diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 0256cb17..89a2af5b 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -197,13 +197,13 @@ currently using a minimum version of PyCharm 2019.2.4.
 * To run the automatic formatter and check for lint issues run
 
     ``` bash
-    > nox -s format`
+    > nox -s format
     ```
 
 * To test specific versions of Python run
 
     ``` bash
-    > nox -s test-3.8`
+    > nox -s test-3.8
     ```
 
 ### Documentation
diff --git a/eland/field_mappings.py b/eland/field_mappings.py
index 45724a43..d8449e34 100644
--- a/eland/field_mappings.py
+++ b/eland/field_mappings.py
@@ -202,6 +202,11 @@ def __init__(
             )
 
         get_mapping = client.indices.get_mapping(index=index_pattern)
+        if not get_mapping:  # dict is empty
+            raise ValueError(
+                f"Can not get mapping for {index_pattern} "
+                f"check indexes exist and client has permission to get mapping."
+            )
 
         # Get all fields (including all nested) and then all field_caps
         all_fields = FieldMappings._extract_fields_from_mapping(get_mapping)
@@ -307,7 +312,10 @@ def flatten(x, name=""):
                         if field_type == "date" and "format" in x:
                             date_format = x["format"]
                         # If there is a conflicting type, warn - first values added wins
-                        if field_name in fields and fields[field_name] != field_type:
+                        if field_name in fields and fields[field_name] != (
+                            field_type,
+                            date_format,
+                        ):
                             warnings.warn(
                                 f"Field {field_name} has conflicting types "
                                 f"{fields[field_name]} != {field_type}",
@@ -403,14 +411,14 @@ def _create_capability_matrix(all_fields, source_fields, all_fields_caps):
 
                     if "non_aggregatable_indices" in vv:
                         warnings.warn(
-                            "Field {} has conflicting aggregatable fields across indexes {}",
-                            format(field, vv["non_aggregatable_indices"]),
+                            f"Field {field} has conflicting aggregatable fields across indexes "
+                            f"{str(vv['non_aggregatable_indices'])}",
                             UserWarning,
                         )
                     if "non_searchable_indices" in vv:
                         warnings.warn(
-                            "Field {} has conflicting searchable fields across indexes {}",
-                            format(field, vv["non_searchable_indices"]),
+                            f"Field {field} has conflicting searchable fields across indexes "
+                            f"{str(vv['non_searchable_indices'])}",
                             UserWarning,
                         )
 
diff --git a/eland/ml/transformers/__init__.py b/eland/ml/transformers/__init__.py
index 7c56f39a..8f08d904 100644
--- a/eland/ml/transformers/__init__.py
+++ b/eland/ml/transformers/__init__.py
@@ -65,19 +65,15 @@ def get_model_transformer(model: Any, **kwargs: Any) -> ModelTransformer:
 try:
     from .xgboost import _MODEL_TRANSFORMERS as _XGBOOST_MODEL_TRANSFORMERS
     from .xgboost import (
-        XGBClassifier,
         XGBoostClassifierTransformer,
         XGBoostForestTransformer,
         XGBoostRegressorTransformer,
-        XGBRegressor,
     )
 
     __all__ += [
         "XGBoostClassifierTransformer",
-        "XGBClassifier",
         "XGBoostForestTransformer",
         "XGBoostRegressorTransformer",
-        "XGBRegressor",
     ]
     _MODEL_TRANSFORMERS.update(_XGBOOST_MODEL_TRANSFORMERS)
 except ImportError:
@@ -86,16 +82,12 @@ def get_model_transformer(model: Any, **kwargs: Any) -> ModelTransformer:
 try:
     from .lightgbm import _MODEL_TRANSFORMERS as _LIGHTGBM_MODEL_TRANSFORMERS
     from .lightgbm import (
-        LGBMClassifier,
         LGBMClassifierTransformer,
         LGBMForestTransformer,
-        LGBMRegressor,
         LGBMRegressorTransformer,
     )
 
     __all__ += [
-        "LGBMRegressor",
-        "LGBMClassifier",
         "LGBMForestTransformer",
         "LGBMRegressorTransformer",
         "LGBMClassifierTransformer",
diff --git a/tests/notebook/test_demo_notebook.ipynb b/tests/notebook/test_demo_notebook.ipynb
index 181bcaf3..5ebbfc5a 100644
--- a/tests/notebook/test_demo_notebook.ipynb
+++ b/tests/notebook/test_demo_notebook.ipynb
@@ -10,11 +10,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import eland as ed\n",
@@ -25,7 +21,12 @@
     "from elasticsearch import Elasticsearch\n",
     "\n",
     "# Import standard test settings for consistent results\n",
-    "from eland.conftest import *"
+    "from eland.conftest import *\n",
+    "\n",
+    "# Remove pandas/matplotlib deprecation warning\n",
+    "import warnings\n",
+    "import matplotlib.cbook\n",
+    "warnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)"
    ]
   },
   {
@@ -45,11 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ed_flights = ed.DataFrame('localhost', 'flights')"
@@ -58,17 +55,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "eland.dataframe.DataFrame"
-      ]
+      "text/plain": "eland.dataframe.DataFrame"
      },
      "execution_count": 3,
      "metadata": {},
@@ -89,11 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pd_flights = ed.eland_to_pandas(ed_flights)"
@@ -102,17 +89,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
+      "text/plain": "pandas.core.frame.DataFrame"
      },
      "execution_count": 5,
      "metadata": {},
@@ -140,23 +121,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n",
-       "       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n",
-       "       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n",
-       "       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n",
-       "       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n",
-       "       'timestamp'],\n",
-       "      dtype='object')"
-      ]
+      "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n       'timestamp'],\n      dtype='object')"
      },
      "execution_count": 6,
      "metadata": {},
@@ -170,23 +139,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n",
-       "       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n",
-       "       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n",
-       "       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n",
-       "       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n",
-       "       'timestamp'],\n",
-       "      dtype='object')"
-      ]
+      "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n       'timestamp'],\n      dtype='object')"
      },
      "execution_count": 7,
      "metadata": {},
@@ -207,28 +164,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice           float64\n",
-       "Cancelled                   bool\n",
-       "Carrier                   object\n",
-       "Dest                      object\n",
-       "DestAirportID             object\n",
-       "                       ...      \n",
-       "OriginLocation            object\n",
-       "OriginRegion              object\n",
-       "OriginWeather             object\n",
-       "dayOfWeek                  int64\n",
-       "timestamp         datetime64[ns]\n",
-       "Length: 27, dtype: object"
-      ]
+      "text/plain": "AvgTicketPrice           float64\nCancelled                   bool\nCarrier                   object\nDest                      object\nDestAirportID             object\n                       ...      \nOriginLocation            object\nOriginRegion              object\nOriginWeather             object\ndayOfWeek                  int64\ntimestamp         datetime64[ns]\nLength: 27, dtype: object"
      },
      "execution_count": 8,
      "metadata": {},
@@ -242,28 +182,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice           float64\n",
-       "Cancelled                   bool\n",
-       "Carrier                   object\n",
-       "Dest                      object\n",
-       "DestAirportID             object\n",
-       "                       ...      \n",
-       "OriginLocation            object\n",
-       "OriginRegion              object\n",
-       "OriginWeather             object\n",
-       "dayOfWeek                  int64\n",
-       "timestamp         datetime64[ns]\n",
-       "Length: 27, dtype: object"
-      ]
+      "text/plain": "AvgTicketPrice           float64\nCancelled                   bool\nCarrier                   object\nDest                      object\nDestAirportID             object\n                       ...      \nOriginLocation            object\nOriginRegion              object\nOriginWeather             object\ndayOfWeek                  int64\ntimestamp         datetime64[ns]\nLength: 27, dtype: object"
      },
      "execution_count": 9,
      "metadata": {},
@@ -284,150 +207,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>DistanceKilometers</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FlightTimeMin</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>841.265642</td>\n",
-       "      <td>16492.326654</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1030.770416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>882.982662</td>\n",
-       "      <td>8823.400140</td>\n",
-       "      <td>...</td>\n",
-       "      <td>464.389481</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>190.636904</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>181.694216</td>\n",
-       "      <td>555.737767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>222.749059</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>730.041778</td>\n",
-       "      <td>13358.244200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>785.779071</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>1080.446279</td>\n",
-       "      <td>8058.581753</td>\n",
-       "      <td>...</td>\n",
-       "      <td>402.929088</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>646.612941</td>\n",
-       "      <td>7088.598322</td>\n",
-       "      <td>...</td>\n",
-       "      <td>644.418029</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>997.751876</td>\n",
-       "      <td>10920.652972</td>\n",
-       "      <td>...</td>\n",
-       "      <td>937.540811</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>1102.814465</td>\n",
-       "      <td>18748.859647</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1697.404971</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>858.144337</td>\n",
-       "      <td>16809.141923</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1610.761827</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>13059 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin  dayOfWeek\n",
-       "0          841.265642        16492.326654  ...    1030.770416          0\n",
-       "1          882.982662         8823.400140  ...     464.389481          0\n",
-       "2          190.636904            0.000000  ...       0.000000          0\n",
-       "3          181.694216          555.737767  ...     222.749059          0\n",
-       "4          730.041778        13358.244200  ...     785.779071          0\n",
-       "...               ...                 ...  ...            ...        ...\n",
-       "13054     1080.446279         8058.581753  ...     402.929088          6\n",
-       "13055      646.612941         7088.598322  ...     644.418029          6\n",
-       "13056      997.751876        10920.652972  ...     937.540811          6\n",
-       "13057     1102.814465        18748.859647  ...    1697.404971          6\n",
-       "13058      858.144337        16809.141923  ...    1610.761827          6\n",
-       "\n",
-       "[13059 rows x 7 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin  dayOfWeek\n0          841.265642        16492.326654  ...    1030.770416          0\n1          882.982662         8823.400140  ...     464.389481          0\n2          190.636904            0.000000  ...       0.000000          0\n3          181.694216          555.737767  ...     222.749059          0\n4          730.041778        13358.244200  ...     785.779071          0\n...               ...                 ...  ...            ...        ...\n13054     1080.446279         8058.581753  ...     402.929088          6\n13055      646.612941         7088.598322  ...     644.418029          6\n13056      997.751876        10920.652972  ...     937.540811          6\n13057     1102.814465        18748.859647  ...    1697.404971          6\n13058      858.144337        16809.141923  ...    1610.761827          6\n\n[13059 rows x 7 columns]",
+      "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>AvgTicketPrice</th>\n      <th>DistanceKilometers</th>\n      <th>...</th>\n      <th>FlightTimeMin</th>\n      <th>dayOfWeek</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>841.265642</td>\n      <td>16492.326654</td>\n      <td>...</td>\n      <td>1030.770416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>882.982662</td>\n      <td>8823.400140</td>\n      <td>...</td>\n      <td>464.389481</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>190.636904</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>181.694216</td>\n      <td>555.737767</td>\n      <td>...</td>\n      <td>222.749059</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>730.041778</td>\n      <td>13358.244200</td>\n      <td>...</td>\n      <td>785.779071</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13054</th>\n      <td>1080.446279</td>\n      <td>8058.581753</td>\n      <td>...</td>\n      <td>402.929088</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>646.612941</td>\n      <td>7088.598322</td>\n      <td>...</td>\n      <td>644.418029</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>997.751876</td>\n      <td>10920.652972</td>\n      <td>...</td>\n      <td>937.540811</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>1102.814465</td>\n      <td>18748.859647</td>\n      <td>...</td>\n      <td>1697.404971</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>858.144337</td>\n      <td>16809.141923</td>\n      <td>...</td>\n      <td>1610.761827</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n<p>13059 rows × 7 columns</p>\n</div>"
      },
      "execution_count": 10,
      "metadata": {},
@@ -441,150 +226,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>DistanceKilometers</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FlightTimeMin</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>841.265642</td>\n",
-       "      <td>16492.326654</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1030.770416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>882.982662</td>\n",
-       "      <td>8823.400140</td>\n",
-       "      <td>...</td>\n",
-       "      <td>464.389481</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>190.636904</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>181.694216</td>\n",
-       "      <td>555.737767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>222.749059</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>730.041778</td>\n",
-       "      <td>13358.244200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>785.779071</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>1080.446279</td>\n",
