python-csv-ingresos-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de los Presupuestos económicos (Ingresos 2018) del Ayuntamiento de Alcobendas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalación de las librerías con las que vamos a trabajar \n",
    "Según la [Universidad de Alcalá](https://www.master-data-scientist.com/pandas-herramienta-data-science/#:~:text=%C2%BFQu%C3%A9%20es%20pandas%3F,tablas%20num%C3%A9ricas%20y%20series%20temporales), en Computación y Ciencia de datos, Pandas es una biblioteca de software escrita como extensión de Numpy (biblioteca para el lenguaje de programación Python que da soporte para crear vectores y matrices grandes multidimensionales, junto con una gran colección de funciones matemáticas de alto nivel para operar con ellas) para manipulación y análisis de datos para el lenguaje de programación Python. En particular, ofrece estructuras de datos y operaciones para manipular tablas numéricas y series temporales."
   ]
  },
  {
   "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)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de Pandas\n",
    "En primer lugar, se importa la librería Pandas y se renombra como \"pd\" para utilizarla en el resto de notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tras haber importado la librería, mediante `=` se crea la variable que contiene los datos que se van a emplear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_pres = \"https://datos.alcobendas.org/dataset/f1b27265-9654-447f-baef-2f3619a59baa/resource/93b1db99-c3ae-4596-b745-ae6924f05cad/download/ingresos.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, se comprueba la creación de dicha variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://datos.alcobendas.org/dataset/f1b27265-9654-447f-baef-2f3619a59baa/resource/93b1db99-c3ae-4596-b745-ae6924f05cad/download/ingresos.csv'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_pres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de un nuevo DataFrame\n",
    "Según [Datacarpentry](https://datacarpentry.org/python-ecology-lesson-es/02-starting-with-data/#:~:text=Un%20DataFrame%20es%20una%20estructura,de%20SQL%20o%20el%20data), un DataFrame es una estructura de datos con dos dimensiones en la cual se puede guardar datos de distintos tipos (como caractéres, enteros, valores de punto flotante, factores y más) en columnas. \n",
    "\n",
    "Una vez definida la nueva variable, se crea un nuevo DataFrame de Pandas: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url_pres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, se expone el nuevo DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Crédito inicial del ejercicio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>IMPUESTOS DIRECTOS</td>\n",
       "      <td>76893259.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Impuesto sobre la Renta</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>Impuestos sobre la Renta de las Personas Físicas</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101.0</td>\n",
       "      <td>Impuesto sobre Sociedades</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102.0</td>\n",
       "      <td>Impuesto sobre la Renta de No Residentes</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>930.0</td>\n",
       "      <td>Préstamos recibidos en moneda distinta del eur...</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>931.0</td>\n",
       "      <td>Préstamos recibidos en moneda distinta del eur...</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>94.0</td>\n",
       "      <td>Depósitos y fianzas recibidos</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>940.0</td>\n",
       "      <td>Depósitos recibidos</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>941.0</td>\n",
       "      <td>Fianzas recibidas</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>329 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Código                                             Nombre  \\\n",
       "0       1.0                                 IMPUESTOS DIRECTOS   \n",
       "1      10.0                            Impuesto sobre la Renta   \n",
       "2     100.0   Impuestos sobre la Renta de las Personas Físicas   \n",
       "3     101.0                          Impuesto sobre Sociedades   \n",
       "4     102.0           Impuesto sobre la Renta de No Residentes   \n",
       "..      ...                                                ...   \n",
       "324   930.0  Préstamos recibidos en moneda distinta del eur...   \n",
       "325   931.0  Préstamos recibidos en moneda distinta del eur...   \n",
       "326    94.0                      Depósitos y fianzas recibidos   \n",
       "327   940.0                                Depósitos recibidos   \n",
       "328   941.0                                  Fianzas recibidas   \n",
       "\n",
       "     Crédito inicial del ejercicio  \n",
       "0                      76893259.18  \n",
       "1                       4994977.89  \n",
       "2                       4994977.89  \n",
       "3                             0.00  \n",
       "4                             0.00  \n",
       "..                             ...  \n",
