{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# European Environment Agency (EEA) Air Quality Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{hint} \n",
    "<a href='https://dust.ltpy.adamplatform.eu/hub/user-redirect/lab/tree/01_observations/2_ground-based/23_EEA_Air_Quality.ipynb' target='_blank'>Execute the notebook on the training platform >></a>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The European Environment Agency (EEA) has a network of air quality monitoring stations across Europe. These stations record data for a number of pollutants, e.g. Particulate Matter 2.5 and Particulate Matter 10, and are placed in different environments to capture what is happening across both urban and rural areas. To learn more about the stations and look up station codes, explore the <a href='https://www.eea.europa.eu/themes/air/air-quality-index/index' target='_blank'>European Air Quality Index map</a>.\n",
    "\n",
    "In the air quality directive (2008/EC/50), the EU has set two limit values for particulate matter (PM10) for the protection of human health: the PM10 daily mean value may not exceed 50 micrograms per cubic metre (µg/m3) more than 35 times in a year and the PM10 annual mean value may not exceed 40 micrograms per cubic metre (µg/m3). <a href='https://www.eea.europa.eu/data-and-maps/figures/particulate-matter-pm10-annual-limit-value-for-the-protection-of-human-health-3' target='_blank'>(Source)</a>\n",
    "\n",
    "As for PM2.5, no daily limit has been set by the EU, but the World Health Organisation's daily limit is 25 micrograms per cubic metre (µg/m3). Read more <a href='https://www.eea.europa.eu/themes/air/air-quality-concentrations/air-quality-standards' target='_blank'>here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Basic facts\n",
    "**Spatial coverage**: `Observation stations across Europe` <br>\n",
    "**Temporal resolution**: `daily aggregates` <br>\n",
    "**Data format**: `csv`\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} How to access the data\n",
    "The EEA air quality data is available for download via the <a href='https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm' target='_blank'>EEA Air Quality Portal</a>.\n",
    "The Python library `airbase` allows you to download all EEA Air Quality data for a country at once. You can specify which pollutant, country and time period you want to download. Read more about the library <a href='https://airbase.readthedocs.io/en/latest/' target='_blank'>here</a>.\n",
    "\n",
    "The first part of this notebook (1 - Download EEA Air Quality data for specific pollutants and countries) shows you how to request EEA Air Quality data with the library `airbase`.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import airbase\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Optional: Download EEA Air Quality data for specific pollutants and countries*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python library `airbase` allows you to download all EEA Air Quality data for a country at once. You can specify which pollutant, country and time period you want to download. Read more about the library [here](https://airbase.readthedocs.io/en/latest/). Due to the large file sizes, it is recommended to download data for each country and pollutant (PM10 and PM2.5) separately. We have predownloaded data for the station Palma de Mallorca, Spain for you.\n",
    "\n",
    "The first step is to store the `airbase` client in a variable called `client`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = airbase.AirbaseClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define a download request by stating which country, pollutant and year we want the data from. The abbreviation for Spain is `\"ES\"` and the pollutant is `\"PM10\"`. We only want data from `2024`, so we specify both, the `year_from` and `year_to` as `2024`. \n",
    "\n",
    "By using the function `download_to_file()`, all the 490 resulting CSVs are concatenated into a single CSV file called `ES_PM10_2024.csv`. Each CSV contains data from a single station. It is recommended to download all the data for a country into one CSV to make it easier to search for a station using the station code. You are able to change the file name to anything you wish. We also specify where the data should be stored by stating the file path `\"../../eodata/2_observations/eea/ES/ES_PM10_2024.csv\"`. \n",
    "\n",
    "We have commented out the code as these data have already been downloaded for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r = client.request(country=[\"ES\"], pl=[\"PM10\"], year_from=2024, year_to=2024)\n",
    "#r.download_to_file(\"../../eodata/2_observations/eea/ES/ES_PM10_2024.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we download the PM2.5 data for Spain and store it in a CSV called `ES_PM2pt5_2024.csv`. You will notice that there are fewer stations that collect PM2.5 data, just 226."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r = client.request(country=[\"ES\"], pl=[\"PM2.5\"], year_from=2024, year_to=2024)\n",
