{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Information\n",
    "__Section__: Examine the datasets  \n",
    "__Goal__: Understand the content and the distribution of the datasets we are using in this part.  \n",
    "__Time needed__: 30 min  \n",
    "__Prerequisites__: Understanding of AIS data\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the data <a class=\"anchor\" id=\"import-data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you understand the meaning of each attribute, you can take a deeper look into the dataset itself, to learn from the distribution of the attributes and their possible relationships and interdependencies. This step will allow you to have a good overview on your data to better solve the diverse tasks for your customers later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "First, you need to load the data again.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dynamic_data = pd.read_csv('./dynamic_data.csv')\n",
    "static_data = pd.read_csv('./static_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic data - let's do it together <a class=\"anchor\" id=\"dyn-data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can use various methods to get an overview of the dataset. Let's have a look at those together:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "The method [info()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) returns the count of instances and attributes in the dataset, the method [describe()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) prints the distribution of each numerical attribute.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 26 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   MMSI          100000 non-null  int64  \n",
      " 1   BaseDateTime  100000 non-null  object \n",
      " 2   LAT           100000 non-null  float64\n",
      " 3   LON           100000 non-null  float64\n",
      " 4   SOG           100000 non-null  float64\n",
      " 5   COG           100000 non-null  float64\n",
      " 6   Heading       100000 non-null  float64\n",
      " 7   VesselName    95405 non-null   object \n",
      " 8   IMO           47669 non-null   object \n",
      " 9   CallSign      80589 non-null   object \n",
      " 10  VesselType    88926 non-null   float64\n",
      " 11  Status        70249 non-null   object \n",
      " 12  Length        85415 non-null   float64\n",
      " 13  Width         71123 non-null   float64\n",
      " 14  Draft         42189 non-null   float64\n",
      " 15  TripID        100000 non-null  int64  \n",
      " 16  DepTime       100000 non-null  object \n",
      " 17  ArrTime       100000 non-null  object \n",
      " 18  DepLat        100000 non-null  float64\n",
      " 19  DepLon        100000 non-null  float64\n",
      " 20  ArrLat        100000 non-null  float64\n",
      " 21  ArrLon        100000 non-null  float64\n",
      " 22  DepCountry    100000 non-null  object \n",
      " 23  DepCity       100000 non-null  object \n",
      " 24  ArrCountry    100000 non-null  object \n",
      " 25  ArrCity       100000 non-null  object \n",
      "dtypes: float64(13), int64(2), object(11)\n",
      "memory usage: 19.8+ MB\n"
     ]
    }
   ],
   "source": [
    "dynamic_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ``RangeIndex: 100000 entries, 0 to 99999``: this means that the dataset contains 100000 instances, or 100000 lines. This is the number of AIS messages that are represented in the dataset.\n",
    "+ ``Data columns (total 26 columns)``: this shows that the dataset contains 26 attributes (represented as columns in the dataset).\n",
    "+ Then follows a list of each attribute, with the number of recorded (non-null) values for each and their [type](./../../introduction/0-2-supervised-learning.html). \n",
    "+ Finally, we see a summary of the types and the number of attributes of each type, and the memory used by this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMSI</th>\n",
       "      <th>BaseDateTime</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>SOG</th>\n",
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       "      <th>TripID</th>\n",
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       "      <th>ArrTime</th>\n",
       "      <th>DepLat</th>\n",
       "      <th>DepLon</th>\n",
       "      <th>ArrLat</th>\n",
       "      <th>ArrLon</th>\n",
       "      <th>DepCountry</th>\n",
       "      <th>DepCity</th>\n",
       "      <th>ArrCountry</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>367114690</td>\n",
       "      <td>2017-01-01 00:00:06</td>\n",
       "      <td>48.51094</td>\n",
       "      <td>-122.60705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-49.6</td>\n",
       "      <td>511.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>under way using engine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01 00:00:06</td>\n",
       "      <td>2017-01-01 02:40:45</td>\n",
       "      <td>48.51094</td>\n",
       "      <td>-122.60705</td>\n",
       "      <td>48.51095</td>\n",
       "      <td>-122.60705</td>\n",
       "      <td>US</td>\n",
       "      <td>Anacortes</td>\n",
       "      <td>US</td>\n",
       "      <td>Anacortes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MMSI         BaseDateTime       LAT        LON  SOG   COG  Heading  \\\n",
