NYC Taxi Data.ipynb

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    "**The competition dataset is based on the 2016 NYC Yellow Cab trip record data made available in Big Query on Google Cloud Platform. The data was originally published by the NYC Taxi and Limousine Commission (TLC). The data was sampled and cleaned for the purposes of this playground competition. Based on individual trip attributes, participants should predict the duration of each trip in the test set.**\n",
    "\n",
    "The training set (contains 1458644 trip records)\n",
    "https://www.kaggle.com/c/nyc-taxi-trip-duration/data"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data fields\n",
    "- **id** - a unique identifier for each trip\n",
    "- **vendor_id** - a code indicating the provider associated with the trip record\n",
    "- **pickup_datetime** - date and time when the meter was engaged\n",
    "- **dropoff_datetime** - date and time when the meter was disengaged\n",
    "- **passenger_count** - the number of passengers in the vehicle (driver entered value)\n",
    "- **pickup_longitude** - the longitude where the meter was engaged\n",
    "- **pickup_latitude** - the latitude where the meter was engaged\n",
    "- **dropoff_longitude** - the longitude where the meter was disengaged\n",
    "- **dropoff_latitude** - the latitude where the meter was disengaged\n",
    "- **store_and_fwd_flag** - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip\n",
    "- **trip_duration** - duration of the trip in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The challenge is to predict journey time of taxi trips in New York city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### install Libraries.\n",
    "\n",
    "Open console and write each of the following one at a time:\n",
    "   - conda install -c anaconda basemap\n",
    "   - conda install -c conda-forge haversine\n",
    "   - conda install -c conda-forge geopy\n",
    "   - conda install -c conda-forge folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib import cm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None \n",
    "import numpy as np\n",
    "\n",
    "from haversine import haversine\n",
    "from scipy.spatial.distance import euclidean , cityblock\n",
    "from geopy.distance import great_circle\n",
    "from math import *\n",
    "\n",
    "from bokeh.io import output_notebook,show\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.palettes import Spectral4\n",
    "\n",
    "import folium \n",
    "from folium import plugins\n",
    "from folium.plugins import HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
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       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
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       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
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       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  \n",
       "3         40.706718                  N            429  \n",
       "4         40.782520                  N            435  "
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = pd.read_csv('Dataset/train.csv', nrows = 50000)\n",
    "#data.to_csv('NYC sub.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plan and workflow:\n",
    "\n",
    "- First we will perform general EDA (Eexploratory Data Analysis.\n",
    "- Visualize features and explore data types and correlations.\n",
    "- Then we proceed with preprocessing:\n",
    "    - Missing values.\n",
    "    - Check outliers.\n",
    "- Then we conclude our Analysis.\n",
    "-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just a cool action to show you a visualized map of the dropoff points between January to June. If you want to understand the code by all means go ahead and check the source from here: https://www.kaggle.com/poonaml/last-cab-to-new-york-animated-heatmap-trips-folium"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "west, south, east, north = -74.03, 40.63, -73.77, 40.85\n",
    "train = data\n",
    "train = train[(train.pickup_latitude> south) & (train.pickup_latitude < north)]\n",
    "train = train[(train.dropoff_latitude> south) & (train.dropoff_latitude < north)]\n",
    "train = train[(train.pickup_longitude> west) & (train.pickup_longitude < east)]\n",
    "train = train[(train.dropoff_longitude> west) & (train.dropoff_longitude < east)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract the month column from pickup datetime variable and take subset of data\n",
    "train['dropoff_datetime'] = pd.to_datetime(train.dropoff_datetime)\n",
    "train['dropoff_month'] = train['dropoff_datetime'].dt.month\n",
    "\n",
    "heat_df =train.sample(n=2500)\n",
    "\n",
    "#Extract required columns\n",
    "heat_df = heat_df[['dropoff_latitude', 'dropoff_longitude','dropoff_month']]\n",
    "\n",
    "\n",
    "# Ensure you're handing it floats\n",
    "heat_df['dropoff_latitude'] = heat_df['dropoff_latitude'].astype(float)\n",
    "heat_df['dropoff_longitude'] = heat_df['dropoff_longitude'].astype(float)\n",
    "\n",
    "\n",
    "#remove NANs\n",
    "heat_df = heat_df.dropna(axis=0)\n",
    "\n",
    "\n",
    "# Create weight column, using date\n",
    "heat_df['Weight'] = heat_df['dropoff_month']\n",
    "heat_df['Weight'] = heat_df['Weight'].astype(float)\n",
    "heat_df = heat_df.dropna(axis=0, subset=['dropoff_latitude','dropoff_longitude', 'Weight'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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[40.725910186767578, -73.977729797363281], [40.828338623046882, -73.935104370117188], [40.720039367675774, -74.010147094726562], [40.758468627929688, -73.986412048339844], [40.750762939453132, -73.978370666503906], [40.716251373291016, -73.961578369140625], [40.779170989990241, -73.944938659667983], [40.739952087402337, -74.003288269042983], [40.777309417724609, -73.978187561035156], [40.772449493408203, -73.963081359863281], [40.724143981933601, -73.976951599121094], [40.739032745361328, -74.005500793457017], [40.776115417480469, -73.959587097167983], [40.711727142333984, -74.00762939453125], [40.755687713623047, -73.979270935058594], [40.795791625976562, -73.9722900390625], [40.7376708984375, -73.99261474609375], [40.753318786621094, -73.970451354980483], [40.821109771728523, -73.937553405761733], [40.781070709228523, -73.98126220703125], [40.743160247802734, -74.00750732421875], [40.765464782714851, -73.980560302734375], [40.724632263183601, -73.981552124023423], [40.752628326416016, -73.968879699707017], [40.765659332275391, -73.9576416015625], [40.786354064941406, -73.948539733886719], [40.755012512207031, -73.977249145507812], [40.769989013671882, -73.980537414550781], [40.766159057617188, -73.967643737792983], [40.790351867675774, -73.971450805664062], [40.646808624267578, -73.782707214355469], [40.777168273925774, -73.975372314453125], [40.745498657226562, -73.990898132324219], [40.777553558349609, -73.958831787109375], [40.748451232910156, -73.984489440917969], [40.759090423583984, -73.915626525878906], [40.798290252685547, -73.972671508789062], [40.795558929443359, -73.943817138671875], [40.754970550537109, -73.991615295410142], [40.739620208740241, -73.988555908203125], [40.764629364013672, -73.976966857910142], [40.774551391601562, -73.988868713378906], [40.707042694091797, -74.00469970703125], [40.754562377929688, -73.997062683105469], [40.633762359619134, -73.970657348632812], [40.784549713134773, -73.956146240234375], [40.749828338623047, -73.991065979003906], [40.759983062744148, -73.98114013671875], [40.742862701416016, -73.996749877929688], [40.792320251464851, -73.952423095703125], [40.7593994140625, -73.979423522949233], [40.777690887451172, -73.98590087890625], [40.750400543212891, -73.994834899902344]]],
                {heatmapOptions: {
                        radius: 15,
                        minOpacity: 0,
                        maxOpacity: 0.8,
                        scaleRadius: false,
                        useLocalExtrema: false,
                        defaultWeight: 1,
                        
                    }
                })
                .addTo(map_dc925ddf79dc44629f226231f4467d94);

        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a14c319e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newyork_on_heatmap = folium.Map(location=[40.767937,-73.982155 ],tiles= \"Stamen Terrain\",\n",
    "                    zoom_start = 13) \n",
    "\n",
    "# List comprehension to make out list of lists\n",
    "heat_data = [[[row['dropoff_latitude'],row['dropoff_longitude']] \n",
    "                for index, row in heat_df[heat_df['Weight'] == i].iterrows()] \n",
    "                 for i in range(0,6)]\n",
    "\n",
    "# Plot it on the map\n",
    "hm = plugins.HeatMapWithTime(heat_data,auto_play=True,max_opacity=0.8)\n",
    "hm.add_to(newyork_on_heatmap)\n",
    "\n",
    "# Display the map\n",
    "newyork_on_heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another one\n",
    "Pickup and dropoff points in separate maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True,figsize=(15,10))\n",
    "\n",
    "train.plot(kind='scatter', x='pickup_longitude', y='pickup_latitude',\n",
    "                color='yellow', \n",
    "                s=.02, alpha=.6, subplots=True, ax=ax1)\n",
    "ax1.set_title(\"Pickups\")\n",
    "ax1.set_facecolor('black')\n",
    "\n",
    "train.plot(kind='scatter', x='dropoff_longitude', y='dropoff_latitude',\n",
    "                color='yellow', \n",
    "                s=.02, alpha=.6, subplots=True, ax=ax2)\n",
    "ax2.set_title(\"Dropoffs\")\n",
    "ax2.set_facecolor('black') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# And Another one \n",
    "Trips from La Guradia Airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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            marker_824c95923d5f46ccb9542c094a74cc4d.bindPopup(popup_44d26aac2ec046958adb7ae80f43f4b5)
