{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"top\"></a>\n",
    "<div style=\"width:1000 px\">\n",
    "\n",
    "<div style=\"float:right; width:98 px; height:98px;\">\n",
    "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n",
    "</div>\n",
    "\n",
    "<h1>Advanced Surface Observations: Working with Mesonet Data</h1>\n",
    "<h3>Unidata Python Workshop</h3>\n",
    "\n",
    "<div style=\"clear:both\"></div>\n",
    "</div>\n",
    "\n",
    "<hr style=\"height:2px;\">\n",
    "\n",
    "<div style=\"float:right; width:250 px\"><img src=\"http://weather-geek.net/images/metar_what.png\" alt=\"METAR\" style=\"height: 200px;\"></div>\n",
    "\n",
    "### Questions\n",
    "1. How do I read in complicated mesonet data with Pandas?\n",
    "1. How do I merge multiple Pandas DataFrames?\n",
    "1. What's the best way to make a station plot of data?\n",
    "1. How can I make a time series of data from one station?\n",
    "\n",
    "### Objectives\n",
    "1. <a href=\"#reading\">Read Mesonet data with Pandas</a>\n",
    "2. <a href=\"#merge\">Merge multiple Pandas DataFrames together </a>\n",
    "3. <a href=\"#plot\">Plot mesonet data with MetPy and CartoPy</a>\n",
    "4. <a href=\"#timeseries\">Create time series plots of station data</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"reading\"></a>\n",
    "# Reading Mesonet Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we're going to use the Pandas library to read text-based data. Pandas is excellent at handling text, csv, and other files. However, you have to help Pandas figure out how your data is formatted sometimes. Lucky for you, mesonet data frequently comes in forms that are not the most user-friendly. Through this notebook, we'll see how these complicated datasets can be handled nicely by Pandas to create useful station plots for hand analysis or publication. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### West Texas Mesonet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [West Texas Mesonet](http://www.depts.ttu.edu/nwi/research/facilities/wtm/index.php) is a wonderful data source for researchers and storm chasers alike! We have some 5-minute observations from the entire network on 22 March 2019 that we'll analyze in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can parse time into a nice internal storage format as we read in the file. If the time is specified in the file in a somewhat standard form, pandas will even guess at the format if you tell it which column to use. However, in this case the time is reported in a horrible format: between one and four characters that, if there are four characters, represent hours and minutes as HHMM. Let's turn take a charater string, turn it into an integer, and then use integer string formatting to write out a four character string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000\n",
      "0005\n",
      "0100\n",
      "1005\n"
     ]
    }
   ],
   "source": [
    "for t in ['0', '05', '100', '1005']:\n",
    "    print('{0:04d}'.format(int(t)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can be told how to parse non-standard dates formats by writing an arbitrary function that takes a string and returns a datetime. Here's what that function looks like in this case. We can use timedelta to convert hours and minutes, and then add them to the start date using date math."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_tx_date(v, start_date=None):\n",
    "    s = '{0:04d}'.format(int(v)) # regularize the data to a four character string\n",
    "    hour = pd.to_timedelta(int(s[0:2]), 'hour') \n",
    "    minute = pd.to_timedelta(int(s[2:4]), 'minute')\n",
    "    return start_date + hour + minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-03-22 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/miniconda/envs/unidata/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
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       "       0   1                   2   3       4       5       6       7       8   \\\n",
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       "...    ..  ..                 ...  ..     ...     ...     ...     ...     ...   \n",
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       "\n",
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       "4       0.407   0.700   0.094   0.033   1.218   12.49   659.60     NaN    NaN   \n",
       "...       ...     ...     ...     ...     ...     ...      ...     ...    ...   \n",
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       "\n",
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# Read in the data and handle the lines that cause issues\n",
    "\n",
    "# Get a nice date variable cooresponding to the start time\n",
    "start_date = pd.datetime.strptime('2019-03-22', '%Y-%m-%d')\n",
    "print(start_date)\n",
    "\n",
    "# Pre-apply the start date to our date parsing function, so that pandas only passes one value\n",
    "from functools import partial\n",
    "date_parser = partial(parse_tx_date, start_date=start_date)\n",
    "\n",
    "filename = 'West_Texas_data/FIVEMIN_82.txt'\n",
    "tx_data = pd.read_csv(filename, delimiter=',', header=None, error_bad_lines=False, warn_bad_lines=False,\n",
    "                     parse_dates=[2], date_parser=date_parser\n",
    "                     )\n",
    "tx_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>1.5m_relative_humidity</th>\n",
       "      <th>station_pressure</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>dewpoint</th>\n",
       "      <th>2m_wind_speed</th>\n",
       "      <th>solar_radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>2.337</td>\n",
       "      <td>2.334</td>\n",
       "      <td>84.90</td>\n",
       "      <td>2.864</td>\n",
       "      <td>0.199</td>\n",
       "      <td>2.842</td>\n",
       "      <td>11.810</td>\n",
       "      <td>12.950</td>\n",
       "      <td>12.030</td>\n",
       "      <td>55.080</td>\n",
       "      <td>350.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.855</td>\n",
       "      <td>1.195</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>15.050</td>\n",
       "      <td>15.180</td>\n",
       "      <td>14.45</td>\n",
       "      <td>15.480</td>\n",
       "      <td>15.180</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.26</td>\n",
       "      <td>2812.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>32</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.774</td>\n",
       "      <td>92.80</td>\n",
       "      <td>4.638</td>\n",
       "      <td>0.181</td>\n",
       "      <td>2.058</td>\n",
       "      <td>6.974</td>\n",
       "      <td>12.090</td>\n",
       "      <td>7.400</td>\n",
       "      <td>71.800</td>\n",
       "      <td>339.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>32</td>\n",
       "      <td>11.840</td>\n",
       "      <td>12.540</td>\n",
       "      <td>12.23</td>\n",
       "      <td>11.520</td>\n",
       "      <td>12.860</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>7.650</td>\n",
       "      <td>12.36</td>\n",
       "      <td>2570.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>42</td>\n",
       "      <td>9.700</td>\n",
       "      <td>11.140</td>\n",
       "      <td>9.62</td>\n",
       "      <td>9.020</td>\n",
       "      <td>9.790</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.218</td>\n",
       "      <td>12.49</td>\n",
       "      <td>659.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22938</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:55:00</td>\n",
       "      <td>23</td>\n",
       "      <td>9.340</td>\n",
       "      <td>9.280</td>\n",
       "      <td>269.20</td>\n",
       "      <td>6.259</td>\n",
       "      <td>1.323</td>\n",
       "      <td>13.950</td>\n",
       "      <td>13.550</td>\n",
       "      <td>14.300</td>\n",
       "      <td>13.540</td>\n",
       "      <td>68.380</td>\n",
       "      <td>302.40</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.050</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22939</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:56:00</td>\n",
       "      <td>60</td>\n",
       "      <td>5.947</td>\n",
       "      <td>5.898</td>\n",
       "      <td>267.00</td>\n",
       "      <td>7.330</td>\n",
       "      <td>0.949</td>\n",
       "      <td>8.560</td>\n",
       "      <td>13.300</td>\n",
       "      <td>13.650</td>\n",
       "      <td>13.460</td>\n",
       "      <td>13.880</td>\n",
       "      <td>229.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.660</td>\n",
       "      <td>4.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22940</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:57:00</td>\n",
       "      <td>60</td>\n",
       "      <td>8.720</td>\n",
       "      <td>8.650</td>\n",
       "      <td>277.30</td>\n",
       "      <td>7.250</td>\n",
       "      <td>0.738</td>\n",
       "      <td>10.060</td>\n",
       "      <td>13.270</td>\n",
       "      <td>13.790</td>\n",
       "      <td>13.510</td>\n",
       "      <td>13.730</td>\n",
       "      <td>229.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.810</td>\n",
       "      <td>6.325</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22941</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:58:00</td>\n",
       "      <td>60</td>\n",
       "      <td>9.530</td>\n",
       "      <td>9.440</td>\n",
       "      <td>275.10</td>\n",
       "      <td>8.220</td>\n",
       "      <td>1.509</td>\n",
       "      <td>12.670</td>\n",
       "      <td>13.270</td>\n",
       "      <td>13.730</td>\n",
       "      <td>13.420</td>\n",
       "      <td>14.020</td>\n",
       "      <td>229.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.560</td>\n",
       "      <td>6.112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22942</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:59:00</td>\n",
       "      <td>60</td>\n",
       "      <td>9.520</td>\n",
       "      <td>9.440</td>\n",
       "      <td>265.30</td>\n",
       "      <td>7.660</td>\n",
       "      <td>1.217</td>\n",
       "      <td>11.370</td>\n",
       "      <td>13.250</td>\n",
       "      <td>13.910</td>\n",
       "      <td>13.570</td>\n",
       "      <td>13.680</td>\n",
       "      <td>229.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.870</td>\n",
       "      <td>6.675</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22943 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Array_ID  QC_flag                Time  Station_ID  \\\n",
       "0             1       81 2019-03-22 00:00:00          54   \n",
       "1             2       81 2019-03-22 00:00:00          54   \n",
       "2             1       81 2019-03-22 00:00:00          32   \n",
       "3             2       81 2019-03-22 00:00:00          32   \n",
       "4             2       81 2019-03-22 00:00:00          42   \n",
       "...         ...      ...                 ...         ...   \n",
       "22938         1       81 2019-03-22 23:55:00          23   \n",
       "22939         1       81 2019-03-22 23:56:00          60   \n",
       "22940         1       81 2019-03-22 23:57:00          60   \n",
       "22941         1       81 2019-03-22 23:58:00          60   \n",
       "22942         1       81 2019-03-22 23:59:00          60   \n",
       "\n",
       "       10m_scalar_wind_speed  10m_vector_wind_speed  10m_wind_direction  \\\n",
       "0                      2.337                  2.334               84.90   \n",
       "1                     15.050                 15.180               14.45   \n",
       "2                      1.780                  1.774               92.80   \n",
       "3                     11.840                 12.540               12.23   \n",
       "4                      9.700                 11.140                9.62   \n",
       "...                      ...                    ...                 ...   \n",
       "22938                  9.340                  9.280              269.20   \n",
       "22939                  5.947                  5.898              267.00   \n",
       "22940                  8.720                  8.650              277.30   \n",
       "22941                  9.530                  9.440              275.10   \n",
       "22942                  9.520                  9.440              265.30   \n",
       "\n",
       "       10m_wind_direction_std  10m_wind_speed_std  10m_gust_wind_speed  \\\n",
       "0                       2.864               0.199                2.842   \n",
       "1                      15.480              15.180                0.272   \n",
       "2                       4.638               0.181                2.058   \n",
       "3                      11.520              12.860                0.239   \n",
       "4                       9.020               9.790                0.407   \n",
       "...                       ...                 ...                  ...   \n",
       "22938                   6.259               1.323               13.950   \n",
       "22939                   7.330               0.949                8.560   \n",
       "22940                   7.250               0.738               10.060   \n",
       "22941                   8.220               1.509               12.670   \n",
       "22942                   7.660               1.217               11.370   \n",
       "\n",
       "       1.5m_temperature  9m_temperature  2m_temperature  \\\n",
       "0                11.810          12.950          12.030   \n",
       "1                 0.380           0.395           0.262   \n",
       "2                 6.974          12.090           7.400   \n",
       "3                 0.233           0.165          -0.073   \n",
       "4                 0.700           0.094           0.033   \n",
       "...                 ...             ...             ...   \n",
       "22938            13.550          14.300          13.540   \n",
       "22939            13.300          13.650          13.460   \n",
       "22940            13.270          13.790          13.510   \n",
       "22941            13.270          13.730          13.420   \n",
       "22942            13.250          13.910          13.570   \n",
       "\n",
       "       1.5m_relative_humidity  station_pressure  rainfall  dewpoint  \\\n",
       "0                      55.080            350.80      0.00     2.855   \n",
       "1                       0.000             13.26   2812.00       NaN   \n",
