calc_yearly_air_quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0f40e20-5149-41aa-92f1-1bd9b2706834",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb90b49-bcf1-4f0a-bd7e-c43e2d68b8c0",
   "metadata": {},
   "source": [
    "## Read data in and review columns and the head of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "782eef9d-577d-471d-b73b-50a585f63398",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/63/jzv4h4hj37j3wl3_r5y5828h0000gn/T/ipykernel_50993/3925539028.py:1: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  daily_df = pd.read_csv('int_data/aqi_data_daily_raw.csv')\n"
     ]
    }
   ],
   "source": [
    "daily_df = pd.read_csv('int_data/aqi_data_daily_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9a52362-141c-45e5-8ecb-40d3ad635d6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32374 entries, 0 to 32373\n",
      "Data columns (total 33 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Unnamed: 0            32374 non-null  int64  \n",
      " 1   state_code            32374 non-null  int64  \n",
      " 2   county_code           32374 non-null  int64  \n",
      " 3   site_number           32374 non-null  int64  \n",
      " 4   parameter_code        32374 non-null  int64  \n",
      " 5   poc                   32374 non-null  int64  \n",
      " 6   latitude              32374 non-null  float64\n",
      " 7   longitude             32374 non-null  float64\n",
      " 8   datum                 32374 non-null  object \n",
      " 9   parameter             32374 non-null  object \n",
      " 10  sample_duration_code  32374 non-null  object \n",
      " 11  sample_duration       32374 non-null  object \n",
      " 12  pollutant_standard    30746 non-null  object \n",
      " 13  date_local            32374 non-null  object \n",
      " 14  units_of_measure      32374 non-null  object \n",
      " 15  event_type            32374 non-null  object \n",
      " 16  observation_count     32374 non-null  int64  \n",
      " 17  observation_percent   32374 non-null  float64\n",
      " 18  validity_indicator    32374 non-null  object \n",
      " 19  arithmetic_mean       32374 non-null  float64\n",
      " 20  first_max_value       32374 non-null  float64\n",
      " 21  first_max_hour        32374 non-null  int64  \n",
      " 22  aqi                   30746 non-null  float64\n",
      " 23  method_code           32374 non-null  int64  \n",
      " 24  method                32374 non-null  object \n",
      " 25  local_site_name       32374 non-null  object \n",
      " 26  site_address          32374 non-null  object \n",
      " 27  state                 32374 non-null  object \n",
      " 28  county                32374 non-null  object \n",
      " 29  city                  32374 non-null  object \n",
      " 30  cbsa_code             32374 non-null  int64  \n",
      " 31  cbsa                  32374 non-null  object \n",
      " 32  date_of_last_change   32374 non-null  object \n",
      "dtypes: float64(6), int64(10), object(17)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "daily_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e3684bbb-049e-4122-b663-75bdbbd890da",
   "metadata": {},
   "outputs": [
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       "      <td>2000-05-18</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>13.2</td>\n",
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       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
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       "      <td>2000-05-24</td>\n",
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       "      <td>Y</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
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       "      <td>8.5</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  state_code  county_code  site_number  parameter_code  poc  \\\n",
       "0           0          48          355           32           88101    2   \n",
       "1           1          48          355           32           88101    2   \n",
       "2           2          48          355           32           88101    2   \n",
       "3           3          48          355           32           88101    2   \n",
       "4           4          48          355           32           88101    2   \n",
       "\n",
       "    latitude  longitude  datum                 parameter sample_duration_code  \\\n",
       "0  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "1  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "2  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "3  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "4  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "\n",
       "  sample_duration pollutant_standard  date_local             units_of_measure  \\\n",
       "0         24 HOUR  PM25 24-hour 2006  2000-05-12  Micrograms/cubic meter (LC)   \n",
       "1         24 HOUR  PM25 24-hour 2006  2000-05-18  Micrograms/cubic meter (LC)   \n",
       "2         24 HOUR  PM25 24-hour 2006  2000-05-24  Micrograms/cubic meter (LC)   \n",
       "3         24 HOUR  PM25 24-hour 2006  2000-05-30  Micrograms/cubic meter (LC)   \n",
       "4         24 HOUR  PM25 24-hour 2006  2000-06-03  Micrograms/cubic meter (LC)   \n",
       "\n",
       "  event_type  observation_count  observation_percent validity_indicator  \\\n",
       "0  No Events                  1                100.0                  Y   \n",
       "1  No Events                  1                100.0                  Y   \n",
       "2  No Events                  1                100.0                  Y   \n",
       "3  No Events                  1                100.0                  Y   \n",
