final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31e89db9",
   "metadata": {},
   "source": [
    "## PART A: Create an Artificial Dataset (Total 30 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "967017c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kutuphaneler\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e071ea9",
   "metadata": {},
   "source": [
    "### Q1 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "b4324ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# age listesini olustur\n",
    "age = random.choices(\n",
    "    range(18, 26),\n",
    "    k=10000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "279e7588",
   "metadata": {},
   "outputs": [],
   "source": [
    "# team listesini olustur\n",
    "team = random.choices(\n",
    "    ['Fenerbahce', 'Galatasaray', 'Besiktas', 'Trabzonspor', 'Other'],\n",
    "    weights=(25, 25, 25, 10, 15),\n",
    "    k=10000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "060f9861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rating listesini olustur\n",
    "rating = random.choices(\n",
    "    range(1, 6),\n",
    "    k=10000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "75ae85b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# income listesini olustur\n",
    "income = random.choices(\n",
    "    range(3000, 10001, 100),\n",
    "    k=10000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "4d72618d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe olustur\n",
    "Q1 = pd.DataFrame.from_dict({'age': age, 'team': team, 'rating': rating, 'income': income})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "235e2497",
   "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>age</th>\n",
       "      <th>team</th>\n",
       "      <th>rating</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>2</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>5</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>4</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>4</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         team  rating  income\n",
       "0   24  Galatasaray       2    5500\n",
       "1   19        Other       3    4700\n",
       "2   19   Fenerbahce       5    3000\n",
       "3   25  Galatasaray       4    5700\n",
       "4   25  Trabzonspor       4    3800"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ciktiyi kontrol et\n",
    "Q1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "794187d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6525.32, 3.0042, 21.5294)"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ciktinin ortalamalarini kontrol et\n",
    "Q1.income.mean(), Q1.rating.mean(), Q1.age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94bbd9d",
   "metadata": {},
   "source": [
    "### Q2) (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "3dd2cbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# olasiliklari tanimlayacak fonksiyonu olusturdum\n",
    "gen_probs = lambda probs: [probs[0]] * 30 + [probs[1]] * 10 + [probs[2]] * 20 + [probs[3]] * 30 + [probs[4]] * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "70b628e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply fonksiyonuna verecegim sorgu fonksiyonu\n",
    "def grade(row):\n",
    "    if row.team == 'Fenerbahce' and (row.rating == 4 or row.rating == 5):\n",
    "        return random.choices(range(1, 101), weights=gen_probs([0, 1, 30, 60, 9]), k=1)[0]\n",
    "    \n",
    "    elif row.team == 'Fenerbahce' and (row.rating == 1 or row.rating == 2 or row.rating == 3):\n",
    "        return random.choices(range(1, 101), weights=gen_probs([30, 20, 20, 30, 0]), k=1)[0]\n",
    "    \n",
    "    elif row.team != 'Fenerbahce' and (row.rating == 4 or row.rating == 5):\n",
    "        return random.choices(range(1, 101), weights=gen_probs([30, 10, 20, 30 ,10]), k=1)[0]\n",
    "    \n",
    "    elif row.team != 'Fenerbahce' and (row.rating == 1 or row.rating == 2 or row.rating == 3):\n",
    "        return random.choices(range(1, 101), weights=gen_probs([60, 10, 30, 0, 0]), k=1)[0]\n",
    "    \n",
    "    else:\n",
    "        # hicbir sorgunun tutmamasi halinde goreyim diye yazdim. (imkansiz sonuc)\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "a2bce280",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>age</th>\n",
       "      <th>team</th>\n",
       "      <th>rating</th>\n",
       "      <th>income</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>2</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>4700</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>5</td>\n",
       "      <td>3000</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>4</td>\n",
       "      <td>5700</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>4</td>\n",
       "      <td>3800</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>3</td>\n",
       "      <td>9700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>2</td>\n",
       "      <td>7700</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>3</td>\n",
       "      <td>3900</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>8900</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>1</td>\n",
       "      <td>4200</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>5</td>\n",
       "      <td>5600</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>5</td>\n",
       "      <td>8300</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24</td>\n",
       "      <td>Besiktas</td>\n",
       "      <td>4</td>\n",
       "      <td>5300</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24</td>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>4</td>\n",
       "      <td>8200</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>4</td>\n",
       "      <td>7400</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age         team  rating  income  grade\n",
       "0    24  Galatasaray       2    5500     16\n",
       "1    19        Other       3    4700      9\n",
       "2    19   Fenerbahce       5    3000     79\n",
       "3    25  Galatasaray       4    5700     26\n",
       "4    25  Trabzonspor       4    3800     70\n",
       "5    22  Trabzonspor       3    9700     29\n",
       "6    22   Fenerbahce       2    7700     25\n",
       "7    24  Trabzonspor       3    3900      8\n",
       "8    24        Other       3    8900      5\n",
       "9    24   Fenerbahce       1    4200     30\n",
       "10   21   Fenerbahce       5    5600     47\n",
       "11   22  Galatasaray       5    8300     67\n",
       "12   24     Besiktas       4    5300     54\n",
       "13   24  Galatasaray       4    8200     24\n",
       "14   18   Fenerbahce       4    7400     71"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1 dataframe ini Q2 dataframe ine esitle ve Q2 dataframe inde grade isimli bir sutun olustur\n",
    "# bu sutunlari grade fonksiyonunun ciktilariyla doldur\n",
    "Q2 = Q1\n",
    "\n",
    "Q2['grade'] = Q2.apply(grade, axis=1)\n",
    "Q2.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e931d8",
   "metadata": {},
   "source": [
    "### Q3) (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "2e6baf64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>team</th>\n",
       "      <th>rating</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>5</td>\n",