            ;

            
        
    
            L.Control.TimeDimensionCustom = L.Control.TimeDimension.extend({
                _getDisplayDateFormat: function(date){
                    var newdate = new moment(date);
                    console.log(newdate)
                    return newdate.format("YYYY-MM-DD HH:mm:ss");
                }
            });
            map_4dc13417147b415f8ac1fdcbf1607064.timeDimension = L.timeDimension({period:"PT24H"});
            var timeDimensionControl = new L.Control.TimeDimensionCustom({
  "autoPlay": true,
  "loopButton": false,
  "maxSpeed": 10,
  "minSpeed": 0.1,
  "playerOptions": {
    "loop": true,
    "startOver": true,
    "transitionTime": 200
  },
  "position": "bottomleft",
  "timeSliderDragUpdate": false
});
            map_4dc13417147b415f8ac1fdcbf1607064.addControl(this.timeDimensionControl);

            console.log("");

            var geoJsonLayer = L.geoJson({"features": [{"geometry": {"coordinates": [[-73.98067474365234, 40.74800109863281], [-73.98192596435547, 40.76882553100585]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-01-15 15:08:59", "2016-01-15 15:23:59"]}}, {"geometry": {"coordinates": [[-73.98277282714845, 40.78404998779297], [-73.95549774169923, 40.76593017578125]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-05-22 19:26:00", "2016-05-22 19:41:00"]}}, {"geometry": {"coordinates": [[-73.98759460449219, 40.74165344238281], [-73.8709716796875, 40.77408218383789]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-03-06 07:03:32", "2016-03-06 07:18:32"]}}, {"geometry": {"coordinates": [[-73.94076538085938, 40.80307388305664], [-73.95105743408203, 40.76919174194336]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-05-31 19:57:13", "2016-05-31 20:12:13"]}}, {"geometry": {"coordinates": [[-73.97738647460938, 40.75354766845703], [-73.95115661621094, 40.77884292602539]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-03-24 18:49:15", "2016-03-24 19:04:15"]}}, {"geometry": {"coordinates": [[-73.93864440917969, 40.80482482910156], [-73.94593048095702, 40.77862548828125]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-02-12 18:29:31", "2016-02-12 18:44:31"]}}, {"geometry": {"coordinates": [[-73.95600891113281, 40.77893829345703], [-73.98648834228516, 40.76176071166992]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-02-10 07:39:21", "2016-02-10 07:54:21"]}}, {"geometry": {"coordinates": [[-73.99903869628906, 40.732398986816406], [-73.98400115966798, 40.762943267822266]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-06-07 20:05:10", "2016-06-07 20:20:10"]}}, {"geometry": {"coordinates": [[-73.95755004882812, 40.76554870605469], [-73.98445129394531, 40.76411819458008]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-04-09 12:09:53", "2016-04-09 12:24:53"]}}, {"geometry": {"coordinates": [[-73.9861831665039, 40.76248550415039], [-73.9660415649414, 40.7620964050293]], "type": "LineString"}, "type": "Feature", "properties": {"style": {"color": "gold", "weight": 10}, "times": ["2016-05-09 19:08:14", "2016-05-09 19:23:14"]}}], "type": "FeatureCollection"}, {
                    pointToLayer: function (feature, latLng) {
                        if (feature.properties.icon == 'marker') {
                            if(feature.properties.iconstyle){
                                return new L.Marker(latLng, {
                                    icon: L.icon(feature.properties.iconstyle)});
                            }
                            //else
                            return new L.Marker(latLng);
                        }
                        if (feature.properties.icon == 'circle') {
                            if (feature.properties.iconstyle) {
                                return new L.circleMarker(latLng, feature.properties.iconstyle)
                                };
                            //else
                            return new L.circleMarker(latLng);
                        }
                        //else

                        return new L.Marker(latLng);
                    },
                    style: function (feature) {
                        return feature.properties.style;
                    },
                    onEachFeature: function(feature, layer) {
                        if (feature.properties.popup) {
                        layer.bindPopup(feature.properties.popup);
                        }
                    }
                })

            var timestamped_geo_json_bb556ef0f5d14531ae1e4ebc8d73ad0c = L.timeDimension.layer.geoJson(geoJsonLayer,
                {updateTimeDimension: true,
                 addlastPoint: true,
                 duration: undefined,
                }).addTo(map_4dc13417147b415f8ac1fdcbf1607064);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a14923e48>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#co-ordinates\n",
    "LaGuardia = {\n",
    "    \"minLat\": 40.76,\n",
    "    \"maxLat\": 40.78,\n",
    "    \"minLong\": -73.895,\n",
    "    \"maxLong\": -73.855\n",
    "}\n",
    "train['pickup_datetime'] = pd.to_datetime(train.pickup_datetime)\n",
    "train['dropoff_datetime'] = pd.to_datetime(train.dropoff_datetime)\n",
    "\n",
    "LaGuardiaData = train[(train['pickup_longitude'].apply(lambda x: (x >=LaGuardia[\"minLong\"]) & (x <= LaGuardia[\"maxLong\"])))]\n",
    "LaGuardiaData = train[(train['pickup_latitude'].apply(lambda x: (x >=LaGuardia[\"minLat\"]) & (x <= LaGuardia[\"maxLat\"])))]\n",
    "LaGuardiaData = train[(train['dropoff_longitude'].apply(lambda x: (x >=LaGuardia[\"minLong\"]) & (x <= LaGuardia[\"maxLong\"])))]\n",
    "LaGuardiaData = train[(train['dropoff_latitude'].apply(lambda x: (x >=LaGuardia[\"minLat\"]) & (x <= LaGuardia[\"maxLat\"])))]\n",
    "\n",
    "m = folium.Map(\n",
    "    location=[40.7769, -73.8740],\n",
    "    zoom_start=12\n",
    ")\n",
    "folium.Marker(location=[40.7769, -73.8740],icon=folium.Icon(color='black') ,popup='LA Guardia International Airport').add_to(m)\n",
    "\n",
    "shortTripsDF=LaGuardiaData[LaGuardiaData.trip_duration==900]\n",
    "\n",
    "lines = [\n",
    "    {\n",
    "        'coordinates': [\n",
    "            [shortTripsDF.pickup_longitude.iloc[index], shortTripsDF.pickup_latitude.iloc[index]],\n",
    "            [shortTripsDF.dropoff_longitude.iloc[index], shortTripsDF.dropoff_latitude.iloc[index]],\n",
    "        ],\n",
    "        'dates': [\n",
    "        str(shortTripsDF.pickup_datetime.iloc[index]),\n",
    "        str(shortTripsDF.dropoff_datetime.iloc[index])\n",
    "        ],\n",
    "        'color': 'gold'\n",
    "    }\n",
    "    for index in range(10)\n",
    "]\n",
    "features = [\n",
    "    {\n",
    "        'type': 'Feature',\n",
    "        'geometry': {\n",
    "            'type': 'LineString',\n",
    "            'coordinates': line['coordinates'],\n",
    "        },\n",
    "        'properties': {\n",
    "            'times': line['dates'],\n",
    "            'style': {\n",
    "                'color': line['color'],\n",
    "                'weight': line['weight'] if 'weight' in line else 10\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    for line in lines\n",
    "]\n",
    "\n",
    "\n",
    "plugins.TimestampedGeoJson({\n",
    "    'type': 'FeatureCollection',\n",
    "    'features': features,\n",
    "}, period='PT24H', add_last_point=True).add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's get back and be serious lol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 11 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null object\n",
      "dropoff_datetime      1458644 non-null object\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 122.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# getting to know our dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clearly there is no missing values, you are lucky bro!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.534950e+00</td>\n",
       "      <td>1.664530e+00</td>\n",
       "      <td>-7.397349e+01</td>\n",
       "      <td>4.075092e+01</td>\n",
       "      <td>-7.397342e+01</td>\n",
       "      <td>4.075180e+01</td>\n",
       "      <td>9.594923e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.987772e-01</td>\n",
       "      <td>1.314242e+00</td>\n",
       "      <td>7.090186e-02</td>\n",
       "      <td>3.288119e-02</td>\n",
       "      <td>7.064327e-02</td>\n",
       "      <td>3.589056e-02</td>\n",
       "      <td>5.237432e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.435970e+01</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.218114e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.399187e+01</td>\n",
       "      <td>4.073735e+01</td>\n",
       "      <td>-7.399133e+01</td>\n",
       "      <td>4.073588e+01</td>\n",
       "      <td>3.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.398174e+01</td>\n",
       "      <td>4.075410e+01</td>\n",
       "      <td>-7.397975e+01</td>\n",
       "      <td>4.075452e+01</td>\n",
       "      <td>6.620000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-7.396733e+01</td>\n",
       "      <td>4.076836e+01</td>\n",
       "      <td>-7.396301e+01</td>\n",
       "      <td>4.076981e+01</td>\n",
       "      <td>1.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>5.188108e+01</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>4.392103e+01</td>\n",
       "      <td>3.526282e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
       "count  1.458644e+06     1.458644e+06      1.458644e+06     1.458644e+06   \n",
       "mean   1.534950e+00     1.664530e+00     -7.397349e+01     4.075092e+01   \n",
       "std    4.987772e-01     1.314242e+00      7.090186e-02     3.288119e-02   \n",
       "min    1.000000e+00     0.000000e+00     -1.219333e+02     3.435970e+01   \n",
       "25%    1.000000e+00     1.000000e+00     -7.399187e+01     4.073735e+01   \n",
       "50%    2.000000e+00     1.000000e+00     -7.398174e+01     4.075410e+01   \n",
       "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076836e+01   \n",
