n",
       "      <th>9: PASIVOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Clasificación por programas</td>\n",
       "      <td>77198659.02</td>\n",
       "      <td>73573032.97</td>\n",
       "      <td>613271.26</td>\n",
       "      <td>6294124.25</td>\n",
       "      <td>528.74</td>\n",
       "      <td>33174136.93</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>195100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DEUDA PÚBLICA</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Deuda Pública</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Deuda Pública</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>SERVICIOS PÚBLICOS BÁSICOS</td>\n",
       "      <td>19082029.47</td>\n",
       "      <td>30794122.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>128000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18514157.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>94.0</td>\n",
       "      <td>Transferencias a otras Administraciones Públicas</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>941.0</td>\n",
       "      <td>Transferencias a Comunidades Autónomas</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>942.0</td>\n",
       "      <td>Transferencias a Entidades Locales territoriales</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>943.0</td>\n",
       "      <td>Transferencias a otras Entidades Locales</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>944.0</td>\n",
       "      <td>Transferencias a la Administración General del...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Código                                             Nombre  \\\n",
       "0       NaN                        Clasificación por programas   \n",
       "1       0.0                                      DEUDA PÚBLICA   \n",
       "2       1.0                                      Deuda Pública   \n",
       "3      11.0                                      Deuda Pública   \n",
       "4       1.0                         SERVICIOS PÚBLICOS BÁSICOS   \n",
       "..      ...                                                ...   \n",
       "130    94.0   Transferencias a otras Administraciones Públicas   \n",
       "131   941.0             Transferencias a Comunidades Autónomas   \n",
       "132   942.0   Transferencias a Entidades Locales territoriales   \n",
       "133   943.0           Transferencias a otras Entidades Locales   \n",
       "134   944.0  Transferencias a la Administración General del...   \n",
       "\n",
       "     1: GASTOS DE PERSONAL  2: GASTOS CORRIENTES EN BIENES Y SERVICIOS  \\\n",
       "0              77198659.02                                 73573032.97   \n",
       "1                     0.00                                        0.00   \n",
       "2                     0.00                                        0.00   \n",
       "3                     0.00                                        0.00   \n",
       "4              19082029.47                                 30794122.81   \n",
       "..                     ...                                         ...   \n",
       "130                   0.00                                        0.00   \n",
       "131                   0.00                                        0.00   \n",
       "132                   0.00                                        0.00   \n",
       "133                   0.00                                        0.00   \n",
       "134                   0.00                                        0.00   \n",
       "\n",
       "     3: GASTOS FINANCIEROS  4: TRANSFERENCIAS CORRIENTES  \\\n",
       "0                613271.26                    6294124.25   \n",
       "1                 15000.00                          0.00   \n",
       "2                 15000.00                          0.00   \n",
       "3                 15000.00                          0.00   \n",
       "4                     0.00                     128000.00   \n",
       "..                     ...                           ...   \n",
       "130                   0.00                          0.00   \n",
       "131                   0.00                          0.00   \n",
       "132                   0.00                          0.00   \n",
       "133                   0.00                          0.00   \n",
       "134                   0.00                          0.00   \n",
       "\n",
       "     5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  6: INVERSIONES REALES  \\\n",
       "0                                          528.74            33174136.93   \n",
       "1                                            0.00                   0.00   \n",
       "2                                            0.00                   0.00   \n",
       "3                                            0.00                   0.00   \n",
       "4                                            0.00            18514157.84   \n",
       "..                                            ...                    ...   \n",
       "130                                          0.00                   0.00   \n",
       "131                                          0.00                   0.00   \n",
       "132                                          0.00                   0.00   \n",
       "133                                          0.00                   0.00   \n",
       "134                                          0.00                   0.00   \n",
       "\n",
       "     7: TRANSFERENCIAS DE CAPITAL  8: ACTIVOS FINANCIEROS  \\\n",
       "0                         80000.0                195100.0   \n",
       "1                             0.0                     0.0   \n",
       "2                             0.0                     0.0   \n",
       "3                             0.0                     0.0   \n",
       "4                             0.0                     0.0   \n",
       "..                            ...                     ...   \n",
       "130                           0.0                     0.0   \n",
       "131                           0.0                     0.0   \n",
       "132                           0.0                     0.0   \n",
       "133                           0.0                     0.0   \n",
       "134                           0.0                     0.0   \n",
       "\n",
       "     9: PASIVOS FINANCIEROS  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         0  \n",
       "..                      ...  \n",
       "130                       0  \n",
       "131                       0  \n",
       "132                       0  \n",
       "133                       0  \n",
       "134                       0  \n",
       "\n",
       "[135 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración del dataframe\n",
    "\n",
    "### Información general \n",
    "\n",
    "Mediante ```df.info()``` se muestra la información de la tabla \n",
    "\n",
    "Según la [página web de pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html), muestra información sobre el dataframe entre la que incluye el índice (RangeIndex) con el número de entradas, el número de columnas que hay (Data Columns), el nombre de las columnas (Colum), la cantidad de datos nulos que posee la tabla (Non-Null Count), el tipo de variable de las columnas (Dtype) y el tamaño (memory usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135 entries, 0 to 134\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                        Non-Null Count  Dtype  \n",
      "---  ------                                        --------------  -----  \n",
      " 0   Código                                        134 non-null    float64\n",
      " 1   Nombre                                        135 non-null    object \n",
      " 2   1: GASTOS DE PERSONAL                         135 non-null    float64\n",
      " 3   2: GASTOS CORRIENTES EN BIENES Y SERVICIOS    135 non-null    float64\n",
      " 4   3: GASTOS FINANCIEROS                         135 non-null    float64\n",
      " 5   4: TRANSFERENCIAS CORRIENTES                  135 non-null    float64\n",
      " 6   5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  135 non-null    float64\n",