    "#r.download_to_file(\"../../eodata/2_observations/eea/ES/ES_PM10_2024.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the observation data with pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this exercise, we will focus on the Palma de Mallorca Station, Spain.\n",
    "\n",
    "The EEA air quality station in Palma de Mallorca has the code `ES2097A`. You can find the file in the folder `../../eodata/2_observations/eea/ES/palma/`. You can read `csv` files with the function `read_table()` from the Python library `pandas`. We can set additonal keyword arguments that allow us to specify the columns and rows of interest:\n",
    "* `delimiter`: specify the delimiter in the text file, e.g. comma\n",
    "* `header`: specify the index of the row that shall be set as header.\n",
    "* `index_col`: specify the index of the column that shall be set as index\n",
    "* `low_memory`: this is set to `False` in this case to avoid mixed type interference\n",
    "\n",
    "Because the data is stored in a comma-separated values file, the delimiter is set to `,`. The first row of the file contains the header information, thus we set the header value to `[0]`. Finally, we set the index column to be the 5th column in the file, which is the `AirQualityStationEoICode`.\n",
    "\n",
    "You see below that the resulting dataframe has 3601 rows and 16 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countrycode</th>\n",
       "      <th>Namespace</th>\n",
       "      <th>AirQualityNetwork</th>\n",
       "      <th>AirQualityStation</th>\n",
       "      <th>SamplingPoint</th>\n",
       "      <th>SamplingProcess</th>\n",
       "      <th>Sample</th>\n",
       "      <th>AirPollutant</th>\n",
       "      <th>AirPollutantCode</th>\n",
       "      <th>AveragingTime</th>\n",
       "      <th>Concentration</th>\n",
       "      <th>UnitOfMeasurement</th>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th>DatetimeEnd</th>\n",
       "      <th>Validity</th>\n",
       "      <th>Verification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AirQualityStationEoICode</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",
       "      <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>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>32.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-01-01 01:00:00 +01:00</td>\n",
       "      <td>2024-01-01 02:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>42.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-01-01 02:00:00 +01:00</td>\n",
       "      <td>2024-01-01 03:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>30.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-01-01 04:00:00 +01:00</td>\n",
       "      <td>2024-01-01 05:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>25.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-01-01 06:00:00 +01:00</td>\n",
       "      <td>2024-01-01 07:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>24.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-01-01 07:00:00 +01:00</td>\n",
       "      <td>2024-01-01 08:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-05-29 13:00:00 +01:00</td>\n",
       "      <td>2024-05-29 14:00:00 +01:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-05-29 14:00:00 +01:00</td>\n",
       "      <td>2024-05-29 15:00:00 +01:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>16.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-05-29 21:00:00 +01:00</td>\n",
       "      <td>2024-05-29 22:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>14.0</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-05-29 22:00:00 +01:00</td>\n",
       "      <td>2024-05-29 23:00:00 +01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>ES</td>\n",
       "      <td>ES.BDCA.AQD</td>\n",
       "      <td>NET_ES205A</td>\n",
       "      <td>STA_ES2097A</td>\n",
       "      <td>SP_35016014_10_49</td>\n",
       "      <td>SPP_35016014_10_49.1</td>\n",
       "      <td>SAM_35016014_10_49</td>\n",
       "      <td>PM10</td>\n",
       "      <td>http://dd.eionet.europa.eu/vocabulary/aq/pollu...</td>\n",
       "      <td>hour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>µg/m3</td>\n",
       "      <td>2024-05-29 23:00:00 +01:00</td>\n",
       "      <td>2024-05-30 00:00:00 +01:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3601 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Countrycode    Namespace AirQualityNetwork  \\\n",
       "AirQualityStationEoICode                                              \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "...                              ...          ...               ...   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "ES2097A                           ES  ES.BDCA.AQD        NET_ES205A   \n",
       "\n",
       "                         AirQualityStation      SamplingPoint  \\\n",
