local_network_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization/Analysis of Local GTFS-RT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "from datetime import datetime, timezone\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytz\n",
    "import seaborn as sns\n",
    "\n",
    "TZ = pytz.timezone('America/Los_Angeles')\n",
    "sns.set_style('whitegrid')\n",
    "path = './transit_vis/data/to_upload'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the Local Summarize_RDS Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join all the data which is saved in different csv for each day\n",
    "# files = os.listdir(path)\n",
    "# frames = [pd.read_csv(f\"{path}/{filename}\", dtype={'trip_short_name': str, 'route_short_name': str}) for filename in files]\n",
    "# all_data = pd.concat(frames)\n",
    "# all_data.to_csv(f\"{path}/../all_data.csv\")\n",
    "# all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month: 1\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './transit_vis/data/to_upload/1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6p/76scpql109v8rhf5gm61sr4c0000gn/T/ipykernel_4527/761838084.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmonth_num\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Month: {month_num}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{path}/{month_num}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mday_num\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Day: {day_num}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './transit_vis/data/to_upload/1'"
     ]
    }
   ],
   "source": [
    "read_cols = ['tripid',\n",
    "             'dist_diff',\n",
    "             'speed_m_s',\n",
    "             'deviation_change_s',\n",
    "             'seg_compkey']\n",
    "col_types = {'tripid':int,\n",
    "             'dist_diff':float,\n",
    "             'speed_m_s':float,\n",
    "             'deviation_change_s':int,\n",
    "             'seg_compkey':int}\n",
    "\n",
    "all_data = []\n",
    "test_samples = []\n",
    "chunksize = 10 ** 5\n",
    "\n",
    "def ff_percentile(data):\n",
    "    return np.percentile(data, 95)\n",
    "\n",
    "def process(chunk):\n",
    "    # Calculate Pace\n",
    "    chunk['speed_mph'] = chunk['speed_m_s']*2.237\n",
    "    chunk['pace_s_mi'] = 1 / (chunk['speed_m_s']*.0006214)\n",
    "\n",
    "#     # Add date to the data; very time consuming avoid if possible\n",
    "#     chunk['day'] = pd.to_datetime(chunk['locationtime'], unit='s')\n",
    "#     chunk['day'] = chunk['day'].dt.strftime('%Y-%m-%d')\n",
    "    \n",
    "    # Get 95th percentile pace for segments\n",
    "    freeflow = chunk[['seg_compkey','pace_s_mi']].groupby('seg_compkey').agg(ff_percentile).reset_index()    \n",
    "    chunk = pd.merge(chunk, freeflow, on='seg_compkey', suffixes=[None, '_95'])\n",
    "        \n",
    "    # Calculate metrics\n",
    "    chunk['delay_stochastic'] = chunk['deviation_change_s']\n",
    "    chunk['delay_total'] = (chunk['pace_s_mi_95'] - chunk['pace_s_mi']) * chunk['dist_diff'] * 0.0006214\n",
    "    chunk['delay_systematic'] = chunk['delay_total'] - chunk['delay_stochastic']\n",
    "    \n",
    "    # Aggregate by segments\n",
    "    # Save 10% of data as samples\n",
    "    return [chunk.sample(int(len(chunk)/10)), chunk.groupby('seg_compkey').agg([np.median,\n",
    "                                             ff_percentile,\n",
    "                                             pd.Series.count])]\n",
    "\n",
    "# Read each month of data, a day at a time, broken into chunks for faster processing\n",
    "# Calculate segment metrics + 10% sample from each chunk, and save\n",
    "for month_num in range(1,13):\n",
    "    print(f\"Month: {month_num}\")\n",
    "    files = os.listdir(f\"{path}/{month_num}\")\n",
    "    for day_num in files:\n",
    "        print(f\"Day: {day_num}\")\n",
    "        i = 0\n",
    "        for chunk in pd.read_csv(f\"{path}/{month_num}/{day_num}\", chunksize=chunksize, usecols=read_cols, dtype=col_types):\n",
    "            i+=1\n",
    "            print(f\"Chunk: {i}\")\n",
    "            result = process(chunk)\n",
    "            test_samples.append(result[0])\n",
    "            all_data.append(result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine metrics calculated for each segment across all chunks\n",
    "data = pd.concat(all_data).dropna().groupby('seg_compkey').mean()\n",
    "data.head()\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seg_compkey</th>\n",
       "      <th>delay_total</th>\n",
       "      <th>delay_systematic</th>\n",
       "      <th>delay_stochastic</th>\n",
       "      <th>pace_s_mi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>89.541048</td>\n",
       "      <td>97.580449</td>\n",
       "      <td>-7.473896</td>\n",
       "      <td>160.683648</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>137.391883</td>\n",
       "      <td>142.405076</td>\n",
       "      <td>-5.086124</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>171.563356</td>\n",
       "      <td>176.705473</td>\n",
       "      <td>-3.893130</td>\n",
       "      <td>160.242180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>4.461799</td>\n",
       "      <td>12.799148</td>\n",
       "      <td>-8.385542</td>\n",