       "2                      71.800            339.50      0.00     2.025   \n",
       "3                       7.650             12.36   2570.00       NaN   \n",
       "4                       1.218             12.49    659.60       NaN   \n",
       "...                       ...               ...       ...       ...   \n",
       "22938                  68.380            302.40      0.01     7.450   \n",
       "22939                  13.880            229.60      0.00   -13.660   \n",
       "22940                  13.730            229.50      0.00   -13.810   \n",
       "22941                  14.020            229.60      0.00   -13.560   \n",
       "22942                  13.680            229.50      0.00   -13.870   \n",
       "\n",
       "       2m_wind_speed  solar_radiation  \n",
       "0              1.195              0.0  \n",
       "1                NaN              NaN  \n",
       "2              0.786              0.0  \n",
       "3                NaN              NaN  \n",
       "4                NaN              NaN  \n",
       "...              ...              ...  \n",
       "22938          7.050              0.0  \n",
       "22939          4.250              0.0  \n",
       "22940          6.325              0.0  \n",
       "22941          6.112              0.0  \n",
       "22942          6.675              0.0  \n",
       "\n",
       "[22943 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns to be understandable\n",
    "tx_data.columns = ['Array_ID', 'QC_flag', 'Time', 'Station_ID', '10m_scalar_wind_speed',\n",
    "                 '10m_vector_wind_speed', '10m_wind_direction',\n",
    "                 '10m_wind_direction_std', '10m_wind_speed_std', \n",
    "                 '10m_gust_wind_speed', '1.5m_temperature', \n",
    "                 '9m_temperature', '2m_temperature', \n",
    "                 '1.5m_relative_humidity', 'station_pressure', 'rainfall', \n",
    "                 'dewpoint', '2m_wind_speed', 'solar_radiation']\n",
    "tx_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The West Texas mesonet provides data on weather, agriculture, and radiation. These different observations are encoded 1, 2, and 3, respectively in the Array ID column. Let's parse out only the meteorological data for this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Array_ID</th>\n",
       "      <th>QC_flag</th>\n",
       "      <th>Time</th>\n",
       "      <th>Station_ID</th>\n",
       "      <th>10m_scalar_wind_speed</th>\n",
       "      <th>10m_vector_wind_speed</th>\n",
       "      <th>10m_wind_direction</th>\n",
       "      <th>10m_wind_direction_std</th>\n",
       "      <th>10m_wind_speed_std</th>\n",
       "      <th>10m_gust_wind_speed</th>\n",
       "      <th>1.5m_temperature</th>\n",
       "      <th>9m_temperature</th>\n",
       "      <th>2m_temperature</th>\n",
       "      <th>1.5m_relative_humidity</th>\n",
       "      <th>station_pressure</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>dewpoint</th>\n",
       "      <th>2m_wind_speed</th>\n",
       "      <th>solar_radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>2.337</td>\n",
       "      <td>2.334</td>\n",
       "      <td>84.9</td>\n",
       "      <td>2.864</td>\n",
       "      <td>0.199</td>\n",
       "      <td>2.842</td>\n",
       "      <td>11.810</td>\n",
       "      <td>12.95</td>\n",
       "      <td>12.03</td>\n",
       "      <td>55.08</td>\n",
       "      <td>350.8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.855</td>\n",
       "      <td>1.195</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>32</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.774</td>\n",
       "      <td>92.8</td>\n",
       "      <td>4.638</td>\n",
       "      <td>0.181</td>\n",
       "      <td>2.058</td>\n",
       "      <td>6.974</td>\n",
       "      <td>12.09</td>\n",
       "      <td>7.40</td>\n",
       "      <td>71.80</td>\n",
       "      <td>339.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>3.103</td>\n",
       "      <td>3.030</td>\n",
       "      <td>189.6</td>\n",
       "      <td>12.440</td>\n",
       "      <td>0.150</td>\n",
       "      <td>3.365</td>\n",
       "      <td>10.680</td>\n",
       "      <td>12.65</td>\n",
       "      <td>11.10</td>\n",
       "      <td>55.47</td>\n",
       "      <td>328.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.927</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>39</td>\n",
       "      <td>2.423</td>\n",
       "      <td>2.422</td>\n",
       "      <td>118.3</td>\n",
       "      <td>1.353</td>\n",
       "      <td>0.097</td>\n",
       "      <td>2.744</td>\n",
       "      <td>11.740</td>\n",
       "      <td>13.91</td>\n",
       "      <td>12.35</td>\n",
       "      <td>48.61</td>\n",
       "      <td>349.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.091</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>3.464</td>\n",
       "      <td>3.451</td>\n",
       "      <td>124.2</td>\n",
       "      <td>4.929</td>\n",
       "      <td>0.292</td>\n",
       "      <td>4.279</td>\n",
       "      <td>13.950</td>\n",
       "      <td>14.99</td>\n",
       "      <td>14.06</td>\n",
       "      <td>49.66</td>\n",
       "      <td>363.2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.354</td>\n",
       "      <td>2.530</td>\n",
       "      <td>0.013</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22938</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:55:00</td>\n",
       "      <td>23</td>\n",
       "      <td>9.340</td>\n",
       "      <td>9.280</td>\n",
       "      <td>269.2</td>\n",
       "      <td>6.259</td>\n",
       "      <td>1.323</td>\n",
       "      <td>13.950</td>\n",
       "      <td>13.550</td>\n",
       "      <td>14.30</td>\n",
       "      <td>13.54</td>\n",
       "      <td>68.38</td>\n",
       "      <td>302.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.050</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22939</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:56:00</td>\n",
       "      <td>60</td>\n",
       "      <td>5.947</td>\n",
       "      <td>5.898</td>\n",
       "      <td>267.0</td>\n",
       "      <td>7.330</td>\n",
       "      <td>0.949</td>\n",
       "      <td>8.560</td>\n",
       "      <td>13.300</td>\n",
       "      <td>13.65</td>\n",
       "      <td>13.46</td>\n",
       "      <td>13.88</td>\n",
       "      <td>229.6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.660</td>\n",
       "      <td>4.250</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22940</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:57:00</td>\n",
       "      <td>60</td>\n",
       "      <td>8.720</td>\n",
       "      <td>8.650</td>\n",
       "      <td>277.3</td>\n",
       "      <td>7.250</td>\n",
       "      <td>0.738</td>\n",
       "      <td>10.060</td>\n",
       "      <td>13.270</td>\n",
       "      <td>13.79</td>\n",
       "      <td>13.51</td>\n",
       "      <td>13.73</td>\n",
       "      <td>229.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.810</td>\n",
       "      <td>6.325</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22941</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:58:00</td>\n",
       "      <td>60</td>\n",
       "      <td>9.530</td>\n",
       "      <td>9.440</td>\n",
       "      <td>275.1</td>\n",
       "      <td>8.220</td>\n",
       "      <td>1.509</td>\n",
       "      <td>12.670</td>\n",
       "      <td>13.270</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.42</td>\n",
       "      <td>14.02</td>\n",
       "      <td>229.6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.560</td>\n",
       "      <td>6.112</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22942</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:59:00</td>\n",
       "      <td>60</td>\n",
       "      <td>9.520</td>\n",
       "      <td>9.440</td>\n",
       "      <td>265.3</td>\n",
       "      <td>7.660</td>\n",
       "      <td>1.217</td>\n",
       "      <td>11.370</td>\n",
       "      <td>13.250</td>\n",
       "      <td>13.91</td>\n",
       "      <td>13.57</td>\n",
       "      <td>13.68</td>\n",
       "      <td>229.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-13.870</td>\n",
       "      <td>6.675</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17279 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Array_ID  QC_flag                Time  Station_ID  \\\n",
       "0             1       81 2019-03-22 00:00:00          54   \n",
       "2             1       81 2019-03-22 00:00:00          32   \n",
       "5             1       81 2019-03-22 00:00:00           7   \n",
       "7             1       81 2019-03-22 00:00:00          39   \n",
       "9             1       81 2019-03-22 00:00:00          56   \n",
       "...         ...      ...                 ...         ...   \n",
       "22938         1       81 2019-03-22 23:55:00          23   \n",
       "22939         1       81 2019-03-22 23:56:00          60   \n",
       "22940         1       81 2019-03-22 23:57:00          60   \n",
       "22941         1       81 2019-03-22 23:58:00          60   \n",
       "22942         1       81 2019-03-22 23:59:00          60   \n",
       "\n",
       "       10m_scalar_wind_speed  10m_vector_wind_speed  10m_wind_direction  \\\n",
       "0                      2.337                  2.334                84.9   \n",
       "2                      1.780                  1.774                92.8   \n",
       "5                      3.103                  3.030               189.6   \n",
       "7                      2.423                  2.422               118.3   \n",
       "9                      3.464                  3.451               124.2   \n",
       "...                      ...                    ...                 ...   \n",
       "22938                  9.340                  9.280               269.2   \n",
       "22939                  5.947                  5.898               267.0   \n",
       "22940                  8.720                  8.650               277.3   \n",
       "22941                  9.530                  9.440               275.1   \n",
       "22942                  9.520                  9.440               265.3   \n",
       "\n",
       "       10m_wind_direction_std  10m_wind_speed_std  10m_gust_wind_speed  \\\n",
       "0                       2.864               0.199                2.842   \n",
       "2                       4.638               0.181                2.058   \n",
       "5                      12.440               0.150                3.365   \n",
       "7                       1.353               0.097                2.744   \n",
       "9                       4.929               0.292                4.279   \n",
       "...                       ...                 ...                  ...   \n",
       "22938                   6.259               1.323               13.950   \n",
       "22939                   7.330               0.949                8.560   \n",
       "22940                   7.250               0.738               10.060   \n",
       "22941                   8.220               1.509               12.670   \n",
       "22942                   7.660               1.217               11.370   \n",
       "\n",
       "       1.5m_temperature  9m_temperature  2m_temperature  \\\n",
       "0                11.810           12.95           12.03   \n",
       "2                 6.974           12.09            7.40   \n",
       "5                10.680           12.65           11.10   \n",
       "7                11.740           13.91           12.35   \n",
       "9                13.950           14.99           14.06   \n",
       "...                 ...             ...             ...   \n",
       "22938            13.550           14.30           13.54   \n",
       "22939            13.300           13.65           13.46   \n",
       "22940            13.270           13.79           13.51   \n",
       "22941            13.270           13.73           13.42   \n",
       "22942            13.250           13.91           13.57   \n",
       "\n",
       "       1.5m_relative_humidity  station_pressure  rainfall  dewpoint  \\\n",
       "0                       55.08             350.8      0.00     2.855   \n",
       "2                       71.80             339.5      0.00     2.025   \n",
       "5                       55.47             328.5      0.00     1.927   \n",
       "7                       48.61             349.1      0.00     1.091   \n",
       "9                       49.66             363.2      0.00     3.354   \n",
       "...                       ...               ...       ...       ...   \n",
       "22938                   68.38             302.4      0.01     7.450   \n",
       "22939                   13.88             229.6      0.00   -13.660   \n",
       "22940                   13.73             229.5      0.00   -13.810   \n",
       "22941                   14.02             229.6      0.00   -13.560   \n",
       "22942                   13.68             229.5      0.00   -13.870   \n",
       "\n",
       "       2m_wind_speed  solar_radiation  \n",
       "0              1.195            0.000  \n",
       "2              0.786            0.000  \n",
       "5              2.000            0.000  \n",
       "7              1.047            0.000  \n",
       "9              2.530            0.013  \n",
       "...              ...              ...  \n",
       "22938          7.050            0.000  \n",
       "22939          4.250            0.000  \n",
       "22940          6.325            0.000  \n",
       "22941          6.112            0.000  \n",
       "22942          6.675            0.000  \n",
       "\n",
       "[17279 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove non-meteorological rows\n",
    "tx_data = tx_data[tx_data['Array_ID'] == 1]\n",
    "tx_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Station pressure is 600 hPa lower than it should be, so let's correct that as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/miniconda/envs/unidata/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        950.8\n",
       "2        939.5\n",
       "5        928.5\n",
       "7        949.1\n",
       "9        963.2\n",
       "         ...  \n",
       "22938    902.4\n",
       "22939    829.6\n",
       "22940    829.5\n",
       "22941    829.6\n",
       "22942    829.5\n",