      " 7   6: INVERSIONES REALES                         135 non-null    float64\n",
      " 8   7: TRANSFERENCIAS DE CAPITAL                  135 non-null    float64\n",
      " 9   8: ACTIVOS FINANCIEROS                        135 non-null    float64\n",
      " 10  9: PASIVOS FINANCIEROS                        135 non-null    int64  \n",
      "dtypes: float64(9), int64(1), object(1)\n",
      "memory usage: 11.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cabecera\n",
    "\n",
    "Mediante ```df.head()``` se muestran las primeras entradas de la tabla, entre paréntesis se pueden indicar cuántas entradas se quieren mostrar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Código</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>1: GASTOS DE PERSONAL</th>\n",
       "      <th>2: GASTOS CORRIENTES EN BIENES Y SERVICIOS</th>\n",
       "      <th>3: GASTOS FINANCIEROS</th>\n",
       "      <th>4: TRANSFERENCIAS CORRIENTES</th>\n",
       "      <th>5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS</th>\n",
       "      <th>6: INVERSIONES REALES</th>\n",
       "      <th>7: TRANSFERENCIAS DE CAPITAL</th>\n",
       "      <th>8: ACTIVOS FINANCIEROS</th>\n",
       "      <th>9: PASIVOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Clasificación por programas</td>\n",
       "      <td>77198659.02</td>\n",
       "      <td>73573032.97</td>\n",
       "      <td>613271.26</td>\n",
       "      <td>6294124.25</td>\n",
       "      <td>528.74</td>\n",
       "      <td>33174136.93</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>195100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DEUDA PÚBLICA</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Deuda Pública</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Deuda Pública</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>SERVICIOS PÚBLICOS BÁSICOS</td>\n",
       "      <td>19082029.47</td>\n",
       "      <td>30794122.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>128000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18514157.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Código                       Nombre  1: GASTOS DE PERSONAL  \\\n",
       "0     NaN  Clasificación por programas            77198659.02   \n",
       "1     0.0                DEUDA PÚBLICA                   0.00   \n",
       "2     1.0                Deuda Pública                   0.00   \n",
       "3    11.0                Deuda Pública                   0.00   \n",
       "4     1.0   SERVICIOS PÚBLICOS BÁSICOS            19082029.47   \n",
       "\n",
       "   2: GASTOS CORRIENTES EN BIENES Y SERVICIOS  3: GASTOS FINANCIEROS  \\\n",
       "0                                 73573032.97              613271.26   \n",
       "1                                        0.00               15000.00   \n",
       "2                                        0.00               15000.00   \n",
       "3                                        0.00               15000.00   \n",
       "4                                 30794122.81                   0.00   \n",
       "\n",
       "   4: TRANSFERENCIAS CORRIENTES  5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  \\\n",
       "0                    6294124.25                                        528.74   \n",
       "1                          0.00                                          0.00   \n",
       "2                          0.00                                          0.00   \n",
       "3                          0.00                                          0.00   \n",
       "4                     128000.00                                          0.00   \n",
       "\n",
       "   6: INVERSIONES REALES  7: TRANSFERENCIAS DE CAPITAL  \\\n",
       "0            33174136.93                       80000.0   \n",
       "1                   0.00                           0.0   \n",
       "2                   0.00                           0.0   \n",
       "3                   0.00                           0.0   \n",
       "4            18514157.84                           0.0   \n",
       "\n",
       "   8: ACTIVOS FINANCIEROS  9: PASIVOS FINANCIEROS  \n",
       "0                195100.0                       0  \n",
       "1                     0.0                       0  \n",
       "2                     0.0                       0  \n",
       "3                     0.0                       0  \n",
       "4                     0.0                       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cola \n",
    "\n",
    "Ahora se muestran las últimas entradas de la tabla (tail) mediante ```df.tail()``` \n",
    "\n",
    "De nuevo, entre paréntesis se puede espeficiar el número de entradas que se quieren mostrar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Código</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>1: GASTOS DE PERSONAL</th>\n",
       "      <th>2: GASTOS CORRIENTES EN BIENES Y SERVICIOS</th>\n",
       "      <th>3: GASTOS FINANCIEROS</th>\n",
       "      <th>4: TRANSFERENCIAS CORRIENTES</th>\n",
       "      <th>5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS</th>\n",
       "      <th>6: INVERSIONES REALES</th>\n",
       "      <th>7: TRANSFERENCIAS DE CAPITAL</th>\n",
       "      <th>8: ACTIVOS FINANCIEROS</th>\n",
       "      <th>9: PASIVOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>94.0</td>\n",
       "      <td>Transferencias a otras Administraciones Públicas</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>941.0</td>\n",
       "      <td>Transferencias a Comunidades Autónomas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>942.0</td>\n",
       "      <td>Transferencias a Entidades Locales territoriales</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>943.0</td>\n",
       "      <td>Transferencias a otras Entidades Locales</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>944.0</td>\n",
       "      <td>Transferencias a la Administración General del...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     Código                                             Nombre  \\\n",
       "130    94.0   Transferencias a otras Administraciones Públicas   \n",
       "131   941.0             Transferencias a Comunidades Autónomas   \n",
       "132   942.0   Transferencias a Entidades Locales territoriales   \n",
       "133   943.0           Transferencias a otras Entidades Locales   \n",
       "134   944.0  Transferencias a la Administración General del...   \n",
       "\n",
       "     1: GASTOS DE PERSONAL  2: GASTOS CORRIENTES EN BIENES Y SERVICIOS  \\\n",
       "130                    0.0                                         0.0   \n",
       "131                    0.0                                         0.0   \n",
       "132                    0.0                                         0.0   \n",
       "133                    0.0                                         0.0   \n",
       "134                    0.0                                         0.0   \n",
       "\n",
       "     3: GASTOS FINANCIEROS  4: TRANSFERENCIAS CORRIENTES  \\\n",
       "130                    0.0                           0.0   \n",
       "131                    0.0                           0.0   \n",
       "132                    0.0                           0.0   \n",
       "133                    0.0                           0.0   \n",
       "134                    0.0                           0.0   \n",
       "\n",
       "     5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  6: INVERSIONES REALES  \\\n",
       "130                                           0.0                    0.0   \n",
       "131                                           0.0                    0.0   \n",
       "132                                           0.0                    0.0   \n",
       "133                                           0.0                    0.0   \n",
       "134                                           0.0                    0.0   \n",
       "\n",
       "     7: TRANSFERENCIAS DE CAPITAL  8: ACTIVOS FINANCIEROS  \\\n",
       "130                           0.0                     0.0   \n",
       "131                           0.0                     0.0   \n",
