From 1c54ea57dbf4c788cdb14bbded0db0f7f7a89a69 Mon Sep 17 00:00:00 2001
From: bearecinos <recinos@uni-bremen.de>
Date: Thu, 10 Dec 2020 10:37:27 +0000
Subject: [PATCH] jupyter notebooks

---
 docs/.gitignore                               |    2 -
 docs/figures/c99differences.png               |  Bin 0 -> 136327 bytes
 docs/figures/code_tables_schema_one.png       |  Bin 0 -> 30072 bytes
 docs/figures/code_tables_schema_two.png       |  Bin 0 -> 76012 bytes
 docs/figures/deckschema.png                   |  Bin 0 -> 8154 bytes
 docs/figures/elements.png                     |  Bin 0 -> 76413 bytes
 docs/figures/new_schema.png                   |  Bin 0 -> 34918 bytes
 docs/figures/schema.png                       |  Bin 0 -> 13092 bytes
 .../create_data_model-checkpoint.ipynb        | 1851 ++++++++++++++
 .../mdf_reader_test_overview-checkpoint.ipynb | 2179 +++++++++++++++++
 docs/notebooks/create_data_model.ipynb        | 1851 ++++++++++++++
 docs/notebooks/mdf_reader_test_overview.ipynb | 2179 +++++++++++++++++
 12 files changed, 8060 insertions(+), 2 deletions(-)
 create mode 100644 docs/figures/c99differences.png
 create mode 100644 docs/figures/code_tables_schema_one.png
 create mode 100644 docs/figures/code_tables_schema_two.png
 create mode 100644 docs/figures/deckschema.png
 create mode 100644 docs/figures/elements.png
 create mode 100644 docs/figures/new_schema.png
 create mode 100644 docs/figures/schema.png
 create mode 100644 docs/notebooks/.ipynb_checkpoints/create_data_model-checkpoint.ipynb
 create mode 100644 docs/notebooks/.ipynb_checkpoints/mdf_reader_test_overview-checkpoint.ipynb
 create mode 100644 docs/notebooks/create_data_model.ipynb
 create mode 100644 docs/notebooks/mdf_reader_test_overview.ipynb

diff --git a/docs/.gitignore b/docs/.gitignore
index 000fc41..91f9cd9 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,4 +1,2 @@
-*
-*/
 !.gitignore
 !User_manual.docx
diff --git a/docs/figures/c99differences.png b/docs/figures/c99differences.png
new file mode 100644
index 0000000000000000000000000000000000000000..3b093023b6784d363c2d4f58011431c9c82c5436
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diff --git a/docs/notebooks/.ipynb_checkpoints/create_data_model-checkpoint.ipynb b/docs/notebooks/.ipynb_checkpoints/create_data_model-checkpoint.ipynb
new file mode 100644
index 0000000..b1d8169
--- /dev/null
+++ b/docs/notebooks/.ipynb_checkpoints/create_data_model-checkpoint.ipynb
@@ -0,0 +1,1851 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a data model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will create and apply a new **data model/schema** to a raw `.imma` file, using the [mdf_reader](https://git.noc.ac.uk/iregon/mdf_reader) tool. We will add supplemental metadata to the basic `imma1` data model and display supplemental data as a pandas dataframe. \n",
+    "\n",
+    "Lets first import all the tools that we will need. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:36,906 - root - INFO - init basic configure of logging success\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('/home/bea/')\n",
+    "import mdf_reader\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mdf_reader` tool comes with data model templates of `.json` files, that we can use to build our models. For more information see the following [manual](https://git.noc.ac.uk/iregon/mdf_reader/-/blob/master/docs/User_manual.docx)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fixed_width_complex_exc',\n",
+       " 'delimited_sections',\n",
+       " 'delimited_basic',\n",
+       " 'fixed_width_complex_opt',\n",
+       " 'fixed_width_sections',\n",
+       " 'fixed_width_basic']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_data_models = '/home/bea/c3s_work/mdf_reader/data_models/lib/'\n",
+    "\n",
+    "template_names = mdf_reader.schemas.templates()\n",
+    "template_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to the manual, ICOADS data stored with the [IMMA format](https://icoads.noaa.gov/e-doc/imma/R3.0-imma1.pdf) represents a complex data model, since the data includes blocks of sections which are exclusive to certain DCK's (e.g. data coming from the NOAA National Climatic Data Center (NCDC) TD-11 formats). Most of the ICOADS data however will need a **schema** based on the `imma1.json` format, which is based on the template: `*_complex_opt.json`.\n",
+    "\n",
+    "Lets try to build our own **schema** based on this template for a new dck. In this notebook we will organise the data and metadata from the **US Maury collection** that corresponds to `source/dck 069-701`.\n",
+    "\n",
+    "1. First lets read a raw `.imma` file from dck 701 as an example, for a subset of the data collected in April/1845. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:38,691 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 10:11:38,702 - root - INFO - EXTRACTING DATA FROM MODEL: imma1\n",
+      "2020-12-10 10:11:38,704 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 10:11:38,731 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 10:11:38,737 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 10:11:38,740 - root - INFO - 1000 ...\n",
+      "2020-12-10 10:11:38,825 - root - INFO - done\n",
+      "2020-12-10 10:11:38,827 - root - INFO - 211000 ...\n",
+      "2020-12-10 10:11:38,907 - root - INFO - done\n",
+      "2020-12-10 10:11:38,908 - root - INFO - 29211000 ...\n",
+      "2020-12-10 10:11:38,972 - root - INFO - done\n",
+      "2020-12-10 10:11:38,973 - root - INFO - 2929211000 ...\n",
+      "2020-12-10 10:11:38,985 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:41,663 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE')\n",
+      "2020-12-10 10:11:41,664 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section c99\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:47,198 - root - INFO - Wrapping output....\n",
+      "2020-12-10 10:11:47,820 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "schema = 'imma1'\n",
+    "\n",
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/069-701_1845-04_subset.imma'\n",
+    "\n",
+    "data_raw = mdf_reader.read(data_file_path, data_model = schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>c99</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>99 0 300850118450401  5404N 2354W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>99 0 810348118450401  4836N 2330W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>99 0 370731118450401  4643N15147W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>99 0 260597118450401  4454N 3015W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>99 0 250661118450401  4356N 2220W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>99 0 210803118450401  4311N 3936W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>99 0 200280118450401  4221N 6524W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>99 0 780477118450401  4123N 4554W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>99 0 780253118450401  3753N 7340W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>99 0 870009118450401  3720N 7346W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>99 0 130116118450401  3650N                   ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>99 0 250791118450401  3620N16445W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>99 0 730765118450401  3521N 5940W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>99 0 350695118450401  3514N15506E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>99 0 860756118450401  3443N16036W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>99 0 220807118450401  3411N     E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>99 0 370279118450401  3315N17945W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>99 0 420271118450401  3030N 6638W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>99 0 780545118450401  2940N 7945W       10300 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>99 0 740832118450401  2832N 1612W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>99 0 350731118450401  2810N 7254W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>99 0 780005118450401  2800N16002W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>99 0 760672118450401  2751N17814W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>99 0 090323118450401  2719N 2208W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>99 0 210767118450401  2632N 3451W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>99 0 260564118450401  2557N17625E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>99 0 760474118450401  2552N 6510W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>99 0 780288118450401  2449N                   ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>99 0 400600118450401      N     W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>99 0 130004118450401  2350N16140W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3656</th>\n",
+       "      <td>99 0 200238118450430  1558S 3058W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3657</th>\n",
+       "      <td>99 0 370089118450430  1629S 3028W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3658</th>\n",
+       "      <td>99 0 220364118450430  1700S 7512W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3659</th>\n",
+       "      <td>99 0 180453118450430  1742S15648W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3660</th>\n",
+       "      <td>99 0 130626118450430  2228S10950E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3661</th>\n",
+       "      <td>99 0 340310118450430  2312S11030E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3662</th>\n",
+       "      <td>99 0 830130118450430  2454S  442E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3663</th>\n",
+       "      <td>99 0 760484118450430  2512S  300W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3664</th>\n",
+       "      <td>99 0 400706118450430  2626S 5502E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3665</th>\n",
+       "      <td>99 0 200360118450430  2701S  840E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3666</th>\n",
+       "      <td>99 0 180254118450430  2711S  312E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3667</th>\n",
+       "      <td>99 0 090235118450430  2733S 3226W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3668</th>\n",
+       "      <td>99 0 800105118450430  2748S 2905W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3669</th>\n",
+       "      <td>99 0 220774118450430      S     E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3670</th>\n",
+       "      <td>99 0 780336118450430  3122S 4950W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3671</th>\n",
+       "      <td>99 0 190828118450430  3329S 1603E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3672</th>\n",
+       "      <td>99 0 280269118450430  3500S17437E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3673</th>\n",
+       "      <td>99 0 830234118450430  3617S 7651WW      10100 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3674</th>\n",
+       "      <td>99 0 100106118450430  3707S 5031W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3675</th>\n",
+       "      <td>99 0 370187118450430  3817S  205E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3676</th>\n",
+       "      <td>99 0 250459118450430  3828S  111E             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3677</th>\n",
+       "      <td>99 0 810274118450430  3951S12759W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3678</th>\n",
+       "      <td>99 0 330362118450430  4011S 8240W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3679</th>\n",
+       "      <td>99 0 790099118450430  4307S                   ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3680</th>\n",
+       "      <td>99 0 340809118450430  4405S 5031W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3681</th>\n",
+       "      <td>99 0 130088118450430  4737S                   ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3682</th>\n",
+       "      <td>99 0 060598118450430  5444S 9045W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3683</th>\n",
+       "      <td>99 0 260061118450430  5619S 6744W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3684</th>\n",
+       "      <td>99 0 350904118450430  5620S 7159W             ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3685</th>\n",
+       "      <td>99 0 180361118450431  2940N 6403W             ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3686 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    c99\n",
+       "0     99 0 300850118450401  5404N 2354W             ...\n",
+       "1     99 0 810348118450401  4836N 2330W             ...\n",
+       "2     99 0 370731118450401  4643N15147W             ...\n",
+       "3     99 0 260597118450401  4454N 3015W             ...\n",
+       "4     99 0 250661118450401  4356N 2220W             ...\n",
+       "5     99 0 210803118450401  4311N 3936W             ...\n",
+       "6     99 0 200280118450401  4221N 6524W             ...\n",
+       "7     99 0 780477118450401  4123N 4554W             ...\n",
+       "8     99 0 780253118450401  3753N 7340W             ...\n",
+       "9     99 0 870009118450401  3720N 7346W             ...\n",
+       "10    99 0 130116118450401  3650N                   ...\n",
+       "11    99 0 250791118450401  3620N16445W             ...\n",
+       "12    99 0 730765118450401  3521N 5940W             ...\n",
+       "13    99 0 350695118450401  3514N15506E             ...\n",
+       "14    99 0 860756118450401  3443N16036W             ...\n",
+       "15    99 0 220807118450401  3411N     E             ...\n",
+       "16    99 0 370279118450401  3315N17945W             ...\n",
+       "17    99 0 420271118450401  3030N 6638W             ...\n",
+       "18    99 0 780545118450401  2940N 7945W       10300 ...\n",
+       "19    99 0 740832118450401  2832N 1612W             ...\n",
+       "20    99 0 350731118450401  2810N 7254W             ...\n",
+       "21    99 0 780005118450401  2800N16002W             ...\n",
+       "22    99 0 760672118450401  2751N17814W             ...\n",
+       "23    99 0 090323118450401  2719N 2208W             ...\n",
+       "24    99 0 210767118450401  2632N 3451W             ...\n",
+       "25    99 0 260564118450401  2557N17625E             ...\n",
+       "26    99 0 760474118450401  2552N 6510W             ...\n",
+       "27    99 0 780288118450401  2449N                   ...\n",
+       "28    99 0 400600118450401      N     W             ...\n",
+       "29    99 0 130004118450401  2350N16140W             ...\n",
+       "...                                                 ...\n",
+       "3656  99 0 200238118450430  1558S 3058W             ...\n",
+       "3657  99 0 370089118450430  1629S 3028W             ...\n",
+       "3658  99 0 220364118450430  1700S 7512W             ...\n",
+       "3659  99 0 180453118450430  1742S15648W             ...\n",
+       "3660  99 0 130626118450430  2228S10950E             ...\n",
+       "3661  99 0 340310118450430  2312S11030E             ...\n",
+       "3662  99 0 830130118450430  2454S  442E             ...\n",
+       "3663  99 0 760484118450430  2512S  300W             ...\n",
+       "3664  99 0 400706118450430  2626S 5502E             ...\n",
+       "3665  99 0 200360118450430  2701S  840E             ...\n",
+       "3666  99 0 180254118450430  2711S  312E             ...\n",
+       "3667  99 0 090235118450430  2733S 3226W             ...\n",
+       "3668  99 0 800105118450430  2748S 2905W             ...\n",
+       "3669  99 0 220774118450430      S     E             ...\n",
+       "3670  99 0 780336118450430  3122S 4950W             ...\n",
+       "3671  99 0 190828118450430  3329S 1603E             ...\n",
+       "3672  99 0 280269118450430  3500S17437E             ...\n",
+       "3673  99 0 830234118450430  3617S 7651WW      10100 ...\n",
+       "3674  99 0 100106118450430  3707S 5031W             ...\n",
+       "3675  99 0 370187118450430  3817S  205E             ...\n",
+       "3676  99 0 250459118450430  3828S  111E             ...\n",
+       "3677  99 0 810274118450430  3951S12759W             ...\n",
+       "3678  99 0 330362118450430  4011S 8240W             ...\n",
+       "3679  99 0 790099118450430  4307S                   ...\n",
+       "3680  99 0 340809118450430  4405S 5031W             ...\n",
+       "3681  99 0 130088118450430  4737S                   ...\n",
+       "3682  99 0 060598118450430  5444S 9045W             ...\n",
+       "3683  99 0 260061118450430  5619S 6744W             ...\n",
+       "3684  99 0 350904118450430  5620S 7159W             ...\n",
+       "3685  99 0 180361118450431  2940N 6403W             ...\n",
+       "\n",
+       "[3686 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw.data['c99']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `c99` column is a bit messy. Here, we will need to separate the Suplememal Metadata ingestied in ICOADS as an entire string and sort each row out according to the source&dck documentation. \n",
+    "\n",
+    "2. We then need to make a new data model or **schema** to be stored in the library folder of the `mdf_reader`. For this we create a folder with the name `imma1_d701` in the lib directory.\n",
+    "3. Under this folder (`/data_models/lib/imma1_d701`) we will need to add a `.json` file with the same name. This `imma1_d701.json` file will contain all the data model information with instructions on how to subdivide the metadata added to `c99`. The name of the file is `imma1_d701.json` because the data model for this deck is based on the `imma1` template shown above, but the `c99` will be further subdivided into other columns/sections. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_folder = '/home/bea/c3s_work/mdf_reader/data_models/lib/'\n",
+    "model_name = 'imma1_d701'\n",
+    "model_path = os.path.join(path_to_folder, model_name)\n",
+    "model_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Uncomment the following lines to create new data models. This folder is already withing the repository so you dont need to run the lines below. They only serve as a guide for further schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if not os.path.exists(model_path):\n",