       "4  No Events                  1                100.0                  Y   \n",
       "\n",
       "   arithmetic_mean  first_max_value  first_max_hour   aqi  method_code  \\\n",
       "0             20.6             20.6               0  72.0          118   \n",
       "1             13.2             13.2               0  59.0          118   \n",
       "2             14.0             14.0               0  60.0          118   \n",
       "3              6.7              6.7               0  37.0          118   \n",
       "4              8.5              8.5               0  47.0          118   \n",
       "\n",
       "                                              method          local_site_name  \\\n",
       "0  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "1  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "2  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "3  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "4  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "\n",
       "           site_address  state  county            city  cbsa_code  \\\n",
       "0  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "1  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "2  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "3  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "4  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "\n",
       "                 cbsa date_of_last_change  \n",
       "0  Corpus Christi, TX          2024-05-19  \n",
       "1  Corpus Christi, TX          2024-05-19  \n",
       "2  Corpus Christi, TX          2024-05-19  \n",
       "3  Corpus Christi, TX          2024-05-19  \n",
       "4  Corpus Christi, TX          2024-05-19  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d758a7-841b-4e58-9c64-254cc3ad8734",
   "metadata": {},
   "source": [
    "### Extract year to see the number of unique stations reporting annually "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "286b2694-2168-4cae-8c2d-edb5e3859262",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df['date_local'] = pd.to_datetime(daily_df['date_local'])\n",
    "daily_df['year'] = daily_df['date_local'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bf42ddd-332b-4477-a7f4-46a63114bd52",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2000-05-12</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
       "      <td>Corpus Christi Huisache</td>\n",
       "      <td>3810 Huisache Street</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>18580</td>\n",
       "      <td>Corpus Christi, TX</td>\n",
       "      <td>2024-05-19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>355</td>\n",
       "      <td>32</td>\n",
       "      <td>88101</td>\n",
       "      <td>2</td>\n",
       "      <td>27.804489</td>\n",
       "      <td>-97.431553</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>PM2.5 - Local Conditions</td>\n",
       "      <td>7</td>\n",
       "      <td>24 HOUR</td>\n",
       "      <td>PM25 24-hour 2006</td>\n",
       "      <td>2000-05-18</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>13.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
       "      <td>Corpus Christi Huisache</td>\n",
       "      <td>3810 Huisache Street</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>18580</td>\n",
       "      <td>Corpus Christi, TX</td>\n",
       "      <td>2024-05-19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>355</td>\n",
       "      <td>32</td>\n",
       "      <td>88101</td>\n",
       "      <td>2</td>\n",
       "      <td>27.804489</td>\n",
       "      <td>-97.431553</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>PM2.5 - Local Conditions</td>\n",
       "      <td>7</td>\n",
       "      <td>24 HOUR</td>\n",
       "      <td>PM25 24-hour 2006</td>\n",
       "      <td>2000-05-24</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
       "      <td>Corpus Christi Huisache</td>\n",
       "      <td>3810 Huisache Street</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>18580</td>\n",
       "      <td>Corpus Christi, TX</td>\n",
       "      <td>2024-05-19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>355</td>\n",
       "      <td>32</td>\n",
       "      <td>88101</td>\n",
       "      <td>2</td>\n",
       "      <td>27.804489</td>\n",
       "      <td>-97.431553</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>PM2.5 - Local Conditions</td>\n",
       "      <td>7</td>\n",
       "      <td>24 HOUR</td>\n",
       "      <td>PM25 24-hour 2006</td>\n",
       "      <td>2000-05-30</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
       "      <td>Corpus Christi Huisache</td>\n",
       "      <td>3810 Huisache Street</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>18580</td>\n",
       "      <td>Corpus Christi, TX</td>\n",
       "      <td>2024-05-19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>355</td>\n",
       "      <td>32</td>\n",
       "      <td>88101</td>\n",
       "      <td>2</td>\n",
       "      <td>27.804489</td>\n",
       "      <td>-97.431553</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>PM2.5 - Local Conditions</td>\n",
       "      <td>7</td>\n",
       "      <td>24 HOUR</td>\n",
       "      <td>PM25 24-hour 2006</td>\n",
       "      <td>2000-06-03</td>\n",
       "      <td>Micrograms/cubic meter (LC)</td>\n",
       "      <td>No Events</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>118</td>\n",
       "      <td>R &amp; P Model 2025 PM2.5 Sequential w/WINS - GRA...</td>\n",
       "      <td>Corpus Christi Huisache</td>\n",
       "      <td>3810 Huisache Street</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Nueces</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>18580</td>\n",
