Flood_Analysis.ipynb

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   "source": [
    "#Import the packages\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import scipy.stats as scs\n",
    "from scipy.stats import pearson3\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86a66e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "station = 2378300\n",
    "state = 'al'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54e93322",
   "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</th>\n",
       "      <th>Date</th>\n",
       "      <th>Yearly max</th>\n",
       "      <th>Exceedance probability</th>\n",
       "      <th>Return periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2014-04-30 00:00:00</td>\n",
       "      <td>8059.29150</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2020-09-16 00:00:00</td>\n",
       "      <td>6727.20850</td>\n",
       "      <td>8.695652</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2005-04-01 00:00:00</td>\n",
       "      <td>6595.70850</td>\n",
       "      <td>13.043478</td>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2017-01-03 00:00:00</td>\n",
       "      <td>2881.83330</td>\n",
       "      <td>17.391304</td>\n",
       "      <td>5.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2003-07-01 00:00:00</td>\n",
       "      <td>2181.18750</td>\n",
       "      <td>21.739130</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2004-09-16 00:00:00</td>\n",
       "      <td>1910.37500</td>\n",
       "      <td>26.086957</td>\n",
       "      <td>3.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2002-09-26 00:00:00</td>\n",
       "      <td>1561.47910</td>\n",
       "      <td>30.434783</td>\n",
       "      <td>3.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2009-03-28 00:00:00</td>\n",
       "      <td>1305.20840</td>\n",
       "      <td>34.782609</td>\n",
       "      <td>2.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2018-09-05 00:00:00</td>\n",
       "      <td>640.77710</td>\n",
       "      <td>39.130435</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2007-10-19 00:00:00</td>\n",
       "      <td>606.35420</td>\n",
       "      <td>43.478261</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2011-09-05 00:00:00</td>\n",
       "      <td>541.25000</td>\n",
       "      <td>47.826087</td>\n",
       "      <td>2.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2016-08-11 00:00:00</td>\n",
       "      <td>529.43750</td>\n",
       "      <td>52.173913</td>\n",
       "      <td>1.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2015-04-13 00:00:00</td>\n",
       "      <td>525.87085</td>\n",
       "      <td>56.521739</td>\n",
       "      <td>1.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2013-08-18 00:00:00</td>\n",
       "      <td>485.00000</td>\n",
       "      <td>60.869565</td>\n",
       "      <td>1.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2010-03-02 00:00:00</td>\n",
       "      <td>483.16250</td>\n",
       "      <td>65.217391</td>\n",
       "      <td>1.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2012-02-19 00:00:00</td>\n",
       "      <td>352.76874</td>\n",
       "      <td>69.565217</td>\n",
       "      <td>1.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2008-02-22 00:00:00</td>\n",
       "      <td>351.93750</td>\n",
       "      <td>73.913043</td>\n",
       "      <td>1.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2378300</td>\n",
       "      <td>1999-08-10 00:00:00</td>\n",
       "      <td>342.02084</td>\n",
       "      <td>78.260870</td>\n",
       "      <td>1.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2001-08-01 00:00:00</td>\n",
       "      <td>286.95834</td>\n",
       "      <td>82.608696</td>\n",
       "      <td>1.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2006-05-11 00:00:00</td>\n",
       "      <td>277.91666</td>\n",
       "      <td>86.956522</td>\n",
       "      <td>1.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2019-07-14 00:00:00</td>\n",
       "      <td>224.49791</td>\n",
       "      <td>91.304348</td>\n",
       "      <td>1.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>2378300</td>\n",
       "      <td>2000-03-30 00:00:00</td>\n",
       "      <td>214.92308</td>\n",
       "      <td>95.652174</td>\n",
       "      <td>1.045455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Station                 Date  Yearly max  Exceedance probability  \\\n",
       "465  2378300  2014-04-30 00:00:00  8059.29150                4.347826   \n",
       "466  2378300  2020-09-16 00:00:00  6727.20850                8.695652   \n",
       "467  2378300  2005-04-01 00:00:00  6595.70850               13.043478   \n",
       "468  2378300  2017-01-03 00:00:00  2881.83330               17.391304   \n",
       "469  2378300  2003-07-01 00:00:00  2181.18750               21.739130   \n",
       "470  2378300  2004-09-16 00:00:00  1910.37500               26.086957   \n",
       "471  2378300  2002-09-26 00:00:00  1561.47910               30.434783   \n",
       "472  2378300  2009-03-28 00:00:00  1305.20840               34.782609   \n",
       "473  2378300  2018-09-05 00:00:00   640.77710               39.130435   \n",
       "474  2378300  2007-10-19 00:00:00   606.35420               43.478261   \n",
       "475  2378300  2011-09-05 00:00:00   541.25000               47.826087   \n",
       "476  2378300  2016-08-11 00:00:00   529.43750               52.173913   \n",
       "477  2378300  2015-04-13 00:00:00   525.87085               56.521739   \n",
       "478  2378300  2013-08-18 00:00:00   485.00000               60.869565   \n",
       "479  2378300  2010-03-02 00:00:00   483.16250               65.217391   \n",
       "480  2378300  2012-02-19 00:00:00   352.76874               69.565217   \n",
       "481  2378300  2008-02-22 00:00:00   351.93750               73.913043   \n",
       "482  2378300  1999-08-10 00:00:00   342.02084               78.260870   \n",
       "483  2378300  2001-08-01 00:00:00   286.95834               82.608696   \n",
       "484  2378300  2006-05-11 00:00:00   277.91666               86.956522   \n",
       "485  2378300  2019-07-14 00:00:00   224.49791               91.304348   \n",
       "486  2378300  2000-03-30 00:00:00   214.92308               95.652174   \n",
       "\n",
       "     Return periods  \n",
       "465       23.000000  \n",
       "466       11.500000  \n",
       "467        7.666667  \n",
       "468        5.750000  \n",
       "469        4.600000  \n",
       "470        3.833333  \n",
       "471        3.285714  \n",
       "472        2.875000  \n",
       "473        2.555556  \n",
       "474        2.300000  \n",
       "475        2.090909  \n",
       "476        1.916667  \n",
       "477        1.769231  \n",
       "478        1.642857  \n",
       "479        1.533333  \n",
       "480        1.437500  \n",
       "481        1.352941  \n",
       "482        1.277778  \n",
       "483        1.210526  \n",
       "484        1.150000  \n",
       "485        1.095238  \n",
       "486        1.045455  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the csv files for a state for further analysis\n",
    "filename = f\"./Data/Flood/flood_event_1980-2020_{state}.csv\"\n",
    "\n",
    "#Read the csv just constructed using above command \n",
    "df = pd.read_csv(filename)\n",
    "\n",
    "#Select rows from the imported csv file corresponding to the station id\n",
    "station_rows = df[df['Station'] == station]\n",
    "station_rows\n"
   ]
  },
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   "cell_type": "code",
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