       "AirQualityStationEoICode                                        \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "...                                    ...                ...   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "ES2097A                        STA_ES2097A  SP_35016014_10_49   \n",
       "\n",
       "                               SamplingProcess              Sample  \\\n",
       "AirQualityStationEoICode                                             \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "...                                        ...                 ...   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "ES2097A                   SPP_35016014_10_49.1  SAM_35016014_10_49   \n",
       "\n",
       "                         AirPollutant  \\\n",
       "AirQualityStationEoICode                \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "...                               ...   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "ES2097A                          PM10   \n",
       "\n",
       "                                                           AirPollutantCode  \\\n",
       "AirQualityStationEoICode                                                      \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "...                                                                     ...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "ES2097A                   http://dd.eionet.europa.eu/vocabulary/aq/pollu...   \n",
       "\n",
       "                         AveragingTime  Concentration UnitOfMeasurement  \\\n",
       "AirQualityStationEoICode                                                  \n",
       "ES2097A                           hour           32.0             µg/m3   \n",
       "ES2097A                           hour           42.0             µg/m3   \n",
       "ES2097A                           hour           30.0             µg/m3   \n",
       "ES2097A                           hour           25.0             µg/m3   \n",
       "ES2097A                           hour           24.0             µg/m3   \n",
       "...                                ...            ...               ...   \n",
       "ES2097A                           hour            NaN             µg/m3   \n",
       "ES2097A                           hour            NaN             µg/m3   \n",
       "ES2097A                           hour           16.0             µg/m3   \n",
       "ES2097A                           hour           14.0             µg/m3   \n",
       "ES2097A                           hour            NaN             µg/m3   \n",
       "\n",
       "                                       DatetimeBegin  \\\n",
       "AirQualityStationEoICode                               \n",
       "ES2097A                   2024-01-01 01:00:00 +01:00   \n",
       "ES2097A                   2024-01-01 02:00:00 +01:00   \n",
       "ES2097A                   2024-01-01 04:00:00 +01:00   \n",
       "ES2097A                   2024-01-01 06:00:00 +01:00   \n",
       "ES2097A                   2024-01-01 07:00:00 +01:00   \n",
       "...                                              ...   \n",
       "ES2097A                   2024-05-29 13:00:00 +01:00   \n",
       "ES2097A                   2024-05-29 14:00:00 +01:00   \n",
       "ES2097A                   2024-05-29 21:00:00 +01:00   \n",
       "ES2097A                   2024-05-29 22:00:00 +01:00   \n",
       "ES2097A                   2024-05-29 23:00:00 +01:00   \n",
       "\n",
       "                                         DatetimeEnd  Validity  Verification  \n",
       "AirQualityStationEoICode                                                      \n",
       "ES2097A                   2024-01-01 02:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-01-01 03:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-01-01 05:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-01-01 07:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-01-01 08:00:00 +01:00         1             3  \n",
       "...                                              ...       ...           ...  \n",
       "ES2097A                   2024-05-29 14:00:00 +01:00        -1             3  \n",
       "ES2097A                   2024-05-29 15:00:00 +01:00        -1             3  \n",
       "ES2097A                   2024-05-29 22:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-05-29 23:00:00 +01:00         1             3  \n",
       "ES2097A                   2024-05-30 00:00:00 +01:00        -1             3  \n",
       "\n",
