04c_pandas_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a132f14e-55b7-4894-8a09-5f08be34e4c7",
   "metadata": {},
   "source": [
    "<left><img src=\"https://github.com/pandas-dev/pandas/raw/main/web/pandas/static/img/pandas.svg\" alt=\"pandas Logo\" style=\"width: 200px;\"/></left>\n",
    "<right><img src=\"https://matplotlib.org/stable/_images/sphx_glr_logos2_003.png\" style=\"width: 200px;\"/></right>\n",
    "\n",
    "# Pandas and Matplotlib - EMODNET\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb62aa16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import datetime\n",
    "from pathlib import Path\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96a6aed-52ec-4d0b-bcba-5a8cb1044b5a",
   "metadata": {},
   "source": [
    "## Load ERDDAP data\n",
    "\n",
    " [ERDDAP](https://coastwatch.pfeg.noaa.gov/erddapinfo/) is a data server that gives you a simple, consistent way to download data in the format and the spatial and temporal coverage that you want. ERDDAP is a web application with an interface for people to use. It is also a RESTful web service that allows data access directly from any computer program (e.g. Matlab, R, or webpages).\"\n",
    "\n",
    "This notebook uses the python client [erddapy](https://pyoceans.github.io/erddapy) to help construct the RESTful URLs and translate the responses into Pandas and Xarray objects. \n",
    "\n",
    "A typical ERDDAP RESTful URL looks like:\n",
    "\n",
    "[https://data.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time>=2016-07-10T00:00:00Z&time<=2017-02-10T00:00:00Z &latitude>=38.0&latitude<=41.0&longitude>=-72.0&longitude<=-69.0](https://data.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time>=2016-07-10T00:00:00Z&time<=2017-02-10T00:00:00Z&latitude>=38.0&latitude<=41.0&longitude>=-72.0&longitude<=-69.0)\n",
    "\n",
    "Let's break it down to smaller parts:\n",
    "\n",
    "- **server**: https://data.ioos.us/gliders/erddap/\n",
    "- **protocol**: tabledap\n",
    "- **dataset_id**: whoi_406-20160902T1700\n",
    "- **response**: .mat\n",
    "- **variables**: depth,latitude,longitude,temperature,time\n",
    "- **constraints**:\n",
    "    - time>=2016-07-10T00:00:00Z\n",
    "    - time<=2017-02-10T00:00:00Z\n",
    "    - latitude>=38.0\n",
    "    - latitude<=41.0\n",
    "    - longitude>=-72.0\n",
    "    - longitude<=-69.0\n",
    "\n",
    "### EMODNET:  \n",
    "https://emodnet.ec.europa.eu/en/emodnet-web-service-documentation#non-ogc-web-services\n",
    "\n",
    "erddap EMODNET physics:  \n",
    "https://prod-erddap.emodnet-physics.eu/erddap/index.html  \n",
    "https://prod-erddap.emodnet-physics.eu/erddap/tabledap/documentation.html  \n",
    "\n",
    "### erddapy  \n",
    "https://github.com/ioos/erddapy\n",
    "\n",
    ">pip install erddapy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb78765c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from erddapy import ERDDAP\n",
    "from erddapy.core.url import urlopen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5160c5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ERDDAP for EMODNET Physics\n",
    "server = 'https://coastwatch.pfeg.noaa.gov/erddap'\n",
    "protocol = 'tabledap'\n",
    "emodnet = ERDDAP(server=server, protocol=protocol)\n",
    "\n",
    "\n",
    "server = 'https://prod-erddap.emodnet-physics.eu/erddap'\n",
    "protocol = 'tabledap'\n",
    "emodnet = ERDDAP(server=server, protocol=protocol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8d8ad4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_time = '2010-01-01T00:00:00Z'\n",
    "max_time = '2020-12-31T23:00:00Z'\n",
    "min_lon, max_lon = -17, -15\n",
    "min_lat, max_lat = 44.1, 44.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8e6e14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Institution</th>\n",
       "      <th>Dataset ID</th>\n",
       "      <th>tabledap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLODAP</td>\n",
       "      <td>GLODAPv2_2021</td>\n",
       "      <td>https://prod-erddap.emodnet-physics.eu/erddap/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Institution     Dataset ID  \\\n",
       "0      GLODAP  GLODAPv2_2021   \n",
       "\n",
       "                                            tabledap  \n",
       "0  https://prod-erddap.emodnet-physics.eu/erddap/...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw = {\n",
    "    'min_lon': min_lon,'max_lon': max_lon,'min_lat': min_lat,'max_lat': max_lat,\n",
    "    'min_time': min_time,'max_time': max_time\n",
    "}\n",
    "\n",
    "search_url = emodnet.get_search_url(response='csv', **kw)\n",
    "search_df = pd.read_csv(urlopen(search_url))\n",
    "search_df = search_df[['Institution', 'Dataset ID','tabledap']]\n",
    "search_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "48495e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_id = 'GLODAPv2_2021'\n",
    "emodnet.dataset_id = dataset_id\n",
    "emodnet.response = \"csv\"\n",
    "emodnet.constraints = {\n",
    "#     \"time>=\": min_time,\n",
    "#     \"time<=\": max_time,\n",
    "    \"latitude>=\": min_lat,\n",
    "    \"latitude<=\": max_lat,\n",
    "    \"longitude>=\": min_lon,\n",
    "    \"longitude<=\": max_lon,\n",
    "}\n",
    "emodnet.variables = [\"longitude\", \"latitude\", \"time\",\n",
    "    \"G2temperature\", \"G2salinity\", \"G2pressure\"\n",
    "]\n",
    "\n",
    "df = emodnet.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a2d305f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>longitude (degrees_east)</th>\n",
       "      <th>latitude (degrees_north)</th>\n",
       "      <th>time (UTC)</th>\n",
       "      <th>G2temperature</th>\n",
       "      <th>G2salinity (PSU)</th>\n",
       "      <th>G2pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.490</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.534</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>23.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>51.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>152.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4200</td>\n",
       "      <td>35.648</td>\n",
       "      <td>202.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.8600</td>\n",
       "      <td>35.579</td>\n",
       "      <td>303.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.4000</td>\n",
       "      <td>35.539</td>\n",
       "      <td>403.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.9800</td>\n",
       "      <td>35.507</td>\n",
       "      <td>502.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.5400</td>\n",
       "      <td>35.506</td>\n",
       "      <td>604.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.7900</td>\n",
       "      <td>35.601</td>\n",
       "      <td>805.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.1700</td>\n",
       "      <td>35.705</td>\n",
       "      <td>1000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>4.2900</td>\n",
       "      <td>35.003</td>\n",
       "      <td>1500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>3.2600</td>\n",
       "      <td>34.900</td>\n",
       "      <td>2000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7120</td>\n",
       "      <td>35.719</td>\n",
       "      <td>3.6793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7080</td>\n",
       "      <td>35.719</td>\n",
       "      <td>18.7020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>16.5560</td>\n",
       "      <td>35.770</td>\n",
       "      <td>49.6010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>13.4130</td>\n",
       "      <td>35.731</td>\n",
       "      <td>100.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.9300</td>\n",
       "      <td>35.709</td>\n",
       "      <td>150.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.4790</td>\n",
       "      <td>35.661</td>\n",
       "      <td>200.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.9430</td>\n",
       "      <td>35.606</td>\n",
       "      <td>300.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.4980</td>\n",
       "      <td>35.565</td>\n",
       "      <td>399.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.0650</td>\n",
       "      <td>35.528</td>\n",
       "      <td>501.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.6210</td>\n",
       "      <td>35.511</td>\n",
       "      <td>600.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.1840</td>\n",
       "      <td>35.657</td>\n",
       "      <td>800.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>9.4920</td>\n",
       "      <td>35.720</td>\n",
       "      <td>1001.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>5.1460</td>\n",
       "      <td>35.138</td>\n",
       "      <td>1500.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>3.4244</td>\n",
       "      <td>34.910</td>\n",
       "      <td>1993.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-16.225</td>\n",
       "      <td>44.133</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3900</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-16.222</td>\n",
       "      <td>44.205</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>35.696</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-16.217</td>\n",
       "      <td>44.277</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>35.718</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-16.210</td>\n",
       "      <td>44.349</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3100</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-16.206</td>\n",
       "      <td>44.419</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2300</td>\n",
       "      <td>35.695</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-16.203</td>\n",
       "      <td>44.490</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2500</td>\n",
       "      <td>35.705</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude (degrees_east)  latitude (degrees_north)            time (UTC)  \\\n",
       "0                    -16.094                    44.420  1992-05-17T00:00:00Z   \n",
       "1                    -16.094                    44.420  1992-05-17T00:00:00Z   \n",
       "2                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "3                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "4                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "5                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "6                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "7                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "8                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "9                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "10                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "11                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "12                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "13                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "14                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "15                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "16                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "17                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "18                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "19                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "20                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "21                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "22                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "23                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "24                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "25                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "26                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "27                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "28                   -16.225                    44.133  1991-05-01T00:00:00Z   \n",
       "29                   -16.222                    44.205  1991-05-01T00:00:00Z   \n",
       "30                   -16.217                    44.277  1991-05-01T00:00:00Z   \n",
       "31                   -16.210                    44.349  1991-05-01T00:00:00Z   \n",
       "32                   -16.206                    44.419  1991-05-01T00:00:00Z   \n",
       "33                   -16.203                    44.490  1991-05-01T00:00:00Z   \n",
       "\n",
       "    G2temperature  G2salinity (PSU)  G2pressure  \n",
       "0         14.8000            35.490      5.0381  \n",
       "1         14.8000            35.534      5.0381  \n",
       "2         12.4400            35.650     23.2000  \n",
       "3         12.4400            35.650     51.3000  \n",
       "4         12.4400            35.650    152.0000  \n",
       "5         12.4200            35.648    202.5000  \n",
       "6         11.8600            35.579    303.0000  \n",
       "7         11.4000            35.539    403.0000  \n",
       "8         10.9800            35.507    502.0000  \n",
       "9         10.5400            35.506    604.0000  \n",
       "10         9.7900            35.601    805.0000  \n",
       "11         9.1700            35.705   1000.0000  \n",
       "12         4.2900            35.003   1500.0000  \n",
       "13         3.2600            34.900   2000.0000  \n",
       "14        19.7120            35.719      3.6793  \n",
       "15        19.7080            35.719     18.7020  \n",
       "16        16.5560            35.770     49.6010  \n",
       "17        13.4130            35.731    100.0400  \n",
       "18        12.9300            35.709    150.5000  \n",
       "19        12.4790            35.661    200.9400  \n",
       "20        11.9430            35.606    300.1600  \n",
       "21        11.4980            35.565    399.6500  \n",
       "22        11.0650            35.528    501.0400  \n",
       "23        10.6210            35.511    600.4900  \n",
       "24        10.1840            35.657    800.4800  \n",
       "25         9.4920            35.720   1001.3000  \n",
       "26         5.1460            35.138   1500.2000  \n",
       "27         3.4244            34.910   1993.2000  \n",
       "28        12.3900            35.699      5.0381  \n",
       "29        12.3700            35.696      5.0381  \n",
       "30        12.5000            35.718      5.0381  \n",
       "31        12.3100            35.699      5.0381  \n",
       "32        12.2300            35.695      5.0381  \n",
       "33        12.2500            35.705      5.0381  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c805eb8-a545-4ba2-a3bb-e8e0232da2c9",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab3da7f",
   "metadata": {},
   "source": [
    "## The pandas [`DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe)...\n",
    "...is a **labeled**, two-dimensional columnar structure, similar to a table, spreadsheet, or the R `data.frame`.\n",
    "\n",
    "![dataframe schematic](https://github.com/pandas-dev/pandas/raw/main/doc/source/_static/schemas/01_table_dataframe.svg \"Schematic of a pandas DataFrame\")\n",
    "\n",
    "The `columns` that make up our `DataFrame` can be lists, dictionaries, NumPy arrays, pandas `Series`, or many other data types not mentioned here. Within these `columns`, you can have data values of many different data types used in Python and NumPy, including text, numbers, and dates/times. The first column of a `DataFrame`, shown in the image above in dark gray, is uniquely referred to as an `index`; this column contains information characterizing each row of our `DataFrame`. Similar to any other `column`, the `index` can label rows by text, numbers, datetime objects, and many other data types. Datetime objects are a quite popular way to label rows.\n",
    "\n",
    "For our first example using Pandas DataFrames, we start by reading in some data in comma-separated value (`.csv`) format. We retrieve this dataset from the Pythia DATASETS class (imported at the top of this page); however, the dataset was originally contained within the NCDC teleconnections database. This dataset contains many types of geoscientific data, including El Nino/Southern Oscillation indices. For more information on this dataset, review the description [here](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0be820cd",
   "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>longitude (degrees_east)</th>\n",
       "      <th>latitude (degrees_north)</th>\n",
       "      <th>time (UTC)</th>\n",
       "      <th>G2temperature</th>\n",
       "      <th>G2salinity (PSU)</th>\n",
       "      <th>G2pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.490</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.534</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>23.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>51.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>152.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4200</td>\n",