       "324                           0.00  \n",
       "325                           0.00  \n",
       "326                           0.00  \n",
       "327                           0.00  \n",
       "328                           0.00  \n",
       "\n",
       "[329 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración del DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Información general\n",
    "A través de `df.info` se presenta la información de la tabla:\n",
    "\n",
    "Según la [página web de Pandas](https://aprendeconalf.es/docencia/python/manual/pandas/), 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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 329 entries, 0 to 328\n",
      "Data columns (total 3 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Código                         329 non-null    float64\n",
      " 1   Nombre                         329 non-null    object \n",
      " 2   Crédito inicial del ejercicio  329 non-null    float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 7.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df. info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cabecera\n",
    "Se emplea `df.head()` para mostrar las primeras entradas de la tabla. Entre paréntesis se pueden indicar cuántas entradas se quieren mostrar. En este caso se exponen cuatro: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>IMPUESTOS DIRECTOS</td>\n",
       "      <td>76893259.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Impuesto sobre la Renta</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>Impuestos sobre la Renta de las Personas Físicas</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101.0</td>\n",
       "      <td>Impuesto sobre Sociedades</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Código                                            Nombre  \\\n",
       "0     1.0                                IMPUESTOS DIRECTOS   \n",
       "1    10.0                           Impuesto sobre la Renta   \n",
       "2   100.0  Impuestos sobre la Renta de las Personas Físicas   \n",
       "3   101.0                         Impuesto sobre Sociedades   \n",
       "\n",
       "   Crédito inicial del ejercicio  \n",
       "0                    76893259.18  \n",
       "1                     4994977.89  \n",
       "2                     4994977.89  \n",
       "3                           0.00  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cola\n",
    "Después, se muestran las últimas entradas de la tabla (tail) mediante `df.tail()`.\n",
    "\n",
    "De nuevo, entre paréntesis se puede especificar el número de entradas que se quieren mostrar. En este caso, se volverá a mostrar cuatro entradas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Código</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Crédito inicial del ejercicio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>931.0</td>\n",
       "      <td>Préstamos recibidos en moneda distinta del eur...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>94.0</td>\n",
       "      <td>Depósitos y fianzas recibidos</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>940.0</td>\n",
       "      <td>Depósitos recibidos</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>941.0</td>\n",
       "      <td>Fianzas recibidas</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "     Código                                             Nombre  \\\n",
       "325   931.0  Préstamos recibidos en moneda distinta del eur...   \n",
       "326    94.0                      Depósitos y fianzas recibidos   \n",
       "327   940.0                                Depósitos recibidos   \n",
       "328   941.0                                  Fianzas recibidas   \n",
       "\n",
       "     Crédito inicial del ejercicio  \n",
       "325                            0.0  \n",
       "326                            0.0  \n",
       "327                            0.0  \n",
       "328                            0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df. tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columnas\n",
    "Además, mediante `df.columns` se expone el nombre de las columnas de la tabla:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Código', 'Nombre', 'Crédito inicial del ejercicio'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se pueden mostrar columnas concretas mediante `df['nombre de la columna']`.\n",
    "\n",
    "A continuación, se ejemplifica lo comentado mediante la columna \"Crédito inicial del ejercicio\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      76893259.18\n",
       "1       4994977.89\n",
       "2       4994977.89\n",
       "3             0.00\n",
       "4             0.00\n",
       "          ...     \n",
       "324           0.00\n",
       "325           0.00\n",
       "326           0.00\n",
       "327           0.00\n",
       "328           0.00\n",