       "      <td>Corpus Christi, TX</td>\n",
       "      <td>2024-05-19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  state_code  county_code  site_number  parameter_code  poc  \\\n",
       "0           0          48          355           32           88101    2   \n",
       "1           1          48          355           32           88101    2   \n",
       "2           2          48          355           32           88101    2   \n",
       "3           3          48          355           32           88101    2   \n",
       "4           4          48          355           32           88101    2   \n",
       "\n",
       "    latitude  longitude  datum                 parameter sample_duration_code  \\\n",
       "0  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "1  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "2  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "3  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "4  27.804489 -97.431553  NAD83  PM2.5 - Local Conditions                    7   \n",
       "\n",
       "  sample_duration pollutant_standard date_local             units_of_measure  \\\n",
       "0         24 HOUR  PM25 24-hour 2006 2000-05-12  Micrograms/cubic meter (LC)   \n",
       "1         24 HOUR  PM25 24-hour 2006 2000-05-18  Micrograms/cubic meter (LC)   \n",
       "2         24 HOUR  PM25 24-hour 2006 2000-05-24  Micrograms/cubic meter (LC)   \n",
       "3         24 HOUR  PM25 24-hour 2006 2000-05-30  Micrograms/cubic meter (LC)   \n",
       "4         24 HOUR  PM25 24-hour 2006 2000-06-03  Micrograms/cubic meter (LC)   \n",
       "\n",
       "  event_type  observation_count  observation_percent validity_indicator  \\\n",
       "0  No Events                  1                100.0                  Y   \n",
       "1  No Events                  1                100.0                  Y   \n",
       "2  No Events                  1                100.0                  Y   \n",
       "3  No Events                  1                100.0                  Y   \n",
       "4  No Events                  1                100.0                  Y   \n",
       "\n",
       "   arithmetic_mean  first_max_value  first_max_hour   aqi  method_code  \\\n",
       "0             20.6             20.6               0  72.0          118   \n",
       "1             13.2             13.2               0  59.0          118   \n",
       "2             14.0             14.0               0  60.0          118   \n",
       "3              6.7              6.7               0  37.0          118   \n",
       "4              8.5              8.5               0  47.0          118   \n",
       "\n",
       "                                              method          local_site_name  \\\n",
       "0  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "1  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "2  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "3  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "4  R & P Model 2025 PM2.5 Sequential w/WINS - GRA...  Corpus Christi Huisache   \n",
       "\n",
       "           site_address  state  county            city  cbsa_code  \\\n",
       "0  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "1  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "2  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "3  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "4  3810 Huisache Street  Texas  Nueces  Corpus Christi      18580   \n",
       "\n",
       "                 cbsa date_of_last_change  year  \n",
       "0  Corpus Christi, TX          2024-05-19  2000  \n",
       "1  Corpus Christi, TX          2024-05-19  2000  \n",
       "2  Corpus Christi, TX          2024-05-19  2000  \n",
       "3  Corpus Christi, TX          2024-05-19  2000  \n",
       "4  Corpus Christi, TX          2024-05-19  2000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4673f4ed-194c-488c-ae7d-e6386ed6fda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "local_site_name\n",
       "Corpus Christi Huisache                    18718\n",
       "Dona Park                                   3272\n",
       "Isla Blanca State Park Road                 3375\n",
       "National Seashore                           4153\n",
       "SOUTH OF NEFF & NAVIGATION INTERSECTION      936\n",
       "San Benito                                  1920\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.groupby('local_site_name').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0a819e-a805-4fa4-83b5-9d86fc16d491",
   "metadata": {},
   "source": [
    "## Review number of stations per year reporting data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fbae730b-6e4e-4722-9aa6-9f2c2c72b065",
   "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>year</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  0\n",
       "0   2000  3\n",
       "1   2001  4\n",
       "2   2002  4\n",
       "3   2003  4\n",
       "4   2004  4\n",
       "5   2005  5\n",
       "6   2006  3\n",
       "7   2007  3\n",
       "8   2008  3\n",
       "9   2009  3\n",
       "10  2010  3\n",
       "11  2011  3\n",
       "12  2012  3\n",
       "13  2013  3\n",
       "14  2014  2\n",
       "15  2015  2\n",
       "16  2016  2\n",
       "17  2017  2\n",
       "18  2018  3\n",
       "19  2019  3\n",
       "20  2020  4\n",
       "21  2021  4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readings_per_station_per_year = daily_df.groupby(['year','local_site_name']).size().reset_index()\n",
    "readings_per_station_per_year.groupby('year').size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb6cca8-5044-4947-ba54-25a10b29343f",
   "metadata": {},
   "source": [