-       "      <td>8058.581753</td>\n",
-       "      <td>...</td>\n",
-       "      <td>402.929088</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>646.612941</td>\n",
-       "      <td>7088.598322</td>\n",
-       "      <td>...</td>\n",
-       "      <td>644.418029</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>997.751876</td>\n",
-       "      <td>10920.652972</td>\n",
-       "      <td>...</td>\n",
-       "      <td>937.540811</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>1102.814465</td>\n",
-       "      <td>18748.859647</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1697.404971</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>858.144337</td>\n",
-       "      <td>16809.141923</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1610.761827</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>13059 rows × 7 columns</p>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin  dayOfWeek\n",
-       "0          841.265642        16492.326654  ...    1030.770416          0\n",
-       "1          882.982662         8823.400140  ...     464.389481          0\n",
-       "2          190.636904            0.000000  ...       0.000000          0\n",
-       "3          181.694216          555.737767  ...     222.749059          0\n",
-       "4          730.041778        13358.244200  ...     785.779071          0\n",
-       "...               ...                 ...  ...            ...        ...\n",
-       "13054     1080.446279         8058.581753  ...     402.929088          6\n",
-       "13055      646.612941         7088.598322  ...     644.418029          6\n",
-       "13056      997.751876        10920.652972  ...     937.540811          6\n",
-       "13057     1102.814465        18748.859647  ...    1697.404971          6\n",
-       "13058      858.144337        16809.141923  ...    1610.761827          6\n",
-       "\n",
-       "[13059 rows x 7 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin  dayOfWeek\n0          841.265642        16492.326654  ...    1030.770416          0\n1          882.982662         8823.400140  ...     464.389481          0\n2          190.636904            0.000000  ...       0.000000          0\n3          181.694216          555.737767  ...     222.749059          0\n4          730.041778        13358.244200  ...     785.779071          0\n...               ...                 ...  ...            ...        ...\n13054     1080.446279         8058.581753  ...     402.929088          6\n13055      646.612941         7088.598322  ...     644.418029          6\n13056      997.751876        10920.652972  ...     937.540811          6\n13057     1102.814465        18748.859647  ...    1697.404971          6\n13058      858.144337        16809.141923  ...    1610.761827          6\n\n[13059 rows x 7 columns]",
+      "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>AvgTicketPrice</th>\n      <th>DistanceKilometers</th>\n      <th>...</th>\n      <th>FlightTimeMin</th>\n      <th>dayOfWeek</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>841.265642</td>\n      <td>16492.326654</td>\n      <td>...</td>\n      <td>1030.770416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>882.982662</td>\n      <td>8823.400140</td>\n      <td>...</td>\n      <td>464.389481</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>190.636904</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>181.694216</td>\n      <td>555.737767</td>\n      <td>...</td>\n      <td>222.749059</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>730.041778</td>\n      <td>13358.244200</td>\n      <td>...</td>\n      <td>785.779071</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13054</th>\n      <td>1080.446279</td>\n      <td>8058.581753</td>\n      <td>...</td>\n      <td>402.929088</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>646.612941</td>\n      <td>7088.598322</td>\n      <td>...</td>\n      <td>644.418029</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>997.751876</td>\n      <td>10920.652972</td>\n      <td>...</td>\n      <td>937.540811</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>1102.814465</td>\n      <td>18748.859647</td>\n      <td>...</td>\n      <td>1697.404971</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>858.144337</td>\n      <td>16809.141923</td>\n      <td>...</td>\n      <td>1610.761827</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>13059 rows × 7 columns</p>"
      },
      "execution_count": 11,
      "metadata": {},
@@ -605,17 +252,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "False"
-      ]
+      "text/plain": "False"
      },
      "execution_count": 12,
      "metadata": {},
@@ -629,17 +270,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "False"
-      ]
+      "text/plain": "False"
      },
      "execution_count": 13,
      "metadata": {},
@@ -660,17 +295,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(13059, 27)"
-      ]
+      "text/plain": "(13059, 27)"
      },
      "execution_count": 14,
      "metadata": {},
@@ -684,17 +313,11 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(13059, 27)"
-      ]
+      "text/plain": "(13059, 27)"
      },
      "execution_count": 15,
      "metadata": {},
@@ -717,20 +340,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',\n",
-       "       ...\n",
-       "       '13049', '13050', '13051', '13052', '13053', '13054', '13055', '13056', '13057', '13058'],\n",
-       "      dtype='object', length=13059)"
-      ]
+      "text/plain": "Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',\n       ...\n       '13049', '13050', '13051', '13052', '13053', '13054', '13055', '13056', '13057', '13058'],\n      dtype='object', length=13059)"
      },
      "execution_count": 16,
      "metadata": {},
@@ -744,17 +358,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<eland.index.Index at 0x1f302117940>"
-      ]
+      "text/plain": "<eland.index.Index at 0x7ff2f000de50>"
      },
      "execution_count": 17,
      "metadata": {},
@@ -769,17 +377,11 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "'_id'"
-      ]
+      "text/plain": "'_id'"
      },
      "execution_count": 18,
      "metadata": {},
@@ -802,29 +404,11 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "array([[841.2656419677076, False, 'Kibana Airlines', ..., 'Sunny', 0,\n",
-       "        Timestamp('2018-01-01 00:00:00')],\n",
-       "       [882.9826615595518, False, 'Logstash Airways', ..., 'Clear', 0,\n",
-       "        Timestamp('2018-01-01 18:27:00')],\n",
-       "       [190.6369038508356, False, 'Logstash Airways', ..., 'Rain', 0,\n",
-       "        Timestamp('2018-01-01 17:11:14')],\n",
-       "       ...,\n",
-       "       [997.7518761454494, False, 'Logstash Airways', ..., 'Sunny', 6,\n",
-       "        Timestamp('2018-02-11 04:09:27')],\n",
-       "       [1102.8144645388556, False, 'JetBeats', ..., 'Hail', 6,\n",
-       "        Timestamp('2018-02-11 08:28:21')],\n",
-       "       [858.1443369038839, False, 'JetBeats', ..., 'Rain', 6,\n",
-       "        Timestamp('2018-02-11 14:54:34')]], dtype=object)"
-      ]
+      "text/plain": "array([[841.2656419677076, False, 'Kibana Airlines', ..., 'Sunny', 0,\n        Timestamp('2018-01-01 00:00:00')],\n       [882.9826615595518, False, 'Logstash Airways', ..., 'Clear', 0,\n        Timestamp('2018-01-01 18:27:00')],\n       [190.6369038508356, False, 'Logstash Airways', ..., 'Rain', 0,\n        Timestamp('2018-01-01 17:11:14')],\n       ...,\n       [997.7518761454494, False, 'Logstash Airways', ..., 'Sunny', 6,\n        Timestamp('2018-02-11 04:09:27')],\n       [1102.8144645388556, False, 'JetBeats', ..., 'Hail', 6,\n        Timestamp('2018-02-11 08:28:21')],\n       [858.1443369038839, False, 'JetBeats', ..., 'Rain', 6,\n        Timestamp('2018-02-11 14:54:34')]], dtype=object)"
      },
      "execution_count": 19,
      "metadata": {},
@@ -838,11 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -876,96 +456,12 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>841.265642</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>882.982662</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 18:27:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>190.636904</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 17:11:14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>181.694216</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 10:33:28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>730.041778</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 05:13:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "0      841.265642      False  ...         0 2018-01-01 00:00:00\n",
-       "1      882.982662      False  ...         0 2018-01-01 18:27:00\n",
-       "2      190.636904      False  ...         0 2018-01-01 17:11:14\n",
-       "3      181.694216       True  ...         0 2018-01-01 10:33:28\n",
-       "4      730.041778      False  ...         0 2018-01-01 05:13:00\n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
+      "text/plain": "   AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n0      841.265642      False  ...         0 2018-01-01 00:00:00\n1      882.982662      False  ...         0 2018-01-01 18:27:00\n2      190.636904      False  ...         0 2018-01-01 17:11:14\n3      181.694216       True  ...         0 2018-01-01 10:33:28\n4      730.041778      False  ...         0 2018-01-01 05:13:00\n\n[5 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>841.265642</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 00:00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>882.982662</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 18:27:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>190.636904</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 17:11:14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>181.694216</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 10:33:28</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>730.041778</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 05:13:00</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
      },
      "execution_count": 21,
      "metadata": {},
@@ -979,96 +475,12 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>841.265642</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>882.982662</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 18:27:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>190.636904</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 17:11:14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>181.694216</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 10:33:28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>730.041778</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 05:13:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>5 rows × 27 columns</p>"
-      ],
-      "text/plain": [
-       "   AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "0      841.265642      False  ...         0 2018-01-01 00:00:00\n",
-       "1      882.982662      False  ...         0 2018-01-01 18:27:00\n",
-       "2      190.636904      False  ...         0 2018-01-01 17:11:14\n",
-       "3      181.694216       True  ...         0 2018-01-01 10:33:28\n",
-       "4      730.041778      False  ...         0 2018-01-01 05:13:00\n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
+      "text/plain": "   AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n0      841.265642      False  ...         0 2018-01-01 00:00:00\n1      882.982662      False  ...         0 2018-01-01 18:27:00\n2      190.636904      False  ...         0 2018-01-01 17:11:14\n3      181.694216       True  ...         0 2018-01-01 10:33:28\n4      730.041778      False  ...         0 2018-01-01 05:13:00\n\n[5 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>841.265642</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 00:00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>882.982662</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 18:27:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>190.636904</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 17:11:14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>181.694216</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 10:33:28</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>730.041778</td>\n      <td>False</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 05:13:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>5 rows × 27 columns</p>"
      },
      "execution_count": 22,
      "metadata": {},
@@ -1089,96 +501,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>1080.446279</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 20:42:25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>646.612941</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 01:41:57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>997.751876</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 04:09:27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>1102.814465</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 08:28:21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>858.144337</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 14:54:34</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "13054     1080.446279      False  ...         6 2018-02-11 20:42:25\n",
-       "13055      646.612941      False  ...         6 2018-02-11 01:41:57\n",
-       "13056      997.751876      False  ...         6 2018-02-11 04:09:27\n",
-       "13057     1102.814465      False  ...         6 2018-02-11 08:28:21\n",
-       "13058      858.144337      False  ...         6 2018-02-11 14:54:34\n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n13054     1080.446279      False  ...         6 2018-02-11 20:42:25\n13055      646.612941      False  ...         6 2018-02-11 01:41:57\n13056      997.751876      False  ...         6 2018-02-11 04:09:27\n13057     1102.814465      False  ...         6 2018-02-11 08:28:21\n13058      858.144337      False  ...         6 2018-02-11 14:54:34\n\n[5 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13054</th>\n      <td>1080.446279</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 20:42:25</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>646.612941</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 01:41:57</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>997.751876</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 04:09:27</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>1102.814465</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 08:28:21</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>858.144337</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 14:54:34</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
      },
      "execution_count": 23,
      "metadata": {},
@@ -1192,96 +520,12 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>1080.446279</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 20:42:25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>646.612941</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 01:41:57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>997.751876</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 04:09:27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>1102.814465</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 08:28:21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>858.144337</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 14:54:34</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>5 rows × 27 columns</p>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "13054     1080.446279      False  ...         6 2018-02-11 20:42:25\n",