+    "#     os.makedirs(model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In that path we will copy the template that we will based our **schema** from. In this case the `imma1` schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import shutil\n",
+    "# shutil.copyfile(os.path.join(path_to_folder, 'imma1/imma1.json'),  os.path.join(model_path, model_name+'.json'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to make a directory called `code_tables` and copy all `code_tables` from the `imma1` folder template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import shutil\n",
+    "# shutil.copytree(os.path.join(path_to_folder, 'imma1/code_tables'), os.path.join(model_path,'code_tables'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We end up with something like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/deckschema.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/code_tables_schema_one.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the key will be to modify the `c99` section of the `imma1_d701.json`. See the highlighted text in the figure below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/c99differences.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `c99_sentinal` section identifies where in the data, we will have a new section. In this case we will have a new section corresponding to Supplemental Metadata. \n",
+    "\n",
+    "In our example this supplemental metadata will come from the documentation of the US Maury collection stored in the [ICOADS website](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_transpec). \n",
+    "\n",
+    "4. We will need to add the metadata information from the website inside that `c99_sentinal` section and create as many sections as the data requires.\n",
+    "\n",
+    ">sentinal: section identifier\n",
+    "applies to: format.fixed_width\n",
+    "is mandatory: it is not mandatory if the section is unique, unique in a parsing_order block, or\n",
+    "part of a sequential parsing_order block.\n",
+    "type: string\n",
+    "comments: the element bearing the sentinal needs to be, additionally, declared in the\n",
+    "elements block\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5. We will have to build additional `.json` files to be saved under the `code_tables` folder of our schema. Each `.json` file inside the `code_tables` are dictionaries that will help decode metadata observations (e.g. wind force scales or weather codes).  For each encoded variable that we add, we will need to add a new `ICOADS.C99_Variable.json` to the **schema**. Files need to be named after the section that they represent, in this case `ICOADS.C99_Variable.json`. See images below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/code_tables_schema_two.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the US Maury collection [ICOADS documentation](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_transpec), we find out that the `c99` for this deck is compose of the following sections:\n",
+    "\n",
+    "- Data\n",
+    "- Header information\n",
+    "- Quality control information (qc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "Data stored in the supplemental attachment consisted of the entire data record\n",
+    "(173 characters); followed by a selection of fields from, or derived from, the\n",
+    "associated header record (through character 241); and selected fields from the\n",
+    "qc file (total 250 characters):\n",
+    "  # Pos.     Total #  Field  Record\n",
+    "    range    of pos.   name    type  Description of field (of derived field)\n",
+    "--- -------  -------  -----  ------  ----------------------------------------\n",
+    "  1 1-7         7     cvoyd    data  voyage number\n",
+    "... ...               ...       ...  ...\n",
+    " 47 172-173     2     cmvq     data  magnetic variation QC indicator\n",
+    " NA 174-175     2     cts2   header  (fr ship type, ctship, according to [5])\n",
+    "  4 176-177     2     cft    header  form type\n",
+    "  5 178-193    16     comm   header  commander (first 16 positions only) [6]\n",
+    "  6 194-217    24     cfr    header  from city\n",
+    "  7 218-241    24     cto    header  to city\n",
+    "  2 242-246     5     qc2    qc      reel sequence number\n",
+    "  5 247-248     2     qc5    qc      day  (local time) (99 indicates missing)\n",
+    "  6 249-250     2     qc6*   qc      hour (local time) (99 indicates missing)\n",
+    "--- -------  -------  -----  ------  ----------------------------------------\n",
+    "* Whenever qc6 was 24, zero was inadvertently written out to the supplemental\n",
+    "attachment.  This resulted from an error in the conversion program, but can\n",
+    "be fixed by interpretation of hour zero as hour 24 of qc5 + 1 (as noted in [2],\n",
+    "qc6 originally ranged 1-24, with 24 signifying hour 0 of the next day.  As\n",
+    "intended, qc5 was included in the supplementary attachment in original form.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the raw data file the information looks like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c99 = data_raw.data['c99']\n",
+    "line = c99.iloc[63] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'99 0 400706118450401   240S 9513ESE     757                                                                 NTW    51 SEXS   50 SW     50                                          201J.S.KIMBALL     SUMATRA                 BOSTON                  20204 199'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "line.values[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then need to divide all this string accoding to the documentation above and the format of the data specified in the [US Maury data docs](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'99 0 '"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sentinal = 5\n",
+    "part_1 = line.values[0][0:5]\n",
+    "part_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'4007061'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cvoyd voyage number = 7\n",
+    "part_2 = line.values[0][5:5+7]\n",
+    "part_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'18450401  '"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# date = 10\n",
+    "part_3 = line.values[0][12:12+10]\n",
+    "part_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6. We build our `.json` file reflecting each data field from the ICOADS documentation as a new section. And add each parameter from the data as a new element. Having a `sentinal` section at the beginning of the `c99` is important since in the `.imma` format, regardless of the source/dck, will have 5 characters that will always be the same. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/new_schema.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To each section we add the corresponding elements/parameters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/elements.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we feed this new data model to the `mdf_reader.read` function. It is important that we save this data model under the right directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:17:41,871 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 10:17:41,877 - root - INFO - EXTRACTING DATA FROM MODEL: /home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701\n",
+      "2020-12-10 10:17:41,880 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 10:17:41,916 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 10:17:41,918 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 10:17:41,919 - root - INFO - 1000 ...\n",
+      "2020-12-10 10:17:41,966 - root - INFO - done\n",
+      "2020-12-10 10:17:41,972 - root - INFO - 211000 ...\n",
+      "2020-12-10 10:17:42,038 - root - INFO - done\n",
+      "2020-12-10 10:17:42,039 - root - INFO - 29211000 ...\n",
+      "2020-12-10 10:17:42,096 - root - INFO - done\n",
+      "2020-12-10 10:17:42,099 - root - INFO - 3029211000 ...\n",
+      "2020-12-10 10:17:42,109 - root - INFO - done\n",
+      "2020-12-10 10:17:42,113 - root - INFO - 303029211000 ...\n",
+      "2020-12-10 10:17:42,138 - root - INFO - done\n",
+      "2020-12-10 10:17:42,139 - root - INFO - 30303029211000 ...\n",
+      "2020-12-10 10:17:42,158 - root - INFO - done\n",
+      "2020-12-10 10:17:42,171 - root - INFO - 3030303029211000 ...\n",
+      "2020-12-10 10:17:42,186 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n",
+      "Reading section c99_sentinal\n",
+      "Reading section c99_data\n",
+      "Reading section c99_header\n",
+      "Reading section c99_qc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:17:46,149 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE'),('c99_data', 'reel_number'),('c99_data', 'frame_number'),('c99_data', 'voyage_sequence'),('c99_data', 'year'),('c99_data', 'month'),('c99_data', 'day'),('c99_data', 'hour'),('c99_data', 'lat_deg_an'),('c99_data', 'lat_min_an'),('c99_data', 'lon_deg_an'),('c99_data', 'lon_min_an'),('c99_data', 'min_drift_coord'),('c99_data', 'attached_thermometer_one'),('c99_data', 'attached_thermometer_two'),('c99_data', 'attached_thermometer_three'),('c99_data', 'air_temperature_one'),('c99_data', 'sea_surface_temperature_one'),('c99_data', 'sea_depth_temperature'),('c99_data', 'air_temperature_two'),('c99_data', 'sea_surface_temperature_two'),('c99_data', 'air_temperature_three'),('c99_data', 'sea_surface_temperature_three')\n",
+      "2020-12-10 10:17:46,149 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n",
+      "2020-12-10 10:17:53,016 - root - INFO - Wrapping output....\n",
+      "2020-12-10 10:17:53,746 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/069-701_1845-04_subset.imma'\n",
+    "\n",
+    "data = mdf_reader.read(data_file_path, data_model_path= model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And magically all the messy string is separated! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">c99_sentinal</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ATTI</th>\n",
+       "      <th>ATTL</th>\n",
+       "      <th>BLK</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_sentinal          \n",
+       "          ATTI ATTL  BLK\n",
+       "0           99    0  NaN\n",
+       "1           99    0  NaN\n",
+       "2           99    0  NaN\n",
+       "3           99    0  NaN\n",
+       "4           99    0  NaN"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pd.options.display.max_columns = None\n",
+    "data.data[[\"c99_sentinal\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The section above is the sentinal section that is the same in all ICOADS dck's/c99 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"56\" halign=\"left\">c99_data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>reel_number</th>\n",
+       "      <th>frame_number</th>\n",
+       "      <th>voyage_sequence</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>lat_deg_an</th>\n",
+       "      <th>lat_min_an</th>\n",
+       "      <th>lat_hemis_an</th>\n",
+       "      <th>lon_deg_an</th>\n",
+       "      <th>lon_min_an</th>\n",
+       "      <th>lon_hemis_an</th>\n",
+       "      <th>current_dir</th>\n",
+       "      <th>current_speed_ind</th>\n",
+       "      <th>current_speed</th>\n",
+       "      <th>min_drift_coord</th>\n",
+       "      <th>period_drift</th>\n",
+       "      <th>mag_var_ind</th>\n",
+       "      <th>mag_var</th>\n",
+       "      <th>baro_obs_time</th>\n",
+       "      <th>baro_pressure_one</th>\n",
+       "      <th>temp_ind</th>\n",
+       "      <th>attached_thermometer_one</th>\n",
+       "      <th>attached_thermometer_two</th>\n",
+       "      <th>attached_thermometer_three</th>\n",
+       "      <th>hour_air_temp_one</th>\n",
+       "      <th>air_temperature_one</th>\n",
+       "      <th>sea_surface_temperature_one</th>\n",
+       "      <th>sea_depth_temperature</th>\n",
+       "      <th>hour_air_temp_two</th>\n",
+       "      <th>air_temperature_two</th>\n",
+       "      <th>sea_surface_temperature_two</th>\n",
+       "      <th>hour_air_temp_three</th>\n",
+       "      <th>air_temperature_three</th>\n",
+       "      <th>sea_surface_temperature_three</th>\n",
+       "      <th>wind_dir_start</th>\n",
+       "      <th>wind_force_start</th>\n",
+       "      <th>wind_info_start</th>\n",
+       "      <th>wind_dir_middle</th>\n",
+       "      <th>wind_force_middle</th>\n",
+       "      <th>wind_info_middle</th>\n",
+       "      <th>wind_dir_later</th>\n",
+       "      <th>wind_force_later</th>\n",
+       "      <th>wind_info_later</th>\n",
+       "      <th>cloud_one</th>\n",
+       "      <th>cloud_dir_one</th>\n",
+       "      <th>cloud_two</th>\n",
+       "      <th>cloud_dir_two</th>\n",
+       "      <th>cloud_three</th>\n",
+       "      <th>cloud_dir_three</th>\n",
+       "      <th>sky_clear</th>\n",
+       "      <th>hour_of_weather</th>\n",
+       "      <th>weather_indic</th>\n",
+       "      <th>weather</th>\n",
+       "      <th>qc_magnetic_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>850</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>N</td>\n",
+       "      <td>23</td>\n",
+       "      <td>54</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>51</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WTWSW</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>81</td>\n",
+       "      <td>348</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48</td>\n",
+       "      <td>36</td>\n",
+       "      <td>N</td>\n",
+       "      <td>23</td>\n",
+       "      <td>30</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2929</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SWXS</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SSW</td>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>N</td>\n",
+       "      <td>151</td>\n",
+       "      <td>47</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WSW</td>\n",
+       "      <td>40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WXN</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SHQ</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26</td>\n",
+       "      <td>597</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>44</td>\n",
+       "      <td>54</td>\n",
+       "      <td>N</td>\n",
+       "      <td>30</td>\n",
+       "      <td>15</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0200W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>W</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NE</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>661</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>43</td>\n",
+       "      <td>56</td>\n",
+       "      <td>N</td>\n",
+       "      <td>22</td>\n",
+       "      <td>20</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SWXW</td>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>W</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>V</td>\n",
+       "      <td>29</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     c99_data                                                               \\\n",
+       "  reel_number frame_number voyage_sequence  year month day hour lat_deg_an   \n",
+       "0          30          850               1  1845     4   1  NaN         54   \n",
+       "1          81          348               1  1845     4   1  NaN         48   \n",
+       "2          37          731               1  1845     4   1  NaN         46   \n",
+       "3          26          597               1  1845     4   1  NaN         44   \n",
+       "4          25          661               1  1845     4   1  NaN         43   \n",
+       "\n",
+       "                                                                          \\\n",
+       "  lat_min_an lat_hemis_an lon_deg_an lon_min_an lon_hemis_an current_dir   \n",
+       "0          4            N         23         54            W         NaN   \n",
+       "1         36            N         23         30            W         NaN   \n",
+       "2         43            N        151         47            W         NaN   \n",
+       "3         54            N         30         15            W         NaN   \n",
+       "4         56            N         22         20            W         NaN   \n",
+       "\n",
+       "                                                                            \\\n",
+       "  current_speed_ind current_speed min_drift_coord period_drift mag_var_ind   \n",
+       "0               NaN           NaN             NaN          NaN         NaN   \n",
+       "1               NaN           NaN             NaN          NaN         NaN   \n",
+       "2               NaN           NaN             NaN          NaN         NaN   \n",
+       "3               NaN           NaN             NaN          NaN           2   \n",
+       "4               NaN           NaN             NaN          NaN         NaN   \n",
+       "\n",
+       "                                                                             \\\n",
+       "  mag_var baro_obs_time baro_pressure_one temp_ind attached_thermometer_one   \n",
+       "0     NaN           NaN               NaN      NaN                      NaN   \n",
+       "1     NaN           NaN              2929        1                      NaN   \n",
+       "2     NaN           NaN               NaN      NaN                      NaN   \n",
+       "3   0200W           NaN               NaN      NaN                      NaN   \n",
+       "4     NaN           NaN               NaN      NaN                      NaN   \n",
+       "\n",
+       "                                                                         \\\n",
+       "  attached_thermometer_two attached_thermometer_three hour_air_temp_one   \n",
+       "0                      NaN                        NaN               NaN   \n",
+       "1                      NaN                        NaN                 9   \n",
+       "2                      NaN                        NaN               NaN   \n",
+       "3                      NaN                        NaN               NaN   \n",
+       "4                      NaN                        NaN               NaN   \n",
+       "\n",
+       "                                                                         \\\n",
