preview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48f74e0d-c0fc-41b2-82f3-bbb0c2d5dda2",
   "metadata": {},
   "source": [
    "# Dataset\n",
    "\n",
    "## OBD2 Car diagnostic scanner\n",
    "\n",
    " * total_seconds (s)\n",
    " * speed (km/h)\n",
    " * distance (m)\n",
    "\n",
    "## Author\n",
    "\n",
    "Kalinin Alexandr\n",
    "\n",
    "Email: fantaziya84@rambler.ru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39cb97ac-7d71-4e5b-ae22-32193a13632b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11670aea-2353-44f0-a73c-a7d38f3ecd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"bmh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c04cc996-0be6-4851-b990-0aed95e7b984",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/obd2_car_diagnostic_scanner_2024_04_28.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3341609f-fd54-4331-9f82-0e75766ae2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = df[df[\"point\"] == 1].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57639d2d-e00a-4d0e-b0e3-6afcb2dc37b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "report[\"meter_per_second\"] = report[\"speed\"] / 3.6\n",
    "report[\"meter_per_minute\"] = report[\"meter_per_second\"] * 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c754f0f-d2af-419e-b91a-ae8a21b91cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_seconds</th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>meter_per_second</th>\n",
       "      <th>meter_per_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.635</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>3.606</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5.293</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.248</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>9.861</td>\n",
       "      <td>74</td>\n",
       "      <td>50</td>\n",
       "      <td>13.888889</td>\n",
       "      <td>833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>12.237</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>15.013</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>19.444444</td>\n",
       "      <td>1166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>18.050</td>\n",
       "      <td>223</td>\n",
       "      <td>80</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>1333.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_seconds  distance  speed  meter_per_second  meter_per_minute\n",
       "0            0.000         0      0          0.000000          0.000000\n",
       "24           1.635         2     10          2.777778        166.666667\n",
       "51           3.606        10     20          5.555556        333.333333\n",
       "74           5.293        22     30          8.333333        500.000000\n",
       "102          7.248        41     40         11.111111        666.666667\n",
       "138          9.861        74     50         13.888889        833.333333\n",
       "172         12.237       110     60         16.666667       1000.000000\n",
       "211         15.013       160     70         19.444444       1166.666667\n",
       "255         18.050       223     80         22.222222       1333.333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report[[\"total_seconds\", \"distance\", \"speed\", \"meter_per_second\", \"meter_per_minute\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e334ea0-acf3-4304-8099-e3ae327f0f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"total_seconds\", y=\"speed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78fce4c9-8440-44f3-942a-00e80eac2f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"total_seconds\", y=\"distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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