       "      <td>35.648</td>\n",
       "      <td>202.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.8600</td>\n",
       "      <td>35.579</td>\n",
       "      <td>303.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.4000</td>\n",
       "      <td>35.539</td>\n",
       "      <td>403.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.9800</td>\n",
       "      <td>35.507</td>\n",
       "      <td>502.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.5400</td>\n",
       "      <td>35.506</td>\n",
       "      <td>604.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.7900</td>\n",
       "      <td>35.601</td>\n",
       "      <td>805.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.1700</td>\n",
       "      <td>35.705</td>\n",
       "      <td>1000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>4.2900</td>\n",
       "      <td>35.003</td>\n",
       "      <td>1500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>3.2600</td>\n",
       "      <td>34.900</td>\n",
       "      <td>2000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7120</td>\n",
       "      <td>35.719</td>\n",
       "      <td>3.6793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7080</td>\n",
       "      <td>35.719</td>\n",
       "      <td>18.7020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>16.5560</td>\n",
       "      <td>35.770</td>\n",
       "      <td>49.6010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>13.4130</td>\n",
       "      <td>35.731</td>\n",
       "      <td>100.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.9300</td>\n",
       "      <td>35.709</td>\n",
       "      <td>150.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.4790</td>\n",
       "      <td>35.661</td>\n",
       "      <td>200.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.9430</td>\n",
       "      <td>35.606</td>\n",
       "      <td>300.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.4980</td>\n",
       "      <td>35.565</td>\n",
       "      <td>399.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.0650</td>\n",
       "      <td>35.528</td>\n",
       "      <td>501.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.6210</td>\n",
       "      <td>35.511</td>\n",
       "      <td>600.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.1840</td>\n",
       "      <td>35.657</td>\n",
       "      <td>800.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>9.4920</td>\n",
       "      <td>35.720</td>\n",
       "      <td>1001.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>5.1460</td>\n",
       "      <td>35.138</td>\n",
       "      <td>1500.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>3.4244</td>\n",
       "      <td>34.910</td>\n",
       "      <td>1993.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-16.225</td>\n",
       "      <td>44.133</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3900</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-16.222</td>\n",
       "      <td>44.205</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>35.696</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-16.217</td>\n",
       "      <td>44.277</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>35.718</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-16.210</td>\n",
       "      <td>44.349</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3100</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-16.206</td>\n",
       "      <td>44.419</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2300</td>\n",
       "      <td>35.695</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-16.203</td>\n",
       "      <td>44.490</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2500</td>\n",
       "      <td>35.705</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude (degrees_east)  latitude (degrees_north)            time (UTC)  \\\n",
       "0                    -16.094                    44.420  1992-05-17T00:00:00Z   \n",
       "1                    -16.094                    44.420  1992-05-17T00:00:00Z   \n",
       "2                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "3                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "4                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "5                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "6                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "7                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "8                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "9                    -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "10                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "11                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "12                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "13                   -16.666                    44.500  2001-02-07T00:00:00Z   \n",
       "14                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "15                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "16                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "17                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "18                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "19                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "20                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "21                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "22                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "23                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "24                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "25                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "26                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "27                   -16.670                    44.484  2001-08-26T00:00:00Z   \n",
       "28                   -16.225                    44.133  1991-05-01T00:00:00Z   \n",
       "29                   -16.222                    44.205  1991-05-01T00:00:00Z   \n",
       "30                   -16.217                    44.277  1991-05-01T00:00:00Z   \n",
       "31                   -16.210                    44.349  1991-05-01T00:00:00Z   \n",
       "32                   -16.206                    44.419  1991-05-01T00:00:00Z   \n",
       "33                   -16.203                    44.490  1991-05-01T00:00:00Z   \n",
       "\n",
       "    G2temperature  G2salinity (PSU)  G2pressure  \n",
       "0         14.8000            35.490      5.0381  \n",
       "1         14.8000            35.534      5.0381  \n",
       "2         12.4400            35.650     23.2000  \n",
       "3         12.4400            35.650     51.3000  \n",
       "4         12.4400            35.650    152.0000  \n",
       "5         12.4200            35.648    202.5000  \n",
       "6         11.8600            35.579    303.0000  \n",
       "7         11.4000            35.539    403.0000  \n",
       "8         10.9800            35.507    502.0000  \n",
       "9         10.5400            35.506    604.0000  \n",
       "10         9.7900            35.601    805.0000  \n",
       "11         9.1700            35.705   1000.0000  \n",
       "12         4.2900            35.003   1500.0000  \n",
       "13         3.2600            34.900   2000.0000  \n",
       "14        19.7120            35.719      3.6793  \n",
       "15        19.7080            35.719     18.7020  \n",
       "16        16.5560            35.770     49.6010  \n",
       "17        13.4130            35.731    100.0400  \n",
       "18        12.9300            35.709    150.5000  \n",
       "19        12.4790            35.661    200.9400  \n",
       "20        11.9430            35.606    300.1600  \n",
       "21        11.4980            35.565    399.6500  \n",
       "22        11.0650            35.528    501.0400  \n",
       "23        10.6210            35.511    600.4900  \n",
       "24        10.1840            35.657    800.4800  \n",
       "25         9.4920            35.720   1001.3000  \n",
       "26         5.1460            35.138   1500.2000  \n",
       "27         3.4244            34.910   1993.2000  \n",
       "28        12.3900            35.699      5.0381  \n",
       "29        12.3700            35.696      5.0381  \n",
       "30        12.5000            35.718      5.0381  \n",
       "31        12.3100            35.699      5.0381  \n",
       "32        12.2300            35.695      5.0381  \n",
       "33        12.2500            35.705      5.0381  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f25621c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set index\n",
    "df.set_index(pd.to_datetime(df['time (UTC)']), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d71e6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude (degrees_east)</th>\n",
       "      <th>latitude (degrees_north)</th>\n",
       "      <th>time (UTC)</th>\n",
       "      <th>G2temperature</th>\n",
       "      <th>G2salinity (PSU)</th>\n",
       "      <th>G2pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time (UTC)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-05-17 00:00:00+00:00</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.490</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-17 00:00:00+00:00</th>\n",
       "      <td>-16.094</td>\n",
       "      <td>44.420</td>\n",
       "      <td>1992-05-17T00:00:00Z</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>35.534</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>23.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>51.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4400</td>\n",
       "      <td>35.650</td>\n",
       "      <td>152.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>12.4200</td>\n",
       "      <td>35.648</td>\n",
       "      <td>202.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.8600</td>\n",
       "      <td>35.579</td>\n",
       "      <td>303.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>11.4000</td>\n",
       "      <td>35.539</td>\n",
       "      <td>403.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.9800</td>\n",
       "      <td>35.507</td>\n",
       "      <td>502.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>10.5400</td>\n",
       "      <td>35.506</td>\n",
       "      <td>604.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.7900</td>\n",
       "      <td>35.601</td>\n",
       "      <td>805.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>9.1700</td>\n",
       "      <td>35.705</td>\n",
       "      <td>1000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>4.2900</td>\n",
       "      <td>35.003</td>\n",
       "      <td>1500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-07 00:00:00+00:00</th>\n",
       "      <td>-16.666</td>\n",
       "      <td>44.500</td>\n",
       "      <td>2001-02-07T00:00:00Z</td>\n",
       "      <td>3.2600</td>\n",
       "      <td>34.900</td>\n",
       "      <td>2000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7120</td>\n",
       "      <td>35.719</td>\n",
       "      <td>3.6793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>19.7080</td>\n",
       "      <td>35.719</td>\n",
       "      <td>18.7020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>16.5560</td>\n",
       "      <td>35.770</td>\n",
       "      <td>49.6010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>13.4130</td>\n",
       "      <td>35.731</td>\n",
       "      <td>100.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.9300</td>\n",
       "      <td>35.709</td>\n",
       "      <td>150.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>12.4790</td>\n",
       "      <td>35.661</td>\n",
       "      <td>200.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.9430</td>\n",
       "      <td>35.606</td>\n",
       "      <td>300.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.4980</td>\n",
       "      <td>35.565</td>\n",
       "      <td>399.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>11.0650</td>\n",
       "      <td>35.528</td>\n",
       "      <td>501.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.6210</td>\n",
       "      <td>35.511</td>\n",
       "      <td>600.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>10.1840</td>\n",
       "      <td>35.657</td>\n",
       "      <td>800.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>9.4920</td>\n",
       "      <td>35.720</td>\n",
       "      <td>1001.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>5.1460</td>\n",
       "      <td>35.138</td>\n",
       "      <td>1500.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08-26 00:00:00+00:00</th>\n",
       "      <td>-16.670</td>\n",
       "      <td>44.484</td>\n",
       "      <td>2001-08-26T00:00:00Z</td>\n",
       "      <td>3.4244</td>\n",
       "      <td>34.910</td>\n",
       "      <td>1993.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.225</td>\n",
       "      <td>44.133</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3900</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.222</td>\n",
       "      <td>44.205</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>35.696</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.217</td>\n",
       "      <td>44.277</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>35.718</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.210</td>\n",
       "      <td>44.349</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.3100</td>\n",
       "      <td>35.699</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.206</td>\n",
       "      <td>44.419</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2300</td>\n",
       "      <td>35.695</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-01 00:00:00+00:00</th>\n",
       "      <td>-16.203</td>\n",
       "      <td>44.490</td>\n",
       "      <td>1991-05-01T00:00:00Z</td>\n",
       "      <td>12.2500</td>\n",
       "      <td>35.705</td>\n",
       "      <td>5.0381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           longitude (degrees_east)  latitude (degrees_north)  \\\n",
       "time (UTC)                                                                      \n",
       "1992-05-17 00:00:00+00:00                   -16.094                    44.420   \n",
       "1992-05-17 00:00:00+00:00                   -16.094                    44.420   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-02-07 00:00:00+00:00                   -16.666                    44.500   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "2001-08-26 00:00:00+00:00                   -16.670                    44.484   \n",
       "1991-05-01 00:00:00+00:00                   -16.225                    44.133   \n",
       "1991-05-01 00:00:00+00:00                   -16.222                    44.205   \n",
       "1991-05-01 00:00:00+00:00                   -16.217                    44.277   \n",
       "1991-05-01 00:00:00+00:00                   -16.210                    44.349   \n",
       "1991-05-01 00:00:00+00:00                   -16.206                    44.419   \n",
       "1991-05-01 00:00:00+00:00                   -16.203                    44.490   \n",
       "\n",
       "                                     time (UTC)  G2temperature  \\\n",
       "time (UTC)                                                       \n",
       "1992-05-17 00:00:00+00:00  1992-05-17T00:00:00Z        14.8000   \n",
       "1992-05-17 00:00:00+00:00  1992-05-17T00:00:00Z        14.8000   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        12.4400   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        12.4400   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        12.4400   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        12.4200   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        11.8600   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        11.4000   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        10.9800   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z        10.5400   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z         9.7900   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z         9.1700   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z         4.2900   \n",