       "Name: Crédito inicial del ejercicio, Length: 329, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Crédito inicial del ejercicio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Además, se puede mostrar una fila en concreto de la columna especificada. Esto se realiza añadiendo el número de dicha fila a continuación del nombre. En este caso se muestra la fila número dos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4994977.89"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Crédito inicial del ejercicio'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demostración de las variables estadísticas\n",
    "Mediante `df.describe()` se muestran las variables estadísticas del DataFrame, algo que no se muestra normalmente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>329.000000</td>\n",
       "      <td>3.290000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>403.929514</td>\n",
       "      <td>1.309417e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>275.541759</td>\n",
       "      <td>6.444360e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>1.560000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>941.000000</td>\n",
       "      <td>7.689326e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Código  Crédito inicial del ejercicio\n",
       "count  329.000000                   3.290000e+02\n",
       "mean   403.929514                   1.309417e+06\n",
       "std    275.541759                   6.444360e+06\n",
       "min      1.000000                   0.000000e+00\n",
       "25%    115.000000                   0.000000e+00\n",
       "50%    394.000000                   0.000000e+00\n",
       "75%    600.000000                   1.560000e+01\n",
       "max    941.000000                   7.689326e+07"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexar el DataFrame según una columna\n",
    "También es posible indexar -ordenar los datos- en función de una columna a través de `df.set_index('')`\n",
    "\n",
    "En este caso, se indexa el DataFrame según la columna \"Nombre\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Código</th>\n",
       "      <th>Crédito inicial del ejercicio</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>IMPUESTOS DIRECTOS</th>\n",
       "      <td>1.0</td>\n",
       "      <td>76893259.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre la Renta</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuestos sobre la Renta de las Personas Físicas</th>\n",
       "      <td>100.0</td>\n",
       "      <td>4994977.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre Sociedades</th>\n",
       "      <td>101.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre la Renta de No Residentes</th>\n",
       "      <td>102.0</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>Préstamos recibidos en moneda distinta del euro a corto plazo</th>\n",
       "      <td>930.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Préstamos recibidos en moneda distinta del euro a largo plazo</th>\n",
       "      <td>931.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depósitos y fianzas recibidos</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depósitos recibidos</th>\n",
       "      <td>940.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fianzas recibidas</th>\n",
       "      <td>941.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>329 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Código  \\\n",
       "Nombre                                                       \n",
       "IMPUESTOS DIRECTOS                                     1.0   \n",
       "Impuesto sobre la Renta                               10.0   \n",
       "Impuestos sobre la Renta de las Personas Físicas     100.0   \n",
       "Impuesto sobre Sociedades                            101.0   \n",
       "Impuesto sobre la Renta de No Residentes             102.0   \n",
       "...                                                    ...   \n",
       "Préstamos recibidos en moneda distinta del euro...   930.0   \n",
       "Préstamos recibidos en moneda distinta del euro...   931.0   \n",
       "Depósitos y fianzas recibidos                         94.0   \n",
       "Depósitos recibidos                                  940.0   \n",
       "Fianzas recibidas                                    941.0   \n",
       "\n",
       "                                                    Crédito inicial del ejercicio  \n",
       "Nombre                                                                             \n",
       "IMPUESTOS DIRECTOS                                                    76893259.18  \n",
       "Impuesto sobre la Renta                                                4994977.89  \n",
       "Impuestos sobre la Renta de las Personas Físicas                       4994977.89  \n",
       "Impuesto sobre Sociedades                                                    0.00  \n",
       "Impuesto sobre la Renta de No Residentes                                     0.00  \n",
       "...                                                                           ...  \n",
       "Préstamos recibidos en moneda distinta del euro...                           0.00  \n",
       "Préstamos recibidos en moneda distinta del euro...                           0.00  \n",
       "Depósitos y fianzas recibidos                                                0.00  \n",
       "Depósitos recibidos                                                          0.00  \n",