       "max    2.000000e+00     9.000000e+00     -6.133553e+01     5.188108e+01   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
       "count       1.458644e+06      1.458644e+06   1.458644e+06  \n",
       "mean       -7.397342e+01      4.075180e+01   9.594923e+02  \n",
       "std         7.064327e-02      3.589056e-02   5.237432e+03  \n",
       "min        -1.219333e+02      3.218114e+01   1.000000e+00  \n",
       "25%        -7.399133e+01      4.073588e+01   3.970000e+02  \n",
       "50%        -7.397975e+01      4.075452e+01   6.620000e+02  \n",
       "75%        -7.396301e+01      4.076981e+01   1.075000e+03  \n",
       "max        -6.133553e+01      4.392103e+01   3.526282e+06  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.53</td>\n",
       "      <td>1.66</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.75</td>\n",
       "      <td>959.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5237.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>34.36</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>32.18</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>397.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.75</td>\n",
       "      <td>662.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>40.77</td>\n",
       "      <td>1075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>-61.34</td>\n",
       "      <td>51.88</td>\n",
       "      <td>-61.34</td>\n",
       "      <td>43.92</td>\n",
       "      <td>3526282.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
       "count 1458644.00       1458644.00        1458644.00       1458644.00   \n",
       "mean        1.53             1.66            -73.97            40.75   \n",
       "std         0.50             1.31              0.07             0.03   \n",
       "min         1.00             0.00           -121.93            34.36   \n",
       "25%         1.00             1.00            -73.99            40.74   \n",
       "50%         2.00             1.00            -73.98            40.75   \n",
       "75%         2.00             2.00            -73.97            40.77   \n",
       "max         2.00             9.00            -61.34            51.88   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
       "count         1458644.00        1458644.00     1458644.00  \n",
       "mean              -73.97             40.75         959.49  \n",
       "std                 0.07              0.04        5237.43  \n",
       "min              -121.93             32.18           1.00  \n",
       "25%               -73.99             40.74         397.00  \n",
       "50%               -73.98             40.75         662.00  \n",
       "75%               -73.96             40.77        1075.00  \n",
       "max               -61.34             43.92     3526282.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis\n",
    "We need to explore the features and perform Univariate Analysis. Anlysing single feature is called Univariate Analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trip Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.trip_duration, kde=False, bins = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the trips are taking so much time and they are long trips. Thought they are few compared to the rest but this could harm the model. Let transform the duration and the the log.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column that take the log values of the trip duration\n",
    "train['log_trip_duration'] = np.log1p(train['trip_duration'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot the log trip duration\n",
    "sns.distplot(train.log_trip_duration, kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you see we smoothed out the outliers effect by mathematically transforming the values of the trip duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passenger Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.passenger_count, kde=False, bins = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vendor ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.vendor_id, kde=False, bins = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>40.77</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.79</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.78</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1            -73.98            40.77             -73.96   \n",
       "1                1            -73.98            40.74             -74.00   \n",
       "2                1            -73.98            40.76             -74.01   \n",
       "3                1            -74.01            40.72             -74.01   \n",
       "4                1            -73.97            40.79             -73.97   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0             40.77                  N            455  \n",
       "1             40.73                  N            663  \n",
       "2             40.71                  N           2124  \n",
       "3             40.71                  N            429  \n",
       "4             40.78                  N            435  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look again at what we have\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's address the following:\n",
    "1. Pickup/Dropoff datetime is in timestamp.\n",
    "2. Trip duration has outliers.\n",
    "3. latitude and longitude values are raw.\n",
    "4. Store and froward flag is Categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Enigneering date and time:\n",
    "- Extract Dates.\n",
    "- Day of the month.\n",
    "- Month of the Year.\n",
    "- weekday.\n",
    "- week of the year.\n",
    "- hour of the day.\n",
    "- minute of the hour.\n",
    "- day of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 11 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null object\n",
      "dropoff_datetime      1458644 non-null object\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 122.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Let's check the datatype for our set\n",
    "data.info()\n",
    "\n",
    "#Observe that pickup_datetime & dropoff_datetime are objects aka string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert our objects into dates\n",
    "data['pickup_datetime'] = pd.to_datetime(data['pickup_datetime'])\n",
    "data['dropoff_datetime'] = pd.to_datetime(data['dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 11 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null datetime64[ns]\n",
      "dropoff_datetime      1458644 non-null datetime64[ns]\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(4), int64(3), object(2)\n",
      "memory usage: 122.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pickup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dates\n",
    "data['pickup_date'] = data['pickup_datetime'].dt.date\n",
    "\n",
    "# day of month 1 day to 30/31 days\n",
    "data['pickup_day'] = data['pickup_datetime'].dt.day\n",
    "\n",
    "#month of year 1 to 12\n",
    "data['pickup_month'] = data['pickup_datetime'].dt.month\n",
    "\n",
    "#weekday 0 to 6\n",
    "data['pickup_weekday'] = data['pickup_datetime'].dt.weekday\n",
    "\n",
    "#week of year\n",
    "data['pickup_weekofyear'] = data['pickup_datetime'].dt.weekofyear\n",
    "\n",
    "#hour of day 0 to 23\n",
    "data['pickup_hour'] = data['pickup_datetime'].dt.hour\n",
    "\n",
    "#minute of hour\n",
    "data['pickup_minute'] = data['pickup_datetime'].dt.minute\n",
    "\n",
    "# day of year\n",
    "data['pickup_dayofyear'] = data['pickup_datetime'].dt.dayofyear\n",
    "\n",
    "# pickup date in seconds\n",
    "data['pickup_dt'] = (data['pickup_datetime'] - data['pickup_datetime'].min()).dt.total_seconds()\n",
    "\n",
    "# pickup week per hour\n",
    "data['pickup_week_hour'] = data['pickup_weekday'] * 24 + data['pickup_hour']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dates\n",
    "data['dropoff_date'] = data['dropoff_datetime'].dt.date\n",
    "\n",
    "# day of month 1 day to 30/31 days\n",
    "data['dropoff_day'] = data['dropoff_datetime'].dt.day\n",
    "\n",
    "#month of year 1 to 12\n",
    "data['dropoff_month'] = data['dropoff_datetime'].dt.month\n",
    "\n",
    "#weekday 0 to 6\n",
    "data['dropoff_weekday'] = data['dropoff_datetime'].dt.weekday\n",
    "\n",
    "#week of year\n",
    "data['dropoff_weekofyear'] = data['dropoff_datetime'].dt.weekofyear\n",
    "\n",
    "#hour of day 0 to 23\n",
    "data['dropoff_hour'] = data['dropoff_datetime'].dt.hour\n",
    "\n",
    "#minute of hour\n",
    "data['dropoff_minute'] = data['dropoff_datetime'].dt.minute\n",
    "\n",
    "# day of year\n",
    "data['dropoff_dayofyear'] = data['dropoff_datetime'].dt.dayofyear\n",
    "\n",
    "# pickup date in seconds\n",
    "data['dropoff_dt'] = (data['dropoff_datetime'] - data['dropoff_datetime'].min()).dt.total_seconds()\n",
    "\n",
    "# dropoff week per hour\n",
    "data['dropoff_week_hour'] = data['dropoff_weekday'] * 24 + data['dropoff_hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 31 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null datetime64[ns]\n",
      "dropoff_datetime      1458644 non-null datetime64[ns]\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "pickup_date           1458644 non-null datetime64[ns]\n",
      "pickup_day            1458644 non-null int64\n",
      "pickup_month          1458644 non-null int64\n",
      "pickup_weekday        1458644 non-null int64\n",
      "pickup_weekofyear     1458644 non-null int64\n",
      "pickup_hour           1458644 non-null int64\n",
      "pickup_minute         1458644 non-null int64\n",
      "pickup_dayofyear      1458644 non-null int64\n",
      "pickup_dt             1458644 non-null float64\n",
      "pickup_week_hour      1458644 non-null int64\n",
      "dropoff_date          1458644 non-null datetime64[ns]\n",
      "dropoff_day           1458644 non-null int64\n",
      "dropoff_month         1458644 non-null int64\n",