       "      <td>208.826880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>78.254590</td>\n",
       "      <td>100.198954</td>\n",
       "      <td>-21.502326</td>\n",
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       "      <th>8874</th>\n",
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       "      <td>10.805218</td>\n",
       "      <td>31.137516</td>\n",
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       "      <td>249.454482</td>\n",
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       "      <td>10.971203</td>\n",
       "      <td>11.410937</td>\n",
       "      <td>-0.584559</td>\n",
       "      <td>205.550236</td>\n",
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       "      <td>-3.965860</td>\n",
       "      <td>5.148438</td>\n",
       "      <td>158.418008</td>\n",
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       "      <th>8877</th>\n",
       "      <td>140751</td>\n",
       "      <td>12.724919</td>\n",
       "      <td>0.383465</td>\n",
       "      <td>12.273256</td>\n",
       "      <td>161.717226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8878</th>\n",
       "      <td>140753</td>\n",
       "      <td>3.104356</td>\n",
       "      <td>-10.097891</td>\n",
       "      <td>13.134831</td>\n",
       "      <td>215.414070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8879 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seg_compkey  delay_total  delay_systematic  delay_stochastic   pace_s_mi\n",
       "0              51    89.541048         97.580449         -7.473896  160.683648\n",
       "1              52   137.391883        142.405076         -5.086124  168.413027\n",
       "2              53   171.563356        176.705473         -3.893130  160.242180\n",
       "3              54     4.461799         12.799148         -8.385542  208.826880\n",
       "4              55    78.254590        100.198954        -21.502326  144.388831\n",
       "...           ...          ...               ...               ...         ...\n",
       "8874       140652    10.805218         31.137516        -20.107843  249.454482\n",
       "8875       140653    10.971203         11.410937         -0.584559  205.550236\n",
       "8876       140749     1.182577         -3.965860          5.148438  158.418008\n",
       "8877       140751    12.724919          0.383465         12.273256  161.717226\n",
       "8878       140753     3.104356        -10.097891         13.134831  215.414070\n",
       "\n",
       "[8879 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save segment values for GIS mapping\n",
    "segment_metrics_data = data[[('delay_total','median'),\n",
    "                             ('delay_systematic','median'),\n",
    "                             ('delay_stochastic','median'),\n",
    "                             ('pace_s_mi','median')]]\n",
    "segment_metrics_data = segment_metrics_data[segment_metrics_data < np.inf]\n",
    "segment_metrics_data = segment_metrics_data[segment_metrics_data > -np.inf]\n",
    "segment_metrics_data = segment_metrics_data.dropna().reset_index()\n",
    "segment_metrics_data.columns = segment_metrics_data.columns.droplevel(1)\n",
    "segment_metrics_data.to_csv(f\"{path}/../segment_metrics_data.csv\")\n",
    "segment_metrics_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripid</th>\n",
       "      <th>dist_diff</th>\n",
       "      <th>speed_m_s</th>\n",
       "      <th>deviation_change_s</th>\n",
       "      <th>seg_compkey</th>\n",
       "      <th>speed_mph</th>\n",
       "      <th>pace_s_mi</th>\n",
       "      <th>pace_s_mi_95</th>\n",
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       "      <th>82566</th>\n",
       "      <td>46413193</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>-113</td>\n",
       "      <td>118.936180</td>\n",
       "      <td>231.936180</td>\n",
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       "      <td>-23</td>\n",
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       "      <td>91625</td>\n",
       "      <td>44.740</td>\n",
       "      <td>80.463470</td>\n",
       "      <td>149.751457</td>\n",
       "      <td>-19</td>\n",
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       "      <td>42.281557</td>\n",
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       "      <th>8743</th>\n",
       "      <td>49788127</td>\n",
       "      <td>65.716496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46</td>\n",
       "      <td>63158</td>\n",
       "      <td>2.237</td>\n",
       "      <td>1609.269392</td>\n",
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       "      <td>46</td>\n",
       "      <td>-9.857474</td>\n",
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       "      <td>328.554606</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>230.996897</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-6</td>\n",
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       "      <td>33.555</td>\n",
       "      <td>107.284626</td>\n",
       "      <td>229.895627</td>\n",
       "      <td>-6</td>\n",
       "      <td>17.599764</td>\n",
       "      <td>23.599764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2785439 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tripid   dist_diff  speed_m_s  deviation_change_s  seg_compkey  \\\n",