       "132                           0.0                     0.0   \n",
       "133                           0.0                     0.0   \n",
       "134                           0.0                     0.0   \n",
       "\n",
       "     9: PASIVOS FINANCIEROS  \n",
       "130                       0  \n",
       "131                       0  \n",
       "132                       0  \n",
       "133                       0  \n",
       "134                       0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columnas\n",
    "\n",
    "Se puede mostrar el nombre de las columnas de la tabla mediante ```df.columns```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Código', 'Nombre', '1: GASTOS DE PERSONAL',\n",
       "       '2: GASTOS CORRIENTES EN BIENES Y SERVICIOS', '3: GASTOS FINANCIEROS',\n",
       "       '4: TRANSFERENCIAS CORRIENTES',\n",
       "       '5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS', '6: INVERSIONES REALES',\n",
       "       '7: TRANSFERENCIAS DE CAPITAL', '8: ACTIVOS FINANCIEROS',\n",
       "       '9: PASIVOS FINANCIEROS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se pueden mostrar columnas en concreto mediante ```df['nombre de la columna']``` \n",
    "\n",
    "A continuación se ejemplifica con la columna \"1: GASTOS DE PERSONAL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      77198659.02\n",
       "1             0.00\n",
       "2             0.00\n",
       "3             0.00\n",
       "4      19082029.47\n",
       "          ...     \n",
       "130           0.00\n",
       "131           0.00\n",
       "132           0.00\n",
       "133           0.00\n",
       "134           0.00\n",
       "Name: 1: GASTOS DE PERSONAL, Length: 135, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['1: GASTOS DE PERSONAL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se puede mostrar una fila en concreto de la columna especificada, añadiendo el número de dicha fila a contuación del nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19082029.47"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['1: GASTOS DE PERSONAL'][4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostramos las variables estadísticas\n",
    "\n",
    "Mediante ```df_es.describe()``` se muestran las variables estadísticas del dataframe, algo que no se muestra normalmente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9: PASIVOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134.000000</td>\n",
       "      <td>1.350000e+02</td>\n",
       "      <td>1.350000e+02</td>\n",
       "      <td>135.000000</td>\n",
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       "      <td>135.000000</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>663.664179</td>\n",
       "      <td>2.307653e+06</td>\n",
       "      <td>2.268701e+06</td>\n",
       "      <td>18171.000296</td>\n",
       "      <td>1.881666e+05</td>\n",
       "      <td>15.666370</td>\n",
       "      <td>1.047493e+06</td>\n",
       "      <td>2370.370370</td>\n",
       "      <td>5780.740741</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1180.397046</td>\n",
       "      <td>7.959743e+06</td>\n",
       "      <td>7.526748e+06</td>\n",
       "      <td>98585.496389</td>\n",
       "      <td>6.834606e+05</td>\n",
       "      <td>89.988814</td>\n",
       "      <td>3.767106e+06</td>\n",
       "      <td>13615.586295</td>\n",
       "      <td>33205.011077</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>133.250000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>333.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>491.750000</td>\n",
       "      <td>1.203482e+06</td>\n",
       "      <td>1.446545e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.122241e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9231.000000</td>\n",
       "      <td>7.719866e+07</td>\n",
       "      <td>7.357303e+07</td>\n",
       "      <td>613271.260000</td>\n",
       "      <td>6.294124e+06</td>\n",
       "      <td>528.740000</td>\n",
       "      <td>3.317414e+07</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>195100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Código  1: GASTOS DE PERSONAL  \\\n",
       "count   134.000000           1.350000e+02   \n",
       "mean    663.664179           2.307653e+06   \n",
       "std    1180.397046           7.959743e+06   \n",
       "min       0.000000           0.000000e+00   \n",
       "25%     133.250000           0.000000e+00   \n",
       "50%     333.500000           0.000000e+00   \n",
       "75%     491.750000           1.203482e+06   \n",
       "max    9231.000000           7.719866e+07   \n",
       "\n",
       "       2: GASTOS CORRIENTES EN BIENES Y SERVICIOS  3: GASTOS FINANCIEROS  \\\n",
       "count                                1.350000e+02             135.000000   \n",
       "mean                                 2.268701e+06           18171.000296   \n",
       "std                                  7.526748e+06           98585.496389   \n",
       "min                                  0.000000e+00               0.000000   \n",
       "25%                                  0.000000e+00               0.000000   \n",
       "50%                                  1.000000e+04               0.000000   \n",
       "75%                                  1.446545e+06               0.000000   \n",
       "max                                  7.357303e+07          613271.260000   \n",
       "\n",
       "       4: TRANSFERENCIAS CORRIENTES  \\\n",
       "count                  1.350000e+02   \n",
       "mean                   1.881666e+05   \n",
       "std                    6.834606e+05   \n",
       "min                    0.000000e+00   \n",
       "25%                    0.000000e+00   \n",
       "50%                    0.000000e+00   \n",
       "75%                    3.000000e+03   \n",
       "max                    6.294124e+06   \n",
       "\n",
       "       5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  6: INVERSIONES REALES  \\\n",
       "count                                    135.000000           1.350000e+02   \n",
       "mean                                      15.666370           1.047493e+06   \n",
       "std                                       89.988814           3.767106e+06   \n",
       "min                                        0.000000           0.000000e+00   \n",
       "25%                                        0.000000           0.000000e+00   \n",
       "50%                                        0.000000           0.000000e+00   \n",
       "75%                                        0.000000           1.122241e+05   \n",
       "max                                      528.740000           3.317414e+07   \n",
       "\n",
       "       7: TRANSFERENCIAS DE CAPITAL  8: ACTIVOS FINANCIEROS  \\\n",
       "count                    135.000000              135.000000   \n",
       "mean                    2370.370370             5780.740741   \n",
       "std                    13615.586295            33205.011077   \n",
       "min                        0.000000                0.000000   \n",
       "25%                        0.000000                0.000000   \n",
       "50%                        0.000000                0.000000   \n",
       "75%                        0.000000                0.000000   \n",
       "max                    80000.000000           195100.000000   \n",
       "\n",
       "       9: PASIVOS FINANCIEROS  \n",
       "count                   135.0  \n",
       "mean                      0.0  \n",
       "std                       0.0  \n",
       "min                       0.0  \n",
       "25%                       0.0  \n",
       "50%                       0.0  \n",
       "75%                       0.0  \n",
       "max                       0.0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexar el dataframe según una columna\n",
    "\n",
    "También es posible indexar -ordenar los datos- en función de una columna mediante ```df.set_index('')```\n",
    "\n",
    "En este caso, se indexa el dataframe según la columna \"Nombre\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Código</th>\n",
       "      <th>1: GASTOS DE PERSONAL</th>\n",