       "2001-02-07 00:00:00+00:00  2001-02-07T00:00:00Z         3.2600   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        19.7120   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        19.7080   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        16.5560   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        13.4130   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        12.9300   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        12.4790   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        11.9430   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        11.4980   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        11.0650   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        10.6210   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z        10.1840   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z         9.4920   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z         5.1460   \n",
       "2001-08-26 00:00:00+00:00  2001-08-26T00:00:00Z         3.4244   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.3900   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.3700   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.5000   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.3100   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.2300   \n",
       "1991-05-01 00:00:00+00:00  1991-05-01T00:00:00Z        12.2500   \n",
       "\n",
       "                           G2salinity (PSU)  G2pressure  \n",
       "time (UTC)                                               \n",
       "1992-05-17 00:00:00+00:00            35.490      5.0381  \n",
       "1992-05-17 00:00:00+00:00            35.534      5.0381  \n",
       "2001-02-07 00:00:00+00:00            35.650     23.2000  \n",
       "2001-02-07 00:00:00+00:00            35.650     51.3000  \n",
       "2001-02-07 00:00:00+00:00            35.650    152.0000  \n",
       "2001-02-07 00:00:00+00:00            35.648    202.5000  \n",
       "2001-02-07 00:00:00+00:00            35.579    303.0000  \n",
       "2001-02-07 00:00:00+00:00            35.539    403.0000  \n",
       "2001-02-07 00:00:00+00:00            35.507    502.0000  \n",
       "2001-02-07 00:00:00+00:00            35.506    604.0000  \n",
       "2001-02-07 00:00:00+00:00            35.601    805.0000  \n",
       "2001-02-07 00:00:00+00:00            35.705   1000.0000  \n",
       "2001-02-07 00:00:00+00:00            35.003   1500.0000  \n",
       "2001-02-07 00:00:00+00:00            34.900   2000.0000  \n",
       "2001-08-26 00:00:00+00:00            35.719      3.6793  \n",
       "2001-08-26 00:00:00+00:00            35.719     18.7020  \n",
       "2001-08-26 00:00:00+00:00            35.770     49.6010  \n",
       "2001-08-26 00:00:00+00:00            35.731    100.0400  \n",
       "2001-08-26 00:00:00+00:00            35.709    150.5000  \n",
       "2001-08-26 00:00:00+00:00            35.661    200.9400  \n",
       "2001-08-26 00:00:00+00:00            35.606    300.1600  \n",
       "2001-08-26 00:00:00+00:00            35.565    399.6500  \n",
       "2001-08-26 00:00:00+00:00            35.528    501.0400  \n",
       "2001-08-26 00:00:00+00:00            35.511    600.4900  \n",
       "2001-08-26 00:00:00+00:00            35.657    800.4800  \n",
       "2001-08-26 00:00:00+00:00            35.720   1001.3000  \n",
       "2001-08-26 00:00:00+00:00            35.138   1500.2000  \n",
       "2001-08-26 00:00:00+00:00            34.910   1993.2000  \n",
       "1991-05-01 00:00:00+00:00            35.699      5.0381  \n",
       "1991-05-01 00:00:00+00:00            35.696      5.0381  \n",
       "1991-05-01 00:00:00+00:00            35.718      5.0381  \n",
       "1991-05-01 00:00:00+00:00            35.699      5.0381  \n",
       "1991-05-01 00:00:00+00:00            35.695      5.0381  \n",
       "1991-05-01 00:00:00+00:00            35.705      5.0381  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ac54db92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1992-05-17 00:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68316c6c",
   "metadata": {},
   "source": [
    "### Read file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e99652d5",
   "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>G2cruise</th>\n",
       "      <th>G2region</th>\n",
       "      <th>G2station</th>\n",
       "      <th>G2cast</th>\n",
       "      <th>G2year</th>\n",
       "      <th>G2month</th>\n",
       "      <th>G2day</th>\n",
       "      <th>G2hour</th>\n",
       "      <th>G2minute</th>\n",
       "      <th>G2latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>G2toc</th>\n",
       "      <th>G2tocf</th>\n",
       "      <th>G2doc</th>\n",
       "      <th>G2docf</th>\n",
       "      <th>G2don</th>\n",
       "      <th>G2donf</th>\n",
       "      <th>G2tdn</th>\n",
       "      <th>G2tdnf</th>\n",
       "      <th>G2chla</th>\n",
       "      <th>G2chlaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>80.567</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>80.567</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>80.567</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>80.567</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>80.567</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>81.197</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>81.197</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>81.197</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>81.197</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>81.197</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    G2cruise  G2region  G2station  G2cast  G2year  G2month  G2day  G2hour  \\\n",
       "0          1         4        319       1    1984        7     20      14   \n",
       "1          1         4        319       1    1984        7     20      14   \n",
       "2          1         4        319       1    1984        7     20      14   \n",
       "3          1         4        319       1    1984        7     20      14   \n",
       "4          1         4        319       1    1984        7     20      14   \n",
       "..       ...       ...        ...     ...     ...      ...    ...     ...   \n",
       "84         1         4        324       1    1984        7     22       2   \n",
       "85         1         4        324       1    1984        7     22       2   \n",
       "86         1         4        324       1    1984        7     22       2   \n",
       "87         1         4        324       1    1984        7     22       2   \n",
       "88         1         4        324       1    1984        7     22       2   \n",
       "\n",
       "    G2minute  G2latitude  ...  G2toc  G2tocf  G2doc  G2docf  G2don  G2donf  \\\n",
       "0         46      80.567  ...  -9999       9  -9999       9  -9999       9   \n",
       "1         46      80.567  ...  -9999       9  -9999       9  -9999       9   \n",
       "2         46      80.567  ...  -9999       9  -9999       9  -9999       9   \n",
       "3         46      80.567  ...  -9999       9  -9999       9  -9999       9   \n",
       "4         46      80.567  ...  -9999       9  -9999       9  -9999       9   \n",
       "..       ...         ...  ...    ...     ...    ...     ...    ...     ...   \n",
       "84        48      81.197  ...  -9999       9  -9999       9  -9999       9   \n",
       "85        48      81.197  ...  -9999       9  -9999       9  -9999       9   \n",
       "86        48      81.197  ...  -9999       9  -9999       9  -9999       9   \n",
       "87        48      81.197  ...  -9999       9  -9999       9  -9999       9   \n",
       "88        48      81.197  ...  -9999       9  -9999       9  -9999       9   \n",
       "\n",
       "    G2tdn  G2tdnf  G2chla  G2chlaf  \n",
       "0   -9999       9   -9999        9  \n",
       "1   -9999       9   -9999        9  \n",
       "2   -9999       9   -9999        9  \n",
       "3   -9999       9   -9999        9  \n",
       "4   -9999       9   -9999        9  \n",
       "..    ...     ...     ...      ...  \n",
       "84  -9999       9   -9999        9  \n",
       "85  -9999       9   -9999        9  \n",
       "86  -9999       9   -9999        9  \n",
       "87  -9999       9   -9999        9  \n",
       "88  -9999       9   -9999        9  \n",
       "\n",
       "[89 rows x 106 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_file = Path('__file__').resolve()\n",
    "dir_data = p_file.parents[0] / 'data'\n",
    "\n",
    "fnd = dir_data / 'GLODAPv2.2021.csv'\n",
    "df2 = pd.read_table(fnd, sep=',')\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377d4803",
   "metadata": {},
   "source": [
    "The `DataFrame` index, as described above, contains information characterizing rows; each row has a unique ID value, which is displayed in the index column.  By default, the IDs for rows in a `DataFrame` are represented as sequential integers, which start at 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cde6999b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1992-05-17 00:00:00+00:00', '1992-05-17 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-02-07 00:00:00+00:00', '2001-02-07 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '2001-08-26 00:00:00+00:00', '2001-08-26 00:00:00+00:00',\n",
       "               '1991-05-01 00:00:00+00:00', '1991-05-01 00:00:00+00:00',\n",
       "               '1991-05-01 00:00:00+00:00', '1991-05-01 00:00:00+00:00',\n",
       "               '1991-05-01 00:00:00+00:00', '1991-05-01 00:00:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', name='time (UTC)', freq=None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af49ac9",
   "metadata": {},
   "source": [
    "At the moment, the index column of our `DataFrame` is not very helpful for humans. However, Pandas has clever ways to make index columns more human-readable. The next example demonstrates how to use optional keyword arguments to convert `DataFrame` index IDs to a human-friendly datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b4657f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>G2cruise</th>\n",
       "      <th>G2region</th>\n",
       "      <th>G2station</th>\n",
       "      <th>G2cast</th>\n",
       "      <th>G2latitude</th>\n",
       "      <th>G2longitude</th>\n",
       "      <th>G2bottomdepth</th>\n",
       "      <th>G2maxsampdepth</th>\n",
       "      <th>G2bottle</th>\n",
       "      <th>...</th>\n",
       "      <th>G2toc</th>\n",
       "      <th>G2tocf</th>\n",
       "      <th>G2doc</th>\n",
       "      <th>G2docf</th>\n",
       "      <th>G2don</th>\n",
       "      <th>G2donf</th>\n",
       "      <th>G2tdn</th>\n",
       "      <th>G2tdnf</th>\n",
       "      <th>G2chla</th>\n",
       "      <th>G2chlaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1984 7 20 14 46</td>\n",
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       "      <td>1984 7 20 14 46</td>\n",
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       "      <th>2</th>\n",
       "      <td>1984 7 20 14 46</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984 7 20 14 46</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
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       "      <td>733</td>\n",
       "      <td>724</td>\n",
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       "      <td>-9999</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984 7 20 14 46</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1984 7 22 2 48</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1984 7 22 2 48</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1984 7 22 2 48</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
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       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1984 7 22 2 48</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1984 7 22 2 48</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               time  G2cruise  G2region  G2station  G2cast  G2latitude  \\\n",
       "0   1984 7 20 14 46         1         4        319       1      80.567   \n",
       "1   1984 7 20 14 46         1         4        319       1      80.567   \n",
       "2   1984 7 20 14 46         1         4        319       1      80.567   \n",
       "3   1984 7 20 14 46         1         4        319       1      80.567   \n",
       "4   1984 7 20 14 46         1         4        319       1      80.567   \n",
       "..              ...       ...       ...        ...     ...         ...   \n",
       "84   1984 7 22 2 48         1         4        324       1      81.197   \n",
       "85   1984 7 22 2 48         1         4        324       1      81.197   \n",
       "86   1984 7 22 2 48         1         4        324       1      81.197   \n",
       "87   1984 7 22 2 48         1         4        324       1      81.197   \n",
       "88   1984 7 22 2 48         1         4        324       1      81.197   \n",
       "\n",
       "    G2longitude  G2bottomdepth  G2maxsampdepth  G2bottle  ...  G2toc  G2tocf  \\\n",
       "0        7.2267            733             724         1  ...  -9999       9   \n",
       "1        7.2267            733             724         2  ...  -9999       9   \n",
       "2        7.2267            733             724         3  ...  -9999       9   \n",
       "3        7.2267            733             724         4  ...  -9999       9   \n",
       "4        7.2267            733             724         5  ...  -9999       9   \n",
       "..          ...            ...             ...       ...  ...    ...     ...   \n",
       "84      16.7930           1258            1252        22  ...  -9999       9   \n",
       "85      16.7930           1258            1252        21  ...  -9999       9   \n",
       "86      16.7930           1258            1252        19  ...  -9999       9   \n",
       "87      16.7930           1258            1252        18  ...  -9999       9   \n",
       "88      16.7930           1258            1252        17  ...  -9999       9   \n",
       "\n",
       "    G2doc  G2docf  G2don  G2donf  G2tdn  G2tdnf  G2chla  G2chlaf  \n",
       "0   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "1   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "2   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "3   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "4   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "..    ...     ...    ...     ...    ...     ...     ...      ...  \n",
       "84  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "85  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "86  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "87  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "88  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "\n",
       "[89 rows x 102 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For pandas version > 2.0\n",
    "# df2 = pd.read_table(fnd, sep=',', dtype={'G2year': int, 'G2month': int, 'G2day': int, \n",
    "#                                                        'G2hour': int, 'G2minute': int},\n",
    "#                     parse_dates={'time': ['G2year', 'G2month', 'G2day', 'G2hour', 'G2minute']},\n",
    "#                     date_format='%Y %m %d %H %M', \n",
    "# )\n",
    "\n",
    "df2 = pd.read_table(fnd, sep=',', dtype={'G2year': int, 'G2month': int, 'G2day': int, \n",
    "                                                       'G2hour': int, 'G2minute': int},\n",
    "                    parse_dates={'time': ['G2year', 'G2month', 'G2day', 'G2hour', 'G2minute']},\n",
    "                    infer_datetime_format=True, \n",
    ")\n",
    "df2 \n",
    "# date was not recognized!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0d3ae28",
   "metadata": {},
   "outputs": [
    {
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       "      <th>G2region</th>\n",
       "      <th>G2station</th>\n",
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       "      <th>G2latitude</th>\n",
       "      <th>G2longitude</th>\n",
       "      <th>G2bottomdepth</th>\n",
       "      <th>G2maxsampdepth</th>\n",
       "      <th>G2bottle</th>\n",
       "      <th>G2pressure</th>\n",
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       "      <th>G2toc</th>\n",
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       "      <th>G2don</th>\n",
       "      <th>G2donf</th>\n",
       "      <th>G2tdn</th>\n",
       "      <th>G2tdnf</th>\n",
       "      <th>G2chla</th>\n",
       "      <th>G2chlaf</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>3</td>\n",
       "      <td>48.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>4</td>\n",
       "      <td>48.9</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>80.567</td>\n",
       "      <td>7.2267</td>\n",
       "      <td>733</td>\n",
       "      <td>724</td>\n",
       "      <td>5</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>21</td>\n",
       "      <td>104.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>19</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>18</td>\n",
       "      <td>197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81.197</td>\n",