+       "  air_temperature_one sea_surface_temperature_one sea_depth_temperature   \n",
+       "0                 NaN                         NaN                   NaN   \n",
+       "1                53.0                        52.0                   NaN   \n",
+       "2                 NaN                         NaN                   NaN   \n",
+       "3                 NaN                         NaN                   NaN   \n",
+       "4                 NaN                         NaN                   NaN   \n",
+       "\n",
+       "                                                                     \\\n",
+       "  hour_air_temp_two air_temperature_two sea_surface_temperature_two   \n",
+       "0               NaN                 NaN                         NaN   \n",
+       "1               NaN                 NaN                         NaN   \n",
+       "2               NaN                 NaN                         NaN   \n",
+       "3               NaN                 NaN                         NaN   \n",
+       "4               NaN                 NaN                         NaN   \n",
+       "\n",
+       "                                                                           \\\n",
+       "  hour_air_temp_three air_temperature_three sea_surface_temperature_three   \n",
+       "0                 NaN                   NaN                           NaN   \n",
+       "1                 NaN                   NaN                           NaN   \n",
+       "2                 NaN                   NaN                           NaN   \n",
+       "3                 NaN                   NaN                           NaN   \n",
+       "4                 NaN                   NaN                           NaN   \n",
+       "\n",
+       "                                                                   \\\n",
+       "  wind_dir_start wind_force_start wind_info_start wind_dir_middle   \n",
+       "0             NW               51             NaN           WTWSW   \n",
+       "1           SWXS               57             NaN             SSW   \n",
+       "2            WSW               40             NaN              NW   \n",
+       "3              W               44             NaN              NE   \n",
+       "4           SWXW               28             NaN               W   \n",
+       "\n",
+       "                                                                      \\\n",
+       "  wind_force_middle wind_info_middle wind_dir_later wind_force_later   \n",
+       "0                44              NaN              S               57   \n",
+       "1                28              NaN             NW               30   \n",
+       "2                44              NaN            WXN               57   \n",
+       "3                57              NaN              N               57   \n",
+       "4                57              NaN              V               29   \n",
+       "\n",
+       "                                                                               \\\n",
+       "  wind_info_later cloud_one cloud_dir_one cloud_two cloud_dir_two cloud_three   \n",
+       "0             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "1             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "2             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "3             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "4             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "\n",
+       "                                                                   \\\n",
+       "  cloud_dir_three sky_clear hour_of_weather weather_indic weather   \n",
+       "0             NaN       NaN             NaN           NaN     NaN   \n",
+       "1             NaN       NaN             NaN           NaN     NaN   \n",
+       "2             NaN       NaN             NaN             1     SHQ   \n",
+       "3             NaN       NaN             NaN           NaN     NaN   \n",
+       "4             NaN       NaN             NaN           NaN     NaN   \n",
+       "\n",
+       "                   \n",
+       "  qc_magnetic_var  \n",
+       "0             NaN  \n",
+       "1             NaN  \n",
+       "2             NaN  \n",
+       "3             NaN  \n",
+       "4             NaN  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_data\"]].head(n=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">c99_header</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>rig</th>\n",
+       "      <th>form_type</th>\n",
+       "      <th>commander</th>\n",
+       "      <th>from_city</th>\n",
+       "      <th>to_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>WM.CALLAGAN</td>\n",
+       "      <td>BOSTON</td>\n",
+       "      <td>ST.PETERSBURG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIVERPOOL</td>\n",
+       "      <td>NEW YORK &amp; RETURN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>01</td>\n",
+       "      <td>CLEMENT NORTON</td>\n",
+       "      <td>LAHAINA</td>\n",
+       "      <td>SAN DIEGO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>R.MCCARRAN</td>\n",
+       "      <td>NEW YORK</td>\n",
+       "      <td>LIVERPOOL &amp; RETURN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>J.P.GANNETT</td>\n",
+       "      <td>LIVERPOOL</td>\n",
+       "      <td>APALACHICOLA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_header                                                         \n",
+       "         rig form_type       commander  from_city             to_city\n",
+       "0          2        01     WM.CALLAGAN     BOSTON       ST.PETERSBURG\n",
+       "1          2        01             NaN  LIVERPOOL   NEW YORK & RETURN\n",
+       "2         37        01  CLEMENT NORTON    LAHAINA           SAN DIEGO\n",
+       "3          2        01      R.MCCARRAN   NEW YORK  LIVERPOOL & RETURN\n",
+       "4          2        01     J.P.GANNETT  LIVERPOOL        APALACHICOLA"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_header\"]].head(n=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">c99_qc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>qc2</th>\n",
+       "      <th>qc5</th>\n",
+       "      <th>qc6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15641</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15524</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17671</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15062</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15516</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_qc        \n",
+       "     qc2 qc5 qc6\n",
+       "0  15641   1  99\n",
+       "1  15524   1  99\n",
+       "2  17671   1  99\n",
+       "3  15062   1  99\n",
+       "4  15516   1  99"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_qc\"]].head(n=5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (c3s)",
+   "language": "python",
+   "name": "c3s"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/notebooks/.ipynb_checkpoints/mdf_reader_test_overview-checkpoint.ipynb b/docs/notebooks/.ipynb_checkpoints/mdf_reader_test_overview-checkpoint.ipynb
new file mode 100644
index 0000000..f54965b
--- /dev/null
+++ b/docs/notebooks/.ipynb_checkpoints/mdf_reader_test_overview-checkpoint.ipynb
@@ -0,0 +1,2179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test and overview of the `mdf_reader` tool "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First clone the [gitlab repository](https://git.noc.ac.uk/brecinosrivas/mdf_reader) and modify the path in `sys.path.append()` with the directory path where you store the `mdf_reader` repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:49:38,429 - root - INFO - init basic configure of logging success\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('/home/bea/')\n",
+    "import mdf_reader\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mdf_reader` is a python3 tool designed to read data files compliant with a user specified [data\n",
+    "model](https://cds.climate.copernicus.eu/toolbox/doc/how-to/15_how_to_understand_the_common_data_model/15_how_to_understand_the_common_data_model.html). \n",
+    "\n",
+    "It was developed with the initial idea to read the [IMMA](https://icoads.noaa.gov/e-doc/imma/R3.0-imma1.pdf) data format, but it was further enhanced to account for other meteorological data formats. \n",
+    "\n",
+    "Lets see an example for a typical file from [ICOADSv3.0.](https://icoads.noaa.gov/r3.html). We pick an specific montly output for a Source/Deck. In this case data from the Marine Meterological Journals data set SID/DCK: **125-704 for Oct 1878.**\n",
+    "\n",
+    "The `.imma` file looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bea/.virtualenvs/c3s/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data_path = os.path.join('~/c3s_work','mdf_reader/tests/data/125-704_1878-10_subset.imma')\n",
+    "data_ori = pd.read_table(data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>18781020 600 4228 29159 130623  10Panay      12325123       9961                         4                   165 17128704125 5 0  1                1FF111F11AAA1AAAA1AAA     9815020N163002199 0 100200180003Panay                     78011118737S.P.Bray,Jr    013231190214        Bulkhead of cabin        1- .1022200200180014Boston              Rio de Janeiro      300200180014001518781020               4220N 6630W 10 E      400200180014001518781020102 85 EXS             WSW           0629601 58             BOC  CU05R</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18781020 800 4231 29197 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>187810201000 4233 29236 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>187810201200 4235 29271 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>187810201400 4237 29310 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>187810201600 4233 29350 130423  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  18781020 600 4228 29159 130623  10Panay      12325123       9961                         4                   165 17128704125 5 0  1                1FF111F11AAA1AAAA1AAA     9815020N163002199 0 100200180003Panay                     78011118737S.P.Bray,Jr    013231190214        Bulkhead of cabin        1- .1022200200180014Boston              Rio de Janeiro      300200180014001518781020               4220N 6630W 10 E      400200180014001518781020102 85 EXS             WSW           0629601 58             BOC  CU05R\n",
+       "0  18781020 800 4231 29197 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "1  187810201000 4233 29236 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "2  187810201200 4235 29271 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "3  187810201400 4237 29310 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "4  187810201600 4233 29350 130423  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very messy to just read into python! \n",
+    "\n",
+    "This is why we need the `mdf_reader` tool, to helps us put those imma files in a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) format. For that we need need a **schema**.\n",
+    "\n",
+    "A **schema** file gathers a collection of descriptors that enable the `mdf_reader` tool to access the content\n",
+    "of a `data model/ schema` and extract the sections of the raw data file that contains meaningful information. These **schema files** are the `bones` of the data model, basically `.json` files outlining the structure of the incoming raw data.\n",
+    "\n",
+    "The `mdf_reader` takes this information and translate the characteristics of the data to a python pandas dataframe.\n",
+    "\n",
+    "The tool has several **schema** templates build in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_names = mdf_reader.schemas.templates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fixed_width_complex_exc',\n",
+       " 'delimited_sections',\n",
+       " 'delimited_basic',\n",
+       " 'fixed_width_complex_opt',\n",
+       " 'fixed_width_sections',\n",
+       " 'fixed_width_basic']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "template_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As well as templates for `code_tables` which are `.json` files containing keys to describe complex metereological variables like weather forcast or sea state conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['nested', 'range_keyed_nested', 'simple', 'range_keyed_simple']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "template_tables = mdf_reader.code_tables.templates()\n",
+    "template_tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Schemas** can be desinged to be deck specific like the example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:54:39,637 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 09:54:39,650 - root - INFO - EXTRACTING DATA FROM MODEL: imma1_d704\n",
+      "2020-12-10 09:54:39,652 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 09:54:39,672 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 09:54:39,678 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 09:54:39,679 - root - INFO - 1000 ...\n",
+      "2020-12-10 09:54:39,713 - root - INFO - done\n",
+      "2020-12-10 09:54:39,718 - root - INFO - 211000 ...\n",
+      "2020-12-10 09:54:39,761 - root - INFO - done\n",
+      "2020-12-10 09:54:39,762 - root - INFO - 29211000 ...\n",
+      "2020-12-10 09:54:39,793 - root - INFO - done\n",
+      "2020-12-10 09:54:39,794 - root - INFO - 3029211000 ...\n",
+      "2020-12-10 09:54:39,802 - root - INFO - done\n",
+      "2020-12-10 09:54:39,803 - root - INFO - 303029211000 ...\n",
+      "2020-12-10 09:54:39,809 - root - INFO - done\n",
+      "2020-12-10 09:54:39,813 - root - INFO - 30303029211000 ...\n",
+      "2020-12-10 09:54:39,821 - root - INFO - done\n",
+      "2020-12-10 09:54:39,822 - root - INFO - 3030303029211000 ...\n",
+      "2020-12-10 09:54:39,851 - root - INFO - done\n",
+      "2020-12-10 09:54:39,854 - root - INFO - 413030303029211000 ...\n",
+      "2020-12-10 09:54:39,863 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n",
+      "Reading section c99_sentinal\n",
+      "Reading section c99_journal\n",
+      "Reading section c99_voyage\n",
+      "Reading section c99_daily\n",
+      "Reading section c99_data4\n",
+      "Reading section c99_data5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:54:43,137 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE'),('c99_journal', 'vessel_length'),('c99_journal', 'vessel_beam'),('c99_journal', 'hold_depth'),('c99_journal', 'tonnage'),('c99_journal', 'baro_height'),('c99_daily', 'year'),('c99_daily', 'month'),('c99_daily', 'day'),('c99_daily', 'distance'),('c99_daily', 'lat_deg_an'),('c99_daily', 'lat_min_an'),('c99_daily', 'lon_deg_an'),('c99_daily', 'lon_min_an'),('c99_daily', 'lat_deg_on'),('c99_daily', 'lat_min_on'),('c99_daily', 'lon_deg_of'),('c99_daily', 'lon_min_of'),('c99_daily', 'current_speed'),('c99_data4', 'year'),('c99_data4', 'month'),('c99_data4', 'day'),('c99_data4', 'hour'),('c99_data4', 'ship_speed'),('c99_data4', 'compass_correction'),('c99_data4', 'attached_thermometer'),('c99_data4', 'air_temperature'),('c99_data4', 'wet_bulb_temperature'),('c99_data4', 'sea_temperature'),('c99_data4', 'sky_clear'),('c99_data5', 'year'),('c99_data5', 'month'),('c99_data5', 'day'),('c99_data5', 'hour'),('c99_data5', 'ship_speed'),('c99_data5', 'attached_thermometer'),('c99_data5', 'air_temperature'),('c99_data5', 'wet_bulb_temperature'),('c99_data5', 'sea_temperature'),('c99_data5', 'sky_clear'),('c99_data5', 'compass_correction')\n",
+      "2020-12-10 09:54:43,138 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n",
+      "2020-12-10 09:54:44,849 - root - ERROR - Code table not defined for element ('c99_data4', 'present_weather')\n",
+      "2020-12-10 09:54:44,851 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,853 - root - ERROR - Code table not defined for element ('c99_data4', 'clouds')\n",
+      "2020-12-10 09:54:44,862 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,875 - root - ERROR - Code table not defined for element ('c99_data4', 'sea_state')\n",
+      "2020-12-10 09:54:44,880 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,883 - root - ERROR - Code table not defined for element ('c99_data5', 'time_ind')\n",
+      "2020-12-10 09:54:44,885 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,889 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_ind')\n",
+      "2020-12-10 09:54:44,891 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,893 - root - ERROR - Code table not defined for element ('c99_data5', 'ship_course_compass')\n",
+      "2020-12-10 09:54:44,895 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,902 - root - ERROR - Code table not defined for element ('c99_data5', 'ship_course_true')\n",
+      "2020-12-10 09:54:44,908 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,912 - root - ERROR - Code table not defined for element ('c99_data5', 'wind_dir_mag')\n",
+      "2020-12-10 09:54:44,916 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,918 - root - ERROR - Code table not defined for element ('c99_data5', 'wind_force')\n",
+      "2020-12-10 09:54:44,920 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,922 - root - ERROR - Code table not defined for element ('c99_data5', 'temp_ind')\n",
+      "2020-12-10 09:54:44,926 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,928 - root - ERROR - Code table not defined for element ('c99_data5', 'present_weather')\n",
+      "2020-12-10 09:54:44,932 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,936 - root - ERROR - Code table not defined for element ('c99_data5', 'clouds')\n",
+      "2020-12-10 09:54:44,938 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,940 - root - ERROR - Code table not defined for element ('c99_data5', 'sea_state')\n",
+      "2020-12-10 09:54:44,942 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,947 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_correction_ind')\n",
+      "2020-12-10 09:54:44,949 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,954 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_correction_dir')\n",
+      "2020-12-10 09:54:44,960 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:46,245 - root - INFO - Wrapping output....\n",