       "Name: station_pressure, Length: 17279, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correct presssure \n",
    "tx_data['station_pressure'] += 600\n",
    "tx_data['station_pressure']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's read in the station metadata file for the West Texas mesonet, so that we can have coordinates to plot data later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Location</th>\n",
       "      <th>Area</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>ID</th>\n",
       "      <th>4 Letter ID</th>\n",
       "      <th>LDM ID</th>\n",
       "      <th>Node</th>\n",
       "      <th>Logger ID</th>\n",
       "      <th>SHEF ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5ENE Abernathy</td>\n",
       "      <td>Abernathy/Hale County</td>\n",
       "      <td>33.87538</td>\n",
       "      <td>-101.75718</td>\n",
       "      <td>3333 ft.</td>\n",
       "      <td>ABER</td>\n",
       "      <td>KARS</td>\n",
       "      <td>XARS</td>\n",
       "      <td>1365</td>\n",
       "      <td>2</td>\n",
       "      <td>ARST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6WSW Adrian</td>\n",
       "      <td>Adrian/Oldham County</td>\n",
       "      <td>35.25347</td>\n",
       "      <td>-102.76158</td>\n",
       "      <td>4260 ft.</td>\n",
       "      <td>ADRI</td>\n",
       "      <td>KAD1</td>\n",
       "      <td>XAD1</td>\n",
       "      <td>1526</td>\n",
       "      <td>109</td>\n",
       "      <td>ADXT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3WSW Aiken</td>\n",
       "      <td>Aiken/Hale County</td>\n",
       "      <td>34.13305</td>\n",
       "      <td>-101.56952</td>\n",
       "      <td>3321 ft.</td>\n",
       "      <td>AIKE</td>\n",
       "      <td>KAI1</td>\n",
       "      <td>XAI1</td>\n",
       "      <td>1496</td>\n",
       "      <td>79</td>\n",
       "      <td>AIKT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7SSE Amarillo</td>\n",
       "      <td>Amarillo/Randall County</td>\n",
       "      <td>35.11270</td>\n",
       "      <td>-101.79964</td>\n",
       "      <td>3611 ft.</td>\n",
       "      <td>AMAS</td>\n",
       "      <td>KAM1</td>\n",
       "      <td>XAM1</td>\n",
       "      <td>1492</td>\n",
       "      <td>75</td>\n",
       "      <td>ASOT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9NNE Amarillo</td>\n",
       "      <td>Amarillo/Potter County</td>\n",
       "      <td>35.33597</td>\n",
       "      <td>-101.80627</td>\n",
       "      <td>3346 ft.</td>\n",
       "      <td>AMAN</td>\n",
       "      <td>KAM2</td>\n",
       "      <td>XAM2</td>\n",
       "      <td>1495</td>\n",
       "      <td>78</td>\n",
       "      <td>AMNT2</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>121</th>\n",
       "      <td>2E Wall</td>\n",
       "      <td>Wall/Tom Green County</td>\n",
       "      <td>31.37882</td>\n",
       "      <td>-100.26628</td>\n",
       "      <td>1870 ft</td>\n",
       "      <td>WALL2</td>\n",
       "      <td>KWA1</td>\n",
       "      <td>XWA1</td>\n",
       "      <td>1421</td>\n",
       "      <td>57</td>\n",
       "      <td>WGST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2NNE Weinert</td>\n",
       "      <td>Weinert/Haskell County</td>\n",
       "      <td>33.34468</td>\n",
       "      <td>-99.66590</td>\n",
       "      <td>1504 ft</td>\n",
       "      <td>WEIN</td>\n",
       "      <td>KWE1</td>\n",
       "      <td>XWE1</td>\n",
       "      <td>1490</td>\n",
       "      <td>73</td>\n",
       "      <td>EIST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Welch</td>\n",
       "      <td>Welch/Dawson County</td>\n",
       "      <td>32.92570</td>\n",
       "      <td>-102.13232</td>\n",
       "      <td>3121 ft</td>\n",
       "      <td>WELC</td>\n",
       "      <td>KWE2</td>\n",
       "      <td>XWE2</td>\n",
       "      <td>1512</td>\n",
       "      <td>96</td>\n",
       "      <td>WEHT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6NW White River Lake</td>\n",
       "      <td>White River Lake/Crosby County</td>\n",
       "      <td>33.52533</td>\n",
       "      <td>-101.16506</td>\n",
       "      <td>2704 ft.</td>\n",
       "      <td>WHIT</td>\n",
       "      <td>KWVS</td>\n",
       "      <td>XWVS</td>\n",
       "      <td>1390</td>\n",
       "      <td>27</td>\n",
       "      <td>WLST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6SSW Wolfforth</td>\n",
       "      <td>Wolfforth/Lubbock County</td>\n",
       "      <td>33.42068</td>\n",
       "      <td>-102.04983</td>\n",
       "      <td>3307 ft.</td>\n",
       "      <td>WOLF</td>\n",
       "      <td>KWOS</td>\n",
       "      <td>XWOS</td>\n",
       "      <td>1411</td>\n",
       "      <td>47</td>\n",
       "      <td>WOST2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Location                            Area       Lat  \\\n",
       "0          5ENE Abernathy           Abernathy/Hale County  33.87538   \n",
       "1             6WSW Adrian            Adrian/Oldham County  35.25347   \n",
       "2              3WSW Aiken               Aiken/Hale County  34.13305   \n",
       "3           7SSE Amarillo         Amarillo/Randall County  35.11270   \n",
       "4           9NNE Amarillo          Amarillo/Potter County  35.33597   \n",
       "..                    ...                             ...       ...   \n",
       "121               2E Wall           Wall/Tom Green County  31.37882   \n",
       "122          2NNE Weinert          Weinert/Haskell County  33.34468   \n",
       "123                 Welch             Welch/Dawson County  32.92570   \n",
       "124  6NW White River Lake  White River Lake/Crosby County  33.52533   \n",
       "125        6SSW Wolfforth        Wolfforth/Lubbock County  33.42068   \n",
       "\n",
       "          Long Elevation     ID 4 Letter ID LDM ID  Node  Logger ID SHEF ID  \n",
       "0   -101.75718  3333 ft.   ABER        KARS   XARS  1365          2   ARST2  \n",
       "1   -102.76158  4260 ft.   ADRI        KAD1   XAD1  1526        109   ADXT2  \n",
       "2   -101.56952  3321 ft.   AIKE        KAI1   XAI1  1496         79   AIKT2  \n",
       "3   -101.79964  3611 ft.   AMAS        KAM1   XAM1  1492         75   ASOT2  \n",
       "4   -101.80627  3346 ft.   AMAN        KAM2   XAM2  1495         78   AMNT2  \n",
       "..         ...       ...    ...         ...    ...   ...        ...     ...  \n",
       "121 -100.26628   1870 ft  WALL2        KWA1   XWA1  1421         57   WGST2  \n",
       "122  -99.66590   1504 ft   WEIN        KWE1   XWE1  1490         73   EIST2  \n",
       "123 -102.13232   3121 ft   WELC        KWE2   XWE2  1512         96   WEHT2  \n",
       "124 -101.16506  2704 ft.   WHIT        KWVS   XWVS  1390         27   WLST2  \n",
       "125 -102.04983  3307 ft.   WOLF        KWOS   XWOS  1411         47   WOST2  \n",
       "\n",
       "[126 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_stations = pd.read_csv('WestTexas_stations.csv')\n",
    "tx_stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oklahoma Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try reading in the Oklahoma Mesonet data located in the `201903222300.mdf` file using Pandas. Check out the documentation on Pandas if you run into issues! Make sure to handle missing values as well. Also read in the Oklahoma station data from the `Oklahoma_stations.csv` file. Only read in the station ID, latitude, and longitude columns from that file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_ok_date(v, start_date=None):\n",
    "    s = '{0:04d}'.format(int(v)) # regularize the data to a four character string\n",
    "    minute = pd.to_timedelta(int(s), 'minute')\n",
    "    return start_date + minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   STID  STNM                TIME  RELH  TAIR  WSPD  WVEC  WDIR  WDSD  WSSD  \\\n",
      "0  ACME   110 2019-03-22 23:00:00    31  21.3   7.3   7.2   131   8.7   1.0   \n",
      "1  ADAX     1 2019-03-22 23:00:00    30  20.9   3.3   3.2   136  17.4   0.8   \n",
      "2  ALTU     2 2019-03-22 23:00:00    54  20.9   7.9   7.9   151   6.1   1.2   \n",
      "3  ALV2   116 2019-03-22 23:00:00    35  20.4   8.0   7.9   114   9.1   1.2   \n",
      "4  ANT2   135 2019-03-22 23:00:00    27  22.0   2.6   2.5   137  19.5   0.7   \n",
      "\n",
      "   ...  TA9M  WS2M  TS10  TB10  TS05  TS25   TS60  TR05  TR25    TR60  \n",
      "0  ...  21.3   6.0  15.1  17.2  15.9  11.6   10.8  1.59  1.49    1.41  \n",
      "1  ...  20.6   2.3  14.9  19.5  16.6  13.1 -998.0  1.51  1.46 -998.00  \n",
      "2  ...  20.9   6.0  12.9  15.4  15.0  11.6 -998.0  2.25  2.21 -998.00  \n",
      "3  ...  19.8   6.7  10.3  15.7  11.5   9.0 -998.0  1.50  1.39 -998.00  \n",
      "4  ...  21.6   1.9  14.4  19.1  16.4  12.5   12.0  1.67  1.38    1.33  \n",
      "\n",
      "[5 rows x 24 columns]\n",
      "   stid      nlat      elon\n",
      "0  ACME  34.80833 -98.02325\n",
      "1  ADAX  34.79851 -96.66909\n",
      "2  ALTU  34.58722 -99.33808\n",
      "3  ALV2  36.70823 -98.70974\n",
      "4  ALVA  36.77970 -98.67170\n"
     ]
    }
   ],
   "source": [
    "# %load solutions/read_ok.py\n",
    "\n",
    "\n",
    "# Cell content replaced by load magic replacement.\n",
    "ok_data = pd.read_csv('201903222300.mdf', skiprows=2, delim_whitespace=True, na_values=-999,\n",
    "                                          parse_dates=[2], date_parser=partial(parse_ok_date, start_date=start_date))\n",
    "ok_stations = pd.read_csv('Oklahoma_stations.csv', usecols=[1,7,8])\n",
    "print(ok_data.head())\n",
    "print(ok_stations.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"merge\"></a>\n",
    "# Merging DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have two data files per mesonet - one for the data itself and one for the metadata. It would be really nice to combine these DataFrames together into one for each mesonet. Pandas has some built in methods to do this - see [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html). For this example, we'll be using the `merge` method. First, let's rename columns in the Oklahoma station DataFrame to be more understandable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns so merging can occur\n",
    "ok_stations.columns = ['STID', 'LAT', 'LON']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conveniently, we have a `STID` column in both DataFrames. Let's base our merge on that and see what we get!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>STID</th>\n",
       "      <th>STNM</th>\n",
       "      <th>TIME</th>\n",
       "      <th>RELH</th>\n",
       "      <th>TAIR</th>\n",
       "      <th>WSPD</th>\n",
       "      <th>WVEC</th>\n",
       "      <th>WDIR</th>\n",
       "      <th>WDSD</th>\n",
       "      <th>WSSD</th>\n",
       "      <th>...</th>\n",
       "      <th>TS10</th>\n",
       "      <th>TB10</th>\n",
       "      <th>TS05</th>\n",
       "      <th>TS25</th>\n",
       "      <th>TS60</th>\n",
       "      <th>TR05</th>\n",
       "      <th>TR25</th>\n",
       "      <th>TR60</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACME</td>\n",
       "      <td>110</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>31</td>\n",
       "      <td>21.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>131</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.1</td>\n",
       "      <td>17.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.41</td>\n",
       "      <td>34.80833</td>\n",
       "      <td>-98.02325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADAX</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>20.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>136</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>14.9</td>\n",
       "      <td>19.5</td>\n",
       "      <td>16.6</td>\n",
       "      <td>13.1</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.46</td>\n",
       "      <td>-998.00</td>\n",
       "      <td>34.79851</td>\n",
       "      <td>-96.66909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALTU</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>20.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>151</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>12.9</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.21</td>\n",
       "      <td>-998.00</td>\n",
       "      <td>34.58722</td>\n",
       "      <td>-99.33808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALV2</td>\n",
       "      <td>116</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>20.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>114</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-998.00</td>\n",
       "      <td>36.70823</td>\n",
       "      <td>-98.70974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANT2</td>\n",
       "      <td>135</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>137</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>14.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.33</td>\n",
       "      <td>34.24967</td>\n",
       "      <td>-95.66844</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>115</th>\n",
       "      <td>WILB</td>\n",
       "      <td>105</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>20.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>69</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>13.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.47</td>\n",
       "      <td>34.90092</td>\n",
       "      <td>-95.34805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>WIST</td>\n",
       "      <td>106</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>18</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>80</td>\n",
       "      <td>21.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>11.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.41</td>\n",
       "      <td>34.98426</td>\n",
       "      <td>-94.68778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>WOOD</td>\n",
       "      <td>107</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>20.1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>135</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>11.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.79</td>\n",
       "      <td>36.42329</td>\n",
       "      <td>-99.41682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>WYNO</td>\n",
       "      <td>108</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>122</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>9.9</td>\n",