       "Fianzas recibidas                                                            0.00  \n",
       "\n",
       "[329 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo mismo ocurre indexando la variable \"Crédito inicial del ejercicio\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre\n",
       "IMPUESTOS DIRECTOS                                               76893259.18\n",
       "Impuesto sobre la Renta                                           4994977.89\n",
       "Impuestos sobre la Renta de las Personas Físicas                  4994977.89\n",
       "Impuesto sobre Sociedades                                               0.00\n",
       "Impuesto sobre la Renta de No Residentes                                0.00\n",
       "                                                                    ...     \n",
       "Préstamos recibidos en moneda distinta del euro a corto plazo           0.00\n",
       "Préstamos recibidos en moneda distinta del euro a largo plazo           0.00\n",
       "Depósitos y fianzas recibidos                                           0.00\n",
       "Depósitos recibidos                                                     0.00\n",
       "Fianzas recibidas                                                       0.00\n",
       "Name: Crédito inicial del ejercicio, Length: 329, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')['Crédito inicial del ejercicio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de un gráfico a partir de \"Nombre\" y \"Crédito inicial del ejercicio\"\n",
    "Una vez indexados los datos según \"Nombre\" y \"Crédito inicial del ejercicio\", mediante `.plot` se presentan los datos en un gráfico. A continuación, para añadir un título al gráfico, lo hacemos a través de `title` como argumento de `.plot`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Crédito inicial del ejercicio'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 34,
     "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')['Crédito inicial del ejercicio'].plot(title=\"Crédito inicial del ejercicio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Debido a la longitud de las etiquetas de \"Nombre\", no se puede distinguir a qué nombre corresponde cada dato en el gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de otro gráfico a partir de \"Nombre\" y \"Código\"\n",
    "En esta ocasión se va a indexar el DataFrame según las variables \"Nombre\" y \"Código\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nombre\n",
       "IMPUESTOS DIRECTOS                                                 1.0\n",
       "Impuesto sobre la Renta                                           10.0\n",
       "Impuestos sobre la Renta de las Personas Físicas                 100.0\n",
       "Impuesto sobre Sociedades                                        101.0\n",
       "Impuesto sobre la Renta de No Residentes                         102.0\n",
       "                                                                 ...  \n",
       "Préstamos recibidos en moneda distinta del euro a corto plazo    930.0\n",
       "Préstamos recibidos en moneda distinta del euro a largo plazo    931.0\n",
       "Depósitos y fianzas recibidos                                     94.0\n",
       "Depósitos recibidos                                              940.0\n",
       "Fianzas recibidas                                                941.0\n",
       "Name: Código, Length: 329, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Nombre')['Código']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un gráfico a partir de \"Nombre\" y \"Código\"\n",
    "Una vez indexados los datos según \"Nombre\" y \"Código\", mediante `.plot` se muestran los datos en un gráfico. Además, al igual que se ha realizado anteriormente, se añade `title=` como argumento de `.plot` para poner un título al gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Códigos'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 37,
     "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')['Código'].plot(title=\"Códigos\")"
   ]
  },
  {
   "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 a qué nombre corresponde cada dato del gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparación de Código y Crédito inicial del ejercicio\n",
    "A continuación, se van a comparar los \"codigos\" y los \"créditos iniciales del ejercicio\" en base al \"Nombre\". \n",
    "\n",
    "Se definen dos nuevas variables, una para cada variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre_credit = df.set_index('Nombre')['Crédito inicial del ejercicio']\n",
    "nombre_codig = df.set_index('Nombre')['Código']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de crear un DataFrame y un gráfico con estos datos, es necesario concatenarlos mediante `vs`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Crédito inicial del ejercicio</th>\n",
       "      <th>Código</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>IMPUESTOS DIRECTOS</th>\n",
       "      <td>76893259.18</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre la Renta</th>\n",