    "## Average at the daily level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "19662c37-8d9e-4a34-9aee-2a333c22af42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>date_local</th>\n",
       "      <th>days_readings_mean_values</th>\n",
       "      <th>count_days_readings</th>\n",
       "      <th>min_days_readings</th>\n",
       "      <th>max_days_readings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-05-01</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-05-03</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-05-12</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>16</td>\n",
       "      <td>16.7</td>\n",
       "      <td>20.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-05-15</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-05-16</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>2021-08-27</td>\n",
       "      <td>4.107407</td>\n",
       "      <td>27</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>2021-08-28</td>\n",
       "      <td>5.637037</td>\n",
       "      <td>27</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>9.939198</td>\n",
       "      <td>27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>11.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>2021-08-30</td>\n",
       "      <td>17.973457</td>\n",
       "      <td>27</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>20.538889</td>\n",
       "      <td>27</td>\n",
       "      <td>18.8</td>\n",
       "      <td>23.208333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1420 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     date_local  days_readings_mean_values  count_days_readings  \\\n",
       "0    2000-05-01                  15.500000                    8   \n",
       "1    2000-05-03                  14.600000                    8   \n",
       "2    2000-05-12                  18.650000                   16   \n",
       "3    2000-05-15                  10.100000                    8   \n",
       "4    2000-05-16                  10.400000                    8   \n",
       "...         ...                        ...                  ...   \n",
       "1415 2021-08-27                   4.107407                   27   \n",
       "1416 2021-08-28                   5.637037                   27   \n",
       "1417 2021-08-29                   9.939198                   27   \n",
       "1418 2021-08-30                  17.973457                   27   \n",
       "1419 2021-08-31                  20.538889                   27   \n",
       "\n",
       "      min_days_readings  max_days_readings  \n",
       "0                  15.5          15.500000  \n",
       "1                  14.6          14.600000  \n",
       "2                  16.7          20.600000  \n",
       "3                  10.1          10.100000  \n",
       "4                  10.4          10.400000  \n",
       "...                 ...                ...  \n",
       "1415                3.3           5.666667  \n",
       "1416                4.4           6.375000  \n",
       "1417                7.8          11.250000  \n",
       "1418               16.2          19.083333  \n",
       "1419               18.8          23.208333  \n",
       "\n",
       "[1420 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_agg = daily_df.groupby('date_local').agg(\n",
    "    days_readings_mean_values=('arithmetic_mean', 'mean'),  # Mean of 'values_to_aggregate'\n",
    "    count_days_readings=('arithmetic_mean', 'count'),    # Count of 'other_column'\n",
    "    min_days_readings=('arithmetic_mean', 'min'),\n",
    "    max_days_readings=('arithmetic_mean', 'max')\n",
    ").reset_index()\n",
    "daily_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b1e0abc-71d4-4e30-aef5-5a09ac281e65",
   "metadata": {},
   "source": [
    "## Extract year from date of reading to agg at year level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "973801c4-db86-41ec-a349-79ece9cd4cff",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_local</th>\n",
       "      <th>days_readings_mean_values</th>\n",
       "      <th>count_days_readings</th>\n",
       "      <th>min_days_readings</th>\n",
       "      <th>max_days_readings</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-05-01</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-05-03</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-05-12</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>16</td>\n",
       "      <td>16.7</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-05-15</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-05-16</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>2021-08-27</td>\n",
       "      <td>4.107407</td>\n",
       "      <td>27</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>2021-08-28</td>\n",
       "      <td>5.637037</td>\n",
       "      <td>27</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6.375000</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>9.939198</td>\n",
       "      <td>27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>2021-08-30</td>\n",
       "      <td>17.973457</td>\n",
       "      <td>27</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.083333</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>20.538889</td>\n",
       "      <td>27</td>\n",
       "      <td>18.8</td>\n",
       "      <td>23.208333</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1420 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     date_local  days_readings_mean_values  count_days_readings  \\\n",
       "0    2000-05-01                  15.500000                    8   \n",
       "1    2000-05-03                  14.600000                    8   \n",
       "2    2000-05-12                  18.650000                   16   \n",