       "      <td>-998.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>-998.00</td>\n",
       "      <td>36.51806</td>\n",
       "      <td>-96.34222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>YUKO</td>\n",
       "      <td>142</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>28</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>146</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.40</td>\n",
       "      <td>35.55671</td>\n",
       "      <td>-97.75538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     STID  STNM                TIME  RELH  TAIR  WSPD  WVEC  WDIR  WDSD  WSSD  \\\n",
       "0    ACME   110 2019-03-22 23:00:00    31  21.3   7.3   7.2   131   8.7   1.0   \n",
       "1    ADAX     1 2019-03-22 23:00:00    30  20.9   3.3   3.2   136  17.4   0.8   \n",
       "2    ALTU     2 2019-03-22 23:00:00    54  20.9   7.9   7.9   151   6.1   1.2   \n",
       "3    ALV2   116 2019-03-22 23:00:00    35  20.4   8.0   7.9   114   9.1   1.2   \n",
       "4    ANT2   135 2019-03-22 23:00:00    27  22.0   2.6   2.5   137  19.5   0.7   \n",
       "..    ...   ...                 ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "115  WILB   105 2019-03-22 23:00:00    26  20.8   3.3   3.3    69   7.0   0.5   \n",
       "116  WIST   106 2019-03-22 23:00:00    18  21.1   1.5   1.4    80  21.7   0.3   \n",
       "117  WOOD   107 2019-03-22 23:00:00    27  20.1   8.4   8.3   135   8.4   1.1   \n",
       "118  WYNO   108 2019-03-22 23:00:00    26  19.0   3.6   3.5   122  11.8   0.5   \n",
       "119  YUKO   142 2019-03-22 23:00:00    28  21.0   4.8   4.7   146  11.3   1.0   \n",
       "\n",
       "     ...  TS10  TB10  TS05  TS25   TS60  TR05  TR25    TR60       LAT  \\\n",
       "0    ...  15.1  17.2  15.9  11.6   10.8  1.59  1.49    1.41  34.80833   \n",
       "1    ...  14.9  19.5  16.6  13.1 -998.0  1.51  1.46 -998.00  34.79851   \n",
       "2    ...  12.9  15.4  15.0  11.6 -998.0  2.25  2.21 -998.00  34.58722   \n",
       "3    ...  10.3  15.7  11.5   9.0 -998.0  1.50  1.39 -998.00  36.70823   \n",
       "4    ...  14.4  19.1  16.4  12.5   12.0  1.67  1.38    1.33  34.24967   \n",
       "..   ...   ...   ...   ...   ...    ...   ...   ...     ...       ...   \n",
       "115  ...  13.1  16.0  15.8  11.9   11.3  1.48  1.44    1.47  34.90092   \n",
       "116  ...  11.8  17.0  13.0  10.4   10.0  1.99  1.51    1.41  34.98426   \n",
       "117  ...  11.3  15.0  12.3  10.1    9.5  1.54  1.56    1.79  36.42329   \n",
       "118  ...  13.7  17.0  14.9   9.9 -998.0  1.85  1.60 -998.00  36.51806   \n",
       "119  ...  12.9   NaN  12.8  11.6    9.9  1.55  1.41    1.40  35.55671   \n",
       "\n",
       "          LON  \n",
       "0   -98.02325  \n",
       "1   -96.66909  \n",
       "2   -99.33808  \n",
       "3   -98.70974  \n",
       "4   -95.66844  \n",
       "..        ...  \n",
       "115 -95.34805  \n",
       "116 -94.68778  \n",
       "117 -99.41682  \n",
       "118 -96.34222  \n",
       "119 -97.75538  \n",
       "\n",
       "[120 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two data frames based on the Station ID\n",
    "ok_data = pd.merge(ok_data, ok_stations, on='STID')\n",
    "ok_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was nice! But what if our DataFrames don't have the same column name, and we want to avoid renaming columns? Check out the documentation for `pd.merge` and see how we can merge the West Texas DataFrames together. Also, subset the data to only be from 2300 UTC, which is when our Oklahoma data was taken. Call the new DataFrame `tx_one_time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Array_ID</th>\n",
       "      <th>QC_flag</th>\n",
       "      <th>Time</th>\n",
       "      <th>Station_ID</th>\n",
       "      <th>10m_scalar_wind_speed</th>\n",
       "      <th>10m_vector_wind_speed</th>\n",
       "      <th>10m_wind_direction</th>\n",
       "      <th>10m_wind_direction_std</th>\n",
       "      <th>10m_wind_speed_std</th>\n",
       "      <th>10m_gust_wind_speed</th>\n",
       "      <th>...</th>\n",
       "      <th>Area</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>ID</th>\n",
       "      <th>4 Letter ID</th>\n",
       "      <th>LDM ID</th>\n",
       "      <th>Node</th>\n",
       "      <th>Logger ID</th>\n",
       "      <th>SHEF ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>3.201</td>\n",
       "      <td>3.191</td>\n",
       "      <td>124.60</td>\n",
       "      <td>4.494</td>\n",
       "      <td>0.274</td>\n",
       "      <td>3.757</td>\n",
       "      <td>...</td>\n",
       "      <td>San Angelo/Tom Green</td>\n",
       "      <td>31.54263</td>\n",
       "      <td>-100.51328</td>\n",
       "      <td>1957 ft.</td>\n",
       "      <td>SASU</td>\n",
       "      <td>KASU</td>\n",
       "      <td>XASU</td>\n",
       "      <td>1418</td>\n",
       "      <td>54</td>\n",
       "      <td>SJST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>32</td>\n",
       "      <td>2.363</td>\n",
       "      <td>1.928</td>\n",
       "      <td>283.10</td>\n",
       "      <td>34.770</td>\n",
       "      <td>0.708</td>\n",
       "      <td>3.430</td>\n",
       "      <td>...</td>\n",
       "      <td>Spur/Dickens County</td>\n",
       "      <td>33.48085</td>\n",
       "      <td>-100.87636</td>\n",
       "      <td>2287 ft.</td>\n",
       "      <td>SPUR</td>\n",
       "      <td>KSP1</td>\n",
       "      <td>XSPR</td>\n",
       "      <td>1396</td>\n",
       "      <td>32</td>\n",
       "      <td>SPST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>11.410</td>\n",
       "      <td>11.310</td>\n",
       "      <td>156.30</td>\n",
       "      <td>7.390</td>\n",
       "      <td>1.815</td>\n",
       "      <td>16.040</td>\n",
       "      <td>...</td>\n",
       "      <td>Roaring S./Motley County</td>\n",
       "      <td>33.93635</td>\n",
       "      <td>-100.84540</td>\n",
       "      <td>2615 ft.</td>\n",
       "      <td>ROAR</td>\n",
       "      <td>KRRS</td>\n",
       "      <td>XRRS</td>\n",
       "      <td>1372</td>\n",
       "      <td>7</td>\n",
       "      <td>RRST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>39</td>\n",
       "      <td>2.422</td>\n",
       "      <td>2.392</td>\n",
       "      <td>164.40</td>\n",
       "      <td>9.020</td>\n",
       "      <td>0.535</td>\n",
       "      <td>4.279</td>\n",
       "      <td>...</td>\n",
       "      <td>Jayton (Kent Co. Airport)</td>\n",
       "      <td>33.23241</td>\n",
       "      <td>-100.56778</td>\n",
       "      <td>2010 ft.</td>\n",
       "      <td>JAYT</td>\n",
       "      <td>KJTS</td>\n",
       "      <td>XJTS</td>\n",
       "      <td>1404</td>\n",
       "      <td>39</td>\n",
       "      <td>JTST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>4.857</td>\n",
       "      <td>4.793</td>\n",
       "      <td>165.20</td>\n",
       "      <td>9.300</td>\n",
       "      <td>0.821</td>\n",
       "      <td>6.664</td>\n",
       "      <td>...</td>\n",
       "      <td>Haskell/Haskell County</td>\n",
       "      <td>33.17473</td>\n",
       "      <td>-99.74420</td>\n",
       "      <td>1605 ft.</td>\n",
       "      <td>HASK</td>\n",
       "      <td>KHA1</td>\n",
       "      <td>XHA1</td>\n",
       "      <td>1420</td>\n",
       "      <td>56</td>\n",
       "      <td>HAXT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>59</td>\n",
       "      <td>6.825</td>\n",
       "      <td>6.811</td>\n",
       "      <td>167.30</td>\n",
       "      <td>3.654</td>\n",
       "      <td>0.781</td>\n",
       "      <td>8.720</td>\n",
       "      <td>...</td>\n",
       "      <td>Knox City/Knox County</td>\n",
       "      <td>33.44557</td>\n",
       "      <td>-99.86497</td>\n",
       "      <td>1470 ft.</td>\n",
       "      <td>KNOX</td>\n",
       "      <td>KKN1</td>\n",
       "      <td>XKN1</td>\n",
       "      <td>1423</td>\n",
       "      <td>59</td>\n",
       "      <td>KXST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>24</td>\n",
       "      <td>5.571</td>\n",
       "      <td>5.456</td>\n",
       "      <td>192.70</td>\n",
       "      <td>11.620</td>\n",
       "      <td>1.399</td>\n",
       "      <td>9.280</td>\n",
       "      <td>...</td>\n",
       "      <td>Post/Garza County</td>\n",
       "      <td>33.20033</td>\n",
       "      <td>-101.36804</td>\n",
       "      <td>2598 ft.</td>\n",
       "      <td>POST2</td>\n",
       "      <td>KPT1</td>\n",
       "      <td>XPTS</td>\n",
       "      <td>1387</td>\n",
       "      <td>24</td>\n",
       "      <td>PTST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>3.585</td>\n",
       "      <td>3.581</td>\n",
       "      <td>320.80</td>\n",
       "      <td>2.779</td>\n",
       "      <td>0.252</td>\n",
       "      <td>4.214</td>\n",
       "      <td>...</td>\n",
       "      <td>Friona/Parmer County</td>\n",
       "      <td>34.65450</td>\n",
       "      <td>-102.69097</td>\n",
       "      <td>4005 ft.</td>\n",
       "      <td>FRIO</td>\n",
       "      <td>KFAS</td>\n",
       "      <td>XFAS</td>\n",
       "      <td>1383</td>\n",
       "      <td>20</td>\n",
       "      <td>FAST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>5.826</td>\n",
       "      <td>5.721</td>\n",
       "      <td>193.90</td>\n",
       "      <td>10.870</td>\n",
       "      <td>0.996</td>\n",
       "      <td>8.620</td>\n",
       "      <td>...</td>\n",
       "      <td>Plainview/Hale County</td>\n",
       "      <td>34.17872</td>\n",
       "      <td>-101.70788</td>\n",
       "      <td>3358 ft.</td>\n",
       "      <td>PLVW</td>\n",
       "      <td>KPVS</td>\n",
       "      <td>XPVS</td>\n",
       "      <td>1367</td>\n",
       "      <td>3</td>\n",
       "      <td>PVST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2868</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>17</td>\n",
       "      <td>5.328</td>\n",
       "      <td>5.305</td>\n",
       "      <td>175.90</td>\n",
       "      <td>5.349</td>\n",
       "      <td>0.508</td>\n",
       "      <td>6.893</td>\n",
       "      <td>...</td>\n",
       "      <td>Plains/Yoakum County</td>\n",
       "      <td>33.22814</td>\n",
       "      <td>-102.83936</td>\n",
       "      <td>3711 ft.</td>\n",
       "      <td>PLAI</td>\n",
       "      <td>KPPS</td>\n",
       "      <td>XPPS</td>\n",
       "      <td>1380</td>\n",
       "      <td>17</td>\n",
       "      <td>PPST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>6.357</td>\n",
       "      <td>6.345</td>\n",
       "      <td>173.20</td>\n",
       "      <td>3.463</td>\n",
       "      <td>0.417</td>\n",
       "      <td>7.450</td>\n",
       "      <td>...</td>\n",
       "      <td>Anton/Hockley County</td>\n",
       "      <td>33.72525</td>\n",
       "      <td>-102.19082</td>\n",
       "      <td>3405 ft.</td>\n",
       "      <td>ANTO</td>\n",
       "      <td>KAOS</td>\n",
       "      <td>XAOS</td>\n",
       "      <td>1393</td>\n",
       "      <td>30</td>\n",
       "      <td>AOST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>40</td>\n",
       "      <td>3.763</td>\n",
       "      <td>3.723</td>\n",
       "      <td>182.60</td>\n",
       "      <td>8.360</td>\n",
       "      <td>0.582</td>\n",
       "      <td>5.357</td>\n",
       "      <td>...</td>\n",
       "      <td>Pampa/Gray County</td>\n",
       "      <td>35.53950</td>\n",
       "      <td>-100.92772</td>\n",
       "      <td>3216 ft.</td>\n",
       "      <td>PAMP</td>\n",
       "      <td>KPMS</td>\n",
       "      <td>XPMS</td>\n",
       "      <td>1405</td>\n",
       "      <td>40</td>\n",
       "      <td>PAMT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3732</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>4.048</td>\n",
       "      <td>4.032</td>\n",
       "      <td>193.70</td>\n",
       "      <td>4.998</td>\n",
       "      <td>0.311</td>\n",
       "      <td>4.900</td>\n",
       "      <td>...</td>\n",
       "      <td>Seagraves/Gaines County</td>\n",
       "      <td>32.93644</td>\n",
       "      <td>-102.57442</td>\n",
       "      <td>3360 ft.</td>\n",
       "      <td>SEAG</td>\n",
       "      <td>KSGV</td>\n",
       "      <td>XSGV</td>\n",
       "      <td>1392</td>\n",
       "      <td>15</td>\n",
       "      <td>SGST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>5.309</td>\n",
       "      <td>5.186</td>\n",
       "      <td>222.80</td>\n",
       "      <td>12.330</td>\n",
       "      <td>0.901</td>\n",
       "      <td>7.770</td>\n",
       "      <td>...</td>\n",
       "      <td>Morton/Cochran County</td>\n",
       "      <td>33.73476</td>\n",
       "      <td>-102.73978</td>\n",
       "      <td>3754 ft.</td>\n",
       "      <td>MORT</td>\n",
       "      <td>KMNS</td>\n",
       "      <td>XMNS</td>\n",
       "      <td>1375</td>\n",
       "      <td>12</td>\n",
       "      <td>MNST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4308</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>5.711</td>\n",
       "      <td>5.679</td>\n",
       "      <td>187.00</td>\n",
       "      <td>6.086</td>\n",
       "      <td>0.616</td>\n",
       "      <td>7.710</td>\n",
       "      <td>...</td>\n",
       "      <td>Brownfield/Terry County</td>\n",
       "      <td>33.15188</td>\n",
       "      <td>-102.27102</td>\n",
       "      <td>3314 ft.</td>\n",
       "      <td>BROW</td>\n",
       "      <td>KBWS</td>\n",
       "      <td>XBWS</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>BWST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>4.280</td>\n",
       "      <td>4.271</td>\n",
       "      <td>181.70</td>\n",
       "      <td>3.595</td>\n",
       "      <td>0.383</td>\n",
       "      <td>5.880</td>\n",
       "      <td>...</td>\n",
       "      <td>Ralls/Crosby County</td>\n",
       "      <td>33.66840</td>\n",
       "      <td>-101.37576</td>\n",
       "      <td>3097 ft.</td>\n",
       "      <td>RALL</td>\n",
       "      <td>KRLS</td>\n",
       "      <td>XRLS</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>RAST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>6.002</td>\n",
       "      <td>5.938</td>\n",
       "      <td>218.30</td>\n",
       "      <td>8.390</td>\n",
       "      <td>0.698</td>\n",
       "      <td>7.640</td>\n",
       "      <td>...</td>\n",
       "      <td>Hart/Castro County</td>\n",
       "      <td>34.42319</td>\n",
       "      <td>-102.10735</td>\n",
       "      <td>3694 ft.</td>\n",