       "[3601 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table('../../eodata/2_observations/eea/ES/palma/ES_5_68795_2024_timeseries.csv', delimiter=',', header=[0], index_col=4, low_memory=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we select only columns that contain data of interest, the `Concentration` of PM10 and `DatetimeBegin`, and reorder them so that `DatetimeBegin` is the first column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th>Concentration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AirQualityStationEoICode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-01-01 01:00:00 +01:00</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-01-01 02:00:00 +01:00</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-01-01 04:00:00 +01:00</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-01-01 06:00:00 +01:00</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-01-01 07:00:00 +01:00</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-05-29 13:00:00 +01:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-05-29 14:00:00 +01:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-05-29 21:00:00 +01:00</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-05-29 22:00:00 +01:00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2097A</th>\n",
       "      <td>2024-05-29 23:00:00 +01:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3601 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       DatetimeBegin  Concentration\n",
       "AirQualityStationEoICode                                           \n",
       "ES2097A                   2024-01-01 01:00:00 +01:00           32.0\n",
       "ES2097A                   2024-01-01 02:00:00 +01:00           42.0\n",
       "ES2097A                   2024-01-01 04:00:00 +01:00           30.0\n",
       "ES2097A                   2024-01-01 06:00:00 +01:00           25.0\n",
       "ES2097A                   2024-01-01 07:00:00 +01:00           24.0\n",
       "...                                              ...            ...\n",
       "ES2097A                   2024-05-29 13:00:00 +01:00            NaN\n",
       "ES2097A                   2024-05-29 14:00:00 +01:00            NaN\n",
       "ES2097A                   2024-05-29 21:00:00 +01:00           16.0\n",
       "ES2097A                   2024-05-29 22:00:00 +01:00           14.0\n",
       "ES2097A                   2024-05-29 23:00:00 +01:00            NaN\n",
       "\n",
       "[3601 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns by name\n",
    "df = df.filter(items=['Concentration','DatetimeBegin'])\n",
    "\n",
    "# Reset DataFrame with columns in desired order\n",
    "df = df[['DatetimeBegin','Concentration']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us change the index column to the start time column `DatetimeBegin` by using `set_index()`. Then we will resample the hourly data into daily data using `.resample()`, passing in `D` for day, and `.mean()` to calculate the mean. \n",
    "\n",
    "Finally, in order to differentiate the data later on, we rename the column from `Concentration` to `PM10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 00:00:00+01:00</th>\n",
       "      <td>31.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-02 00:00:00+01:00</th>\n",
       "      <td>25.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-03 00:00:00+01:00</th>\n",
       "      <td>21.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-04 00:00:00+01:00</th>\n",
       "      <td>15.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 00:00:00+01:00</th>\n",
       "      <td>3.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26 00:00:00+01:00</th>\n",
       "      <td>12.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-27 00:00:00+01:00</th>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-28 00:00:00+01:00</th>\n",
       "      <td>16.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-29 00:00:00+01:00</th>\n",
       "      <td>15.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-30 00:00:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PM10\n",
       "DatetimeBegin                       \n",
       "2024-01-01 00:00:00+01:00  31.666667\n",
       "2024-01-02 00:00:00+01:00  25.333333\n",
       "2024-01-03 00:00:00+01:00  21.208333\n",
       "2024-01-04 00:00:00+01:00  15.833333\n",
       "2024-01-05 00:00:00+01:00   3.708333\n",
       "...                              ...\n",
       "2024-05-26 00:00:00+01:00  12.166667\n",
       "2024-05-27 00:00:00+01:00  13.833333\n",
       "2024-05-28 00:00:00+01:00  16.541667\n",
       "2024-05-29 00:00:00+01:00  15.647059\n",
       "2024-05-30 00:00:00+01:00        NaN\n",
       "\n",
       "[151 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set date column as index \n",
    "df = df.set_index('DatetimeBegin')\n",
    "\n",
    "# Converting the Index to a DatetimeIndex\n",
    "df.index = pd.to_datetime(df.index)\n",
    "\n",
    "# Resample hourly data to daily mean of PM10\n",
    "pm10_daily = df.resample('D').mean()\n",
    "\n",
    "# Rename Concentration column\n",
    "pm10_daily.rename(columns={'Concentration': 'PM10'}, inplace=True)\n",