       "0  367114690  2017-01-01 00:00:06  48.51094 -122.60705  0.0 -49.6    511.0   \n",
       "\n",
       "  VesselName  IMO CallSign  VesselType                  Status  Length  Width  \\\n",
       "0        NaN  NaN      NaN         NaN  under way using engine     NaN    NaN   \n",
       "\n",
       "   Draft  TripID              DepTime              ArrTime    DepLat  \\\n",
       "0    NaN       1  2017-01-01 00:00:06  2017-01-01 02:40:45  48.51094   \n",
       "\n",
       "      DepLon    ArrLat     ArrLon DepCountry    DepCity ArrCountry    ArrCity  \n",
       "0 -122.60705  48.51095 -122.60705         US  Anacortes         US  Anacortes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dynamic_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we printed the first instance of the dataset. As we see, some attributes have the value ``NaN``. This means that this value is missing (it has not been recorded or saved). This is the reason why some attributes above don't have ``100000 non-null`` but rather a lower number of instances: some of these values are missing, and we call them __missing values__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMSI</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>SOG</th>\n",
       "      <th>COG</th>\n",
       "      <th>Heading</th>\n",
       "      <th>VesselType</th>\n",
       "      <th>Length</th>\n",
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       "      <th>ArrLon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>88926.000000</td>\n",
       "      <td>85415.000000</td>\n",
       "      <td>71123.000000</td>\n",
       "      <td>42189.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.590440e+08</td>\n",
       "      <td>46.221127</td>\n",
       "      <td>-122.893343</td>\n",
       "      <td>1.775973</td>\n",
       "      <td>-16.296728</td>\n",
       "      <td>369.527000</td>\n",
       "      <td>952.373142</td>\n",
       "      <td>60.899165</td>\n",
       "      <td>13.653366</td>\n",
       "      <td>6.055567</td>\n",
       "      <td>668.135890</td>\n",
       "      <td>46.222409</td>\n",
       "      <td>-122.892360</td>\n",
       "      <td>46.220749</td>\n",
       "      <td>-122.897869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.927431e+07</td>\n",
       "      <td>3.850865</td>\n",
       "      <td>0.703268</td>\n",
       "      <td>4.491950</td>\n",
       "      <td>118.459501</td>\n",
       "      <td>174.132613</td>\n",
       "      <td>237.057544</td>\n",
       "      <td>74.529841</td>\n",
       "      <td>10.556948</td>\n",
       "      <td>4.340102</td>\n",
       "      <td>409.245317</td>\n",
       "      <td>3.854998</td>\n",
       "      <td>0.706539</td>\n",
       "      <td>3.846843</td>\n",
       "      <td>0.705792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.160089e+06</td>\n",
       "      <td>32.209370</td>\n",
       "      <td>-125.998590</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-204.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.220640</td>\n",
       "      <td>-125.995610</td>\n",
       "      <td>32.209370</td>\n",
       "      <td>-125.998590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.160334e+08</td>\n",
       "      <td>46.137208</td>\n",
       "      <td>-123.190290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-116.600000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>18.140000</td>\n",
       "      <td>6.430000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>46.137290</td>\n",
       "      <td>-123.204420</td>\n",
       "      <td>46.144140</td>\n",
       "      <td>-123.192990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.669768e+08</td>\n",
       "      <td>47.645370</td>\n",
       "      <td>-122.688210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-49.600000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>26.490000</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>646.000000</td>\n",
       "      <td>47.645390</td>\n",
       "      <td>-122.683800</td>\n",
       "      <td>47.645110</td>\n",
       "      <td>-122.688210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.675157e+08</td>\n",
       "      <td>48.621405</td>\n",
       "      <td>-122.385730</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>77.900000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>60.840000</td>\n",
       "      <td>18.280000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>48.621300</td>\n",
       "      <td>-122.385500</td>\n",
       "      <td>48.621200</td>\n",
       "      <td>-122.385970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.876543e+08</td>\n",
       "      <td>49.890740</td>\n",
       "      <td>-120.002420</td>\n",
       "      <td>42.100000</td>\n",
       "      <td>204.700000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>49.890740</td>\n",
       "      <td>-120.002920</td>\n",
       "      <td>49.832120</td>\n",
       "      <td>-120.002420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MMSI            LAT            LON            SOG  \\\n",
       "count  1.000000e+05  100000.000000  100000.000000  100000.000000   \n",
       "mean   3.590440e+08      46.221127    -122.893343       1.775973   \n",