       "      <td>70.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>4</td>\n",
       "      <td>68.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>48.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Besiktas</td>\n",
       "      <td>5</td>\n",
       "      <td>48.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>5</td>\n",
       "      <td>48.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Besiktas</td>\n",
       "      <td>4</td>\n",
       "      <td>47.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>5</td>\n",
       "      <td>46.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>2</td>\n",
       "      <td>46.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>4</td>\n",
       "      <td>46.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>1</td>\n",
       "      <td>46.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fenerbahce</td>\n",
       "      <td>3</td>\n",
       "      <td>45.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Other</td>\n",
       "      <td>5</td>\n",
       "      <td>45.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>4</td>\n",
       "      <td>45.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>27.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>2</td>\n",
       "      <td>26.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>1</td>\n",
       "      <td>26.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Other</td>\n",
       "      <td>2</td>\n",
       "      <td>25.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Besiktas</td>\n",
       "      <td>1</td>\n",
       "      <td>25.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Galatasaray</td>\n",
       "      <td>3</td>\n",
       "      <td>25.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>2</td>\n",
       "      <td>24.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Besiktas</td>\n",
       "      <td>2</td>\n",
       "      <td>24.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Besiktas</td>\n",
       "      <td>3</td>\n",
       "      <td>23.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>1</td>\n",
       "      <td>23.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Trabzonspor</td>\n",
       "      <td>3</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           team  rating  grade\n",
       "9    Fenerbahce       5  70.45\n",
       "8    Fenerbahce       4  68.94\n",
       "18        Other       4  48.47\n",
       "4      Besiktas       5  48.28\n",
       "24  Trabzonspor       5  48.21\n",
       "3      Besiktas       4  47.67\n",
       "14  Galatasaray       5  46.94\n",
       "6    Fenerbahce       2  46.92\n",
       "13  Galatasaray       4  46.14\n",
       "5    Fenerbahce       1  46.08\n",
       "7    Fenerbahce       3  45.83\n",
       "19        Other       5  45.79\n",
       "23  Trabzonspor       4  45.66\n",
       "15        Other       1  27.47\n",
       "11  Galatasaray       2  26.59\n",
       "10  Galatasaray       1  26.14\n",
       "16        Other       2  25.94\n",
       "0      Besiktas       1  25.89\n",
       "12  Galatasaray       3  25.38\n",
       "21  Trabzonspor       2  24.59\n",
       "1      Besiktas       2  24.38\n",
       "2      Besiktas       3  23.70\n",
       "20  Trabzonspor       1  23.41\n",
       "22  Trabzonspor       3  23.10\n",
       "17        Other       3  22.98"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ortalamalar tam tutmuyor sebebini bilmiyorum\n",
    "# team, rating, grade sutunlarini al\n",
    "# team ve rating ile grupla\n",
    "# team ve rating sutunlarini index olarak alma\n",
    "# gruplanmis verilerin ortalamasini al ve 0 dan sonra 2 basamakli olacak sekilde yuvarla\n",
    "Q3 = Q2\n",
    "\n",
    "Q3 = Q3[['team', 'rating', 'grade']].groupby(['team', 'rating'], as_index=False).mean().round(2)\n",
    "Q3.sort_values(by=['grade'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac3984b",
   "metadata": {},
   "source": [
    "### Q4) (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "3a7b9bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xkl1nltktyeWttRuTHD22m6q6d5IrW2snJfnrZdYPi/L1JHuNZ+Olqnaoqgds4TVnJHnOzH1P9quqXZZZ9t9sWHeSJyX5TG76Qnv1WI9HLvnK3seTPLvGmzpW1R5b2XfYWp9Mcueqes7MtJ3HxyU//zex3DLbsi25W4Yd7B+N95J41Gb6v2uSy8e6ffIm896V5D0ZLk+DqfpEkp2r6qnJxhtT/3mGMPSK9HW0uXUcWVX3GNexx1hbcHuwW5LvjuPHbDLv8PH9fpckRyT57HjA/EVJnjJuc5Lh9hsPreG+rdtl2Hdb7mB5kqSq7tta+2Jr7U+SXJ2b7j90szaTnJPkiBruo7dLksdlOPjBHYAzjJiKu1TV+nG8kjxtPGL08ar6lSSfH09DvibJU5L8UpLXV9WNSX6e4V4pN9Nau6GqnpjktKr6lyQnJbm+qs7PsLPyliTvr6rHJzkrN6Xkhyb5o6r6+djmU+f758LWa8MvaxyZ5C+rarcMn+X/I8OR3OW8PcOlYl+uoZiuyrAjsJSvJXlaVZ2Y5BtJTmit/WtVnZThGvbLMlxqsyVvz3BpzlfHWjqptfbmreg7bJXWWquqI5K8Ybyc8qoMn/N/nOEGnUt9/s9abhvx1WzltqS19s2q+kqG9/ylGXbGl/PyDF8EvpWh9ma/XP9dhvsavecW/WPAAow1+Lgkb6mql2c4WP3RJP81w/1QXjLu/y3767ittYur6mUZ9gnvlGG/77kZ6gJu616X4ZK0P8hwkGPWZzLcM/KXkry7tbauqt6R4bYAZ43fjda11p5RVS/NsP2pJB9trX14C+2+frwMrjKEsudn+OXCm7WZDDfrTvKl8bVvb619ZTwjitu5uukMNQAAbgvG4PWxrbWjF90XAG77xkvd1rbWnrelZbnjcIYRAMBtSFW9KcOlbI9edF8AgNsvZxgBAAAA0HHTawAAAAA6AiMAAAAAOgIjAAAAADoCIwDgdquq9qyq9ePwz1X13ZnnO65gu2dX1bqZ52ur6uyVag8AYN78ShoAcLvVWvt+koOSpKqOS3JNa+34VWr+HlX1qNba/16l9gAA5sYZRgDAHUpVHVxVn6qq86rqjKrae5z+zKo6t6rOr6r3V9XO4/RTquqEqjqrqi6tqodW1clV9bWqOmUzTb0+ycuWaH/fqvp0VX15HB48Tj907Nd7q+ofq+o1VfXkqvpSVV1QVfcdl9tr7N+54/Bbc/9HAgDu8ARGAMAdSSV5U5IjW2sHJzk5yavGeR9orT2wtXZgkq8l+f2Z1/1CkocleWGS05K8IckDkvxqVR20TFufT3JtVR22yfQrkxzeWvv1JEcl+cuZeQcm+c9JfjXJ0Un2a609KMnbkzx/XOaNSd7QWntgkt8b5wEAzJVL0gCAO5KdkhyQ5MyqSpLtklw+zjugql6ZZPckd01yxszrTmuttaq6IMkVrbULkqSqLkqyb5L1y7T3ygxnGf3xzLQdkrx5DJpuSLLfzLxzW2uXj+u+JMnHx+kXJNkQPD08yf5j/5PkblW1a2vtx1v+8wEAbhmBEQBwR1JJLmqtHbLEvFOSHNFaO7+qjkly6My8a8fHG2fGNzxfdn+qtfbJqnpFkt+cmfzCJFdkOJvoTkl+tkQ7m7Y1286dkhzSWvvpcu0CAGwrl6QBAHck1ybZq6oOSZKq2qGqHjDO2zXJ5VW1Q5Inz7HNVyV58czz3ZJc3lq7McNlZ9vdyvV9PMnzNjzZzCVxAABbTWAEANyR3JjkyCSvrarzM1xK9uBx3suTfDHJmUn+YV4NttY+muSqmUlvSfK0qvpChsvRfnIrV/mCJGur6qtVdXGSZ8+npwAAN6nW2qL7AAAAAMCEOMMIAAAAgI7ACAAAAICOwAgAAACAjsAIAAAAgI7ACAAAAICOwAgAAACAjsAIAAAAgM7/B9Oe247QZj1AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = Q3.pivot(index='team', \n",
    "         columns='rating', \n",
    "         values='grade').plot(kind='bar',\n",
    "                              figsize=(20, 15),\n",
    "                              width=.85,\n",
    "                              color=['#2E8B57', '#2E688B', '#8B2E43', '#D8D92F', '#2FD9C6'])\n",
    "\n",
    "plt.grid() # grafigi karelere bol\n",
    "\n",
    "plt.title('Average grade based on Team Name and Rating') # grafige baslik ver\n",
    "\n",
    "plt.xlabel('Team Name') # grafigin x ekseninin basligi\n",
    "plt.ylabel('Average Grade') # grafigin y ekseninin basligi\n",
    "\n",
    "plt.yticks(range(0, 85, 5)) # grafigin y ekseninin araliklari\n",
    "plt.xticks(rotation=0) # grafigin x ekseninin elemanlarinin yatay gozukmesi\n",
    "\n",
    "plt.legend(title='Rating Level', loc='upper center', ncol=5, fontsize=15) # legend i duzenle\n",
    "\n",
    "# her bir barin uzerine degerlerini yazdir\n",
    "for container in fig.containers:\n",
    "    fig.bar_label(container)\n",
    "\n",
    "plt.show() # grafigi goster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef73b62",
   "metadata": {},
   "source": [
    "# PART B: Water Quality Data (Total 70 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "cca59309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# veriyi oku ve data degiskenine ata\n",