-       "13055      646.612941      False  ...         6 2018-02-11 01:41:57\n",
-       "13056      997.751876      False  ...         6 2018-02-11 04:09:27\n",
-       "13057     1102.814465      False  ...         6 2018-02-11 08:28:21\n",
-       "13058      858.144337      False  ...         6 2018-02-11 14:54:34\n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n13054     1080.446279      False  ...         6 2018-02-11 20:42:25\n13055      646.612941      False  ...         6 2018-02-11 01:41:57\n13056      997.751876      False  ...         6 2018-02-11 04:09:27\n13057     1102.814465      False  ...         6 2018-02-11 08:28:21\n13058      858.144337      False  ...         6 2018-02-11 14:54:34\n\n[5 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13054</th>\n      <td>1080.446279</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 20:42:25</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>646.612941</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 01:41:57</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>997.751876</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 04:09:27</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>1102.814465</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 08:28:21</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>858.144337</td>\n      <td>False</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 14:54:34</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>5 rows × 27 columns</p>"
      },
      "execution_count": 24,
      "metadata": {},
@@ -1302,23 +546,11 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n",
-       "       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n",
-       "       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n",
-       "       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n",
-       "       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n",
-       "       'timestamp'],\n",
-       "      dtype='object')"
-      ]
+      "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n       'timestamp'],\n      dtype='object')"
      },
      "execution_count": 25,
      "metadata": {},
@@ -1332,23 +564,11 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n",
-       "       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n",
-       "       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n",
-       "       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n",
-       "       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n",
-       "       'timestamp'],\n",
-       "      dtype='object')"
-      ]
+      "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n       'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n       'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n       'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n       'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n       'timestamp'],\n      dtype='object')"
      },
      "execution_count": 26,
      "metadata": {},
@@ -1369,28 +589,11 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "0         Kibana Airlines\n",
-       "1        Logstash Airways\n",
-       "2        Logstash Airways\n",
-       "3         Kibana Airlines\n",
-       "4         Kibana Airlines\n",
-       "               ...       \n",
-       "13054    Logstash Airways\n",
-       "13055    Logstash Airways\n",
-       "13056    Logstash Airways\n",
-       "13057            JetBeats\n",
-       "13058            JetBeats\n",
-       "Name: Carrier, Length: 13059, dtype: object"
-      ]
+      "text/plain": "0         Kibana Airlines\n1        Logstash Airways\n2        Logstash Airways\n3         Kibana Airlines\n4         Kibana Airlines\n               ...       \n13054    Logstash Airways\n13055    Logstash Airways\n13056    Logstash Airways\n13057            JetBeats\n13058            JetBeats\nName: Carrier, Length: 13059, dtype: object"
      },
      "execution_count": 27,
      "metadata": {},
@@ -1404,28 +607,11 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "0         Kibana Airlines\n",
-       "1        Logstash Airways\n",
-       "2        Logstash Airways\n",
-       "3         Kibana Airlines\n",
-       "4         Kibana Airlines\n",
-       "               ...       \n",
-       "13054    Logstash Airways\n",
-       "13055    Logstash Airways\n",
-       "13056    Logstash Airways\n",
-       "13057            JetBeats\n",
-       "13058            JetBeats\n",
-       "Name: Carrier, Length: 13059, dtype: object"
-      ]
+      "text/plain": "0         Kibana Airlines\n1        Logstash Airways\n2        Logstash Airways\n3         Kibana Airlines\n4         Kibana Airlines\n               ...       \n13054    Logstash Airways\n13055    Logstash Airways\n13056    Logstash Airways\n13057            JetBeats\n13058            JetBeats\nName: Carrier, Length: 13059, dtype: object"
      },
      "execution_count": 28,
      "metadata": {},
@@ -1439,114 +625,12 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>Carrier</th>\n",
-       "      <th>Origin</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>Frankfurt am Main Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>Cape Town International Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>Venice Marco Polo Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>Naples International Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>Licenciado Benito Juarez International Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>Pisa International Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>Winnipeg / James Armstrong Richardson Internat...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>Licenciado Benito Juarez International Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>Itami Airport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>Adelaide International Airport</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>13059 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Carrier                                             Origin\n",
-       "0       Kibana Airlines                          Frankfurt am Main Airport\n",
-       "1      Logstash Airways                    Cape Town International Airport\n",
-       "2      Logstash Airways                          Venice Marco Polo Airport\n",
-       "3       Kibana Airlines                       Naples International Airport\n",
-       "4       Kibana Airlines     Licenciado Benito Juarez International Airport\n",
-       "...                 ...                                                ...\n",
-       "13054  Logstash Airways                         Pisa International Airport\n",
-       "13055  Logstash Airways  Winnipeg / James Armstrong Richardson Internat...\n",
-       "13056  Logstash Airways     Licenciado Benito Juarez International Airport\n",
-       "13057          JetBeats                                      Itami Airport\n",
-       "13058          JetBeats                     Adelaide International Airport\n",
-       "\n",
-       "[13059 rows x 2 columns]"
-      ]
+      "text/plain": "                Carrier                                             Origin\n0       Kibana Airlines                          Frankfurt am Main Airport\n1      Logstash Airways                    Cape Town International Airport\n2      Logstash Airways                          Venice Marco Polo Airport\n3       Kibana Airlines                       Naples International Airport\n4       Kibana Airlines     Licenciado Benito Juarez International Airport\n...                 ...                                                ...\n13054  Logstash Airways                         Pisa International Airport\n13055  Logstash Airways  Winnipeg / James Armstrong Richardson Internat...\n13056  Logstash Airways     Licenciado Benito Juarez International Airport\n13057          JetBeats                                      Itami Airport\n13058          JetBeats                     Adelaide International Airport\n\n[13059 rows x 2 columns]",
+      "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>Carrier</th>\n      <th>Origin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Kibana Airlines</td>\n      <td>Frankfurt am Main Airport</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Logstash Airways</td>\n      <td>Cape Town International Airport</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Logstash Airways</td>\n      <td>Venice Marco Polo Airport</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Kibana Airlines</td>\n      <td>Naples International Airport</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kibana Airlines</td>\n      <td>Licenciado Benito Juarez International Airport</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13054</th>\n      <td>Logstash Airways</td>\n      <td>Pisa International Airport</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>Logstash Airways</td>\n      <td>Winnipeg / James Armstrong Richardson Internat...</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>Logstash Airways</td>\n      <td>Licenciado Benito Juarez International Airport</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>JetBeats</td>\n      <td>Itami Airport</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>JetBeats</td>\n      <td>Adelaide International Airport</td>\n    </tr>\n  </tbody>\n</table>\n<p>13059 rows × 2 columns</p>\n</div>"
      },
      "execution_count": 29,
      "metadata": {},
@@ -1567,11 +651,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1598,150 +678,12 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>960.869736</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 12:09:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>975.812632</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 15:38:32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>311</th>\n",
-       "      <td>946.358410</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 11:51:12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>651</th>\n",
-       "      <td>975.383864</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 21:13:17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>950</th>\n",
-       "      <td>907.836523</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 05:14:51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12820</th>\n",
-       "      <td>909.973606</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2018-02-10 05:11:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12906</th>\n",
-       "      <td>983.429244</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 06:19:58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12918</th>\n",
-       "      <td>1136.678150</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 16:03:10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12919</th>\n",
-       "      <td>1105.211803</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 05:36:05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13013</th>\n",
-       "      <td>1055.350213</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 13:20:16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>68 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "8          960.869736       True  ...         0 2018-01-01 12:09:35\n",
-       "26         975.812632       True  ...         0 2018-01-01 15:38:32\n",
-       "311        946.358410       True  ...         0 2018-01-01 11:51:12\n",
-       "651        975.383864       True  ...         2 2018-01-03 21:13:17\n",
-       "950        907.836523       True  ...         2 2018-01-03 05:14:51\n",
-       "...               ...        ...  ...       ...                 ...\n",
-       "12820      909.973606       True  ...         5 2018-02-10 05:11:35\n",
-       "12906      983.429244       True  ...         6 2018-02-11 06:19:58\n",
-       "12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n",
-       "12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n",
-       "13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n",
-       "\n",
-       "[68 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n8          960.869736       True  ...         0 2018-01-01 12:09:35\n26         975.812632       True  ...         0 2018-01-01 15:38:32\n311        946.358410       True  ...         0 2018-01-01 11:51:12\n651        975.383864       True  ...         2 2018-01-03 21:13:17\n950        907.836523       True  ...         2 2018-01-03 05:14:51\n...               ...        ...  ...       ...                 ...\n12820      909.973606       True  ...         5 2018-02-10 05:11:35\n12906      983.429244       True  ...         6 2018-02-11 06:19:58\n12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n\n[68 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>960.869736</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 12:09:35</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>975.812632</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 15:38:32</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>946.358410</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 11:51:12</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>975.383864</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 21:13:17</td>\n    </tr>\n    <tr>\n      <th>950</th>\n      <td>907.836523</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 05:14:51</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12820</th>\n      <td>909.973606</td>\n      <td>True</td>\n      <td>...</td>\n      <td>5</td>\n      <td>2018-02-10 05:11:35</td>\n    </tr>\n    <tr>\n      <th>12906</th>\n      <td>983.429244</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 06:19:58</td>\n    </tr>\n    <tr>\n      <th>12918</th>\n      <td>1136.678150</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 16:03:10</td>\n    </tr>\n    <tr>\n      <th>12919</th>\n      <td>1105.211803</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 05:36:05</td>\n    </tr>\n    <tr>\n      <th>13013</th>\n      <td>1055.350213</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 13:20:16</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 27 columns</p>\n</div>"
      },
      "execution_count": 31,
      "metadata": {},
@@ -1764,150 +706,12 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>960.869736</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 12:09:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>975.812632</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 15:38:32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>311</th>\n",
-       "      <td>946.358410</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 11:51:12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>651</th>\n",
-       "      <td>975.383864</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 21:13:17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>950</th>\n",
-       "      <td>907.836523</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 05:14:51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12820</th>\n",
-       "      <td>909.973606</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2018-02-10 05:11:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12906</th>\n",