       "std    5.927431e+07       3.850865       0.703268       4.491950   \n",
       "min    3.160089e+06      32.209370    -125.998590      -0.100000   \n",
       "25%    3.160334e+08      46.137208    -123.190290       0.000000   \n",
       "50%    3.669768e+08      47.645370    -122.688210       0.000000   \n",
       "75%    3.675157e+08      48.621405    -122.385730       0.100000   \n",
       "max    9.876543e+08      49.890740    -120.002420      42.100000   \n",
       "\n",
       "                 COG        Heading    VesselType        Length         Width  \\\n",
       "count  100000.000000  100000.000000  88926.000000  85415.000000  71123.000000   \n",
       "mean      -16.296728     369.527000    952.373142     60.899165     13.653366   \n",
       "std       118.459501     174.132613    237.057544     74.529841     10.556948   \n",
       "min      -204.800000       0.000000      0.000000      6.710000      0.000000   \n",
       "25%      -116.600000     205.000000   1004.000000     18.140000      6.430000   \n",
       "50%       -49.600000     511.000000   1018.000000     26.490000      9.350000   \n",
       "75%        77.900000     511.000000   1019.000000     60.840000     18.280000   \n",
       "max       204.700000     511.000000   1025.000000    349.000000     50.000000   \n",
       "\n",
       "              Draft         TripID         DepLat         DepLon  \\\n",
       "count  42189.000000  100000.000000  100000.000000  100000.000000   \n",
       "mean       6.055567     668.135890      46.222409    -122.892360   \n",
       "std        4.340102     409.245317       3.854998       0.706539   \n",
       "min        0.000000       1.000000      32.220640    -125.995610   \n",
       "25%        3.000000     317.000000      46.137290    -123.204420   \n",
       "50%        4.500000     646.000000      47.645390    -122.683800   \n",
       "75%        9.100000    1001.000000      48.621300    -122.385500   \n",
       "max       18.800000    1520.000000      49.890740    -120.002920   \n",
       "\n",
       "              ArrLat         ArrLon  \n",
       "count  100000.000000  100000.000000  \n",
       "mean       46.220749    -122.897869  \n",
       "std         3.846843       0.705792  \n",
       "min        32.209370    -125.998590  \n",
       "25%        46.144140    -123.192990  \n",
       "50%        47.645110    -122.688210  \n",
       "75%        48.621200    -122.385970  \n",
       "max        49.832120    -120.002420  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dynamic_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns some statistics about the distribution of the numerical attributes in the dataset. For example, we can see that the 3rd quartile ('75%') of the attribute SOG has a value of 0.1, which means that 75% of the recorded values for SOG are less than 0.1 Knot: we can conclude from this information that most of the recorded datapoints in this dataset concern immobile ships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, with some simple histograms, we can have a visualization the distribution of each attribute in the dataset. This will allow us to look a little deeper than with the above functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "For that, we use the method [plot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html) of a Pandas Series, which allows to produce different types of plot for an attribute, here we choose [hist()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.hist.html).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dynamic_data['LAT'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the distribution of the latitude attribute in the dataset. We can see that many values are comprised in the range [45.0 ; 50.0]. This means that many positions are recorded in this area, while the range [32.5 ; 45.0] is less dense in recorded positions. If you want to know more about the histograms and the different kinds of plots used in the course, you can visit [this page](./../../introduction/0-3-graphs.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create this type of plot for every numerical attribute in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "In the previous cell, try to change the name of the plotted attribute to visualize the other attributes. You can even try to put the name of a non-numerical attribute to see what happens.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a9c6ab969a341bcb3fd5db75b808ba3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute:', options=('MMSI', 'LAT', 'LON', 'SOG', 'COG', 'Heading…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_hist(att)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "num_attributes = dynamic_data.select_dtypes([np.number]).columns\n",
    "\n",
    "def plot_hist(att):\n",
    "    dynamic_data[att].plot.hist()\n",
    "\n",
    "interact(plot_hist, att = widgets.Dropdown(\n",
    "    options = num_attributes,\n",
    "    value = num_attributes[0],\n",
    "    description = 'Attribute:',\n",
    "    disabled = False,\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can see the different values of each attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "For that, we use the method [unique()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.unique.html):\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 1012., 1019., 1001., 1025., 1024., 1004., 1018., 1005.,\n",