       "      <td>16.7930</td>\n",
       "      <td>1258</td>\n",
       "      <td>1252</td>\n",
       "      <td>17</td>\n",
       "      <td>247.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "      <td>-9999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    G2cruise  G2region  G2station  G2cast  G2latitude  G2longitude  \\\n",
       "0          1         4        319       1      80.567       7.2267   \n",
       "1          1         4        319       1      80.567       7.2267   \n",
       "2          1         4        319       1      80.567       7.2267   \n",
       "3          1         4        319       1      80.567       7.2267   \n",
       "4          1         4        319       1      80.567       7.2267   \n",
       "..       ...       ...        ...     ...         ...          ...   \n",
       "84         1         4        324       1      81.197      16.7930   \n",
       "85         1         4        324       1      81.197      16.7930   \n",
       "86         1         4        324       1      81.197      16.7930   \n",
       "87         1         4        324       1      81.197      16.7930   \n",
       "88         1         4        324       1      81.197      16.7930   \n",
       "\n",
       "    G2bottomdepth  G2maxsampdepth  G2bottle  G2pressure  ...  G2toc  G2tocf  \\\n",
       "0             733             724         1         8.6  ...  -9999       9   \n",
       "1             733             724         2         8.6  ...  -9999       9   \n",
       "2             733             724         3        48.9  ...  -9999       9   \n",
       "3             733             724         4        48.9  ...  -9999       9   \n",
       "4             733             724         5       148.0  ...  -9999       9   \n",
       "..            ...             ...       ...         ...  ...    ...     ...   \n",
       "84           1258            1252        22        68.0  ...  -9999       9   \n",
       "85           1258            1252        21       104.0  ...  -9999       9   \n",
       "86           1258            1252        19       148.0  ...  -9999       9   \n",
       "87           1258            1252        18       197.0  ...  -9999       9   \n",
       "88           1258            1252        17       247.0  ...  -9999       9   \n",
       "\n",
       "    G2doc  G2docf  G2don  G2donf  G2tdn  G2tdnf  G2chla  G2chlaf  \n",
       "0   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "1   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "2   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "3   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "4   -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "..    ...     ...    ...     ...    ...     ...     ...      ...  \n",
       "84  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "85  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "86  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "87  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "88  -9999       9  -9999       9  -9999       9   -9999        9  \n",
       "\n",
       "[89 rows x 101 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('time', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d26ed71",
   "metadata": {},
   "source": [
    "Each of our data rows is now helpfully labeled by a datetime-object-like index value; this means that we can now easily identify data values not only by named columns, but also by date labels on rows. This is a sneak preview of the `DatetimeIndex` functionality of Pandas; this functionality enables a large portion of Pandas' timeseries-related usage. Don't worry; `DatetimeIndex` will be discussed in full detail later on this page. In the meantime, let's look at the columns of data read in from the `.csv` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "847347f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude (degrees_east)', 'latitude (degrees_north)', 'time (UTC)',\n",
       "       'G2temperature', 'G2salinity (PSU)', 'G2pressure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d3b2fb",
   "metadata": {},
   "source": [
    "## The pandas [`Series`](https://pandas.pydata.org/docs/user_guide/dsintro.html#series)...\n",
    "\n",
    "...is essentially any one of the columns of our `DataFrame`. A `Series` also includes the index column from the source `DataFrame`, in order to provide a label for each value in the `Series`.\n",
    "\n",
    "![pandas Series](https://github.com/pandas-dev/pandas/raw/main/doc/source/_static/schemas/01_table_series.svg \"Schematic of a pandas Series\")\n",
    "\n",
    "The pandas `Series` is a fast and capable 1-dimensional array of nearly any data type we could want, and it can behave very similarly to a NumPy `ndarray` or a Python `dict`. You can take a look at any of the `Series` that make up your `DataFrame`, either by using its column name and the Python `dict` notation, or by using dot-shorthand with the column name:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e93573",
   "metadata": {},
   "source": [
    "### Get columns informations  \n",
    "\n",
    "df['name']  \n",
    "df.name \n",
    "\n",
    "if name is a number  \n",
    "df[145]  \n",
    "df.15 is not valid!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee085815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time (UTC)\n",
       "1992-05-17 00:00:00+00:00    14.8000\n",
       "1992-05-17 00:00:00+00:00    14.8000\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4200\n",
       "2001-02-07 00:00:00+00:00    11.8600\n",
       "2001-02-07 00:00:00+00:00    11.4000\n",
       "2001-02-07 00:00:00+00:00    10.9800\n",
       "2001-02-07 00:00:00+00:00    10.5400\n",
       "2001-02-07 00:00:00+00:00     9.7900\n",
       "2001-02-07 00:00:00+00:00     9.1700\n",
       "2001-02-07 00:00:00+00:00     4.2900\n",
       "2001-02-07 00:00:00+00:00     3.2600\n",
       "2001-08-26 00:00:00+00:00    19.7120\n",
       "2001-08-26 00:00:00+00:00    19.7080\n",
       "2001-08-26 00:00:00+00:00    16.5560\n",
       "2001-08-26 00:00:00+00:00    13.4130\n",
       "2001-08-26 00:00:00+00:00    12.9300\n",
       "2001-08-26 00:00:00+00:00    12.4790\n",
       "2001-08-26 00:00:00+00:00    11.9430\n",
       "2001-08-26 00:00:00+00:00    11.4980\n",
       "2001-08-26 00:00:00+00:00    11.0650\n",
       "2001-08-26 00:00:00+00:00    10.6210\n",
       "2001-08-26 00:00:00+00:00    10.1840\n",
       "2001-08-26 00:00:00+00:00     9.4920\n",
       "2001-08-26 00:00:00+00:00     5.1460\n",
       "2001-08-26 00:00:00+00:00     3.4244\n",
       "1991-05-01 00:00:00+00:00    12.3900\n",
       "1991-05-01 00:00:00+00:00    12.3700\n",
       "1991-05-01 00:00:00+00:00    12.5000\n",
       "1991-05-01 00:00:00+00:00    12.3100\n",
       "1991-05-01 00:00:00+00:00    12.2300\n",
       "1991-05-01 00:00:00+00:00    12.2500\n",
       "Name: G2temperature, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['G2temperature']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fcc97fa",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> You can also use the dot notation illustrated below to specify a column name, but this syntax is mostly provided for convenience. For the most part, this notation is interchangeable with the dictionary notation; however, if the column name is not a valid Python identifier (e.g., it starts with a number or space), you cannot use dot notation.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d46cbb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time (UTC)\n",
       "1992-05-17 00:00:00+00:00    14.8000\n",
       "1992-05-17 00:00:00+00:00    14.8000\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4400\n",
       "2001-02-07 00:00:00+00:00    12.4200\n",
       "2001-02-07 00:00:00+00:00    11.8600\n",
       "2001-02-07 00:00:00+00:00    11.4000\n",
       "2001-02-07 00:00:00+00:00    10.9800\n",
       "2001-02-07 00:00:00+00:00    10.5400\n",
       "2001-02-07 00:00:00+00:00     9.7900\n",
       "2001-02-07 00:00:00+00:00     9.1700\n",
       "2001-02-07 00:00:00+00:00     4.2900\n",
       "2001-02-07 00:00:00+00:00     3.2600\n",
       "2001-08-26 00:00:00+00:00    19.7120\n",
       "2001-08-26 00:00:00+00:00    19.7080\n",
       "2001-08-26 00:00:00+00:00    16.5560\n",
       "2001-08-26 00:00:00+00:00    13.4130\n",
       "2001-08-26 00:00:00+00:00    12.9300\n",
       "2001-08-26 00:00:00+00:00    12.4790\n",
       "2001-08-26 00:00:00+00:00    11.9430\n",
       "2001-08-26 00:00:00+00:00    11.4980\n",
       "2001-08-26 00:00:00+00:00    11.0650\n",
       "2001-08-26 00:00:00+00:00    10.6210\n",
       "2001-08-26 00:00:00+00:00    10.1840\n",
       "2001-08-26 00:00:00+00:00     9.4920\n",
       "2001-08-26 00:00:00+00:00     5.1460\n",
       "2001-08-26 00:00:00+00:00     3.4244\n",
       "1991-05-01 00:00:00+00:00    12.3900\n",
       "1991-05-01 00:00:00+00:00    12.3700\n",
       "1991-05-01 00:00:00+00:00    12.5000\n",
       "1991-05-01 00:00:00+00:00    12.3100\n",
       "1991-05-01 00:00:00+00:00    12.2300\n",
       "1991-05-01 00:00:00+00:00    12.2500\n",
       "Name: G2temperature, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.G2temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "866ca05c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-01 05:00:00</th>\n",
       "      <td>2.817</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>226.8</td>\n",
       "      <td>223.4</td>\n",
       "      <td>27.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>76.40</td>\n",
       "      <td>93.04</td>\n",
       "      <td>10.30</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 06:00:00</th>\n",
       "      <td>3.407</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>229.8</td>\n",
       "      <td>226.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>91.55</td>\n",
       "      <td>112.80</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 07:00:00</th>\n",
       "      <td>4.644</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>231.5</td>\n",
       "      <td>229.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>110.38</td>\n",
       "      <td>135.01</td>\n",
       "      <td>17.11</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 08:00:00</th>\n",
       "      <td>5.131</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>230.7</td>\n",
       "      <td>225.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>122.50</td>\n",
       "      <td>152.12</td>\n",
       "      <td>16.92</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 09:00:00</th>\n",
       "      <td>4.297</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>228.4</td>\n",
       "      <td>221.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>123.26</td>\n",
       "      <td>153.09</td>\n",
       "      <td>10.32</td>\n",
       "      <td>-4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31 19:00:00</th>\n",
       "      <td>0.618</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>217.1</td>\n",
       "      <td>218.6</td>\n",
       "      <td>62.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>124.21</td>\n",
       "      <td>204.12</td>\n",
       "      <td>-5.79</td>\n",
       "      <td>-4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31 20:00:00</th>\n",
       "      <td>0.579</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12.1</td>\n",
       "      <td>197.1</td>\n",
       "      <td>219.6</td>\n",
       "      <td>71.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>103.68</td>\n",
       "      <td>225.18</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>-4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31 21:00:00</th>\n",
       "      <td>0.537</td>\n",
       "      <td>6.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>221.5</td>\n",
       "      <td>73.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>88.59</td>\n",
       "      <td>219.36</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31 22:00:00</th>\n",
       "      <td>0.496</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>144.5</td>\n",
       "      <td>223.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>79.42</td>\n",
       "      <td>178.05</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31 23:00:00</th>\n",
       "      <td>0.432</td>\n",
       "      <td>6.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>166.6</td>\n",
       "      <td>225.2</td>\n",
       "      <td>70.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>84.11</td>\n",
       "      <td>185.49</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-2.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192859 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm    tp   dirm     dp   spr      h      lm  \\\n",
       "YY_mm_DD_time                                                              \n",
       "1979-01-01 05:00:00  2.817  6.8   7.7  226.8  223.4  27.2  80.02   76.40   \n",
       "1979-01-01 06:00:00  3.407  7.4   8.5  229.8  226.5  25.6  80.02   91.55   \n",
       "1979-01-01 07:00:00  4.644  8.1   9.3  231.5  229.2  24.7  80.02  110.38   \n",
       "1979-01-01 08:00:00  5.131  8.6   9.9  230.7  225.0  25.3  80.02  122.50   \n",
       "1979-01-01 09:00:00  4.297  8.6   9.9  228.4  221.1  27.5  80.02  123.26   \n",
       "...                    ...  ...   ...    ...    ...   ...    ...     ...   \n",
       "2000-12-31 19:00:00  0.618  7.7  11.5  217.1  218.6  62.5  80.02  124.21   \n",
       "2000-12-31 20:00:00  0.579  6.8  12.1  197.1  219.6  71.1  80.02  103.68   \n",
       "2000-12-31 21:00:00  0.537  6.2  12.0  165.5  221.5  73.3  80.02   88.59   \n",
       "2000-12-31 22:00:00  0.496  5.8  10.7  144.5  223.4  72.0  80.02   79.42   \n",
       "2000-12-31 23:00:00  0.432  6.1  10.9  166.6  225.2  70.3  80.02   84.11   \n",
       "\n",
       "                         lp     uw    vw  \n",
       "YY_mm_DD_time                             \n",
       "1979-01-01 05:00:00   93.04  10.30  3.32  \n",
       "1979-01-01 06:00:00  112.80  13.27  3.68  \n",
       "1979-01-01 07:00:00  135.01  17.11  6.84  \n",
       "1979-01-01 08:00:00  152.12  16.92  6.68  \n",
       "1979-01-01 09:00:00  153.09  10.32 -4.45  \n",
       "...                     ...    ...   ...  \n",
       "2000-12-31 19:00:00  204.12  -5.79 -4.57  \n",
       "2000-12-31 20:00:00  225.18  -5.20 -4.69  \n",
       "2000-12-31 21:00:00  219.36  -5.06 -4.21  \n",
       "2000-12-31 22:00:00  178.05  -5.68 -2.88  \n",
       "2000-12-31 23:00:00  185.49  -4.90 -2.32  \n",
       "\n",
       "[192859 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table('data/data_waves.dat', header=None, delim_whitespace=True, \n",
    "                   names=['YY', 'mm', 'DD', 'time', 'hs', 'tm', 'tp', 'dirm', 'dp', 'spr', 'h', 'lm', 'lp', \n",
    "                          'uw', 'vw'],\n",
    "                  parse_dates=[[0, 1, 2, 3]], index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1dcc04",
   "metadata": {},
   "source": [
    "### Using `.iloc` and `.loc` to index\n",
    "\n",
    "In this section, we introduce ways to access data that are preferred by Pandas over the methods listed above. When accessing by label, it is preferred to use the `.loc` method, and when accessing by index, the `.iloc` method is preferred. These methods behave similarly to the notation introduced above, but provide more speed, security, and rigor in your value selection. Using these methods can also help you avoid [chained assignment warnings](https://pandas.pydata.org/docs/user_guide/indexing.html#returning-a-view-versus-a-copy) generated by pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "970ba89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-01 00:00:00</th>\n",
       "      <td>1.297</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>208.7</td>\n",
       "      <td>227.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.95</td>\n",
       "      <td>124.17</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 01:00:00</th>\n",
       "      <td>1.212</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>211.7</td>\n",
       "      <td>226.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>77.72</td>\n",
       "      <td>116.02</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 02:00:00</th>\n",
       "      <td>1.152</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>212.7</td>\n",
       "      <td>226.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>76.28</td>\n",
       "      <td>110.40</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 03:00:00</th>\n",
       "      <td>1.086</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>213.6</td>\n",
       "      <td>225.9</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.78</td>\n",
       "      <td>108.50</td>\n",
       "      <td>-4.65</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 04:00:00</th>\n",
       "      <td>1.045</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>212.1</td>\n",