-       "      <td>983.429244</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 06:19:58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12918</th>\n",
-       "      <td>1136.678150</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 16:03:10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12919</th>\n",
-       "      <td>1105.211803</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 05:36:05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13013</th>\n",
-       "      <td>1055.350213</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 13:20:16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>68 rows × 27 columns</p>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "8          960.869736       True  ...         0 2018-01-01 12:09:35\n",
-       "26         975.812632       True  ...         0 2018-01-01 15:38:32\n",
-       "311        946.358410       True  ...         0 2018-01-01 11:51:12\n",
-       "651        975.383864       True  ...         2 2018-01-03 21:13:17\n",
-       "950        907.836523       True  ...         2 2018-01-03 05:14:51\n",
-       "...               ...        ...  ...       ...                 ...\n",
-       "12820      909.973606       True  ...         5 2018-02-10 05:11:35\n",
-       "12906      983.429244       True  ...         6 2018-02-11 06:19:58\n",
-       "12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n",
-       "12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n",
-       "13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n",
-       "\n",
-       "[68 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n8          960.869736       True  ...         0 2018-01-01 12:09:35\n26         975.812632       True  ...         0 2018-01-01 15:38:32\n311        946.358410       True  ...         0 2018-01-01 11:51:12\n651        975.383864       True  ...         2 2018-01-03 21:13:17\n950        907.836523       True  ...         2 2018-01-03 05:14:51\n...               ...        ...  ...       ...                 ...\n12820      909.973606       True  ...         5 2018-02-10 05:11:35\n12906      983.429244       True  ...         6 2018-02-11 06:19:58\n12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n\n[68 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>960.869736</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 12:09:35</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>975.812632</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 15:38:32</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>946.358410</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 11:51:12</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>975.383864</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 21:13:17</td>\n    </tr>\n    <tr>\n      <th>950</th>\n      <td>907.836523</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 05:14:51</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12820</th>\n      <td>909.973606</td>\n      <td>True</td>\n      <td>...</td>\n      <td>5</td>\n      <td>2018-02-10 05:11:35</td>\n    </tr>\n    <tr>\n      <th>12906</th>\n      <td>983.429244</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 06:19:58</td>\n    </tr>\n    <tr>\n      <th>12918</th>\n      <td>1136.678150</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 16:03:10</td>\n    </tr>\n    <tr>\n      <th>12919</th>\n      <td>1105.211803</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 05:36:05</td>\n    </tr>\n    <tr>\n      <th>13013</th>\n      <td>1055.350213</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 13:20:16</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>68 rows × 27 columns</p>"
      },
      "execution_count": 32,
      "metadata": {},
@@ -1928,150 +732,12 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>960.869736</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 12:09:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>975.812632</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 15:38:32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>311</th>\n",
-       "      <td>946.358410</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 11:51:12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>651</th>\n",
-       "      <td>975.383864</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 21:13:17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>950</th>\n",
-       "      <td>907.836523</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 05:14:51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12820</th>\n",
-       "      <td>909.973606</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2018-02-10 05:11:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12906</th>\n",
-       "      <td>983.429244</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 06:19:58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12918</th>\n",
-       "      <td>1136.678150</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 16:03:10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12919</th>\n",
-       "      <td>1105.211803</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 05:36:05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13013</th>\n",
-       "      <td>1055.350213</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 13:20:16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>68 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "8          960.869736       True  ...         0 2018-01-01 12:09:35\n",
-       "26         975.812632       True  ...         0 2018-01-01 15:38:32\n",
-       "311        946.358410       True  ...         0 2018-01-01 11:51:12\n",
-       "651        975.383864       True  ...         2 2018-01-03 21:13:17\n",
-       "950        907.836523       True  ...         2 2018-01-03 05:14:51\n",
-       "...               ...        ...  ...       ...                 ...\n",
-       "12820      909.973606       True  ...         5 2018-02-10 05:11:35\n",
-       "12906      983.429244       True  ...         6 2018-02-11 06:19:58\n",
-       "12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n",
-       "12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n",
-       "13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n",
-       "\n",
-       "[68 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n8          960.869736       True  ...         0 2018-01-01 12:09:35\n26         975.812632       True  ...         0 2018-01-01 15:38:32\n311        946.358410       True  ...         0 2018-01-01 11:51:12\n651        975.383864       True  ...         2 2018-01-03 21:13:17\n950        907.836523       True  ...         2 2018-01-03 05:14:51\n...               ...        ...  ...       ...                 ...\n12820      909.973606       True  ...         5 2018-02-10 05:11:35\n12906      983.429244       True  ...         6 2018-02-11 06:19:58\n12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n\n[68 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>960.869736</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 12:09:35</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>975.812632</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 15:38:32</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>946.358410</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 11:51:12</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>975.383864</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 21:13:17</td>\n    </tr>\n    <tr>\n      <th>950</th>\n      <td>907.836523</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 05:14:51</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12820</th>\n      <td>909.973606</td>\n      <td>True</td>\n      <td>...</td>\n      <td>5</td>\n      <td>2018-02-10 05:11:35</td>\n    </tr>\n    <tr>\n      <th>12906</th>\n      <td>983.429244</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 06:19:58</td>\n    </tr>\n    <tr>\n      <th>12918</th>\n      <td>1136.678150</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 16:03:10</td>\n    </tr>\n    <tr>\n      <th>12919</th>\n      <td>1105.211803</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 05:36:05</td>\n    </tr>\n    <tr>\n      <th>13013</th>\n      <td>1055.350213</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 13:20:16</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 27 columns</p>\n</div>"
      },
      "execution_count": 33,
      "metadata": {},
@@ -2087,150 +753,12 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>Cancelled</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>960.869736</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 12:09:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>975.812632</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 15:38:32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>311</th>\n",
-       "      <td>946.358410</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 11:51:12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>651</th>\n",
-       "      <td>975.383864</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 21:13:17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>950</th>\n",
-       "      <td>907.836523</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2018-01-03 05:14:51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12820</th>\n",
-       "      <td>909.973606</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2018-02-10 05:11:35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12906</th>\n",
-       "      <td>983.429244</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 06:19:58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12918</th>\n",
-       "      <td>1136.678150</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 16:03:10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12919</th>\n",
-       "      <td>1105.211803</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 05:36:05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13013</th>\n",
-       "      <td>1055.350213</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 13:20:16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>68 rows × 27 columns</p>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n",
-       "8          960.869736       True  ...         0 2018-01-01 12:09:35\n",
-       "26         975.812632       True  ...         0 2018-01-01 15:38:32\n",
-       "311        946.358410       True  ...         0 2018-01-01 11:51:12\n",
-       "651        975.383864       True  ...         2 2018-01-03 21:13:17\n",
-       "950        907.836523       True  ...         2 2018-01-03 05:14:51\n",
-       "...               ...        ...  ...       ...                 ...\n",
-       "12820      909.973606       True  ...         5 2018-02-10 05:11:35\n",
-       "12906      983.429244       True  ...         6 2018-02-11 06:19:58\n",
-       "12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n",
-       "12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n",
-       "13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n",
-       "\n",
-       "[68 rows x 27 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp\n8          960.869736       True  ...         0 2018-01-01 12:09:35\n26         975.812632       True  ...         0 2018-01-01 15:38:32\n311        946.358410       True  ...         0 2018-01-01 11:51:12\n651        975.383864       True  ...         2 2018-01-03 21:13:17\n950        907.836523       True  ...         2 2018-01-03 05:14:51\n...               ...        ...  ...       ...                 ...\n12820      909.973606       True  ...         5 2018-02-10 05:11:35\n12906      983.429244       True  ...         6 2018-02-11 06:19:58\n12918     1136.678150       True  ...         6 2018-02-11 16:03:10\n12919     1105.211803       True  ...         6 2018-02-11 05:36:05\n13013     1055.350213       True  ...         6 2018-02-11 13:20:16\n\n[68 rows x 27 columns]",
+      "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>AvgTicketPrice</th>\n      <th>Cancelled</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>960.869736</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 12:09:35</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>975.812632</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 15:38:32</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>946.358410</td>\n      <td>True</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 11:51:12</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>975.383864</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 21:13:17</td>\n    </tr>\n    <tr>\n      <th>950</th>\n      <td>907.836523</td>\n      <td>True</td>\n      <td>...</td>\n      <td>2</td>\n      <td>2018-01-03 05:14:51</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12820</th>\n      <td>909.973606</td>\n      <td>True</td>\n      <td>...</td>\n      <td>5</td>\n      <td>2018-02-10 05:11:35</td>\n    </tr>\n    <tr>\n      <th>12906</th>\n      <td>983.429244</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 06:19:58</td>\n    </tr>\n    <tr>\n      <th>12918</th>\n      <td>1136.678150</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 16:03:10</td>\n    </tr>\n    <tr>\n      <th>12919</th>\n      <td>1105.211803</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 05:36:05</td>\n    </tr>\n    <tr>\n      <th>13013</th>\n      <td>1055.350213</td>\n      <td>True</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 13:20:16</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>68 rows × 27 columns</p>"
      },
      "execution_count": 34,
      "metadata": {},
@@ -2260,63 +788,12 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>DistanceKilometers</th>\n",
-       "      <th>AvgTicketPrice</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sum</th>\n",
-       "      <td>9.261629e+07</td>\n",
-       "      <td>8.204365e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000205e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.578438e+03</td>\n",
-       "      <td>2.663969e+02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     DistanceKilometers  AvgTicketPrice\n",
-       "sum        9.261629e+07    8.204365e+06\n",
-       "min        0.000000e+00    1.000205e+02\n",
-       "std        4.578438e+03    2.663969e+02"
-      ]
+      "text/plain": "     DistanceKilometers  AvgTicketPrice\nsum        9.261629e+07    8.204365e+06\nmin        0.000000e+00    1.000205e+02\nstd        4.578438e+03    2.663969e+02",
+      "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>DistanceKilometers</th>\n      <th>AvgTicketPrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sum</th>\n      <td>9.261629e+07</td>\n      <td>8.204365e+06</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000e+00</td>\n      <td>1.000205e+02</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4.578438e+03</td>\n      <td>2.663969e+02</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 35,
      "metadata": {},
@@ -2337,63 +814,12 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>DistanceKilometers</th>\n",
-       "      <th>AvgTicketPrice</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sum</th>\n",
-       "      <td>9.261629e+07</td>\n",
-       "      <td>8.204365e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000205e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.578614e+03</td>\n",