       "         70.,   30., 1020.,   99., 1003.,   80., 1013.,   31., 1002.,\n",
       "         52.,   53., 1011.,   50.,   35., 1023.,   69.,   37.,    7.,\n",
       "         60., 1022.,   90.,   51., 1010.,    0.,   79.,   71., 1017.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dynamic_data['VesselType'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this result, and by comparing the values with the AIS documention available [here](https://coast.noaa.gov/data/marinecadastre/ais/VesselTypeCodes2018.pdf), you can analyze what kind of ships are present in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "Have a look at the different values for the other attributes by changing the name of the attribute in the previous cell.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "448006a60f634a9d9e191df60b34f168",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute:', options=('MMSI', 'BaseDateTime', 'LAT', 'LON', 'SOG',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.get_unique(att)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "attributes = dynamic_data.columns\n",
    "\n",
    "def get_unique(att):\n",
    "    print(dynamic_data[att].unique())\n",
    "\n",
    "interact(get_unique, att = widgets.Dropdown(\n",
    "    options = attributes,\n",
    "    value = attributes[0],\n",
    "    description = 'Attribute:',\n",
    "    disabled = False,\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionaly, we can create a 2 dimensional plot, to represent 2 attributes against each other. For example, on this picture, we represent the attributes longitude and latitude, to get a geographical representation of the recorded datapoints:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "For this, we use the library [pyplot](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.html) and the function [plot()](https://matplotlib.org/3.1.3/api/_as_gen/matplotlib.pyplot.plot.html). \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20f57e05668>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(dynamic_data['Length'], dynamic_data['SOG'], 'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datapoints are represented as crosses, and on this plot, each separated blue line is a very likely one recorded trip."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{toggle} Advanced level\n",
    "Change the name of the attributes and try to compare other (numerical) attributes.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6553ac13c3f44ff2bbca2695ef14bea8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute (x):', options=('MMSI', 'LAT', 'LON', 'SOG', 'COG', 'Hea…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_2att(att1, att2)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "num_attributes = dynamic_data.select_dtypes([np.number]).columns\n",
    "\n",
    "def plot_2att(att1, att2):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(dynamic_data[att1], dynamic_data[att2], 'x')\n",
    "    plt.xlabel(att1)\n",
    "    plt.ylabel(att2)\n",
    "\n",
    "interact(plot_2att,\n",
    "         att1 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (x):',\n",
    "                                 disabled = False,),\n",
    "         att2 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (y):',\n",
    "                                 disabled = False,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also choose to visualize the information of this dataset for only one trip. For example, we want to see the longitude and latitude values for the trip with the TripID 106:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20f56893860>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for one trip\n",
    "\n",
    "trip = dynamic_data.loc[dynamic_data['TripID'] == 106]\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(trip['LON'], trip['LAT'], 'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the value of the TripID attribute, and the names of the attributes, to plot anything else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab3e33e3223346d59eeb338d3e8285ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(BoundedIntText(value=1, description='TripID [1 ; 1520]:', max=1520, min=1), Dropdown(des…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_1trip(tripid, att1, att2)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "num_attributes = dynamic_data.select_dtypes([np.number]).columns\n",
    "\n",
    "def plot_1trip(tripid, att1, att2):\n",
    "    trip = dynamic_data.loc[dynamic_data['TripID'] == tripid]\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(trip[att1], trip[att2], 'x')\n",
    "    plt.xlabel(att1)\n",
    "    plt.ylabel(att2)\n",
    "\n",
    "interact(plot_1trip,\n",
    "         tripid = widgets.BoundedIntText(value = 1,\n",
    "                                         min = 1,\n",
    "                                         max = 1520,\n",
    "                                         step = 1,\n",
    "                                         description = 'TripID [1 ; 1520]:',\n",
    "                                         disabled = False),\n",