      "dropoff_weekday       1458644 non-null int64\n",
      "dropoff_weekofyear    1458644 non-null int64\n",
      "dropoff_hour          1458644 non-null int64\n",
      "dropoff_minute        1458644 non-null int64\n",
      "dropoff_dayofyear     1458644 non-null int64\n",
      "dropoff_dt            1458644 non-null float64\n",
      "dropoff_week_hour     1458644 non-null int64\n",
      "dtypes: datetime64[ns](4), float64(6), int64(19), object(2)\n",
      "memory usage: 345.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data['pickup_date'] = pd.to_datetime(data['pickup_date'])\n",
    "data['dropoff_date'] = pd.to_datetime(data['dropoff_date'])\n",
    "\n",
    "\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>dropoff_date</th>\n",
       "      <th>dropoff_day</th>\n",
       "      <th>dropoff_month</th>\n",
       "      <th>dropoff_weekday</th>\n",
       "      <th>dropoff_weekofyear</th>\n",
       "      <th>dropoff_hour</th>\n",
       "      <th>dropoff_minute</th>\n",
       "      <th>dropoff_dayofyear</th>\n",
       "      <th>dropoff_dt</th>\n",
       "      <th>dropoff_week_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>40.77</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>74</td>\n",
       "      <td>6370139.00</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>164</td>\n",
       "      <td>14086267.00</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1598837.00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-04-06</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>97</td>\n",
       "      <td>8364969.00</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.79</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.78</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-03-26</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>86</td>\n",
       "      <td>7392879.00</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2377394          1 2016-06-12 00:43:35 2016-06-12 00:54:38   \n",
       "2  id3858529          2 2016-01-19 11:35:24 2016-01-19 12:10:48   \n",
       "3  id3504673          2 2016-04-06 19:32:31 2016-04-06 19:39:40   \n",
       "4  id2181028          2 2016-03-26 13:30:55 2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1            -73.98            40.77             -73.96   \n",
       "1                1            -73.98            40.74             -74.00   \n",
       "2                1            -73.98            40.76             -74.01   \n",
       "3                1            -74.01            40.72             -74.01   \n",
       "4                1            -73.97            40.79             -73.97   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag        ...          dropoff_date  \\\n",
       "0             40.77                  N        ...            2016-03-14   \n",
       "1             40.73                  N        ...            2016-06-12   \n",
       "2             40.71                  N        ...            2016-01-19   \n",
       "3             40.71                  N        ...            2016-04-06   \n",
       "4             40.78                  N        ...            2016-03-26   \n",
       "\n",
       "  dropoff_day  dropoff_month  dropoff_weekday  dropoff_weekofyear  \\\n",
       "0          14              3                0                  11   \n",
       "1          12              6                6                  23   \n",
       "2          19              1                1                   3   \n",
       "3           6              4                2                  14   \n",
       "4          26              3                5                  12   \n",
       "\n",
       "   dropoff_hour  dropoff_minute  dropoff_dayofyear  dropoff_dt  \\\n",
       "0            17              32                 74  6370139.00   \n",
       "1             0              54                164 14086267.00   \n",
       "2            12              10                 19  1598837.00   \n",
       "3            19              39                 97  8364969.00   \n",
       "4            13              38                 86  7392879.00   \n",
       "\n",
       "   dropoff_week_hour  \n",
       "0                 17  \n",
       "1                144  \n",
       "2                 36  \n",
       "3                 67  \n",
       "4                133  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pickup month destribution\n",
    "sns.distplot(data.pickup_month, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pickup weekday destribution\n",
    "sns.distplot(data.pickup_weekday, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEPCAYAAACzwehFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFipJREFUeJzt3X2QXNV55/HvIAEWBkFixEqsgCwBPVKMjRIMzpoX44RkS3HWOLaBhVkwW2tegnGSSuwkVZaCYXFSqbgUg4MSSqCIWgnMLiSpXSNtJRG2GdvgGGNwAtZTVMDCgASEFwsQEnqZ/eOeMY00o9MzmqG7Z76fKpX6PvfcvufO7e5f33u7T/cNDg4iSdLe7NfpDkiSup9hIUmqMiwkSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElV0zvdgbGKiAOBk4GNwM4Od0eSesU0YA7wnczc1u5CPRsWNEEx0OlOSFKPOh34RruNezksNgKsXr2a2bNnd7ovktQTNm3aRH9/P5TX0Hb1cljsBJg9ezZz587tdF8kqdeM6vS9F7glSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVJVL3/PQupqL2/ZwZZte36U/aADp3HIQT711FvaesRGxFeBI4DtpXQZ8LPAYmB/4IuZeUNpexawFJgB3J6Zi0t9IXATMBO4B7g8M3dExNHAqnL/CfRn5ivjs3lS52zZtpPvPrrnQ/mk4w82LNRzqqehIqIPmAecmJkLM3Mh8CTweeA0YCFwaUT8XETMAFYAZwMLgJMjYlG5q1XAlZk5D+gDLin1ZcCyzJwP3A8sGbetk/bi5S07eObFbXv8e3nLjk53Teo67by9ifL/30fEO4DlwMvA3Zn5AkBE3AF8DPg68GhmPl7qq4BzIuIRYEZm3lfuayVwdUTcBJwBfLil/nXgD/Zxu6Qq3/lL7WvnGfFTwDrgUzSnnL4G3M6bB6HaCJwCHDlMfe5e6ocDmzNzx271Kclz3HX+jaTOqD67MvNe4N6h6Yi4meaaxLUtzfqAXTSntQb3oU6pT0m+063r5N/IoNJUVn2ER8RpwIGZua6U+oAf0vx4xpDZwNM01zJGU38WODQipmXmztLm6TFtSQ8Z6UXn9e2752b3moovnIa5prJ2HuGHAddExPtoTkN9HPivwKqImAW8CnwUuBT4PhARcRzwOHABsCIzN0TE1og4NTO/CVwIrM3M7RExAJwH3ApcBKwd303sPiO96Mw/asao7qeTL9i98sI50t8IeiucR2MqBrkmXjunob4SEe8Fvkfzc3w3ZOY3I+KzwFeBA4CbMvOfACLiYuBO4G3AGuCOclf9wPKImAk8AFxf6lcAt0TEYuAJ4Pxx2rZJr1desDtppL8RjD6ce8VI27zw2LcbIhqzth4hmbmE3T7Smpm30hwN7N52HXDiMPWHaC6C717fAJzZXnfVq0Z6t7vffn3s2rXnO/zJ/AK2c+cgz7y4508fT/Q2b92+i/WPvbZH3TcXaoePkCmmU6co9nbqbf2PptYLmC/a6kU+MqcYT1FoNLz+oSHubQG+29XwvC6mIe7tceC7L00kH1/qBj7ShjHaJ6fvvjSRfHyNnUE7fvxrDcMnpzQ5+FweP/61JI0b38lPXu49SeOm19/J7+0b/6MNvMkWnL3X43E0GcZokjR+9vaN/9EGXq8H5+56r8fjaLzGaJLUWb7xm3hTOiwkTQ6+8Zt4hoUk7YOpclRjWEjSPpgqRzX7dboDkqTuZ1hIkqoMC0lSlWEhSaoyLCRJVYaFJKnKj85K6jqTbVylycC/uqSuM9nGVZoMPA0lSaoyLCRJVYaFJKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpXfbpE04XbuHOSZF7ftUfcb2b3DvSRpwm3dvov1j722R91vZPeOtvdSRHwBODwzL46IhcBNwEzgHuDyzNwREUcDq4AjgAT6M/OViDgMWA0cCzwHnJuZmyLiAOBm4D3Aa8AFmbl+HLdPkjQO2rpmERG/DHy8pbQKuDIz5wF9wCWlvgxYlpnzgfuBJaV+LTCQmQuA5cB1pf5bwKul/jvAyrFviiRpolTDIiJ+Gvg88Mdl+hhgRmbeV5qsBM6JiP2BM4A7Wuvl9gdpjiwAbgMWlfY/qWfmPcCscnQiSeoi7RxZ3Ah8FnixTB8JbGyZvxGYCxwObM7MHbvV37RMmb8ZmLWX+5IkdZG9hkVEfAL4UWau222ZwZbpPmDXMHVKfahNq5GW6WtZRpLUJWoXuM8D5kTEg8BPAwfTvLjPaWkzG3gaeBY4NCKmZebO0ubp0uap0u7JiJgOHAI8DzxZ2v3rbvclSeoiez2yyMxfycwTMnMh8EfA/8nM/wZsjYhTS7MLgbWZuR0YoAkYgIuAteX2mjJNmT9Q2v+kHhGnAVsz84nx2TRJ0ngZ6wec+4HlETETeAC4vtSvAG6JiMXAE8D5pb4EWBkRDwMvleUBvgTcWOrbaIJHkqacbv8p2bZ7kJkrKR9tzcyHgFOGabMBOHOY+gvAh4apb+XNH8mVpCmp239K1rGhJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaoyLCRJVYaFJKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaqa3k6jiLgG+BgwCNycmUsj4ixgKTADuD0zF5e2C4GbgJnAPcDlmbkjIo4GVgFHAAn0Z+YrEXEYsBo4FngOODczN43nRkqS9k31yCIi3g/8EvBu4D3ApyLiRGAFcDawADg5IhaVRVYBV2bmPKAPuKTUlwHLMnM+cD+wpNSvBQYycwGwHLhuPDZMkjR+qmGRmV8HPpCZO2iOCqYDhwGPZubjpb4KOCcijgFmZOZ9ZfGVpb4/cAZwR2u93P4gzZEFwG3AotJektQl2rpmkZnbI+Jq4BFgHXAksLGlyUZg7l7qhwObS7C01mldpszfDMway8ZIkiZG2xe4M/Mqmhfxo4B5NNcvhvQBu8r9tVOn1IfatOprmSdJ6gLtXLOYXy5ak5lbgL8BzgTmtDSbDTwNPDlC/Vng0IiYVupzSh3gqdKOiJgOHAI8P7bNkSRNhHaOLI4FlkfEgRFxAM1F7RuBiIjjSgBcAKzNzA3A1og4tSx7YalvBwaA80r9ImBtub2mTFPmD5T2kqQu0c4F7jXAXcD3gO8C38rMLwMXA3fSXMdYzxsXr/uBP4+I9cDBwPWlfgVwaUQ8ApwOLC71JcAvRsTDpc0n932zJEnjqa3vWWTm54DP7VZbB5w4TNuHgFOGqW+gOX21e/0F4EPt9EOS1Bl+g1uSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaoyLCRJVYaFJKnKsJAkVU3vdAckSSPbuXOQZ17ctkf9oAOncchBb91LuGEhSV1s6/ZdrH/stT3qJx1/8FsaFp6GkiRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSapq63NXEXEVcG6ZvCszfz8izgKWAjOA2zNzcWm7ELgJmAncA1yemTsi4mhgFXAEkEB/Zr4SEYcBq4FjgeeAczNz07htoSRpn1WPLEoo/Crw88BC4KSIOB9YAZwNLABOjohFZZFVwJWZOQ/oAy4p9WXAssycD9wPLCn1a4GBzFwALAeuG48NkySNn3ZOQ20Efi8zX8/M7cAPgHnAo5n5eGbuoAmIcyLiGGBGZt5Xll1Z6vsDZwB3tNbL7Q/SHFkA3AYsKu0lSV2iGhaZ+fDQi39EHE9zOmoXTYgM2QjMBY4coX44sLkES2ud1mXK/M3ArDFujyRpArR9gTsi3gn8A/AZ4DFgsGV2H02A7NdmnVIfatOqr2WeJKkLtBUWEXEqsA74w8y8BXgSmNPSZDbw9F7qzwKHRsS0Up9T6gBPlXZExHTgEOD5sWyMJGlitHOB+yjg74ALMvPLpfztZlYcVwLgAmBtZm4AtpZwAbiw1LcDA8B5pX4RsLbcXlOmKfMHSntJUpdo56OznwbeBiyNiKHaXwEXA3eWeWt44+J1P7A8ImYCDwDXl/oVwC0RsRh4Aji/1JcAKyPiYeClsrwkqYtUwyIzfxv47RFmnzhM+4eAU4apbwDOHKb+AvChWj8kSZ3jN7glSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaoyLCRJVYaFJKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqprebsOImAl8C/j1zPxhRJwFLAVmALdn5uLSbiFwEzATuAe4PDN3RMTRwCrgCCCB/sx8JSIOA1YDxwLPAedm5qZx20JJ0j5r68giIt4LfAOYV6ZnACuAs4EFwMkRsag0XwVcmZnzgD7gklJfBizLzPnA/cCSUr8WGMjMBcBy4Lp93ShJ0vhq9zTUJcAngafL9CnAo5n5eGbuoAmIcyLiGGBGZt5X2q0s9f2BM4A7Wuvl9gdpjiwAbgMWlfaSpC7RVlhk5icyc6CldCSwsWV6IzB3L/XDgc0lWFrrb7qvMn8zMGt0myFJmkhjvcC9HzDYMt0H7BpFnVIfatOqr2WeJKkLjDUsngTmtEzPpjlFNVL9WeDQiJhW6nN445TWU6UdETEdOAR4foz9kiRNgLGGxbeBiIjjSgBcAKzNzA3A1og4tbS7sNS3AwPAeaV+EbC23F5TpinzB0p7SVKXGFNYZOZW4GLgTuARYD1vXLzuB/48ItYDBwPXl/oVwKUR8QhwOrC41JcAvxgRD5c2nxxLnyRJE6ft71kAZObPtNxeB5w4TJuHaD4ttXt9A3DmMPUXgA+Nph+SpLeW3+CWJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaoyLCRJVYaFJKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSpyrCQJFUZFpKkKsNCklRlWEiSqgwLSVKVYSFJqjIsJElVhoUkqWp6pzsAEBEXAIuB/YEvZuYNHe6SJKlFx48sIuLfA58HTgMWApdGxM91tleSpFbdcGRxFnB3Zr4AEBF3AB8DrqksNw1g06ZNY17xv/34dV54bsse9Wemv40Xntu6R33j2w9i+6sHtH0/o20/0evt5Lq7bb2dXHevrLeT6+629XZy3WPZb3vT8po5bTTLdUNYHAlsbJneCJzSxnJzAPr7+yeiT5I02c0B/rXdxt0QFvsBgy3TfcCuNpb7DnA6TbjsnIB+SdJkNI0mKL4zmoW6ISyepHnRHzIbeLq2UGZuA74xUZ2SpEms7SOKId0QFv8IfC4iZgGvAh8FLu1slyRJrTr+aajMfAr4LPBV4EHg1sz8p872SpLUqm9wcLDeSpI0pXX8yEKS1P0MC0lSlWEhSaoyLCRJVd3w0dlxM5kGJIyIrwJHANtL6bLM/HYHuzQqETET+Bbw65n5w4g4C1gKzABuz8zFHe3gKAyzLX9NM5bZq6XJ1Zn5tx3rYJsi4irg3DJ5V2b+fi/ulxG2o1f3yTU0wxsNAjdn5tJu3SeT5tNQZUDCbwAnAdtontznZ+YjHe3YGEREH82XFY/JzB2d7s9oRcR7geXAfGAe8AyQwPuBHwF30YT52o51sk27b0sJi38GfjUzN+596e5RXoCuBj5A88L0/4CbgD+lh/bLCNvxFzRjyfXaPnk/zSCqZ9K8wX0E+DDwf+nCfTKZTkP9ZEDCzHwVGBqQsBdF+f/vI+KhiLiyo70ZvUuAT/LGN/FPAR7NzMdL+K0CzulU50bpTdsSEQcBRwMrIuL7EXF1RPTC82gj8HuZ+Xpmbgd+QBPkvbZfhtuOo+nBfZKZXwc+UP72R9Cc6TmMLt0nk+k01FgHJOxGPwWsAz5F847jaxGRmfkPne1WezLzEwARQ5k37L6Z+xZ3a0yG2ZbZwN3AFcCPga8A/53m6KNrZebDQ7cj4nia0zhfosf2ywjbcTrNu/Oe2icAmbk9Iq4GPg38b7r4uTKZwmKsAxJ2ncy8F7h3aDoibgZ+DeiJsBjGZNo3jwG/MTQdEV8CLqIHXpgAIuKdNKc2PgPsoDm6GNIz+6V1OzIz6eF9kplXRcSf0px+mkeXPle6/lBtFJ6kDFtetDUgYTeKiNMi4pdbSn28caG7F02mffOuiPhoS6ln9k1EnEpzxPqHmXkLPbpfdt+OXt0nETE/IhYCZOYW4G9ojpC6cp9MpiOLyTQg4WHANRHxPprTUB8HLu9sl/bJt4GIiOOAx4ELgBWd7dKY9QFfjIi7gVdoHmO3dLZLdRFxFPB3wHmZeXcp99x+GWE7enKfAMcCV0fEaTRHE2cDNwJ/1o37ZNIcWUymAQkz8ys0h9jfA74LrCinpnpSZm4FLgbupPnEx3qaDyD0nMz8PvAnwDdptuXBzLyts71qy6eBtwFLI+LBiHiQZp9cTG/tl+G243304D7JzDW8+Xn+rcz8Ml26TybNR2clSRNn0hxZSJImjmEhSaoyLCRJVYaFJKnKsJAkVRkWmrIi4pqIuKjSZjAiDn+r+lTWeXhE+DFFdZXJ9KU8aVQy84863QepVxgWmvQi4kyaobg30Aw1/hrNF5/+APiXzPxCGYr8euDtwOvAp1u+IUxEzKYZJeAvM/OG8s5/Vmb+W5k/CMwCThhuXZn5g0ofP0IzXPUW4Dst9bcDfwkcD7wDeJnmW72vAf8CHJWZPy7D2idwTmY+NLa/lDQyT0NpqngP8KXMfDfw18D/HJoREfvTDCFxTWaeQDMs+XUtw1zPpRmL6E/a/EGtEdc1nIj4dzRDOnw0M0+iCZohi4CXMvM/ZuY8miC5MjOfoBn9tr+0+wDwvEGhiWJYaKp4KDMHyu0VwM/TvFMHeBewMzPvAsjM72bmuzJzaLTPNTTjjd061nVFxDv20v404J9bfqjrxqEZmXkHsDIiPhUR19EMNHdwmX0DTbABXEZzBCJNCMNCU0XrLw72lf93tsx70wXliDghIoZO015GM0z07+52n32l7QGjWNdI+lpu/2T5iPhN4Gaa01O3Are1tP1H4KAyQvEZwP+qrEMaM8NCU8XCiHh3uX0pzc/uvlSmExiMiF8BiIhfoDnFM/T8uJdm5N/FEXFCqT1Hc7oJmmsIe11XZr7EyO4B3hkRJ5bpi1vm/SdgZWbeXPr5n4FpAJk5CCyj+XnUW8uAjdKEMCw0VWwCPl9+P/vDwIVDMzJzG/AR4KoyiulfAR/JzNdb2iTwP4BV5Ujit4AbIuIBYAFv/nWzEdc1nMx8jiZwVpf7+w8ts78AXBYR3wcGgAeA41rm3wIcRcupK2kiOOqsJr3yaai/KBevJ826yvr+C/DxzFz0VqxPU5cfnZXeAhHxGd745NLu/iwzV4/hPr9G83Hds/eha1JbPLKQJFV5zUKSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSp6v8D9pygAvACbwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pickup day of the month destribution\n",
    "sns.distplot(data.pickup_day, kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Trip duration\n",
    "There are some outliers and we need to smooth them out by taking the mathematical log to transform the values into a small distance reducing the effect of the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.trip_duration, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEPCAYAAACDTflkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFrNJREFUeJzt3XuQ3XWZ5/F3EgJ0IAEdYBLk4jiQJ+iOZOQ2u1w2WzBaWRkYS4GCCLIKyCIlMyXOumuy6gzu3LYYhTEFBTJhBFlqoZx1lMzoAA7xAgoMOAPmqZRAWCABVnCS0HSnu9P7x+/beOx7vp1wTjfvV1VXznl+lzyn65zz6d/t+5s1ODiIJEk1Zre7AUnS9GWISJKqGSKSpGqGiCSpmiEiSapmiEiSqhkikqRqhogkqZohIkmqZohIkqoZIpKkanu0u4FdLSL2Ao4DNgEDbW5HkqaLOcAi4EeZ2TvZhWZciNAEyLp2NyFJ09TJwHcnO/NMDJFNALfeeisLFy5sdy+SNC1s3ryZFStWQPkOnayZGCIDAAsXLuSQQw5pdy+SNN3s1GEAD6xLkqoZIpKkaoaIJKmaISJJqmaISJKqGSKSpGqGiCSp2ky8TkSa0Nbufrp7Rz8dft5ec5g/z4+GNBl+UvSG1N07wEMbto067Zgj9zVEpElyd5YkqZohIkmqZohIkqoZIpKkaoaIJKmaISJJqmaISJKqGSKSpGpeUaUZbawr07f3DbahG2nmMUQ0o411ZfqSQ7va0I0087g7S5JUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdW8TkQaZmBgkOdf7h1R97a50kiT+kRExGeAs8vTb2bmH0TEacDVQBdwe2auLPMuBW4EFgD3AZdmZn9EHAbcAhwEJLAiM7dFxP7ArcDbgBeBszNzc0TsCXwZOBZ4FTgvM9fvklctjaOnbwfrn3h1RN3b5kojTbg7q4TFu4HfBJYCx0TEucBNwJnAUcBxEbG8LHILcHlmLgZmAReX+mpgdWYuAR4EVpX6VcC6zDwKuAH4Yql/HHil1H8PWDOF1ylJ2g0mc0xkE/CJzNyemX3AT4DFwIbMfDIz+2mC46yIOBzoysz7y7JrSn0ucApwR2u9PH4vzZYIwG3A8jL/a/XMvA84sGzNSJI6xIQhkpmPDYVCRBxJs1trB024DNkEHAIcPEb9AGBLCZzWOq3LlOlbgAPHWZckqUNM+uysiHgH8G3gk8ATQOswqLNogmX2JOuU+tA8rSZalySpQ0wqRCLiROBu4FOZeTPwDLCoZZaFwHPj1F8A9ouIOaW+qNQBni3zERF7APOBn42zLklSh5jMgfVDgb+hOTvqf5XyA82kOKIEw3nA2szcCPSU0AE4v9T7gHXAOaV+AbC2PL6rPKdMX1fmf60eEScBPZn5dP1LlSTtapM5X/FKYG/g6ogYql0HXAjcWabdxS8Omq8AboiIBcDDwDWlfhlwc0SsBJ4Gzi31VcCaiHgM+HlZHuBa4PpS76UJJElSB5kwRDLzCuCKMSYfPcr8jwLHj1LfCCwbpf4ScMYo9R7gQxP1J0lqH4c9kSRVM0QkSdUMEUlSNUNEklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNEJEkVZvM7XGljre1u5/u3oER9e19g23oRnrjMEQ0I3T3DvDQhm0j6ksO7WpDN9Ibh7uzJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFXzplTSJA0MDPL8y70j6vP2msP8eX6U9MbkO1+apJ6+Hax/4tUR9WOO3NcQ0RvWpN/5EbEA+D5wemY+FRF/BZwEvFJm+Vxmfi0iTgOuBrqA2zNzZVl+KXAjsAC4D7g0M/sj4jDgFuAgIIEVmbktIvYHbgXeBrwInJ2Zm6f+kiVJu8qkjolExAnAd4HFLeVjgVMyc2n5+VpEdAE3AWcCRwHHRcTyMv8twOWZuRiYBVxc6quB1Zm5BHgQWFXqVwHrMvMo4Abgi7UvUpK0e0z2wPrFwMeA5wAiYh5wGHBTRPw4Ij4XEbOB44ENmflkZvbTBMdZEXE40JWZ95f1rSn1ucApwB2t9fL4vTRbIgC3AcvL/JKkDjGpEMnMizJzXUtpIXAP8GHgt4CTgY8ABwObWubbBBwyTv0AYEsJnNY6rcuU6VuAAyf7wiRJu1/V0cDMfAJ439DziLgWuIBmi2KwZdZZwA6asJpMnVIfmqfVrJZpkqQOUHWdSET8RkS8v6U0C+gDngEWtdQX0uwCG6v+ArBfRMwp9UWlDvBsmY+I2AOYD/yspl9J0u5Re7HhLOALEfGmcpziEuBrwANARMQRJRjOA9Zm5kagJyJOLMufX+p9wDrgnFK/AFhbHt9VnlOmryvzS5I6RFWIZOaPgT8Gvgc8DjySmbdlZg9wIXBnqa/nFwfNVwB/ERHrgX2Ba0r9MuCSiHic5tjKylJfBfxWRDxW5vlYTa+SpN1np46JZOZbWx6vpjk9d/g8dwNHj1J/lObsreH1jcCyUeovAWfsTH+SpNeXY2dJkqoZIpKkaoaIJKmaISJJqmaISJKqGSKSpGqGiCSpmiEiSapmiEiSqhkikqRqhogkqZohIkmqZohIkqoZIpKkaoaIJKla1T3WpXbZ2t1Pd+/AiPr2vsE2dCPJENG00t07wEMbto2oLzm0qw3dSHJ3liSpmiEiSapmiEiSqhkikqRqhogkqZohIkmqZohIkqoZIpKkaoaIJKmaISJJqmaISJKqGSKSpGqGiCSpmiEiSapmiEiSqhkikqRqhogkqZohIkmqZohIkqoZIpKkantMZqaIWAB8Hzg9M5+KiNOAq4Eu4PbMXFnmWwrcCCwA7gMuzcz+iDgMuAU4CEhgRWZui4j9gVuBtwEvAmdn5uaI2BP4MnAs8CpwXmau32WvWpK0S0y4JRIRJwDfBRaX513ATcCZwFHAcRGxvMx+C3B5Zi4GZgEXl/pqYHVmLgEeBFaV+lXAusw8CrgB+GKpfxx4pdR/D1gzhdcoSdpNJrM762LgY8Bz5fnxwIbMfDIz+2mC46yIOBzoysz7y3xrSn0ucApwR2u9PH4vzZYIwG3A8jL/a/XMvA84sGzNSJI6yIQhkpkXZea6ltLBwKaW55uAQ8apHwBsKYHTWv+ldZXpW4ADx1mXJKmD1BxYnw0MtjyfBezYiTqlPjRPq4nWJUnqIDUh8gywqOX5QppdXWPVXwD2i4g5pb6IX+wae7bMR0TsAcwHfjbOuiRJHaQmRB4AIiKOKMFwHrA2MzcCPRFxYpnv/FLvA9YB55T6BcDa8viu8pwyfV2Z/7V6RJwE9GTm0xW9SrvdwMAgz7/cO+Jna3f/xAtL09ykTvFtlZk9EXEhcCewN80X/tBB8xXADeWU4IeBa0r9MuDmiFgJPA2cW+qrgDUR8Rjw87I8wLXA9aXeSxNIUkfq6dvB+ideHVE/5sh9mT9vpz9i0rQy6Xd4Zr615fHdwNGjzPMozdlbw+sbgWWj1F8Czhil3gN8aLK9SZLawyvWJUnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNEJEkVXOIUXWkrd39dPcOjKhv7xt+fzNJ7WSIqCN19w7w0IZtI+pLDu1qQzeSxuLuLElSNUNEklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRV22MqC0fEvcBBQF8pfRT4dWAlMBf4QmZ+qcx7GnA10AXcnpkrS30pcCOwALgPuDQz+yPiMOCWsv4EVmTmtqn0K0nataq3RCJiFrAYODozl2bmUuAZ4PPAScBS4JKIeHtEdAE3AWcCRwHHRcTysqpbgMszczEwC7i41FcDqzNzCfAgsKq2V0nS7jGV3VlR/v1WRDwaEZcDpwH3ZOZLmfkKcAfwAeB4YENmPpmZ/TTBcVZEHA50Zeb9ZV1rSn0ucEpZ/rX6FHqVJO0GUwmRNwF3A+8DTgUuBQ4DNrXMswk4BDh4J+sHAFtK4LTWJUkdpPqYSGb+APjB0POI+DLNMY+rWmabBeygCavBKdQpdWnaGBgY5PmXe0fU5+01h/nzpnQ4UuoY1e/kiDgJ2Csz7y6lWcBTwKKW2RYCz9EcK9mZ+gvAfhExJzMHyjzP1fYqtUNP3w7WP/HqiPoxR+5riGjGmMrurP2BP4+IvSNiPvAh4IPAqRFxYETMA94P/B3wABARcUREzAHOA9Zm5kagJyJOLOs8v9T7gHXAOaV+AbB2Cr1KknaDqezO+kZEnAD8EzAH+FJmfi8iPg3cC+wJ3JiZPwSIiAuBO4G9gbv4xUHzFcANEbEAeBi4ptQvA26OiJXA08C5tb2qc23t7qe7d2BEfXvf8L2ZkjrRlLapM3MVw069zcyvAl8dZd67gaNHqT9Kc/bW8PpGYNlU+lPn6+4d4KENIy//WXJoVxu6kbSzvGJdklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNEJEkVTNEJEnVDBFJUjVDRJJUzRCRJFUzRCRJ1aZ0j3VJO29gYJDnX+4dUZ+31xzmz/MjqenFd6xeF1u7++nuHRhR39432IZu2qunbwfrn3h1RP2YI/c1RDTt+I7V66K7d4CHNmwbUV9yaFcbupG0q3hMRJJUzRCRJFUzRCRJ1QwRSVI1Q0SSVM0QkSRVM0QkSdUMEUlSNUNEklTNK9a1Szm8ifTGYohol3J4k3oOzKjpyHem1CEcmFHTkcdEJEnVDBFJUjW3kVXFA+iSoMNDJCLOA1YCc4EvZOaX2tySCg+gS4IODpGIeAvweeAYoBf4fkTcm5mPt7ezNxa3ONrPs7bUyTr5HXgacE9mvgQQEXcAHwD+cILl5gBs3rx593bXwV7pGeDVUb74Z8+exY4do3/5jzWtr3+QfGbkGUO/vmhvXnqxZ0T9+T2md70Te9q4x978dNPI+r956zwO2G/PUV/DdDfWe7hrrznss/ecNnQ087V8Z+7UL7iTQ+RgYFPL803A8ZNYbhHAihUrdkdPkjTTLQJ+OtmZOzlEZgOtfxrPAnZMYrkfASfThM7IP2UkSaOZQxMgP9qZhTo5RJ6hCYMhC4HnJlooM3uB7+6upiRpBpv0FsiQTg6RfwA+GxEHAq8A7wcuaW9LkqRWHXuxYWY+C3wauBd4BPhqZv6wvV1JklrNGhz0VE1JUp2O3RKRJHU+Q0SSVM0QkSRVM0QkSdU6+RTfKtNp0MaI+Axwdnn6zcz8g3b2M5GI+J/AAZl5Ybt7GU1E/A7wGWAf4FuZeUWbWxpTRHwQ+K/l6drMvLKd/QwXEQuA7wOnZ+ZTEXEacDXQBdyemSvb2mAxSp+XAB+nuVD5QeCjmbm9nT3CyD5b6pcDH8jMZW1qbcpm1JZIy6CNJwFLgUsi4u3t7Wp05UP5buA3aXo9JiLe196uxhYRpwIfancfY4mItwHXAb8LvBN4V0Qsb29Xo4uIecA1wL8HjgZOLu+HjhARJ9BcsLu4PO8CbgLOBI4CjuuE3+0ofS4GPgn8O5r3wGzgY21rsBjeZ0v97cCn2tLULjSjQoSWQRsz8xVgaNDGTrQJ+ERmbs/MPuAnwGFt7mlUEfFmmnD+H+3uZRzvo/kL+Zny+zwHeKDNPY1lDs1nbx+aLea5wMhRLtvnYpov36ERIo4HNmTmk5nZD9wCnNWu5loM77MXuCwzt2TmIPDPdMZnanifRMRewPXAf29XU7vKTNudVTto4+suMx8behwRR9Ls1jqxfR2N63qaCz8PbXcj4zgC2B4RX6f54vgGsKq9LY0uM7dGxCpgPdAN/CPNro6OkJkXAUTEUGm0z9Uhr3NbIwzvMzM3AhtL7UDgcuDCNrX3mlF+nwB/TLN192Q7etqVZtqWSO2gjW0TEe8Avg18MjM3tLuf4SLiIuD/Zubd7e5lAnvQbIl+BPi3wAl06O63iHgn8GHgcJov6AGgo46JDDOtPldlt/bdwJcz8zttbmeEiPht4LDM/Kt297IrzLQQeYYyFHwxqUEb2yUiTqR5s38qM29udz9jOAd4d0Q8QnMvlzMi4i/a3NNoNgP/kJkvZuarwNfo0K1Q4D3A3Zn5QhkwdA2wrK0djW/afK4iYgnNVt3NmflH7e5nDOcC7yifqRuBYyPi9jb3VG2m7c6aNoM2RsShwN8A52TmPe3uZyyZ+dtDjyPiQmBZZv5++zoa0zeAmyNif2ArsJzm99uJHgX+LCL2odmd9Tvs5PDbr7MHgIiII2h2v5xHsyumo0TEfOBbwKcz8yvt7mcsmfnhoccRsQz4bGae076OpmZGbYlMs0EbrwT2Bq6OiEfKz6Xtbmq6yswHgD+jOQvmcZp94x25uyAzvwXcBjwE/JjmwPqftLWpcWRmD82xhTtpfrfraU5a6TQXAb8KfKLlMzXRnVA1RQ7AKEmqNqO2RCRJry9DRJJUzRCRJFUzRCRJ1QwRSVI1Q0TTXkQsi4h/2Q3rfe9Yp4hGxBkRcc0u/L/+MiI+uwvX92sRcWd5fHBEdMywKppZZtrFhtKudBzw5tEmZObXga+/vu3slMOBAMjM52hGtpV2OUNEM0ZE7Ad8iWZo/UFgLfDfMrM/Iv4j8Kc041Q9QjPO1kmt93YYtq4TgEuBORHxr8AGmnG59gH+FbiZ5j4Qp0fEd4CHaW5BcADwlcz8zAS9LqAZ8uJomgEN+2kulCQinirrfrD1OfD/gHU0Iz6/lWYo+f9EM0R7V+ntSppwuxF4S0T8PfBR4F8yc9+ImEtzX5BTy+/iAeD3y6CQT9EMwXIqzSCWf52ZHTmIpTqHu7M0k1wD/Az4DeBYmi/oKyPiV4CvAB/MzKU0Ixq8ZbwVlSvgr6MZXv7TpfwOmmFf/sMoiwTNKMzvAs6JiNMn6PVzNMO/L6EZVj3Gn/01hwB/lJmLgT1pwnBZZr6TZrSGP8zMAZqrt3+ame8ZtvxKmkEfjy4/s4E/b5m+b2aeTLPlcmVE/Nok+9IblCGimWQ58JeZOVgGNryu1E4BHs/MRwHKYJdbKtb/48wca7nrM7MvM38O/G+aQRbHcxrNX/qDmfkizYCRk9EP/ABeG/r8AmBFRPwJzZbTvhMsvxy4rvS6A7i21Ib8n7LuZ4EXGGN3njTEENFMMnzI8tk041L10wxf3qpmKPNt40zrH/b/Dkxifa09tS4/OGzani2Pe8uNoYiId9EEygKagQf/lJGvc7g5jP47GtJ6c6zhfUgjGCKaSf4euDwiZpU7x11Cc6+W7wGLy308iIj3A/vzy1+mo+nnl79gx/PBiJgdEW+iucHY304w/1rgIy3LnNky7UWa3XFDo7wuGrk40GxhPZiZV9Pc2Op3aUJivN7/DvjPETE3IoZuH/vtiV6cNBZDRDPJx4GDaG6L+s9AAp/PzJdo7uHw1xHxMM2upn6aYdjHcw/wnoi4dhL/dxfwQ+B+YPUkbuL1WaCPZkTcvy39DvkvwBXlfhPn04z2O5rbgAMi4ic0o+tuA95chkR/HOiJiB/yy1sTV9Hce+URmgP0c4ErJvH6pFE5iq9mvHIm1Eqa+zZ0l91A3wQOLvfinur6v0NzLKYTh0eXditP8dWMl5lbImI78KOI6KPZAjgbWDbOXRrvncrNt8rWwLoxJm8tZ0BJ055bIpKkah4TkSRVM0QkSdUMEUlSNUNEklTNEJEkVTNEJEnV/j9jPwqnLWuMJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['log_trip_duration'] = np.log1p(data['trip_duration'].values)\n",