-       "      <td>2.664071e+02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     DistanceKilometers  AvgTicketPrice\n",
-       "sum        9.261629e+07    8.204365e+06\n",
-       "min        0.000000e+00    1.000205e+02\n",
-       "std        4.578614e+03    2.664071e+02"
-      ]
+      "text/plain": "     DistanceKilometers  AvgTicketPrice\nsum        9.261629e+07    8.204365e+06\nmin        0.000000e+00    1.000205e+02\nstd        4.578614e+03    2.664071e+02",
+      "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>DistanceKilometers</th>\n      <th>AvgTicketPrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sum</th>\n      <td>9.261629e+07</td>\n      <td>8.204365e+06</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000e+00</td>\n      <td>1.000205e+02</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4.578614e+03</td>\n      <td>2.664071e+02</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 36,
      "metadata": {},
@@ -2421,28 +847,11 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    13059\n",
-       "Cancelled         13059\n",
-       "Carrier           13059\n",
-       "Dest              13059\n",
-       "DestAirportID     13059\n",
-       "                  ...  \n",
-       "OriginLocation    13059\n",
-       "OriginRegion      13059\n",
-       "OriginWeather     13059\n",
-       "dayOfWeek         13059\n",
-       "timestamp         13059\n",
-       "Length: 27, dtype: int64"
-      ]
+      "text/plain": "AvgTicketPrice    13059\nCancelled         13059\nCarrier           13059\nDest              13059\nDestAirportID     13059\n                  ...  \nOriginLocation    13059\nOriginRegion      13059\nOriginWeather     13059\ndayOfWeek         13059\ntimestamp         13059\nLength: 27, dtype: int64"
      },
      "execution_count": 37,
      "metadata": {},
@@ -2456,28 +865,11 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    13059\n",
-       "Cancelled         13059\n",
-       "Carrier           13059\n",
-       "Dest              13059\n",
-       "DestAirportID     13059\n",
-       "                  ...  \n",
-       "OriginLocation    13059\n",
-       "OriginRegion      13059\n",
-       "OriginWeather     13059\n",
-       "dayOfWeek         13059\n",
-       "timestamp         13059\n",
-       "Length: 27, dtype: int64"
-      ]
+      "text/plain": "AvgTicketPrice    13059\nCancelled         13059\nCarrier           13059\nDest              13059\nDestAirportID     13059\n                  ...  \nOriginLocation    13059\nOriginRegion      13059\nOriginWeather     13059\ndayOfWeek         13059\ntimestamp         13059\nLength: 27, dtype: int64"
      },
      "execution_count": 38,
      "metadata": {},
@@ -2498,123 +890,12 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>DistanceKilometers</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FlightTimeMin</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>13059.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>628.253689</td>\n",
-       "      <td>7092.142455</td>\n",
-       "      <td>...</td>\n",
-       "      <td>511.127842</td>\n",
-       "      <td>2.835975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>266.396861</td>\n",
-       "      <td>4578.438497</td>\n",
-       "      <td>...</td>\n",
-       "      <td>334.753952</td>\n",
-       "      <td>1.939439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>100.020528</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>409.893816</td>\n",
-       "      <td>2459.705673</td>\n",
-       "      <td>...</td>\n",
-       "      <td>252.333192</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>640.556668</td>\n",
-       "      <td>7610.330866</td>\n",
-       "      <td>...</td>\n",
-       "      <td>503.045170</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>842.185470</td>\n",
-       "      <td>9736.637600</td>\n",
-       "      <td>...</td>\n",
-       "      <td>720.416036</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1199.729053</td>\n",
-       "      <td>19881.482315</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1902.902032</td>\n",
-       "      <td>6.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin     dayOfWeek\n",
-       "count    13059.000000        13059.000000  ...   13059.000000  13059.000000\n",
-       "mean       628.253689         7092.142455  ...     511.127842      2.835975\n",
-       "std        266.396861         4578.438497  ...     334.753952      1.939439\n",
-       "min        100.020528            0.000000  ...       0.000000      0.000000\n",
-       "25%        409.893816         2459.705673  ...     252.333192      1.000000\n",
-       "50%        640.556668         7610.330866  ...     503.045170      3.000000\n",
-       "75%        842.185470         9736.637600  ...     720.416036      4.000000\n",
-       "max       1199.729053        19881.482315  ...    1902.902032      6.000000\n",
-       "\n",
-       "[8 rows x 7 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin     dayOfWeek\ncount    13059.000000        13059.000000  ...   13059.000000  13059.000000\nmean       628.253689         7092.142455  ...     511.127842      2.835975\nstd        266.396861         4578.438497  ...     334.753952      1.939439\nmin        100.020528            0.000000  ...       0.000000      0.000000\n25%        409.893816         2459.705673  ...     252.333192      1.000000\n50%        640.556668         7610.330866  ...     503.045170      3.000000\n75%        842.185470         9736.637600  ...     720.416036      4.000000\nmax       1199.729053        19881.482315  ...    1902.902032      6.000000\n\n[8 rows x 7 columns]",
+      "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>AvgTicketPrice</th>\n      <th>DistanceKilometers</th>\n      <th>...</th>\n      <th>FlightTimeMin</th>\n      <th>dayOfWeek</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>13059.000000</td>\n      <td>13059.000000</td>\n      <td>...</td>\n      <td>13059.000000</td>\n      <td>13059.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>628.253689</td>\n      <td>7092.142455</td>\n      <td>...</td>\n      <td>511.127842</td>\n      <td>2.835975</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>266.396861</td>\n      <td>4578.438497</td>\n      <td>...</td>\n      <td>334.753952</td>\n      <td>1.939439</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>100.020528</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>409.893816</td>\n      <td>2459.705673</td>\n      <td>...</td>\n      <td>252.333192</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>640.556668</td>\n      <td>7610.330866</td>\n      <td>...</td>\n      <td>503.045170</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>842.185470</td>\n      <td>9736.637600</td>\n      <td>...</td>\n      <td>720.416036</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1199.729053</td>\n      <td>19881.482315</td>\n      <td>...</td>\n      <td>1902.902032</td>\n      <td>6.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 7 columns</p>\n</div>"
      },
      "execution_count": 39,
      "metadata": {},
@@ -2635,123 +916,12 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>AvgTicketPrice</th>\n",
-       "      <th>DistanceKilometers</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FlightTimeMin</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13059.000000</td>\n",
-       "      <td>13059.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>628.253689</td>\n",
-       "      <td>7092.142457</td>\n",
-       "      <td>...</td>\n",
-       "      <td>511.127842</td>\n",
-       "      <td>2.835975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>266.386661</td>\n",
-       "      <td>4578.263193</td>\n",
-       "      <td>...</td>\n",
-       "      <td>334.741135</td>\n",
-       "      <td>1.939365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>100.020531</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>410.008918</td>\n",
-       "      <td>2470.545974</td>\n",
-       "      <td>...</td>\n",
-       "      <td>251.938710</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>640.387285</td>\n",
-       "      <td>7612.072403</td>\n",
-       "      <td>...</td>\n",
-       "      <td>503.148975</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>842.213490</td>\n",
-       "      <td>9735.660463</td>\n",
-       "      <td>...</td>\n",
-       "      <td>720.505705</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>1199.729004</td>\n",
-       "      <td>19881.482422</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1902.901978</td>\n",
-       "      <td>6.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin     dayOfWeek\n",
-       "count    13059.000000        13059.000000  ...   13059.000000  13059.000000\n",
-       "mean       628.253689         7092.142457  ...     511.127842      2.835975\n",
-       "std        266.386661         4578.263193  ...     334.741135      1.939365\n",
-       "min        100.020531            0.000000  ...       0.000000      0.000000\n",
-       "25%        410.008918         2470.545974  ...     251.938710      1.000000\n",
-       "50%        640.387285         7612.072403  ...     503.148975      3.000000\n",
-       "75%        842.213490         9735.660463  ...     720.505705      4.000000\n",
-       "max       1199.729004        19881.482422  ...    1902.901978      6.000000\n",
-       "\n",
-       "[8 rows x 7 columns]"
-      ]
+      "text/plain": "       AvgTicketPrice  DistanceKilometers  ...  FlightTimeMin     dayOfWeek\ncount    13059.000000        13059.000000  ...   13059.000000  13059.000000\nmean       628.253689         7092.142457  ...     511.127842      2.835975\nstd        266.386661         4578.263193  ...     334.741135      1.939365\nmin        100.020531            0.000000  ...       0.000000      0.000000\n25%        410.008918         2470.545974  ...     251.938710      1.000000\n50%        640.387285         7612.072403  ...     503.148975      3.000000\n75%        842.213490         9735.660463  ...     720.505705      4.000000\nmax       1199.729004        19881.482422  ...    1902.901978      6.000000\n\n[8 rows x 7 columns]",
+      "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>AvgTicketPrice</th>\n      <th>DistanceKilometers</th>\n      <th>...</th>\n      <th>FlightTimeMin</th>\n      <th>dayOfWeek</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>13059.000000</td>\n      <td>13059.000000</td>\n      <td>...</td>\n      <td>13059.000000</td>\n      <td>13059.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>628.253689</td>\n      <td>7092.142457</td>\n      <td>...</td>\n      <td>511.127842</td>\n      <td>2.835975</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>266.386661</td>\n      <td>4578.263193</td>\n      <td>...</td>\n      <td>334.741135</td>\n      <td>1.939365</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>100.020531</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>410.008918</td>\n      <td>2470.545974</td>\n      <td>...</td>\n      <td>251.938710</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>640.387285</td>\n      <td>7612.072403</td>\n      <td>...</td>\n      <td>503.148975</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>842.213490</td>\n      <td>9735.660463</td>\n      <td>...</td>\n      <td>720.505705</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1199.729004</td>\n      <td>19881.482422</td>\n      <td>...</td>\n      <td>1902.901978</td>\n      <td>6.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 7 columns</p>\n</div>"
      },
      "execution_count": 40,
      "metadata": {},
@@ -2773,11 +943,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2827,11 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2871,7 +1033,7 @@
       " 26  timestamp           13059 non-null  datetime64[ns]\n",
       "dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)\n",
       "memory usage: 64.000 bytes\n",
-      "Elasticsearch storage usage: 5.043 MB\n"
+      "Elasticsearch storage usage: 5.046 MB\n"
      ]
     }
    ],
@@ -2897,26 +1059,11 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice         1199.729053\n",
-       "Cancelled                 1.000000\n",
-       "DistanceKilometers    19881.482315\n",
-       "DistanceMiles         12353.780369\n",
-       "FlightDelay               1.000000\n",
-       "FlightDelayMin          360.000000\n",
-       "FlightTimeHour           31.715034\n",
-       "FlightTimeMin          1902.902032\n",
-       "dayOfWeek                 6.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice         1199.729053\nCancelled                 1.000000\nDistanceKilometers    19881.482315\nDistanceMiles         12353.780369\nFlightDelay               1.000000\nFlightDelayMin          360.000000\nFlightTimeHour           31.715034\nFlightTimeMin          1902.902032\ndayOfWeek                 6.000000\ndtype: float64"
      },
      "execution_count": 43,
      "metadata": {},
@@ -2937,26 +1084,11 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice         1199.729004\n",
-       "Cancelled                 1.000000\n",
-       "DistanceKilometers    19881.482422\n",
-       "DistanceMiles         12353.780273\n",
-       "FlightDelay               1.000000\n",
-       "FlightDelayMin          360.000000\n",
-       "FlightTimeHour           31.715034\n",
-       "FlightTimeMin          1902.901978\n",
-       "dayOfWeek                 6.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice         1199.729004\nCancelled                 1.000000\nDistanceKilometers    19881.482422\nDistanceMiles         12353.780273\nFlightDelay               1.000000\nFlightDelayMin          360.000000\nFlightTimeHour           31.715034\nFlightTimeMin          1902.901978\ndayOfWeek                 6.000000\ndtype: float64"
      },
      "execution_count": 44,
      "metadata": {},
@@ -2977,26 +1109,11 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice        100.020528\n",
-       "Cancelled               0.000000\n",
-       "DistanceKilometers      0.000000\n",
-       "DistanceMiles           0.000000\n",
-       "FlightDelay             0.000000\n",
-       "FlightDelayMin          0.000000\n",
-       "FlightTimeHour          0.000000\n",
-       "FlightTimeMin           0.000000\n",
-       "dayOfWeek               0.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice        100.020528\nCancelled               0.000000\nDistanceKilometers      0.000000\nDistanceMiles           0.000000\nFlightDelay             0.000000\nFlightDelayMin          0.000000\nFlightTimeHour          0.000000\nFlightTimeMin           0.000000\ndayOfWeek               0.000000\ndtype: float64"
      },
      "execution_count": 45,
      "metadata": {},
@@ -3010,26 +1127,11 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice        100.020531\n",
-       "Cancelled               0.000000\n",
-       "DistanceKilometers      0.000000\n",
-       "DistanceMiles           0.000000\n",
-       "FlightDelay             0.000000\n",
-       "FlightDelayMin          0.000000\n",
-       "FlightTimeHour          0.000000\n",
-       "FlightTimeMin           0.000000\n",