       "3    2000-05-15                  10.100000                    8   \n",
       "4    2000-05-16                  10.400000                    8   \n",
       "...         ...                        ...                  ...   \n",
       "1415 2021-08-27                   4.107407                   27   \n",
       "1416 2021-08-28                   5.637037                   27   \n",
       "1417 2021-08-29                   9.939198                   27   \n",
       "1418 2021-08-30                  17.973457                   27   \n",
       "1419 2021-08-31                  20.538889                   27   \n",
       "\n",
       "      min_days_readings  max_days_readings  year  \n",
       "0                  15.5          15.500000  2000  \n",
       "1                  14.6          14.600000  2000  \n",
       "2                  16.7          20.600000  2000  \n",
       "3                  10.1          10.100000  2000  \n",
       "4                  10.4          10.400000  2000  \n",
       "...                 ...                ...   ...  \n",
       "1415                3.3           5.666667  2021  \n",
       "1416                4.4           6.375000  2021  \n",
       "1417                7.8          11.250000  2021  \n",
       "1418               16.2          19.083333  2021  \n",
       "1419               18.8          23.208333  2021  \n",
       "\n",
       "[1420 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_agg['date_local'] = pd.to_datetime(daily_agg['date_local'])\n",
    "daily_agg['year'] = daily_agg['date_local'].dt.year\n",
    "daily_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab63ddc5-a1eb-46f0-8c28-299c8dddaa03",
   "metadata": {},
   "source": [
    "## Average the per day particulate level from all reporting stations on the annual level\n",
    "\n",
    "Then save the data as a csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "573115d5-5630-4024-bdbb-7edad1289eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_annual_air_quality = daily_agg.groupby('year').agg(\n",
    "    years_avg_air_quality=('days_readings_mean_values', 'mean')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9db9feb1-66f2-4157-80f6-187300789553",
   "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>year</th>\n",
       "      <th>years_avg_air_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>11.603472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>10.193173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>10.894341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>12.061240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>9.562841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>11.360600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>12.257895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>13.186491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>12.767517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>13.656589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>12.496970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>10.885470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>12.154563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>10.413618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>12.485000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>13.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>10.404878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>9.104386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>12.563715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>10.997073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>12.101809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>10.184033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  years_avg_air_quality\n",
       "0   2000              11.603472\n",
       "1   2001              10.193173\n",
       "2   2002              10.894341\n",
       "3   2003              12.061240\n",
       "4   2004               9.562841\n",
       "5   2005              11.360600\n",
       "6   2006              12.257895\n",
       "7   2007              13.186491\n",
       "8   2008              12.767517\n",
       "9   2009              13.656589\n",
       "10  2010              12.496970\n",
       "11  2011              10.885470\n",
       "12  2012              12.154563\n",
       "13  2013              10.413618\n",
       "14  2014              12.485000\n",
       "15  2015              13.300000\n",
       "16  2016              10.404878\n",
       "17  2017               9.104386\n",
       "18  2018              12.563715\n",
       "19  2019              10.997073\n",
       "20  2020              12.101809\n",
       "21  2021              10.184033"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_annual_air_quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b3b3c84-dedd-419d-b944-9a55f7719701",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_annual_air_quality.to_csv('int_data/annual_avg_particulate_matter.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97aad3e9-e95f-446e-9c12-53f5e1558f1f",
   "metadata": {},
   "source": [
    "### Visualize Annual Average Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8bdda79f-e473-48a6-8812-d1558120e4b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(avg_annual_air_quality['year'], avg_annual_air_quality['years_avg_air_quality'], marker='o', linestyle='-', label='Yearly Avg Air Quality')\n",
    "\n",
    "plt.title('Yearly Average Air Quality Over Time', fontsize=14)\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.ylabel('Average Air Quality', fontsize=12)\n",
    "plt.xticks(avg_annual_air_quality['year'], rotation=45)\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}