       "      <td>225.8</td>\n",
       "      <td>31.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>72.94</td>\n",
       "      <td>106.77</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 19:00:00</th>\n",
       "      <td>2.038</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>35.87</td>\n",
       "      <td>43.14</td>\n",
       "      <td>-10.85</td>\n",
       "      <td>-13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 20:00:00</th>\n",
       "      <td>1.984</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>43.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>34.67</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>-12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 21:00:00</th>\n",
       "      <td>1.806</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>37.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>32.62</td>\n",
       "      <td>40.64</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 22:00:00</th>\n",
       "      <td>1.803</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>36.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>31.77</td>\n",
       "      <td>40.11</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>-13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 23:00:00</th>\n",
       "      <td>1.971</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>35.3</td>\n",
       "      <td>30.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>33.59</td>\n",
       "      <td>41.12</td>\n",
       "      <td>-12.06</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8040 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm   tp   dirm     dp   spr      h     lm  \\\n",
       "YY_mm_DD_time                                                            \n",
       "1982-01-01 00:00:00  1.297  6.6  8.9  208.7  227.7  36.1  80.02  75.95   \n",
       "1982-01-01 01:00:00  1.212  6.7  8.6  211.7  226.9  30.9  80.02  77.72   \n",
       "1982-01-01 02:00:00  1.152  6.6  8.4  212.7  226.4  32.3  80.02  76.28   \n",
       "1982-01-01 03:00:00  1.086  6.6  8.3  213.6  225.9  30.2  80.02  75.78   \n",
       "1982-01-01 04:00:00  1.045  6.5  8.3  212.1  225.8  31.5  80.02  72.94   \n",
       "...                    ...  ...  ...    ...    ...   ...    ...    ...   \n",
       "1982-12-01 19:00:00  2.038  4.7  5.3   42.9   39.6  31.3  80.02  35.87   \n",
       "1982-12-01 20:00:00  1.984  4.6  5.2   43.1   38.9  31.3  80.02  34.67   \n",
       "1982-12-01 21:00:00  1.806  4.4  5.1   41.9   37.8  31.7  80.02  32.62   \n",
       "1982-12-01 22:00:00  1.803  4.4  5.1   36.3   34.2  30.2  80.02  31.77   \n",
       "1982-12-01 23:00:00  1.971  4.5  5.1   38.9   35.3  30.4  80.02  33.59   \n",
       "\n",
       "                         lp     uw     vw  \n",
       "YY_mm_DD_time                              \n",
       "1982-01-01 00:00:00  124.17  -4.07  -1.07  \n",
       "1982-01-01 01:00:00  116.02  -4.07  -1.07  \n",
       "1982-01-01 02:00:00  110.40  -5.33  -3.31  \n",
       "1982-01-01 03:00:00  108.50  -4.65   0.10  \n",
       "1982-01-01 04:00:00  106.77  -5.44   2.38  \n",
       "...                     ...    ...    ...  \n",
       "1982-12-01 19:00:00   43.14 -10.85 -13.53  \n",
       "1982-12-01 20:00:00   41.79 -11.79 -12.64  \n",
       "1982-12-01 21:00:00   40.64 -10.66 -11.85  \n",
       "1982-12-01 22:00:00   40.11  -9.60 -13.30  \n",
       "1982-12-01 23:00:00   41.12 -12.06 -13.19  \n",
       "\n",
       "[8040 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"1982-01-01\":\"1982-12-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "07ae81b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hs        5.131\n",
       "tm        8.600\n",
       "tp        9.900\n",
       "dirm    230.700\n",
       "dp      225.000\n",
       "spr      25.300\n",
       "h        80.020\n",
       "lm      122.500\n",
       "lp      152.120\n",
       "uw       16.920\n",
       "vw        6.680\n",
       "Name: 1979-01-01 08:00:00, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32758ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
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       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-01 05:00:00</th>\n",
       "      <td>2.817</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>226.8</td>\n",
       "      <td>223.4</td>\n",
       "      <td>27.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>76.40</td>\n",
       "      <td>93.04</td>\n",
       "      <td>10.30</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 06:00:00</th>\n",
       "      <td>3.407</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>229.8</td>\n",
       "      <td>226.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>91.55</td>\n",
       "      <td>112.80</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 07:00:00</th>\n",
       "      <td>4.644</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>231.5</td>\n",
       "      <td>229.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>110.38</td>\n",
       "      <td>135.01</td>\n",
       "      <td>17.11</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 08:00:00</th>\n",
       "      <td>5.131</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>230.7</td>\n",
       "      <td>225.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>122.50</td>\n",
       "      <td>152.12</td>\n",
       "      <td>16.92</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 09:00:00</th>\n",
       "      <td>4.297</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>228.4</td>\n",
       "      <td>221.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>123.26</td>\n",
       "      <td>153.09</td>\n",
       "      <td>10.32</td>\n",
       "      <td>-4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 10:00:00</th>\n",
       "      <td>3.510</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>225.5</td>\n",
       "      <td>223.1</td>\n",
       "      <td>26.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>117.35</td>\n",
       "      <td>141.12</td>\n",
       "      <td>8.26</td>\n",
       "      <td>-4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 11:00:00</th>\n",
       "      <td>3.059</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>223.4</td>\n",
       "      <td>222.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>114.52</td>\n",
       "      <td>137.93</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 12:00:00</th>\n",
       "      <td>2.972</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>224.3</td>\n",
       "      <td>223.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>117.64</td>\n",
       "      <td>141.84</td>\n",
       "      <td>7.90</td>\n",
       "      <td>-6.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 13:00:00</th>\n",
       "      <td>3.007</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>222.8</td>\n",
       "      <td>219.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>126.77</td>\n",
       "      <td>149.27</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 14:00:00</th>\n",
       "      <td>3.151</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>227.3</td>\n",
       "      <td>220.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>116.27</td>\n",
       "      <td>157.18</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-15.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 15:00:00</th>\n",
       "      <td>2.990</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>225.7</td>\n",
       "      <td>221.2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>126.92</td>\n",
       "      <td>162.69</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01 16:00:00</th>\n",
       "      <td>3.019</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>226.0</td>\n",
       "      <td>222.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>136.47</td>\n",
       "      <td>171.07</td>\n",
       "      <td>1.45</td>\n",
       "      <td>-15.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm    tp   dirm     dp   spr      h      lm  \\\n",
       "YY_mm_DD_time                                                              \n",
       "1979-01-01 05:00:00  2.817  6.8   7.7  226.8  223.4  27.2  80.02   76.40   \n",
       "1979-01-01 06:00:00  3.407  7.4   8.5  229.8  226.5  25.6  80.02   91.55   \n",
       "1979-01-01 07:00:00  4.644  8.1   9.3  231.5  229.2  24.7  80.02  110.38   \n",
       "1979-01-01 08:00:00  5.131  8.6   9.9  230.7  225.0  25.3  80.02  122.50   \n",
       "1979-01-01 09:00:00  4.297  8.6   9.9  228.4  221.1  27.5  80.02  123.26   \n",
       "1979-01-01 10:00:00  3.510  8.4   9.5  225.5  223.1  26.8  80.02  117.35   \n",
       "1979-01-01 11:00:00  3.059  8.3   9.4  223.4  222.0  23.8  80.02  114.52   \n",
       "1979-01-01 12:00:00  2.972  8.3   9.5  224.3  223.4  25.0  80.02  117.64   \n",
       "1979-01-01 13:00:00  3.007  8.7   9.8  222.8  219.3  25.5  80.02  126.77   \n",
       "1979-01-01 14:00:00  3.151  8.1  10.1  227.3  220.1  47.1  80.02  116.27   \n",
       "1979-01-01 15:00:00  2.990  8.6  10.2  225.7  221.2  42.0  80.02  126.92   \n",
       "1979-01-01 16:00:00  3.019  8.9  10.5  226.0  222.3  40.3  80.02  136.47   \n",
       "\n",
       "                         lp     uw     vw  \n",
       "YY_mm_DD_time                              \n",
       "1979-01-01 05:00:00   93.04  10.30   3.32  \n",
       "1979-01-01 06:00:00  112.80  13.27   3.68  \n",
       "1979-01-01 07:00:00  135.01  17.11   6.84  \n",
       "1979-01-01 08:00:00  152.12  16.92   6.68  \n",
       "1979-01-01 09:00:00  153.09  10.32  -4.45  \n",
       "1979-01-01 10:00:00  141.12   8.26  -4.84  \n",
       "1979-01-01 11:00:00  137.93   7.17   0.81  \n",
       "1979-01-01 12:00:00  141.84   7.90  -6.86  \n",
       "1979-01-01 13:00:00  149.27  -2.89  -9.10  \n",
       "1979-01-01 14:00:00  157.18  -4.13 -15.34  \n",
       "1979-01-01 15:00:00  162.69  -2.20 -13.19  \n",
       "1979-01-01 16:00:00  171.07   1.45 -15.09  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "918f9268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
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       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-04-01 00:00:00</th>\n",
       "      <td>0.397</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>203.9</td>\n",
       "      <td>218.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.02</td>\n",
       "      <td>29.05</td>\n",
       "      <td>2.89</td>\n",
       "      <td>-6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 01:00:00</th>\n",
       "      <td>0.441</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>260.5</td>\n",
       "      <td>219.6</td>\n",
       "      <td>62.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.13</td>\n",
       "      <td>27.67</td>\n",
       "      <td>2.89</td>\n",
       "      <td>-6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 02:00:00</th>\n",
       "      <td>0.463</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>268.4</td>\n",
       "      <td>241.1</td>\n",
       "      <td>58.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.50</td>\n",
       "      <td>24.18</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 03:00:00</th>\n",
       "      <td>0.496</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>280.1</td>\n",
       "      <td>261.0</td>\n",
       "      <td>60.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.43</td>\n",
       "      <td>24.90</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 04:00:00</th>\n",
       "      <td>0.500</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>290.2</td>\n",
       "      <td>263.8</td>\n",
       "      <td>63.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>23.01</td>\n",
       "      <td>28.47</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-4.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 05:00:00</th>\n",
       "      <td>0.466</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>292.7</td>\n",
       "      <td>264.0</td>\n",
       "      <td>64.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>24.60</td>\n",
       "      <td>34.83</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 06:00:00</th>\n",
       "      <td>0.413</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>280.7</td>\n",
       "      <td>256.1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>27.05</td>\n",
       "      <td>43.40</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 07:00:00</th>\n",
       "      <td>0.365</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>265.2</td>\n",
       "      <td>260.3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>29.89</td>\n",
       "      <td>43.85</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 08:00:00</th>\n",
       "      <td>0.335</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>259.6</td>\n",
       "      <td>261.8</td>\n",
       "      <td>41.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>29.96</td>\n",
       "      <td>43.16</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 09:00:00</th>\n",
       "      <td>0.310</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>257.4</td>\n",
       "      <td>259.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>28.84</td>\n",
       "      <td>42.23</td>\n",
       "      <td>1.42</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 10:00:00</th>\n",
       "      <td>0.296</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>258.8</td>\n",
       "      <td>254.1</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>27.35</td>\n",
       "      <td>39.41</td>\n",
       "      <td>1.93</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 11:00:00</th>\n",
       "      <td>0.295</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>263.5</td>\n",
       "      <td>272.3</td>\n",
       "      <td>40.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.39</td>\n",
       "      <td>36.48</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 12:00:00</th>\n",
       "      <td>0.299</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>267.1</td>\n",
       "      <td>275.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.64</td>\n",
       "      <td>36.42</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 13:00:00</th>\n",
       "      <td>0.296</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>267.2</td>\n",
       "      <td>276.1</td>\n",
       "      <td>39.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.94</td>\n",
       "      <td>36.68</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 14:00:00</th>\n",
       "      <td>0.285</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>264.1</td>\n",
       "      <td>273.8</td>\n",
       "      <td>41.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.25</td>\n",
       "      <td>36.51</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 15:00:00</th>\n",
       "      <td>0.273</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>259.4</td>\n",
       "      <td>267.7</td>\n",
       "      <td>46.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>24.18</td>\n",
       "      <td>35.96</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 16:00:00</th>\n",
       "      <td>0.268</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>256.6</td>\n",
       "      <td>259.8</td>\n",
       "      <td>50.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.67</td>\n",
       "      <td>35.11</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 17:00:00</th>\n",
       "      <td>0.267</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>253.2</td>\n",
       "      <td>249.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>21.04</td>\n",
       "      <td>33.23</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>-4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 18:00:00</th>\n",
       "      <td>0.406</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>60.1</td>\n",
       "      <td>58.9</td>\n",
       "      <td>63.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>12.88</td>\n",
       "      <td>7.94</td>\n",
       "      <td>-6.35</td>\n",
       "      <td>-5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 19:00:00</th>\n",
       "      <td>0.538</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>58.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>49.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>12.62</td>\n",
       "      <td>11.74</td>\n",
       "      <td>-6.24</td>\n",
       "      <td>-6.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 20:00:00</th>\n",
       "      <td>0.651</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.9</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>13.91</td>\n",
       "      <td>14.74</td>\n",
       "      <td>-6.54</td>\n",
       "      <td>-6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 21:00:00</th>\n",
