emiliom/flowpilot_db_access.ipynb

## flowpilot_db_access.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb70c83a-7a41-43bd-87ce-9b4fa66b18e4",
   "metadata": {},
   "source": [
    "# Opening and merging FlowPilot tables with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de1b15ae-db4b-4efc-a298-3d897d835590",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28d4c515-22f2-4a15-a374-11433b7c510a",
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlite3.connect(\"/my/directory/path/flowpilot.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b4b41ca-974f-4ba0-bbde-63fdd7ffc78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hindcast_df = pd.read_sql_query(\"SELECT * from hindcast\", con)\n",
    "float_mission_df = pd.read_sql_query(\"SELECT * from float_mission\", con)\n",
    "float_data_file_df = pd.read_sql_query(\"SELECT * from float_data_file\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e1f82d2-c1c1-4231-9fdf-87f3b75b2f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>file_name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Float_0_00001.nc</td>\n",
       "      <td>1</td>\n",
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       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Float_1_00001.nc</td>\n",
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       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Float_2_00001.nc</td>\n",
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       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Float_3_00001.nc</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Float_2_00002.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   record_id         file_name  float_mission_record_id dive_record_id   \n",
       "0          1  Float_0_00001.nc                        1           None  \\\n",
       "1          2  Float_1_00001.nc                        2           None   \n",
       "2          3  Float_2_00001.nc                        3           None   \n",
       "3          4  Float_3_00001.nc                        4           None   \n",
       "4          5  Float_2_00002.nc                        3           None   \n",
       "\n",
       "  parsed_file_text     file_time  \n",
       "0             None  1.685574e+09  \n",
       "1             None  1.685574e+09  \n",
       "2             None  1.685574e+09  \n",
       "3             None  1.685574e+09  \n",
       "4             None  1.685574e+09  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_data_file_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eef47140-7140-4e49-a566-332cab5ab745",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>float_mission_record_id</th>\n",
       "      <th>dive_num</th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_id</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>distance</th>\n",
       "      <th>bearing</th>\n",
       "      <th>speed</th>\n",
       "      <th>distance_error</th>\n",
       "      <th>bearing_error</th>\n",
       "      <th>speed_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>70.337215</td>\n",
       "      <td>-12.491143</td>\n",
       "      <td>1002.827621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_single</td>\n",
       "      <td>Float_2_0001_single_obsvel.nc</td>\n",
       "      <td>4.490214</td>\n",
       "      <td>140.595164</td>\n",
       "      <td>66.529149</td>\n",
       "      <td>-5.002446</td>\n",
       "      <td>948.534402</td>\n",
       "      <td>62.606564</td>\n",
       "      <td>-5.196565</td>\n",
       "      <td>892.608440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_multi</td>\n",
       "      <td>Float_2_0001_multiple_obsvel.nc</td>\n",
       "      <td>4.561072</td>\n",
       "      <td>139.971657</td>\n",
       "      <td>3.478011</td>\n",
       "      <td>143.422226</td>\n",
       "      <td>49.587488</td>\n",
       "      <td>7.071406</td>\n",
       "      <td>161.845061</td>\n",
       "      <td>100.820052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.301574</td>\n",
       "      <td>140.615653</td>\n",
       "      <td>70.735653</td>\n",
       "      <td>-12.693107</td>\n",
       "      <td>70.539710</td>\n",
       "      <td>15.560581</td>\n",
       "      <td>-46.259580</td>\n",
       "      <td>15.517477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_single</td>\n",
       "      <td>Float_0_0001_single_obsvel.nc</td>\n",
       "      <td>4.382540</td>\n",
       "      <td>140.563268</td>\n",
       "      <td>63.537511</td>\n",
       "      <td>-5.913183</td>\n",
       "      <td>63.361507</td>\n",
       "      <td>5.439028</td>\n",
       "      <td>-24.381205</td>\n",
       "      <td>5.423962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_multi</td>\n",
       "      <td>Float_0_0001_multiple_obsvel.nc</td>\n",
       "      <td>4.487508</td>\n",
       "      <td>139.940752</td>\n",
       "      <td>7.712325</td>\n",
       "      <td>138.421196</td>\n",
       "      <td>7.690962</td>\n",
       "      <td>64.701518</td>\n",
       "      <td>171.629584</td>\n",
       "      <td>64.522290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.308863</td>\n",
       "      <td>140.716989</td>\n",
       "      <td>70.663741</td>\n",
       "      <td>-11.958221</td>\n",
       "      <td>70.467997</td>\n",
       "      <td>15.124327</td>\n",
       "      <td>-38.473893</td>\n",
       "      <td>15.082431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_single</td>\n",
       "      <td>Float_1_0001_single_obsvel.nc</td>\n",
       "      <td>4.376060</td>\n",
       "      <td>140.665062</td>\n",
       "      <td>63.778939</td>\n",
       "      <td>-6.458907</td>\n",
       "      <td>63.602267</td>\n",
       "      <td>6.389508</td>\n",
       "      <td>-17.702030</td>\n",
       "      <td>6.371808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>obsvel_multi</td>\n",