    "data = pd.read_csv('data_.csv', encoding= 'unicode_escape', sep=';')\n",
    "data.rename(columns={'Unnamed: 0': 'X',\n",
    "                 'Unnamed: 1': 'X.1',\n",
    "                 'Parameter': 'Parameter',\n",
    "                 'Unit': 'Unit',\n",
    "                 'Year': 'Year',\n",
    "                 '1': 'X1',\n",
    "                 '2': 'X2',\n",
    "                 '3': 'X3',\n",
    "                 '4': 'X4',\n",
    "                 '5': 'X5',\n",
    "                 '6': 'X6',\n",
    "                 '7': 'X7',\n",
    "                 '8': 'X8',\n",
    "                 '9': 'X9',\n",
    "                 '10': 'X10',\n",
    "                 '11': 'X11',\n",
    "                 '12': 'X12'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "b4538661",
   "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>X</th>\n",
       "      <th>X.1</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Year</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Al</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Al</td>\n",
       "      <td>µg/L</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>As</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As</td>\n",
       "      <td>µg/L</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As</td>\n",
       "      <td>µg/L</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As</td>\n",
       "      <td>µg/L</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As</td>\n",
       "      <td>µg/L</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   X   X.1 Parameter  Unit  \\\n",
       "0  Station No: 01-11-00-001; Station Name: MERIC ...   NaN       NaN   NaN   \n",
       "1                                                NaN    Al       NaN   NaN   \n",
       "2                                                NaN   NaN        Al  µg/L   \n",
       "3                                                NaN    As       NaN   NaN   \n",
       "4                                                NaN   NaN        As  µg/L   \n",
       "5                                                NaN   NaN        As  µg/L   \n",
       "6                                                NaN   NaN        As  µg/L   \n",
       "7                                                NaN   NaN        As  µg/L   \n",
       "8                                                NaN  BOD5       NaN   NaN   \n",
       "9                                                NaN   NaN      BOD5  mg/L   \n",
       "\n",
       "     Year  X1    X2    X3      X4  X5      X6  X7   X8  X9     X10  X11  X12  \n",
       "0     NaN NaN   NaN   NaN     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "1     NaN NaN   NaN   NaN     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "2  2013.0 NaN   NaN   NaN  168.25 NaN  156.87 NaN  NaN NaN  161.21  NaN  NaN  \n",
       "3     NaN NaN   NaN   NaN     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "4  1987.0 NaN   NaN   NaN    4.00 NaN    1.00 NaN  NaN NaN    6.00  NaN  NaN  \n",
       "5  1992.0 NaN   NaN  32.0     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "6  1994.0 NaN   NaN   NaN     NaN NaN    9.00 NaN  NaN NaN     NaN  NaN  NaN  \n",
       "7  2013.0 NaN  3.49   NaN    1.17 NaN   52.20 NaN  NaN NaN    2.74  NaN  NaN  \n",
       "8     NaN NaN   NaN   NaN     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "9  1985.0 NaN   NaN   NaN    5.10 NaN    6.10 NaN  7.2 NaN    7.70  NaN  7.3  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d48c14f",
   "metadata": {},
   "source": [
    "### Q5 (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "7a4226c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
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       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Station_Name Parameter    Year  X1  \\\n",
       "2   Station No: 01-11-00-001; Station Name: MERIC ...        Al  2013.0 NaN   \n",
       "4   Station No: 01-11-00-001; Station Name: MERIC ...        As  1987.0 NaN   \n",
       "5   Station No: 01-11-00-001; Station Name: MERIC ...        As  1992.0 NaN   \n",
       "6   Station No: 01-11-00-001; Station Name: MERIC ...        As  1994.0 NaN   \n",
       "7   Station No: 01-11-00-001; Station Name: MERIC ...        As  2013.0 NaN   \n",
       "9   Station No: 01-11-00-001; Station Name: MERIC ...      BOD5  1985.0 NaN   \n",
       "10  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5  1986.0 NaN   \n",
       "11  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5  1987.0 NaN   \n",
       "12  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5  1988.0 NaN   \n",
       "13  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5  1989.0 NaN   \n",
       "\n",
       "      X2    X3      X4  X5      X6  X7   X8  X9     X10  X11  X12  \n",
       "2    NaN   NaN  168.25 NaN  156.87 NaN  NaN NaN  161.21  NaN  NaN  \n",
       "4    NaN   NaN    4.00 NaN    1.00 NaN  NaN NaN    6.00  NaN  NaN  \n",
       "5    NaN  32.0     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "6    NaN   NaN     NaN NaN    9.00 NaN  NaN NaN     NaN  NaN  NaN  \n",
       "7   3.49   NaN    1.17 NaN   52.20 NaN  NaN NaN    2.74  NaN  NaN  \n",
       "9    NaN   NaN    5.10 NaN    6.10 NaN  7.2 NaN    7.70  NaN  7.3  \n",
       "10   NaN   6.5    7.00 NaN    4.90 NaN  3.6 NaN    6.80  NaN  9.7  \n",
       "11  9.20   NaN    3.80 NaN    4.20 NaN  NaN NaN    3.80  NaN  9.6  \n",
       "12  9.30   NaN    2.70 NaN    5.70 NaN  7.0 NaN    2.10  NaN  6.7  \n",
       "13  5.00   NaN    3.60 NaN    6.00 NaN  6.7 NaN    4.50  NaN  8.0  "
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q5 = data\n",
    "\n",
    "Q5['X'].fillna(method='ffill', inplace=True) # X sutunundaki NaN degerleri doldur\n",
    "Q5 = Q5[Q5['Parameter'].notnull()] # Parameter sutunundaki NaN degerlerinin hepsini sil\n",
    "Q5.rename(columns={'X': 'Station_Name'}, inplace=True) # X sutununu Station_Name olarak degistir\n",
    "Q5.drop(columns=['X.1', 'Unit'], axis=1, inplace=True) # X.1 ve Unit sutunlarini sil\n",
    "\n",
    "Q5.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01081a18",
   "metadata": {},
   "source": [
    "### Q6 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "1b7cc812",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>X12</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.25</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>BOD5</td>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Station_Name Parameter       Year  \\\n",
       "2   Station No: 01-11-00-001; Station Name: MERIC ...        Al 2013-01-01   \n",
       "4   Station No: 01-11-00-001; Station Name: MERIC ...        As 1987-01-01   \n",
       "5   Station No: 01-11-00-001; Station Name: MERIC ...        As 1992-01-01   \n",
       "6   Station No: 01-11-00-001; Station Name: MERIC ...        As 1994-01-01   \n",
       "7   Station No: 01-11-00-001; Station Name: MERIC ...        As 2013-01-01   \n",
       "9   Station No: 01-11-00-001; Station Name: MERIC ...      BOD5 1985-01-01   \n",
       "10  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5 1986-01-01   \n",
       "11  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5 1987-01-01   \n",
       "12  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5 1988-01-01   \n",