       "      <th>2: GASTOS CORRIENTES EN BIENES Y SERVICIOS</th>\n",
       "      <th>3: GASTOS FINANCIEROS</th>\n",
       "      <th>4: TRANSFERENCIAS CORRIENTES</th>\n",
       "      <th>5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS</th>\n",
       "      <th>6: INVERSIONES REALES</th>\n",
       "      <th>7: TRANSFERENCIAS DE CAPITAL</th>\n",
       "      <th>8: ACTIVOS FINANCIEROS</th>\n",
       "      <th>9: PASIVOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nombre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clasificación por programas</th>\n",
       "      <td>NaN</td>\n",
       "      <td>77198659.02</td>\n",
       "      <td>73573032.97</td>\n",
       "      <td>613271.26</td>\n",
       "      <td>6294124.25</td>\n",
       "      <td>528.74</td>\n",
       "      <td>33174136.93</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>195100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEUDA PÚBLICA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deuda Pública</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deuda Pública</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERVICIOS PÚBLICOS BÁSICOS</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19082029.47</td>\n",
       "      <td>30794122.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>128000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18514157.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a otras Administraciones Públicas</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a Comunidades Autónomas</th>\n",
       "      <td>941.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a Entidades Locales territoriales</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a otras Entidades Locales</th>\n",
       "      <td>943.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a la Administración General del Estado</th>\n",
       "      <td>944.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Código  \\\n",
       "Nombre                                                       \n",
       "Clasificación por programas                            NaN   \n",
       "DEUDA PÚBLICA                                          0.0   \n",
       "Deuda Pública                                          1.0   \n",
       "Deuda Pública                                         11.0   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                             1.0   \n",
       "...                                                    ...   \n",
       "Transferencias a otras Administraciones Públicas      94.0   \n",
       "Transferencias a Comunidades Autónomas               941.0   \n",
       "Transferencias a Entidades Locales territoriales     942.0   \n",
       "Transferencias a otras Entidades Locales             943.0   \n",
       "Transferencias a la Administración General del ...   944.0   \n",
       "\n",
       "                                                    1: GASTOS DE PERSONAL  \\\n",
       "Nombre                                                                      \n",
       "Clasificación por programas                                   77198659.02   \n",
       "DEUDA PÚBLICA                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                    19082029.47   \n",
       "...                                                                   ...   \n",
       "Transferencias a otras Administraciones Públicas                     0.00   \n",
       "Transferencias a Comunidades Autónomas                               0.00   \n",
       "Transferencias a Entidades Locales territoriales                     0.00   \n",
       "Transferencias a otras Entidades Locales                             0.00   \n",
       "Transferencias a la Administración General del ...                   0.00   \n",
       "\n",
       "                                                    2: GASTOS CORRIENTES EN BIENES Y SERVICIOS  \\\n",
       "Nombre                                                                                           \n",
       "Clasificación por programas                                                        73573032.97   \n",
       "DEUDA PÚBLICA                                                                             0.00   \n",
       "Deuda Pública                                                                             0.00   \n",
       "Deuda Pública                                                                             0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                                         30794122.81   \n",
       "...                                                                                        ...   \n",
       "Transferencias a otras Administraciones Públicas                                          0.00   \n",
       "Transferencias a Comunidades Autónomas                                                    0.00   \n",
       "Transferencias a Entidades Locales territoriales                                          0.00   \n",
       "Transferencias a otras Entidades Locales                                                  0.00   \n",
       "Transferencias a la Administración General del ...                                        0.00   \n",
       "\n",
       "                                                    3: GASTOS FINANCIEROS  \\\n",
       "Nombre                                                                      \n",
       "Clasificación por programas                                     613271.26   \n",
       "DEUDA PÚBLICA                                                    15000.00   \n",
       "Deuda Pública                                                    15000.00   \n",
       "Deuda Pública                                                    15000.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                           0.00   \n",
       "...                                                                   ...   \n",
       "Transferencias a otras Administraciones Públicas                     0.00   \n",
       "Transferencias a Comunidades Autónomas                               0.00   \n",
       "Transferencias a Entidades Locales territoriales                     0.00   \n",
       "Transferencias a otras Entidades Locales                             0.00   \n",
       "Transferencias a la Administración General del ...                   0.00   \n",
       "\n",
       "                                                    4: TRANSFERENCIAS CORRIENTES  \\\n",
       "Nombre                                                                             \n",
       "Clasificación por programas                                           6294124.25   \n",
       "DEUDA PÚBLICA                                                               0.00   \n",
       "Deuda Pública                                                               0.00   \n",
       "Deuda Pública                                                               0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                             128000.00   \n",
       "...                                                                          ...   \n",
       "Transferencias a otras Administraciones Públicas                            0.00   \n",
       "Transferencias a Comunidades Autónomas                                      0.00   \n",
       "Transferencias a Entidades Locales territoriales                            0.00   \n",
       "Transferencias a otras Entidades Locales                                    0.00   \n",
       "Transferencias a la Administración General del ...                          0.00   \n",
       "\n",
       "                                                    5: FONDO DE CONTINGENCIA Y OTROS IMPREVISTOS  \\\n",
       "Nombre                                                                                             \n",
       "Clasificación por programas                                                               528.74   \n",
       "DEUDA PÚBLICA                                                                               0.00   \n",
       "Deuda Pública                                                                               0.00   \n",