    "\n",
    "sns.distplot(data.log_trip_duration, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"pickup_hour\", y=\"log_trip_duration\", data=data,color='red',height=7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. latitude and longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new feautes \n",
    "\n",
    "data['haversine_distance'] = data.apply(lambda row: haversine( (row['pickup_latitude'], row['pickup_longitude']), (row['dropoff_latitude'], row['dropoff_longitude']) ), axis=1)\n",
    "data['log_haversine_distance'] = np.log1p(data['haversine_distance'])\n",
    "\n",
    "def manhattan_distance(x,y):\n",
    "  return sum(abs(a-b) for a,b in zip(x,y))\n",
    "\n",
    "def euclidean_distance(x,y):\n",
    "  return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))\n",
    "\n",
    "data['euclidean_distance'] = data.apply(lambda row: euclidean_distance( (row['pickup_latitude'], row['pickup_longitude']), (row['dropoff_latitude'], row['dropoff_longitude']) ), axis=1)\n",
    "\n",
    "data['manhattan_distance'] = data.apply(lambda row: manhattan_distance( (row['pickup_latitude'], row['pickup_longitude']), (row['dropoff_latitude'], row['dropoff_longitude']) ), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 36 columns):\n",
      "id                        1458644 non-null object\n",
      "vendor_id                 1458644 non-null int64\n",
      "pickup_datetime           1458644 non-null datetime64[ns]\n",
      "dropoff_datetime          1458644 non-null datetime64[ns]\n",
      "passenger_count           1458644 non-null int64\n",
      "pickup_longitude          1458644 non-null float64\n",
      "pickup_latitude           1458644 non-null float64\n",
      "dropoff_longitude         1458644 non-null float64\n",
      "dropoff_latitude          1458644 non-null float64\n",
      "store_and_fwd_flag        1458644 non-null object\n",
      "trip_duration             1458644 non-null int64\n",
      "pickup_date               1458644 non-null datetime64[ns]\n",
      "pickup_day                1458644 non-null int64\n",
      "pickup_month              1458644 non-null int64\n",
      "pickup_weekday            1458644 non-null int64\n",
      "pickup_weekofyear         1458644 non-null int64\n",
      "pickup_hour               1458644 non-null int64\n",
      "pickup_minute             1458644 non-null int64\n",
      "pickup_dayofyear          1458644 non-null int64\n",
      "pickup_dt                 1458644 non-null float64\n",
      "pickup_week_hour          1458644 non-null int64\n",
      "dropoff_date              1458644 non-null datetime64[ns]\n",
      "dropoff_day               1458644 non-null int64\n",
      "dropoff_month             1458644 non-null int64\n",
      "dropoff_weekday           1458644 non-null int64\n",
      "dropoff_weekofyear        1458644 non-null int64\n",
      "dropoff_hour              1458644 non-null int64\n",
      "dropoff_minute            1458644 non-null int64\n",
      "dropoff_dayofyear         1458644 non-null int64\n",
      "dropoff_dt                1458644 non-null float64\n",
      "dropoff_week_hour         1458644 non-null int64\n",
      "log_trip_duration         1458644 non-null float64\n",
      "haversine_distance        1458644 non-null float64\n",
      "log_haversine_distance    1458644 non-null float64\n",
      "euclidean_distance        1458644 non-null float64\n",
      "manhattan_distance        1458644 non-null float64\n",
      "dtypes: datetime64[ns](4), float64(11), int64(19), object(2)\n",
      "memory usage: 400.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's look into the distribution of the distance algorithms we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.euclidean_distance, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.manhattan_distance, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.haversine_distance, kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see clearly that there are outliers in these destince measures though we have taken the log of the haversine_distance, we still need to perform the same for the euclidean and manhattan distance. Look below how the log of the haversine_distance distribution appears."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.log_haversine_distance, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now let's take the log\n",
    "data['log_euclidean_distance'] = np.log1p(data['euclidean_distance'])\n",
    "data['log_manhattan_distance'] = np.log1p(data['manhattan_distance'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at them again\n",
    "\n",
    "sns.distplot(data.log_euclidean_distance, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEPCAYAAACOU4kjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGS5JREFUeJzt3Xu0XWV57/HvJtwCIQQPgYRGboPkIUg1mgCtINKTqCOCioMChQh4IZZBKVpvR21SHT3YWmxRQFM5ARosFDwHq4cq8ZbSJmBRCAfpQfLIUS7dkECKYAwkMZd9/pjvjsu9k52dvd5kX/x+xtiDNZ/5zrneuSZj/jIv610dXV1dSJJU0x6D3QFJ0shjuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnV7TnYHdidImIf4ARgJbB5kLsjScPBKGAicF9mbujvQr9R4UITLMsGuxOSNAy9Dri7v41/08JlJcAtt9zChAkTBrsvkjTkrVq1ijlz5kA5fvbXb1q4bAaYMGECkyZNGuy+SNJwslO3EryhL0mqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKm637TvubTlFy9t4qUNvR/13m+fURywnx+lJHXziLgTXtqwmeWPru1Vnz55jOEiSS28LCZJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqq5fP58YEWOB7wFnZObjETELuAoYDXw5M+eVdtOA64GxwFLgkszcFBGHAzcDhwAJzMnMtRExDrgFOBpYDZyTmasiYm/gBmAGsA44PzNXREQH8BngDGALMDcz76nySUiSqtnhmUtEnATcDUwp06OBG4G3AVOBEyJidml+M3BZZk4BOoC5pb4AWJCZxwL3A/NL/QpgWWZOBRYCV5f65cCLpf5+YFGpn1Xe8zjgTGBRRPj7wpI0xPTnsthc4I+Ap8v0icCjmflYZm6iCZSzI+IIYHRm3lvaLSr1vYBTgdtb6+X16TRnLgC3ArNL+631zFwKjC9nP6cDt2Xmlsz8MfAk8Nqd3mpJ0i61w3DJzIszc1lL6TBgZcv0SmBSH/WDgTUliFrrv7auMn8NMH4A7yFJGkIGckN/D6CrZbqD5v5Hf+uUenebVju7ru66JGkIGUi4dAITW6Yn0Fwy2179WeDAiBhV6hP51SW2p0o7yr2TA4DnBvAekqQhZCDh8n0gIuKYEhjnA4sz8wlgfUScXNpdUOobgWXAuaV+IbC4vL6zTFPmLyvtt9Yj4hRgfWY+WepzImJURBxD85DBfQPYBknSLrTTT1pl5vqIeCfwFWBfmgN+9836OcDC8ujyA8A1pX4pcFNEzKO5CX9eqc+neeLrYeCFsjzAtcB1pb6BJqgo73MS8FCZfk9mrtvZbZAk7VodXV09b4eMXBFxJPDYkiVLmDRp558DeOb5DSx/dG2v+vTJYzj0oH3a76AkDTGdnZ3MnDkT4KjMfLy/y/kNfUlSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqbs92Fo6IdwAfK5OLM/NDETENuB4YCywFLsnMTRFxOHAzcAiQwJzMXBsR44BbgKOB1cA5mbkqIvYGbgBmAOuA8zNzRUR0AJ8BzgC2AHMz8552tkOSVNeAz1wiYj/gGuD1wKuA10XELJoAuSwzpwAdwNyyyAJgQWYeC9wPzC/1K4BlmTkVWAhcXeqXAy+W+vuBRaV+FjAVOA44E1gUEW2FpCSprnYui40qy+8P7FX+NgKjM/Pe0mYRcHZE7AWcCtzeWi+vT6c5cwG4FZhd2m+tZ+ZSYHw5+zkduC0zt2Tmj4Engde2sR2SpMoGHC6Z+Quas48VQCfwOPBLYGVLs5XAJOBgYE1mbupRBzise5kyfw0wvrXeY5nt1SVJQ0Q7l8VeCbwbOILmgL8ZeCPQ1dKsg+a+yB496pR6d5tW21tmR3VJ0hDRzmWxNwFLMvPZzNxAc6nrNGBiS5sJwNPAs8CBETGq1CeWOsBTpR3l3skBwHM0Z0PbWtf26pKkIaKdcPkhMCsi9i9PcL0F+FdgfUScXNpcQPMU2UZgGXBuqV8ILC6v7yzTlPnLSvut9Yg4BVifmU+W+pyIGBURxwBTgPva2A5JUmUDfsoqM78dEa8GltPcyP8B8Gngq8DCiBgLPEDzRBnApcBNETGP5ib8eaU+n+aJr4eBF4A5pX4tcF2pb6AJKmgeCjgJeKhMvycz1w10OyRJ9XV0dfW8FTJyRcSRwGNLlixh0qSdfwbgmec3sPzRtb3q0yeP4dCD9mm/g5I0xHR2djJz5kyAozLz8f4u5zf0JUnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKm6PdtZOCLeAnwC2B/4dma+LyJmAVcBo4EvZ+a80nYacD0wFlgKXJKZmyLicOBm4BAggTmZuTYixgG3AEcDq4FzMnNVROwN3ADMANYB52fmina2Q5JU14DPXCLiaOCLwJnAK4HXRMRs4EbgbcBU4IRSgyZALsvMKUAHMLfUFwALMvNY4H5gfqlfASzLzKnAQuDqUr8ceLHU3w8sGug2SJJ2jXYui72d5sykMzM3AucCLwGPZuZjmbmJJlDOjogjgNGZeW9ZdlGp7wWcCtzeWi+vT6c5cwG4FZhd2m+tZ+ZSYHw5+5EkDRHthMsxwKiIuCMiHgQuBQ4DVra0WQlM6qN+MLCmBFFrndZlyvw1wPg+1iVJGiLaueeyJ81Zx2nAWuAOmnsgXS1tOoAtNCHWnzql3t2m1Y7WJUkaIto5c1kFfDczV2fmOuCrwCxgYkubCcDTQOd26s8CB0bEqFKfWOoAT5V2RMSewAHAc32sS5I0RLQTLl8H3hQR40o4zKa5dxIRcUypnQ8szswngPURcXJZ9oJS3wgso7lfA3AhsLi8vrNMU+YvK+231iPiFGB9Zj7ZxnZIkiob8GWxzPx+RFwJ3A3sBXwH+FtgBfAVYF+aIOi+WT8HWBgRY4EHgGtK/VLgpoiYBzwJnFfq84FFEfEw8EJZHuBa4LpS30ATVJKkIaSjq6vnLY+RKyKOBB5bsmQJkybt/DMAzzy/geWPru1Vnz55DIcetE/7HZSkIaazs5OZM2cCHJWZj/d3Ob+hL0mqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnV7dnuCiLir4GDM/OdETENuB4YCywFLsnMTRFxOHAzcAiQwJzMXBsR44BbgKOB1cA5mbkqIvYGbgBmAOuA8zNzRUR0AJ8BzgC2AHMz8552t0GSVFdbZy4RMRO4qKV0M3BZZk4BOoC5pb4AWJCZxwL3A/NL/QpgWWZOBRYCV5f65cCLpf5+YFGpnwVMBY4DzgQWRUTbASlJqmvA4RIRLwM+BfxFmT4CGJ2Z95Ymi4CzI2Iv4FTg9tZ6eX06zZkLwK3A7NJ+az0zlwLjy9nP6cBtmbklM38MPAm8dqDbIEnaNdo5c7kO+FPg+TJ9GLCyZf5KYBJwMLAmMzf1qP/aMmX+GmB8H+vaXl2SNIQMKFwi4mLgPzJzSY91dbVMd9DcF+lZp9S727Ta3jI7qkuShpCB3q84F5gYEQ8CLwPG0Bz0J7a0mQA8DTwLHBgRozJzc2nzdGnzVGnXWe6dHAA8B3SWdj/psa7ues/3kCQNIQM6c8nMN2Tm8Zk5Dfgz4I7MfBewPiJOLs0uABZn5kZgGU0gAVwILC6v7yzTlPnLSvut9Yg4BVifmU+W+pyIGBURxwBTgPsGsg2SpF2n9pNWc4CFETEWeAC4ptQvBW6KiHk0N+HPK/X5NE98PQy8UJYHuBa4rtQ30AQVNA8FnAQ8VKbfk5nrKm+DJKlNHV1dPW+HjFwRcSTw2JIlS5g0aeefA3jm+Q0sf3Rtr/r0yWM49KB92u+gJA0xnZ2dzJw5E+CozHy8v8v5DX1JUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqm7PdhaOiE8A55TJb2TmRyJiFnAVMBr4cmbOK22nAdcDY4GlwCWZuSkiDgduBg4BEpiTmWsjYhxwC3A0sBo4JzNXRcTewA3ADGAdcH5mrmhnOyRJdQ34zKWEyBuBVwPTgOkRcR5wI/A2YCpwQkTMLovcDFyWmVOADmBuqS8AFmTmscD9wPxSvwJYlplTgYXA1aV+OfBiqb8fWDTQbZAk7RrtXBZbCXwwM3+ZmRuBR4ApwKOZ+VhmbqIJlLMj4ghgdGbeW5ZdVOp7AacCt7fWy+vTac5cAG4FZpf2W+uZuRQYX85+Bs3mzV088/yGXn+/eGnTYHZLkgbNgC+LZebD3a8jYjLN5bFraUKn20pgEnDYduoHA2tKELXWaV2mXD5bA4zvY11PDnRb2rV+4xZW/HRdr/r0yWM4YL+2rjxK0rDU9g39iHgF8B3gw8BPga6W2R3AlvI+/alT6t1tWu1oXZKkIaKtcImIk4ElwEcz8yagE5jY0mQC8HQf9WeBAyNiVKlPLHWAp0o7ImJP4ADguT7WJUkaItq5of9y4Gs0T2vdVsrfb2bFMSUwzgcWZ+YTwPoSRgAXlPpGYBlwbqlfCCwur+8s05T5y0r7rfWIOAVYn5mDdklMktRbOzcEPgTsC1wVEd21LwLvBL5S5t3Jr27WzwEWRsRY4AHgmlK/FLgpIubR3Dc5r9TnA4si4mHghbI8NPd1riv1DTRBJUkaQtq5of8+4H3bmf2qbbT/IXDiNupPAKdto/4z4K3bqK8HLtrJ7kqSdiO/oS9Jqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqozXCRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs5wkSRVZ7hIkqrbc7A7MJJt3tzFM89v6FXfb59RHLCfH72kkcsj3C60fuMWVvx0Xa/69MljDBdJI5qXxSRJ1RkukqTqDBdJUnWGiySpOsNFklSd4SJJqs7nYQeB33+RNNINyyNZRJwPzAP2Aj6XmV8Y5C7tlO19/2Xa0fvz0obNvep77NHBli1dveqGkaShatgdmSLit4BPAdOBDcD3IuKuzPzR4PasfdsLnWNfPpoV/+GXMSUNH8PxyDQL+OfM/BlARNwO/D7w5/1YdhTAqlWrBvTG//nzX/Kz1S/1qj+z5778bPX63V5fuf9+bHxx7/52X5J2WsvxctTOLDccw+UwYGXL9ErgxH4uOxFgzpw5tfskSSPdROAn/W08HMNlD6D1BkQHsKWfy94HvI4mkHrf3JAk9TSKJlju25mFhmO4dNIERLcJwNP9WTAzNwB374pOSdII1u8zlm7DMVy+C3wyIsYDLwJnAe8d3C5JkloNuy9RZuZTwJ8CdwEPAv+QmT8Y3F5Jklp1dHX1/v6EJEntGHZnLpKkoc9wkSRVZ7hIkqozXCRJ1Q3HR5F3uR0NjBkR04DrgbHAUuCSzNy02zvahn5s4yeAdwPPl9LC4TZAaLeIGAt8DzgjMx/vMW/Y78tuO9jOEbE/y3acUya/kZkf6TF/ROzPfmznkN+fnrn00DIw5inANOC9EXFcj2Y3A5dl5hSaEQLm7t5etqef2zgD+IPMnFb+htT/uP0VESfRfHF2ynaaDOt92a0f2zns92dEzALeCLya5v/b6RHx9h7Nhv3+7Od2Dvn9abj0tnVgzMx8EegeGBOAiDgCGJ2Z95bSIuDs3d7L9vS5jcUM4OMR8VBEfD4i9t3tvaxjLvBHbGMUhxGyL7ttdzuLkbA/VwIfzMxfZuZG4BHg8O6ZI2h/9rmdxZDfn4ZLb9saGHPSTswfDvrchogYA/wf4MPAa4BxwPzd2cFaMvPizFy2ndkjYV8CfW/nSNmfmflwd3BExGSay0Z3tjQZEftzR9s5XPan91x629HAmO0MnDlU9LkNmbkWeHP3dET8DXAjzcgII8lI2Jc7NNL2Z0S8AvgG8OHMfLRl1ojan9vbzuGyPz1z6a2TMjR/0XNgzB3NHw763IaIODwi3t0yvwPYuJv6tjuNhH25QyNpf0bEycAS4KOZeVOP2SNmf/a1ncNlfxouvX0XmBkR4yNiP5qBMb/ZPTMznwDWl50PcAGwePd3sy19biOwDrgyIo6KiA6aa/lfHYR+7lIjZF/2x4jYnxHxcuBrwPmZeVvP+SNlf+5oOxkm+9Nw6WF7A2NGxJ0RMaM0mwN8NiJWAGOAawantwOzo23MzNXAHwL/BCTNv4z+ZtA6XNlI2pd9GYH780PAvsBVEfFg+btkBO7PPrdzuOxPB66UJFXnmYskqTrDRZJUneEiSarOcJEkVWe4SJKqM1w0ZETEaRHxfwe7HzsjIj4ZEZ8fwHLfjoiDy+uLI+LS+r3b5vt+PSLeWV4/GBHj+mh7YET88+7ol0Yeh3+RBscbWl6fAuz2UM3MaTtochBw4u7oi0Yew0VDTkQcCHyBZrjxLppvWX88MzdFxJuBvwI203wBdBZwSs/fL+mxvn8BlgO/AxwC/A+aoUFeD+wPnJOZ/x4RvwNcCexDM4zIdzLzPRFxJM1QHHcCJ9EcdD+Smd3fij42Iu4qyzxDMxT6yog4A/g4sHd535syc35E/F1Z7q6IuBJ4K/CGiFhHM0L1dcChpY9PlP49GxGP04z0O5NmlNwvZWafAxZGxGHATTSDOj5R+tE9rwsYT3Mc+BJwcJn1jbLevwNGR8SDwHTgIpov7+0NvAz4dGb+bTkTejvNOF6TgZeAizLzkYiYAHwROLbM/2JmXlP28dXAb9P8ptASmjG0ht1vr2jbvCymoega4DmaA88M4FXAhyLivwB/D7yj/Kv7LuC3+rnOIzPzZOAdNAHyL5k5g2bYmz8ubd4H/FlmngQcB7w1IqaXeUcD38rME4GPAp9rWffRNAFwLM2PN11chuX4IM1BdgZNsH0sIg7OzHeV5X4vM/8euAP4bPlNjj8A/i0zf7es9yWaYUy6jcnM1wGvLZ/JUTvY7i8A92bmK4DLaQ7yPc0FfpqZrwFeB0wuB/93AevKZz26tHtzZr4aOLd8jt1eD/xxZh4PfL98RgALgB+Xz+Z3aX476Bjgs8DyzJxO87slBwMf2MG2aBgxXDQUzQY+n5ldmbmB5l++s4FTgR9l5g8ByoB+a/q5zn8s//1J+e83W6ZfVl5fBIyLiI/THBRH0wwhAs3AgN3Dnj/Qsgw0Zziry+sfAodkZhfwFpofevoEcBXNMB3799XJzLwa+F5EfKD04fiWPgD879LuKeDZHv3Yllk0Zztk5v8DtnUP5ZvAWRFxJ82ZyUcz8+c9+rUWOAM4PSL+O83wQa39Wp6ZneV16+czi+ZMkcz8eWYeX/pxBvCH5axoOc3lt9/ewbZoGPGymIainkOn70Fz6WQTzQG6VX+HVN/QOlF+hKmnpcBDNAfb/0lzCaz7/X6Zmd3v1dWjH63r6gI6ImJ/mt/c+CqwjGZI9DO30f9fExF/RXOgvZHmzGyvHsus6/lefa1vG216XXbKzPvKGdAs4L8CP4iI2TRnj939mgT8G01Q3E1z+e6MfvRrEy37MiKOBv4TGAWcnZmPlPo4fn2fa5jzzEVD0beAyyKiIyL2Ad4LfAe4B5gSEa8EiIizaH4oqe2DUjm4nQD8t8z8R5ofmTqG5iA4EJNpfsd9Xmb+E3Aazb2c7vVtpgkOaA7A3a/fBHyuXC57lubG/0D7AE1QvheaodqB3+vZICI+DczPzK/RXBp8mOaMaRMwqlzimwGsBq4Avk0JlojYUd++S3N5rfte2hKaz+ZbwJ+07OM7gMva2E4NMYaLhqLLaW48/3v5S+BTmfkz4DzgSxHxAM2BeBPNfYm2ZOYLwF8CD5THoT9KE2bHDHCVDwFfB1ZExCM0l8h+1LK+/wX8a0QcT/PAwiUR8THgz4G/joiHaA64d7fRB2iGYz+u9OEGmocgevocMK1s9/3AY8BtNL/k+AOasLmP5vdSkl/97O7qfvTtMmBq2Z57gL/MzOU0+3h/mv37UPnvldtdi4YdR0XWsBERY4F5wCcz86WIeA3NL/UdVu5xSBoiDBcNKxFxBc1jrxvL3wdo7h1+djuL3JWZf7KburfbRUQAX97O7MzMc3dnf6RuhoskqTrvuUiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVN3/B7RLThprNI1dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.log_manhattan_distance, kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the unique attributes of this column\n",
    "data['store_and_fwd_flag'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Machine Learning algorithms expects all features to be numberic therefore we have convert all categorical features into a numeric representation. There are several methods that performs the same prupose but with variations. Lable encoding is one of the methods we will use here. The example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Label mapping:\n",
      "black --> 0\n",
      "green --> 1\n",
      "red --> 2\n",
      "white --> 3\n",
      "yellow --> 4\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing \n",
    "\n",
    "# input labels \n",
    "input_labels = ['red', 'black', 'red', 'green', 'black', 'yellow', 'white'] \n",
    "\n",
    "\n",
    "# Create label encoder and fit the labels \n",
    "encoder = preprocessing.LabelEncoder() \n",
    "encoder.fit(input_labels) \n",
    "\n",
    "\n",
    "# Print the mapping  \n",
    "print(\"\\nLabel mapping:\") \n",
    "for i, item in enumerate(encoder.classes_): \n",
    "    print(item, '-->', i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Labels = ['green', 'red', 'black']\n",
      "Encoded values = [1, 2, 0]\n"
     ]
    }
   ],
   "source": [
    "# Encode a set of randomly ordered labels using the encoder \n",
    "test_labels = ['green', 'red', 'black'] \n",
    "encoded_values = encoder.transform(test_labels) \n",
    "print(\"\\nLabels =\", test_labels) \n",
    "print(\"Encoded values =\", list(encoded_values)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Encoded values = [3, 0, 4, 1]\n",
      "Decoded labels = ['white', 'black', 'yellow', 'green']\n"
     ]
    }
   ],
   "source": [
    "# Decode a set of values using the encoder \n",
    "encoded_values = [3, 0, 4, 1] \n",
    "decoded_list = encoder.inverse_transform(encoded_values) \n",
    "print(\"\\nEncoded values =\", encoded_values) \n",
    "print(\"Decoded labels =\", list(decoded_list)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's apply this method for our *store_and_fwd_flag* column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Label mapping:\n",
      "N --> 0\n",
      "Y --> 1\n"
     ]
    }
   ],
   "source": [
    "# first define the varaibles\n",
    "input_labels = ['N', 'Y'] \n",
    "\n",
    "# Create label encoder and fit the labels \n",
    "encoder = preprocessing.LabelEncoder() \n",
    "encoder.fit(input_labels) \n",
    "\n",
    "\n",
    "# Print the mapping  \n",
    "print(\"\\nLabel mapping:\") \n",
    "for i, item in enumerate(encoder.classes_): \n",
    "    print(item, '-->', i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode our column\n",
    "data['En_store_and_fwd_flag'] = encoder.fit_transform(data['store_and_fwd_flag'].astype('str'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>dropoff_dt</th>\n",
       "      <th>dropoff_week_hour</th>\n",
       "      <th>log_trip_duration</th>\n",
       "      <th>haversine_distance</th>\n",
       "      <th>log_haversine_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>manhattan_distance</th>\n",
       "      <th>log_euclidean_distance</th>\n",
       "      <th>log_manhattan_distance</th>\n",
       "      <th>En_store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>40.77</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>6370139.00</td>\n",
       "      <td>17</td>\n",
       "      <td>6.12</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>14086267.00</td>\n",
       "      <td>144</td>\n",
       "      <td>6.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>1598837.00</td>\n",
       "      <td>36</td>\n",
       "      <td>7.66</td>\n",
       "      <td>6.39</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>40.71</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>8364969.00</td>\n",
       "      <td>67</td>\n",
       "      <td>6.06</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.79</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.78</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>7392879.00</td>\n",
       "      <td>133</td>\n",
       "      <td>6.08</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2377394          1 2016-06-12 00:43:35 2016-06-12 00:54:38   \n",
       "2  id3858529          2 2016-01-19 11:35:24 2016-01-19 12:10:48   \n",
       "3  id3504673          2 2016-04-06 19:32:31 2016-04-06 19:39:40   \n",
       "4  id2181028          2 2016-03-26 13:30:55 2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1            -73.98            40.77             -73.96   \n",
       "1                1            -73.98            40.74             -74.00   \n",
       "2                1            -73.98            40.76             -74.01   \n",
       "3                1            -74.01            40.72             -74.01   \n",
       "4                1            -73.97            40.79             -73.97   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag          ...            dropoff_dt  \\\n",
       "0             40.77                  N          ...            6370139.00   \n",
       "1             40.73                  N          ...           14086267.00   \n",
       "2             40.71                  N          ...            1598837.00   \n",
       "3             40.71                  N          ...            8364969.00   \n",
       "4             40.78                  N          ...            7392879.00   \n",
       "\n",
       "  dropoff_week_hour  log_trip_duration  haversine_distance  \\\n",
       "0                17               6.12                1.50   \n",
       "1               144               6.50                1.81   \n",
       "2                36               7.66                6.39   \n",
       "3                67               6.06                1.49   \n",
       "4               133               6.08                1.19   \n",
       "\n",
       "   log_haversine_distance  euclidean_distance  manhattan_distance  \\\n",
       "0                    0.92                0.02                0.02   \n",
       "1                    1.03                0.02                0.03   \n",
       "2                    2.00                0.06                0.08   \n",
       "3                    0.91                0.01                0.02   \n",
       "4                    0.78                0.01                0.01   \n",
       "\n",
       "   log_euclidean_distance  log_manhattan_distance  En_store_and_fwd_flag  \n",
       "0                    0.02                    0.02                      0  \n",
       "1                    0.02                    0.03                      0  \n",
       "2                    0.06                    0.08                      0  \n",