       "13  Station No: 01-11-00-001; Station Name: MERIC ...      BOD5 1989-01-01   \n",
       "\n",
       "    X1    X2    X3      X4  X5      X6  X7   X8  X9     X10  X11  X12  \n",
       "2  NaN   NaN   NaN  168.25 NaN  156.87 NaN  NaN NaN  161.21  NaN  NaN  \n",
       "4  NaN   NaN   NaN    4.00 NaN    1.00 NaN  NaN NaN    6.00  NaN  NaN  \n",
       "5  NaN   NaN  32.0     NaN NaN     NaN NaN  NaN NaN     NaN  NaN  NaN  \n",
       "6  NaN   NaN   NaN     NaN NaN    9.00 NaN  NaN NaN     NaN  NaN  NaN  \n",
       "7  NaN  3.49   NaN    1.17 NaN   52.20 NaN  NaN NaN    2.74  NaN  NaN  \n",
       "9  NaN   NaN   NaN    5.10 NaN    6.10 NaN  7.2 NaN    7.70  NaN  7.3  \n",
       "10 NaN   NaN   6.5    7.00 NaN    4.90 NaN  3.6 NaN    6.80  NaN  9.7  \n",
       "11 NaN  9.20   NaN    3.80 NaN    4.20 NaN  NaN NaN    3.80  NaN  9.6  \n",
       "12 NaN  9.30   NaN    2.70 NaN    5.70 NaN  7.0 NaN    2.10  NaN  6.7  \n",
       "13 NaN  5.00   NaN    3.60 NaN    6.00 NaN  6.7 NaN    4.50  NaN  8.0  "
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q6 = Q5\n",
    "\n",
    "Q6 = Q6.astype({'Year':'int'}) # Year sutununu ondalikli sayidan tamsayiya cevir (.0 i sil)\n",
    "Q6['Year'] = pd.to_datetime(Q6['Year'].astype(str) + '-01-01', format='%Y-%m-%d') # Year sutununu datetime objesi yap\n",
    "\n",
    "Q6.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e528ed",
   "metadata": {},
   "source": [
    "### Q7 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "1ffb9c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q7 = Q6\n",
    "\n",
    "# zaman araliklarini al\n",
    "min_date = Q7['Year'].min()\n",
    "max_date = Q7['Year'].max()\n",
    "\n",
    "date_range = pd.date_range(start=min_date, end=max_date, freq='AS')\n",
    "\n",
    "Q7_yearly = pd.DataFrame(list(itertools.product(Q7['Station_Name'].unique(), Q7['Parameter'].unique(), date_range)), \n",
    "                         columns=['Station_Name', 'Parameter', 'Year'])\n",
    "Q7 = pd.merge(Q7, Q7_yearly, on=['Station_Name', 'Parameter', 'Year'], how='right')\n",
    "\n",
    "# Station Name ve Parameter sutunlarina gore sirala\n",
    "Q7 = Q7.sort_values(by=['Station_Name', 'Parameter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "5e2c144c",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1991-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>Al</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Station_Name Parameter       Year  \\\n",
       "0   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1985-01-01   \n",
       "1   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1986-01-01   \n",
       "2   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1987-01-01   \n",
       "3   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1988-01-01   \n",
       "4   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1989-01-01   \n",
       "5   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1990-01-01   \n",
       "6   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1991-01-01   \n",
       "7   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1992-01-01   \n",
       "8   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1993-01-01   \n",
       "9   Station No: 01-11-00-001; Station Name: MERIC ...        Al 1994-01-01   \n",
       "10  Station No: 01-11-00-001; Station Name: MERIC ...        Al 1995-01-01   \n",
       "11  Station No: 01-11-00-001; Station Name: MERIC ...        Al 1996-01-01   \n",
       "12  Station No: 01-11-00-001; Station Name: MERIC ...        Al 1997-01-01   \n",
       "13  Station No: 01-11-00-001; Station Name: MERIC ...        Al 1998-01-01   \n",
       "14  Station No: 01-11-00-001; Station Name: MERIC ...        Al 1999-01-01   \n",
       "15  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2000-01-01   \n",
       "16  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2001-01-01   \n",
       "17  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2002-01-01   \n",
       "18  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2003-01-01   \n",
       "19  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2004-01-01   \n",
       "20  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2005-01-01   \n",
       "21  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2006-01-01   \n",
       "22  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2007-01-01   \n",
       "23  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2008-01-01   \n",
       "24  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2009-01-01   \n",
       "25  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2010-01-01   \n",
       "26  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2011-01-01   \n",
       "27  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2012-01-01   \n",
       "28  Station No: 01-11-00-001; Station Name: MERIC ...        Al 2013-01-01   \n",
       "29  Station No: 01-11-00-001; Station Name: MERIC ...        As 1985-01-01   \n",
       "\n",
       "    X1  X2  X3      X4  X5      X6  X7  X8  X9     X10  X11  X12  \n",
       "0  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "1  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "2  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "3  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "4  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "5  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "6  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "7  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "8  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "9  NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "10 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "11 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "12 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "13 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "14 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "15 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "16 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "17 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "18 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "19 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "20 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "21 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "22 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "23 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "24 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "25 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "26 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "27 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  \n",
       "28 NaN NaN NaN  168.25 NaN  156.87 NaN NaN NaN  161.21  NaN  NaN  \n",
       "29 NaN NaN NaN     NaN NaN     NaN NaN NaN NaN     NaN  NaN  NaN  "
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q7.iloc[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "c63d31d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1991-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>As</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>B</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>Station No: 01-11-00-001; Station Name: MERIC ...</td>\n",
       "      <td>B</td>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Station_Name Parameter       Year  \\\n",