       "Deuda Pública                                                                               0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                                                  0.00   \n",
       "...                                                                                          ...   \n",
       "Transferencias a otras Administraciones Públicas                                            0.00   \n",
       "Transferencias a Comunidades Autónomas                                                      0.00   \n",
       "Transferencias a Entidades Locales territoriales                                            0.00   \n",
       "Transferencias a otras Entidades Locales                                                    0.00   \n",
       "Transferencias a la Administración General del ...                                          0.00   \n",
       "\n",
       "                                                    6: INVERSIONES REALES  \\\n",
       "Nombre                                                                      \n",
       "Clasificación por programas                                   33174136.93   \n",
       "DEUDA PÚBLICA                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                    18514157.84   \n",
       "...                                                                   ...   \n",
       "Transferencias a otras Administraciones Públicas                     0.00   \n",
       "Transferencias a Comunidades Autónomas                               0.00   \n",
       "Transferencias a Entidades Locales territoriales                     0.00   \n",
       "Transferencias a otras Entidades Locales                             0.00   \n",
       "Transferencias a la Administración General del ...                   0.00   \n",
       "\n",
       "                                                    7: TRANSFERENCIAS DE CAPITAL  \\\n",
       "Nombre                                                                             \n",
       "Clasificación por programas                                              80000.0   \n",
       "DEUDA PÚBLICA                                                                0.0   \n",
       "Deuda Pública                                                                0.0   \n",
       "Deuda Pública                                                                0.0   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                                   0.0   \n",
       "...                                                                          ...   \n",
       "Transferencias a otras Administraciones Públicas                             0.0   \n",
       "Transferencias a Comunidades Autónomas                                       0.0   \n",
       "Transferencias a Entidades Locales territoriales                             0.0   \n",
       "Transferencias a otras Entidades Locales                                     0.0   \n",
       "Transferencias a la Administración General del ...                           0.0   \n",
       "\n",
       "                                                    8: ACTIVOS FINANCIEROS  \\\n",
       "Nombre                                                                       \n",
       "Clasificación por programas                                       195100.0   \n",
       "DEUDA PÚBLICA                                                          0.0   \n",
       "Deuda Pública                                                          0.0   \n",
       "Deuda Pública                                                          0.0   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                             0.0   \n",
       "...                                                                    ...   \n",
       "Transferencias a otras Administraciones Públicas                       0.0   \n",
       "Transferencias a Comunidades Autónomas                                 0.0   \n",
       "Transferencias a Entidades Locales territoriales                       0.0   \n",
       "Transferencias a otras Entidades Locales                               0.0   \n",
       "Transferencias a la Administración General del ...                     0.0   \n",
       "\n",
       "                                                    9: PASIVOS FINANCIEROS  \n",
       "Nombre                                                                      \n",
       "Clasificación por programas                                              0  \n",
       "DEUDA PÚBLICA                                                            0  \n",
       "Deuda Pública                                                            0  \n",
       "Deuda Pública                                                            0  \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                               0  \n",
       "...                                                                    ...  \n",
       "Transferencias a otras Administraciones Públicas                         0  \n",
       "Transferencias a Comunidades Autónomas                                   0  \n",
       "Transferencias a Entidades Locales territoriales                         0  \n",
       "Transferencias a otras Entidades Locales                                 0  \n",
       "Transferencias a la Administración General del ...                       0  \n",
       "\n",
       "[135 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre\n",
       "Clasificación por programas                              77198659.02\n",
       "DEUDA PÚBLICA                                                   0.00\n",
       "Deuda Pública                                                   0.00\n",
       "Deuda Pública                                                   0.00\n",
       "SERVICIOS PÚBLICOS BÁSICOS                               19082029.47\n",
       "                                                            ...     \n",
       "Transferencias a otras Administraciones Públicas                0.00\n",
       "Transferencias a Comunidades Autónomas                          0.00\n",
       "Transferencias a Entidades Locales territoriales                0.00\n",
       "Transferencias a otras Entidades Locales                        0.00\n",
       "Transferencias a la Administración General del Estado           0.00\n",
       "Name: 1: GASTOS DE PERSONAL, Length: 135, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')['1: GASTOS DE PERSONAL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un gráfico a partir de \"Nombre\" y \"1: GASTOS DE PERSONAL\"\n",
    "\n",
    "Una vez indexados los datos según \"Nombre\" y \"1: GASTOS DE PERSONAL\", mediante ```.plot``` se muestran los datos en un gráfico\n",
    "\n",
    "Añadiendo ```title=``` como argumento de ```.plot``` se pone un título al gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gastos de personal'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DEDPtJyqEEKJMRNTM3gRsd87N28d515nZXDOb29DQUHB8ciwSQggBZSKiwGuBy81sLfAb4Hwzuz33JOfcLc652c652fX19QVHJsciIYQQUCYi6pz7gnNuinNuGvBO4DHn3FWlik+ORUIIIaBMRHSgiWvtXCGEEEDFYCeg2DjnngCeKGUc3pxbyhiEEEIcCqgnWgAy5wohhACJaEH4/UQlokIIMdSRiBZALIa8c4UQQkhEC0HmXCGEECARLQjNExVCCAES0YLQfqJCCCFAIloQfgF6iagQQgx1JKIFEDMjWfJl7oUQQhzsSEQLIB7TLi5CCCEkogUhc64QQgiQiBaEHIuEEEKARLQg1BMVQggBEtGC0LJ/QgghQCJaEDGtWCSEEAKJaEHEzZCGCiGEkIgWQExjokIIIZCIFoTmiQohhIAyEVEzqzaz581sgZktMbPrSxlfXAvQCyGEACoGOwFFogM43znXbGaVwNNm9hfn3N9LEVksJu9cIYQQZSKizjkHNIevleGvZCoX12ILQgghKBNzLoCZxc1sPrAdeMQ591yp4tJiC0IIIaCMRNQ5l3TOzQKmAGeY2Ym555jZdWY218zmNjQ0FByXnydaeFqFEEKUB2UjohHOuT3A48AleX67xTk32zk3u76+vuA45FgkhBACykREzazezEaHzzXAhcDyUsUXM+RYJIQQojwci4BJwK/MLI5vGNztnLuvVJHFYgb4uaLRZyGEEEOPshBR59xC4NSBii9uXjiTzhFDIirEUGJ7UzsTRlYPdjLEQUJZmHMHmqj3KZOuEEOLJZv3csZX/8or25oGOyniIEEiWgDxyJwr5yIhhhTbGtsB2Lq3fZBTIg4WJKIFkDbnqicqxJCio8vPbWvrSg5ySsTBgkS0ADKORYOcEJFm5fZmlm1pHOxkiDKnI+Ff+naJqAhIRAsgHnyJZM49ePj6A8v4jz8sGuxkiDInEk+JqIiQiBZANCaqBRcOHpraEzR3JAY7GaLMyfREZYYSHoloAWTPExUHB+2JpMapRMnpSPgyprImIiSiBZA9T1QcHLR3JdU7ECUnKmMy54oIiWgBaJ7owUd7V0oVmyg56omKXCSiBRD1ROWde/DQ1pVMTz8QolREZUxlTURIRAtAjkUHH+1dSTqTKVkHRElpT8g7V3RHIloAMucefHRorEoMAFpsQeQiES2AtDlXPdGDgmTK0ZmUiIrSo8UWRC4S0QKIh1xTT/TgIHL2APUQRGnJOBZpTFR4JKIFYFo796Aie2qLprmIUqIpLiIXiWgByJx7cJFdoalyE6WkQ45FIgeJaAHE5Vh0UJFdoWWbdoUoNhoTFbmUhYia2RFm9riZLTWzJWb28VLGF9N+ogcV2eOgbZ0y54rS0S7vXJFDxWAnoEgkgE875140s5HAPDN7xDm3tBSRZfYTLUXoor90HxNV5SZKR8acq5dfeMqiJ+qc2+KcezF8bgKWAZNLFV9M3rkHFR3ZY6Iy54oSkp6P3KlyJjxlIaLZmNk04FTguTy/XWdmc81sbkNDQ8FxyLHo4CJbONtUuYkSku6JqrEmAmUlomY2Avgd8AnnXGPu7865W5xzs51zs+vr6wuOJ76PMdGm9i6ZFQeQbubchMxsonREPdGupCORM57zxMvbuewHT8m5bYhRNiJqZpV4Ab3DOff7Usa1r2X/rv7lC3zpj4tLmQSRRTfvXDVeRAnpSKSoCO9/boNtwYa9LNncyCtbmwcjaWKQKAsRNb/6wc+BZc65/y11fPsy527e08ZTKxpwMvcOCNk9UZlzRalIheUlRw+vBHqWteaOLgCWbN474GkTg0dZiCjwWuA9wPlmNj/8XVqqyDLzRPP/3tKRYFtjB5v3tpcqCSKLdjkWiQEgmiM6qsaLaO6QTVN7AoClW3qMJIkypiymuDjnngZsoOKL9bHsn3OO1tBCnbduN5NH1wxUsoYs0Zy9yrhp6oEoGdFY5+jhVUBLTxHt8CK6ZLNEdChRLj3RAaUvx6LOZIpEENcX1+0e0HQNVTq6kphBXXWlHLpEyejZE+3eYIt6osu2NJLS9Lchg0S0APraxaW1I1OJz5OIDgjtiRTDKmJUV8a1kowoGVEDbXQQ0dyy1tzux0RbO5Os29U6sIkTg4ZEtABifTgWtXT61ujEumEs3dJISzDxiNLR3pWkujJOdWUsPQVBiGKT7okO731M9IixfvhGzkVDB4loAfS1AH00HnruzHqSKceCjXsGMmlDkvauJNUVcaor4zLnipIRNdBG11QBeXqiHQlOmzqGipixVOOiQwaJaAH05VgU9TzPOXo8oHHRgaC9K0VNVZwamXNFCWlPOxb13hMdW1vF0RNGyEN3CCERLYC+HIuiuWOTRlUzc8IIjYsOAO1dyfSYqHqiolSke6J5RDSVcjR3JBhZXckJh4+Sh+4QQiJaALE+dnFpCSJaO6yCU6eOZtEmjY2UmvZEKj0mqikuolREU1zq8njnNgdfiJHDKjj+8DoamjrY3qR54kMBiWgBpHdxydMTbQ0v0/CqOBNGVrOrpVMrF5WY9s4k1ZUxhqknKkpI5FiUzzu3OUxvGVldwfGT6gBYtqVpgFMoBgOJaAGkl/3LOybqX6zhVRXU1VSQcpneaTLluOR7c3hw8ZaBS+wQoD3hvXNrJKKihERlK9+KRdEc0RHVFRw+uhqAhqaOAU6hGAwkogXQt3du6IkOi1NX7V+2xjY/f2xvWxfLtzbJxFtkMt65Me3iIkpG1BMdXlVBVUWse080rJs7srqyx3svyhuJaAHE+nAsSvdEK+PpsZOolbo3vFSNbZo7Wkzau1JUV8aorlBPVJSOaIegYRUxqiu6z0lujHqiwyoYWV0RjklEhwIS0QLoaxeX1s4EwypiVMRjPV6mtIjq5Soq0WILNVV+iovGoEUpiKwcwypjvqx19hwTrauuoCIeY8SwivT7LsobiWgB9LWLS0tngtphXjzzmXOzv4vikFmxKI5zfv1iIYpN1PMcFi3skcg/JgpeTGVxGhpIRAugr2X/WjuSDK+KAxlX+J49Ub1cxSSa4jKswhdnTXMRpaAjkaQybsRj1sOJLXtMFPy7L4vT0EAiWgB9ORa1dCaorcq0RiEzBtqonmjRSaUcnQk/JloTGi8aFxWloL0rxbAKX8aGVcZpy2qsNbUnMPO+EOBFVObcoYFEtACChva6du7wYf5FGtmLObdJPdGiEXlMVlfGqa6QiIrS0ZFIpq0dNZWxHlNcRgyrSDsd1lVXqrE8RCgLETWzX5jZdjNbPEDxEbPeHIuS6Z5oVUWM6spYerPeRjkWFZ1omkF1WPYPZM4VpaEjDBsAPZaYbGpPMDL4QoCfS6rG8tCgLEQUuBW4ZCAjjMes1wXoozFR6N4ijXqirZ1JuuT8UhSiiixa9i/7mBDFpCPsWwvkHRONLE8AdTXyzh0qlIWIOufmALsGMs6YWS/L/iXT3rnQ3cEg+6VSK7U4ZItoTeglaCcXUQrau5JUBRHN3QC+qT2R9swF33hu7kiQUGO57CkLER0M4jHLu+xfa2ci7eAC3V3ds824Gi8pDpHpNlo71x+TiIri05FIpctY7mYHfgeX7ubc6Lgob4aUiJrZdWY218zmNjQ0HFBYcbP880Q7ktRmi2hOTzRyStK4aHGI5uoN62bOVetfFJ+OriTVWT3R9s6ejkUR0fQ2mXTLnyElos65W5xzs51zs+vr6w8orFjMejgWJVOOtq4kw6syL9PI6spuy/5NGlUDyJxbLNLm3IqMOXegeqLtXUm+8PtFWmh8iNDerSfac7GFbmOiOdPbRPkypES0mORzLIrGSGqH5ZpzQ0+0tYspY7yIypxbHKJVZGqq4lneuQMjogs37uXO59fz2PJtAxKfGFw6upLdHIu6ki495tnU3pXXnCuLU/lTFiJqZncCzwLHmtlGM7u21HHGrOd+oq0d0V6iPR2LUilHU0eCI8YOB/RyFYuMY1FswEU02nR57c7WAYlPDC6d3aa4hKGDRIrORIqORKrbFBeZc4cOFfs+5eDHOfeugY4zZj0di6J9Q7v3RCvpSjoamjtwDo4YE0RUZp6i0JZlzo0qtrZexkSXbWlk6952Xv+qCUWJe3ujN+Oul4gOCdpzeqIAbZ1JumK+vHXzzq3RdmhDhbLoiQ4G+cy56b1Eu42J+s8bd/uKdtKoamKmnmixyHjn7nvFohsfW8FnfrugaHFvD2Oha3e2FC3MiNbOBEs3NxY9XFE42fNEsz3BI/+G7DFRmXOHDhLRAsk3T7Q16olW9WyRbtjVBsCo4ZWM1JJgRSPbnBuLGVUVsW4OH9ls2t3GzpZOWoo07SAy567b2Vr07dd+9cw6rvjh0zSpEj5o8CLqxTPbia0pLD6f7Z1bWxX3jWVZnMoeiWiB5JsnGlXOw3MciwA27PI90VE1ldTVVGgnlyIRCWZ6rCpns+RsNu1p6/b/QIm8cps7Euxq6SxKmBErtjfRlXSs3SFT8cFCRyKZHjLIXmKyKWsv0Qgz0yL0QwSJaIHEY0buWgt99UQ37g490ZpK6qor1cMoEu3pPR7DWFXOZsmZ85LsaPZCFzVoDpTtjR3pSrXYzkXROOvqHc1FDVcURjLl6Eq6Hj3Rtq5kekPu7DFR8O+6zLnlj0S0QPJ556Z7ojlr5wJs2J3VE62ulJmnSETTDizs8Zo7fy8iu/cZNWgOlIbmDmYdMRqA9buKOy66Lgj9qoae4W5vamftjuKPw4re6Ugv6hH1RDPrNDfl7CUaoZ1chgYS0QLJZ86NeqLDq3qac6OKu66mkpHVFWqhFon2rmTatAbeSzefY9HmLBEtRk+0M5FiV0snpx85BjOKanZt7UykTcVr8ojl9X9eynt/9ULR4hP7JhoiyF6xCHJ6osO690S1CP3QQCJaIDHr6Z3bErxzcxegB1+Jx2NGbVXczx3Vy1UU2rtSadMaQHVVPO8Ul02hETNiWEVReqI7mr3ITR49nMNH1bC+SCZiyDihxQxWN/Q05768tYk1O1rymq1FacheXhLoNie5Me2dm8+cK4tTuSMRLZB4nmX/WjuSxCwzPgf+c1U8RiLlGFVT6R0OqvVyFYu2royzB/ieQr6e6KY9bcQMTp06Om1aPxCi6S0TRg5j6tjhRZ3msi6EderUMazZ0dLN8zeZcqzb2YJzsCqPwIrS0JFn7B28iDZ3JKiMW7f3HmTOHSpIRAsk3zzRls4EtVUV6fE5iLz0fAs1mjtWV1NBc0ci736kon/0MOdWxunIJ6K72zisrppp42oLNuf+4aWN3PXCegC2N/rpLRPqhjFt/HDWFdGxKOrVvv7Yelo7k2wNcYGfb9yV9OVGIjpwdCQiEc14gUPknev3Es1+74ED9s5NJFOc883HuOO5dQWHIUqPRLRA/DzR7sdaO5LdprdERM5F0fho9L15CPdGizUlJHtRcPAOH/n2E920p43JY2qYMqaGxvZEQZXbjY+t5PuPrgCye6LVHDmull0tnUUb5163s5W66gpOnToGgDVZzkWrsz6v2CYRHSiy5yNDpicajYnmjoeCbzR3JFIFL0O5sqGZjbvb+O3cjQWmWgwEEtECyetY1JXsNr0lIhorqUv3RIf2aibrdrZwxlcf5dGlB75we3vW9lTgpx7k2wpt0542Jo+uSa9dvLGfJt3mjgRrdrSweW87O5s72N7UgRmMH1HFkSHMYi3/t25XK0eOq2VGfS0Aq7Kci6Le57jaKlZul4gOFD17ov7/zU+u4uGl23qMh0Km0Vzojk1LNvkVq+Zv2MPWve37OFsMFhLRAonncSxq7Ujk74kG0YzMudELt6/e0B3PreObDy4vRnIPKp5ZtZNEyjFnxYHt6Qphj8ccc25uyz+Zcmzd287ho2vSu+j017loyaa9REOTizbtpaGpnXG1VVTEYxw5zotdsUy663e2MHXscA6rq6amMt7NuWj1jhZG1VRy2pFjWClz7oCRO8UlFjM+c9ExvG5mPRccN5HrXjejxzUHugj94s17iYcNiB9Zpp2CDlbKYgH6wSAWyzNPtDPRbd3ciMh8mx4Trd6/nuhP56xm8552Pnb+zLT5aP3OVqaMqSEWsz6vPZiZu3Z3t/8HQntXqrtjUR4R3d7UTiLlmDymJr0BQH/HRRdt2pv+vGRzIw1NHdSPrAbgyHE+zGI4FyVTjo2723jjSZMwM6aPr+1mwl3T0MKM+lpmThjB48u305VMURlXW7jU5DoWAXzk/Jl9XnOgFqclmxs5Zcoo9rR28fCSrbznzCMLCkeUFr19BdLbPNHaqnw90Z6ORdD3upobdrWydmcrnckUz6/dBcDK7c2c9+3H+e28DUW5h8Fi3jp/P8u3NtK8H+vYfvPB5Vz83Tlc/N05fPjXL3YTyfZEvp5od3NuNL1l8ugaRg+v7HOay6NLt/G5exb2WAt38aa9TKwbxvTxtSzauJftTR3UjxwG+ClNR4yt4cHFWwtyFkulHJ+6ez5Pr9jB5j1tJFIubSKeUV/bba7o6h3NzBg/gpkTR5AInrqi9OQuL7k/pBvL++iJPr9mF8u2dN9sIJVyLN3cyImTR3HhCRN5dtVOzTk9SJGIFki+BehbOhIMz+NgEK1kUtePnujfVu4AwAyeDmbP+xZuJuXgT/M3H/gNDBINTR2s3dnKuTPHk3Lw0vq+e6O7Wzr56ZzVmMERY2u4f+GWbiZuPyba3bGoM5nqJmbRakVTxtRgZkwZU9PrmOiNj6/krrkbWJKzg8qiTXs5afIoTpw8ikWb9rK9sYMJQUQBPnPRsSzatJfbnl2733kR8dyaXfz+xU188Q+L0mOeU8dFIjqCjbtb6Uj4qRTbGjuYUV/L0fUjATkXDRT5eqL7YlTNvodtGtu7uPbWF/jYnS91a7it29VKc0eCEw8fxUXHH0Yi5Xhs+TZWbGti0ca9vYYnBh6JaIH01hMdnqelGjkYjMp1LOrj5Xp65Q4mjBzGmdPH8fTKnQD8ZdFWAP6+eic7w2T/Q41567xoXnvOdGK2b5PuXxZvJZFyfPttp/Czf301V589jV/+bS2PLfdjRG2dybSpGzJrmv512bZ0pRT1Og8f7cdDp4wZnrcnunZHCws27AHgT/M3pY83dyRYvaOFEyeP4qTJdWza08a2pvZuInr5KYdz7szxfPvhV/rtBHLvgk3EY8b6Xa38z0MvAzA19ESPqq8l5fx4a+SlO2N8LUdN8OOwci4aGHIdi/aHTGO5d2vL7X9fR1NHghXbm3luza708SWbvVAef3gdpx4xmvqRw/jkXQu48LtzuPyHT/Py1qZCbkOUAIlogfTWE63N0xPt4Vg0rAKz3r32UinHM6t2cs7R4zln5niWbWnkudU7eXlbE2+fPYWUg4eL4Nk6GMxbt4uqihhnHTWOYw+rS4tqb9y7YBMz6ms54fA6AD7/xldx3KQ6PnX3Av7t/+bS3JFIO3sAvPHESUwfX8t1t83jPT9/no27W9m8p40xwyvT49VTxtSwYVfP7cv+vGAzZnDKlFHcu2Bzuje7bEsjzpHuiQI4RzcRNTO+8uYT6UqmeMctz/KuW/7OJ37zUjdrw/bGdva2dm84dSSSPLBoK5efcjhnHzWOJZsbqYwbk0Z5wT/2MN/jfGz59vRi9DPqRzC8qoLJo2tYIREdEHKnuOwP+2ost3cl+cXTazlzxljqqiu4/e+Z+aCLN/lycMzEkcRixpcuO46rzpzKN/7pJGqrKvj+X185gLsRxaRsRNTMLjGzl81spZl9vtTx+WX/Mt+dc74nmm9MNMexKBYzRgzrff3cpVsa2dXSyTkzx3PuzPEA/Ne9SwD45IXHMG3ccB5YtKWYtzNgzF23m5Mnj2JYRZxXTxvDS+t3k0jm37ps6952nluziytOmdxtgfmb/vlUjp04ko272zhuUh3nHD0+fc3UccN56BOv47/+8XgWbNjDW370DM+v2cXk4JULcMTY4bTkLGLgnOOP8zfx6mljufbcGWxr7OD50DOIzGfZIgowoa66W3qPHFfLt648mYkjq0mkUty7YDNfvW8Z4E3KF39vDq//zhM8vnx7+ponX25gb1sXV8w6nC+88TifvjHD016ZrzqsjvNfNYEfPraSuWt3Y5ZxZJo5cYR6ogNEIT3R6so4VRUxFm7cw18WbeH+hVv484LNPLJ0G43tXdwzbyM7mjv4+BuO4W2zj+DBxVvTe9Qu2byXYw8bSVUwH18xazJfefNJvPOMqbz3tdN4YNHW9KbtHYlk0fezFftPWXjnmlkc+CFwIbAReMHM7nXOLS1VnPEY3cy5nckUiZTL2xOdUV9LZdzSJjqgz51covHQ1x49nvEjhjGqppLlW5s4bepoJo2q4Y0nTeKWOavZ3dLJmNqqbtc+s3IH1/95KecdW8+nLjoG5+Abf1nOc2t2ccMVJ/DqaWOLcfvd6EykuOnxldy/cDPO+fVFP3TeUbzp5EndVnFp70qyeNNe3nvOdABOP3IM//fsOpZvbeomThH3hfAun3V4t+NH1Y/grvef1Wt6qipiXPPa6Zxz9Hiu/uULrNjezMUnTEz/fvZR46iKx3j/bfO4/X2voa66kqVbGlnV0MJ7z5nOBcdNYHhVnHsXbOKso8alnYoi0Zw2bjhrd7Z264lGXDFrMlfMmgz4fL/5yVVccPxEbnpsBV1Jx+TR1Vxz6wtcffY0PnnhMfxpwWbG1Vbx2qPHUxmP8cHzjuo2xgvwxUuP45LvzeH259YxZUxN2rnl6PoRPLtqpzx0i4hzrsfKQ5CZ4lLVjzFR8M5sDy3ZxkNLuluO/DKBcWYdMZozZ4xlYt0wfv70Gu56fgMfOf9olmxu5MLjJuYN89pzZvDLZ9byv4+8zOxpY/nhYys57vA6bnrXqT0adqL0lIWIAmcAK51zqwHM7DfAFUAJRdTY29bF/Qt9jzBafD5fT/TkKaNZfP3F3VqxI6srWNnQzP0Lt5BIpWhsT/h5plVx7lu4hZkTRjAxvBBnHzWOvyzeyqUnTQLg0hMn8eMnVvHDx1emV7UBP974i7+tYfyIYfxkzmrmrNhBVzLFyu3NjB9RxTt+8iz/du4MTp4yumj50JVMcfOTq1i+tYlzZ45n9HC/CMBH73yJBxZt4bKTJ2H4Smn9Lr9k3ewjvZDPDoJ+5/PrOfuo8T3C/u3cjZw8ZRTTx9cWlLaZE0fyhw+fzafvXsDrj52QPn7cpDp+fNVpfOD2eVz9i+e59pwZPLhkKxUx49ITJzG8qoKLjp/IA4u28tqjx/PCul2clCXyJ04eFUS07wrrExfM5NFl23j/bXNJObj5qtM579h6vvGX5fzq2bX8af4mWjqTvPPVR6RF8HOXvKpHOEdPGMFVZx7Jrc+sZfr4Eenjpx05hp89vYbz/ucJ3n3mVI4cW1g+DTQp59jZ3MHmve1pU2dlPMZho6o5fHQ1VfH97+0Vi9UNzTy8dBvLtzZyxvSxXHDcRCaMrCblHIs27eXe+ZupqoilLQT7y+8+eDZb97Zj5q1XMfOrdT26bBvPrNrJZy8+FjNjRv0Izjl6fPpd2tXSyYmT6/KGOWp4JdeeM53vPbqCR5dt5+yjxvHS+j1cduPT/PtFx+ZtyGdTETcuPuGwft2H6J1yEdHJQPa8j43Aa3JPMrPrgOsApk