       "      <td>0.764</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>52.7</td>\n",
       "      <td>53.8</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>15.55</td>\n",
       "      <td>17.24</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>-6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 22:00:00</th>\n",
       "      <td>0.941</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>53.3</td>\n",
       "      <td>55.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>18.08</td>\n",
       "      <td>21.28</td>\n",
       "      <td>-8.74</td>\n",
       "      <td>-7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 23:00:00</th>\n",
       "      <td>1.053</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>52.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>33.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>20.22</td>\n",
       "      <td>24.14</td>\n",
       "      <td>-8.91</td>\n",
       "      <td>-7.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm   tp   dirm     dp   spr      h     lm     lp  \\\n",
       "YY_mm_DD_time                                                                   \n",
       "1982-04-01 00:00:00  0.397  3.7  4.3  203.9  218.8  60.0  80.02  26.02  29.05   \n",
       "1982-04-01 01:00:00  0.441  3.4  4.2  260.5  219.6  62.9  80.02  22.13  27.67   \n",
       "1982-04-01 02:00:00  0.463  3.5  3.9  268.4  241.1  58.4  80.02  22.50  24.18   \n",
       "1982-04-01 03:00:00  0.496  3.5  4.0  280.1  261.0  60.3  80.02  22.43  24.90   \n",
       "1982-04-01 04:00:00  0.500  3.6  4.3  290.2  263.8  63.4  80.02  23.01  28.47   \n",
       "1982-04-01 05:00:00  0.466  3.7  4.7  292.7  264.0  64.6  80.02  24.60  34.83   \n",
       "1982-04-01 06:00:00  0.413  3.9  5.3  280.7  256.1  61.3  80.02  27.05  43.40   \n",
       "1982-04-01 07:00:00  0.365  4.1  5.3  265.2  260.3  51.0  80.02  29.89  43.85   \n",
       "1982-04-01 08:00:00  0.335  4.2  5.3  259.6  261.8  41.8  80.02  29.96  43.16   \n",
       "1982-04-01 09:00:00  0.310  4.1  5.2  257.4  259.8  37.7  80.02  28.84  42.23   \n",
       "1982-04-01 10:00:00  0.296  4.0  5.0  258.8  254.1  38.5  80.02  27.35  39.41   \n",
       "1982-04-01 11:00:00  0.295  3.9  4.8  263.5  272.3  40.4  80.02  26.39  36.48   \n",
       "1982-04-01 12:00:00  0.299  3.9  4.8  267.1  275.7  40.1  80.02  26.64  36.42   \n",
       "1982-04-01 13:00:00  0.296  4.0  4.8  267.2  276.1  39.6  80.02  26.94  36.68   \n",
       "1982-04-01 14:00:00  0.285  3.9  4.8  264.1  273.8  41.4  80.02  26.25  36.51   \n",
       "1982-04-01 15:00:00  0.273  3.8  4.8  259.4  267.7  46.1  80.02  24.18  35.96   \n",
       "1982-04-01 16:00:00  0.268  3.6  4.7  256.6  259.8  50.7  80.02  22.67  35.11   \n",
       "1982-04-01 17:00:00  0.267  3.5  4.6  253.2  249.6  58.9  80.02  21.04  33.23   \n",
       "1982-04-01 18:00:00  0.406  2.7  2.3   60.1   58.9  63.3  80.02  12.88   7.94   \n",
       "1982-04-01 19:00:00  0.538  2.7  2.7   58.9   61.7  49.7  80.02  12.62  11.74   \n",
       "1982-04-01 20:00:00  0.651  2.9  3.1   54.0   52.9  43.0  80.02  13.91  14.74   \n",
       "1982-04-01 21:00:00  0.764  3.1  3.3   52.7   53.8  38.5  80.02  15.55  17.24   \n",
       "1982-04-01 22:00:00  0.941  3.3  3.7   53.3   55.9  35.0  80.02  18.08  21.28   \n",
       "1982-04-01 23:00:00  1.053  3.5  3.9   52.5   54.9  33.1  80.02  20.22  24.14   \n",
       "\n",
       "                       uw    vw  \n",
       "YY_mm_DD_time                    \n",
       "1982-04-01 00:00:00  2.89 -6.59  \n",
       "1982-04-01 01:00:00  2.89 -6.59  \n",
       "1982-04-01 02:00:00 -1.40 -5.39  \n",
       "1982-04-01 03:00:00 -3.49 -5.21  \n",
       "1982-04-01 04:00:00 -3.95 -4.81  \n",
       "1982-04-01 05:00:00 -3.33 -4.60  \n",
       "1982-04-01 06:00:00 -2.18 -4.28  \n",
       "1982-04-01 07:00:00 -1.55 -2.35  \n",
       "1982-04-01 08:00:00 -0.89 -0.78  \n",
       "1982-04-01 09:00:00  1.42 -0.18  \n",
       "1982-04-01 10:00:00  1.93 -0.20  \n",
       "1982-04-01 11:00:00 -0.64 -0.65  \n",
       "1982-04-01 12:00:00 -2.12 -0.05  \n",
       "1982-04-01 13:00:00 -2.27 -0.95  \n",
       "1982-04-01 14:00:00 -1.10 -2.21  \n",
       "1982-04-01 15:00:00 -1.84 -2.68  \n",
       "1982-04-01 16:00:00 -3.48 -3.02  \n",
       "1982-04-01 17:00:00 -5.63 -4.60  \n",
       "1982-04-01 18:00:00 -6.35 -5.36  \n",
       "1982-04-01 19:00:00 -6.24 -6.09  \n",
       "1982-04-01 20:00:00 -6.54 -6.54  \n",
       "1982-04-01 21:00:00 -7.39 -6.85  \n",
       "1982-04-01 22:00:00 -8.74 -7.46  \n",
       "1982-04-01 23:00:00 -8.91 -7.93  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-04-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c475777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-01 00:00:00</th>\n",
       "      <td>1.297</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>208.7</td>\n",
       "      <td>227.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.95</td>\n",
       "      <td>124.17</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 01:00:00</th>\n",
       "      <td>1.212</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>211.7</td>\n",
       "      <td>226.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>77.72</td>\n",
       "      <td>116.02</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 02:00:00</th>\n",
       "      <td>1.152</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>212.7</td>\n",
       "      <td>226.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>76.28</td>\n",
       "      <td>110.40</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 03:00:00</th>\n",
       "      <td>1.086</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>213.6</td>\n",
       "      <td>225.9</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.78</td>\n",
       "      <td>108.50</td>\n",
       "      <td>-4.65</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 04:00:00</th>\n",
       "      <td>1.045</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>212.1</td>\n",
       "      <td>225.8</td>\n",
       "      <td>31.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>72.94</td>\n",
       "      <td>106.77</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 19:00:00</th>\n",
       "      <td>2.038</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>35.87</td>\n",
       "      <td>43.14</td>\n",
       "      <td>-10.85</td>\n",
       "      <td>-13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 20:00:00</th>\n",
       "      <td>1.984</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>43.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>34.67</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>-12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 21:00:00</th>\n",
       "      <td>1.806</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>37.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>32.62</td>\n",
       "      <td>40.64</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 22:00:00</th>\n",
       "      <td>1.803</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>36.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>31.77</td>\n",
       "      <td>40.11</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>-13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 23:00:00</th>\n",
       "      <td>1.971</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>35.3</td>\n",
       "      <td>30.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>33.59</td>\n",
       "      <td>41.12</td>\n",
       "      <td>-12.06</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8040 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm   tp   dirm     dp   spr      h     lm  \\\n",
       "YY_mm_DD_time                                                            \n",
       "1982-01-01 00:00:00  1.297  6.6  8.9  208.7  227.7  36.1  80.02  75.95   \n",
       "1982-01-01 01:00:00  1.212  6.7  8.6  211.7  226.9  30.9  80.02  77.72   \n",
       "1982-01-01 02:00:00  1.152  6.6  8.4  212.7  226.4  32.3  80.02  76.28   \n",
       "1982-01-01 03:00:00  1.086  6.6  8.3  213.6  225.9  30.2  80.02  75.78   \n",
       "1982-01-01 04:00:00  1.045  6.5  8.3  212.1  225.8  31.5  80.02  72.94   \n",
       "...                    ...  ...  ...    ...    ...   ...    ...    ...   \n",
       "1982-12-01 19:00:00  2.038  4.7  5.3   42.9   39.6  31.3  80.02  35.87   \n",
       "1982-12-01 20:00:00  1.984  4.6  5.2   43.1   38.9  31.3  80.02  34.67   \n",
       "1982-12-01 21:00:00  1.806  4.4  5.1   41.9   37.8  31.7  80.02  32.62   \n",
       "1982-12-01 22:00:00  1.803  4.4  5.1   36.3   34.2  30.2  80.02  31.77   \n",
       "1982-12-01 23:00:00  1.971  4.5  5.1   38.9   35.3  30.4  80.02  33.59   \n",
       "\n",
       "                         lp     uw     vw  \n",
       "YY_mm_DD_time                              \n",
       "1982-01-01 00:00:00  124.17  -4.07  -1.07  \n",
       "1982-01-01 01:00:00  116.02  -4.07  -1.07  \n",
       "1982-01-01 02:00:00  110.40  -5.33  -3.31  \n",
       "1982-01-01 03:00:00  108.50  -4.65   0.10  \n",
       "1982-01-01 04:00:00  106.77  -5.44   2.38  \n",
       "...                     ...    ...    ...  \n",
       "1982-12-01 19:00:00   43.14 -10.85 -13.53  \n",
       "1982-12-01 20:00:00   41.79 -11.79 -12.64  \n",
       "1982-12-01 21:00:00   40.64 -10.66 -11.85  \n",
       "1982-12-01 22:00:00   40.11  -9.60 -13.30  \n",
       "1982-12-01 23:00:00   41.12 -12.06 -13.19  \n",
       "\n",
       "[8040 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-01-01\":\"1982-12-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0dda57c",
   "metadata": {},
   "source": [
    "The `.loc` and `.iloc` methods also allow us to pull entire rows out of a `DataFrame`, as shown in these examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f6693b7",
   "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>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-04-01 00:00:00</th>\n",
       "      <td>0.397</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>203.9</td>\n",
       "      <td>218.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.02</td>\n",
       "      <td>29.05</td>\n",
       "      <td>2.89</td>\n",
       "      <td>-6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 01:00:00</th>\n",
       "      <td>0.441</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>260.5</td>\n",
       "      <td>219.6</td>\n",
       "      <td>62.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.13</td>\n",
       "      <td>27.67</td>\n",
       "      <td>2.89</td>\n",
       "      <td>-6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 02:00:00</th>\n",
       "      <td>0.463</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>268.4</td>\n",
       "      <td>241.1</td>\n",
       "      <td>58.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.50</td>\n",
       "      <td>24.18</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 03:00:00</th>\n",
       "      <td>0.496</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>280.1</td>\n",
       "      <td>261.0</td>\n",
       "      <td>60.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.43</td>\n",
       "      <td>24.90</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 04:00:00</th>\n",
       "      <td>0.500</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>290.2</td>\n",
       "      <td>263.8</td>\n",
       "      <td>63.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>23.01</td>\n",
       "      <td>28.47</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-4.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 05:00:00</th>\n",
       "      <td>0.466</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>292.7</td>\n",
       "      <td>264.0</td>\n",
       "      <td>64.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>24.60</td>\n",
       "      <td>34.83</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 06:00:00</th>\n",
       "      <td>0.413</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>280.7</td>\n",
       "      <td>256.1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>27.05</td>\n",
       "      <td>43.40</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 07:00:00</th>\n",
       "      <td>0.365</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>265.2</td>\n",
       "      <td>260.3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>29.89</td>\n",
       "      <td>43.85</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 08:00:00</th>\n",
       "      <td>0.335</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>259.6</td>\n",
       "      <td>261.8</td>\n",
       "      <td>41.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>29.96</td>\n",
       "      <td>43.16</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 09:00:00</th>\n",
       "      <td>0.310</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>257.4</td>\n",
       "      <td>259.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>28.84</td>\n",
       "      <td>42.23</td>\n",
       "      <td>1.42</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 10:00:00</th>\n",
       "      <td>0.296</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>258.8</td>\n",
       "      <td>254.1</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>27.35</td>\n",
       "      <td>39.41</td>\n",
       "      <td>1.93</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 11:00:00</th>\n",
       "      <td>0.295</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>263.5</td>\n",
       "      <td>272.3</td>\n",
       "      <td>40.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.39</td>\n",
       "      <td>36.48</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 12:00:00</th>\n",
       "      <td>0.299</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>267.1</td>\n",
       "      <td>275.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.64</td>\n",
       "      <td>36.42</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 13:00:00</th>\n",
       "      <td>0.296</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>267.2</td>\n",
       "      <td>276.1</td>\n",
       "      <td>39.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.94</td>\n",
       "      <td>36.68</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 14:00:00</th>\n",
       "      <td>0.285</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>264.1</td>\n",
       "      <td>273.8</td>\n",
       "      <td>41.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>26.25</td>\n",
       "      <td>36.51</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 15:00:00</th>\n",
       "      <td>0.273</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>259.4</td>\n",
       "      <td>267.7</td>\n",
       "      <td>46.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>24.18</td>\n",
       "      <td>35.96</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 16:00:00</th>\n",
       "      <td>0.268</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>256.6</td>\n",
       "      <td>259.8</td>\n",
       "      <td>50.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>22.67</td>\n",
       "      <td>35.11</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 17:00:00</th>\n",
       "      <td>0.267</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>253.2</td>\n",
       "      <td>249.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>21.04</td>\n",
       "      <td>33.23</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>-4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 18:00:00</th>\n",
       "      <td>0.406</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>60.1</td>\n",
       "      <td>58.9</td>\n",
       "      <td>63.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>12.88</td>\n",
       "      <td>7.94</td>\n",
       "      <td>-6.35</td>\n",
       "      <td>-5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 19:00:00</th>\n",
       "      <td>0.538</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>58.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>49.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>12.62</td>\n",
       "      <td>11.74</td>\n",
       "      <td>-6.24</td>\n",
       "      <td>-6.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 20:00:00</th>\n",
       "      <td>0.651</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.9</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>13.91</td>\n",
       "      <td>14.74</td>\n",
       "      <td>-6.54</td>\n",
       "      <td>-6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 21:00:00</th>\n",
       "      <td>0.764</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>52.7</td>\n",
       "      <td>53.8</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>15.55</td>\n",
       "      <td>17.24</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>-6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 22:00:00</th>\n",