       "30    Station No: 01-11-00-001; Station Name: MERIC ...        As 1986-01-01   \n",
       "31    Station No: 01-11-00-001; Station Name: MERIC ...        As 1987-01-01   \n",
       "32    Station No: 01-11-00-001; Station Name: MERIC ...        As 1988-01-01   \n",
       "33    Station No: 01-11-00-001; Station Name: MERIC ...        As 1989-01-01   \n",
       "34    Station No: 01-11-00-001; Station Name: MERIC ...        As 1990-01-01   \n",
       "35    Station No: 01-11-00-001; Station Name: MERIC ...        As 1991-01-01   \n",
       "36    Station No: 01-11-00-001; Station Name: MERIC ...        As 1992-01-01   \n",
       "37    Station No: 01-11-00-001; Station Name: MERIC ...        As 1993-01-01   \n",
       "38    Station No: 01-11-00-001; Station Name: MERIC ...        As 1994-01-01   \n",
       "39    Station No: 01-11-00-001; Station Name: MERIC ...        As 1995-01-01   \n",
       "40    Station No: 01-11-00-001; Station Name: MERIC ...        As 1996-01-01   \n",
       "41    Station No: 01-11-00-001; Station Name: MERIC ...        As 1997-01-01   \n",
       "42    Station No: 01-11-00-001; Station Name: MERIC ...        As 1998-01-01   \n",
       "43    Station No: 01-11-00-001; Station Name: MERIC ...        As 1999-01-01   \n",
       "44    Station No: 01-11-00-001; Station Name: MERIC ...        As 2000-01-01   \n",
       "45    Station No: 01-11-00-001; Station Name: MERIC ...        As 2001-01-01   \n",
       "46    Station No: 01-11-00-001; Station Name: MERIC ...        As 2002-01-01   \n",
       "47    Station No: 01-11-00-001; Station Name: MERIC ...        As 2003-01-01   \n",
       "48    Station No: 01-11-00-001; Station Name: MERIC ...        As 2004-01-01   \n",
       "49    Station No: 01-11-00-001; Station Name: MERIC ...        As 2005-01-01   \n",
       "50    Station No: 01-11-00-001; Station Name: MERIC ...        As 2006-01-01   \n",
       "51    Station No: 01-11-00-001; Station Name: MERIC ...        As 2007-01-01   \n",
       "52    Station No: 01-11-00-001; Station Name: MERIC ...        As 2008-01-01   \n",
       "53    Station No: 01-11-00-001; Station Name: MERIC ...        As 2009-01-01   \n",
       "54    Station No: 01-11-00-001; Station Name: MERIC ...        As 2010-01-01   \n",
       "55    Station No: 01-11-00-001; Station Name: MERIC ...        As 2011-01-01   \n",
       "56    Station No: 01-11-00-001; Station Name: MERIC ...        As 2012-01-01   \n",
       "57    Station No: 01-11-00-001; Station Name: MERIC ...        As 2013-01-01   \n",
       "1276  Station No: 01-11-00-001; Station Name: MERIC ...         B 1985-01-01   \n",
       "1277  Station No: 01-11-00-001; Station Name: MERIC ...         B 1986-01-01   \n",
       "\n",
       "      X1    X2    X3    X4  X5    X6  X7  X8  X9   X10  X11  X12  \n",
       "30   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "31   NaN   NaN   NaN  4.00 NaN   1.0 NaN NaN NaN  6.00  NaN  NaN  \n",
       "32   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "33   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "34   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "35   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "36   NaN   NaN  32.0   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "37   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "38   NaN   NaN   NaN   NaN NaN   9.0 NaN NaN NaN   NaN  NaN  NaN  \n",
       "39   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "40   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "41   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "42   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "43   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "44   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "45   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "46   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "47   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "48   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "49   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "50   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "51   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "52   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "53   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "54   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "55   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "56   NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "57   NaN  3.49   NaN  1.17 NaN  52.2 NaN NaN NaN  2.74  NaN  NaN  \n",
       "1276 NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  \n",
       "1277 NaN   NaN   NaN   NaN NaN   NaN NaN NaN NaN   NaN  NaN  NaN  "
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q7.iloc[30:60]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea32e20f",
   "metadata": {},
   "source": [
    "### Q8 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "67ce5d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q8 = Q7\n",
    "\n",
    "# Station Code sutununu olustur\n",
    "Q8['Station_Code'] = Q8['Station_Name'].apply(lambda val: val.split(';')[0].split('Station No: ')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "509a8829",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Station Name sutununu olustur\n",
    "Q8['Station_Name'] = Q8['Station_Name'].apply(lambda val: val.split(';')[1].split('Station Name: ')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "d62af570",
   "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>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1991-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Station_Code          Station_Name Parameter       Year  X1  X2  X3  X4  \\\n",
       "0  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01 NaN NaN NaN NaN   \n",
       "1  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1986-01-01 NaN NaN NaN NaN   \n",
       "2  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1987-01-01 NaN NaN NaN NaN   \n",
       "3  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1988-01-01 NaN NaN NaN NaN   \n",
       "4  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1989-01-01 NaN NaN NaN NaN   \n",
       "5  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1990-01-01 NaN NaN NaN NaN   \n",
       "6  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1991-01-01 NaN NaN NaN NaN   \n",
       "7  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1992-01-01 NaN NaN NaN NaN   \n",
       "8  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1993-01-01 NaN NaN NaN NaN   \n",
       "9  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1994-01-01 NaN NaN NaN NaN   \n",
       "\n",
       "   X5  X6  X7  X8  X9  X10  X11  X12  \n",
       "0 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "1 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "2 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "3 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "4 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "5 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "6 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "7 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "8 NaN NaN NaN NaN NaN  NaN  NaN  NaN  \n",
       "9 NaN NaN NaN NaN NaN  NaN  NaN  NaN  "
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sutunlarin siralarini tekrar duzenle (Station_Code sutunu sondaydi)\n",