+      "2020-12-10 09:54:46,490 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "schema = 'imma1_d704'\n",
+    "\n",
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/125-704_1878-10_subset.imma'\n",
+    "\n",
+    "data = mdf_reader.read(data_file_path, data_model = schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A new **schema** can be build for a particular deck and source as shown in this notebook. The `imma1_d704` schema was build upon the `imma1` schema/data model but extra sections have been added to the `.json` files to include suplemental data from ICOADS documentation. This is a snapshot of the data inside the `imma1_d704.json`.\n",
+    "```\n",
+    "\"c99_journal\": {\n",
+    "            \"header\": {\"sentinal\": \"1\", \"field_layout\":\"fixed_width\",\"length\": 117},\n",
+    "            \"elements\": {\n",
+    "              \"sentinal\":{\n",
+    "                  \"description\": \"Journal header record identifier\",\n",
+    "                  \"field_length\": 1,\n",
+    "                  \"column_type\": \"str\"\n",
+    "              },\n",
+    "              \"reel_no\":{\n",
+    "                  \"description\": \"Microfilm reel number. See if we want the zero padding or not...\",\n",
+    "                  \"field_length\": 3,\n",
+    "                  \"column_type\": \"str\",\n",
+    "                  \"LMR6\": true\n",
+    "              }\n",
+    "\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now metadata information can be extracted as a component of the padas dataframe. "
+   ]
+  },
+  {
+   "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>sentinal</th>\n",
+       "      <th>reel_no</th>\n",
+       "      <th>journal_no</th>\n",
+       "      <th>frame_no</th>\n",
+       "      <th>ship_name</th>\n",
+       "      <th>journal_ed</th>\n",
+       "      <th>rig</th>\n",
+       "      <th>ship_material</th>\n",
+       "      <th>vessel_type</th>\n",
+       "      <th>vessel_length</th>\n",
+       "      <th>...</th>\n",
+       "      <th>hold_depth</th>\n",
+       "      <th>tonnage</th>\n",
+       "      <th>baro_type</th>\n",
+       "      <th>baro_height</th>\n",
+       "      <th>baro_cdate</th>\n",
+       "      <th>baro_loc</th>\n",
+       "      <th>baro_units</th>\n",
+       "      <th>baro_cor</th>\n",
+       "      <th>thermo_mount</th>\n",
+       "      <th>SST_I</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
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+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
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+       "      <td>14</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
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+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>465 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sentinal reel_no journal_no frame_no          ship_name journal_ed rig  \\\n",
+       "0          1     002       0018     0003              Panay         78  01   \n",
+       "1          1     002       0018     0003              Panay         78  01   \n",
+       "2          1     002       0018     0003              Panay         78  01   \n",
+       "3          1     002       0018     0003              Panay         78  01   \n",
+       "4          1     002       0018     0003              Panay         78  01   \n",
+       "5          1     002       0018     0003              Panay         78  01   \n",
+       "6          1     007       0129     0410               Emma         78  02   \n",
+       "7          1     002       0018     0003              Panay         78  01   \n",
+       "8          1     007       0129     0410               Emma         78  02   \n",
+       "9          1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "10         1     002       0018     0003              Panay         78  01   \n",
+       "11         1     007       0129     0410               Emma         78  02   \n",
+       "12         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "13         1     002       0018     0003              Panay         78  01   \n",
+       "14         1     007       0129     0410               Emma         78  02   \n",
+       "15         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "16         1     002       0018     0003              Panay         78  01   \n",
+       "17         1     007       0129     0410               Emma         78  02   \n",
+       "18         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "19         1     002       0018     0003              Panay         78  01   \n",
+       "20         1     007       0129     0410               Emma         78  02   \n",
+       "21         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "22         1     002       0018     0003              Panay         78  01   \n",
+       "23         1     007       0129     0410               Emma         78  02   \n",
+       "24         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "25         1     002       0018     0003              Panay         78  01   \n",
+       "26         1     007       0129     0410               Emma         78  02   \n",
+       "27         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "28         1     002       0018     0003              Panay         78  01   \n",
+       "29         1     007       0129     0410               Emma         78  02   \n",
+       "..       ...     ...        ...      ...                ...        ...  ..   \n",
+       "435        1     007       0129     0410               Emma         78  02   \n",
+       "436        1     002       0018     0003              Panay         78  01   \n",
+       "437        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "438        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "439        1     007       0129     0410               Emma         78  02   \n",
+       "440        1     002       0018     0003              Panay         78  01   \n",
+       "441        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "442        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "443        1     007       0129     0410               Emma         78  02   \n",
+       "444        1     002       0018     0003              Panay         78  01   \n",
+       "445        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "446        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "447        1     007       0129     0410               Emma         78  02   \n",
+       "448        1     002       0018     0003              Panay         78  01   \n",
+       "449        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "450        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "451        1     007       0129     0410               Emma         78  02   \n",
+       "452        1     002       0018     0003              Panay         78  01   \n",
+       "453        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "454        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "455        1     007       0129     0410               Emma         78  02   \n",
+       "456        1     002       0018     0003              Panay         78  01   \n",
+       "457        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "458        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "459        1     007       0129     0410               Emma         78  02   \n",
+       "460        1     002       0018     0003              Panay         78  01   \n",
+       "461        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "462        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "463        1     007       0129     0410               Emma         78  02   \n",
+       "464        1     002       0018     0003              Panay         78  01   \n",
+       "\n",
+       "    ship_material vessel_type  vessel_length  ...  hold_depth tonnage  \\\n",
+       "0               1           1            187  ...          23    1190   \n",
+       "1               1           1            187  ...          23    1190   \n",
+       "2               1           1            187  ...          23    1190   \n",
+       "3               1           1            187  ...          23    1190   \n",
+       "4               1           1            187  ...          23    1190   \n",
+       "5               1           1            187  ...          23    1190   \n",
+       "6               1           1            136  ...          19     468   \n",
+       "7               1           1            187  ...          23    1190   \n",
+       "8               1           1            136  ...          19     468   \n",
+       "9               1           1            128  ...          17     483   \n",
+       "10              1           1            187  ...          23    1190   \n",
+       "11              1           1            136  ...          19     468   \n",
+       "12              1           1            128  ...          17     483   \n",
+       "13              1           1            187  ...          23    1190   \n",
+       "14              1           1            136  ...          19     468   \n",
+       "15              1           1            128  ...          17     483   \n",
+       "16              1           1            187  ...          23    1190   \n",
+       "17              1           1            136  ...          19     468   \n",
+       "18              1           1            128  ...          17     483   \n",
+       "19              1           1            187  ...          23    1190   \n",
+       "20              1           1            136  ...          19     468   \n",
+       "21              1           1            128  ...          17     483   \n",
+       "22              1           1            187  ...          23    1190   \n",
+       "23              1           1            136  ...          19     468   \n",
+       "24              1           1            128  ...          17     483   \n",
+       "25              1           1            187  ...          23    1190   \n",
+       "26              1           1            136  ...          19     468   \n",
+       "27              1           1            128  ...          17     483   \n",
+       "28              1           1            187  ...          23    1190   \n",
+       "29              1           1            136  ...          19     468   \n",
+       "..            ...         ...            ...  ...         ...     ...   \n",
+       "435             1           1            136  ...          19     468   \n",
+       "436             1           1            187  ...          23    1190   \n",
+       "437             1           1            217  ...          28    1904   \n",
+       "438             1           1            128  ...          17     483   \n",
+       "439             1           1            136  ...          19     468   \n",
+       "440             1           1            187  ...          23    1190   \n",
+       "441             1           1            217  ...          28    1904   \n",
+       "442             1           1            128  ...          17     483   \n",
+       "443             1           1            136  ...          19     468   \n",
+       "444             1           1            187  ...          23    1190   \n",
+       "445             1           1            217  ...          28    1904   \n",
+       "446             1           1            128  ...          17     483   \n",
+       "447             1           1            136  ...          19     468   \n",
+       "448             1           1            187  ...          23    1190   \n",
+       "449             1           1            217  ...          28    1904   \n",
+       "450             1           1            128  ...          17     483   \n",
+       "451             1           1            136  ...          19     468   \n",
+       "452             1           1            187  ...          23    1190   \n",
+       "453             1           1            217  ...          28    1904   \n",
+       "454             1           1            128  ...          17     483   \n",
+       "455             1           1            136  ...          19     468   \n",
+       "456             1           1            187  ...          23    1190   \n",
+       "457             1           1            217  ...          28    1904   \n",
+       "458             1           1            128  ...          17     483   \n",
+       "459             1           1            136  ...          19     468   \n",
+       "460             1           1            187  ...          23    1190   \n",
+       "461             1           1            217  ...          28    1904   \n",
+       "462             1           1            128  ...          17     483   \n",
+       "463             1           1            136  ...          19     468   \n",
+       "464             1           1            187  ...          23    1190   \n",
+       "\n",
+       "    baro_type baro_height  baro_cdate                   baro_loc baro_units  \\\n",
+       "0           2          14         NaN          Bulkhead of cabin          1   \n",
+       "1           2          14         NaN          Bulkhead of cabin          1   \n",
+       "2           2          14         NaN          Bulkhead of cabin          1   \n",
+       "3           2          14         NaN          Bulkhead of cabin          1   \n",
+       "4           2          14         NaN          Bulkhead of cabin          1   \n",
+       "5           2          14         NaN          Bulkhead of cabin          1   \n",
+       "6           1          10         NaN         In the after cabin          1   \n",
+       "7           2          14         NaN          Bulkhead of cabin          1   \n",
+       "8           1          10         NaN         In the after cabin          1   \n",
+       "9           1          17    01101878                After Cabin          1   \n",
+       "10          2          14         NaN          Bulkhead of cabin          1   \n",
+       "11          1          10         NaN         In the after cabin          1   \n",
+       "12          1          17    01101878                After Cabin          1   \n",
+       "13          2          14         NaN          Bulkhead of cabin          1   \n",
+       "14          1          10         NaN         In the after cabin          1   \n",
+       "15          1          17    01101878                After Cabin          1   \n",
+       "16          2          14         NaN          Bulkhead of cabin          1   \n",
+       "17          1          10         NaN         In the after cabin          1   \n",
+       "18          1          17    01101878                After Cabin          1   \n",
+       "19          2          14         NaN          Bulkhead of cabin          1   \n",
+       "20          1          10         NaN         In the after cabin          1   \n",
+       "21          1          17    01101878                After Cabin          1   \n",
+       "22          2          14         NaN          Bulkhead of cabin          1   \n",
+       "23          1          10         NaN         In the after cabin          1   \n",
+       "24          1          17    01101878                After Cabin          1   \n",
+       "25          2          14         NaN          Bulkhead of cabin          1   \n",
+       "26          1          10         NaN         In the after cabin          1   \n",
+       "27          1          17    01101878                After Cabin          1   \n",
+       "28          2          14         NaN          Bulkhead of cabin          1   \n",
+       "29          1          10         NaN         In the after cabin          1   \n",
+       "..        ...         ...         ...                        ...        ...   \n",
+       "435         1          10         NaN         In the after cabin          1   \n",
+       "436         2          14         NaN          Bulkhead of cabin          1   \n",
+       "437         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "438         1          17    01101878                After Cabin          1   \n",
+       "439         1          10         NaN         In the after cabin          1   \n",
+       "440         2          14         NaN          Bulkhead of cabin          1   \n",
+       "441         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "442         1          17    01101878                After Cabin          1   \n",
+       "443         1          10         NaN         In the after cabin          1   \n",
+       "444         2          14         NaN          Bulkhead of cabin          1   \n",
+       "445         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "446         1          17    01101878                After Cabin          1   \n",
+       "447         1          10         NaN         In the after cabin          1   \n",
+       "448         2          14         NaN          Bulkhead of cabin          1   \n",
+       "449         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "450         1          17    01101878                After Cabin          1   \n",
+       "451         1          10         NaN         In the after cabin          1   \n",
+       "452         2          14         NaN          Bulkhead of cabin          1   \n",
+       "453         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "454         1          17    01101878                After Cabin          1   \n",
+       "455         1          10         NaN         In the after cabin          1   \n",
+       "456         2          14         NaN          Bulkhead of cabin          1   \n",
+       "457         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "458         1          17    01101878                After Cabin          1   \n",
+       "459         1          10         NaN         In the after cabin          1   \n",
+       "460         2          14         NaN          Bulkhead of cabin          1   \n",