       "      <td>0.941</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>53.3</td>\n",
       "      <td>55.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>18.08</td>\n",
       "      <td>21.28</td>\n",
       "      <td>-8.74</td>\n",
       "      <td>-7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01 23:00:00</th>\n",
       "      <td>1.053</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>52.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>33.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>20.22</td>\n",
       "      <td>24.14</td>\n",
       "      <td>-8.91</td>\n",
       "      <td>-7.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm   tp   dirm     dp   spr      h     lm     lp  \\\n",
       "YY_mm_DD_time                                                                   \n",
       "1982-04-01 00:00:00  0.397  3.7  4.3  203.9  218.8  60.0  80.02  26.02  29.05   \n",
       "1982-04-01 01:00:00  0.441  3.4  4.2  260.5  219.6  62.9  80.02  22.13  27.67   \n",
       "1982-04-01 02:00:00  0.463  3.5  3.9  268.4  241.1  58.4  80.02  22.50  24.18   \n",
       "1982-04-01 03:00:00  0.496  3.5  4.0  280.1  261.0  60.3  80.02  22.43  24.90   \n",
       "1982-04-01 04:00:00  0.500  3.6  4.3  290.2  263.8  63.4  80.02  23.01  28.47   \n",
       "1982-04-01 05:00:00  0.466  3.7  4.7  292.7  264.0  64.6  80.02  24.60  34.83   \n",
       "1982-04-01 06:00:00  0.413  3.9  5.3  280.7  256.1  61.3  80.02  27.05  43.40   \n",
       "1982-04-01 07:00:00  0.365  4.1  5.3  265.2  260.3  51.0  80.02  29.89  43.85   \n",
       "1982-04-01 08:00:00  0.335  4.2  5.3  259.6  261.8  41.8  80.02  29.96  43.16   \n",
       "1982-04-01 09:00:00  0.310  4.1  5.2  257.4  259.8  37.7  80.02  28.84  42.23   \n",
       "1982-04-01 10:00:00  0.296  4.0  5.0  258.8  254.1  38.5  80.02  27.35  39.41   \n",
       "1982-04-01 11:00:00  0.295  3.9  4.8  263.5  272.3  40.4  80.02  26.39  36.48   \n",
       "1982-04-01 12:00:00  0.299  3.9  4.8  267.1  275.7  40.1  80.02  26.64  36.42   \n",
       "1982-04-01 13:00:00  0.296  4.0  4.8  267.2  276.1  39.6  80.02  26.94  36.68   \n",
       "1982-04-01 14:00:00  0.285  3.9  4.8  264.1  273.8  41.4  80.02  26.25  36.51   \n",
       "1982-04-01 15:00:00  0.273  3.8  4.8  259.4  267.7  46.1  80.02  24.18  35.96   \n",
       "1982-04-01 16:00:00  0.268  3.6  4.7  256.6  259.8  50.7  80.02  22.67  35.11   \n",
       "1982-04-01 17:00:00  0.267  3.5  4.6  253.2  249.6  58.9  80.02  21.04  33.23   \n",
       "1982-04-01 18:00:00  0.406  2.7  2.3   60.1   58.9  63.3  80.02  12.88   7.94   \n",
       "1982-04-01 19:00:00  0.538  2.7  2.7   58.9   61.7  49.7  80.02  12.62  11.74   \n",
       "1982-04-01 20:00:00  0.651  2.9  3.1   54.0   52.9  43.0  80.02  13.91  14.74   \n",
       "1982-04-01 21:00:00  0.764  3.1  3.3   52.7   53.8  38.5  80.02  15.55  17.24   \n",
       "1982-04-01 22:00:00  0.941  3.3  3.7   53.3   55.9  35.0  80.02  18.08  21.28   \n",
       "1982-04-01 23:00:00  1.053  3.5  3.9   52.5   54.9  33.1  80.02  20.22  24.14   \n",
       "\n",
       "                       uw    vw  \n",
       "YY_mm_DD_time                    \n",
       "1982-04-01 00:00:00  2.89 -6.59  \n",
       "1982-04-01 01:00:00  2.89 -6.59  \n",
       "1982-04-01 02:00:00 -1.40 -5.39  \n",
       "1982-04-01 03:00:00 -3.49 -5.21  \n",
       "1982-04-01 04:00:00 -3.95 -4.81  \n",
       "1982-04-01 05:00:00 -3.33 -4.60  \n",
       "1982-04-01 06:00:00 -2.18 -4.28  \n",
       "1982-04-01 07:00:00 -1.55 -2.35  \n",
       "1982-04-01 08:00:00 -0.89 -0.78  \n",
       "1982-04-01 09:00:00  1.42 -0.18  \n",
       "1982-04-01 10:00:00  1.93 -0.20  \n",
       "1982-04-01 11:00:00 -0.64 -0.65  \n",
       "1982-04-01 12:00:00 -2.12 -0.05  \n",
       "1982-04-01 13:00:00 -2.27 -0.95  \n",
       "1982-04-01 14:00:00 -1.10 -2.21  \n",
       "1982-04-01 15:00:00 -1.84 -2.68  \n",
       "1982-04-01 16:00:00 -3.48 -3.02  \n",
       "1982-04-01 17:00:00 -5.63 -4.60  \n",
       "1982-04-01 18:00:00 -6.35 -5.36  \n",
       "1982-04-01 19:00:00 -6.24 -6.09  \n",
       "1982-04-01 20:00:00 -6.54 -6.54  \n",
       "1982-04-01 21:00:00 -7.39 -6.85  \n",
       "1982-04-01 22:00:00 -8.74 -7.46  \n",
       "1982-04-01 23:00:00 -8.91 -7.93  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-04-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "123a5ea6",
   "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>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-01 00:00:00</th>\n",
       "      <td>1.297</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>208.7</td>\n",
       "      <td>227.7</td>\n",
       "      <td>36.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.95</td>\n",
       "      <td>124.17</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 01:00:00</th>\n",
       "      <td>1.212</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>211.7</td>\n",
       "      <td>226.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>77.72</td>\n",
       "      <td>116.02</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 02:00:00</th>\n",
       "      <td>1.152</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>212.7</td>\n",
       "      <td>226.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>76.28</td>\n",
       "      <td>110.40</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 03:00:00</th>\n",
       "      <td>1.086</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>213.6</td>\n",
       "      <td>225.9</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>75.78</td>\n",
       "      <td>108.50</td>\n",
       "      <td>-4.65</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01 04:00:00</th>\n",
       "      <td>1.045</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>212.1</td>\n",
       "      <td>225.8</td>\n",
       "      <td>31.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>72.94</td>\n",
       "      <td>106.77</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 19:00:00</th>\n",
       "      <td>2.038</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>35.87</td>\n",
       "      <td>43.14</td>\n",
       "      <td>-10.85</td>\n",
       "      <td>-13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 20:00:00</th>\n",
       "      <td>1.984</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>43.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>80.02</td>\n",
       "      <td>34.67</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>-12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 21:00:00</th>\n",
       "      <td>1.806</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>37.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>32.62</td>\n",
       "      <td>40.64</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 22:00:00</th>\n",
       "      <td>1.803</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>36.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>30.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>31.77</td>\n",
       "      <td>40.11</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>-13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-01 23:00:00</th>\n",
       "      <td>1.971</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>35.3</td>\n",
       "      <td>30.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>33.59</td>\n",
       "      <td>41.12</td>\n",
       "      <td>-12.06</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8040 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hs   tm   tp   dirm     dp   spr      h     lm  \\\n",
       "YY_mm_DD_time                                                            \n",
       "1982-01-01 00:00:00  1.297  6.6  8.9  208.7  227.7  36.1  80.02  75.95   \n",
       "1982-01-01 01:00:00  1.212  6.7  8.6  211.7  226.9  30.9  80.02  77.72   \n",
       "1982-01-01 02:00:00  1.152  6.6  8.4  212.7  226.4  32.3  80.02  76.28   \n",
       "1982-01-01 03:00:00  1.086  6.6  8.3  213.6  225.9  30.2  80.02  75.78   \n",
       "1982-01-01 04:00:00  1.045  6.5  8.3  212.1  225.8  31.5  80.02  72.94   \n",
       "...                    ...  ...  ...    ...    ...   ...    ...    ...   \n",
       "1982-12-01 19:00:00  2.038  4.7  5.3   42.9   39.6  31.3  80.02  35.87   \n",
       "1982-12-01 20:00:00  1.984  4.6  5.2   43.1   38.9  31.3  80.02  34.67   \n",
       "1982-12-01 21:00:00  1.806  4.4  5.1   41.9   37.8  31.7  80.02  32.62   \n",
       "1982-12-01 22:00:00  1.803  4.4  5.1   36.3   34.2  30.2  80.02  31.77   \n",
       "1982-12-01 23:00:00  1.971  4.5  5.1   38.9   35.3  30.4  80.02  33.59   \n",
       "\n",
       "                         lp     uw     vw  \n",
       "YY_mm_DD_time                              \n",
       "1982-01-01 00:00:00  124.17  -4.07  -1.07  \n",
       "1982-01-01 01:00:00  116.02  -4.07  -1.07  \n",
       "1982-01-01 02:00:00  110.40  -5.33  -3.31  \n",
       "1982-01-01 03:00:00  108.50  -4.65   0.10  \n",
       "1982-01-01 04:00:00  106.77  -5.44   2.38  \n",
       "...                     ...    ...    ...  \n",
       "1982-12-01 19:00:00   43.14 -10.85 -13.53  \n",
       "1982-12-01 20:00:00   41.79 -11.79 -12.64  \n",
       "1982-12-01 21:00:00   40.64 -10.66 -11.85  \n",
       "1982-12-01 22:00:00   40.11  -9.60 -13.30  \n",
       "1982-12-01 23:00:00   41.12 -12.06 -13.19  \n",
       "\n",
       "[8040 rows x 11 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-01-01\":\"1982-12-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105ee937",
   "metadata": {},
   "source": [
    "### Exercise A\n",
    "\n",
    "- Define a new dataframe with the hs, tm, dirm data\n",
    "- Select the 1980-1990 data\n",
    "- Get the maximum and mean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a5969ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3229e5f6",
   "metadata": {},
   "source": [
    "### Exercise B\n",
    "\n",
    "- Define a new dataframe with the tp, uw, uv data\n",
    "- Select the 1990-2000 data\n",
    "- Get the minimum and mean data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203f495d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b02030",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1aac512a",
   "metadata": {},
   "source": [
    "### Get stats on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e8fb4068",
   "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>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.00000</td>\n",
       "      <td>1.928590e+05</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "      <td>192859.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.638451</td>\n",
       "      <td>4.627077</td>\n",
       "      <td>5.524533</td>\n",
       "      <td>191.059599</td>\n",
       "      <td>194.873016</td>\n",
       "      <td>34.29020</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>41.037628</td>\n",
       "      <td>54.070865</td>\n",
       "      <td>-2.033231</td>\n",
       "      <td>-0.439350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.576429</td>\n",
       "      <td>1.478183</td>\n",
       "      <td>2.061372</td>\n",
       "      <td>53.658589</td>\n",
       "      <td>52.406684</td>\n",
       "      <td>13.34258</td>\n",
       "      <td>3.208677e-10</td>\n",
       "      <td>26.426497</td>\n",
       "      <td>40.656567</td>\n",
       "      <td>3.590493</td>\n",
       "      <td>3.727733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.30000</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>3.260000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-19.890000</td>\n",
       "      <td>-21.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.235000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.300000</td>\n",
       "      <td>24.20000</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>24.850000</td>\n",
       "      <td>-4.170000</td>\n",
       "      <td>-2.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>203.400000</td>\n",
       "      <td>210.900000</td>\n",
       "      <td>31.50000</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>-1.980000</td>\n",
       "      <td>-0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.865000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>218.500000</td>\n",
       "      <td>222.900000</td>\n",
       "      <td>41.70000</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>53.675000</td>\n",
       "      <td>74.245000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.934000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>150.600000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>80.80000</td>\n",
       "      <td>8.002000e+01</td>\n",
       "      <td>219.840000</td>\n",
       "      <td>4209.630000</td>\n",
       "      <td>20.370000</td>\n",
       "      <td>18.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hs             tm             tp           dirm  \\\n",
       "count  192859.000000  192859.000000  192859.000000  192859.000000   \n",
       "mean        0.638451       4.627077       5.524533     191.059599   \n",
       "std         0.576429       1.478183       2.061372      53.658589   \n",
       "min         0.005000       1.400000       0.000000       0.000000   \n",
       "25%         0.235000       3.500000       4.000000     170.000000   \n",
       "50%         0.450000       4.300000       5.200000     203.400000   \n",
       "75%         0.865000       5.500000       6.900000     218.500000   \n",
       "max         5.934000      11.700000     150.600000     360.000000   \n",
       "\n",
       "                  dp           spr             h             lm  \\\n",
       "count  192859.000000  192859.00000  1.928590e+05  192859.000000   \n",
       "mean      194.873016      34.29020  8.002000e+01      41.037628   \n",
       "std        52.406684      13.34258  3.208677e-10      26.426497   \n",
       "min         0.000000       4.30000  8.002000e+01       3.260000   \n",
       "25%       170.300000      24.20000  8.002000e+01      21.600000   \n",
       "50%       210.900000      31.50000  8.002000e+01      33.200000   \n",
       "75%       222.900000      41.70000  8.002000e+01      53.675000   \n",
       "max       360.000000      80.80000  8.002000e+01     219.840000   \n",
       "\n",
       "                  lp             uw             vw  \n",
       "count  192859.000000  192859.000000  192859.000000  \n",
       "mean       54.070865      -2.033231      -0.439350  \n",
       "std        40.656567       3.590493       3.727733  \n",
       "min         0.000000     -19.890000     -21.920000  \n",
       "25%        24.850000      -4.170000      -2.490000  \n",
       "50%        41.500000      -1.980000      -0.010000  \n",
       "75%        74.245000       0.200000       1.990000  \n",
       "max      4209.630000      20.370000      18.710000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3f04a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hs         5.934\n",
       "tm        11.700\n",
       "tp       150.600\n",
       "dirm     360.000\n",
       "dp       360.000\n",
       "spr       80.800\n",
       "h         80.020\n",
       "lm       219.840\n",
       "lp      4209.630\n",
       "uw        20.370\n",
       "vw        18.710\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622d60f7",
   "metadata": {},
   "source": [
    "## Resampling, Shifting, and Windowing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1f987f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YY_mm_DD_time\n",
       "1979-01-01 05:00:00    2.817\n",
       "1979-01-01 06:00:00    3.407\n",
       "1979-01-01 07:00:00    4.644\n",
       "1979-01-01 08:00:00    5.131\n",
       "1979-01-01 09:00:00    4.297\n",
       "                       ...  \n",
       "2000-12-31 19:00:00    0.618\n",
       "2000-12-31 20:00:00    0.579\n",
       "2000-12-31 21:00:00    0.537\n",
       "2000-12-31 22:00:00    0.496\n",
       "2000-12-31 23:00:00    0.432\n",
       "Name: hs, Length: 192859, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "80badb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='YY_mm_DD_time'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hs[:100].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7dde70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='YY_mm_DD_time'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling('12H').mean().hs[:100].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4b3be82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfi = df.iloc[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d189848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='YY_mm_DD_time'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfi.hs.resample('24H').mean().plot(style=':', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6d1a1ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YY_mm_DD_time\n",
       "1979-12-31    0.716788\n",
       "1980-12-31    0.669013\n",
       "1981-12-31    0.690018\n",
       "1982-12-31    0.620169\n",