-       "dayOfWeek               0.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice        100.020531\nCancelled               0.000000\nDistanceKilometers      0.000000\nDistanceMiles           0.000000\nFlightDelay             0.000000\nFlightDelayMin          0.000000\nFlightTimeHour          0.000000\nFlightTimeMin           0.000000\ndayOfWeek               0.000000\ndtype: float64"
      },
      "execution_count": 46,
      "metadata": {},
@@ -3050,26 +1152,11 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice         628.253689\n",
-       "Cancelled                0.128494\n",
-       "DistanceKilometers    7092.142455\n",
-       "DistanceMiles         4406.853013\n",
-       "FlightDelay              0.251168\n",
-       "FlightDelayMin          47.335171\n",
-       "FlightTimeHour           8.518797\n",
-       "FlightTimeMin          511.127842\n",
-       "dayOfWeek                2.835975\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice         628.253689\nCancelled                0.128494\nDistanceKilometers    7092.142455\nDistanceMiles         4406.853013\nFlightDelay              0.251168\nFlightDelayMin          47.335171\nFlightTimeHour           8.518797\nFlightTimeMin          511.127842\ndayOfWeek                2.835975\ndtype: float64"
      },
      "execution_count": 47,
      "metadata": {},
@@ -3083,26 +1170,11 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice         628.253689\n",
-       "Cancelled                0.128494\n",
-       "DistanceKilometers    7092.142457\n",
-       "DistanceMiles         4406.853010\n",
-       "FlightDelay              0.251168\n",
-       "FlightDelayMin          47.335171\n",
-       "FlightTimeHour           8.518797\n",
-       "FlightTimeMin          511.127842\n",
-       "dayOfWeek                2.835975\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice         628.253689\nCancelled                0.128494\nDistanceKilometers    7092.142457\nDistanceMiles         4406.853010\nFlightDelay              0.251168\nFlightDelayMin          47.335171\nFlightTimeHour           8.518797\nFlightTimeMin          511.127842\ndayOfWeek                2.835975\ndtype: float64"
      },
      "execution_count": 48,
      "metadata": {},
@@ -3123,26 +1195,11 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice        8.204365e+06\n",
-       "Cancelled             1.678000e+03\n",
-       "DistanceKilometers    9.261629e+07\n",
-       "DistanceMiles         5.754909e+07\n",
-       "FlightDelay           3.280000e+03\n",
-       "FlightDelayMin        6.181500e+05\n",
-       "FlightTimeHour        1.112470e+05\n",
-       "FlightTimeMin         6.674818e+06\n",
-       "dayOfWeek             3.703500e+04\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice        8.204365e+06\nCancelled             1.678000e+03\nDistanceKilometers    9.261629e+07\nDistanceMiles         5.754909e+07\nFlightDelay           3.280000e+03\nFlightDelayMin        6.181500e+05\nFlightTimeHour        1.112470e+05\nFlightTimeMin         6.674818e+06\ndayOfWeek             3.703500e+04\ndtype: float64"
      },
      "execution_count": 49,
      "metadata": {},
@@ -3156,26 +1213,11 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice        8.204365e+06\n",
-       "Cancelled             1.678000e+03\n",
-       "DistanceKilometers    9.261629e+07\n",
-       "DistanceMiles         5.754909e+07\n",
-       "FlightDelay           3.280000e+03\n",
-       "FlightDelayMin        6.181500e+05\n",
-       "FlightTimeHour        1.112470e+05\n",
-       "FlightTimeMin         6.674818e+06\n",
-       "dayOfWeek             3.703500e+04\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice        8.204365e+06\nCancelled             1.678000e+03\nDistanceKilometers    9.261629e+07\nDistanceMiles         5.754909e+07\nFlightDelay           3.280000e+03\nFlightDelayMin        6.181500e+05\nFlightTimeHour        1.112470e+05\nFlightTimeMin         6.674818e+06\ndayOfWeek             3.703500e+04\ndtype: float64"
      },
      "execution_count": 50,
      "metadata": {},
@@ -3196,20 +1238,11 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Carrier      4\n",
-       "Origin     156\n",
-       "Dest       156\n",
-       "dtype: int64"
-      ]
+      "text/plain": "Carrier      4\nOrigin     156\nDest       156\ndtype: int64"
      },
      "execution_count": 51,
      "metadata": {},
@@ -3223,20 +1256,11 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Carrier      4\n",
-       "Origin     156\n",
-       "Dest       156\n",
-       "dtype: int64"
-      ]
+      "text/plain": "Carrier      4\nOrigin     156\nDest       156\ndtype: int64"
      },
      "execution_count": 52,
      "metadata": {},
@@ -3257,150 +1281,12 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>Carrier</th>\n",
-       "      <th>DestRegion</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 18:27:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 17:11:14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 10:33:28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 05:13:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 20:42:25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>CH-ZH</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 01:41:57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>RU-AMU</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 04:09:27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 08:28:21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>US-DC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 14:54:34</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>13059 rows × 20 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Carrier DestRegion  ... dayOfWeek           timestamp\n",
-       "0       Kibana Airlines      SE-BD  ...         0 2018-01-01 00:00:00\n",
-       "1      Logstash Airways      IT-34  ...         0 2018-01-01 18:27:00\n",
-       "2      Logstash Airways      IT-34  ...         0 2018-01-01 17:11:14\n",
-       "3       Kibana Airlines      IT-34  ...         0 2018-01-01 10:33:28\n",
-       "4       Kibana Airlines      SE-BD  ...         0 2018-01-01 05:13:00\n",
-       "...                 ...        ...  ...       ...                 ...\n",
-       "13054  Logstash Airways      SE-BD  ...         6 2018-02-11 20:42:25\n",
-       "13055  Logstash Airways      CH-ZH  ...         6 2018-02-11 01:41:57\n",
-       "13056  Logstash Airways     RU-AMU  ...         6 2018-02-11 04:09:27\n",
-       "13057          JetBeats      SE-BD  ...         6 2018-02-11 08:28:21\n",
-       "13058          JetBeats      US-DC  ...         6 2018-02-11 14:54:34\n",
-       "\n",
-       "[13059 rows x 20 columns]"
-      ]
+      "text/plain": "                Carrier DestRegion  ... dayOfWeek           timestamp\n0       Kibana Airlines      SE-BD  ...         0 2018-01-01 00:00:00\n1      Logstash Airways      IT-34  ...         0 2018-01-01 18:27:00\n2      Logstash Airways      IT-34  ...         0 2018-01-01 17:11:14\n3       Kibana Airlines      IT-34  ...         0 2018-01-01 10:33:28\n4       Kibana Airlines      SE-BD  ...         0 2018-01-01 05:13:00\n...                 ...        ...  ...       ...                 ...\n13054  Logstash Airways      SE-BD  ...         6 2018-02-11 20:42:25\n13055  Logstash Airways      CH-ZH  ...         6 2018-02-11 01:41:57\n13056  Logstash Airways     RU-AMU  ...         6 2018-02-11 04:09:27\n13057          JetBeats      SE-BD  ...         6 2018-02-11 08:28:21\n13058          JetBeats      US-DC  ...         6 2018-02-11 14:54:34\n\n[13059 rows x 20 columns]",
+      "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>Carrier</th>\n      <th>DestRegion</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Kibana Airlines</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 00:00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Logstash Airways</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 18:27:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Logstash Airways</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 17:11:14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Kibana Airlines</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 10:33:28</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kibana Airlines</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 05:13:00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13054</th>\n      <td>Logstash Airways</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 20:42:25</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>Logstash Airways</td>\n      <td>CH-ZH</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 01:41:57</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>Logstash Airways</td>\n      <td>RU-AMU</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 04:09:27</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>JetBeats</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 08:28:21</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>JetBeats</td>\n      <td>US-DC</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 14:54:34</td>\n    </tr>\n  </tbody>\n</table>\n<p>13059 rows × 20 columns</p>\n</div>"
      },
      "execution_count": 53,
      "metadata": {},
@@ -3420,150 +1306,12 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "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>Carrier</th>\n",
-       "      <th>DestRegion</th>\n",
-       "      <th>...</th>\n",
-       "      <th>dayOfWeek</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 18:27:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 17:11:14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>IT-34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 10:33:28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Kibana Airlines</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2018-01-01 05:13:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13054</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 20:42:25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13055</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>CH-ZH</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 01:41:57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13056</th>\n",
-       "      <td>Logstash Airways</td>\n",
-       "      <td>RU-AMU</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 04:09:27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13057</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>SE-BD</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 08:28:21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13058</th>\n",
-       "      <td>JetBeats</td>\n",
-       "      <td>US-DC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2018-02-11 14:54:34</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>13059 rows × 20 columns</p>"
-      ],
-      "text/plain": [
-       "                Carrier DestRegion  ... dayOfWeek           timestamp\n",
-       "0       Kibana Airlines      SE-BD  ...         0 2018-01-01 00:00:00\n",
-       "1      Logstash Airways      IT-34  ...         0 2018-01-01 18:27:00\n",
-       "2      Logstash Airways      IT-34  ...         0 2018-01-01 17:11:14\n",
-       "3       Kibana Airlines      IT-34  ...         0 2018-01-01 10:33:28\n",
-       "4       Kibana Airlines      SE-BD  ...         0 2018-01-01 05:13:00\n",
-       "...                 ...        ...  ...       ...                 ...\n",
-       "13054  Logstash Airways      SE-BD  ...         6 2018-02-11 20:42:25\n",
-       "13055  Logstash Airways      CH-ZH  ...         6 2018-02-11 01:41:57\n",
-       "13056  Logstash Airways     RU-AMU  ...         6 2018-02-11 04:09:27\n",
-       "13057          JetBeats      SE-BD  ...         6 2018-02-11 08:28:21\n",
-       "13058          JetBeats      US-DC  ...         6 2018-02-11 14:54:34\n",
-       "\n",
-       "[13059 rows x 20 columns]"
-      ]
+      "text/plain": "                Carrier DestRegion  ... dayOfWeek           timestamp\n0       Kibana Airlines      SE-BD  ...         0 2018-01-01 00:00:00\n1      Logstash Airways      IT-34  ...         0 2018-01-01 18:27:00\n2      Logstash Airways      IT-34  ...         0 2018-01-01 17:11:14\n3       Kibana Airlines      IT-34  ...         0 2018-01-01 10:33:28\n4       Kibana Airlines      SE-BD  ...         0 2018-01-01 05:13:00\n...                 ...        ...  ...       ...                 ...\n13054  Logstash Airways      SE-BD  ...         6 2018-02-11 20:42:25\n13055  Logstash Airways      CH-ZH  ...         6 2018-02-11 01:41:57\n13056  Logstash Airways     RU-AMU  ...         6 2018-02-11 04:09:27\n13057          JetBeats      SE-BD  ...         6 2018-02-11 08:28:21\n13058          JetBeats      US-DC  ...         6 2018-02-11 14:54:34\n\n[13059 rows x 20 columns]",
+      "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>Carrier</th>\n      <th>DestRegion</th>\n      <th>...</th>\n      <th>dayOfWeek</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Kibana Airlines</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 00:00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Logstash Airways</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 18:27:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Logstash Airways</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 17:11:14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Kibana Airlines</td>\n      <td>IT-34</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 10:33:28</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kibana Airlines</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2018-01-01 05:13:00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13054</th>\n      <td>Logstash Airways</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 20:42:25</td>\n    </tr>\n    <tr>\n      <th>13055</th>\n      <td>Logstash Airways</td>\n      <td>CH-ZH</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 01:41:57</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>Logstash Airways</td>\n      <td>RU-AMU</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 04:09:27</td>\n    </tr>\n    <tr>\n      <th>13057</th>\n      <td>JetBeats</td>\n      <td>SE-BD</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 08:28:21</td>\n    </tr>\n    <tr>\n      <th>13058</th>\n      <td>JetBeats</td>\n      <td>US-DC</td>\n      <td>...</td>\n      <td>6</td>\n      <td>2018-02-11 14:54:34</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>13059 rows × 20 columns</p>"
      },
      "execution_count": 54,
      "metadata": {},
@@ -3590,18 +1338,12 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 9 Axes>"
-      ]
+      "text/plain": "<Figure size 720x720 with 9 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -3617,18 +1359,12 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 9 Axes>"
-      ]
+      "text/plain": "<Figure size 720x720 with 9 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -3651,11 +1387,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ed_flights2 = ed_flights[(ed_flights.OriginAirportID == 'AMS') & (ed_flights.FlightDelayMin > 60)]\n",
@@ -3666,11 +1398,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3720,17 +1448,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.5"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/tests/notebook/test_etl.ipynb b/tests/notebook/test_etl.ipynb
index 2c741e75..5f16c202 100644
--- a/tests/notebook/test_etl.ipynb
+++ b/tests/notebook/test_etl.ipynb
@@ -18,9 +18,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "False"
-      ]
+      "text/plain": "False"
      },