6dekARjqsdxtbGdj786xe7HT+sl5ZgrhloypgaHl22vcf1Ee//h8zk7YtOmMhjy7fzxiCiJ06uY0Z9LT97eg2wptt1V505lS9eehzPrtrJZ+9ZSEXcuO3aMzh16hhu+PMSfjJndX9vdZ9MGDmMn//rbN4QWs6JZIqfPrWG7z7yCn9ZvLXbuVUVMWYf6YV/8ugapo+v5Y7n1nPHc+vzhn395SccYNqque3aHkWBNxw3kRvfdSof+fVL6Wdw8QkT0z37t54+hT/O38xHfv0SAO9+TWaO3tlHjefJVxqYUNezJ5pNdWWcb7/tFN5+87O879zpXHKir7i+fPkJXHn6FL72wDL+vnonbz1tyj7v4xMXzOS+hZs5OUvM33jiYfzkPadz69/W8q0HX95nGAcbwypiYVMGby7d0zq4UzhOnTqad50xlWdW7eT6P2fa35Vx46yjxvO20/f9nHIZW1vF2BxrEcBrZozrceyGK07gu4+uSHvjz+7DanTtOdPZ09rFhcdP5LVHj+eVbU184LZ5fPZ3C/eZppHVFRLRImLlYEs3syuBS5xz7wvf3wO8xjn3kd6umT17tps7d27BcXYlUz32eqyMx5g2bnhec1AubZ3J9G4iMTPqqisYPqyCts4kLR0JpoypoSL0Tpxz7G3rYvTwzMu4t62LbY3dvUCHV8WZMiZjMm7pSGBGtwUgNuxqzbu27IFwxJjh3TxkI3Y2d7AzZ43c0TWV3UxOe1u72NaU35s1HjOmj6st6cISDU0d7G71aZw6dni3eYDrd7bSnkgSC4seRL2QVMrR3JlIj3Xvi2iB8lzyPdd9hVNTGU+Xi2w27WlLbwp/KDC2topxtVXd3pXWzgRb9/qFMQYjPeNHZBpF2fk5aVR13udXKlIpx86WzvQ85P2lI5Hcr1WzYgZHTxhZUNrMbJ5zbnZBF5cp5SKiZwFfds5dHL5/AcA59/XerjlQERVCiKGGRLQn5eKN8AIw08ymm1kV8E7g3kFOkxBCiDKnLMZEnXMJM/sI8BAQB37hnFsyyMkSQghR5pSFiAI45x4AHhjsdAghhBg6lIs5VwghhBhwJKJCCCFEgUhEhRBCiAKRiAohhBAFUhbzRAvBzBqAdfs8MT/jgR1FTM5AcCimGQ7NdB+KaYZDM91K88AxHqh1ztUPdkIOJoasiB4IZjb3UJtwfCimGQ7NdB+KaYZDM91K88BxqKa71MicK4QQQhSIRFQIIYQoEIloYdwy2AkogEMxzXBopvtQTDMcmulWmgeOQzXdJUVjokIIIUSBqCcqhBBCFMiQE1EzqzWzD5rZIXnvZvZ2M5s22OkAMLPRZvbBAq+92Mxm7ee5HzazEYXEU2rMLB7Sl3839jLkYCqDpcDM3mdmve+IfRBgZtPM7J8HOx1iP0TUzA4zs9+Y2Sozm2dmD5jZMeEhLi4k0hDG6PD5Y2a2zMzuMLNrzGypma0I8X0/bG2Gmc0ys0vD56SZzTezxWb2WzMb3ks8l5vZ57O+VwA3AU8D15nZvxSS/v28x1lmdoOZHV/g9T8zs5dyjl0FHOmcW3sA6Sr4ueXhB8BiM3uzmTkze1Uf8T5hZrPD5/OBi4F54TkuMLMXzezs8PvhZvZXM/uBmf0E+BfnXHN4nreY2X3hvGYzeyacf4+ZrTWz8eG3Z8L/L4b/6fJTKGY228xeNrOFZvbJcPjbwDLnXP6dxXsPa4SZ/TiU8xfDu/WomW3qq4GXfY856dpjZqtDOM+a2VvCbzeY2QX7kZ7zzOy+KL+yjj9jZs1Z35cBrwVujZ5nOH6umS0Jz7Mm6/itZnZlTpjpMpivPIZ7PDXP8Teb2QVm1hbiWWpmN0f5Fe7h7D7u8XIz68j6frWZHZ5zzueANufcrj6yK1/YT5jZerPMLuNm9sfsvOsvufVX1vE48EP8+/PMAYSffif7OOe86H3bzzDzPc8vm9ln9nFdj3ISjs82sx9kpaXX57sfaRttZh8q9Ppecc71+gcY8CzwgaxjpwDnAtOAxX1dvz9/wHJgSojreeCacDwO/Bz4n/D9auCm8Lk56/o7gM8caDr2I50V/Tz/amAZcCUQL3V8vTy7WJ7jxXpuE4HLw+e7gKeA6/s4/wlgds6x7Od4MfBknut+Gj338P084L7c68P3tcD4fHFkl5++8mcf93wYsLJI5ek3wNeiNAATgN3A34HX91Ymcu8x3MeLwKasY0cCH+1PmYryNTdP8zynJ4DZuc8TuBm4Ks+1vwKu7K0M5iuP4R5PzXP8VuCDWddWAHOAfwqfv9xbXRDdey/3st/vWm/nhrAWAueE76OB5/Ll54HEE347FnhdEcpgt2fYV7noR5j5nmevzyXn2V65j3P2GU5/01aMv31Fej4wZ18JCp+fCi/zi8DZ4fikUMjnA4uBc8PxtfjVL24GOoFFwI+Al8kI5UTgz0Ai/L4VaAD2AilgO77CWQmsAL4LJMNvCeAy4BqgCfgbsBRoDvE9Fc7/THh4ifBbKvz+FuD3QCMwD3gJ2BU+J8P5W4HjgTdkHUuEa6aEYy4rrW8BVgEtQBewIeTVK8D94bpOoA2YC+wBWoE/4ldW6gI6Qh68D3gkhN0U4moD/hLy8K4Q3oIQ9xqgHdgZvreFsP43fG8J6VgCbAvnz8c3YtYC68OxVeG+VwKPhXAaQ/zNIcwXw3W/wq/KkgrntYZ0fyqc35D12+/CZxfi/2M4vznEtzbr2XSG+B8J57vwbNeGe2wMz8Hhy0oS2JJ1rgv3sTnkR1c4vwHYGO6zM9xLEtiEb9x9JYTvwn1dii8XnVnP4bfA10O6nwBWZ+VZJTArpG9NyPOxWZXZPfhnfifeC/Lf8ZvNbw9ht5IpB+tDWtrCsZZwrxvCsd3AKSHs6Fgq/P0A3zBuzcr/rnD/UZndCvw6pCnKs0S4JvtZv4AvM51kynoz8Eb8O7Y8nL8+5HcqxBW9a8tDnnWF85L4598Z8rgr3OcvwzNOhXPagevDfbeGv/vIvPt7gAvC9XeE/IvyyAHfCMcdmTrjmHDe0vBc3x/C+U98Y3hTOHc1Peu7O8Jz3R7yfn7Iz00hPcvx5TVq/J0efluL3wN5Q7ifpnDfi4FPh2M7Qng/JPNOrwT+FsJqDc9zRbi/J/B1wM1kGmg/xtcpS8hq6NKLiAKXhDS/iC8vUbprgV/g34eXgCv6K6LAv+HLzQL8ez88S0RvDul8BXhTtoiHcLeGfJuP78j9I76h8hLwKDAxK75fkHkHPxaO/wZf3ueT6ZxF79nCKG/Cfd4f0rgYeMeBiOjHgO/uh4gOB6rD55nA3PD508B/hM9xYGS2iOYI6sfwBSoS0buAT4QMmoVvgd4EjMW/hF8JGfJgyIhNwLHh2v8OGX41/uX9HV6QbwAeB96LLySRiCbxYn88vqC24xsA/xfiOA3/wu0BjgsPaCH+5doQfvvXcP6TwJfwhX87oXUF1AETwueHwj18DP+SJoDPhvRuwvcknsBXAt/Gv5Dzw99TIU1fC/FGFfuNZCqqh8i0iE8K55wX0vQAvmCsw1eUX8I3KBbhK5A5wLasl++/gWfIvPRLw3O5Ey9C94RwLsZXVG8O6ViDL5xfDfmWwIvoopDet5CpeKNK+E58RbAoHJsLfD9cm8SXgaZw7v34ijwBfBj/vHeHfPllyN9XwrkLQ7p/FeK7INzzVfjy9D8hj+fgK/FFIc2vD7/XAfXAO/Hl4zZ8efpGuNeLQ/yfxb/Au4CL8OW6ncx7sjz8XR7+fy+rMlsKvCfEtQPfAzd8JdEe0nwNmWd9XIj7LnyjxRHeVXyFsgQv3J34RkRVCHcncHTI5/UhH3eHND8a8nMHvgH9FBkBfU84zwH/HPK+Bfh/ZBq6v8GXq/vCb7MIPYyQn7vwjahESMdt4Vgr/j14gEx53ogv/0uAnwDvxpenB8KxzSF920P44/Dvx6/wdcNHw/Vbge/gy/CvQ36MBd6EL1OX48vlE/iyNR54VbhuOfBwuO+leFFZh6/jovruS/h344nwXDqBo/BltzHkQyyk+W/hmTwD/Az/vr8jxPvREMaTwM9C2BuBe8LnZcCWrM7Ng+FzM74Xfl6I+0F8XfsImbpnbFYd/ARwcm8iClTj67SZ+PJ3NxkR/RrB2oDvab+CXwYwVxcioYr+tpIR0XFZ536FYDHBl5MHQ17NDPdeTXfL05fJ6okCY8jMMHkf8J2s854BhoXnuTPk+zSyBB7/jt4S7jOGL7evA94K/DTrvFF96WSxNuWuBG4y7yiSxLfqwBf6X5hZJfBH59z8foR5PvAveHGKWszghacW+By+UDTiW2aLgMct4xBQGf6/gm9ZWTj31/iX96asuJLAjc65pWHcNOac22JmKXxFcxj+RRqDf5Fi+ILbGOI9HF8hLwROwD8swu8Ro4A7zOx0fOFweNGuD58fD9c+7JxbF4ZWYiH9DfgW4fX4ynU2voXVha9ITse3mt6IrwBPxT8PQvjDwv1WAyeGtCfxFfa/hOO1IR0TgZFmtghf8e7FV4at4byF+IrsM+H66fjGyVUhnp/iX7AV+Bb6R0I61+Arpz/gy8cfyfQCbw9pPxkYGe756JBvUWUaw5eBTcAM4Mxw3IX7b8VXpO8Pn2vCvTh8D/0a4Ax8pftYyI/r8OUkeg/OynpWSWChC2NjZvZWfIOiFl82Cfd3Hb6iGRGe3xp8BfZPzrmHzawLGGdm4/ANs/8I167Hv7DgG01HAN9yzt1mZrvwlfurQ5gt4Zy/hvO/jjdhjsCX7UQ4/q4wbj46fD823F89/jlW4t+lX4V8Hh7OjQS3PYTVHvJ2aggniW9IPogXrFdCHo/Ai2IMX5anhesm43srR+EtQufjy0oq5Ff0LE8KYVQCXwz/O8j0HA3/zj0dfq/Di8aMEM+Z+F7Yg/gycHTIpz34hiQh/Bi+YRXHC8suM6sP6fnfcH+jQ5w455ab2Zbw+ylkLAd3hfuZAfxnqO+OCOkaFY7HQ/7W4RtAZznnUma2MuTnsfh3cCa+Et+LL3+/x1fedwJXmvcZqcYLEPiyvCV8jnrchDz6Lf4d7QKmOeeSZnYncA6+kft2M7suxDMpPKuF5OdVwBrn3AoAM7sd/8wI6b08a3yzOtzTspwwVjnnZkVfzOzLWb+daGZfwef3CDLPCeBu51wKWGFmq0Na+mIKcJeZTcLXVWuyfrvfOdcBdJjZdnz+5XJR+HspfB+Bfy5PAd8xs2/iBfypvhKxL8eiJfjCuS8+iW/Bn4Kv4KsAnHNz8BXFJrwjQl+OPEvJiE/ESPxDWhm+T8a3yFvwPZw1eDNwAl9gnsNXDJeRubfF+Iozha8AInHNJXI4sJzjCfyLAdDinKvBV/j34ltJ4CuEL+ErqxPxFS0hzoj/xleIZ+OFpA1fCCNcFEcv6ctHMivdyfC/E3/vZ+J7ex3ARufcifgX6k/h90khnR/F9wLuxIv0LfhW/uvxlfWakN6V+Mr1AefcK/g8dviX9y58pU84Jx7ufSH++XfiC3zkZNXlQhMvK8178a1Gy8qL7M/xrPOzr42+t+IbOtUhvC78y2H4vP5/+DI6HN8LmA58HC9mH8GXkTn4xsg9Ib55ZjbOvOftj/C91xZ8QyGG78G+Ad/77gSuCMcXAJeEBt0wvGhfFvL7DnxZz64gmkN6J5rZ2pBXa0NFtBP4kXPu5zn3bPhG6h/xDaqEc+4w5xcH/28yIpTCN2Br8BXGFnzvYCe+9Q6+XI7Kyc82fDkHX/leSE/24IV9C75heg2+kfpQuP5H+MbhHWRMvZeGe7WQx7tC3h2Pf6feRKbnm8RXbNeQKV+vxgvLGHz9tCHkw63h8334clyNfxaOnuUF/LNvw1sX3h/OTZKpN2IhzUvw9dDx4T6SwLvI1HePhfDn4hs2V+LfiTH48hyRCGFaCPNPwH86504KYUXvcQv0q3NThc/ja/AN+m51iplNxzd43+CcOxlveSjUk9yAtzrnZoW/qc65XAHdF7cCHwn3HT2ndHpzzs333LK5EW+5PAn/DLPD6sj6nCR/nhrw9az7Odo59/NQv52Gz8+vmNl/9pWIfYnoY8Cw0IrxsZqdbGbn5pw3Cm9qSOELfzyceyTeNPhTfMV1Wh9x/RVfII7NivvP+EyPxqdG4StK8AU0ain/Hd/z2BXS8JWscEfizbm78T26Y/CmodwdXKJKIp4VRzZjgEozuxZvzpqHb/VOw/dcn8O/dI34yjIS0JHh/yi8gG/Bt5gjj+Kooo96N9VmFlVoKXxrfjy+ot8BvD38dmRWPozJSevDeHGsw7fq28zsH/EV1IwQTgdeID9IRqBWAm8jUy4m4SuaG/EVxijgfvMex+34ym8Vvrd4B77QRZX3anwLdhte4KrC8TeDn2oU4k3iK6iqkCdRYyB63uCFKB7yb1L4/xKZXuS8cI7DV6TbwvlnhWNvwlfcO/Bl4LQQ/mUhvW/Bl59Z+HJxTriuBS8wUWXYFO7hSrz4jg15FQvhV4W/TnzF/v1wzZ14028zcKJzbmU4ngielhPwDZY259w0vInvtNBbWgecaWYTgH8I6bgG/76cjBeyCnyFGU03qgppejncx2uCNehv+Bb56fgyfg6+8QKZchoPYY4iUw4cvkxfHOIaHfKrNtzT2HBsUvh9TNa1HeHZRKI5ISufGkM8hrdkPAd8K1x/XDjeFZ7XRvzzbsBXlkeEdP4ghFGHb/zNwL/fU0MaluMF7Br8OBqhcVMbwh6Jt3ZF+XC6mR2Db2S14s2rRwIbnHNRvVBLpr5rJCOOh+F7ZV/D9yyzGyZN+E7AmvAMopkGleRp2Dvn9uDfsagHtTXkM2QsYYR4N0VpByqCt/I78D34OsJ4sJlNJNMw6o3lwDQzOyp8f1fWbw8BH408kM3s1H2ElY+RwJZw3+/O+e1tZhYLcc/Al99smsiUU/D5G937v+5H3LnXPwS818L0OTObbGYTgsd2q3PudnxDuS/d6ntMNHQWDsebq1bhW1D347u808iM9czE9zoWAN8k4xH5r/ie4Ev4LvL03sZEw+dP4QvZinB8dbh+Pr4VHY0zRE4Jq4DzwrU3knGeiBxKrsaP86wkUykuw1dA3yUzJtqJN8EsDNc/lmWnX4IfwF6ON1FFDhBt+F7bG8iMTe7Gmw5vxVeyr4Tja/AVb9TibCHjPPEK3jzaFI414Sv/J+jdsejfwj1E5uYt+Bb1SnxlMx7fO1yIr2iawl9kKmvHV36T8S/8BnzP5PGQZ53hvufjhXERGQemaGxoWfjeFJ5VC77QPxqOfx7fuGkn44xyORnHosVkHEVawudXwud7wn3uJuOckcRXJMvCOdF4nQtpihxSmsg4FjWFfNoVjkXOS1/HDws0ZoXdGcKNnFUiJ5iN4T6+GeJP4E2D38SX9+ge9uDF/Mv4cnVliCtyRFkOfCiEtRDfY/olGaephcCHs967BeHZNuDL2it4Me7Cl7FF4b524t+XyOGmNXp+IZz14T7byDhepcLnXfgysS5c1xjS14KvgBdn5XEXGceilfjy8TheuFpCvmzHl+l7wv19JYT5cng+LnzfSsbB6DdkHIuidEUOdh0h/OYQR2P4rR1fFzSHtHwe3xjcTMbJb1UI90N4k+/WcL3Dl/n/COG3h3Q/hX8PO8J93oV/B68OaflpyM/78JV/dn3XGdKyLfxfEH7/K5m68KYQ/oqQnh3h2SwJn8eH+N4DPBGuuYGMY9Et4VkvCvf8tMs4Fr0Swo2eY65j0a3hnL/ixf3q3sZEXU/Hou+TGZOswY9PLwrp7uG1y74diz6Irw+fx9fZt2alsVfHovD5mJCv8/ENoivw79c8vNg9kRtf+L4Yb+YGbylZTMax6ONk6rhn8Z2Wi7PieSFfHmX/adk//Lw0fEbt6OX3afgHeWKR4rs1hHdPgdcPA5LOuYSZnQX8OHsM4lBiX3lfDoSW7RPAq0Lv5ZCg2OX+UGQwn52ZjXDONYfPnwcmOec+3su55+GF400Dl0IB/bO9i4OHqcDdwWzTie+ZioOQ4AfwVeBTh5KAioPi2V1mZl/A19Pr8L1icZChnqgQQghRIPtyLBJCCCFEL0hEhRBCiAKRiAohhBAFIhEVogiY38XmO1nfP5OzUsuBhN1cjHCEEMVHIipEcegA/slytikbKMJylUKIAUYiKkRxSOAnxH8y94ewx+Jj5vch/auZTQ3HbzW/p+jfze8Fep6Z/cL8/rq35oTxXfP7df41rGQU7Qf5PTObC3zczE43syfN7yn6UFhTVAhRQiSiQhSPHwLvzlq2MeJG4Fdh7dI78EvVRYzBr1D1Sfzasd/FLzV4UljgHPwyc3Odcyfgl6H7r6zrq5xzs0OYN+IXWD8dv9PQV4t4b0KIPMgEJESRcM41mtn/4ZcUbMv66Sz84uTgF2r/VtZvf3bOubBrzjbn3CIAM1uCX0JtPn45vLvC+bfjl26LiI5Hu4M8EpY2jZPZ9UMIUSIkokIUl+/h1xz95X6eH+02kaL7zhMpen8/s1dIiXb9Mfx6uWflOV8IUSJkzhWiiIT9R+8Grs06/Ax+gwDwi5f3uT9hHmL4Be3B7yD0dJ5zXgbqw1rKmFmlmZ3Qz3iEEP1EIipE8fkO3feS/ChwjZktxO/SkXcR8T5oAc4ws8X4LfNuyD3BOdeJF9pvmtkCvBn47P4nXQjRH7R2rhBCCFEg6okKIYQQBSIRFUIIIQpEIiqEEEIUiERUCCGEKBCJqBBCCFEgElEhhBCiQCSiQgghRIFIRIUQQogC+f9P3d75qIm1/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Nombre')['1: GASTOS DE PERSONAL'].plot(title=\"Gastos de personal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Debido a la longitud de las etiquetas de \"Nombre\" no se puede distinguir bien a qué nombre corresponde cada dato del gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de otro gráfico a partir de \"Nombre\" y \"3: GASTOS FINANCIEROS\"\n",