    "         att1 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (x):',\n",
    "                                 disabled = False,),\n",
    "         att2 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (y):',\n",
    "                                 disabled = False,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"694\"\n",
       "            height=\"600\"\n",
       "            src=\"https://h5p.org/h5p/embed/742748\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x252f844bcf8>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(\"https://h5p.org/h5p/embed/742748\", \"694\", \"600\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static data - your turn <a class=\"anchor\" id=\"sta-data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same tools as for the dynamic dataset, you can now analyze the static dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1520 entries, 0 to 1519\n",
      "Data columns (total 22 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   TripID      1520 non-null   int64  \n",
      " 1   MMSI        1520 non-null   int64  \n",
      " 2   MeanSOG     1520 non-null   float64\n",
      " 3   VesselName  1442 non-null   object \n",
      " 4   IMO         538 non-null    object \n",
      " 5   CallSign    1137 non-null   object \n",
      " 6   VesselType  1287 non-null   float64\n",
      " 7   Length      1220 non-null   float64\n",
      " 8   Width       911 non-null    float64\n",
      " 9   Draft       496 non-null    float64\n",
      " 10  Cargo       378 non-null    float64\n",
      " 11  DepTime     1520 non-null   object \n",
      " 12  ArrTime     1520 non-null   object \n",
      " 13  DepLat      1520 non-null   float64\n",
      " 14  DepLon      1520 non-null   float64\n",
      " 15  ArrLat      1520 non-null   float64\n",
      " 16  ArrLon      1520 non-null   float64\n",
      " 17  DepCountry  1520 non-null   object \n",
      " 18  DepCity     1520 non-null   object \n",
      " 19  ArrCountry  1520 non-null   object \n",
      " 20  ArrCity     1520 non-null   object \n",
      " 21  Duration    1520 non-null   object \n",
      "dtypes: float64(10), int64(2), object(10)\n",
      "memory usage: 261.4+ KB\n"
     ]
    }
   ],
   "source": [
    "static_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>TripID</th>\n",
       "      <th>MMSI</th>\n",
       "      <th>MeanSOG</th>\n",
       "      <th>VesselType</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Draft</th>\n",
       "      <th>Cargo</th>\n",
       "      <th>DepLat</th>\n",
       "      <th>DepLon</th>\n",
       "      <th>ArrLat</th>\n",
       "      <th>ArrLon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1.520000e+03</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>1220.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>760.500000</td>\n",
       "      <td>3.597421e+08</td>\n",
       "      <td>1.034825</td>\n",
       "      <td>971.680653</td>\n",
       "      <td>56.769590</td>\n",
       "      <td>13.104501</td>\n",
       "      <td>6.457056</td>\n",
       "      <td>50.515873</td>\n",
       "      <td>46.354331</td>\n",
       "      <td>-122.868905</td>\n",
       "      <td>46.353671</td>\n",
       "      <td>-122.871346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>438.930518</td>\n",
       "      <td>6.263661e+07</td>\n",
       "      <td>2.936439</td>\n",
       "      <td>198.957887</td>\n",
       "      <td>74.739358</td>\n",
       "      <td>10.903338</td>\n",
       "      <td>4.607529</td>\n",
       "      <td>22.693810</td>\n",
       "      <td>3.766705</td>\n",
       "      <td>0.681947</td>\n",
       "      <td>3.762056</td>\n",
       "      <td>0.680604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.160089e+06</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.220640</td>\n",
       "      <td>-125.995610</td>\n",
       "      <td>32.209370</td>\n",
       "      <td>-125.998590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>380.750000</td>\n",
       "      <td>3.380724e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>14.840000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>46.168652</td>\n",
       "      <td>-123.178480</td>\n",
       "      <td>46.168460</td>\n",
       "      <td>-123.168262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>760.500000</td>\n",
       "      <td>3.669802e+08</td>\n",
       "      <td>0.012633</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>22.340000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>47.647795</td>\n",
       "      <td>-122.651365</td>\n",
       "      <td>47.646925</td>\n",
       "      <td>-122.645290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1140.250000</td>\n",
       "      <td>3.675663e+08</td>\n",
       "      <td>0.072000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>41.277500</td>\n",
       "      <td>16.350000</td>\n",
       "      <td>10.025000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>48.656940</td>\n",
       "      <td>-122.386562</td>\n",
       "      <td>48.665710</td>\n",
       "      <td>-122.386607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1520.000000</td>\n",
       "      <td>9.876543e+08</td>\n",
       "      <td>20.360811</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>49.890740</td>\n",