      "execution_count": 2,
      "metadata": {},
@@ -42,8 +40,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2020-10-28 22:01:46.397163: read 10000 rows\n",
-      "2020-10-28 22:01:47.100938: read 13059 rows\n"
+      "2021-03-30 11:57:39.116425: read 10000 rows\n",
+      "2021-03-30 11:57:39.522722: read 13059 rows\n"
      ]
     }
    ],
@@ -59,9 +57,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
+      "text/plain": "pandas.core.frame.DataFrame"
      },
      "execution_count": 4,
      "metadata": {},
@@ -79,146 +75,8 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e3e6e7371be43aabd4f9a2bb62ed737",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Progress'), FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "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>account length</th>\n",
-       "      <th>area code</th>\n",
-       "      <th>churn</th>\n",
-       "      <th>customer service calls</th>\n",
-       "      <th>international plan</th>\n",
-       "      <th>number vmail messages</th>\n",
-       "      <th>phone number</th>\n",
-       "      <th>state</th>\n",
-       "      <th>total day calls</th>\n",
-       "      <th>total day charge</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total eve calls</th>\n",
-       "      <th>total eve charge</th>\n",
-       "      <th>total eve minutes</th>\n",
-       "      <th>total intl calls</th>\n",
-       "      <th>total intl charge</th>\n",
-       "      <th>total intl minutes</th>\n",
-       "      <th>total night calls</th>\n",
-       "      <th>total night charge</th>\n",
-       "      <th>total night minutes</th>\n",
-       "      <th>voice mail plan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>128</td>\n",
-       "      <td>415</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>no</td>\n",
-       "      <td>25</td>\n",
-       "      <td>382-4657</td>\n",
-       "      <td>KS</td>\n",
-       "      <td>110</td>\n",
-       "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99</td>\n",
-       "      <td>16.78</td>\n",
-       "      <td>197.4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>91</td>\n",
-       "      <td>11.01</td>\n",
-       "      <td>244.7</td>\n",
-       "      <td>yes</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>107</td>\n",
-       "      <td>415</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>no</td>\n",
-       "      <td>26</td>\n",
-       "      <td>371-7191</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>123</td>\n",
-       "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>103</td>\n",
-       "      <td>16.62</td>\n",
-       "      <td>195.5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>103</td>\n",
-       "      <td>11.45</td>\n",
-       "      <td>254.4</td>\n",
-       "      <td>yes</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "<p>2 rows × 21 columns</p>"
-      ],
-      "text/plain": [
-       "   account length  area code  churn  customer service calls  \\\n",
-       "0             128        415      0                       1   \n",
-       "1             107        415      0                       1   \n",
-       "\n",
-       "  international plan  number vmail messages phone number state  \\\n",
-       "0                 no                     25     382-4657    KS   \n",
-       "1                 no                     26     371-7191    OH   \n",
-       "\n",
-       "   total day calls  total day charge  ...  total eve calls  total eve charge  \\\n",
-       "0              110             45.07  ...               99             16.78   \n",
-       "1              123             27.47  ...              103             16.62   \n",
-       "\n",
-       "   total eve minutes  total intl calls  total intl charge  total intl minutes  \\\n",
-       "0              197.4                 3                2.7                10.0   \n",
-       "1              195.5                 3                3.7                13.7   \n",
-       "\n",
-       "   total night calls  total night charge  total night minutes  voice mail plan  \n",
-       "0                 91               11.01                244.7              yes  \n",
-       "1                103               11.45                254.4              yes  \n",
-       "\n",
-       "[2 rows x 21 columns]"
-      ]
+      "text/plain": "   account length  area code  churn  customer service calls  \\\n0             128        415      0                       1   \n1             107        415      0                       1   \n\n  international plan  number vmail messages phone number state  \\\n0                 no                     25     382-4657    KS   \n1                 no                     26     371-7191    OH   \n\n   total day calls  total day charge  ...  total eve calls  total eve charge  \\\n0              110             45.07  ...               99             16.78   \n1              123             27.47  ...              103             16.62   \n\n   total eve minutes  total intl calls  total intl charge  total intl minutes  \\\n0              197.4                 3                2.7                10.0   \n1              195.5                 3                3.7                13.7   \n\n   total night calls  total night charge  total night minutes  voice mail plan  \n0                 91               11.01                244.7              yes  \n1                103               11.45                254.4              yes  \n\n[2 rows x 21 columns]",
+      "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>account length</th>\n      <th>area code</th>\n      <th>churn</th>\n      <th>customer service calls</th>\n      <th>international plan</th>\n      <th>number vmail messages</th>\n      <th>phone number</th>\n      <th>state</th>\n      <th>total day calls</th>\n      <th>total day charge</th>\n      <th>...</th>\n      <th>total eve calls</th>\n      <th>total eve charge</th>\n      <th>total eve minutes</th>\n      <th>total intl calls</th>\n      <th>total intl charge</th>\n      <th>total intl minutes</th>\n      <th>total night calls</th>\n      <th>total night charge</th>\n      <th>total night minutes</th>\n      <th>voice mail plan</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>128</td>\n      <td>415</td>\n      <td>0</td>\n      <td>1</td>\n      <td>no</td>\n      <td>25</td>\n      <td>382-4657</td>\n      <td>KS</td>\n      <td>110</td>\n      <td>45.07</td>\n      <td>...</td>\n      <td>99</td>\n      <td>16.78</td>\n      <td>197.4</td>\n      <td>3</td>\n      <td>2.7</td>\n      <td>10.0</td>\n      <td>91</td>\n      <td>11.01</td>\n      <td>244.7</td>\n      <td>yes</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>107</td>\n      <td>415</td>\n      <td>0</td>\n      <td>1</td>\n      <td>no</td>\n      <td>26</td>\n      <td>371-7191</td>\n      <td>OH</td>\n      <td>123</td>\n      <td>27.47</td>\n      <td>...</td>\n      <td>103</td>\n      <td>16.62</td>\n      <td>195.5</td>\n      <td>3</td>\n      <td>3.7</td>\n      <td>13.7</td>\n      <td>103</td>\n      <td>11.45</td>\n      <td>254.4</td>\n      <td>yes</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<p>2 rows × 21 columns</p>"
      },
      "execution_count": 5,
      "metadata": {},
@@ -237,38 +95,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'took': 0,\n",
-       " 'timed_out': False,\n",
-       " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n",
-       " 'hits': {'total': {'value': 2, 'relation': 'eq'},\n",
-       "  'max_score': 1.0,\n",
-       "  'hits': [{'_index': 'churn',\n",
-       "    '_type': '_doc',\n",
-       "    '_id': '0',\n",
-       "    '_score': 1.0,\n",
-       "    '_source': {'state': 'KS',\n",
-       "     'account length': 128,\n",
-       "     'area code': 415,\n",
-       "     'phone number': '382-4657',\n",
-       "     'international plan': 'no',\n",
-       "     'voice mail plan': 'yes',\n",
-       "     'number vmail messages': 25,\n",
-       "     'total day minutes': 265.1,\n",
-       "     'total day calls': 110,\n",
-       "     'total day charge': 45.07,\n",
-       "     'total eve minutes': 197.4,\n",
-       "     'total eve calls': 99,\n",
-       "     'total eve charge': 16.78,\n",
-       "     'total night minutes': 244.7,\n",
-       "     'total night calls': 91,\n",
-       "     'total night charge': 11.01,\n",
-       "     'total intl minutes': 10.0,\n",
-       "     'total intl calls': 3,\n",
-       "     'total intl charge': 2.7,\n",
-       "     'customer service calls': 1,\n",
-       "     'churn': 0}}]}}"
-      ]
+      "text/plain": "{'took': 0,\n 'timed_out': False,\n '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n 'hits': {'total': {'value': 2, 'relation': 'eq'},\n  'max_score': 1.0,\n  'hits': [{'_index': 'churn',\n    '_id': '0',\n    '_score': 1.0,\n    '_source': {'state': 'KS',\n     'account length': 128,\n     'area code': 415,\n     'phone number': '382-4657',\n     'international plan': 'no',\n     'voice mail plan': 'yes',\n     'number vmail messages': 25,\n     'total day minutes': 265.1,\n     'total day calls': 110,\n     'total day charge': 45.07,\n     'total eve minutes': 197.4,\n     'total eve calls': 99,\n     'total eve charge': 16.78,\n     'total night minutes': 244.7,\n     'total night calls': 91,\n     'total night charge': 11.01,\n     'total intl minutes': 10.0,\n     'total intl calls': 3,\n     'total intl charge': 2.7,\n     'customer service calls': 1,\n     'churn': 0}}]}}"
      },
      "execution_count": 6,
      "metadata": {},
@@ -287,9 +114,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'acknowledged': True}"
-      ]
+      "text/plain": "{'acknowledged': True}"
      },
      "execution_count": 7,
      "metadata": {},
@@ -322,4 +147,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/tests/notebook/test_metrics.ipynb b/tests/notebook/test_metrics.ipynb
index c7fbff79..08f76a40 100644
--- a/tests/notebook/test_metrics.ipynb
+++ b/tests/notebook/test_metrics.ipynb
@@ -32,13 +32,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice                          640.387\n",
-       "Cancelled                                 False\n",
-       "dayOfWeek                                     3\n",
-       "timestamp         2018-01-22 00:05:00.494117676\n",
-       "dtype: object"
-      ]
+      "text/plain": "AvgTicketPrice                       640.387285\nCancelled                                 False\ndayOfWeek                                     3\ntimestamp         2018-01-21 23:43:19.256498944\ndtype: object"
      },
      "execution_count": 3,
      "metadata": {},
@@ -57,12 +51,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    640.387285\n",
-       "Cancelled           0.000000\n",
-       "dayOfWeek           3.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice    640.387285\nCancelled           0.000000\ndayOfWeek           3.000000\ndtype: float64"
      },
      "execution_count": 4,
      "metadata": {},
@@ -81,14 +70,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice                          640.387\n",
-       "Cancelled                                 False\n",
-       "dayOfWeek                                     3\n",
-       "timestamp         2018-01-22 00:05:00.494117676\n",
-       "DestCountry                                 NaN\n",
-       "dtype: object"
-      ]
+      "text/plain": "AvgTicketPrice                       640.387285\nCancelled                                 False\ndayOfWeek                                     3\ntimestamp         2018-01-21 23:43:19.256498944\nDestCountry                                 NaN\ndtype: object"
      },
      "execution_count": 5,
      "metadata": {},
@@ -107,11 +89,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    213.430365\n",
-       "dayOfWeek           2.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice    213.430365\ndayOfWeek           2.000000\ndtype: float64"
      },
      "execution_count": 6,
      "metadata": {},
@@ -130,11 +108,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    213.430365\n",
-       "dayOfWeek           2.000000\n",
-       "dtype: float64"
-      ]
+      "text/plain": "AvgTicketPrice    213.430365\ndayOfWeek           2.000000\ndtype: float64"
      },
      "execution_count": 7,
      "metadata": {},
@@ -153,14 +127,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AvgTicketPrice    213.43\n",
-       "Cancelled            NaN\n",
-       "dayOfWeek              2\n",
-       "timestamp            NaT\n",
-       "DestCountry          NaN\n",
-       "dtype: object"
-      ]
+      "text/plain": "AvgTicketPrice    213.430365\nCancelled                NaN\ndayOfWeek                2.0\ntimestamp                NaT\nDestCountry              NaN\ndtype: object"
      },
      "execution_count": 8,
      "metadata": {},
@@ -194,4 +161,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/tests/notebook/test_plotting.ipynb b/tests/notebook/test_plotting.ipynb
index 0d31ca78..ddd89ecd 100644
--- a/tests/notebook/test_plotting.ipynb
+++ b/tests/notebook/test_plotting.ipynb
@@ -2,19 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import eland as ed\n",
     "import numpy as np\n",
+    "\n",
+    "# Remove pandas/matplotlib deprecation warning\n",
+    "import warnings\n",
+    "import matplotlib.cbook\n",
+    "warnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,25 +29,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
+      "text/plain": "<AxesSubplot:>"