       "      <td>HART</td>\n",
       "      <td>KHRS</td>\n",
       "      <td>XHRS</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>HAST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>4.511</td>\n",
       "      <td>4.499</td>\n",
       "      <td>184.60</td>\n",
       "      <td>4.039</td>\n",
       "      <td>0.333</td>\n",
       "      <td>5.259</td>\n",
       "      <td>...</td>\n",
       "      <td>Reese Center/Lubbock County</td>\n",
       "      <td>33.60759</td>\n",
       "      <td>-102.04597</td>\n",
       "      <td>3343 ft.</td>\n",
       "      <td>REES2</td>\n",
       "      <td>KREE</td>\n",
       "      <td>XREE</td>\n",
       "      <td>1364</td>\n",
       "      <td>1</td>\n",
       "      <td>REST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5460</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>130</td>\n",
       "      <td>6.697</td>\n",
       "      <td>6.667</td>\n",
       "      <td>192.10</td>\n",
       "      <td>5.401</td>\n",
       "      <td>0.681</td>\n",
       "      <td>8.200</td>\n",
       "      <td>...</td>\n",
       "      <td>Olton/Lamb County</td>\n",
       "      <td>34.09378</td>\n",
       "      <td>-102.11808</td>\n",
       "      <td>3566 ft.</td>\n",
       "      <td>OLTO</td>\n",
       "      <td>KONS</td>\n",
       "      <td>XONS</td>\n",
       "      <td>19</td>\n",
       "      <td>130</td>\n",
       "      <td>ONST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5748</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>21</td>\n",
       "      <td>5.958</td>\n",
       "      <td>5.929</td>\n",
       "      <td>204.70</td>\n",
       "      <td>5.675</td>\n",
       "      <td>0.715</td>\n",
       "      <td>7.580</td>\n",
       "      <td>...</td>\n",
       "      <td>Dimmitt/Castro County</td>\n",
       "      <td>34.56751</td>\n",
       "      <td>-102.29317</td>\n",
       "      <td>3876 ft.</td>\n",
       "      <td>DIMM</td>\n",
       "      <td>KDMS</td>\n",
       "      <td>XDMS</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>DMST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6035</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>37</td>\n",
       "      <td>5.043</td>\n",
       "      <td>5.024</td>\n",
       "      <td>164.10</td>\n",
       "      <td>5.022</td>\n",
       "      <td>0.432</td>\n",
       "      <td>6.141</td>\n",
       "      <td>...</td>\n",
       "      <td>Snyder/Scurry County</td>\n",
       "      <td>32.71614</td>\n",
       "      <td>-100.86167</td>\n",
       "      <td>2431 ft.</td>\n",
       "      <td>SNYD</td>\n",
       "      <td>KSYS</td>\n",
       "      <td>XSYS</td>\n",
       "      <td>1401</td>\n",
       "      <td>37</td>\n",
       "      <td>SYST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6323</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>18</td>\n",
       "      <td>5.015</td>\n",
       "      <td>4.987</td>\n",
       "      <td>169.20</td>\n",
       "      <td>6.077</td>\n",
       "      <td>0.461</td>\n",
       "      <td>6.272</td>\n",
       "      <td>...</td>\n",
       "      <td>Lamesa/Dawson County</td>\n",
       "      <td>32.70592</td>\n",
       "      <td>-101.93617</td>\n",
       "      <td>2956 ft.</td>\n",
       "      <td>LAMS</td>\n",
       "      <td>KLES</td>\n",
       "      <td>XLES</td>\n",
       "      <td>1381</td>\n",
       "      <td>18</td>\n",
       "      <td>LMST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6611</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>31</td>\n",
       "      <td>4.942</td>\n",
       "      <td>4.931</td>\n",
       "      <td>174.90</td>\n",
       "      <td>3.871</td>\n",
       "      <td>0.389</td>\n",
       "      <td>5.782</td>\n",
       "      <td>...</td>\n",
       "      <td>Northeast Borden County</td>\n",
       "      <td>32.89903</td>\n",
       "      <td>-101.20189</td>\n",
       "      <td>2705 ft.</td>\n",
       "      <td>FLUV</td>\n",
       "      <td>KFVS</td>\n",
       "      <td>XFVS</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>FVST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6899</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>25</td>\n",
       "      <td>4.725</td>\n",
       "      <td>4.711</td>\n",
       "      <td>179.30</td>\n",
       "      <td>4.324</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.847</td>\n",
       "      <td>...</td>\n",
       "      <td>Seminole/Gaines County</td>\n",
       "      <td>32.74075</td>\n",
       "      <td>-102.63581</td>\n",
       "      <td>3313 ft.</td>\n",
       "      <td>SEMI</td>\n",
       "      <td>KSM1</td>\n",
       "      <td>XSMS</td>\n",
       "      <td>1388</td>\n",
       "      <td>25</td>\n",
       "      <td>SMST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7187</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>33</td>\n",
       "      <td>5.804</td>\n",
       "      <td>5.728</td>\n",
       "      <td>171.20</td>\n",
       "      <td>9.230</td>\n",
       "      <td>0.917</td>\n",
       "      <td>8.260</td>\n",
       "      <td>...</td>\n",
       "      <td>Lubbock/Lubbock County</td>\n",
       "      <td>33.60408</td>\n",
       "      <td>-101.89919</td>\n",
       "      <td>3232 ft.</td>\n",
       "      <td>LBBW2</td>\n",
       "      <td>KLBW</td>\n",
       "      <td>XLBW</td>\n",
       "      <td>1397</td>\n",
       "      <td>33</td>\n",
       "      <td>LWST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7475</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>4.248</td>\n",
       "      <td>4.209</td>\n",
       "      <td>233.70</td>\n",
       "      <td>7.720</td>\n",
       "      <td>0.464</td>\n",
       "      <td>5.325</td>\n",
       "      <td>...</td>\n",
       "      <td>Amherst/Lamb County</td>\n",
       "      <td>34.02178</td>\n",
       "      <td>-102.40453</td>\n",
       "      <td>3647 ft.</td>\n",
       "      <td>AMHE</td>\n",
       "      <td>KAMH</td>\n",
       "      <td>XAMH</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>ATST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>5.211</td>\n",
       "      <td>5.206</td>\n",
       "      <td>184.30</td>\n",
       "      <td>2.629</td>\n",
       "      <td>0.509</td>\n",
       "      <td>6.762</td>\n",
       "      <td>...</td>\n",
       "      <td>Slaton/Lubbock County</td>\n",
       "      <td>33.45690</td>\n",
       "      <td>-101.61723</td>\n",
       "      <td>3065 ft.</td>\n",
       "      <td>SLAT</td>\n",
       "      <td>KSLS</td>\n",
       "      <td>XSLS</td>\n",
       "      <td>1368</td>\n",
       "      <td>5</td>\n",
       "      <td>SLST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8051</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>5.575</td>\n",
       "      <td>5.541</td>\n",
       "      <td>185.70</td>\n",
       "      <td>6.277</td>\n",
       "      <td>0.523</td>\n",
       "      <td>7.090</td>\n",
       "      <td>...</td>\n",
       "      <td>Levelland/Hockley County</td>\n",
       "      <td>33.52650</td>\n",
       "      <td>-102.36000</td>\n",
       "      <td>3496 ft.</td>\n",
       "      <td>LEVE</td>\n",
       "      <td>KLDS</td>\n",
       "      <td>XLDS</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>LDST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8339</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>6.002</td>\n",
       "      <td>5.949</td>\n",
       "      <td>151.70</td>\n",
       "      <td>7.630</td>\n",
       "      <td>0.886</td>\n",
       "      <td>8.260</td>\n",
       "      <td>...</td>\n",
       "      <td>Aspermont (Stonewall Co. Airp.)</td>\n",
       "      <td>33.16789</td>\n",
       "      <td>-100.19602</td>\n",
       "      <td>1740 ft</td>\n",
       "      <td>ASPE</td>\n",
       "      <td>KASR</td>\n",
       "      <td>XASR</td>\n",
       "      <td>1402</td>\n",
       "      <td>41</td>\n",
       "      <td>ASST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>2.817</td>\n",
       "      <td>2.811</td>\n",
       "      <td>161.00</td>\n",
       "      <td>3.893</td>\n",
       "      <td>0.233</td>\n",
       "      <td>3.528</td>\n",
       "      <td>...</td>\n",
       "      <td>Gail/Borden County</td>\n",
       "      <td>32.75508</td>\n",
       "      <td>-101.41439</td>\n",
       "      <td>2547 ft.</td>\n",
       "      <td>GAIL</td>\n",
       "      <td>KGGS</td>\n",
       "      <td>XGGS</td>\n",
       "      <td>1389</td>\n",
       "      <td>26</td>\n",
       "      <td>GGST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8915</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>49</td>\n",
       "      <td>5.988</td>\n",
       "      <td>5.957</td>\n",
       "      <td>151.00</td>\n",
       "      <td>5.881</td>\n",
       "      <td>0.583</td>\n",
       "      <td>7.220</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkey/Hall County</td>\n",
       "      <td>34.37896</td>\n",
       "      <td>-100.93175</td>\n",
       "      <td>2450 ft.</td>\n",
       "      <td>VALL</td>\n",
       "      <td>KTUR</td>\n",
       "      <td>XTUR</td>\n",
       "      <td>1413</td>\n",
       "      <td>49</td>\n",
       "      <td>TUST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9203</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>7.670</td>\n",
       "      <td>7.650</td>\n",
       "      <td>172.30</td>\n",
       "      <td>3.565</td>\n",
       "      <td>0.772</td>\n",
       "      <td>9.670</td>\n",
       "      <td>...</td>\n",
       "      <td>Abernathy/Hale County</td>\n",
       "      <td>33.87538</td>\n",
       "      <td>-101.75718</td>\n",
       "      <td>3333 ft.</td>\n",
       "      <td>ABER</td>\n",
       "      <td>KARS</td>\n",
       "      <td>XARS</td>\n",
       "      <td>1365</td>\n",
       "      <td>2</td>\n",
       "      <td>ARST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9491</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>27</td>\n",
       "      <td>6.712</td>\n",
       "      <td>6.630</td>\n",
       "      <td>188.20</td>\n",
       "      <td>8.930</td>\n",
       "      <td>0.669</td>\n",
       "      <td>8.100</td>\n",
       "      <td>...</td>\n",
       "      <td>White River Lake/Crosby County</td>\n",
       "      <td>33.52533</td>\n",
       "      <td>-101.16506</td>\n",
       "      <td>2704 ft.</td>\n",
       "      <td>WHIT</td>\n",
       "      <td>KWVS</td>\n",
       "      <td>XWVS</td>\n",
       "      <td>1390</td>\n",
       "      <td>27</td>\n",
       "      <td>WLST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9779</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>51</td>\n",
       "      <td>6.559</td>\n",
       "      <td>6.550</td>\n",
       "      <td>252.20</td>\n",
       "      <td>3.006</td>\n",
       "      <td>0.328</td>\n",
       "      <td>7.190</td>\n",
       "      <td>...</td>\n",
       "      <td>Tatum, NM/Lea County</td>\n",
       "      <td>33.23877</td>\n",
       "      <td>-103.35211</td>\n",
       "      <td>4018 ft</td>\n",
       "      <td>TATU</td>\n",
       "      <td>KTAT</td>\n",
       "      <td>XTAT</td>\n",
       "      <td>1415</td>\n",
       "      <td>51</td>\n",
       "      <td>TTSN5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10067</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>3.627</td>\n",
       "      <td>3.623</td>\n",
       "      <td>249.70</td>\n",
       "      <td>2.558</td>\n",
       "      <td>0.118</td>\n",
       "      <td>3.920</td>\n",
       "      <td>...</td>\n",
       "      <td>Muleshoe/Bailey County</td>\n",
       "      <td>34.20635</td>\n",
       "      <td>-102.74240</td>\n",
       "      <td>3806 ft.</td>\n",
       "      <td>MULE</td>\n",
       "      <td>KMUS</td>\n",
       "      <td>XMUS</td>\n",
       "      <td>1373</td>\n",
       "      <td>11</td>\n",
       "      <td>MUST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10355</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>34</td>\n",
       "      <td>4.498</td>\n",
       "      <td>4.474</td>\n",
       "      <td>167.20</td>\n",
       "      <td>5.883</td>\n",
       "      <td>0.545</td>\n",
       "      <td>5.684</td>\n",
       "      <td>...</td>\n",
       "      <td>Guthrie/King County</td>\n",
       "      <td>33.56703</td>\n",
       "      <td>-100.48061</td>\n",
       "      <td>1998 ft.</td>\n",
       "      <td>PITC</td>\n",
       "      <td>KPFS</td>\n",
       "      <td>XPFS</td>\n",
       "      <td>1398</td>\n",
       "      <td>34</td>\n",
       "      <td>PFST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10643</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>7.300</td>\n",
       "      <td>7.260</td>\n",
       "      <td>211.40</td>\n",
       "      <td>5.911</td>\n",
       "      <td>1.009</td>\n",
       "      <td>9.540</td>\n",
       "      <td>...</td>\n",
       "      <td>Tulia/Swisher County</td>\n",
       "      <td>34.54294</td>\n",
       "      <td>-101.74050</td>\n",
       "      <td>3478 ft.</td>\n",
       "      <td>TULI</td>\n",
       "      <td>KTIS</td>\n",
       "      <td>XTIS</td>\n",
       "      <td>1385</td>\n",
       "      <td>22</td>\n",
       "      <td>TLST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10931</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>6.985</td>\n",
       "      <td>6.938</td>\n",
       "      <td>185.00</td>\n",
       "      <td>6.657</td>\n",
       "      <td>0.766</td>\n",
       "      <td>9.280</td>\n",
       "      <td>...</td>\n",
       "      <td>Wolfforth/Lubbock County</td>\n",
       "      <td>33.42068</td>\n",
       "      <td>-102.04983</td>\n",
       "      <td>3307 ft.</td>\n",
       "      <td>WOLF</td>\n",
       "      <td>KWOS</td>\n",
       "      <td>XWOS</td>\n",
       "      <td>1411</td>\n",
       "      <td>47</td>\n",
       "      <td>WOST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11219</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>52</td>\n",
       "      <td>10.420</td>\n",
       "      <td>10.310</td>\n",
       "      <td>166.10</td>\n",
       "      <td>8.430</td>\n",
       "      <td>1.413</td>\n",
       "      <td>13.980</td>\n",
       "      <td>...</td>\n",
       "      <td>Northfield/Motley County</td>\n",
       "      <td>34.27303</td>\n",
       "      <td>-100.60444</td>\n",
       "      <td>2088 ft.</td>\n",
       "      <td>NORT</td>\n",
       "      <td>KNOR</td>\n",
       "      <td>XNOR</td>\n",