       "      <td>Float_1_0001_multiple_obsvel.nc</td>\n",
       "      <td>4.451160</td>\n",
       "      <td>140.050742</td>\n",
       "      <td>4.830870</td>\n",
       "      <td>165.977353</td>\n",
       "      <td>4.817488</td>\n",
       "      <td>62.303140</td>\n",
       "      <td>174.101904</td>\n",
       "      <td>62.130556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.409999</td>\n",
       "      <td>140.757470</td>\n",
       "      <td>74.631405</td>\n",
       "      <td>-11.320347</td>\n",
       "      <td>74.424670</td>\n",
       "      <td>22.728412</td>\n",
       "      <td>0.960672</td>\n",
       "      <td>22.665452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   record_id  float_mission_record_id  dive_num                    model_name   \n",
       "0          1                        3         2  hycom_glbv0.08_expt_53.0_wnp  \\\n",
       "1          2                        3         2                 obsvel_single   \n",
       "2          3                        3         2                  obsvel_multi   \n",
       "3          4                        1         2  hycom_glbv0.08_expt_53.0_wnp   \n",
       "4          5                        1         2                 obsvel_single   \n",
       "5          6                        1         2                  obsvel_multi   \n",
       "6          7                        2         2  hycom_glbv0.08_expt_53.0_wnp   \n",
       "7          8                        2         2                 obsvel_single   \n",
       "8          9                        2         2                  obsvel_multi   \n",
       "9         10                        4         2  hycom_glbv0.08_expt_53.0_wnp   \n",
       "\n",
       "                          model_id   end_lat     end_lon   distance   \n",
       "0                       2015010112  4.404336  140.652207  74.202785  \\\n",
       "1    Float_2_0001_single_obsvel.nc  4.490214  140.595164  66.529149   \n",
       "2  Float_2_0001_multiple_obsvel.nc  4.561072  139.971657   3.478011   \n",
       "3                       2015010112  4.301574  140.615653  70.735653   \n",
       "4    Float_0_0001_single_obsvel.nc  4.382540  140.563268  63.537511   \n",
       "5  Float_0_0001_multiple_obsvel.nc  4.487508  139.940752   7.712325   \n",
       "6                       2015010112  4.308863  140.716989  70.663741   \n",
       "7    Float_1_0001_single_obsvel.nc  4.376060  140.665062  63.778939   \n",
       "8  Float_1_0001_multiple_obsvel.nc  4.451160  140.050742   4.830870   \n",
       "9                       2015010112  4.409999  140.757470  74.631405   \n",
       "\n",
       "      bearing        speed  distance_error  bearing_error  speed_error  \n",
       "0  -11.933926  1057.940691       70.337215     -12.491143  1002.827621  \n",
       "1   -5.002446   948.534402       62.606564      -5.196565   892.608440  \n",
       "2  143.422226    49.587488        7.071406     161.845061   100.820052  \n",
       "3  -12.693107    70.539710       15.560581     -46.259580    15.517477  \n",
       "4   -5.913183    63.361507        5.439028     -24.381205     5.423962  \n",
       "5  138.421196     7.690962       64.701518     171.629584    64.522290  \n",
       "6  -11.958221    70.467997       15.124327     -38.473893    15.082431  \n",
       "7   -6.458907    63.602267        6.389508     -17.702030     6.371808  \n",
       "8  165.977353     4.817488       62.303140     174.101904    62.130556  \n",
       "9  -11.320347    74.424670       22.728412       0.960672    22.665452  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hindcast_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6215e666-22e8-43b6-a3c0-9c46e94a6297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 265 entries, 0 to 264\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   record_id                265 non-null    int64  \n",
      " 1   float_mission_record_id  265 non-null    int64  \n",
      " 2   dive_num                 265 non-null    int64  \n",
      " 3   model_name               265 non-null    object \n",
      " 4   model_id                 265 non-null    object \n",
      " 5   end_lat                  265 non-null    float64\n",
      " 6   end_lon                  265 non-null    float64\n",
      " 7   distance                 265 non-null    float64\n",
      " 8   bearing                  265 non-null    float64\n",
      " 9   speed                    265 non-null    float64\n",
      " 10  distance_error           265 non-null    float64\n",
      " 11  bearing_error            265 non-null    float64\n",
      " 12  speed_error              265 non-null    float64\n",
      "dtypes: float64(8), int64(3), object(2)\n",
      "memory usage: 27.0+ KB\n"
     ]
    }
   ],
   "source": [
    "hindcast_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "134c345a-c243-4871-a6a6-f9b286537080",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(ncols=2, figsize=(14,5))\n",
    "hindcast_df.plot.scatter(ax=ax[0], x='end_lon', y='end_lat', c='bearing')\n",
    "hindcast_df.plot.scatter(ax=ax[1], x='distance', y='speed');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3693ec-7d08-43bd-b994-01daf015e32d",
   "metadata": {},
   "source": [
    "## Merge the three tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f001d87c-1578-4fc4-bfde-53ee9633fc1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = (\n",
    "    hindcast_df\n",
    "    .merge(float_mission_df, left_on='float_mission_record_id', right_on='record_id')\n",
    "    .merge(float_data_file_df, left_on='record_id_y', right_on='float_mission_record_id')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "808d99a6-28eb-4292-9b08-a281a9d09fa0",
   "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>record_id_x</th>\n",
       "      <th>float_mission_record_id_x</th>\n",
       "      <th>dive_num</th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_id</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>distance</th>\n",
