5_series_temporales/5_4_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resample de una serie temporal con pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pandas permite realizar remuestrreos de las series temporales (DataFrames con índices de tiempo)\n",
    "\n",
    "* Los métodos disponibles son \n",
    "    * __resample()__ vuelve a muestrear la serie temporal y aplica un afunción a cada grupo\n",
    "    * __asfreq()__ devuelve uno de los valores del grupo y permite un método de relleno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ambos métodos permiten usar distintas frequencias definidas por ``códigos offset`` que puedes ver en:\n",
    "\n",
    "https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ejemplo de cómo funciona le método __resample()__\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<left>\n",
    "<img src=\"imgs/resample.png\"  alt=\"drawing\" width=\"40%\"/>\n",
    "</left>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Veamos unos ejemplos básicos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install datetime\n",
    "from datetime import datetime, date, time, timedelta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.033667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>1.224089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.009481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.484882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.825253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-05</th>\n",
       "      <td>0.053142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-06</th>\n",
       "      <td>0.032290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-07</th>\n",
       "      <td>0.579863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-08</th>\n",
       "      <td>-0.611814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-09</th>\n",
       "      <td>-1.289051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2000-01-01 -1.033667\n",
       "2000-01-02  1.224089\n",
       "2000-01-03 -0.009481\n",
       "2000-01-04  0.484882\n",
       "2000-01-05  0.825253\n",
       "...              ...\n",
       "2000-04-05  0.053142\n",
       "2000-04-06  0.032290\n",
       "2000-04-07  0.579863\n",
       "2000-04-08 -0.611814\n",
       "2000-04-09 -1.289051\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generamos una serie temporal usando un rango:\n",
    "\n",
    "rng = pd.date_range('2000-01-01', periods=100, freq='D')\n",
    "ts = pd.DataFrame(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos el valor mesual medio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>-0.120286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.035406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-30</th>\n",
       "      <td>0.009768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2000-01-31 -0.120286\n",
       "2000-02-29  0.001348\n",
       "2000-03-31  0.035406\n",
       "2000-04-30  0.009768"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos el valor quincenal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.420006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-0.545838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>0.010613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-15</th>\n",
       "      <td>-1.201015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>-1.552874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-16</th>\n",
       "      <td>1.595595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>-0.336704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2000-01-01  0.420006\n",
       "2000-01-16 -0.545838\n",
       "2000-01-31  0.010613\n",
       "2000-02-15 -1.201015\n",
       "2000-03-01 -1.552874\n",
       "2000-03-16  1.595595\n",
       "2000-03-31 -0.336704"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('15D').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>-0.120286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.035406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-28</th>\n",
       "      <td>0.009768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2000-01-31 -0.120286\n",
       "2000-02-29  0.001348\n",
       "2000-03-31  0.035406\n",
       "2000-04-28  0.009768"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('BM').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extraemos una neva serie temporal cogiendo el valor cada 5 días usando el método __asfreq()__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.420006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.187042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.666231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-0.545838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>-0.563967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2000-01-01  0.420006\n",
       "2000-01-06  0.187042\n",
       "2000-01-11  0.666231\n",
       "2000-01-16 -0.545838\n",
       "2000-01-21 -0.563967"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('5D').head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__OJO:__ si no hay valores nos devuelve NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0.420006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 00:00:00</th>\n",
       "      <td>0.141218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00</th>\n",
       "      <td>0.406539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-07 00:00:00</th>\n",
       "      <td>0.803427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-07 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-08 00:00:00</th>\n",
       "      <td>-1.774450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-08 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-09 00:00:00</th>\n",
       "      <td>2.949957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "2000-01-01 00:00:00  0.420006\n",
       "2000-01-01 12:00:00       NaN\n",
       "2000-01-02 00:00:00  0.141218\n",
       "2000-01-02 12:00:00       NaN\n",
       "2000-01-03 00:00:00  0.406539\n",
       "...                       ...\n",
       "2000-04-07 00:00:00  0.803427\n",
       "2000-04-07 12:00:00       NaN\n",
       "2000-04-08 00:00:00 -1.774450\n",
       "2000-04-08 12:00:00       NaN\n",
       "2000-04-09 00:00:00  2.949957\n",
       "\n",
       "[199 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('12H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0.420006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 00:00:00</th>\n",
       "      <td>0.141218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00</th>\n",
       "      <td>0.406539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-07 00:00:00</th>\n",
       "      <td>0.803427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-07 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-08 00:00:00</th>\n",
       "      <td>-1.774450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-08 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-09 00:00:00</th>\n",
       "      <td>2.949957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "2000-01-01 00:00:00  0.420006\n",
       "2000-01-01 12:00:00  0.000000\n",
       "2000-01-02 00:00:00  0.141218\n",
       "2000-01-02 12:00:00  0.000000\n",