+       "461         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "462         1          17    01101878                After Cabin          1   \n",
+       "463         1          10         NaN         In the after cabin          1   \n",
+       "464         2          14         NaN          Bulkhead of cabin          1   \n",
+       "\n",
+       "     baro_cor thermo_mount SST_I  \n",
+       "0      - .102            2   NaN  \n",
+       "1      - .102            2   NaN  \n",
+       "2      - .102            2   NaN  \n",
+       "3      - .102            2   NaN  \n",
+       "4      - .102            2   NaN  \n",
+       "5      - .102            2   NaN  \n",
+       "6      - .561            1   NaN  \n",
+       "7      - .102            2   NaN  \n",
+       "8      - .561            1   NaN  \n",
+       "9      + .001            1   NaN  \n",
+       "10     - .102            2   NaN  \n",
+       "11     - .561            1   NaN  \n",
+       "12     + .001            1   NaN  \n",
+       "13     - .102            2   NaN  \n",
+       "14     - .561            1   NaN  \n",
+       "15     + .001            1   NaN  \n",
+       "16     - .102            2   NaN  \n",
+       "17     - .561            1   NaN  \n",
+       "18     + .001            1   NaN  \n",
+       "19     - .102            2   NaN  \n",
+       "20     - .561            1   NaN  \n",
+       "21     + .001            1   NaN  \n",
+       "22     - .102            2   NaN  \n",
+       "23     - .561            1   NaN  \n",
+       "24     + .001            1   NaN  \n",
+       "25     - .102            2   NaN  \n",
+       "26     - .561            1   NaN  \n",
+       "27     + .001            1   NaN  \n",
+       "28     - .102            2   NaN  \n",
+       "29     - .561            1   NaN  \n",
+       "..        ...          ...   ...  \n",
+       "435    - .561            1   NaN  \n",
+       "436    - .102            2   NaN  \n",
+       "437    + .003            4   NaN  \n",
+       "438    + .001            1   NaN  \n",
+       "439    - .561            1   NaN  \n",
+       "440    - .102            2   NaN  \n",
+       "441    + .003            4   NaN  \n",
+       "442    + .001            1   NaN  \n",
+       "443    - .561            1   NaN  \n",
+       "444    - .102            2   NaN  \n",
+       "445    + .003            4   NaN  \n",
+       "446    + .001            1   NaN  \n",
+       "447    - .561            1   NaN  \n",
+       "448    - .102            2   NaN  \n",
+       "449    + .003            4   NaN  \n",
+       "450    + .001            1   NaN  \n",
+       "451    - .561            1   NaN  \n",
+       "452    - .102            2   NaN  \n",
+       "453    + .003            4   NaN  \n",
+       "454    + .001            1   NaN  \n",
+       "455    - .561            1   NaN  \n",
+       "456    - .102            2   NaN  \n",
+       "457    + .003            4   NaN  \n",
+       "458    + .001            1   NaN  \n",
+       "459    - .561            1   NaN  \n",
+       "460    - .102            2   NaN  \n",
+       "461    + .003            4   NaN  \n",
+       "462    + .001            1   NaN  \n",
+       "463    - .561            1   NaN  \n",
+       "464    - .102            2   NaN  \n",
+       "\n",
+       "[465 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data.c99_journal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To learn how to construc a schema or data model for a particular deck/source, visit this other tutorial. INSERT LINK TO NEXT NOTEBOOK"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (c3s)",
+   "language": "python",
+   "name": "c3s"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/notebooks/create_data_model.ipynb b/docs/notebooks/create_data_model.ipynb
new file mode 100644
index 0000000..b1d8169
--- /dev/null
+++ b/docs/notebooks/create_data_model.ipynb
@@ -0,0 +1,1851 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a data model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will create and apply a new **data model/schema** to a raw `.imma` file, using the [mdf_reader](https://git.noc.ac.uk/iregon/mdf_reader) tool. We will add supplemental metadata to the basic `imma1` data model and display supplemental data as a pandas dataframe. \n",
+    "\n",
+    "Lets first import all the tools that we will need. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:36,906 - root - INFO - init basic configure of logging success\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('/home/bea/')\n",
+    "import mdf_reader\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mdf_reader` tool comes with data model templates of `.json` files, that we can use to build our models. For more information see the following [manual](https://git.noc.ac.uk/iregon/mdf_reader/-/blob/master/docs/User_manual.docx)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fixed_width_complex_exc',\n",
+       " 'delimited_sections',\n",
+       " 'delimited_basic',\n",
+       " 'fixed_width_complex_opt',\n",
+       " 'fixed_width_sections',\n",
+       " 'fixed_width_basic']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_data_models = '/home/bea/c3s_work/mdf_reader/data_models/lib/'\n",
+    "\n",
+    "template_names = mdf_reader.schemas.templates()\n",
+    "template_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to the manual, ICOADS data stored with the [IMMA format](https://icoads.noaa.gov/e-doc/imma/R3.0-imma1.pdf) represents a complex data model, since the data includes blocks of sections which are exclusive to certain DCK's (e.g. data coming from the NOAA National Climatic Data Center (NCDC) TD-11 formats). Most of the ICOADS data however will need a **schema** based on the `imma1.json` format, which is based on the template: `*_complex_opt.json`.\n",
+    "\n",
+    "Lets try to build our own **schema** based on this template for a new dck. In this notebook we will organise the data and metadata from the **US Maury collection** that corresponds to `source/dck 069-701`.\n",
+    "\n",
+    "1. First lets read a raw `.imma` file from dck 701 as an example, for a subset of the data collected in April/1845. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:38,691 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 10:11:38,702 - root - INFO - EXTRACTING DATA FROM MODEL: imma1\n",
+      "2020-12-10 10:11:38,704 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 10:11:38,731 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 10:11:38,737 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 10:11:38,740 - root - INFO - 1000 ...\n",
+      "2020-12-10 10:11:38,825 - root - INFO - done\n",
+      "2020-12-10 10:11:38,827 - root - INFO - 211000 ...\n",
+      "2020-12-10 10:11:38,907 - root - INFO - done\n",
+      "2020-12-10 10:11:38,908 - root - INFO - 29211000 ...\n",
+      "2020-12-10 10:11:38,972 - root - INFO - done\n",
+      "2020-12-10 10:11:38,973 - root - INFO - 2929211000 ...\n",
+      "2020-12-10 10:11:38,985 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:41,663 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE')\n",
+      "2020-12-10 10:11:41,664 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section c99\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:11:47,198 - root - INFO - Wrapping output....\n",
+      "2020-12-10 10:11:47,820 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "schema = 'imma1'\n",
+    "\n",
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/069-701_1845-04_subset.imma'\n",
+    "\n",
+    "data_raw = mdf_reader.read(data_file_path, data_model = schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    </tr>\n",
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+       "      <th>22</th>\n",
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+       "    </tr>\n",
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+       "      <td>99 0 180254118450430  2711S  312E             ...</td>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
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+       "      <th>3669</th>\n",
+       "      <td>99 0 220774118450430      S     E             ...</td>\n",
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+       "    <tr>\n",
+       "      <th>3670</th>\n",
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+       "    </tr>\n",
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+       "    </tr>\n",
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+       "3674  99 0 100106118450430  3707S 5031W             ...\n",
+       "3675  99 0 370187118450430  3817S  205E             ...\n",
+       "3676  99 0 250459118450430  3828S  111E             ...\n",
+       "3677  99 0 810274118450430  3951S12759W             ...\n",
+       "3678  99 0 330362118450430  4011S 8240W             ...\n",
+       "3679  99 0 790099118450430  4307S                   ...\n",
+       "3680  99 0 340809118450430  4405S 5031W             ...\n",
+       "3681  99 0 130088118450430  4737S                   ...\n",
+       "3682  99 0 060598118450430  5444S 9045W             ...\n",
+       "3683  99 0 260061118450430  5619S 6744W             ...\n",
+       "3684  99 0 350904118450430  5620S 7159W             ...\n",
+       "3685  99 0 180361118450431  2940N 6403W             ...\n",
+       "\n",
+       "[3686 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw.data['c99']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `c99` column is a bit messy. Here, we will need to separate the Suplememal Metadata ingestied in ICOADS as an entire string and sort each row out according to the source&dck documentation. \n",
+    "\n",
+    "2. We then need to make a new data model or **schema** to be stored in the library folder of the `mdf_reader`. For this we create a folder with the name `imma1_d701` in the lib directory.\n",
+    "3. Under this folder (`/data_models/lib/imma1_d701`) we will need to add a `.json` file with the same name. This `imma1_d701.json` file will contain all the data model information with instructions on how to subdivide the metadata added to `c99`. The name of the file is `imma1_d701.json` because the data model for this deck is based on the `imma1` template shown above, but the `c99` will be further subdivided into other columns/sections. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_folder = '/home/bea/c3s_work/mdf_reader/data_models/lib/'\n",
+    "model_name = 'imma1_d701'\n",
+    "model_path = os.path.join(path_to_folder, model_name)\n",
+    "model_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Uncomment the following lines to create new data models. This folder is already withing the repository so you dont need to run the lines below. They only serve as a guide for further schemas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if not os.path.exists(model_path):\n",
+    "#     os.makedirs(model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In that path we will copy the template that we will based our **schema** from. In this case the `imma1` schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import shutil\n",
+    "# shutil.copyfile(os.path.join(path_to_folder, 'imma1/imma1.json'),  os.path.join(model_path, model_name+'.json'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to make a directory called `code_tables` and copy all `code_tables` from the `imma1` folder template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import shutil\n",
+    "# shutil.copytree(os.path.join(path_to_folder, 'imma1/code_tables'), os.path.join(model_path,'code_tables'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We end up with something like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/deckschema.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/code_tables_schema_one.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the key will be to modify the `c99` section of the `imma1_d701.json`. See the highlighted text in the figure below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='/home/bea/c3s_work/figures/c99differences.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `c99_sentinal` section identifies where in the data, we will have a new section. In this case we will have a new section corresponding to Supplemental Metadata. \n",
+    "\n",
+    "In our example this supplemental metadata will come from the documentation of the US Maury collection stored in the [ICOADS website](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_transpec). \n",
+    "\n",
+    "4. We will need to add the metadata information from the website inside that `c99_sentinal` section and create as many sections as the data requires.\n",
+    "\n",
+    ">sentinal: section identifier\n",
+    "applies to: format.fixed_width\n",
+    "is mandatory: it is not mandatory if the section is unique, unique in a parsing_order block, or\n",
+    "part of a sequential parsing_order block.\n",
+    "type: string\n",
+    "comments: the element bearing the sentinal needs to be, additionally, declared in the\n",
+    "elements block\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5. We will have to build additional `.json` files to be saved under the `code_tables` folder of our schema. Each `.json` file inside the `code_tables` are dictionaries that will help decode metadata observations (e.g. wind force scales or weather codes).  For each encoded variable that we add, we will need to add a new `ICOADS.C99_Variable.json` to the **schema**. Files need to be named after the section that they represent, in this case `ICOADS.C99_Variable.json`. See images below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/code_tables_schema_two.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the US Maury collection [ICOADS documentation](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_transpec), we find out that the `c99` for this deck is compose of the following sections:\n",
+    "\n",
+    "- Data\n",
+    "- Header information\n",
+    "- Quality control information (qc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "Data stored in the supplemental attachment consisted of the entire data record\n",
+    "(173 characters); followed by a selection of fields from, or derived from, the\n",
+    "associated header record (through character 241); and selected fields from the\n",
+    "qc file (total 250 characters):\n",
+    "  # Pos.     Total #  Field  Record\n",
+    "    range    of pos.   name    type  Description of field (of derived field)\n",
+    "--- -------  -------  -----  ------  ----------------------------------------\n",
+    "  1 1-7         7     cvoyd    data  voyage number\n",
+    "... ...               ...       ...  ...\n",
+    " 47 172-173     2     cmvq     data  magnetic variation QC indicator\n",
+    " NA 174-175     2     cts2   header  (fr ship type, ctship, according to [5])\n",
+    "  4 176-177     2     cft    header  form type\n",
+    "  5 178-193    16     comm   header  commander (first 16 positions only) [6]\n",
+    "  6 194-217    24     cfr    header  from city\n",
+    "  7 218-241    24     cto    header  to city\n",
+    "  2 242-246     5     qc2    qc      reel sequence number\n",
+    "  5 247-248     2     qc5    qc      day  (local time) (99 indicates missing)\n",
+    "  6 249-250     2     qc6*   qc      hour (local time) (99 indicates missing)\n",
+    "--- -------  -------  -----  ------  ----------------------------------------\n",
+    "* Whenever qc6 was 24, zero was inadvertently written out to the supplemental\n",
+    "attachment.  This resulted from an error in the conversion program, but can\n",
+    "be fixed by interpretation of hour zero as hour 24 of qc5 + 1 (as noted in [2],\n",
+    "qc6 originally ranged 1-24, with 24 signifying hour 0 of the next day.  As\n",
+    "intended, qc5 was included in the supplementary attachment in original form.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the raw data file the information looks like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c99 = data_raw.data['c99']\n",
+    "line = c99.iloc[63] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'99 0 400706118450401   240S 9513ESE     757                                                                 NTW    51 SEXS   50 SW     50                                          201J.S.KIMBALL     SUMATRA                 BOSTON                  20204 199'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "line.values[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then need to divide all this string accoding to the documentation above and the format of the data specified in the [US Maury data docs](https://icoads.noaa.gov/e-doc/other/transpec/maury/maury_format)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'99 0 '"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sentinal = 5\n",
+    "part_1 = line.values[0][0:5]\n",
+    "part_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'4007061'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cvoyd voyage number = 7\n",
+    "part_2 = line.values[0][5:5+7]\n",
+    "part_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'18450401  '"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# date = 10\n",
+    "part_3 = line.values[0][12:12+10]\n",
+    "part_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6. We build our `.json` file reflecting each data field from the ICOADS documentation as a new section. And add each parameter from the data as a new element. Having a `sentinal` section at the beginning of the `c99` is important since in the `.imma` format, regardless of the source/dck, will have 5 characters that will always be the same. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/new_schema.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To each section we add the corresponding elements/parameters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='/home/bea/c3s_work/figures/elements.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we feed this new data model to the `mdf_reader.read` function. It is important that we save this data model under the right directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:17:41,871 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 10:17:41,877 - root - INFO - EXTRACTING DATA FROM MODEL: /home/bea/c3s_work/mdf_reader/data_models/lib/imma1_d701\n",