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD4CAYAAADo30HgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXwklEQVR4nO3df5Dc9X3f8ee7EoiW2ASDcyUHrc6RcOZI09jcgKcTp9cSjHATlKRARD0NjuWq7cA4wdO6MO5QD1PNmHoaTTKGeNSiMWaIhUpMc0zVyIph63bG4pdDEgQRHIgMEhgawDhn50SF3v1jP7L3s9q729273dOZ52Pm5r77+X6+n31/vnfa1+33+92vIjORJOm4v7HcBUiSTi4GgySpYjBIkioGgySpYjBIkiqrl7uApXD22Wfn2rVr+9r2u9/9LqeffvrSFjQg1joYK6XWlVInWOugLHWtjz322F9m5rtPWJGZK/7rwgsvzH49+OCDfW87bNY6GCul1pVSZ6a1DspS1wo8mh1eUz2UJEmqGAySpIrBIEmqGAySpIrBIEmqGAySpIrBIEmqGAySpIrBIEmqdHVLjIjYAPw2sAr4r5n52bb1a4AvARcCrwK/mpnPl3U3AZuBt4BPZOae0r4D+AXglcz8qZaxPgf8IvAm8Czw65n57f6nOL9X/uoI2/Y+Pajh53XDpecvy/NK0nwWfMcQEauA24DLgXHgmogYb+u2GXg9M9cB24Bby7bjwCbgAmADcHsZD+CLpa3dXuCnMvOngaeBm3qckyRpEbo5lHQRMJ2Zz2Xmm8BOYGNbn43AnWX5XuCSiIjSvjMzj2TmQWC6jEdmfh14rf3JMvOrmXm0PNwHnNvjnCRJi9DNoaRR4IWWx4eAi+fqk5lHI+IN4KzSvq9t29Ee6vsYcE+nFRGxBdgCMDIyQqPR6GHYHzjl2BFGZw/2te1iNRov9tR/Zmam73kOm7UuvZVSJ1jroAyr1pP2ttsR8WngKHB3p/WZuR3YDjAxMZGTk5N9Pc+u+/dw+LSxPqtcnKsnezvH0Gg06Heew2atS2+l1AnWOijDqrWbQ0mHgfNaHp9b2jr2iYjVwBk0T0J3s+0JIuKjNE9Mf6TcGlaSNCTdBMMjwPqIGIuIU2meTJ5q6zMFXFuWrwQeKC/oU8CmiFgTEWPAeuDh+Z6sXAH1KeCKzPxe91ORJC2FBYOhnAi+HtgDPAXsysz9EXFLRFxRut0BnBUR08AngRvLtvuBXcCTwB8C12XmWwAR8WXgG8B7I+JQRGwuY30eeAewNyIej4gvLNFcJUld6OocQ2buBna3td3csjwLXDXHtluBrR3ar5mj/7puapIkDYaffJYkVQwGSVLlpL1cVYMzjFuAjM4u361GOvH2I1L3fMcgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkSlfBEBEbIuJARExHxI0d1q+JiHvK+ociYm3LuptK+4GIuKylfUdEvBIRT7SN9a6I2BsRz5TvZy5ifpKkHi0YDBGxCrgNuBwYB66JiPG2bpuB1zNzHbANuLVsOw5sAi4ANgC3l/EAvlja2t0IfC0z1wNfK48lSUPSzTuGi4DpzHwuM98EdgIb2/psBO4sy/cCl0RElPadmXkkMw8C02U8MvPrwGsdnq91rDuBX+p+OpKkxVrdRZ9R4IWWx4eAi+fqk5lHI+IN4KzSvq9t29EFnm8kM18qy98CRjp1iogtwBaAkZERGo3GghPp5JRjRxidPdjXtovVaLzYU/+ZmZm+59lqdPbIosdYyHLu107m29dLtV8HbaXUCdY6KMOqtZtgWDaZmRGRc6zbDmwHmJiYyMnJyb6eY9f9ezh82ljfNS7G1ZPn99S/0WjQ7zxbbdv79KLHWMjo7MFl26+dzLevl2q/DtpKqROsdVCGVWs3h5IOA+e1PD63tHXsExGrgTOAV7vctt3LEXFOGesc4JUuapQkLZFuguERYH1EjEXEqTRPJk+19ZkCri3LVwIPZGaW9k3lqqUxYD3w8ALP1zrWtcAfdFGjJGmJLBgMmXkUuB7YAzwF7MrM/RFxS0RcUbrdAZwVEdPAJylXEmXmfmAX8CTwh8B1mfkWQER8GfgG8N6IOBQRm8tYnwUujYhngJ8vjyVJQ9LVOYbM3A3sbmu7uWV5Frhqjm23Als7tF8zR/9XgUu6qUuStPT85LMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqWIwSJIqBoMkqdJVMETEhog4EBHTEXFjh/VrIuKesv6hiFjbsu6m0n4gIi5baMyIuCQivhkRj0fE/4mIdYucoySpBwsGQ0SsAm4DLgfGgWsiYryt22bg9cxcB2wDbi3bjgObgAuADcDtEbFqgTF/F/hIZv4M8HvAv1/UDCVJPenmHcNFwHRmPpeZbwI7gY1tfTYCd5ble4FLIiJK+87MPJKZB4HpMt58YybwzrJ8BvBif1OTJPVjdRd9RoEXWh4fAi6eq09mHo2IN4CzSvu+tm1Hy/JcY34c2B0Rfw18B/hAp6IiYguwBWBkZIRGo9HFVE50yrEjjM4e7GvbxWo0esu8mZmZvufZanT2yKLHWMhy7tdO5tvXS7VfB22l1AnWOijDqrWbYBi2G4APZ+ZDEfFvgd+iGRaVzNwObAeYmJjIycnJvp5s1/17OHzaWP/VLsLVk+f31L/RaNDvPFtt2/v0osdYyOjswWXbr53Mt6+Xar8O2kqpE6x1UIZVazeHkg4D57U8Pre0dewTEatpHgJ6dZ5tO7ZHxLuBv5+ZD5X2e4B/0NVMJElLoptgeARYHxFjEXEqzZPJU219poBry/KVwAOZmaV9U7lqaQxYDzw8z5ivA2dExPE/7y4Fnup/epKkXi14KKmcM7ge2AOsAnZk5v6IuAV4NDOngDuAuyJiGniN5gs9pd8u4EngKHBdZr4F0GnM0v4vgN+PiGM0g+JjSzpjvS3Nd/hsdPbIwA6v3XBpb4cLpZNBV+cYMnM3sLut7eaW5Vngqjm23Qps7WbM0n4fcF83dUmSlp6ffJYkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVQwGSVLFYJAkVboKhojYEBEHImI6Im7ssH5NRNxT1j8UEWtb1t1U2g9ExGULjRlNWyPi6Yh4KiI+scg5SpJ6sHqhDhGxCrgNuBQ4BDwSEVOZ+WRLt83A65m5LiI2AbcCvxoR48Am4ALgx4E/iojzyzZzjflR4DzgJzPzWET82FJM9GS0be/TPfUfnT3S8zaS1Ktu3jFcBExn5nOZ+SawE9jY1mcjcGdZvhe4JCKitO/MzCOZeRCYLuPNN+a/Bm7JzGMAmflK/9OTJPVqwXcMwCjwQsvjQ8DFc/XJzKMR8QZwVmnf17btaFmea8yfoPlu45eB/wt8IjOfaS8qIrYAWwBGRkZoNBpdTOVEpxw7wujswb62HTZrHYxB1tpovLhkY83MzPT9ez5s1joYw6q1m2AYtjXAbGZORMSvADuAD7Z3ysztwHaAiYmJnJyc7OvJdt2/h8OnjfVf7RCNzh601gEYZK1XT56/cKcuNRoN+v09HzZrHYxh1drNoaTDNI/5H3duaevYJyJWA2cAr86z7XxjHgK+UpbvA366ixolSUukm2B4BFgfEWMRcSrNk8lTbX2mgGvL8pXAA5mZpX1TuWppDFgPPLzAmP8d+Edl+R8Cnm2VpCFa8FBSOWdwPbAHWAXsyMz9EXEL8GhmTgF3AHdFxDTwGs0Xekq/XcCTwFHgusx8C6DTmOUpPwvcHRE3ADPAx5duupKkhXR1jiEzdwO729publmeBa6aY9utwNZuxizt3wb+STd1SZKWnp98liRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUsVgkCRVugqGiNgQEQciYjoibuywfk1E3FPWPxQRa1vW3VTaD0TEZT2M+TsRMdPnvCRJfVowGCJiFXAbcDkwDlwTEeNt3TYDr2fmOmAbcGvZdhzYBFwAbABuj4hVC40ZERPAmYucmySpD928Y7gImM7M5zLzTWAnsLGtz0bgzrJ8L3BJRERp35mZRzLzIDBdxptzzBIanwM+tbipSZL6sbqLPqPACy2PDwEXz9UnM49GxBvAWaV9X9u2o2V5rjGvB6Yy86VmtnQWEVuALQAjIyM0Go0upnKiU44dYXT2YF/bDpu1DsYga911/9KNe8qxI+y6f0/X/X/sHWuW7Ll7NTMz0/e/yWGz1hN1EwxDExE/DlwFTC7UNzO3A9sBJiYmcnJywU062nX/Hg6fNtbXtsM2OnvQWgdgpdTaa51XT54/wGrm12g06Pff5LBZ64m6OZR0GDiv5fG5pa1jn4hYDZwBvDrPtnO1vw9YB0xHxPPA34qI6S7nIklaAt0EwyPA+ogYi4hTaZ5MnmrrMwVcW5avBB7IzCztm8pVS2PAeuDhucbMzP+RmX87M9dm5lrge+WEtiRpSBY8lFTOGVwP7AFWATsyc39E3AI8mplTwB3AXeWv+9dovtBT+u0CngSOAtdl5lsAncZc+ulJknrV1TmGzNwN7G5ru7lleZbmuYFO224FtnYzZoc+P9JNfZKkpeMnnyVJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJla6CISI2RMSBiJiOiBs7rF8TEfeU9Q9FxNqWdTeV9gMRcdlCY0bE3aX9iYjYERGnLHKOkqQeLBgMEbEKuA24HBgHromI8bZum4HXM3MdsA24tWw7DmwCLgA2ALdHxKoFxrwb+Eng7wF/E/j4omYoSepJN+8YLgKmM/O5zHwT2AlsbOuzEbizLN8LXBIRUdp3ZuaRzDwITJfx5hwzM3dnATwMnLu4KUqSerG6iz6jwAstjw8BF8/VJzOPRsQbwFmlfV/btqNled4xyyGkfw78RqeiImILsAVgZGSERqPRxVROdMqxI4zOHuxr22Gz1sFYKbX2Wmej8eIAq5nfzMxM3/8mh81aT9RNMCyX24GvZ+b/7rQyM7cD2wEmJiZycnKyryfZdf8eDp821m+NQzU6e9BaB2Cl1NprnVdPnj/AaubXaDTo99/ksFnriboJhsPAeS2Pzy1tnfociojVwBnAqwtsO+eYEfEfgHcD/7KL+iRJS6ibcwyPAOsjYiwiTqV5Mnmqrc8UcG1ZvhJ4oJwjmAI2lauWxoD1NM8bzDlmRHwcuAy4JjOPLW56kqReLfiOoZwzuB7YA6wCdmTm/oi4BXg0M6eAO4C7ImIaeI3mCz2l3y7gSeAocF1mvgXQaczylF8A/gL4RvP8NV/JzFuWbMaSpHl1dY4hM3cDu9vabm5ZngWummPbrcDWbsYs7SfzeQ9J+qHni7D0Q2rb3qeX5XlvuHT5TnpraXhLDElSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFW8JYakJbVt79OMzh5ZlltyeDuOpeE7BklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFX8gJukHxr9fKhuKT6M98P2wTrfMUiSKgaDJKliMEiSKgaDJKnSVTBExIaIOBAR0xFxY4f1ayLinrL+oYhY27LuptJ+ICIuW2jMiBgrY0yXMU9d5BwlST1YMBgiYhVwG3A5MA5cExHjbd02A69n5jpgG3Br2XYc2ARcAGwAbo+IVQuMeSuwrYz1ehlbkjQk3VyuehEwnZnPAUTETmAj8GRLn43AZ8ryvcDnIyJK+87MPAIcjIjpMh6dxoyIp4B/DPyz0ufOMu7v9jU7SRqCYf3fE50urR3EpbLdBMMo8ELL40PAxXP1ycyjEfEGcFZp39e27WhZ7jTmWcC3M/Noh/6ViNgCbCkPZyLiQBdz6eRs4C/73HbYrHUwVkqtK6VOsNZBOaHWTy5uvL/bqXHFfsAtM7cD2xc7TkQ8mpkTS1DSwFnrYKyUWldKnWCtgzKsWrs5+XwYOK/l8bmlrWOfiFgNnAG8Os+2c7W/CvxoGWOu55IkDVA3wfAIsL5cLXQqzZPJU219poBry/KVwAOZmaV9U7lqaQxYDzw815hlmwfLGJQx/6D/6UmSerXgoaRyzuB6YA+wCtiRmfsj4hbg0cycAu4A7ionl1+j+UJP6beL5onqo8B1mfkWQKcxy1P+O2BnRPxH4I/L2IO06MNRQ2Stg7FSal0pdYK1DspQao3mH+mSJDX5yWdJUsVgkCRV3tbBsNCtPoZcy3kR8WBEPBkR+yPiN0r7ZyLicEQ8Xr4+3LJNx9uNDKne5yPiz0pNj5a2d0XE3oh4pnw/s7RHRPxOqfVPI+L9Q6zzvS377vGI+E5E/ObJsl8jYkdEvBIRT7S09bwfI+La0v+ZiLi203MNqNbPRcSfl3rui4gfLe1rI+KvW/bvF1q2ubD87kyX+cSQau35Zz7o14g56rynpcbnI+Lx0j68fZqZb8svmie9nwXeA5wK/Akwvoz1nAO8vyy/A3ia5u1CPgP8mw79x0vNa4CxMpdVQ6z3eeDstrb/BNxYlm8Ebi3LHwb+JxDAB4CHlvFn/i2aH+o5KfYr8HPA+4En+t2PwLuA58r3M8vymUOq9UPA6rJ8a0uta1v7tY3zcKk/ynwuH1KtPf3Mh/Ea0anOtvX/Gbh52Pv07fyO4fu3+sjMN4Hjt/pYFpn5UmZ+syz/FfAUc3zqu/j+7UYy8yDQeruR5bKR5m1MKN9/qaX9S9m0j+ZnVc5ZhvouAZ7NzL+Yp89Q92tmfp3mlXztNfSyHy8D9mbma5n5OrCX5r3JBl5rZn41f3Cngn00P3s0p1LvOzNzXzZf0b7ED+Y30FrnMdfPfOCvEfPVWf7qvxr48nxjDGKfvp2DodOtPuZ7IR6aaN6d9n3AQ6Xp+vJWfcfxwwosf/0JfDUiHovm7UkARjLzpbL8LWCkLC93rcdtov5HdjLuV+h9P54MNQN8jOZfq8eNRcQfR8T/iogPlrZRmvUdN+xae/mZL/d+/SDwcmY+09I2lH36dg6Gk1JE/Ajw+8BvZuZ3aN5A8CeAnwFeovnW8mTws5n5fpp3yL0uIn6udWX5y+WkuRY6mh+kvAL4b6XpZN2vlZNtP84lIj5N87NKd5eml4C/k5nvo3k7n9+LiHcuV33FiviZt7iG+g+Zoe3Tt3MwdHOrj6GKiFNohsLdmfkVgMx8OTPfysxjwH/hB4c1lrX+zDxcvr8C3Ffqevn4IaLy/ZWTodbicuCbmfkynLz7teh1Py5rzRHxUeAXgI+UIKMclnm1LD9G81j9+aWu1sNNQ6u1j5/5su3XaN4W6FeAe463DXOfvp2DoZtbfQxNOZ54B/BUZv5WS3vrsfhfBo5fvTDX7UaGUevpEfGO48s0T0A+QX1rlNbbmUwBv1auqvkA8EbLoZJhqf76Ohn3a4te9+Me4EMRcWY5PPKh0jZwEbEB+BRwRWZ+r6X93dH8f1eIiPfQ3I/PlXq/ExEfKL/zv8aQbnvTx898OV8jfh7488z8/iGioe7TpTzDvtK+aF7l8TTN5P30MtfyszQPGfwp8Hj5+jBwF/BnpX0KOKdlm0+X2g8wgCs75qn1PTSv0PgTYP/xfUfztulfA54B/gh4V2kPmv8x07NlLhND3ren07xB4xktbSfFfqUZVi8B/4/mseHN/exHmsf3p8vXrw+x1mmax+GP/85+ofT9p+V343Hgm8AvtowzQfNF+Vng85Q7MAyh1p5/5oN+jehUZ2n/IvCv2voObZ96SwxJUuXtfChJktSBwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqTK/weyDp6tFMKzLwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -55,25 +57,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
+      "text/plain": "<AxesSubplot:>"
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD4CAYAAADo30HgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXx0lEQVR4nO3df5Dc9X3f8ee7Ej9ax8YYu1fnoNUlCGeONo3tG/AfcXotwQg3RUkKVDTT4BpXbQcmCZ7WFeMO9TDVTKin0UzGEI86aEwYYqES05ynimXFeOt2xgiwQxwEFpyRM0hg0wDBOTt3VOjdP/YjZz/L7t33dvd+BD0fMzf33c/38/ns+/vdu3vdfr+7343MRJKkU/7aWhcgSVpfDAZJUsVgkCRVDAZJUsVgkCRVNq51AaPw9re/PTdt2jTQ2O9///u86U1vGm1BI2R9w7G+4VjfcNZ7fV/72tf+NDPf8boVmflX/uu9731vDurLX/7ywGNXg/UNx/qGY33DWe/1AY9mj7+pHkqSJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFXeEJfE0PLsOvjUyOYan19oPN/Nl180svuVtHJ8xiBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqjQKhojYEhFHImI2Inb0WH9WRNxX1h+KiE0d624p7Uci4oqO9j0R8UJEPN411ycj4psR8Y2IeCAi3jr45kmSlmvJYIiIDcAdwJXAJHBdREx2dbsBeDkzLwR2AbeXsZPANuBiYAtwZ5kP4DOlrdtB4O9m5k8CTwG3LHObJElDaPKM4RJgNjOfycxXgb3A1q4+W4G7y/L9wGUREaV9b2YuZOZRYLbMR2Z+BXip+84y84uZeaLcfAg4f5nbJEkaQpNgGAee7bh9rLT17FP+qL8CnNdw7GI+DPz+MvpLkoa0bj/BLSI+DpwA7u2zfjuwHWBsbIxWqzXQ/czNzQ08djWsRH3j8wsjm+uMkwuMzx9t1LfVem5k99vU6fj4jpL1DWe919dPk2A4DlzQcfv80tarz7GI2AicA7zYcOzrRMSHgJ8DLsvM7NUnM3cDuwGmpqZyenq6waa8XqvVYtCxq2El6hvtR3se5fjZE436Xju9+h/teTo+vqNkfcNZ7/X10+RQ0iPA5oiYiIgzaZ9MnunqMwNcX5avBh4sf9BngG3lVUsTwGbg4cXuLCK2AB8DrsrMHzTfFEnSKCwZDOWcwU3AAeBJYF9mHo6I2yLiqtLtLuC8iJgFPgrsKGMPA/uAJ4AvADdm5msAEfFZ4KvAuyLiWETcUOb6FPBm4GBEPBYRnx7RtkqSGmh0jiEz9wP7u9pu7VieB67pM3YnsLNH+3V9+l/YpCZJ0srwnc+SpIrBIEmqGAySpIrBIEmqrNs3uK2WF/58YaSv61+Omy9f/df1S9JSfMYgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaqc9p/gptWzFp+UNz7f/oQ+Py1Paq7RM4aI2BIRRyJiNiJ29Fh/VkTcV9YfiohNHetuKe1HIuKKjvY9EfFCRDzeNdfbIuJgRDxdvp87xPZJkpZpyWCIiA3AHcCVwCRwXURMdnW7AXg5My8EdgG3l7GTwDbgYmALcGeZD+Azpa3bDuBLmbkZ+FK5LUlaJU2eMVwCzGbmM5n5KrAX2NrVZytwd1m+H7gsIqK0783Mhcw8CsyW+cjMrwAv9bi/zrnuBn6++eZIkobV5BzDOPBsx+1jwKX9+mTmiYh4BTivtD/UNXZ8ifsby8zny/J3gLFenSJiO7AdYGxsjFarteSG9HLGyQXG548ONHZYrdZzS/aZm5sbeNv6GZ9fGNlca7n/mjhVX5N9vRZW4vEdJesbznqvr591ffI5MzMiss+63cBugKmpqZyenh7oPvZ9/gDHz54YuMZhXDu99AnRVqvFoNvWzyhPAo/PH12z/dfEqfqa7Ou1sBKP7yhZ33DWe339NDmUdBy4oOP2+aWtZ5+I2AicA7zYcGy370bEO8tc7wReaFCjJGlEmgTDI8DmiJiIiDNpn0ye6eozA1xflq8GHszMLO3byquWJoDNwMNL3F/nXNcDv9egRknSiCwZDJl5ArgJOAA8CezLzMMRcVtEXFW63QWcFxGzwEcpryTKzMPAPuAJ4AvAjZn5GkBEfBb4KvCuiDgWETeUuX4duDwingZ+ttyWJK2SRucYMnM/sL+r7daO5Xngmj5jdwI7e7Rf16f/i8BlTeqSJI2el8SQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSpVEwRMSWiDgSEbMRsaPH+rMi4r6y/lBEbOpYd0tpPxIRVyw1Z0RcFhFfj4jHIuL/RMSFQ26jJGkZlgyGiNgA3AFcCUwC10XEZFe3G4CXM/NCYBdwexk7CWwDLga2AHdGxIYl5vwt4Jcy86eA3wH+41BbKElalibPGC4BZjPzmcx8FdgLbO3qsxW4uyzfD1wWEVHa92bmQmYeBWbLfIvNmcBbyvI5wHODbZokaRAbG/QZB57tuH0MuLRfn8w8ERGvAOeV9oe6xo6X5X5zfgTYHxF/AXwPeF+DGiVJI9IkGFbbzcAHM/NQRPx74Ddoh0UlIrYD2wHGxsZotVoD3dkZJxcYnz86eLVDaLWWfjI0Nzc38Lb1Mz6/MLK51nL/NXGqvib7ei2sxOM7StY3nPVeXz9NguE4cEHH7fNLW68+xyJiI+1DQC8uMfZ17RHxDuDvZ+ah0n4f8IVeRWXmbmA3wNTUVE5PTzfYlNfb9/kDHD97YqCxw7p2+qIl+7RaLQbdtn52HXxqZHONzx9ds/3XxKn6muzrtbASj+8oWd9w1nt9/TQ5x/AIsDkiJiLiTNonk2e6+swA15flq4EHMzNL+7byqqUJYDPw8CJzvgycExGnfosvB54cfPMkScu15DOGcs7gJuAAsAHYk5mHI+I24NHMnAHuAu6JiFngJdp/6Cn99gFPACeAGzPzNYBec5b2fwX8bkScpB0UHx7pFkuSFtXoHENm7gf2d7Xd2rE8D1zTZ+xOYGeTOUv7A8ADTeqSJI2e73yWJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUMBklSxWCQJFUafYKbVsaug08t2Wd8fqFRP0kaFZ8xSJIqBoMkqWIwSJIqBoMkqWIwSJIqjYIhIrZExJGImI2IHT3WnxUR95X1hyJiU8e6W0r7kYi4Yqk5o21nRDwVEU9GxK8MuY2SpGVY8uWqEbEBuAO4HDgGPBIRM5n5REe3G4CXM/PCiNgG3A78s4iYBLYBFwM/CvxBRFxUxvSb80PABcBPZObJiPibo9hQnd7W6iW/N19+0dKdpHWmyTOGS4DZzHwmM18F9gJbu/psBe4uy/cDl0VElPa9mbmQmUeB2TLfYnP+W+C2zDwJkJkvDL55kqTlavIGt3Hg2Y7bx4BL+/XJzBMR8QpwXml/qGvseFnuN+eP03628QvA/wV+JTOf7i4qIrYD2wHGxsZotVoNNuX1zji5wPj80YHGrgbrG85a19dqPbfo+rm5uYF/dleD9Q1nvdfXz3p85/NZwHxmTkXELwJ7gPd3d8rM3cBugKmpqZyenh7ozvZ9/gDHz54YvNoVNj5/1PqGsNb1XTu9+KGkVqvFoD+7q8H6hrPe6+unyaGk47SP+Z9yfmnr2SciNgLnAC8uMnaxOY8BnyvLDwA/2aBGSdKINAmGR4DNETEREWfSPpk809VnBri+LF8NPJiZWdq3lVctTQCbgYeXmPN/AP+wLP8DwAsFSdIqWvJQUjlncBNwANgA7MnMwxFxG/BoZs4AdwH3RMQs8BLtP/SUfvuAJ4ATwI2Z+RpArznLXf46cG9E3AzMAR8Z3eZKkpbS6BxDZu4H9ne13dqxPA9c02fsTmBnkzlL+58B/7hJXZKk0fOdz5KkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkSqNgiIgtEXEkImYjYkeP9WdFxH1l/aGI2NSx7pbSfiQirljGnL8ZEXMDbpckaUBLBkNEbADuAK4EJoHrImKyq9sNwMuZeSGwC7i9jJ0EtgEXA1uAOyNiw1JzRsQUcO6Q2yZJGkCTZwyXALOZ+UxmvgrsBbZ29dkK3F2W7wcui4go7XszcyEzjwKzZb6+c5bQ+CTwseE2TZI0iI0N+owDz3bcPgZc2q9PZp6IiFeA80r7Q11jx8tyvzlvAmYy8/l2tvQWEduB7QBjY2O0Wq0Gm/J6Z5xcYHz+6EBjV4P1DWet62u1nlt0/dzc3MA/u6vB+oaz3uvrp0kwrJqI+FHgGmB6qb6ZuRvYDTA1NZXT00sO6Wnf5w9w/OyJgcauhvH5o9Y3hLWu79rpixZd32q1GPRndzVY33DWe339NDmUdBy4oOP2+aWtZ5+I2AicA7y4yNh+7e8GLgRmI+LbwN+IiNmG2yJJGoEmwfAIsDkiJiLiTNonk2e6+swA15flq4EHMzNL+7byqqUJYDPwcL85M/N/ZubfysxNmbkJ+EE5oS1JWiVLHkoq5wxuAg4AG4A9mXk4Im4DHs3MGeAu4J7y3/1LtP/QU/rtA54ATgA3ZuZrAL3mHP3mSZKWq9E5hszcD+zvaru1Y3me9rmBXmN3AjubzNmjz480qU+SNDq+81mSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVFlXn8cgvdHsOvjUouvH5xeW7DOomy9f/LMgpH58xiBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqSKwSBJqhgMkqRKo2CIiC0RcSQiZiNiR4/1Z0XEfWX9oYjY1LHultJ+JCKuWGrOiLi3tD8eEXsi4owht1GStAxLBkNEbADuAK4EJoHrImKyq9sNwMuZeSGwC7i9jJ0EtgEXA1uAOyNiwxJz3gv8BPD3gL8OfGSoLZQkLUuTZwyXALOZ+UxmvgrsBbZ29dkK3F2W7wcui4go7XszcyEzjwKzZb6+c2bm/iyAh4Hzh9tESdJyNLm66jjwbMftY8Cl/fpk5omIeAU4r7Q/1DV2vCwvOmc5hPQvgF/tVVREbAe2A4yNjdFqtRpsyuudcXKB8fmjA41dDdY3nNO5vlbruaHnmJubG/h3azVY38pYz5fdvhP4Smb+714rM3M3sBtgamoqp6enB7qTfZ8/wPGzJwatccWNzx+1viGczvVdOz38ZbdbrRaD/m6tButbGU2C4ThwQcft80tbrz7HImIjcA7w4hJj+84ZEf8JeAfwrxvUJ0kaoSbnGB4BNkfEREScSftk8kxXnxng+rJ8NfBgOUcwA2wrr1qaADbTPm/Qd86I+AhwBXBdZp4cbvMkScu15DOGcs7gJuAAsAHYk5mHI+I24NHMnAHuAu6JiFngJdp/6Cn99gFPACeAGzPzNYBec5a7/DTwJ8BX2+ev+Vxm3jayLZYkLarROYbM3A/s72q7tWN5Hrimz9idwM4mc5b29XzeQ5Le8HznsySpYjBIkioGgySpYjBIkioGgySpYjBIkioGgySp4nsGpDeoXQefGnqO8fmFZc9z8+XDX6NJa8tnDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSaoYDJKkisEgSar4BjdJIzWKN9Y11f0GPN9cNxo+Y5AkVQwGSVLFYJAkVQwGSVLFYJAkVXxVkqQ3jNV8RVSnN9qroRo9Y4iILRFxJCJmI2JHj/VnRcR9Zf2hiNjUse6W0n4kIq5Yas6ImChzzJY5zxxyGyVJy7BkMETEBuAO4EpgErguIia7ut0AvJyZFwK7gNvL2ElgG3AxsAW4MyI2LDHn7cCuMtfLZW5J0ippcijpEmA2M58BiIi9wFbgiY4+W4FPlOX7gU9FRJT2vZm5AByNiNkyH73mjIgngX8E/PPS5+4y728NtHWStAr6HcIa5BPwlmslDmM1CYZx4NmO28eAS/v1ycwTEfEKcF5pf6hr7HhZ7jXnecCfZeaJHv0rEbEd2F5uzkXEkQbb0svbgT8dcOxqsL7hWN9wrG84K17fR4cb/nd6Nf6VPfmcmbuB3cPOExGPZubUCEpaEdY3HOsbjvUNZ73X10+Tk8/HgQs6bp9f2nr2iYiNwDnAi4uM7df+IvDWMke/+5IkraAmwfAIsLm8WuhM2ieTZ7r6zADXl+WrgQczM0v7tvKqpQlgM/BwvznLmC+XOShz/t7gmydJWq4lDyWVcwY3AQeADcCezDwcEbcBj2bmDHAXcE85ufwS7T/0lH77aJ+oPgHcmJmvAfSas9zlfwD2RsR/Bv6wzL2Shj4ctcKsbzjWNxzrG856r6+naP+TLklSm5fEkCRVDAZJUuW0DoalLvWxCvd/QUR8OSKeiIjDEfGrpf0TEXE8Ih4rXx/sGNPzEiMrXOe3I+KPSy2Plra3RcTBiHi6fD+3tEdE/Gap8RsR8Z4VrOtdHfvosYj4XkT82lrvv4jYExEvRMTjHW3L3l8RcX3p/3REXN/rvkZY3ycj4pulhgci4q2lfVNE/EXHvvx0x5j3lp+L2bINsYL1LfsxXanf7z713ddR27cj4rHSvur7byQy87T8on3S+1vAjwFnAn8ETK5yDe8E3lOW3ww8RfsSIZ8A/l2P/pOlzrOAiVL/hlWo89vA27va/guwoyzvAG4vyx8Efh8I4H3AoVV8PL9D+w07a7r/gJ8B3gM8Puj+At4GPFO+n1uWz13B+j4AbCzLt3fUt6mzX9c8D5eao2zDlStY37Ie05X8/e5VX9f6/wrculb7bxRfp/Mzhh9e6iMzXwVOXepj1WTm85n59bL858CT9Hmnd/HDS4xk5lGg8xIjq20r7UuWUL7/fEf7b2fbQ7Tfl/LOVajnMuBbmfkni/RZlf2XmV+h/eq87vtezv66AjiYmS9l5svAQdrXG1uR+jLzi/mXVxx4iPZ7iPoqNb4lMx/K9l+53+7YppHXt4h+j+mK/X4vVl/5r/9a4LOLzbGS+28UTudg6HWpj8X+KK+oaF+R9t3AodJ0U3lav+fUYQfWruYEvhgRX4v2pUgAxjLz+bL8HWBsjWvcRv3LuJ72Hyx/f61lrR+m/R/sKRMR8YcR8b8i4v2lbbzUtJr1LecxXav9937gu5n5dEfbetl/jZ3OwbBuRMSPAL8L/Fpmfo/2RQN/HPgp4HnaT03X0k9n5ntoXw33xoj4mc6V5T+eNXvdc7TfJHkV8N9L03rbf5W13l+LiYiP037P0b2l6Xngb2fmu2lflud3IuIta1Daun5MO1xH/Q/Ketl/y3I6B0OTS32suIg4g3Yo3JuZnwPIzO9m5muZeRL4b/zl4Y41qTkzj5fvLwAPlHq+e+oQUfn+whrWeCXw9cz8bqlzXe2/Yrn7a9VrjYgPAT8H/FIJL8ohmhfL8tdoH7e/qNTSebhpResb4DFdi/23EfhF4L6OutfF/luu0zkYmlzqY0WV45F3AU9m5m90tHcek/8F4NSrH/pdYmQla3xTRLz51DLtk5SPU18GpfPSJTPAL5dX27wPeKXjEMpKqf5LW0/7r8Ny99cB4AMRcW45bPKB0rYiImIL8DHgqsz8QUf7O6L9+SlExI/R3mfPlBq/FxHvKz/Hv8wKXr5mgMd0LX6/fxb4Zmb+8BDRetl/y7bWZ7/X8ov2K0Keop3iH1+D+/9p2ocUvgE8Vr4+CNwD/HFpnwHe2THm46XeI6zCqxhov6rjj8rX4VP7ifYl0r8EPA38AfC20h60P4TpW2Ubpla4vjfRvvjiOR1ta7r/aIfU88D/o33s+IZB9hftY/2z5etfrnB9s7SPyZ/6Ofx06ftPy+P+GPB14J90zDNF+w/0t4BPUa6ksEL1LfsxXanf7171lfbPAP+mq++q779RfHlJDElS5XQ+lCRJ6sFgkCRVDAZJUsVgkCRVDAZJUsVgkCRVDAZJUuX/A9pRnOAdu1V3AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -87,32 +85,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "array([[<AxesSubplot:title={'center':'AvgTicketPrice'}>,\n",
-       "        <AxesSubplot:title={'center':'DistanceKilometers'}>,\n",
-       "        <AxesSubplot:title={'center':'DistanceMiles'}>],\n",
-       "       [<AxesSubplot:title={'center':'FlightDelayMin'}>,\n",
-       "        <AxesSubplot:title={'center':'FlightTimeHour'}>,\n",
-       "        <AxesSubplot:title={'center':'FlightTimeMin'}>],\n",
-       "       [<AxesSubplot:title={'center':'dayOfWeek'}>, <AxesSubplot:>,\n",
-       "        <AxesSubplot:>]], dtype=object)"
-      ]
+      "text/plain": "array([[<AxesSubplot:title={'center':'AvgTicketPrice'}>,\n        <AxesSubplot:title={'center':'DistanceKilometers'}>,\n        <AxesSubplot:title={'center':'DistanceMiles'}>],\n       [<AxesSubplot:title={'center':'FlightDelayMin'}>,\n        <AxesSubplot:title={'center':'FlightTimeHour'}>,\n        <AxesSubplot:title={'center':'FlightTimeMin'}>],\n       [<AxesSubplot:title={'center':'dayOfWeek'}>, <AxesSubplot:>,\n        <AxesSubplot:>]], dtype=object)"
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 9 Axes>"
-      ]
+      "text/plain": "<Figure size 720x720 with 9 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -146,4 +133,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file