    "pm10_daily\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us repeat all the above steps for the PM2.5 data for the same station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>PM2.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 00:00:00+01:00</th>\n",
       "      <td>12.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-02 00:00:00+01:00</th>\n",
       "      <td>8.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-03 00:00:00+01:00</th>\n",
       "      <td>7.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-04 00:00:00+01:00</th>\n",
       "      <td>6.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 00:00:00+01:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26 00:00:00+01:00</th>\n",
       "      <td>7.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-27 00:00:00+01:00</th>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-28 00:00:00+01:00</th>\n",
       "      <td>8.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-29 00:00:00+01:00</th>\n",
       "      <td>7.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-30 00:00:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               PM2.5\n",
       "DatetimeBegin                       \n",
       "2024-01-01 00:00:00+01:00  12.125000\n",
       "2024-01-02 00:00:00+01:00   8.833333\n",
       "2024-01-03 00:00:00+01:00   7.875000\n",
       "2024-01-04 00:00:00+01:00   6.041667\n",
       "2024-01-05 00:00:00+01:00   1.833333\n",
       "...                              ...\n",
       "2024-05-26 00:00:00+01:00   7.208333\n",
       "2024-05-27 00:00:00+01:00   6.750000\n",
       "2024-05-28 00:00:00+01:00   8.083333\n",
       "2024-05-29 00:00:00+01:00   7.352941\n",
       "2024-05-30 00:00:00+01:00        NaN\n",
       "\n",
       "[151 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_table('../../eodata/2_observations/eea/ES/palma/ES_6001_68800_2024_timeseries.csv', delimiter=',', header=[0], index_col=4, low_memory=False)\n",
    "\n",
    "# select columns by name\n",
    "df2 = df2.filter(items=['Concentration','DatetimeBegin'])\n",
    "\n",
    "# Reset DataFrame with columns in desired order\n",
    "df2 = df2[['DatetimeBegin','Concentration']]\n",
    "\n",
    "# Set date column as index \n",
    "df2 = df2.set_index('DatetimeBegin')\n",
    "\n",
    "# Converting the Index to a DatetimeIndex\n",
    "df2.index = pd.to_datetime(df2.index)\n",
    "\n",
    "# Resample hourly data to daily mean of PM2.5\n",
    "pm2pt5_daily = df2.resample('D').mean()\n",
    "\n",
    "# Rename Concentration column\n",
    "pm2pt5_daily.rename(columns={'Concentration': 'PM2.5'}, inplace=True)\n",
    "pm2pt5_daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are interested in plotting only data for April 2024, we have to filter both data frames further. Using `.index.to_series()` enables us to turn the `DatetimeIndex` into a series. We then can pass the start date of `2024-04-01` and the end date of `2024-04-28` to the `.between()` method to filter this series to only April 2024."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-04-01 00:00:00+01:00</th>\n",
       "      <td>16.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-02 00:00:00+01:00</th>\n",
       "      <td>9.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-03 00:00:00+01:00</th>\n",
       "      <td>8.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-04 00:00:00+01:00</th>\n",
       "      <td>7.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-05 00:00:00+01:00</th>\n",
       "      <td>12.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-06 00:00:00+01:00</th>\n",
       "      <td>11.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-07 00:00:00+01:00</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-08 00:00:00+01:00</th>\n",
       "      <td>14.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-09 00:00:00+01:00</th>\n",
       "      <td>25.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-10 00:00:00+01:00</th>\n",
       "      <td>46.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-11 00:00:00+01:00</th>\n",
       "      <td>92.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-12 00:00:00+01:00</th>\n",
       "      <td>31.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-13 00:00:00+01:00</th>\n",
       "      <td>28.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-14 00:00:00+01:00</th>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-15 00:00:00+01:00</th>\n",
       "      <td>101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-16 00:00:00+01:00</th>\n",
       "      <td>80.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-17 00:00:00+01:00</th>\n",
       "      <td>26.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-18 00:00:00+01:00</th>\n",
       "      <td>39.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-19 00:00:00+01:00</th>\n",
       "      <td>48.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-20 00:00:00+01:00</th>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-21 00:00:00+01:00</th>\n",