       "      <td>1416</td>\n",
       "      <td>52</td>\n",
       "      <td>NORT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11507</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>36</td>\n",
       "      <td>5.693</td>\n",
       "      <td>5.661</td>\n",
       "      <td>192.90</td>\n",
       "      <td>6.149</td>\n",
       "      <td>0.667</td>\n",
       "      <td>7.420</td>\n",
       "      <td>...</td>\n",
       "      <td>Paducah/Cottle County</td>\n",
       "      <td>33.89053</td>\n",
       "      <td>-100.39886</td>\n",
       "      <td>2021 ft.</td>\n",
       "      <td>PADU</td>\n",
       "      <td>KPAD</td>\n",
       "      <td>XPAD</td>\n",
       "      <td>1400</td>\n",
       "      <td>36</td>\n",
       "      <td>PADT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11795</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>4.608</td>\n",
       "      <td>4.580</td>\n",
       "      <td>144.20</td>\n",
       "      <td>6.264</td>\n",
       "      <td>0.351</td>\n",
       "      <td>5.521</td>\n",
       "      <td>...</td>\n",
       "      <td>Seymour/Baylor County</td>\n",
       "      <td>33.63233</td>\n",
       "      <td>-99.29098</td>\n",
       "      <td>1302 ft</td>\n",
       "      <td>SEYM</td>\n",
       "      <td>KSE1</td>\n",
       "      <td>XSE1</td>\n",
       "      <td>1422</td>\n",
       "      <td>58</td>\n",
       "      <td>SEST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12083</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>14</td>\n",
       "      <td>4.655</td>\n",
       "      <td>4.606</td>\n",
       "      <td>171.30</td>\n",
       "      <td>8.340</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.403</td>\n",
       "      <td>...</td>\n",
       "      <td>O'Donnell/Lynn County</td>\n",
       "      <td>32.97988</td>\n",
       "      <td>-101.83220</td>\n",
       "      <td>3054 ft.</td>\n",
       "      <td>ODON2</td>\n",
       "      <td>KOES</td>\n",
       "      <td>XOES</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>OEST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12371</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>55</td>\n",
       "      <td>7.660</td>\n",
       "      <td>7.630</td>\n",
       "      <td>170.30</td>\n",
       "      <td>4.870</td>\n",
       "      <td>0.856</td>\n",
       "      <td>10.090</td>\n",
       "      <td>...</td>\n",
       "      <td>St. Lawrence/Glasscock County</td>\n",
       "      <td>31.65645</td>\n",
       "      <td>-101.60019</td>\n",
       "      <td>2693 ft.</td>\n",
       "      <td>STLW</td>\n",
       "      <td>KST1</td>\n",
       "      <td>XST1</td>\n",
       "      <td>1419</td>\n",
       "      <td>55</td>\n",
       "      <td>GCMT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13763</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>7.250</td>\n",
       "      <td>7.210</td>\n",
       "      <td>259.50</td>\n",
       "      <td>6.131</td>\n",
       "      <td>0.802</td>\n",
       "      <td>8.530</td>\n",
       "      <td>...</td>\n",
       "      <td>Guadalupe Mtns NP/Culberson</td>\n",
       "      <td>31.89132</td>\n",
       "      <td>-104.80997</td>\n",
       "      <td>5571 ft.</td>\n",
       "      <td>GUMO</td>\n",
       "      <td>KGU1</td>\n",
       "      <td>XGU1</td>\n",
       "      <td>1424</td>\n",
       "      <td>60</td>\n",
       "      <td>PSST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14099</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>6.963</td>\n",
       "      <td>6.920</td>\n",
       "      <td>163.10</td>\n",
       "      <td>6.384</td>\n",
       "      <td>1.027</td>\n",
       "      <td>9.470</td>\n",
       "      <td>...</td>\n",
       "      <td>Western Hardeman County</td>\n",
       "      <td>34.34389</td>\n",
       "      <td>-99.93972</td>\n",
       "      <td>1644 ft.</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>KGDS</td>\n",
       "      <td>XGDS</td>\n",
       "      <td>1407</td>\n",
       "      <td>43</td>\n",
       "      <td>GDST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14387</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>53</td>\n",
       "      <td>1.759</td>\n",
       "      <td>1.736</td>\n",
       "      <td>281.10</td>\n",
       "      <td>9.440</td>\n",
       "      <td>0.262</td>\n",
       "      <td>2.287</td>\n",
       "      <td>...</td>\n",
       "      <td>Dora NM/Roosevelt County</td>\n",
       "      <td>33.92005</td>\n",
       "      <td>-103.35778</td>\n",
       "      <td>4340 ft.</td>\n",
       "      <td>DORA</td>\n",
       "      <td>KDR1</td>\n",
       "      <td>XDR1</td>\n",
       "      <td>1417</td>\n",
       "      <td>53</td>\n",
       "      <td>DORN5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14675</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>44</td>\n",
       "      <td>5.319</td>\n",
       "      <td>5.294</td>\n",
       "      <td>186.80</td>\n",
       "      <td>5.570</td>\n",
       "      <td>0.587</td>\n",
       "      <td>6.795</td>\n",
       "      <td>...</td>\n",
       "      <td>McLean/Gray County</td>\n",
       "      <td>35.23719</td>\n",
       "      <td>-100.57492</td>\n",
       "      <td>2863 ft.</td>\n",
       "      <td>MCLE</td>\n",
       "      <td>KMCS</td>\n",
       "      <td>XMCS</td>\n",
       "      <td>1408</td>\n",
       "      <td>44</td>\n",
       "      <td>MCST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14963</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>29</td>\n",
       "      <td>4.830</td>\n",
       "      <td>4.797</td>\n",
       "      <td>189.10</td>\n",
       "      <td>6.698</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.337</td>\n",
       "      <td>...</td>\n",
       "      <td>Southeast Cochran County</td>\n",
       "      <td>33.38912</td>\n",
       "      <td>-102.60994</td>\n",
       "      <td>3625 ft.</td>\n",
       "      <td>MALL</td>\n",
       "      <td>KSDS</td>\n",
       "      <td>XSDS</td>\n",
       "      <td>1395</td>\n",
       "      <td>29</td>\n",
       "      <td>SDST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15251</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>1.890</td>\n",
       "      <td>1.850</td>\n",
       "      <td>114.60</td>\n",
       "      <td>11.690</td>\n",
       "      <td>1.011</td>\n",
       "      <td>3.561</td>\n",
       "      <td>...</td>\n",
       "      <td>Clarendon/Donley County</td>\n",
       "      <td>34.92492</td>\n",
       "      <td>-100.93000</td>\n",
       "      <td>2836 ft.</td>\n",
       "      <td>CLAR</td>\n",
       "      <td>KCES</td>\n",
       "      <td>XCES</td>\n",
       "      <td>1399</td>\n",
       "      <td>35</td>\n",
       "      <td>CEST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15539</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>6.925</td>\n",
       "      <td>6.868</td>\n",
       "      <td>198.50</td>\n",
       "      <td>7.330</td>\n",
       "      <td>0.862</td>\n",
       "      <td>8.590</td>\n",
       "      <td>...</td>\n",
       "      <td>Silverton/Briscoe County</td>\n",
       "      <td>34.44540</td>\n",
       "      <td>-101.19050</td>\n",
       "      <td>3202 ft.</td>\n",
       "      <td>SILV</td>\n",
       "      <td>KSVS</td>\n",
       "      <td>XSVS</td>\n",
       "      <td>1386</td>\n",
       "      <td>23</td>\n",
       "      <td>STST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15827</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>50</td>\n",
       "      <td>8.820</td>\n",
       "      <td>8.780</td>\n",
       "      <td>173.40</td>\n",
       "      <td>5.479</td>\n",
       "      <td>1.404</td>\n",
       "      <td>12.580</td>\n",
       "      <td>...</td>\n",
       "      <td>Childress/Childress County</td>\n",
       "      <td>34.45650</td>\n",
       "      <td>-100.19891</td>\n",
       "      <td>1943 ft.</td>\n",
       "      <td>CHIL</td>\n",
       "      <td>KCXS</td>\n",
       "      <td>XCXS</td>\n",
       "      <td>1414</td>\n",
       "      <td>50</td>\n",
       "      <td>CXST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16115</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>8.150</td>\n",
       "      <td>8.100</td>\n",
       "      <td>175.60</td>\n",
       "      <td>6.315</td>\n",
       "      <td>1.177</td>\n",
       "      <td>11.430</td>\n",
       "      <td>...</td>\n",
       "      <td>Floydada/Floyd County</td>\n",
       "      <td>34.00158</td>\n",
       "      <td>-101.32588</td>\n",
       "      <td>3179 ft.</td>\n",
       "      <td>FLOY</td>\n",
       "      <td>KFLS</td>\n",
       "      <td>XFLS</td>\n",
       "      <td>1371</td>\n",
       "      <td>8</td>\n",
       "      <td>FLST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16403</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>28</td>\n",
       "      <td>8.870</td>\n",
       "      <td>8.760</td>\n",
       "      <td>181.70</td>\n",
       "      <td>9.140</td>\n",
       "      <td>1.371</td>\n",
       "      <td>13.030</td>\n",
       "      <td>...</td>\n",
       "      <td>Graham/Garza County</td>\n",
       "      <td>33.08152</td>\n",
       "      <td>-101.51615</td>\n",
       "      <td>2870 ft</td>\n",
       "      <td>MACY</td>\n",
       "      <td>KGHS</td>\n",
       "      <td>XGHS</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>GHST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16691</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>38</td>\n",
       "      <td>4.342</td>\n",
       "      <td>4.260</td>\n",
       "      <td>9.31</td>\n",
       "      <td>11.140</td>\n",
       "      <td>0.911</td>\n",
       "      <td>6.109</td>\n",
       "      <td>...</td>\n",
       "      <td>Memphis/Hall County</td>\n",
       "      <td>34.73136</td>\n",
       "      <td>-100.52543</td>\n",
       "      <td>2057 ft.</td>\n",
       "      <td>MEMP</td>\n",
       "      <td>KMES</td>\n",
       "      <td>XMES</td>\n",
       "      <td>1409</td>\n",
       "      <td>38</td>\n",
       "      <td>MEST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>45</td>\n",
       "      <td>4.517</td>\n",
       "      <td>4.480</td>\n",
       "      <td>183.40</td>\n",
       "      <td>7.360</td>\n",
       "      <td>0.430</td>\n",
       "      <td>5.325</td>\n",
       "      <td>...</td>\n",
       "      <td>Denver City/Yoakum County</td>\n",
       "      <td>32.99082</td>\n",
       "      <td>-102.93871</td>\n",
       "      <td>3652 ft.</td>\n",
       "      <td>DENV</td>\n",
       "      <td>KDVS</td>\n",
       "      <td>XDVS</td>\n",
       "      <td>1403</td>\n",
       "      <td>45</td>\n",
       "      <td>DVST2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17267</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2019-03-22 23:00:00</td>\n",
       "      <td>48</td>\n",
       "      <td>5.423</td>\n",
       "      <td>5.367</td>\n",
       "      <td>167.90</td>\n",
       "      <td>8.210</td>\n",
       "      <td>0.905</td>\n",
       "      <td>8.130</td>\n",
       "      <td>...</td>\n",
       "      <td>Andrews/Andrews County</td>\n",
       "      <td>32.32008</td>\n",
       "      <td>-102.51669</td>\n",
       "      <td>3169 ft.</td>\n",
       "      <td>ANDR</td>\n",
       "      <td>KANS</td>\n",
       "      <td>XANS</td>\n",
       "      <td>1412</td>\n",
       "      <td>48</td>\n",
       "      <td>AWST2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Array_ID  QC_flag                Time  Station_ID  \\\n",
       "276           1       81 2019-03-22 23:00:00          54   \n",
       "564           1       81 2019-03-22 23:00:00          32   \n",
       "852           1       81 2019-03-22 23:00:00           7   \n",
       "1140          1       81 2019-03-22 23:00:00          39   \n",
       "1428          1       81 2019-03-22 23:00:00          56   \n",
       "1716          1       81 2019-03-22 23:00:00          59   \n",
       "2004          1       81 2019-03-22 23:00:00          24   \n",
       "2292          1       81 2019-03-22 23:00:00          20   \n",
       "2580          1       81 2019-03-22 23:00:00           3   \n",
       "2868          1       81 2019-03-22 23:00:00          17   \n",
       "3156          1       81 2019-03-22 23:00:00          30   \n",
       "3444          1       81 2019-03-22 23:00:00          40   \n",
       "3732          1       81 2019-03-22 23:00:00          15   \n",
       "4020          1       81 2019-03-22 23:00:00          12   \n",
       "4308          1       81 2019-03-22 23:00:00          10   \n",
       "4596          1       81 2019-03-22 23:00:00           9   \n",
       "4884          1       81 2019-03-22 23:00:00          19   \n",
       "5172          1       81 2019-03-22 23:00:00           1   \n",
       "5460          1       81 2019-03-22 23:00:00         130   \n",
       "5748          1       81 2019-03-22 23:00:00          21   \n",
       "6035          1       81 2019-03-22 23:00:00          37   \n",
       "6323          1       81 2019-03-22 23:00:00          18   \n",
       "6611          1       81 2019-03-22 23:00:00          31   \n",
       "6899          1       81 2019-03-22 23:00:00          25   \n",
       "7187          1       81 2019-03-22 23:00:00          33   \n",
       "7475          1       81 2019-03-22 23:00:00          16   \n",
       "7763          1       81 2019-03-22 23:00:00           5   \n",
       "8051          1       81 2019-03-22 23:00:00           6   \n",
       "8339          1       81 2019-03-22 23:00:00          41   \n",
       "8627          1       81 2019-03-22 23:00:00          26   \n",
       "8915          1       81 2019-03-22 23:00:00          49   \n",
       "9203          1       81 2019-03-22 23:00:00           2   \n",
       "9491          1       81 2019-03-22 23:00:00          27   \n",
       "9779          1       81 2019-03-22 23:00:00          51   \n",
       "10067         1       81 2019-03-22 23:00:00          11   \n",
       "10355         1       81 2019-03-22 23:00:00          34   \n",
       "10643         1       81 2019-03-22 23:00:00          22   \n",
       "10931         1       81 2019-03-22 23:00:00          47   \n",
       "11219         1       81 2019-03-22 23:00:00          52   \n",
       "11507         1       81 2019-03-22 23:00:00          36   \n",
       "11795         1       81 2019-03-22 23:00:00          58   \n",