+      "2020-12-10 10:17:41,880 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 10:17:41,916 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 10:17:41,918 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 10:17:41,919 - root - INFO - 1000 ...\n",
+      "2020-12-10 10:17:41,966 - root - INFO - done\n",
+      "2020-12-10 10:17:41,972 - root - INFO - 211000 ...\n",
+      "2020-12-10 10:17:42,038 - root - INFO - done\n",
+      "2020-12-10 10:17:42,039 - root - INFO - 29211000 ...\n",
+      "2020-12-10 10:17:42,096 - root - INFO - done\n",
+      "2020-12-10 10:17:42,099 - root - INFO - 3029211000 ...\n",
+      "2020-12-10 10:17:42,109 - root - INFO - done\n",
+      "2020-12-10 10:17:42,113 - root - INFO - 303029211000 ...\n",
+      "2020-12-10 10:17:42,138 - root - INFO - done\n",
+      "2020-12-10 10:17:42,139 - root - INFO - 30303029211000 ...\n",
+      "2020-12-10 10:17:42,158 - root - INFO - done\n",
+      "2020-12-10 10:17:42,171 - root - INFO - 3030303029211000 ...\n",
+      "2020-12-10 10:17:42,186 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n",
+      "Reading section c99_sentinal\n",
+      "Reading section c99_data\n",
+      "Reading section c99_header\n",
+      "Reading section c99_qc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 10:17:46,149 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE'),('c99_data', 'reel_number'),('c99_data', 'frame_number'),('c99_data', 'voyage_sequence'),('c99_data', 'year'),('c99_data', 'month'),('c99_data', 'day'),('c99_data', 'hour'),('c99_data', 'lat_deg_an'),('c99_data', 'lat_min_an'),('c99_data', 'lon_deg_an'),('c99_data', 'lon_min_an'),('c99_data', 'min_drift_coord'),('c99_data', 'attached_thermometer_one'),('c99_data', 'attached_thermometer_two'),('c99_data', 'attached_thermometer_three'),('c99_data', 'air_temperature_one'),('c99_data', 'sea_surface_temperature_one'),('c99_data', 'sea_depth_temperature'),('c99_data', 'air_temperature_two'),('c99_data', 'sea_surface_temperature_two'),('c99_data', 'air_temperature_three'),('c99_data', 'sea_surface_temperature_three')\n",
+      "2020-12-10 10:17:46,149 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n",
+      "2020-12-10 10:17:53,016 - root - INFO - Wrapping output....\n",
+      "2020-12-10 10:17:53,746 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/069-701_1845-04_subset.imma'\n",
+    "\n",
+    "data = mdf_reader.read(data_file_path, data_model_path= model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And magically all the messy string is separated! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">c99_sentinal</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ATTI</th>\n",
+       "      <th>ATTL</th>\n",
+       "      <th>BLK</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_sentinal          \n",
+       "          ATTI ATTL  BLK\n",
+       "0           99    0  NaN\n",
+       "1           99    0  NaN\n",
+       "2           99    0  NaN\n",
+       "3           99    0  NaN\n",
+       "4           99    0  NaN"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pd.options.display.max_columns = None\n",
+    "data.data[[\"c99_sentinal\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The section above is the sentinal section that is the same in all ICOADS dck's/c99 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"56\" halign=\"left\">c99_data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>reel_number</th>\n",
+       "      <th>frame_number</th>\n",
+       "      <th>voyage_sequence</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>lat_deg_an</th>\n",
+       "      <th>lat_min_an</th>\n",
+       "      <th>lat_hemis_an</th>\n",
+       "      <th>lon_deg_an</th>\n",
+       "      <th>lon_min_an</th>\n",
+       "      <th>lon_hemis_an</th>\n",
+       "      <th>current_dir</th>\n",
+       "      <th>current_speed_ind</th>\n",
+       "      <th>current_speed</th>\n",
+       "      <th>min_drift_coord</th>\n",
+       "      <th>period_drift</th>\n",
+       "      <th>mag_var_ind</th>\n",
+       "      <th>mag_var</th>\n",
+       "      <th>baro_obs_time</th>\n",
+       "      <th>baro_pressure_one</th>\n",
+       "      <th>temp_ind</th>\n",
+       "      <th>attached_thermometer_one</th>\n",
+       "      <th>attached_thermometer_two</th>\n",
+       "      <th>attached_thermometer_three</th>\n",
+       "      <th>hour_air_temp_one</th>\n",
+       "      <th>air_temperature_one</th>\n",
+       "      <th>sea_surface_temperature_one</th>\n",
+       "      <th>sea_depth_temperature</th>\n",
+       "      <th>hour_air_temp_two</th>\n",
+       "      <th>air_temperature_two</th>\n",
+       "      <th>sea_surface_temperature_two</th>\n",
+       "      <th>hour_air_temp_three</th>\n",
+       "      <th>air_temperature_three</th>\n",
+       "      <th>sea_surface_temperature_three</th>\n",
+       "      <th>wind_dir_start</th>\n",
+       "      <th>wind_force_start</th>\n",
+       "      <th>wind_info_start</th>\n",
+       "      <th>wind_dir_middle</th>\n",
+       "      <th>wind_force_middle</th>\n",
+       "      <th>wind_info_middle</th>\n",
+       "      <th>wind_dir_later</th>\n",
+       "      <th>wind_force_later</th>\n",
+       "      <th>wind_info_later</th>\n",
+       "      <th>cloud_one</th>\n",
+       "      <th>cloud_dir_one</th>\n",
+       "      <th>cloud_two</th>\n",
+       "      <th>cloud_dir_two</th>\n",
+       "      <th>cloud_three</th>\n",
+       "      <th>cloud_dir_three</th>\n",
+       "      <th>sky_clear</th>\n",
+       "      <th>hour_of_weather</th>\n",
+       "      <th>weather_indic</th>\n",
+       "      <th>weather</th>\n",
+       "      <th>qc_magnetic_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>850</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>N</td>\n",
+       "      <td>23</td>\n",
+       "      <td>54</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>51</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WTWSW</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>81</td>\n",
+       "      <td>348</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48</td>\n",
+       "      <td>36</td>\n",
+       "      <td>N</td>\n",
+       "      <td>23</td>\n",
+       "      <td>30</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2929</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SWXS</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SSW</td>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>N</td>\n",
+       "      <td>151</td>\n",
+       "      <td>47</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WSW</td>\n",
+       "      <td>40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NW</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>WXN</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SHQ</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26</td>\n",
+       "      <td>597</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>44</td>\n",
+       "      <td>54</td>\n",
+       "      <td>N</td>\n",
+       "      <td>30</td>\n",
+       "      <td>15</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0200W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>W</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NE</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>661</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>43</td>\n",
+       "      <td>56</td>\n",
+       "      <td>N</td>\n",
+       "      <td>22</td>\n",
+       "      <td>20</td>\n",
+       "      <td>W</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SWXW</td>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>W</td>\n",
+       "      <td>57</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>V</td>\n",
+       "      <td>29</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     c99_data                                                               \\\n",
+       "  reel_number frame_number voyage_sequence  year month day hour lat_deg_an   \n",
+       "0          30          850               1  1845     4   1  NaN         54   \n",
+       "1          81          348               1  1845     4   1  NaN         48   \n",
+       "2          37          731               1  1845     4   1  NaN         46   \n",
+       "3          26          597               1  1845     4   1  NaN         44   \n",
+       "4          25          661               1  1845     4   1  NaN         43   \n",
+       "\n",
+       "                                                                          \\\n",
+       "  lat_min_an lat_hemis_an lon_deg_an lon_min_an lon_hemis_an current_dir   \n",
+       "0          4            N         23         54            W         NaN   \n",
+       "1         36            N         23         30            W         NaN   \n",
+       "2         43            N        151         47            W         NaN   \n",
+       "3         54            N         30         15            W         NaN   \n",
+       "4         56            N         22         20            W         NaN   \n",
+       "\n",
+       "                                                                            \\\n",
+       "  current_speed_ind current_speed min_drift_coord period_drift mag_var_ind   \n",
+       "0               NaN           NaN             NaN          NaN         NaN   \n",
+       "1               NaN           NaN             NaN          NaN         NaN   \n",
+       "2               NaN           NaN             NaN          NaN         NaN   \n",
+       "3               NaN           NaN             NaN          NaN           2   \n",
+       "4               NaN           NaN             NaN          NaN         NaN   \n",
+       "\n",
+       "                                                                             \\\n",
+       "  mag_var baro_obs_time baro_pressure_one temp_ind attached_thermometer_one   \n",
+       "0     NaN           NaN               NaN      NaN                      NaN   \n",
+       "1     NaN           NaN              2929        1                      NaN   \n",
+       "2     NaN           NaN               NaN      NaN                      NaN   \n",
+       "3   0200W           NaN               NaN      NaN                      NaN   \n",
+       "4     NaN           NaN               NaN      NaN                      NaN   \n",
+       "\n",
+       "                                                                         \\\n",
+       "  attached_thermometer_two attached_thermometer_three hour_air_temp_one   \n",
+       "0                      NaN                        NaN               NaN   \n",
+       "1                      NaN                        NaN                 9   \n",
+       "2                      NaN                        NaN               NaN   \n",
+       "3                      NaN                        NaN               NaN   \n",
+       "4                      NaN                        NaN               NaN   \n",
+       "\n",
+       "                                                                         \\\n",
+       "  air_temperature_one sea_surface_temperature_one sea_depth_temperature   \n",
+       "0                 NaN                         NaN                   NaN   \n",
+       "1                53.0                        52.0                   NaN   \n",
+       "2                 NaN                         NaN                   NaN   \n",
+       "3                 NaN                         NaN                   NaN   \n",
+       "4                 NaN                         NaN                   NaN   \n",
+       "\n",
+       "                                                                     \\\n",
+       "  hour_air_temp_two air_temperature_two sea_surface_temperature_two   \n",
+       "0               NaN                 NaN                         NaN   \n",
+       "1               NaN                 NaN                         NaN   \n",
+       "2               NaN                 NaN                         NaN   \n",
+       "3               NaN                 NaN                         NaN   \n",
+       "4               NaN                 NaN                         NaN   \n",
+       "\n",
+       "                                                                           \\\n",
+       "  hour_air_temp_three air_temperature_three sea_surface_temperature_three   \n",
+       "0                 NaN                   NaN                           NaN   \n",
+       "1                 NaN                   NaN                           NaN   \n",
+       "2                 NaN                   NaN                           NaN   \n",
+       "3                 NaN                   NaN                           NaN   \n",
+       "4                 NaN                   NaN                           NaN   \n",
+       "\n",
+       "                                                                   \\\n",
+       "  wind_dir_start wind_force_start wind_info_start wind_dir_middle   \n",
+       "0             NW               51             NaN           WTWSW   \n",
+       "1           SWXS               57             NaN             SSW   \n",
+       "2            WSW               40             NaN              NW   \n",
+       "3              W               44             NaN              NE   \n",
+       "4           SWXW               28             NaN               W   \n",
+       "\n",
+       "                                                                      \\\n",
+       "  wind_force_middle wind_info_middle wind_dir_later wind_force_later   \n",
+       "0                44              NaN              S               57   \n",
+       "1                28              NaN             NW               30   \n",
+       "2                44              NaN            WXN               57   \n",
+       "3                57              NaN              N               57   \n",
+       "4                57              NaN              V               29   \n",
+       "\n",
+       "                                                                               \\\n",
+       "  wind_info_later cloud_one cloud_dir_one cloud_two cloud_dir_two cloud_three   \n",
+       "0             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "1             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "2             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "3             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "4             NaN       NaN           NaN       NaN           NaN         NaN   \n",
+       "\n",
+       "                                                                   \\\n",
+       "  cloud_dir_three sky_clear hour_of_weather weather_indic weather   \n",
+       "0             NaN       NaN             NaN           NaN     NaN   \n",
+       "1             NaN       NaN             NaN           NaN     NaN   \n",
+       "2             NaN       NaN             NaN             1     SHQ   \n",
+       "3             NaN       NaN             NaN           NaN     NaN   \n",
+       "4             NaN       NaN             NaN           NaN     NaN   \n",
+       "\n",
+       "                   \n",
+       "  qc_magnetic_var  \n",
+       "0             NaN  \n",
+       "1             NaN  \n",
+       "2             NaN  \n",
+       "3             NaN  \n",
+       "4             NaN  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_data\"]].head(n=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">c99_header</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>rig</th>\n",
+       "      <th>form_type</th>\n",
+       "      <th>commander</th>\n",
+       "      <th>from_city</th>\n",
+       "      <th>to_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>WM.CALLAGAN</td>\n",
+       "      <td>BOSTON</td>\n",
+       "      <td>ST.PETERSBURG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>LIVERPOOL</td>\n",
+       "      <td>NEW YORK &amp; RETURN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>01</td>\n",
+       "      <td>CLEMENT NORTON</td>\n",
+       "      <td>LAHAINA</td>\n",
+       "      <td>SAN DIEGO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>R.MCCARRAN</td>\n",
+       "      <td>NEW YORK</td>\n",
+       "      <td>LIVERPOOL &amp; RETURN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>01</td>\n",
+       "      <td>J.P.GANNETT</td>\n",
+       "      <td>LIVERPOOL</td>\n",
+       "      <td>APALACHICOLA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_header                                                         \n",
+       "         rig form_type       commander  from_city             to_city\n",
+       "0          2        01     WM.CALLAGAN     BOSTON       ST.PETERSBURG\n",
+       "1          2        01             NaN  LIVERPOOL   NEW YORK & RETURN\n",
+       "2         37        01  CLEMENT NORTON    LAHAINA           SAN DIEGO\n",
+       "3          2        01      R.MCCARRAN   NEW YORK  LIVERPOOL & RETURN\n",
+       "4          2        01     J.P.GANNETT  LIVERPOOL        APALACHICOLA"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_header\"]].head(n=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">c99_qc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>qc2</th>\n",
+       "      <th>qc5</th>\n",
+       "      <th>qc6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15641</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15524</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17671</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15062</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15516</td>\n",
+       "      <td>1</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  c99_qc        \n",
+       "     qc2 qc5 qc6\n",
+       "0  15641   1  99\n",
+       "1  15524   1  99\n",
+       "2  17671   1  99\n",
+       "3  15062   1  99\n",
+       "4  15516   1  99"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data[[\"c99_qc\"]].head(n=5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (c3s)",
+   "language": "python",
+   "name": "c3s"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/notebooks/mdf_reader_test_overview.ipynb b/docs/notebooks/mdf_reader_test_overview.ipynb