       "3                    0.01                    0.02                      0  \n",
       "4                    0.01                    0.01                      0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's have a look into our dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 39 columns):\n",
      "id                        1458644 non-null object\n",
      "vendor_id                 1458644 non-null int64\n",
      "pickup_datetime           1458644 non-null datetime64[ns]\n",
      "dropoff_datetime          1458644 non-null datetime64[ns]\n",
      "passenger_count           1458644 non-null int64\n",
      "pickup_longitude          1458644 non-null float64\n",
      "pickup_latitude           1458644 non-null float64\n",
      "dropoff_longitude         1458644 non-null float64\n",
      "dropoff_latitude          1458644 non-null float64\n",
      "store_and_fwd_flag        1458644 non-null object\n",
      "trip_duration             1458644 non-null int64\n",
      "pickup_date               1458644 non-null datetime64[ns]\n",
      "pickup_day                1458644 non-null int64\n",
      "pickup_month              1458644 non-null int64\n",
      "pickup_weekday            1458644 non-null int64\n",
      "pickup_weekofyear         1458644 non-null int64\n",
      "pickup_hour               1458644 non-null int64\n",
      "pickup_minute             1458644 non-null int64\n",
      "pickup_dayofyear          1458644 non-null int64\n",
      "pickup_dt                 1458644 non-null float64\n",
      "pickup_week_hour          1458644 non-null int64\n",
      "dropoff_date              1458644 non-null datetime64[ns]\n",
      "dropoff_day               1458644 non-null int64\n",
      "dropoff_month             1458644 non-null int64\n",
      "dropoff_weekday           1458644 non-null int64\n",
      "dropoff_weekofyear        1458644 non-null int64\n",
      "dropoff_hour              1458644 non-null int64\n",
      "dropoff_minute            1458644 non-null int64\n",
      "dropoff_dayofyear         1458644 non-null int64\n",
      "dropoff_dt                1458644 non-null float64\n",
      "dropoff_week_hour         1458644 non-null int64\n",
      "log_trip_duration         1458644 non-null float64\n",
      "haversine_distance        1458644 non-null float64\n",
      "log_haversine_distance    1458644 non-null float64\n",
      "euclidean_distance        1458644 non-null float64\n",
      "manhattan_distance        1458644 non-null float64\n",
      "log_euclidean_distance    1458644 non-null float64\n",
      "log_manhattan_distance    1458644 non-null float64\n",
      "En_store_and_fwd_flag     1458644 non-null int64\n",
      "dtypes: datetime64[ns](4), float64(13), int64(20), object(2)\n",
      "memory usage: 434.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_weekday</th>\n",
       "      <th>...</th>\n",
       "      <th>dropoff_dt</th>\n",
       "      <th>dropoff_week_hour</th>\n",
       "      <th>log_trip_duration</th>\n",
       "      <th>haversine_distance</th>\n",
       "      <th>log_haversine_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>manhattan_distance</th>\n",
       "      <th>log_euclidean_distance</th>\n",
       "      <th>log_manhattan_distance</th>\n",
       "      <th>En_store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "      <td>1458644.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.53</td>\n",
       "      <td>1.66</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.75</td>\n",
       "      <td>959.49</td>\n",
       "      <td>15.50</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.05</td>\n",
       "      <td>...</td>\n",
       "      <td>7899773.43</td>\n",
       "      <td>86.91</td>\n",
       "      <td>6.47</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5237.43</td>\n",
       "      <td>8.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.95</td>\n",
       "      <td>...</td>\n",
       "      <td>4455328.78</td>\n",
       "      <td>46.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>34.36</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>32.18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>397.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4122121.50</td>\n",
       "      <td>45.00</td>\n",
       "      <td>5.99</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>40.75</td>\n",
       "      <td>662.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>...</td>\n",
       "      <td>7925501.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>40.77</td>\n",
       "      <td>1075.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>...</td>\n",
       "      <td>11678840.75</td>\n",
       "      <td>128.00</td>\n",
       "      <td>6.98</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>-61.34</td>\n",
       "      <td>51.88</td>\n",
       "      <td>-61.34</td>\n",
       "      <td>43.92</td>\n",
       "      <td>3526282.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>...</td>\n",
       "      <td>15807512.00</td>\n",
       "      <td>167.00</td>\n",
       "      <td>15.08</td>\n",
       "      <td>1240.91</td>\n",
       "      <td>7.12</td>\n",
       "      <td>11.19</td>\n",
       "      <td>12.91</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
       "count 1458644.00       1458644.00        1458644.00       1458644.00   \n",
       "mean        1.53             1.66            -73.97            40.75   \n",
       "std         0.50             1.31              0.07             0.03   \n",
       "min         1.00             0.00           -121.93            34.36   \n",
       "25%         1.00             1.00            -73.99            40.74   \n",
       "50%         2.00             1.00            -73.98            40.75   \n",
       "75%         2.00             2.00            -73.97            40.77   \n",
       "max         2.00             9.00            -61.34            51.88   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  trip_duration  pickup_day  \\\n",
       "count         1458644.00        1458644.00     1458644.00  1458644.00   \n",
       "mean              -73.97             40.75         959.49       15.50   \n",
       "std                 0.07              0.04        5237.43        8.70   \n",
       "min              -121.93             32.18           1.00        1.00   \n",
       "25%               -73.99             40.74         397.00        8.00   \n",
       "50%               -73.98             40.75         662.00       15.00   \n",
       "75%               -73.96             40.77        1075.00       23.00   \n",
       "max               -61.34             43.92     3526282.00       31.00   \n",
       "\n",
       "       pickup_month  pickup_weekday          ...            dropoff_dt  \\\n",
       "count    1458644.00      1458644.00          ...            1458644.00   \n",
       "mean           3.52            3.05          ...            7899773.43   \n",
       "std            1.68            1.95          ...            4455328.78   \n",
       "min            1.00            0.00          ...                  0.00   \n",
       "25%            2.00            1.00          ...            4122121.50   \n",
       "50%            4.00            3.00          ...            7925501.00   \n",
       "75%            5.00            5.00          ...           11678840.75   \n",
       "max            6.00            6.00          ...           15807512.00   \n",
       "\n",
       "       dropoff_week_hour  log_trip_duration  haversine_distance  \\\n",
       "count         1458644.00         1458644.00          1458644.00   \n",
       "mean               86.91               6.47                3.44   \n",
       "std                46.81               0.80                4.30   \n",
       "min                 0.00               0.69                0.00   \n",
       "25%                45.00               5.99                1.23   \n",
       "50%                89.00               6.50                2.09   \n",
       "75%               128.00               6.98                3.88   \n",
       "max               167.00              15.08             1240.91   \n",
       "\n",
       "       log_haversine_distance  euclidean_distance  manhattan_distance  \\\n",
       "count              1458644.00          1458644.00          1458644.00   \n",
       "mean                     1.25                0.04                0.05   \n",
       "std                      0.63                0.05                0.06   \n",
       "min                      0.00                0.00                0.00   \n",
       "25%                      0.80                0.01                0.02   \n",
       "50%                      1.13                0.02                0.03   \n",
       "75%                      1.58                0.04                0.05   \n",
       "max                      7.12               11.19               12.91   \n",
       "\n",
       "       log_euclidean_distance  log_manhattan_distance  En_store_and_fwd_flag  \n",
       "count              1458644.00              1458644.00             1458644.00  \n",
       "mean                     0.03                    0.04                   0.01  \n",
       "std                      0.04                    0.05                   0.07  \n",
       "min                      0.00                    0.00                   0.00  \n",
       "25%                      0.01                    0.02                   0.00  \n",
       "50%                      0.02                    0.03                   0.00  \n",
       "75%                      0.04                    0.05                   0.00  \n",
       "max                      2.50                    2.63                   1.00  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Produce this into a file\n",
    "data.to_csv('Dataset/Large NYC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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