       "      <td>14.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22 00:00:00+01:00</th>\n",
       "      <td>23.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 00:00:00+01:00</th>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24 00:00:00+01:00</th>\n",
       "      <td>23.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25 00:00:00+01:00</th>\n",
       "      <td>24.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-26 00:00:00+01:00</th>\n",
       "      <td>17.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-27 00:00:00+01:00</th>\n",
       "      <td>10.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-28 00:00:00+01:00</th>\n",
       "      <td>11.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-29 00:00:00+01:00</th>\n",
       "      <td>5.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30 00:00:00+01:00</th>\n",
       "      <td>6.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 PM10\n",
       "DatetimeBegin                        \n",
       "2024-04-01 00:00:00+01:00   16.304348\n",
       "2024-04-02 00:00:00+01:00    9.041667\n",
       "2024-04-03 00:00:00+01:00    8.043478\n",
       "2024-04-04 00:00:00+01:00    7.636364\n",
       "2024-04-05 00:00:00+01:00   12.166667\n",
       "2024-04-06 00:00:00+01:00   11.458333\n",
       "2024-04-07 00:00:00+01:00   16.000000\n",
       "2024-04-08 00:00:00+01:00   14.125000\n",
       "2024-04-09 00:00:00+01:00   25.625000\n",
       "2024-04-10 00:00:00+01:00   46.291667\n",
       "2024-04-11 00:00:00+01:00   92.083333\n",
       "2024-04-12 00:00:00+01:00   31.625000\n",
       "2024-04-13 00:00:00+01:00   28.416667\n",
       "2024-04-14 00:00:00+01:00   37.000000\n",
       "2024-04-15 00:00:00+01:00  101.500000\n",
       "2024-04-16 00:00:00+01:00   80.416667\n",
       "2024-04-17 00:00:00+01:00   26.272727\n",
       "2024-04-18 00:00:00+01:00   39.125000\n",
       "2024-04-19 00:00:00+01:00   48.875000\n",
       "2024-04-20 00:00:00+01:00   20.750000\n",
       "2024-04-21 00:00:00+01:00   14.166667\n",
       "2024-04-22 00:00:00+01:00   23.458333\n",
       "2024-04-23 00:00:00+01:00   23.750000\n",
       "2024-04-24 00:00:00+01:00   23.500000\n",
       "2024-04-25 00:00:00+01:00   24.625000\n",
       "2024-04-26 00:00:00+01:00   17.375000\n",
       "2024-04-27 00:00:00+01:00   10.333333\n",
       "2024-04-28 00:00:00+01:00   11.125000\n",
       "2024-04-29 00:00:00+01:00    5.523810\n",
       "2024-04-30 00:00:00+01:00    6.875000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm10_Apr2024 = pm10_daily[pm10_daily.index.to_series().between('2024-04-01', '2024-04-30')]\n",
    "pm10_Apr2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
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       "    <tr>\n",
       "      <th>DatetimeBegin</th>\n",
       "      <th></th>\n",
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       "      <th>2024-04-01 00:00:00+01:00</th>\n",
       "      <td>7.260870</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-02 00:00:00+01:00</th>\n",
       "      <td>4.125000</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-03 00:00:00+01:00</th>\n",
       "      <td>3.521739</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-04 00:00:00+01:00</th>\n",
       "      <td>3.681818</td>\n",
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       "      <th>2024-04-05 00:00:00+01:00</th>\n",
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       "      <th>2024-04-06 00:00:00+01:00</th>\n",
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       "      <th>2024-04-07 00:00:00+01:00</th>\n",
       "      <td>7.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-08 00:00:00+01:00</th>\n",
       "      <td>6.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-09 00:00:00+01:00</th>\n",
       "      <td>11.833333</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-10 00:00:00+01:00</th>\n",
       "      <td>20.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-11 00:00:00+01:00</th>\n",
       "      <td>27.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-12 00:00:00+01:00</th>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-13 00:00:00+01:00</th>\n",
       "      <td>14.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-14 00:00:00+01:00</th>\n",
       "      <td>16.833333</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-15 00:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2024-04-16 00:00:00+01:00</th>\n",
       "      <td>25.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-17 00:00:00+01:00</th>\n",
       "      <td>12.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-18 00:00:00+01:00</th>\n",
       "      <td>16.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-19 00:00:00+01:00</th>\n",
       "      <td>17.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-20 00:00:00+01:00</th>\n",
       "      <td>8.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-21 00:00:00+01:00</th>\n",