       "1983-12-31    0.610109\n",
       "1984-12-31    0.705193\n",
       "1985-12-31    0.605939\n",
       "1986-12-31    0.637735\n",
       "1987-12-31    0.646592\n",
       "1988-12-31    0.639368\n",
       "1989-12-31    0.541437\n",
       "1990-12-31    0.596926\n",
       "1991-12-31    0.584074\n",
       "1992-12-31    0.623920\n",
       "1993-12-31    0.664763\n",
       "1994-12-31    0.616432\n",
       "1995-12-31    0.653598\n",
       "1996-12-31    0.630572\n",
       "1997-12-31    0.589313\n",
       "1998-12-31    0.646900\n",
       "1999-12-31    0.683747\n",
       "2000-12-31    0.673055\n",
       "Freq: A-DEC, Name: hs, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hs.resample('A').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81bddfc",
   "metadata": {},
   "source": [
    "For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n",
    "In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n",
    "Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n",
    "Here, we will resample the business day data at a daily frequency (i.e., including weekends):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ae2b53fb",
   "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>hs</th>\n",
       "      <th>tm</th>\n",
       "      <th>tp</th>\n",
       "      <th>dirm</th>\n",
       "      <th>dp</th>\n",
       "      <th>spr</th>\n",
       "      <th>h</th>\n",
       "      <th>lm</th>\n",
       "      <th>lp</th>\n",
       "      <th>uw</th>\n",
       "      <th>vw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YY_mm_DD_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>5.131</td>\n",
       "      <td>10.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>359.4</td>\n",
       "      <td>357.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>195.60</td>\n",
       "      <td>286.11</td>\n",
       "      <td>17.97</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3.844</td>\n",
       "      <td>10.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>359.9</td>\n",
       "      <td>359.6</td>\n",
       "      <td>80.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>168.88</td>\n",
       "      <td>271.40</td>\n",
       "      <td>13.64</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>5.507</td>\n",
       "      <td>10.7</td>\n",
       "      <td>13.3</td>\n",
       "      <td>358.7</td>\n",
       "      <td>359.9</td>\n",
       "      <td>79.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>183.37</td>\n",
       "      <td>264.52</td>\n",
       "      <td>17.22</td>\n",
       "      <td>13.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>4.733</td>\n",
       "      <td>9.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>358.5</td>\n",
       "      <td>357.9</td>\n",
       "      <td>80.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>142.52</td>\n",
       "      <td>253.07</td>\n",
       "      <td>17.41</td>\n",
       "      <td>13.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>3.878</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>359.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>80.1</td>\n",
       "      <td>80.02</td>\n",
       "      <td>159.33</td>\n",
       "      <td>227.27</td>\n",
       "      <td>15.89</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>3.787</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>359.0</td>\n",
       "      <td>359.2</td>\n",
       "      <td>80.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>164.74</td>\n",
       "      <td>266.60</td>\n",
       "      <td>13.56</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>4.796</td>\n",
       "      <td>10.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>360.0</td>\n",
       "      <td>359.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>176.13</td>\n",
       "      <td>267.80</td>\n",
       "      <td>16.49</td>\n",
       "      <td>12.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>4.909</td>\n",
       "      <td>10.6</td>\n",
       "      <td>13.3</td>\n",
       "      <td>359.6</td>\n",
       "      <td>357.1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>190.74</td>\n",
       "      <td>264.06</td>\n",
       "      <td>14.99</td>\n",
       "      <td>10.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>4.119</td>\n",
       "      <td>10.8</td>\n",
       "      <td>150.6</td>\n",
       "      <td>359.5</td>\n",
       "      <td>359.9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>196.58</td>\n",
       "      <td>4209.63</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>4.795</td>\n",
       "      <td>9.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>359.9</td>\n",
       "      <td>80.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>162.04</td>\n",
       "      <td>235.95</td>\n",
       "      <td>14.86</td>\n",
       "      <td>13.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>5.202</td>\n",
       "      <td>10.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>359.9</td>\n",
       "      <td>359.5</td>\n",
       "      <td>80.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>180.23</td>\n",
       "      <td>234.08</td>\n",
       "      <td>15.45</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>4.888</td>\n",
       "      <td>10.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>359.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>80.6</td>\n",
       "      <td>80.02</td>\n",
       "      <td>174.16</td>\n",
       "      <td>271.85</td>\n",
       "      <td>15.54</td>\n",
       "      <td>16.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>4.108</td>\n",
       "      <td>10.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>358.3</td>\n",
       "      <td>359.5</td>\n",
       "      <td>80.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>170.14</td>\n",
       "      <td>238.84</td>\n",
       "      <td>14.22</td>\n",
       "      <td>13.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>5.721</td>\n",
       "      <td>9.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>359.7</td>\n",
       "      <td>359.4</td>\n",
       "      <td>79.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>153.37</td>\n",
       "      <td>252.01</td>\n",
       "      <td>15.60</td>\n",
       "      <td>15.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>5.311</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>359.5</td>\n",
       "      <td>359.9</td>\n",
       "      <td>79.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>156.74</td>\n",
       "      <td>240.14</td>\n",
       "      <td>17.47</td>\n",
       "      <td>13.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>4.764</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.7</td>\n",
       "      <td>360.0</td>\n",
       "      <td>359.7</td>\n",
       "      <td>79.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>149.74</td>\n",
       "      <td>210.01</td>\n",
       "      <td>13.19</td>\n",
       "      <td>16.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>4.534</td>\n",
       "      <td>10.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>360.0</td>\n",
       "      <td>359.8</td>\n",
       "      <td>79.8</td>\n",
       "      <td>80.02</td>\n",
       "      <td>180.96</td>\n",
       "      <td>234.74</td>\n",
       "      <td>17.84</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>3.905</td>\n",
       "      <td>11.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>358.2</td>\n",
       "      <td>358.6</td>\n",
       "      <td>80.7</td>\n",
       "      <td>80.02</td>\n",
       "      <td>219.84</td>\n",
       "      <td>278.11</td>\n",
       "      <td>12.25</td>\n",
       "      <td>13.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>4.139</td>\n",
       "      <td>10.1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>359.9</td>\n",
       "      <td>359.0</td>\n",
       "      <td>79.4</td>\n",
       "      <td>80.02</td>\n",
       "      <td>170.28</td>\n",
       "      <td>224.44</td>\n",
       "      <td>14.57</td>\n",
       "      <td>14.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>4.532</td>\n",
       "      <td>9.4</td>\n",
       "      <td>11.5</td>\n",
       "      <td>359.9</td>\n",
       "      <td>359.2</td>\n",
       "      <td>80.2</td>\n",
       "      <td>80.02</td>\n",
       "      <td>146.38</td>\n",
       "      <td>204.17</td>\n",
       "      <td>15.72</td>\n",
       "      <td>13.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>5.934</td>\n",
       "      <td>10.7</td>\n",
       "      <td>12.7</td>\n",
       "      <td>359.8</td>\n",
       "      <td>359.9</td>\n",
       "      <td>80.5</td>\n",
       "      <td>80.02</td>\n",
       "      <td>192.30</td>\n",
       "      <td>243.73</td>\n",
       "      <td>20.37</td>\n",
       "      <td>13.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>5.092</td>\n",
       "      <td>10.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>359.7</td>\n",
       "      <td>360.0</td>\n",
       "      <td>79.9</td>\n",
       "      <td>80.02</td>\n",
       "      <td>180.48</td>\n",
       "      <td>238.75</td>\n",
       "      <td>12.87</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hs    tm     tp   dirm     dp   spr      h      lm       lp  \\\n",
       "YY_mm_DD_time                                                                   \n",
       "1979           5.131  10.8   13.9  359.4  357.7  80.0  80.02  195.60   286.11   \n",
       "1980           3.844  10.1   13.5  359.9  359.6  80.7  80.02  168.88   271.40   \n",
       "1981           5.507  10.7   13.3  358.7  359.9  79.5  80.02  183.37   264.52   \n",
       "1982           4.733   9.2   13.0  358.5  357.9  80.5  80.02  142.52   253.07   \n",
       "1983           3.878   9.8   12.2  359.8  360.0  80.1  80.02  159.33   227.27   \n",
       "1984           3.787  10.0   13.4  359.0  359.2  80.7  80.02  164.74   266.60   \n",
       "1985           4.796  10.3   13.4  360.0  359.5  80.5  80.02  176.13   267.80   \n",
       "1986           4.909  10.6   13.3  359.6  357.1  80.0  80.02  190.74   264.06   \n",
       "1987           4.119  10.8  150.6  359.5  359.9  80.0  80.02  196.58  4209.63   \n",
       "1988           4.795   9.9   12.5  360.0  359.9  80.6  80.02  162.04   235.95   \n",
       "1989           5.202  10.4   12.4  359.9  359.5  80.8  80.02  180.23   234.08   \n",
       "1990           4.888  10.3   13.5  359.5  360.0  80.6  80.02  174.16   271.85   \n",
       "1991           4.108  10.2   12.6  358.3  359.5  80.4  80.02  170.14   238.84   \n",
       "1992           5.721   9.6   12.9  359.7  359.4  79.9  80.02  153.37   252.01   \n",
       "1993           5.311   9.8   12.6  359.5  359.9  79.9  80.02  156.74   240.14   \n",
       "1994           4.764   9.5   11.7  360.0  359.7  79.8  80.02  149.74   210.01   \n",
       "1995           4.534  10.4   12.4  360.0  359.8  79.8  80.02  180.96   234.74   \n",
       "1996           3.905  11.7   13.7  358.2  358.6  80.7  80.02  219.84   278.11   \n",
       "1997           4.139  10.1   12.1  359.9  359.0  79.4  80.02  170.28   224.44   \n",
       "1998           4.532   9.4   11.5  359.9  359.2  80.2  80.02  146.38   204.17   \n",
       "1999           5.934  10.7   12.7  359.8  359.9  80.5  80.02  192.30   243.73   \n",
       "2000           5.092  10.2   12.6  359.7  360.0  79.9  80.02  180.48   238.75   \n",
       "\n",
       "                  uw     vw  \n",
       "YY_mm_DD_time                \n",
       "1979           17.97  13.48  \n",
       "1980           13.64  13.48  \n",
       "1981           17.22  13.52  \n",
       "1982           17.41  13.98  \n",
       "1983           15.89  11.19  \n",
       "1984           13.56  15.17  \n",
       "1985           16.49  12.25  \n",
       "1986           14.99  10.32  \n",
       "1987           14.69  11.88  \n",
       "1988           14.86  13.82  \n",
       "1989           15.45  14.67  \n",
       "1990           15.54  16.24  \n",
       "1991           14.22  13.74  \n",
       "1992           15.60  15.77  \n",
       "1993           17.47  13.78  \n",
       "1994           13.19  16.33  \n",
       "1995           17.84  13.29  \n",
       "1996           12.25  13.42  \n",
       "1997           14.57  14.11  \n",
       "1998           15.72  13.26  \n",
       "1999           20.37  13.95  \n",
       "2000           12.87  18.71  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_max = df.groupby(df.index.year).max()\n",
    "annual_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d27751d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YY_mm_DD_time\n",
       "1979   1979-01-01 08:00:00\n",
       "1980   1980-12-06 09:00:00\n",
       "1981   1981-12-08 19:00:00\n",
       "1982   1982-10-14 19:00:00\n",
       "1983   1983-11-27 14:00:00\n",
       "1984   1984-09-23 16:00:00\n",
       "1985   1985-03-16 16:00:00\n",
       "1986   1986-01-24 06:00:00\n",
       "1987   1987-06-15 21:00:00\n",
       "1988   1988-01-29 20:00:00\n",
       "1989   1989-02-26 20:00:00\n",
       "1990   1990-02-27 14:00:00\n",
       "1991   1991-10-18 18:00:00\n",
       "1992   1992-03-23 22:00:00\n",
       "1993   1993-12-24 11:00:00\n",
       "1994   1994-09-15 17:00:00\n",
       "1995   1995-01-26 22:00:00\n",
       "1996   1996-02-19 01:00:00\n",
       "1997   1997-05-06 21:00:00\n",
       "1998   1998-04-11 22:00:00\n",
       "1999   1999-12-28 04:00:00\n",
       "2000   2000-11-06 13:00:00\n",
       "Name: hs, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_hs_max=df.hs.groupby(df.index.year).idxmax()\n",
    "index_hs_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "597cfeac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hs.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9e3ee506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hs.resample('1Y').mean().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9109fa9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save files\n",
    "df.to_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25822fa",
   "metadata": {},
   "source": [
    "<left><img src=\"https://images.prismic.io/coresignal-website/135e2df3-33e4-456a-adb0-73ebaa07bc88_JSON+vs+CSV.png?auto=compress%2Cformat&fit=max&q=90&w=1200&h=1499\" alt=\"save\" style=\"width: 500px;\"/></left>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5623e442",
   "metadata": {},
   "source": [
    "### CSV\n",
    "Name, Job title  \n",
    "\n",
    "Jane, Analyst  \n",
    "Lukas, Developer\n",
    "\n",
    "### JSON\n",
    "{“name”:”Jane”,”jobTitle”:”Analyst”}\n",
    "\n",
    "{“name”:”Lukas”,”jobTitle”:”Developer”}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ff253d",
   "metadata": {},
   "source": [
    "### Exercise A\n",
    "\n",
    "- With you new dataframe\n",
    "- Create a 2x2 figure\n",
    "- plot variables data, the 1-year resample data, a 3 month rolling month and markers for the  annual maxima: hs for top-left and tm top-right\n",
    "- plot hs-dirm and tm-dirm scatter on bottom-left and bottom-right"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece38b0a",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c94e0b39",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6e9a4163",
   "metadata": {},
   "source": [
    "### Exercise B\n",
    "\n",
    "- With you new dataframe\n",
    "- Create a 2x2 figure\n",
    "- plot variables data, the 1-year resample data, a 3 month rolling month and markers for the  annual maxima: tp for top-left and uw top-right\n",
    "- plot tp-uw and tp-vw scatter on bottom-left and bottom-right"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c666adad",
   "metadata": {},
   "source": [
    "# Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "271570b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f427ed1d",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# facetting histograms by subsets of data\n",
    "sb.set(style=\"darkgrid\")\n",
    "\n",
    "tips = sb.load_dataset(\"tips\")\n",
    "tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "28de98d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdf59703820>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
    "bins = np.linspace(0, 60, 13)\n",
    "g.map(plt.hist, \"total_bill\", color=\"steelblue\", bins=bins, lw=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a369e1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = sb.color_palette()[2]\n",
    "g = sb.jointplot(data=tips, x=\"total_bill\", y=\"tip\", kind=\"reg\",\n",
    "                  xlim=(0, 60), ylim=(0, 12), color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7285eea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = sb.color_palette()[2]\n",
    "g = sb.jointplot(data=tips, x=\"total_bill\", y=\"tip\", kind=\"hex\",\n",
    "                  xlim=(0, 60), ylim=(0, 12), color=color)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}