new file mode 100644
index 0000000..f54965b
--- /dev/null
+++ b/docs/notebooks/mdf_reader_test_overview.ipynb
@@ -0,0 +1,2179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test and overview of the `mdf_reader` tool "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First clone the [gitlab repository](https://git.noc.ac.uk/brecinosrivas/mdf_reader) and modify the path in `sys.path.append()` with the directory path where you store the `mdf_reader` repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:49:38,429 - root - INFO - init basic configure of logging success\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('/home/bea/')\n",
+    "import mdf_reader\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mdf_reader` is a python3 tool designed to read data files compliant with a user specified [data\n",
+    "model](https://cds.climate.copernicus.eu/toolbox/doc/how-to/15_how_to_understand_the_common_data_model/15_how_to_understand_the_common_data_model.html). \n",
+    "\n",
+    "It was developed with the initial idea to read the [IMMA](https://icoads.noaa.gov/e-doc/imma/R3.0-imma1.pdf) data format, but it was further enhanced to account for other meteorological data formats. \n",
+    "\n",
+    "Lets see an example for a typical file from [ICOADSv3.0.](https://icoads.noaa.gov/r3.html). We pick an specific montly output for a Source/Deck. In this case data from the Marine Meterological Journals data set SID/DCK: **125-704 for Oct 1878.**\n",
+    "\n",
+    "The `.imma` file looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bea/.virtualenvs/c3s/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data_path = os.path.join('~/c3s_work','mdf_reader/tests/data/125-704_1878-10_subset.imma')\n",
+    "data_ori = pd.read_table(data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>18781020 600 4228 29159 130623  10Panay      12325123       9961                         4                   165 17128704125 5 0  1                1FF111F11AAA1AAAA1AAA     9815020N163002199 0 100200180003Panay                     78011118737S.P.Bray,Jr    013231190214        Bulkhead of cabin        1- .1022200200180014Boston              Rio de Janeiro      300200180014001518781020               4220N 6630W 10 E      400200180014001518781020102 85 EXS             WSW           0629601 58             BOC  CU05R</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18781020 800 4231 29197 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>187810201000 4233 29236 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>187810201200 4235 29271 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>187810201400 4237 29310 130623  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>187810201600 4233 29350 130423  10Panay      1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  18781020 600 4228 29159 130623  10Panay      12325123       9961                         4                   165 17128704125 5 0  1                1FF111F11AAA1AAAA1AAA     9815020N163002199 0 100200180003Panay                     78011118737S.P.Bray,Jr    013231190214        Bulkhead of cabin        1- .1022200200180014Boston              Rio de Janeiro      300200180014001518781020               4220N 6630W 10 E      400200180014001518781020102 85 EXS             WSW           0629601 58             BOC  CU05R\n",
+       "0  18781020 800 4231 29197 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "1  187810201000 4233 29236 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "2  187810201200 4235 29271 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "3  187810201400 4237 29310 130623  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
+       "4  187810201600 4233 29350 130423  10Panay      1...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very messy to just read into python! \n",
+    "\n",
+    "This is why we need the `mdf_reader` tool, to helps us put those imma files in a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) format. For that we need need a **schema**.\n",
+    "\n",
+    "A **schema** file gathers a collection of descriptors that enable the `mdf_reader` tool to access the content\n",
+    "of a `data model/ schema` and extract the sections of the raw data file that contains meaningful information. These **schema files** are the `bones` of the data model, basically `.json` files outlining the structure of the incoming raw data.\n",
+    "\n",
+    "The `mdf_reader` takes this information and translate the characteristics of the data to a python pandas dataframe.\n",
+    "\n",
+    "The tool has several **schema** templates build in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_names = mdf_reader.schemas.templates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fixed_width_complex_exc',\n",
+       " 'delimited_sections',\n",
+       " 'delimited_basic',\n",
+       " 'fixed_width_complex_opt',\n",
+       " 'fixed_width_sections',\n",
+       " 'fixed_width_basic']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "template_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As well as templates for `code_tables` which are `.json` files containing keys to describe complex metereological variables like weather forcast or sea state conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['nested', 'range_keyed_nested', 'simple', 'range_keyed_simple']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "template_tables = mdf_reader.code_tables.templates()\n",
+    "template_tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Schemas** can be desinged to be deck specific like the example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:54:39,637 - root - INFO - READING DATA MODEL SCHEMA FILE...\n",
+      "2020-12-10 09:54:39,650 - root - INFO - EXTRACTING DATA FROM MODEL: imma1_d704\n",
+      "2020-12-10 09:54:39,652 - root - INFO - Getting data string from source...\n",
+      "2020-12-10 09:54:39,672 - root - INFO - Extracting and reading sections\n",
+      "2020-12-10 09:54:39,678 - root - INFO - Processing section partitioning threads\n",
+      "2020-12-10 09:54:39,679 - root - INFO - 1000 ...\n",
+      "2020-12-10 09:54:39,713 - root - INFO - done\n",
+      "2020-12-10 09:54:39,718 - root - INFO - 211000 ...\n",
+      "2020-12-10 09:54:39,761 - root - INFO - done\n",
+      "2020-12-10 09:54:39,762 - root - INFO - 29211000 ...\n",
+      "2020-12-10 09:54:39,793 - root - INFO - done\n",
+      "2020-12-10 09:54:39,794 - root - INFO - 3029211000 ...\n",
+      "2020-12-10 09:54:39,802 - root - INFO - done\n",
+      "2020-12-10 09:54:39,803 - root - INFO - 303029211000 ...\n",
+      "2020-12-10 09:54:39,809 - root - INFO - done\n",
+      "2020-12-10 09:54:39,813 - root - INFO - 30303029211000 ...\n",
+      "2020-12-10 09:54:39,821 - root - INFO - done\n",
+      "2020-12-10 09:54:39,822 - root - INFO - 3030303029211000 ...\n",
+      "2020-12-10 09:54:39,851 - root - INFO - done\n",
+      "2020-12-10 09:54:39,854 - root - INFO - 413030303029211000 ...\n",
+      "2020-12-10 09:54:39,863 - root - INFO - done\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading section core\n",
+      "Reading section c1\n",
+      "Reading section c5\n",
+      "Reading section c6\n",
+      "Reading section c7\n",
+      "Reading section c8\n",
+      "Reading section c9\n",
+      "Reading section c95\n",
+      "Reading section c96\n",
+      "Reading section c97\n",
+      "Reading section c98\n",
+      "Reading section c99_sentinal\n",
+      "Reading section c99_journal\n",
+      "Reading section c99_voyage\n",
+      "Reading section c99_daily\n",
+      "Reading section c99_data4\n",
+      "Reading section c99_data5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-12-10 09:54:43,137 - root - WARNING - Data numeric elements with missing upper or lower threshold: ('c1', 'BSI'),('c1', 'AQZ'),('c1', 'AQA'),('c1', 'UQZ'),('c1', 'UQA'),('c1', 'VQZ'),('c1', 'VQA'),('c1', 'PQZ'),('c1', 'PQA'),('c1', 'DQZ'),('c1', 'DQA'),('c5', 'OS'),('c5', 'OP'),('c5', 'FM'),('c5', 'IMMV'),('c5', 'IX'),('c5', 'W2'),('c5', 'WMI'),('c5', 'SD2'),('c5', 'SP2'),('c5', 'IS'),('c5', 'RS'),('c5', 'IC1'),('c5', 'IC2'),('c5', 'IC3'),('c5', 'IC4'),('c5', 'IC5'),('c5', 'IR'),('c5', 'RRR'),('c5', 'TR'),('c5', 'NU'),('c5', 'QCI'),('c5', 'QI1'),('c5', 'QI2'),('c5', 'QI3'),('c5', 'QI4'),('c5', 'QI5'),('c5', 'QI6'),('c5', 'QI7'),('c5', 'QI8'),('c5', 'QI9'),('c5', 'QI10'),('c5', 'QI11'),('c5', 'QI12'),('c5', 'QI13'),('c5', 'QI14'),('c5', 'QI15'),('c5', 'QI16'),('c5', 'QI17'),('c5', 'QI18'),('c5', 'QI19'),('c5', 'QI20'),('c5', 'QI21'),('c5', 'QI22'),('c5', 'QI23'),('c5', 'QI24'),('c5', 'QI25'),('c5', 'QI26'),('c5', 'QI27'),('c5', 'QI28'),('c5', 'QI29'),('c5', 'RHI'),('c5', 'AWSI'),('c6', 'FBSRC'),('c6', 'MST'),('c7', 'OPM'),('c7', 'LOT'),('c9', 'CCe'),('c9', 'WWe'),('c9', 'Ne'),('c9', 'NHe'),('c9', 'He'),('c9', 'CLe'),('c9', 'CMe'),('c9', 'CHe'),('c9', 'SBI'),('c95', 'DPRO'),('c95', 'DPRP'),('c95', 'UFR'),('c95', 'ASIR'),('c96', 'ASII'),('c97', 'ASIE'),('c99_journal', 'vessel_length'),('c99_journal', 'vessel_beam'),('c99_journal', 'hold_depth'),('c99_journal', 'tonnage'),('c99_journal', 'baro_height'),('c99_daily', 'year'),('c99_daily', 'month'),('c99_daily', 'day'),('c99_daily', 'distance'),('c99_daily', 'lat_deg_an'),('c99_daily', 'lat_min_an'),('c99_daily', 'lon_deg_an'),('c99_daily', 'lon_min_an'),('c99_daily', 'lat_deg_on'),('c99_daily', 'lat_min_on'),('c99_daily', 'lon_deg_of'),('c99_daily', 'lon_min_of'),('c99_daily', 'current_speed'),('c99_data4', 'year'),('c99_data4', 'month'),('c99_data4', 'day'),('c99_data4', 'hour'),('c99_data4', 'ship_speed'),('c99_data4', 'compass_correction'),('c99_data4', 'attached_thermometer'),('c99_data4', 'air_temperature'),('c99_data4', 'wet_bulb_temperature'),('c99_data4', 'sea_temperature'),('c99_data4', 'sky_clear'),('c99_data5', 'year'),('c99_data5', 'month'),('c99_data5', 'day'),('c99_data5', 'hour'),('c99_data5', 'ship_speed'),('c99_data5', 'attached_thermometer'),('c99_data5', 'air_temperature'),('c99_data5', 'wet_bulb_temperature'),('c99_data5', 'sea_temperature'),('c99_data5', 'sky_clear'),('c99_data5', 'compass_correction')\n",
+      "2020-12-10 09:54:43,138 - root - WARNING - Corresponding upper and/or lower bounds set to +/-inf for validation\n",
+      "2020-12-10 09:54:44,849 - root - ERROR - Code table not defined for element ('c99_data4', 'present_weather')\n",
+      "2020-12-10 09:54:44,851 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,853 - root - ERROR - Code table not defined for element ('c99_data4', 'clouds')\n",
+      "2020-12-10 09:54:44,862 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,875 - root - ERROR - Code table not defined for element ('c99_data4', 'sea_state')\n",
+      "2020-12-10 09:54:44,880 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,883 - root - ERROR - Code table not defined for element ('c99_data5', 'time_ind')\n",
+      "2020-12-10 09:54:44,885 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,889 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_ind')\n",
+      "2020-12-10 09:54:44,891 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,893 - root - ERROR - Code table not defined for element ('c99_data5', 'ship_course_compass')\n",
+      "2020-12-10 09:54:44,895 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,902 - root - ERROR - Code table not defined for element ('c99_data5', 'ship_course_true')\n",
+      "2020-12-10 09:54:44,908 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,912 - root - ERROR - Code table not defined for element ('c99_data5', 'wind_dir_mag')\n",
+      "2020-12-10 09:54:44,916 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,918 - root - ERROR - Code table not defined for element ('c99_data5', 'wind_force')\n",
+      "2020-12-10 09:54:44,920 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,922 - root - ERROR - Code table not defined for element ('c99_data5', 'temp_ind')\n",
+      "2020-12-10 09:54:44,926 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,928 - root - ERROR - Code table not defined for element ('c99_data5', 'present_weather')\n",
+      "2020-12-10 09:54:44,932 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,936 - root - ERROR - Code table not defined for element ('c99_data5', 'clouds')\n",
+      "2020-12-10 09:54:44,938 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,940 - root - ERROR - Code table not defined for element ('c99_data5', 'sea_state')\n",
+      "2020-12-10 09:54:44,942 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,947 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_correction_ind')\n",
+      "2020-12-10 09:54:44,949 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:44,954 - root - ERROR - Code table not defined for element ('c99_data5', 'compass_correction_dir')\n",
+      "2020-12-10 09:54:44,960 - root - WARNING - Element mask set to False\n",
+      "2020-12-10 09:54:46,245 - root - INFO - Wrapping output....\n",
+      "2020-12-10 09:54:46,490 - root - INFO - CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL\n"
+     ]
+    }
+   ],
+   "source": [
+    "schema = 'imma1_d704'\n",
+    "\n",
+    "data_file_path = '/home/bea/c3s_work/mdf_reader/tests/data/125-704_1878-10_subset.imma'\n",
+    "\n",
+    "data = mdf_reader.read(data_file_path, data_model = schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A new **schema** can be build for a particular deck and source as shown in this notebook. The `imma1_d704` schema was build upon the `imma1` schema/data model but extra sections have been added to the `.json` files to include suplemental data from ICOADS documentation. This is a snapshot of the data inside the `imma1_d704.json`.\n",
+    "```\n",
+    "\"c99_journal\": {\n",
+    "            \"header\": {\"sentinal\": \"1\", \"field_layout\":\"fixed_width\",\"length\": 117},\n",
+    "            \"elements\": {\n",
+    "              \"sentinal\":{\n",
+    "                  \"description\": \"Journal header record identifier\",\n",
+    "                  \"field_length\": 1,\n",
+    "                  \"column_type\": \"str\"\n",
+    "              },\n",
+    "              \"reel_no\":{\n",
+    "                  \"description\": \"Microfilm reel number. See if we want the zero padding or not...\",\n",
+    "                  \"field_length\": 3,\n",
+    "                  \"column_type\": \"str\",\n",
+    "                  \"LMR6\": true\n",
+    "              }\n",
+    "\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now metadata information can be extracted as a component of the padas dataframe. "
+   ]
+  },
+  {
+   "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>sentinal</th>\n",
+       "      <th>reel_no</th>\n",
+       "      <th>journal_no</th>\n",
+       "      <th>frame_no</th>\n",
+       "      <th>ship_name</th>\n",
+       "      <th>journal_ed</th>\n",
+       "      <th>rig</th>\n",
+       "      <th>ship_material</th>\n",
+       "      <th>vessel_type</th>\n",
+       "      <th>vessel_length</th>\n",
+       "      <th>...</th>\n",
+       "      <th>hold_depth</th>\n",
+       "      <th>tonnage</th>\n",
+       "      <th>baro_type</th>\n",
+       "      <th>baro_height</th>\n",
+       "      <th>baro_cdate</th>\n",
+       "      <th>baro_loc</th>\n",
+       "      <th>baro_units</th>\n",
+       "      <th>baro_cor</th>\n",
+       "      <th>thermo_mount</th>\n",
+       "      <th>SST_I</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
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+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
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+       "      <td>14</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
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+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>1</td>\n",
+       "      <td>001</td>\n",
+       "      <td>0014</td>\n",
+       "      <td>0437</td>\n",
+       "      <td>Jermiah Thompson</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>101878</td>\n",
+       "      <td>After Bulk Head Capt Room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0033</td>\n",
+       "      <td>0416</td>\n",
+       "      <td>Emma C.Litchfield</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>483</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>01101878</td>\n",
+       "      <td>After Cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>+ .001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>1</td>\n",
+       "      <td>007</td>\n",
+       "      <td>0129</td>\n",
+       "      <td>0410</td>\n",
+       "      <td>Emma</td>\n",
+       "      <td>78</td>\n",
+       "      <td>02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In the after cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .561</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>1</td>\n",
+       "      <td>002</td>\n",
+       "      <td>0018</td>\n",
+       "      <td>0003</td>\n",
+       "      <td>Panay</td>\n",
+       "      <td>78</td>\n",
+       "      <td>01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bulkhead of cabin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>- .102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>465 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sentinal reel_no journal_no frame_no          ship_name journal_ed rig  \\\n",
+       "0          1     002       0018     0003              Panay         78  01   \n",
+       "1          1     002       0018     0003              Panay         78  01   \n",
+       "2          1     002       0018     0003              Panay         78  01   \n",
+       "3          1     002       0018     0003              Panay         78  01   \n",
+       "4          1     002       0018     0003              Panay         78  01   \n",
+       "5          1     002       0018     0003              Panay         78  01   \n",
+       "6          1     007       0129     0410               Emma         78  02   \n",
+       "7          1     002       0018     0003              Panay         78  01   \n",
+       "8          1     007       0129     0410               Emma         78  02   \n",
+       "9          1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "10         1     002       0018     0003              Panay         78  01   \n",