       "      <td>6.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22 00:00:00+01:00</th>\n",
       "      <td>10.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23 00:00:00+01:00</th>\n",
       "      <td>10.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24 00:00:00+01:00</th>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25 00:00:00+01:00</th>\n",
       "      <td>10.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-26 00:00:00+01:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-27 00:00:00+01:00</th>\n",
       "      <td>4.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-28 00:00:00+01:00</th>\n",
       "      <td>4.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-29 00:00:00+01:00</th>\n",
       "      <td>2.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30 00:00:00+01:00</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               PM2.5\n",
       "DatetimeBegin                       \n",
       "2024-04-01 00:00:00+01:00   7.260870\n",
       "2024-04-02 00:00:00+01:00   4.125000\n",
       "2024-04-03 00:00:00+01:00   3.521739\n",
       "2024-04-04 00:00:00+01:00   3.681818\n",
       "2024-04-05 00:00:00+01:00   5.083333\n",
       "2024-04-06 00:00:00+01:00   5.208333\n",
       "2024-04-07 00:00:00+01:00   7.041667\n",
       "2024-04-08 00:00:00+01:00   6.583333\n",
       "2024-04-09 00:00:00+01:00  11.833333\n",
       "2024-04-10 00:00:00+01:00  20.208333\n",
       "2024-04-11 00:00:00+01:00  27.458333\n",
       "2024-04-12 00:00:00+01:00  12.500000\n",
       "2024-04-13 00:00:00+01:00  14.541667\n",
       "2024-04-14 00:00:00+01:00  16.833333\n",
       "2024-04-15 00:00:00+01:00  29.916667\n",
       "2024-04-16 00:00:00+01:00  25.541667\n",
       "2024-04-17 00:00:00+01:00  12.681818\n",
       "2024-04-18 00:00:00+01:00  16.500000\n",
       "2024-04-19 00:00:00+01:00  17.583333\n",
       "2024-04-20 00:00:00+01:00   8.458333\n",
       "2024-04-21 00:00:00+01:00   6.041667\n",
       "2024-04-22 00:00:00+01:00  10.750000\n",
       "2024-04-23 00:00:00+01:00  10.875000\n",
       "2024-04-24 00:00:00+01:00  11.500000\n",
       "2024-04-25 00:00:00+01:00  10.583333\n",
       "2024-04-26 00:00:00+01:00   7.666667\n",
       "2024-04-27 00:00:00+01:00   4.250000\n",
       "2024-04-28 00:00:00+01:00   4.791667\n",
       "2024-04-29 00:00:00+01:00   2.523810\n",
       "2024-04-30 00:00:00+01:00   3.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm2pt5_Apr2024 = pm2pt5_daily[pm2pt5_daily.index.to_series().between('2024-04-01', '2024-04-30')]\n",
    "pm2pt5_Apr2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize daily EEA PM10 and EEA PM2.5 at Palma de Mallorca, Spain for April 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to visualize all points of PM10 and PM2.5 at Palma de Mallorca, Spain for April 2024. \n",
    "\n",
    "You can use the built-in `plot()` function of the pandas library to define a line plot. With the `filter` function, you can select the dataframe columns you wish to visualize. The visualisation code below consists of five main parts:\n",
    "* `Initiate a matplotlib figure`\n",
    "* `Define a line plot with the built-in plot function of the pandas library`\n",
    "* `Set title and axes label information`\n",
    "* `Format axes ticks`\n",
    "* `Add additional features, such as a grid or legend`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initiate a matplotlib figure\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = plt.axes()\n",
    "\n",
    "# Select pandas dataframe columns and define a line plot for PM10 and PM2.5 each\n",
    "pm10_Apr2024.filter(['PM10']).plot(ax=ax, style='o-', label='PM10')\n",
    "pm2pt5_Apr2024.filter(['PM2.5']).plot(ax=ax, style='o-',label='PM2.5')\n",
    "plt.axhline(y=50, color='r', linestyle='dotted', label='PM10 daily limit')\n",
    "\n",
    "# Set title and axes lable information\n",
    "plt.title('\\nPM2.5 and PM10 for April 2024 - Palma de Mallorca, Spain', fontsize=20, pad=20)\n",
    "plt.ylabel('Particulate matter in micrograms per cubic metre (µg/m3)\\n', fontsize=16)\n",
    "\n",
    "plt.xlabel('Day', fontsize=16)\n",
    "\n",
    "# Format the axes ticks\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# Set major ticks on the y-axis every 10, minor ticks every 5\n",
    "major_ticks = np.arange(0, 101, 10)\n",
    "minor_ticks = np.arange(0, 101, 5)\n",
    "ax.set_yticks(major_ticks)\n",
    "ax.set_yticks(minor_ticks, minor=True)\n",
    "\n",
    "# Use different settings for the grids\n",
    "ax.grid(which='minor', alpha=0.2)\n",
    "ax.grid(which='major', alpha=0.8)\n",
    "\n",
    "# Add additionally a legend and grid to the plot\n",
    "plt.legend(fontsize=16,loc=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows you that the PM10 guidelines of 50µg/m3 set by the EU were exceeded multiple times between 10th and 17th April 2024."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}