    "Q8 = Q8[['Station_Code', 'Station_Name', 'Parameter', 'Year',\n",
    "         'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12']]\n",
    "Q8.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8bf6f8",
   "metadata": {},
   "source": [
    "### Q9 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "8e5c157b",
   "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>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18502</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37004</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55506</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74008</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92510</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111012</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129514</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148016</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166518</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>X10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Station_Code          Station_Name Parameter       Year Month  Value\n",
       "0       01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X1    NaN\n",
       "18502   01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X2    NaN\n",
       "37004   01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X3    NaN\n",
       "55506   01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X4    NaN\n",
       "74008   01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X5    NaN\n",
       "92510   01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X6    NaN\n",
       "111012  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X7    NaN\n",
       "129514  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X8    NaN\n",
       "148016  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01    X9    NaN\n",
       "166518  01-11-00-001  MERIC NEHRI-KAPIKULE        Al 1985-01-01   X10    NaN"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q9 = Q8\n",
    "\n",
    "# X ile baslayan sutun isimlerini bul\n",
    "x_columns = [column_name for column_name in Q9.columns if column_name.startswith(\"X\")]\n",
    "\n",
    "# aylari ekle\n",
    "Q9 = pd.melt(Q9, id_vars=['Station_Code', 'Station_Name', 'Parameter', 'Year'], \n",
    "             value_vars=x_columns, \n",
    "             var_name='Month',\n",
    "             value_name='Value')\n",
    "Q9 = Q9.sort_values(by=['Station_Code', 'Station_Name', 'Parameter', 'Year'])\n",
    "Q9.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704ae306",
   "metadata": {},
   "source": [
    "### Q10 (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "fbd062ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q10 = Q9\n",
    "\n",
    "# Month sutunu verilerinden X karakterini sil ve karakterleri integer formatina cevir\n",
    "Q10['Month'] = Q10['Month'].str.replace('X', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "62e34099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year sutunundan sadece yili cek ve sutunun elemanlarini bu sekilde degistir\n",
    "Q10['Year'] = pd.DatetimeIndex(Q10['Year']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "f6d91f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year ve Month sutunlarinin birlesiminden Year_Month sutununu olustur\n",
    "Q10['Year_Month'] = pd.to_datetime(Q10[['Year', 'Month']].assign(Day=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "baabe855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Value</th>\n",
       "      <th>Year_Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18502</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37004</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55506</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74008</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92510</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111012</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129514</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148016</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166518</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185020</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203522</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1985</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18503</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37005</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55507</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74009</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92511</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111013</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129515</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>Al</td>\n",
       "      <td>1986</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Station_Code          Station_Name Parameter  Year  Month  Value  \\\n",
       "0       01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      1    NaN   \n",
       "18502   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      2    NaN   \n",
       "37004   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      3    NaN   \n",
       "55506   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      4    NaN   \n",
       "74008   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      5    NaN   \n",
       "92510   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      6    NaN   \n",
       "111012  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      7    NaN   \n",
       "129514  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      8    NaN   \n",
       "148016  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985      9    NaN   \n",
       "166518  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985     10    NaN   \n",
       "185020  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985     11    NaN   \n",
       "203522  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1985     12    NaN   \n",
       "1       01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      1    NaN   \n",
       "18503   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      2    NaN   \n",
       "37005   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      3    NaN   \n",
       "55507   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      4    NaN   \n",
       "74009   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      5    NaN   \n",
       "92511   01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      6    NaN   \n",
       "111013  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      7    NaN   \n",
       "129515  01-11-00-001  MERIC NEHRI-KAPIKULE        Al  1986      8    NaN   \n",
       "\n",
       "       Year_Month  \n",
       "0      1985-01-01  \n",
       "18502  1985-02-01  \n",
       "37004  1985-03-01  \n",
       "55506  1985-04-01  \n",
       "74008  1985-05-01  \n",
       "92510  1985-06-01  \n",
       "111012 1985-07-01  \n",
       "129514 1985-08-01  \n",
       "148016 1985-09-01  \n",
       "166518 1985-10-01  \n",
       "185020 1985-11-01  \n",
       "203522 1985-12-01  \n",
       "1      1986-01-01  \n",
       "18503  1986-02-01  \n",
       "37005  1986-03-01  \n",
       "55507  1986-04-01  \n",
       "74009  1986-05-01  \n",
       "92511  1986-06-01  \n",