       "12083         1       81 2019-03-22 23:00:00          14   \n",
       "12371         1       81 2019-03-22 23:00:00          55   \n",
       "13763         1       81 2019-03-22 23:00:00          60   \n",
       "14099         1       81 2019-03-22 23:00:00          43   \n",
       "14387         1       81 2019-03-22 23:00:00          53   \n",
       "14675         1       81 2019-03-22 23:00:00          44   \n",
       "14963         1       81 2019-03-22 23:00:00          29   \n",
       "15251         1       81 2019-03-22 23:00:00          35   \n",
       "15539         1       81 2019-03-22 23:00:00          23   \n",
       "15827         1       81 2019-03-22 23:00:00          50   \n",
       "16115         1       81 2019-03-22 23:00:00           8   \n",
       "16403         1       81 2019-03-22 23:00:00          28   \n",
       "16691         1       81 2019-03-22 23:00:00          38   \n",
       "16979         1       81 2019-03-22 23:00:00          45   \n",
       "17267         1       81 2019-03-22 23:00:00          48   \n",
       "\n",
       "       10m_scalar_wind_speed  10m_vector_wind_speed  10m_wind_direction  \\\n",
       "276                    3.201                  3.191              124.60   \n",
       "564                    2.363                  1.928              283.10   \n",
       "852                   11.410                 11.310              156.30   \n",
       "1140                   2.422                  2.392              164.40   \n",
       "1428                   4.857                  4.793              165.20   \n",
       "1716                   6.825                  6.811              167.30   \n",
       "2004                   5.571                  5.456              192.70   \n",
       "2292                   3.585                  3.581              320.80   \n",
       "2580                   5.826                  5.721              193.90   \n",
       "2868                   5.328                  5.305              175.90   \n",
       "3156                   6.357                  6.345              173.20   \n",
       "3444                   3.763                  3.723              182.60   \n",
       "3732                   4.048                  4.032              193.70   \n",
       "4020                   5.309                  5.186              222.80   \n",
       "4308                   5.711                  5.679              187.00   \n",
       "4596                   4.280                  4.271              181.70   \n",
       "4884                   6.002                  5.938              218.30   \n",
       "5172                   4.511                  4.499              184.60   \n",
       "5460                   6.697                  6.667              192.10   \n",
       "5748                   5.958                  5.929              204.70   \n",
       "6035                   5.043                  5.024              164.10   \n",
       "6323                   5.015                  4.987              169.20   \n",
       "6611                   4.942                  4.931              174.90   \n",
       "6899                   4.725                  4.711              179.30   \n",
       "7187                   5.804                  5.728              171.20   \n",
       "7475                   4.248                  4.209              233.70   \n",
       "7763                   5.211                  5.206              184.30   \n",
       "8051                   5.575                  5.541              185.70   \n",
       "8339                   6.002                  5.949              151.70   \n",
       "8627                   2.817                  2.811              161.00   \n",
       "8915                   5.988                  5.957              151.00   \n",
       "9203                   7.670                  7.650              172.30   \n",
       "9491                   6.712                  6.630              188.20   \n",
       "9779                   6.559                  6.550              252.20   \n",
       "10067                  3.627                  3.623              249.70   \n",
       "10355                  4.498                  4.474              167.20   \n",
       "10643                  7.300                  7.260              211.40   \n",
       "10931                  6.985                  6.938              185.00   \n",
       "11219                 10.420                 10.310              166.10   \n",
       "11507                  5.693                  5.661              192.90   \n",
       "11795                  4.608                  4.580              144.20   \n",
       "12083                  4.655                  4.606              171.30   \n",
       "12371                  7.660                  7.630              170.30   \n",
       "13763                  7.250                  7.210              259.50   \n",
       "14099                  6.963                  6.920              163.10   \n",
       "14387                  1.759                  1.736              281.10   \n",
       "14675                  5.319                  5.294              186.80   \n",
       "14963                  4.830                  4.797              189.10   \n",
       "15251                  1.890                  1.850              114.60   \n",
       "15539                  6.925                  6.868              198.50   \n",
       "15827                  8.820                  8.780              173.40   \n",
       "16115                  8.150                  8.100              175.60   \n",
       "16403                  8.870                  8.760              181.70   \n",
       "16691                  4.342                  4.260                9.31   \n",
       "16979                  4.517                  4.480              183.40   \n",
       "17267                  5.423                  5.367              167.90   \n",
       "\n",
       "       10m_wind_direction_std  10m_wind_speed_std  10m_gust_wind_speed  ...  \\\n",
       "276                     4.494               0.274                3.757  ...   \n",
       "564                    34.770               0.708                3.430  ...   \n",
       "852                     7.390               1.815               16.040  ...   \n",
       "1140                    9.020               0.535                4.279  ...   \n",
       "1428                    9.300               0.821                6.664  ...   \n",
       "1716                    3.654               0.781                8.720  ...   \n",
       "2004                   11.620               1.399                9.280  ...   \n",
       "2292                    2.779               0.252                4.214  ...   \n",
       "2580                   10.870               0.996                8.620  ...   \n",
       "2868                    5.349               0.508                6.893  ...   \n",
       "3156                    3.463               0.417                7.450  ...   \n",
       "3444                    8.360               0.582                5.357  ...   \n",
       "3732                    4.998               0.311                4.900  ...   \n",
       "4020                   12.330               0.901                7.770  ...   \n",
       "4308                    6.086               0.616                7.710  ...   \n",
       "4596                    3.595               0.383                5.880  ...   \n",
       "4884                    8.390               0.698                7.640  ...   \n",
       "5172                    4.039               0.333                5.259  ...   \n",
       "5460                    5.401               0.681                8.200  ...   \n",
       "5748                    5.675               0.715                7.580  ...   \n",
       "6035                    5.022               0.432                6.141  ...   \n",
       "6323                    6.077               0.461                6.272  ...   \n",
       "6611                    3.871               0.389                5.782  ...   \n",
       "6899                    4.324               0.544                5.847  ...   \n",
       "7187                    9.230               0.917                8.260  ...   \n",
       "7475                    7.720               0.464                5.325  ...   \n",
       "7763                    2.629               0.509                6.762  ...   \n",
       "8051                    6.277               0.523                7.090  ...   \n",
       "8339                    7.630               0.886                8.260  ...   \n",
       "8627                    3.893               0.233                3.528  ...   \n",
       "8915                    5.881               0.583                7.220  ...   \n",
       "9203                    3.565               0.772                9.670  ...   \n",
       "9491                    8.930               0.669                8.100  ...   \n",
       "9779                    3.006               0.328                7.190  ...   \n",
       "10067                   2.558               0.118                3.920  ...   \n",
       "10355                   5.883               0.545                5.684  ...   \n",
       "10643                   5.911               1.009                9.540  ...   \n",
       "10931                   6.657               0.766                9.280  ...   \n",
       "11219                   8.430               1.413               13.980  ...   \n",
       "11507                   6.149               0.667                7.420  ...   \n",
       "11795                   6.264               0.351                5.521  ...   \n",
       "12083                   8.340               0.740                6.403  ...   \n",
       "12371                   4.870               0.856               10.090  ...   \n",
       "13763                   6.131               0.802                8.530  ...   \n",
       "14099                   6.384               1.027                9.470  ...   \n",
       "14387                   9.440               0.262                2.287  ...   \n",
       "14675                   5.570               0.587                6.795  ...   \n",
       "14963                   6.698               0.614                6.337  ...   \n",
       "15251                  11.690               1.011                3.561  ...   \n",
       "15539                   7.330               0.862                8.590  ...   \n",
       "15827                   5.479               1.404               12.580  ...   \n",
       "16115                   6.315               1.177               11.430  ...   \n",
       "16403                   9.140               1.371               13.030  ...   \n",
       "16691                  11.140               0.911                6.109  ...   \n",
       "16979                   7.360               0.430                5.325  ...   \n",
       "17267                   8.210               0.905                8.130  ...   \n",
       "\n",
       "                                  Area       Lat       Long  Elevation     ID  \\\n",
       "276               San Angelo/Tom Green  31.54263 -100.51328   1957 ft.   SASU   \n",
       "564                Spur/Dickens County  33.48085 -100.87636   2287 ft.   SPUR   \n",
       "852           Roaring S./Motley County  33.93635 -100.84540   2615 ft.   ROAR   \n",
       "1140         Jayton (Kent Co. Airport)  33.23241 -100.56778   2010 ft.   JAYT   \n",
       "1428            Haskell/Haskell County  33.17473  -99.74420   1605 ft.   HASK   \n",
       "1716             Knox City/Knox County  33.44557  -99.86497   1470 ft.   KNOX   \n",
       "2004                 Post/Garza County  33.20033 -101.36804   2598 ft.  POST2   \n",
       "2292              Friona/Parmer County  34.65450 -102.69097   4005 ft.   FRIO   \n",
       "2580             Plainview/Hale County  34.17872 -101.70788   3358 ft.   PLVW   \n",
       "2868              Plains/Yoakum County  33.22814 -102.83936   3711 ft.   PLAI   \n",
       "3156              Anton/Hockley County  33.72525 -102.19082   3405 ft.   ANTO   \n",
       "3444                 Pampa/Gray County  35.53950 -100.92772   3216 ft.   PAMP   \n",
       "3732           Seagraves/Gaines County  32.93644 -102.57442   3360 ft.   SEAG   \n",
       "4020             Morton/Cochran County  33.73476 -102.73978   3754 ft.   MORT   \n",
       "4308           Brownfield/Terry County  33.15188 -102.27102   3314 ft.   BROW   \n",
       "4596               Ralls/Crosby County  33.66840 -101.37576   3097 ft.   RALL   \n",
       "4884                Hart/Castro County  34.42319 -102.10735   3694 ft.   HART   \n",
       "5172       Reese Center/Lubbock County  33.60759 -102.04597   3343 ft.  REES2   \n",
       "5460                 Olton/Lamb County  34.09378 -102.11808   3566 ft.   OLTO   \n",
       "5748             Dimmitt/Castro County  34.56751 -102.29317   3876 ft.   DIMM   \n",
       "6035              Snyder/Scurry County  32.71614 -100.86167   2431 ft.   SNYD   \n",
       "6323              Lamesa/Dawson County  32.70592 -101.93617   2956 ft.   LAMS   \n",
       "6611           Northeast Borden County  32.89903 -101.20189   2705 ft.   FLUV   \n",
       "6899            Seminole/Gaines County  32.74075 -102.63581   3313 ft.   SEMI   \n",
       "7187            Lubbock/Lubbock County  33.60408 -101.89919   3232 ft.  LBBW2   \n",
       "7475               Amherst/Lamb County  34.02178 -102.40453   3647 ft.   AMHE   \n",
       "7763             Slaton/Lubbock County  33.45690 -101.61723   3065 ft.   SLAT   \n",
       "8051          Levelland/Hockley County  33.52650 -102.36000   3496 ft.   LEVE   \n",
       "8339   Aspermont (Stonewall Co. Airp.)  33.16789 -100.19602    1740 ft   ASPE   \n",
       "8627                Gail/Borden County  32.75508 -101.41439   2547 ft.   GAIL   \n",
       "8915                Turkey/Hall County  34.37896 -100.93175   2450 ft.   VALL   \n",
       "9203             Abernathy/Hale County  33.87538 -101.75718   3333 ft.   ABER   \n",
       "9491    White River Lake/Crosby County  33.52533 -101.16506   2704 ft.   WHIT   \n",
       "9779              Tatum, NM/Lea County  33.23877 -103.35211    4018 ft   TATU   \n",
       "10067           Muleshoe/Bailey County  34.20635 -102.74240   3806 ft.   MULE   \n",
       "10355              Guthrie/King County  33.56703 -100.48061   1998 ft.   PITC   \n",