       "      <td>4994977.89</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuestos sobre la Renta de las Personas Físicas</th>\n",
       "      <td>4994977.89</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre Sociedades</th>\n",
       "      <td>0.00</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impuesto sobre la Renta de No Residentes</th>\n",
       "      <td>0.00</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Préstamos recibidos en moneda distinta del euro a corto plazo</th>\n",
       "      <td>0.00</td>\n",
       "      <td>930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Préstamos recibidos en moneda distinta del euro a largo plazo</th>\n",
       "      <td>0.00</td>\n",
       "      <td>931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depósitos y fianzas recibidos</th>\n",
       "      <td>0.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depósitos recibidos</th>\n",
       "      <td>0.00</td>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fianzas recibidas</th>\n",
       "      <td>0.00</td>\n",
       "      <td>941.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>329 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Crédito inicial del ejercicio  \\\n",
       "Nombre                                                                              \n",
       "IMPUESTOS DIRECTOS                                                    76893259.18   \n",
       "Impuesto sobre la Renta                                                4994977.89   \n",
       "Impuestos sobre la Renta de las Personas Físicas                       4994977.89   \n",
       "Impuesto sobre Sociedades                                                    0.00   \n",
       "Impuesto sobre la Renta de No Residentes                                     0.00   \n",
       "...                                                                           ...   \n",
       "Préstamos recibidos en moneda distinta del euro...                           0.00   \n",
       "Préstamos recibidos en moneda distinta del euro...                           0.00   \n",
       "Depósitos y fianzas recibidos                                                0.00   \n",
       "Depósitos recibidos                                                          0.00   \n",
       "Fianzas recibidas                                                            0.00   \n",
       "\n",
       "                                                    Código  \n",
       "Nombre                                                      \n",
       "IMPUESTOS DIRECTOS                                     1.0  \n",
       "Impuesto sobre la Renta                               10.0  \n",
       "Impuestos sobre la Renta de las Personas Físicas     100.0  \n",
       "Impuesto sobre Sociedades                            101.0  \n",
       "Impuesto sobre la Renta de No Residentes             102.0  \n",
       "...                                                    ...  \n",
       "Préstamos recibidos en moneda distinta del euro...   930.0  \n",
       "Préstamos recibidos en moneda distinta del euro...   931.0  \n",
       "Depósitos y fianzas recibidos                         94.0  \n",
       "Depósitos recibidos                                  940.0  \n",
       "Fianzas recibidas                                    941.0  \n",
       "\n",
       "[329 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = pd.concat([nombre_credit,nombre_codig],axis=1)\n",
    "vs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de un gráfico de comparación\n",
    "Una vez concatenados los datos de ambas variables, se puede elaborar un gráfico de comparación con ellos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Código vs Crédito Inicial'}, xlabel='Nombre'>"
      ]
     },
     "execution_count": 41,
     "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 =['Crédito inicial del ejercicio','Código']\n",
    "vs.plot(title=\"Código vs Crédito Inicial\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardamos en un csv\n",
    "Por último, se guarda todo lo realizado en un archivo csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.to_csv('ingresos.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se comprueba que el archivo se ha creado mediante `ls`, y como este es un comando de bash, se indica `!` par poder utilizarlo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "api-pandas-folium.ipynb  phyton-pruebas.ipynb\t\t  Shared_Resources\r\n",
      "escit.png\t\t python-api-covid-pandas.ipynb\t  Untitled.ipynb\r\n",
      "esvit.csv\t\t python-csv-farolas-pandas.ipynb\r\n",
      "ingresos.csv\t\t seaborn-data\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se importa matplotlib.pyplot (Según [Aprende con Alf](https://aprendeconalf.es/docencia/python/manual/matplotlib/) Matplotlib.pyplot es una librería de Python especializada en la creación de gráficos en dos dimensiones. Permite crear y personalizar los tipos de gráficos más comunes, entre ellos: Diagramas de barras) y se redefine como plt.\n",
    "\n",
    "También se guarda el gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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('ingresos.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
}