       "111013 1986-07-01  \n",
       "129515 1986-08-01  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q10.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e29ff9a",
   "metadata": {},
   "source": [
    "### Q11 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "d6316189",
   "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>Parameter</th>\n",
       "      <th>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year_Month</th>\n",
       "      <th>Al</th>\n",
       "      <th>As</th>\n",
       "      <th>B</th>\n",
       "      <th>Ba</th>\n",
       "      <th>BOD5</th>\n",
       "      <th>...</th>\n",
       "      <th>T-Coli</th>\n",
       "      <th>TDS</th>\n",
       "      <th>TH</th>\n",
       "      <th>TKN</th>\n",
       "      <th>TOC</th>\n",
       "      <th>Top.N</th>\n",
       "      <th>Top.P</th>\n",
       "      <th>Tot.Pest.</th>\n",
       "      <th>Turb</th>\n",
       "      <th>Zn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>1985-02-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>1985</td>\n",
       "      <td>3</td>\n",
       "      <td>1985-03-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>1985-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Parameter  Station_Code          Station_Name  Year  Month Year_Month  Al  As  \\\n",
       "0          01-11-00-001  MERIC NEHRI-KAPIKULE  1985      1 1985-01-01 NaN NaN   \n",
       "1          01-11-00-001  MERIC NEHRI-KAPIKULE  1985      2 1985-02-01 NaN NaN   \n",
       "2          01-11-00-001  MERIC NEHRI-KAPIKULE  1985      3 1985-03-01 NaN NaN   \n",
       "3          01-11-00-001  MERIC NEHRI-KAPIKULE  1985      4 1985-04-01 NaN NaN   \n",
       "\n",
       "Parameter   B  Ba  BOD5  ...  T-Coli    TDS     TH  TKN  TOC  Top.N  Top.P  \\\n",
       "0         NaN NaN   NaN  ...     NaN    NaN    NaN  NaN  NaN    NaN    NaN   \n",
       "1         NaN NaN   NaN  ...     NaN    NaN    NaN  NaN  NaN    NaN    NaN   \n",
       "2         NaN NaN   NaN  ...     NaN    NaN    NaN  NaN  NaN    NaN    NaN   \n",
       "3         NaN NaN   5.1  ...     NaN  299.0  250.0  NaN  NaN    NaN    NaN   \n",
       "\n",
       "Parameter  Tot.Pest.  Turb  Zn  \n",
       "0                NaN   NaN NaN  \n",
       "1                NaN   NaN NaN  \n",
       "2                NaN   NaN NaN  \n",
       "3                NaN  96.0 NaN  \n",
       "\n",
       "[4 rows x 63 columns]"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q11 = Q10\n",
    "\n",
    "Q11 = Q11.pivot(index=[\"Station_Code\", \"Station_Name\", \"Year\", \"Month\", \"Year_Month\"], \n",
    "                columns=\"Parameter\", \n",
    "                values=\"Value\")\n",
    "Q11.reset_index(inplace=True)\n",
    "columns = ['Station_Code', 'Station_Name', 'Year', 'Month', 'Year_Month',\n",
    "             'Al','As','B','Ba','BOD5','Ca++','Cd','Cl-','CN-','Co','CO2','COD','Col','Cr','Cu','DO','DO%',\n",
    "             'E-Coli','EC','F-','F-Coli','F-Strp','Fe','Fenoller','Hg','Hidrokarbonlar','K+','M-Al','M.Oil',\n",
    "             'Mg++','Mn','Na+','NH4-N','Ni','NO2-N','NO3-N','o-PO4','P-Al','PAH','Pb','pH','pV','Qanlik','Se',\n",
    "             'SO4=','SS','Surfaktanlar','T','T-Coli','TDS','TH','TKN','TOC','Top.N','Top.P','Tot.Pest.','Turb','Zn']\n",
    "Q11 = Q11[columns]\n",
    "Q11.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9a7450",
   "metadata": {},
   "source": [
    "### Q12 (5 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "44b5f6cd",
   "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>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.229630</td>\n",
       "      <td>2.986572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-11-00-002</td>\n",
       "      <td>TUNCA NEHRI-SU AKACAGI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.489933</td>\n",
       "      <td>3.049296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-11-00-003</td>\n",
       "      <td>ARDA NEHRI-ESKI DEMIRYOLU KOPRUSU</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.238889</td>\n",
       "      <td>2.458612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-11-00-004</td>\n",
       "      <td>MEKAN DERE SARAY AYVACIK GOLETI AKS YERI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.155243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-11-00-008</td>\n",
       "      <td>MERIC NEHRI-ESKIKOY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.271329</td>\n",
       "      <td>2.895860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-11-00-009</td>\n",
       "      <td>MERIC NEHRI-IPSALA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.466225</td>\n",
       "      <td>4.125803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-11-00-011</td>\n",
       "      <td>CORLU SUYU-CERKEZKOY GIRISI</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>20.821429</td>\n",
       "      <td>23.298678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01-11-00-012</td>\n",
       "      <td>CORLU SUYU-CERKEZKOY CIKISI</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>90.782667</td>\n",
       "      <td>224.980282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01-11-00-013</td>\n",
       "      <td>ERGENE NEHRI-CORLU KOPRUSU</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>7.757143</td>\n",
       "      <td>13.830074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01-11-00-014</td>\n",
       "      <td>ERGENE NEHRI-INANLI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>53.127027</td>\n",
       "      <td>41.524838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01-11-00-015</td>\n",
       "      <td>ERGENE NEHRI-LULEBURGAZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>53.233333</td>\n",
       "      <td>45.945184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Station_Code                              Station_Name  min     max  \\\n",
       "0   01-11-00-001                      MERIC NEHRI-KAPIKULE  0.0    23.0   \n",
       "1   01-11-00-002                    TUNCA NEHRI-SU AKACAGI  0.0    16.0   \n",
       "2   01-11-00-003         ARDA NEHRI-ESKI DEMIRYOLU KOPRUSU  0.0    25.0   \n",
       "3   01-11-00-004  MEKAN DERE SARAY AYVACIK GOLETI AKS YERI  0.0    10.0   \n",
       "4   01-11-00-008                       MERIC NEHRI-ESKIKOY  0.0    21.0   \n",
       "5   01-11-00-009                        MERIC NEHRI-IPSALA  0.0    40.0   \n",
       "6   01-11-00-011               CORLU SUYU-CERKEZKOY GIRISI  1.0   108.0   \n",
       "7   01-11-00-012               CORLU SUYU-CERKEZKOY CIKISI  4.0  1988.0   \n",
       "8   01-11-00-013                ERGENE NEHRI-CORLU KOPRUSU  0.0    96.0   \n",
       "9   01-11-00-014                       ERGENE NEHRI-INANLI  0.0   215.0   \n",
       "10  01-11-00-015                   ERGENE NEHRI-LULEBURGAZ  0.0   225.0   \n",
       "\n",
       "         mean         std  \n",
       "0    4.229630    2.986572  \n",
       "1    4.489933    3.049296  \n",
       "2    2.238889    2.458612  \n",
       "3    3.800000    3.155243  \n",
       "4    4.271329    2.895860  \n",
       "5    5.466225    4.125803  \n",
       "6   20.821429   23.298678  \n",
       "7   90.782667  224.980282  \n",
       "8    7.757143   13.830074  \n",
       "9   53.127027   41.524838  \n",
       "10  53.233333   45.945184  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q12 = Q11\n",