       "10643             Tulia/Swisher County  34.54294 -101.74050   3478 ft.   TULI   \n",
       "10931         Wolfforth/Lubbock County  33.42068 -102.04983   3307 ft.   WOLF   \n",
       "11219         Northfield/Motley County  34.27303 -100.60444   2088 ft.   NORT   \n",
       "11507            Paducah/Cottle County  33.89053 -100.39886   2021 ft.   PADU   \n",
       "11795            Seymour/Baylor County  33.63233  -99.29098    1302 ft   SEYM   \n",
       "12083            O'Donnell/Lynn County  32.97988 -101.83220   3054 ft.  ODON2   \n",
       "12371    St. Lawrence/Glasscock County  31.65645 -101.60019   2693 ft.   STLW   \n",
       "13763      Guadalupe Mtns NP/Culberson  31.89132 -104.80997   5571 ft.   GUMO   \n",
       "14099          Western Hardeman County  34.34389  -99.93972   1644 ft.   GOOD   \n",
       "14387         Dora NM/Roosevelt County  33.92005 -103.35778   4340 ft.   DORA   \n",
       "14675               McLean/Gray County  35.23719 -100.57492   2863 ft.   MCLE   \n",
       "14963         Southeast Cochran County  33.38912 -102.60994   3625 ft.   MALL   \n",
       "15251          Clarendon/Donley County  34.92492 -100.93000   2836 ft.   CLAR   \n",
       "15539         Silverton/Briscoe County  34.44540 -101.19050   3202 ft.   SILV   \n",
       "15827       Childress/Childress County  34.45650 -100.19891   1943 ft.   CHIL   \n",
       "16115            Floydada/Floyd County  34.00158 -101.32588   3179 ft.   FLOY   \n",
       "16403              Graham/Garza County  33.08152 -101.51615    2870 ft   MACY   \n",
       "16691              Memphis/Hall County  34.73136 -100.52543   2057 ft.   MEMP   \n",
       "16979        Denver City/Yoakum County  32.99082 -102.93871   3652 ft.   DENV   \n",
       "17267           Andrews/Andrews County  32.32008 -102.51669   3169 ft.   ANDR   \n",
       "\n",
       "       4 Letter ID  LDM ID  Node  Logger ID SHEF ID  \n",
       "276           KASU    XASU  1418         54   SJST2  \n",
       "564           KSP1    XSPR  1396         32   SPST2  \n",
       "852           KRRS    XRRS  1372          7   RRST2  \n",
       "1140          KJTS    XJTS  1404         39   JTST2  \n",
       "1428          KHA1    XHA1  1420         56   HAXT2  \n",
       "1716          KKN1    XKN1  1423         59   KXST2  \n",
       "2004          KPT1    XPTS  1387         24   PTST2  \n",
       "2292          KFAS    XFAS  1383         20   FAST2  \n",
       "2580          KPVS    XPVS  1367          3   PVST2  \n",
       "2868          KPPS    XPPS  1380         17   PPST2  \n",
       "3156          KAOS    XAOS  1393         30   AOST2  \n",
       "3444          KPMS    XPMS  1405         40   PAMT2  \n",
       "3732          KSGV    XSGV  1392         15   SGST2  \n",
       "4020          KMNS    XMNS  1375         12   MNST2  \n",
       "4308          KBWS    XBWS    17         10   BWST2  \n",
       "4596          KRLS    XRLS    14          9   RAST2  \n",
       "4884          KHRS    XHRS    20         19   HAST2  \n",
       "5172          KREE    XREE  1364          1   REST2  \n",
       "5460          KONS    XONS    19        130   ONST2  \n",
       "5748          KDMS    XDMS    21         21   DMST2  \n",
       "6035          KSYS    XSYS  1401         37   SYST2  \n",
       "6323          KLES    XLES  1381         18   LMST2  \n",
       "6611          KFVS    XFVS    32         31   FVST2  \n",
       "6899          KSM1    XSMS  1388         25   SMST2  \n",
       "7187          KLBW    XLBW  1397         33   LWST2  \n",
       "7475          KAMH    XAMH    15         16   ATST2  \n",
       "7763          KSLS    XSLS  1368          5   SLST2  \n",
       "8051          KLDS    XLDS    16          6   LDST2  \n",
       "8339          KASR    XASR  1402         41   ASST2  \n",
       "8627          KGGS    XGGS  1389         26   GGST2  \n",
       "8915          KTUR    XTUR  1413         49   TUST2  \n",
       "9203          KARS    XARS  1365          2   ARST2  \n",
       "9491          KWVS    XWVS  1390         27   WLST2  \n",
       "9779          KTAT    XTAT  1415         51   TTSN5  \n",
       "10067         KMUS    XMUS  1373         11   MUST2  \n",
       "10355         KPFS    XPFS  1398         34   PFST2  \n",
       "10643         KTIS    XTIS  1385         22   TLST2  \n",
       "10931         KWOS    XWOS  1411         47   WOST2  \n",
       "11219         KNOR    XNOR  1416         52   NORT2  \n",
       "11507         KPAD    XPAD  1400         36   PADT2  \n",
       "11795         KSE1    XSE1  1422         58   SEST2  \n",
       "12083         KOES    XOES    18         14   OEST2  \n",
       "12371         KST1    XST1  1419         55   GCMT2  \n",
       "13763         KGU1    XGU1  1424         60   PSST2  \n",
       "14099         KGDS    XGDS  1407         43   GDST2  \n",
       "14387         KDR1    XDR1  1417         53   DORN5  \n",
       "14675         KMCS    XMCS  1408         44   MCST2  \n",
       "14963         KSDS    XSDS  1395         29   SDST2  \n",
       "15251         KCES    XCES  1399         35   CEST2  \n",
       "15539         KSVS    XSVS  1386         23   STST2  \n",
       "15827         KCXS    XCXS  1414         50   CXST2  \n",
       "16115         KFLS    XFLS  1371          8   FLST2  \n",
       "16403         KGHS    XGHS    22         28   GHST2  \n",
       "16691         KMES    XMES  1409         38   MEST2  \n",
       "16979         KDVS    XDVS  1403         45   DVST2  \n",
       "17267         KANS    XANS  1412         48   AWST2  \n",
       "\n",
       "[56 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/merge_texas.py\n",
    "\n",
    "\n",
    "# Cell content replaced by load magic replacement.\n",
    "# Find common time between TX and OK data\n",
    "tx_data = pd.merge(tx_data, tx_stations, left_on='Station_ID', right_on='Logger ID')\n",
    "tx_one_time = tx_data[tx_data['Time'] == '2019-3-22 23:00']\n",
    "tx_one_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"plot\"></a>\n",
    "# Creating a Station Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to plot temperature, dewpoint, and wind barbs. Given our data from the two mesonets, do we have what we need? If not, use MetPy to calculate what you need!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import metpy.calc as mpcalc\n",
    "from metpy.units import units\n",
    "\n",
    "# Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/miniconda/envs/unidata/lib/python3.7/site-packages/metpy/xarray.py:677: MetpyDeprecationWarning: The dewpoint_rh function was deprecated in version 0.12. This function has been renamed dewpoint_from_relative_humidity.\n",
      "  return func(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# %load solutions/data_conversion.py\n",
    "\n",
    "\n",
    "# Cell content replaced by load magic replacement.\n",
    "ok_dewpoint = mpcalc.dewpoint_rh(ok_data['TAIR'].values * units.degC, ok_data['RELH'].values * units.percent)\n",
    "ok_u, ok_v = mpcalc.wind_components(ok_data['WSPD'].values * units.mph, ok_data['WDIR'].values * units.degrees)\n",
    "tx_u, tx_v = mpcalc.wind_components(tx_one_time['10m_scalar_wind_speed'].values * units.mph, tx_one_time['10m_wind_direction'].values * units.degrees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's make a Station Plot with our data using MetPy and CartoPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from metpy.plots import StationPlot\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up a plot with map features\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "proj = ccrs.Stereographic(central_longitude=-100, central_latitude=35)\n",
    "ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
    "ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='black')\n",
    "ax.gridlines()\n",
    "\n",
    "\n",
    "# Create a station plot pointing to an Axes to draw on as well as the location of points\n",
    "stationplot = StationPlot(ax, ok_data['LON'].values, ok_data['LAT'].values, transform=ccrs.PlateCarree(),\n",
    "                          fontsize=10)\n",
    "stationplot.plot_parameter('NW', ok_data['TAIR'], color='red')\n",
    "stationplot.plot_parameter('SW', ok_dewpoint, color='green')\n",
    "stationplot.plot_barb(ok_u, ok_v)\n",
    "\n",
    "# Texas Data\n",
    "stationplot = StationPlot(ax, tx_one_time['Long'].values, tx_one_time['Lat'].values, transform=ccrs.PlateCarree(),\n",
    "                          fontsize=10)\n",
    "stationplot.plot_parameter('NW', tx_one_time['2m_temperature'], color='red')\n",
    "stationplot.plot_parameter('SW', tx_one_time['dewpoint'], color='green')\n",
    "stationplot.plot_barb(tx_u, tx_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an informative plot, but is rather crowded. Using MetPy's `reduce_point_density` function, try cleaning up this plot to something that would be presentable/publishable. This function will return a mask, which you'll apply to all arrays in the plotting commands to filter down the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Oklahoma\n",
    "xy = proj.transform_points(ccrs.PlateCarree(), ok_data['LON'].values, ok_data['LAT'].values)\n",
    "# Reduce point density so that there's only one point within a 50km circle\n",
    "ok_mask = mpcalc.reduce_point_density(xy, 50000)\n",
    "\n",
    "# Texas\n",
    "\n",
    "# Your code here\n",
    "\n",
    "# Plot\n",
    "\n",
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load solutions/reduce_and_plot.py\n",
    "\n",
    "\n",
    "# Cell content replaced by load magic replacement.\n",
    "xy = proj.transform_points(ccrs.PlateCarree(), tx_one_time['Long'].values, tx_one_time['Lat'].values)\n",
    "tx_mask = mpcalc.reduce_point_density(xy, 50000)\n",
    "\n",
    "#Plot\n",
    "\n",
    "# Set up a plot with map features\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "proj = ccrs.Stereographic(central_longitude=-100, central_latitude=35)\n",
    "ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
    "ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='black')\n",
    "ax.gridlines()\n",
    "\n",
    "# Create a station plot pointing to an Axes to draw on as well as the location of points\n",
    "stationplot = StationPlot(ax, ok_data['LON'].values[ok_mask], ok_data['LAT'].values[ok_mask], transform=ccrs.PlateCarree(),\n",
    "                          fontsize=10)\n",
    "stationplot.plot_parameter('NW', ok_data['TAIR'][ok_mask], color='red')\n",
    "stationplot.plot_parameter('SW', ok_dewpoint[ok_mask], color='green')\n",
    "stationplot.plot_barb(ok_u[ok_mask], ok_v[ok_mask])\n",
    "\n",
    "# Texas Data\n",
    "stationplot = StationPlot(ax, tx_one_time['Long'].values[tx_mask], tx_one_time['Lat'].values[tx_mask], transform=ccrs.PlateCarree(),\n",
    "                          fontsize=10)\n",
    "stationplot.plot_parameter('NW', tx_one_time['2m_temperature'][tx_mask], color='red')\n",
    "stationplot.plot_parameter('SW', tx_one_time['dewpoint'][tx_mask], color='green')\n",
    "stationplot.plot_barb(tx_u[tx_mask], tx_v[tx_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"timeseries\"></a>\n",
    "# Creating Time Series for Stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want to take data from all times from a single station to make a time series (or meteogram) plot? How can we easily do that with Pandas without having to aggregate the data by hand? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2019-03-22 06:30:00\n",
      "1    2019-03-22 07:30:00\n",
      "2    2019-03-22 08:30:00\n",
      "3    2019-03-22 09:30:00\n",
      "4    2019-03-22 10:30:00\n",
      "5    2019-03-22 11:30:00\n",
      "6    2019-03-22 12:30:00\n",
      "7    2019-03-22 13:30:00\n",
      "8    2019-03-22 14:30:00\n",
      "9    2019-03-22 15:30:00\n",
      "10   2019-03-22 16:30:00\n",
      "11   2019-03-22 17:30:00\n",
      "12   2019-03-22 18:30:00\n",
      "13   2019-03-22 19:30:00\n",
      "14   2019-03-22 20:30:00\n",
      "Name: Time, dtype: datetime64[ns]\n",
      "Time\n",
      "2019-03-22 06:00:00   2019-03-22 06:00:00\n",
      "2019-03-22 06:05:00   2019-03-22 06:05:00\n",
      "2019-03-22 06:10:00   2019-03-22 06:10:00\n",
      "2019-03-22 06:15:00   2019-03-22 06:15:00\n",
      "2019-03-22 06:20:00   2019-03-22 06:20:00\n",
      "                              ...        \n",
      "2019-03-22 19:40:00   2019-03-22 19:40:00\n",
      "2019-03-22 19:45:00   2019-03-22 19:45:00\n",
      "2019-03-22 19:50:00   2019-03-22 19:50:00\n",
      "2019-03-22 19:55:00   2019-03-22 19:55:00\n",
      "2019-03-22 20:00:00   2019-03-22 20:00:00\n",
      "Name: Time, Length: 169, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Select daylight hours\n",
    "tx_daytime = tx_data[(tx_data['Time'] >= '2019-03-22 06:00') & (tx_data['Time'] <= '2019-03-22 20:00')]\n",
    "\n",
    "# Create sub-tables for each station\n",
    "tx_grp = tx_daytime.groupby('ID')\n",
    "\n",
    "# Get data from station DIMM\n",
    "station_data = tx_grp.get_group('DIMM')\n",
    "\n",
    "# Create hourly averaged data\n",
    "# time_bins = pd.cut(station_data['Time'], np.arange(600, 2100, 100))\n",
    "# xarray has groupby_bins, but pandas has cut\n",
    "station_data.index=station_data['Time']\n",
    "station_hourly = station_data.resample('H')\n",
    "\n",
    "\n",
    "# station_hourly = station_data.groupby(time_bins)\n",
    "station_hourly_mean = station_hourly.mean()\n",
    "station_hourly_mean = station_hourly_mean.reset_index() # no longer index by time so that we get it back as a regular variable.\n",
    "\n",
    "# The times are reported at the beginning of the interval, but really represent \n",
    "# the mean symmetric about the half hour. Let's fix that.\n",
    "# from datetime import timedelta timedelta(minutes=30) #\n",
    "station_hourly_mean['Time'] += pd.to_timedelta(30, 'minutes')\n",
    "print(station_hourly_mean['Time'])\n",
    "print(station_data['Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the data above to make a time series plot of the instantaneous data and the hourly averaged data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a2d658e90>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load solutions/mesonet_timeseries.py\n",
    "\n",
    "\n",
    "# Cell content replaced by load magic replacement.\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax= fig.add_subplot(111)\n",
    "ax.plot(station_hourly_mean['Time'], station_hourly_mean['solar_radiation'])\n",
    "ax.plot(station_data['Time'], station_data['solar_radiation'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}