    "\n",
    "En esta ocasión se va a indexar el dataframe según \"Nombre\" y \"3: GASTOS FINANCIEROS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre\n",
       "Clasificación por programas                              613271.26\n",
       "DEUDA PÚBLICA                                             15000.00\n",
       "Deuda Pública                                             15000.00\n",
       "Deuda Pública                                             15000.00\n",
       "SERVICIOS PÚBLICOS BÁSICOS                                    0.00\n",
       "                                                           ...    \n",
       "Transferencias a otras Administraciones Públicas              0.00\n",
       "Transferencias a Comunidades Autónomas                        0.00\n",
       "Transferencias a Entidades Locales territoriales              0.00\n",
       "Transferencias a otras Entidades Locales                      0.00\n",
       "Transferencias a la Administración General del Estado         0.00\n",
       "Name: 3: GASTOS FINANCIEROS, Length: 135, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')['3: GASTOS FINANCIEROS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un gráfico a partir de \"Nombre\" y \"3: GASTOS FINANCIEROS\"\n",
    "\n",
    "Una vez indexados los datos según \"Nombre\" y \"3: GASTOS FINANCIEROS\", mediante ```.plot``` se muestran los datos en un gráfico\n",
    "\n",
    "Además, al igual que anteriormente, añadiendo ```title=``` como argumento de ```.plot``` se pone un título al gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gastos financieros'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.set_index('Nombre')['3: GASTOS FINANCIEROS'].plot(title=\"Gastos financieros\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al igual que en el anterior gráfico, debido a la longitud de las etiquetas de \"Nombre\" no se puede distinguir bien a qué nombre corresponde cada dato del gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparación de Gastos de personal y Gastos financieros\n",
    "\n",
    "A continuación se van a comparar los Gastos de personal y Gastos financieros en base al nombre\n",
    "\n",
    "Para ello se deben definir dos nuevas variables, una para cada gasto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "gastos_pers = df.set_index('Nombre')['1: GASTOS DE PERSONAL']\n",
    "gastos_fin = df.set_index('Nombre')['3: GASTOS FINANCIEROS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de crear un dataframe y un gráfico con estos datos, es necesario concatearlos mediante ```vs```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>1: GASTOS DE PERSONAL</th>\n",
       "      <th>3: GASTOS FINANCIEROS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nombre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clasificación por programas</th>\n",
       "      <td>77198659.02</td>\n",
       "      <td>613271.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEUDA PÚBLICA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deuda Pública</th>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deuda Pública</th>\n",
       "      <td>0.00</td>\n",
       "      <td>15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERVICIOS PÚBLICOS BÁSICOS</th>\n",
       "      <td>19082029.47</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a otras Administraciones Públicas</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a Comunidades Autónomas</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a Entidades Locales territoriales</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a otras Entidades Locales</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transferencias a la Administración General del Estado</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    1: GASTOS DE PERSONAL  \\\n",
       "Nombre                                                                      \n",
       "Clasificación por programas                                   77198659.02   \n",
       "DEUDA PÚBLICA                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "Deuda Pública                                                        0.00   \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                    19082029.47   \n",
       "...                                                                   ...   \n",
       "Transferencias a otras Administraciones Públicas                     0.00   \n",
       "Transferencias a Comunidades Autónomas                               0.00   \n",
       "Transferencias a Entidades Locales territoriales                     0.00   \n",
       "Transferencias a otras Entidades Locales                             0.00   \n",
       "Transferencias a la Administración General del ...                   0.00   \n",
       "\n",
       "                                                    3: GASTOS FINANCIEROS  \n",
       "Nombre                                                                     \n",
       "Clasificación por programas                                     613271.26  \n",
       "DEUDA PÚBLICA                                                    15000.00  \n",
       "Deuda Pública                                                    15000.00  \n",
       "Deuda Pública                                                    15000.00  \n",
       "SERVICIOS PÚBLICOS BÁSICOS                                           0.00  \n",
       "...                                                                   ...  \n",
       "Transferencias a otras Administraciones Públicas                     0.00  \n",
       "Transferencias a Comunidades Autónomas                               0.00  \n",
       "Transferencias a Entidades Locales territoriales                     0.00  \n",
       "Transferencias a otras Entidades Locales                             0.00  \n",
       "Transferencias a la Administración General del ...                   0.00  \n",
       "\n",
       "[135 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([gastos_pers,gastos_fin],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un gráfico de comparación\n",
    "\n",
    "Una vez concatenados los datos de ambos gastos, se puede crear un gráfico con ellos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gastos de personal vs gastos financieros'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "vs.columns =['1: GASTOS DE PERSONAL', '3: GASTOS FINANCIEROS']\n",
    "vs.plot(title=\"Gastos de personal vs gastos financieros\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardamos en un csv\n",
    "\n",
    "Ahora guardamos todo lo visto en un archivo csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('gastos.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos que el archivo se ha creado con ```ls```, y como este es un comando de bash, se pone ```!``` para poder usarlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  probando-con-r.ipynb\t\t  Shared_Resources\r\n",
      "escit.png\t\t python-api-covid19-pandas.ipynb  Untitled.ipynb\r\n",
      "esvit.csv\t\t python-pruebas.ipynb\r\n",
      "gastos.csv\t\t seaborn-data\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos matplotlib.pyplot y lo redefinimos como plt\n",
    "\n",
    "También uardamos el gráfico "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "vs.plot()\n",
    "plt.savefig('gastos.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}