    "\n",
    "Q12 = Q12.groupby(['Station_Code', 'Station_Name'])['BOD5'].agg(['min','max','mean','std']).reset_index()\n",
    "Q12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02a501f",
   "metadata": {},
   "source": [
    "### Q13 (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "0546ed17",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Parameter</th>\n",
       "      <th>Station_Code</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th colspan=\"4\" halign=\"left\">BOD5</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Ca++</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fe</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Mg++</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Na+</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-00-001</td>\n",
       "      <td>MERIC NEHRI-KAPIKULE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.229630</td>\n",
       "      <td>2.986572</td>\n",
       "      <td>1.48</td>\n",
       "      <td>130.00</td>\n",
       "      <td>71.762778</td>\n",
       "      <td>19.585391</td>\n",
       "      <td>...</td>\n",
       "      <td>967.935000</td>\n",
       "      <td>511.180285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>17.451481</td>\n",
       "      <td>6.670011</td>\n",
       "      <td>5.750</td>\n",
       "      <td>79.12</td>\n",
       "      <td>29.520247</td>\n",
       "      <td>12.103190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-11-00-002</td>\n",
       "      <td>TUNCA NEHRI-SU AKACAGI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.489933</td>\n",
       "      <td>3.049296</td>\n",
       "      <td>46.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>78.891141</td>\n",
       "      <td>14.673304</td>\n",
       "      <td>...</td>\n",
       "      <td>382.233333</td>\n",
       "      <td>291.967113</td>\n",
       "      <td>4.9</td>\n",
       "      <td>48.6</td>\n",
       "      <td>27.510811</td>\n",
       "      <td>8.127800</td>\n",
       "      <td>12.199</td>\n",
       "      <td>107.64</td>\n",
       "      <td>41.400972</td>\n",
       "      <td>18.033151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-11-00-003</td>\n",
       "      <td>ARDA NEHRI-ESKI DEMIRYOLU KOPRUSU</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.238889</td>\n",
       "      <td>2.458612</td>\n",
       "      <td>20.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>40.461597</td>\n",
       "      <td>10.212302</td>\n",
       "      <td>...</td>\n",
       "      <td>284.082353</td>\n",
       "      <td>239.549192</td>\n",
       "      <td>2.4</td>\n",
       "      <td>29.2</td>\n",
       "      <td>9.213846</td>\n",
       "      <td>3.987035</td>\n",
       "      <td>2.300</td>\n",
       "      <td>40.25</td>\n",
       "      <td>14.974565</td>\n",
       "      <td>7.356644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-11-00-004</td>\n",
       "      <td>MEKAN DERE SARAY AYVACIK GOLETI AKS YERI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.155243</td>\n",
       "      <td>10.00</td>\n",
       "      <td>124.34</td>\n",
       "      <td>57.726000</td>\n",
       "      <td>47.920528</td>\n",
       "      <td>...</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>364.359356</td>\n",
       "      <td>1.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>3.554793</td>\n",
       "      <td>3.450</td>\n",
       "      <td>44.35</td>\n",
       "      <td>16.604000</td>\n",
       "      <td>11.974782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Parameter  Station_Code                              Station_Name BOD5        \\\n",
       "                                                                   min   max   \n",
       "0          01-11-00-001                      MERIC NEHRI-KAPIKULE  0.0  23.0   \n",
       "1          01-11-00-002                    TUNCA NEHRI-SU AKACAGI  0.0  16.0   \n",
       "2          01-11-00-003         ARDA NEHRI-ESKI DEMIRYOLU KOPRUSU  0.0  25.0   \n",
       "3          01-11-00-004  MEKAN DERE SARAY AYVACIK GOLETI AKS YERI  0.0  10.0   \n",
       "\n",
       "Parameter                       Ca++                                ...  \\\n",
       "               mean       std    min     max       mean        std  ...   \n",
       "0          4.229630  2.986572   1.48  130.00  71.762778  19.585391  ...   \n",
       "1          4.489933  3.049296  46.00  130.00  78.891141  14.673304  ...   \n",
       "2          2.238889  2.458612  20.00   68.00  40.461597  10.212302  ...   \n",
       "3          3.800000  3.155243  10.00  124.34  57.726000  47.920528  ...   \n",
       "\n",
       "Parameter          Fe             Mg++                                Na+  \\\n",
       "                 mean         std  min   max       mean       std     min   \n",
       "0          967.935000  511.180285  0.0  48.0  17.451481  6.670011   5.750   \n",
       "1          382.233333  291.967113  4.9  48.6  27.510811  8.127800  12.199   \n",
       "2          284.082353  239.549192  2.4  29.2   9.213846  3.987035   2.300   \n",
       "3          538.000000  364.359356  1.2  11.5   4.790000  3.554793   3.450   \n",
       "\n",
       "Parameter                                \n",
       "              max       mean        std  \n",
       "0           79.12  29.520247  12.103190  \n",
       "1          107.64  41.400972  18.033151  \n",
       "2           40.25  14.974565   7.356644  \n",
       "3           44.35  16.604000  11.974782  \n",
       "\n",
       "[4 rows x 38 columns]"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q13 = Q11\n",
    "\n",
    "parameters = ['BOD5' , 'Ca++' , 'Cl-' , 'Cu' , 'DO' , 'EC' , 'Fe' , 'Mg++' , 'Na+']\n",
    "Q13 = Q13.groupby(['Station_Code', 'Station_Name']).agg({col: ['min','max','mean','std'] for col in parameters})\n",
    "Q13 = Q13.reset_index()\n",
    "Q13.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e4ffda",
   "metadata": {},
   "source": [
    "### Q14 (10 PTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "ecc6a473",
   "metadata": {},
   "outputs": [],
   "source": [
    "# istenilen sutunlarin verileri\n",
    "capp = Q11[['Station_Name', 'Year', 'Ca++']]\n",
    "clm = Q11[['Station_Name', 'Year', 'Cl-']]\n",
    "ec = Q11[['Station_Name', 'Year', 'EC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "89c2791a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 ayri subplot olustur\n",
    "fig, axs = plt.subplots(1, 3, figsize=(20, 15))\n",
    "\n",
    "# subplotlari doldur\n",
    "sns.lineplot(data=capp, ax=axs[0], x='Year', y='Ca++', hue='Station_Name', linestyle='--', ci=0)\n",
    "axs[0].set_title('Ca++')\n",
    "\n",
    "sns.lineplot(data=clm, ax=axs[1], x='Year', y='Cl-', hue='Station_Name', linestyle='--', ci=0)\n",
    "axs[1].set_title('Cl-')\n",
    "\n",
    "sns.lineplot(data=ec, ax=axs[2], x='Year', y='EC', hue='Station_Name', linestyle='--', ci=0)\n",
    "axs[2].set_title('EC')\n",
    "\n",
    "for plot in axs:\n",
    "    plot.grid()\n",
    "    plot.legend().set_visible(False)\n",
    "\n",
    "# legend olustur\n",
    "leg = plt.legend(title='Station_Name', bbox_to_anchor=(-.8, -.15), loc='lower center', ncol=3, frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d5efaa8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}