+       "11         1     007       0129     0410               Emma         78  02   \n",
+       "12         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "13         1     002       0018     0003              Panay         78  01   \n",
+       "14         1     007       0129     0410               Emma         78  02   \n",
+       "15         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "16         1     002       0018     0003              Panay         78  01   \n",
+       "17         1     007       0129     0410               Emma         78  02   \n",
+       "18         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "19         1     002       0018     0003              Panay         78  01   \n",
+       "20         1     007       0129     0410               Emma         78  02   \n",
+       "21         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "22         1     002       0018     0003              Panay         78  01   \n",
+       "23         1     007       0129     0410               Emma         78  02   \n",
+       "24         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "25         1     002       0018     0003              Panay         78  01   \n",
+       "26         1     007       0129     0410               Emma         78  02   \n",
+       "27         1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "28         1     002       0018     0003              Panay         78  01   \n",
+       "29         1     007       0129     0410               Emma         78  02   \n",
+       "..       ...     ...        ...      ...                ...        ...  ..   \n",
+       "435        1     007       0129     0410               Emma         78  02   \n",
+       "436        1     002       0018     0003              Panay         78  01   \n",
+       "437        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "438        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "439        1     007       0129     0410               Emma         78  02   \n",
+       "440        1     002       0018     0003              Panay         78  01   \n",
+       "441        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "442        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "443        1     007       0129     0410               Emma         78  02   \n",
+       "444        1     002       0018     0003              Panay         78  01   \n",
+       "445        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "446        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "447        1     007       0129     0410               Emma         78  02   \n",
+       "448        1     002       0018     0003              Panay         78  01   \n",
+       "449        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "450        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "451        1     007       0129     0410               Emma         78  02   \n",
+       "452        1     002       0018     0003              Panay         78  01   \n",
+       "453        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "454        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "455        1     007       0129     0410               Emma         78  02   \n",
+       "456        1     002       0018     0003              Panay         78  01   \n",
+       "457        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "458        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "459        1     007       0129     0410               Emma         78  02   \n",
+       "460        1     002       0018     0003              Panay         78  01   \n",
+       "461        1     001       0014     0437   Jermiah Thompson         78  01   \n",
+       "462        1     002       0033     0416  Emma C.Litchfield         78  02   \n",
+       "463        1     007       0129     0410               Emma         78  02   \n",
+       "464        1     002       0018     0003              Panay         78  01   \n",
+       "\n",
+       "    ship_material vessel_type  vessel_length  ...  hold_depth tonnage  \\\n",
+       "0               1           1            187  ...          23    1190   \n",
+       "1               1           1            187  ...          23    1190   \n",
+       "2               1           1            187  ...          23    1190   \n",
+       "3               1           1            187  ...          23    1190   \n",
+       "4               1           1            187  ...          23    1190   \n",
+       "5               1           1            187  ...          23    1190   \n",
+       "6               1           1            136  ...          19     468   \n",
+       "7               1           1            187  ...          23    1190   \n",
+       "8               1           1            136  ...          19     468   \n",
+       "9               1           1            128  ...          17     483   \n",
+       "10              1           1            187  ...          23    1190   \n",
+       "11              1           1            136  ...          19     468   \n",
+       "12              1           1            128  ...          17     483   \n",
+       "13              1           1            187  ...          23    1190   \n",
+       "14              1           1            136  ...          19     468   \n",
+       "15              1           1            128  ...          17     483   \n",
+       "16              1           1            187  ...          23    1190   \n",
+       "17              1           1            136  ...          19     468   \n",
+       "18              1           1            128  ...          17     483   \n",
+       "19              1           1            187  ...          23    1190   \n",
+       "20              1           1            136  ...          19     468   \n",
+       "21              1           1            128  ...          17     483   \n",
+       "22              1           1            187  ...          23    1190   \n",
+       "23              1           1            136  ...          19     468   \n",
+       "24              1           1            128  ...          17     483   \n",
+       "25              1           1            187  ...          23    1190   \n",
+       "26              1           1            136  ...          19     468   \n",
+       "27              1           1            128  ...          17     483   \n",
+       "28              1           1            187  ...          23    1190   \n",
+       "29              1           1            136  ...          19     468   \n",
+       "..            ...         ...            ...  ...         ...     ...   \n",
+       "435             1           1            136  ...          19     468   \n",
+       "436             1           1            187  ...          23    1190   \n",
+       "437             1           1            217  ...          28    1904   \n",
+       "438             1           1            128  ...          17     483   \n",
+       "439             1           1            136  ...          19     468   \n",
+       "440             1           1            187  ...          23    1190   \n",
+       "441             1           1            217  ...          28    1904   \n",
+       "442             1           1            128  ...          17     483   \n",
+       "443             1           1            136  ...          19     468   \n",
+       "444             1           1            187  ...          23    1190   \n",
+       "445             1           1            217  ...          28    1904   \n",
+       "446             1           1            128  ...          17     483   \n",
+       "447             1           1            136  ...          19     468   \n",
+       "448             1           1            187  ...          23    1190   \n",
+       "449             1           1            217  ...          28    1904   \n",
+       "450             1           1            128  ...          17     483   \n",
+       "451             1           1            136  ...          19     468   \n",
+       "452             1           1            187  ...          23    1190   \n",
+       "453             1           1            217  ...          28    1904   \n",
+       "454             1           1            128  ...          17     483   \n",
+       "455             1           1            136  ...          19     468   \n",
+       "456             1           1            187  ...          23    1190   \n",
+       "457             1           1            217  ...          28    1904   \n",
+       "458             1           1            128  ...          17     483   \n",
+       "459             1           1            136  ...          19     468   \n",
+       "460             1           1            187  ...          23    1190   \n",
+       "461             1           1            217  ...          28    1904   \n",
+       "462             1           1            128  ...          17     483   \n",
+       "463             1           1            136  ...          19     468   \n",
+       "464             1           1            187  ...          23    1190   \n",
+       "\n",
+       "    baro_type baro_height  baro_cdate                   baro_loc baro_units  \\\n",
+       "0           2          14         NaN          Bulkhead of cabin          1   \n",
+       "1           2          14         NaN          Bulkhead of cabin          1   \n",
+       "2           2          14         NaN          Bulkhead of cabin          1   \n",
+       "3           2          14         NaN          Bulkhead of cabin          1   \n",
+       "4           2          14         NaN          Bulkhead of cabin          1   \n",
+       "5           2          14         NaN          Bulkhead of cabin          1   \n",
+       "6           1          10         NaN         In the after cabin          1   \n",
+       "7           2          14         NaN          Bulkhead of cabin          1   \n",
+       "8           1          10         NaN         In the after cabin          1   \n",
+       "9           1          17    01101878                After Cabin          1   \n",
+       "10          2          14         NaN          Bulkhead of cabin          1   \n",
+       "11          1          10         NaN         In the after cabin          1   \n",
+       "12          1          17    01101878                After Cabin          1   \n",
+       "13          2          14         NaN          Bulkhead of cabin          1   \n",
+       "14          1          10         NaN         In the after cabin          1   \n",
+       "15          1          17    01101878                After Cabin          1   \n",
+       "16          2          14         NaN          Bulkhead of cabin          1   \n",
+       "17          1          10         NaN         In the after cabin          1   \n",
+       "18          1          17    01101878                After Cabin          1   \n",
+       "19          2          14         NaN          Bulkhead of cabin          1   \n",
+       "20          1          10         NaN         In the after cabin          1   \n",
+       "21          1          17    01101878                After Cabin          1   \n",
+       "22          2          14         NaN          Bulkhead of cabin          1   \n",
+       "23          1          10         NaN         In the after cabin          1   \n",
+       "24          1          17    01101878                After Cabin          1   \n",
+       "25          2          14         NaN          Bulkhead of cabin          1   \n",
+       "26          1          10         NaN         In the after cabin          1   \n",
+       "27          1          17    01101878                After Cabin          1   \n",
+       "28          2          14         NaN          Bulkhead of cabin          1   \n",
+       "29          1          10         NaN         In the after cabin          1   \n",
+       "..        ...         ...         ...                        ...        ...   \n",
+       "435         1          10         NaN         In the after cabin          1   \n",
+       "436         2          14         NaN          Bulkhead of cabin          1   \n",
+       "437         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "438         1          17    01101878                After Cabin          1   \n",
+       "439         1          10         NaN         In the after cabin          1   \n",
+       "440         2          14         NaN          Bulkhead of cabin          1   \n",
+       "441         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "442         1          17    01101878                After Cabin          1   \n",
+       "443         1          10         NaN         In the after cabin          1   \n",
+       "444         2          14         NaN          Bulkhead of cabin          1   \n",
+       "445         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "446         1          17    01101878                After Cabin          1   \n",
+       "447         1          10         NaN         In the after cabin          1   \n",
+       "448         2          14         NaN          Bulkhead of cabin          1   \n",
+       "449         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "450         1          17    01101878                After Cabin          1   \n",
+       "451         1          10         NaN         In the after cabin          1   \n",
+       "452         2          14         NaN          Bulkhead of cabin          1   \n",
+       "453         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "454         1          17    01101878                After Cabin          1   \n",
+       "455         1          10         NaN         In the after cabin          1   \n",
+       "456         2          14         NaN          Bulkhead of cabin          1   \n",
+       "457         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "458         1          17    01101878                After Cabin          1   \n",
+       "459         1          10         NaN         In the after cabin          1   \n",
+       "460         2          14         NaN          Bulkhead of cabin          1   \n",
+       "461         1          15      101878  After Bulk Head Capt Room          1   \n",
+       "462         1          17    01101878                After Cabin          1   \n",
+       "463         1          10         NaN         In the after cabin          1   \n",
+       "464         2          14         NaN          Bulkhead of cabin          1   \n",
+       "\n",
+       "     baro_cor thermo_mount SST_I  \n",
+       "0      - .102            2   NaN  \n",
+       "1      - .102            2   NaN  \n",
+       "2      - .102            2   NaN  \n",
+       "3      - .102            2   NaN  \n",
+       "4      - .102            2   NaN  \n",
+       "5      - .102            2   NaN  \n",
+       "6      - .561            1   NaN  \n",
+       "7      - .102            2   NaN  \n",
+       "8      - .561            1   NaN  \n",
+       "9      + .001            1   NaN  \n",
+       "10     - .102            2   NaN  \n",
+       "11     - .561            1   NaN  \n",
+       "12     + .001            1   NaN  \n",
+       "13     - .102            2   NaN  \n",
+       "14     - .561            1   NaN  \n",
+       "15     + .001            1   NaN  \n",
+       "16     - .102            2   NaN  \n",
+       "17     - .561            1   NaN  \n",
+       "18     + .001            1   NaN  \n",
+       "19     - .102            2   NaN  \n",
+       "20     - .561            1   NaN  \n",
+       "21     + .001            1   NaN  \n",
+       "22     - .102            2   NaN  \n",
+       "23     - .561            1   NaN  \n",
+       "24     + .001            1   NaN  \n",
+       "25     - .102            2   NaN  \n",
+       "26     - .561            1   NaN  \n",
+       "27     + .001            1   NaN  \n",
+       "28     - .102            2   NaN  \n",
+       "29     - .561            1   NaN  \n",
+       "..        ...          ...   ...  \n",
+       "435    - .561            1   NaN  \n",
+       "436    - .102            2   NaN  \n",
+       "437    + .003            4   NaN  \n",
+       "438    + .001            1   NaN  \n",
+       "439    - .561            1   NaN  \n",
+       "440    - .102            2   NaN  \n",
+       "441    + .003            4   NaN  \n",
+       "442    + .001            1   NaN  \n",
+       "443    - .561            1   NaN  \n",
+       "444    - .102            2   NaN  \n",
+       "445    + .003            4   NaN  \n",
+       "446    + .001            1   NaN  \n",
+       "447    - .561            1   NaN  \n",
+       "448    - .102            2   NaN  \n",
+       "449    + .003            4   NaN  \n",
+       "450    + .001            1   NaN  \n",
+       "451    - .561            1   NaN  \n",
+       "452    - .102            2   NaN  \n",
+       "453    + .003            4   NaN  \n",
+       "454    + .001            1   NaN  \n",
+       "455    - .561            1   NaN  \n",
+       "456    - .102            2   NaN  \n",
+       "457    + .003            4   NaN  \n",
+       "458    + .001            1   NaN  \n",
+       "459    - .561            1   NaN  \n",
+       "460    - .102            2   NaN  \n",
+       "461    + .003            4   NaN  \n",
+       "462    + .001            1   NaN  \n",
+       "463    - .561            1   NaN  \n",
+       "464    - .102            2   NaN  \n",
+       "\n",
+       "[465 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data.c99_journal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To learn how to construc a schema or data model for a particular deck/source, visit this other tutorial. INSERT LINK TO NEXT NOTEBOOK"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (c3s)",
+   "language": "python",
+   "name": "c3s"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
GitLab