       "2000-01-03 00:00:00  0.406539\n",
       "...                       ...\n",
       "2000-04-07 00:00:00  0.803427\n",
       "2000-04-07 12:00:00  0.000000\n",
       "2000-04-08 00:00:00 -1.774450\n",
       "2000-04-08 12:00:00  0.000000\n",
       "2000-04-09 00:00:00  2.949957\n",
       "\n",
       "[199 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('12H', fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejercicios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos un ejemplo con los datos de calidad del aire que usamos en notebooks anteriores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carga el fichero air_quality_no2_long.csv del directorio data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>city</th>\n",
       "      <th>country</th>\n",
       "      <th>datetime</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city country                  datetime location parameter  value   unit\n",
       "0  Paris      FR 2019-06-21 00:00:00+00:00  FR04014       no2   20.0  µg/m³\n",
       "1  Paris      FR 2019-06-20 23:00:00+00:00  FR04014       no2   21.8  µg/m³\n",
       "2  Paris      FR 2019-06-20 22:00:00+00:00  FR04014       no2   26.5  µg/m³\n",
       "3  Paris      FR 2019-06-20 21:00:00+00:00  FR04014       no2   24.9  µg/m³\n",
       "4  Paris      FR 2019-06-20 20:00:00+00:00  FR04014       no2   21.4  µg/m³"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality = pd.read_csv(\"data/air_quality_no2_long.csv\", parse_dates=[\"datetime\"])\n",
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coviértelo a un dataframe con una columna para cada \"location\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:00:00+00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00+00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 04:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>61.9</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 05:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "location                   BETR801  FR04014  London Westminster\n",
       "datetime                                                       \n",
       "2019-05-07 01:00:00+00:00     50.5     25.0                23.0\n",
       "2019-05-07 02:00:00+00:00     45.0     27.7                19.0\n",
       "2019-05-07 03:00:00+00:00      NaN     50.4                19.0\n",
       "2019-05-07 04:00:00+00:00      NaN     61.9                16.0\n",
       "2019-05-07 05:00:00+00:00      NaN     72.4                 NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2 = air_quality.pivot_table(values=\"value\", index=\"datetime\", columns=\"location\")\n",
    "no_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcula los valores medios mensuales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-31 00:00:00+00:00</th>\n",
       "      <td>24.344156</td>\n",
       "      <td>27.908446</td>\n",
       "      <td>28.158433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30 00:00:00+00:00</th>\n",
       "      <td>31.916667</td>\n",
       "      <td>27.499272</td>\n",
       "      <td>19.581152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "location                     BETR801    FR04014  London Westminster\n",
       "datetime                                                           \n",
       "2019-05-31 00:00:00+00:00  24.344156  27.908446           28.158433\n",
       "2019-06-30 00:00:00+00:00  31.916667  27.499272           19.581152"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dibuja los valores medios diarios de las tres ubicaciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_2.resample(\"D\").mean().plot(style=\"-o\", figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genera un nuevo dataframe con valores cada 30 minutos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:00:00+00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:30:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00+00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:30:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:30:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:30:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2159 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location                   BETR801  FR04014  London Westminster\n",
       "datetime                                                       \n",
       "2019-05-07 01:00:00+00:00     50.5     25.0                23.0\n",
       "2019-05-07 01:30:00+00:00      NaN      NaN                 NaN\n",
       "2019-05-07 02:00:00+00:00     45.0     27.7                19.0\n",
       "2019-05-07 02:30:00+00:00      NaN      NaN                 NaN\n",
       "2019-05-07 03:00:00+00:00      NaN     50.4                19.0\n",
       "...                            ...      ...                 ...\n",
       "2019-06-20 22:00:00+00:00      NaN     26.5                 NaN\n",
       "2019-06-20 22:30:00+00:00      NaN      NaN                 NaN\n",
       "2019-06-20 23:00:00+00:00      NaN     21.8                 NaN\n",
       "2019-06-20 23:30:00+00:00      NaN      NaN                 NaN\n",
       "2019-06-21 00:00:00+00:00      NaN     20.0                 NaN\n",
       "\n",
       "[2159 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2.asfreq(freq=\"30T\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genera un nuevo dataframe con valores cada 30 minutos pero rellenando los valores que no existan con un 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:00:00+00:00</th>\n",
       "      <td>50.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 01:30:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:00:00+00:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 02:30:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 03:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:30:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:30:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2159 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location                   BETR801  FR04014  London Westminster\n",
       "datetime                                                       \n",
       "2019-05-07 01:00:00+00:00     50.5     25.0                23.0\n",
       "2019-05-07 01:30:00+00:00      0.0      0.0                 0.0\n",
       "2019-05-07 02:00:00+00:00     45.0     27.7                19.0\n",
       "2019-05-07 02:30:00+00:00      0.0      0.0                 0.0\n",
       "2019-05-07 03:00:00+00:00      NaN     50.4                19.0\n",
       "...                            ...      ...                 ...\n",
       "2019-06-20 22:00:00+00:00      NaN     26.5                 NaN\n",
       "2019-06-20 22:30:00+00:00      0.0      0.0                 0.0\n",
       "2019-06-20 23:00:00+00:00      NaN     21.8                 NaN\n",
       "2019-06-20 23:30:00+00:00      0.0      0.0                 0.0\n",
       "2019-06-21 00:00:00+00:00      NaN     20.0                 NaN\n",
       "\n",
       "[2159 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "no_2.asfreq(freq=\"30T\", fill_value=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "18a6b418ca989a304418b74e14b57df096d22507d5bd0d85ac6f17ef362aab2d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}