       "82566  46413193   54.892262        2.0                  23       134917   \n",
       "90921  46696265  339.872915       21.0                 -37        28133   \n",
       "21664  34746506  626.123666       11.0                 -38        26828   \n",
       "40169  40422206  356.739446       12.0                 -16        72897   \n",
       "30610  34746802  596.867228       13.0                -113        70859   \n",
       "...         ...         ...        ...                 ...          ...   \n",
       "10793  50119253  465.258416       11.0                 -23        56916   \n",
       "1527   49787927  540.732943       20.0                 -19        91625   \n",
       "8743   49788127   65.716496        1.0                  46        63158   \n",
       "8822   49788347  328.554606       11.0                 -94       103422   \n",
       "7719   49788173  230.996897       15.0                  -6       113216   \n",
       "\n",
       "       speed_mph    pace_s_mi  pace_s_mi_95  delay_stochastic  delay_total  \\\n",
       "82566      4.474   804.634696   1609.269392                23    27.446131   \n",
       "90921     46.977    76.631876    172.996460               -37    20.351914   \n",
       "21664     24.607   146.297217   1609.269392               -38   569.203333   \n",
       "40169     26.844   134.105783    750.992383               -16   136.750121   \n",
       "30610     29.081   123.789953    444.464880              -113   118.936180   \n",
       "...          ...          ...           ...               ...          ...   \n",
       "10793     24.607   146.297217    297.714837               -23    43.776587   \n",
       "1527      44.740    80.463470    149.751457               -19    23.281557   \n",
       "8743       2.237  1609.269392   1367.878983                46    -9.857474   \n",
       "8822      24.607   146.297217    670.528913               -94   107.029152   \n",
       "7719      33.555   107.284626    229.895627                -6    17.599764   \n",
       "\n",
       "       delay_systematic  \n",
       "82566          4.446131  \n",
       "90921         57.351914  \n",
       "21664        607.203333  \n",
       "40169        152.750121  \n",
       "30610        231.936180  \n",
       "...                 ...  \n",
       "10793         66.776587  \n",
       "1527          42.281557  \n",
       "8743         -55.857474  \n",
       "8822         201.029152  \n",
       "7719          23.599764  \n",
       "\n",
       "[2785439 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine samples to get 10% of overall dataset stored as individual delays\n",
    "sample_data = pd.concat(test_samples)\n",
    "\n",
    "# Save sample data for analysis in jupyter\n",
    "sample_data = sample_data[sample_data < np.inf]\n",
    "sample_data = sample_data[sample_data > -np.inf]\n",
    "sample_data.dropna(inplace=True)\n",
    "sample_data.to_csv(f\"{path}/../sample_metrics_data.csv\")\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions of Key Characteristics/Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data processed above\n",
    "sample_data = pd.read_csv(f\"{path}/../sample_metrics_data.csv\")\n",
    "segment_data = pd.read_csv(f\"{path}/../segment_metrics_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-19.0\n",
      "95.81883599202668\n",
      "71.91887391375076\n",
      "\n",
      "34.11991948297005\n",
      "185.610828046273\n",
      "169.57152344166067\n"
     ]
    }
   ],
   "source": [
    "print(np.median(sample_data['delay_stochastic']))\n",
    "print(np.median(sample_data['delay_systematic']))\n",
    "print(np.median(sample_data['delay_total']))\n",
    "print()\n",
    "print(np.std(sample_data['delay_stochastic']))\n",
    "print(np.std(sample_data['delay_systematic']))\n",
    "print(np.std(sample_data['delay_total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sample_data['delay_stochastic'].dropna()\n",
    "x = x[x < np.inf]\n",
    "x = x[x > -np.inf]\n",
    "sns.histplot(x.values, binwidth=1).set(xlabel='Delay (s)', xlim=(-120,120), title=f\"Stochastic Delay\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.histplot(segment_data['delay_stochastic']).set(xlabel='Delay (s)', title=f\"Segment Stochastic Delay\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = sample_data['delay_systematic'].dropna()\n",
    "y = y[y < np.inf]\n",
    "y = y[y > -np.inf]\n",
    "sns.histplot(y.values).set(xlabel='Delay (s)', xlim=(-120,600), title=f\"Systematic Delay\")\n",
    "# sns.displot(y.values, kind=\"kde\", bw_adjust=.1).set(xlabel='Delay (s)', xlim=(-120,1000), title=f\"Systematic Delay\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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.histplot(segment_data['delay_systematic']).set(xlabel='Delay (s)', title=f\"Segment Systematic Delay\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = sample_data['delay_total'].dropna()\n",
    "z = z[z < np.inf]\n",
    "z = z[z > -np.inf]\n",
    "sns.histplot(z.values[z!=0]).set(xlabel='Delay (s)', xlim=(-120,600), title=f\"Total Delay\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(segment_data['delay_total']).set(xlabel='Delay (s)', title=f\"Segment Total Delay\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}