       "      <td>-120.002920</td>\n",
       "      <td>49.832120</td>\n",
       "      <td>-120.002420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TripID          MMSI      MeanSOG   VesselType       Length  \\\n",
       "count  1520.000000  1.520000e+03  1520.000000  1287.000000  1220.000000   \n",
       "mean    760.500000  3.597421e+08     1.034825   971.680653    56.769590   \n",
       "std     438.930518  6.263661e+07     2.936439   198.957887    74.739358   \n",
       "min       1.000000  3.160089e+06    -0.100000     0.000000     6.710000   \n",
       "25%     380.750000  3.380724e+08     0.000000  1004.000000    14.840000   \n",
       "50%     760.500000  3.669802e+08     0.012633  1019.000000    22.340000   \n",
       "75%    1140.250000  3.675663e+08     0.072000  1019.000000    41.277500   \n",
       "max    1520.000000  9.876543e+08    20.360811  1025.000000   349.000000   \n",
       "\n",
       "            Width       Draft       Cargo       DepLat       DepLon  \\\n",
       "count  911.000000  496.000000  378.000000  1520.000000  1520.000000   \n",
       "mean    13.104501    6.457056   50.515873    46.354331  -122.868905   \n",
       "std     10.903338    4.607529   22.693810     3.766705     0.681947   \n",
       "min      0.000000    0.000000    0.000000    32.220640  -125.995610   \n",
       "25%      5.500000    3.000000   31.000000    46.168652  -123.178480   \n",
       "50%      8.000000    4.650000   52.000000    47.647795  -122.651365   \n",
       "75%     16.350000   10.025000   70.000000    48.656940  -122.386562   \n",
       "max     50.000000   18.800000   99.000000    49.890740  -120.002920   \n",
       "\n",
       "            ArrLat       ArrLon  \n",
       "count  1520.000000  1520.000000  \n",
       "mean     46.353671  -122.871346  \n",
       "std       3.762056     0.680604  \n",
       "min      32.209370  -125.998590  \n",
       "25%      46.168460  -123.168262  \n",
       "50%      47.646925  -122.645290  \n",
       "75%      48.665710  -122.386607  \n",
       "max      49.832120  -120.002420  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "static_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "#static_data[''].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10865dee12914bffb708dc20b7f5b734",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute:', options=('TripID', 'MMSI', 'MeanSOG', 'VesselType', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_hist(att)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "num_attributes = static_data.select_dtypes([np.number]).columns\n",
    "\n",
    "def plot_hist(att):\n",
    "    static_data[att].plot.hist()\n",
    "\n",
    "interact(plot_hist, att = widgets.Dropdown(\n",
    "    options = num_attributes,\n",
    "    value = num_attributes[0],\n",
    "    description = 'Attribute:',\n",
    "    disabled = False,\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "#static_data[''].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bc01167517a4c57807e79fdcae088c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute:', options=('TripID', 'MMSI', 'MeanSOG', 'VesselName', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.get_unique(att)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "attributes = static_data.columns\n",
    "\n",
    "def get_unique(att):\n",
    "    print(static_data[att].unique())\n",
    "\n",
    "interact(get_unique, att = widgets.Dropdown(\n",
    "    options = attributes,\n",
    "    value = attributes[0],\n",
    "    description = 'Attribute:',\n",
    "    disabled = False,\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "#plt.plot(static_data[''], static_data[''], 'x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f05898b2c124d80bf5542f6edeee257",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Attribute (x):', options=('TripID', 'MMSI', 'MeanSOG', 'VesselType…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_2att(att1, att2)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For beginner version: cell to hide\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "num_attributes = static_data.select_dtypes([np.number]).columns\n",
    "\n",
    "def plot_2att(att1, att2):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(static_data[att1], static_data[att2], 'x')\n",
    "    plt.xlabel(att1)\n",
    "    plt.ylabel(att2)\n",
    "\n",
    "interact(plot_2att,\n",
    "         att1 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (x):',\n",
    "                                 disabled = False,),\n",
    "         att2 = widgets.Dropdown(options = num_attributes,\n",
    "                                 value = num_attributes[0],\n",
    "                                 description = 'Attribute (y):',\n",
    "                                 disabled = False,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"694\"\n",
       "            height=\"600\"\n",
       "            src=\"https://h5p.org/h5p/embed/742791\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x252f84678d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(\"https://h5p.org/h5p/embed/742791\", \"694\", \"600\")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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