       "      <th>bearing</th>\n",
       "      <th>speed</th>\n",
       "      <th>...</th>\n",
       "      <th>speed_error</th>\n",
       "      <th>record_id_y</th>\n",
       "      <th>float_record_id</th>\n",
       "      <th>mission_record_id</th>\n",
       "      <th>record_id</th>\n",
       "      <th>file_name</th>\n",
       "      <th>float_mission_record_id_y</th>\n",
       "      <th>dive_record_id</th>\n",
       "      <th>parsed_file_text</th>\n",
       "      <th>file_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Float_2_00001.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Float_2_00002.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Float_2_00003.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Float_2_00004.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>Float_2_00005.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Float_2_00006.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Float_2_00007.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Float_2_00008.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>Float_2_00009.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>hycom_glbv0.08_expt_53.0_wnp</td>\n",
       "      <td>2015010112</td>\n",
       "      <td>4.404336</td>\n",
       "      <td>140.652207</td>\n",
       "      <td>74.202785</td>\n",
       "      <td>-11.933926</td>\n",
       "      <td>1057.940691</td>\n",
       "      <td>...</td>\n",
       "      <td>1002.827621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>Float_2_00010.nc</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.685574e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   record_id_x  float_mission_record_id_x  dive_num   \n",
       "0            1                          3         2  \\\n",
       "1            1                          3         2   \n",
       "2            1                          3         2   \n",
       "3            1                          3         2   \n",
       "4            1                          3         2   \n",
       "5            1                          3         2   \n",
       "6            1                          3         2   \n",
       "7            1                          3         2   \n",
       "8            1                          3         2   \n",
       "9            1                          3         2   \n",
       "\n",
       "                     model_name    model_id   end_lat     end_lon   distance   \n",
       "0  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785  \\\n",
       "1  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "2  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "3  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "4  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "5  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "6  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "7  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "8  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "9  hycom_glbv0.08_expt_53.0_wnp  2015010112  4.404336  140.652207  74.202785   \n",
       "\n",
       "     bearing        speed  ...  speed_error  record_id_y  float_record_id   \n",
       "0 -11.933926  1057.940691  ...  1002.827621            3                3  \\\n",
       "1 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "2 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "3 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "4 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "5 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "6 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "7 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "8 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "9 -11.933926  1057.940691  ...  1002.827621            3                3   \n",
       "\n",
       "   mission_record_id  record_id         file_name  float_mission_record_id_y   \n",
       "0                  1          3  Float_2_00001.nc                          3  \\\n",
       "1                  1          5  Float_2_00002.nc                          3   \n",
       "2                  1          9  Float_2_00003.nc                          3   \n",
       "3                  1         11  Float_2_00004.nc                          3   \n",
       "4                  1         15  Float_2_00005.nc                          3   \n",
       "5                  1         17  Float_2_00006.nc                          3   \n",
       "6                  1         21  Float_2_00007.nc                          3   \n",
       "7                  1         23  Float_2_00008.nc                          3   \n",
       "8                  1         27  Float_2_00009.nc                          3   \n",
       "9                  1         29  Float_2_00010.nc                          3   \n",
       "\n",
       "  dive_record_id  parsed_file_text     file_time  \n",
       "0           None              None  1.685574e+09  \n",
       "1           None              None  1.685574e+09  \n",
       "2           None              None  1.685574e+09  \n",
       "3           None              None  1.685574e+09  \n",
       "4           None              None  1.685574e+09  \n",
       "5           None              None  1.685574e+09  \n",
       "6           None              None  1.685574e+09  \n",
       "7           None              None  1.685574e+09  \n",
       "8           None              None  1.685574e+09  \n",
       "9           None              None  1.685574e+09  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6759b10c-11c4-4ac4-8466-10273c170880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6930"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "979837f3-ace8-4073-beb2-6a7d4915b3f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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