MergeF1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# predicting will the driver come on the podium or not ie in the top 3 given the feature from the the csv files from kaggle \n",
    "#Using algorithms like deep learing feed feedforward, backpropagation, random forests and feasture selection usign lasso .\n",
    "\n",
    "#I am trying to add the qualifying race time laps as the feature that their fastest lap there\n",
    "# But i am finding that the number of features are less even after adding that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qualifyId          int64\nraceId             int64\ndriverId           int64\nconstructorId      int64\nnumber             int64\nposition           int64\nq1                object\nq2                object\nq3                object\nfastest time      object\ntime             float64\ndtype: object\n   qualifyId  raceId  driverId  constructorId  number  position       q1  \\\n0          1      18         1              1      22         1  01:26.6   \n1          2      18         9              2       4         2  01:26.1   \n2          3      18         5              1      23         3  01:25.7   \n3          4      18        13              6       2         4  01:26.0   \n4          5      18         2              2       3         5  01:26.0   \n\n        q2       q3 fastest time     time  \n0  01:25.2  01:26.7      01:25.2  85200.0  \n1  01:25.3  01:26.9      01:25.3  85300.0  \n2  01:25.5  01:27.1      01:25.5  85500.0  \n3  01:25.7  01:27.2      01:25.7  85700.0  \n4  01:25.5  01:27.2      01:25.5  85500.0  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "main = pd.read_csv('Data/results.csv', encoding=\"ANSI\")\n",
    "circuits = pd.read_csv('Data/circuits.csv', encoding=\"ANSI\")\n",
    "\n",
    "constructors = pd.read_csv('Data/constructors.csv', encoding=\"ANSI\")\n",
    "\n",
    "drivers = pd.read_csv('Data/drivers.csv', encoding=\"ANSI\")\n",
    "qualifying = pd.read_csv('Data/qualifying.csv', encoding=\"ANSI\").dropna()\n",
    "races = pd.read_csv('Data/races.csv', encoding=\"ANSI\")\n",
    "\n",
    "main.drop('position', axis=1, inplace=True)\n",
    "main.drop('positionText', axis=1, inplace=True)\n",
    "main.drop('points', axis=1, inplace=True)\n",
    "main.drop('laps', axis=1, inplace=True)\n",
    "main.drop('time', axis=1, inplace=True)\n",
    "main.drop('milliseconds', axis=1, inplace=True)\n",
    "main.drop('fastestLap', axis=1, inplace=True)\n",
    "main.drop('rank', axis=1, inplace=True)\n",
    "main.drop('fastestLapTime', axis=1, inplace=True)\n",
    "main.drop('fastestLapSpeed', axis=1, inplace=True)\n",
    "main.drop('statusId', axis=1, inplace=True)\n",
    "\n",
    "drivers = drivers[['driverId','nationality']]\n",
    "races = races[['raceId','circuitId','year']]\n",
    "circuits = circuits[['circuitId','lat','lng']]\n",
    "main.dropna(axis=0, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "timemilliseconds = []\n",
    "time = qualifying['fastest time'].str.split(':')\n",
    "for item in time:\n",
    "     timemilliseconds.append((int(item[0])*60000 + float(item[1])*1000))\n",
    "\n",
    "qualifying['time'] = timemilliseconds\n",
    "print(qualifying.dtypes)\n",
    "print(qualifying.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultId           int64\nraceId             int64\ndriverId           int64\nconstructorId      int64\nnumber           float64\ngrid               int64\npositionOrder      int64\nnationality       object\ndtype: object\n"
     ]
    }
   ],
   "source": [
    "# merging drivers csv file\n",
    "main = pd.merge(main,drivers,on='driverId')\n",
    "main = pd.DataFrame(main)\n",
    "print(main.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultId           int64\nraceId             int64\ndriverId           int64\nconstructorId      int64\nnumber           float64\ngrid               int64\npositionOrder      int64\nnationality       object\ncircuitId          int64\nyear               int64\ndtype: object\n"
     ]
    }
   ],
   "source": [
    "# merging races.csv file\n",
    "main = pd.merge(main,races,on='raceId')\n",
    "main = pd.DataFrame(main)\n",
    "print(main.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultId           int64\nraceId             int64\ndriverId           int64\nconstructorId      int64\nnumber           float64\ngrid               int64\npositionOrder      int64\nnationality       object\ncircuitId          int64\nyear               int64\nlat              float64\nlng              float64\ndtype: object\n"
     ]
    }
   ],
   "source": [
    "# merging circuits.csv file\n",
    "main = pd.merge(main,circuits,on='circuitId')\n",
    "main = pd.DataFrame(main)\n",
    "print(main.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultId           int64\nraceId             int64\ndriverId           int64\nconstructorId      int64\nnumber           float64\ngrid               int64\npositionOrder      int64\nnationality       object\ncircuitId          int64\nyear               int64\nlat              float64\nlng              float64\npodium              bool\ndtype: object\n"
     ]
    }
   ],
   "source": [
    "pod = []\n",
    "for position in main['positionOrder']:\n",
    "        pod.append(position<4)\n",
    "\n",
    "main['podium'] = pod\n",
    "\n",
    "print(main.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   resultId  raceId  driverId  constructorId  number  grid  positionOrder  \\\n0         1      18         1              1    22.0     1              1   \n1         2      18         2              2     3.0     5              2   \n2         3      18         3              3     7.0     7              3   \n3         4      18         4              4     5.0    11              4   \n4         5      18         5              1    23.0     3              5   \n\n  nationality  circuitId  year     ...      Portuguese  Rhodesian  Russian  \\\n0     British          1  2008     ...               0          0        0   \n1      German          1  2008     ...               0          0        0   \n2      German          1  2008     ...               0          0        0   \n3     Spanish          1  2008     ...               0          0        0   \n4     Finnish          1  2008     ...               0          0        0   \n\n   South African  Spanish  Swedish  Swiss  Thai  Uruguayan  Venezuelan  \n0              0        0        0      0     0          0           0  \n1              0        0        0      0     0          0           0  \n2              0        0        0      0     0          0           0  \n3              0        1        0      0     0          0           0  \n4              0        0        0      0     0          0           0  \n\n[5 rows x 54 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#df = pd.concat([df, pd.get_dummies(df['YEAR'])], axis=1);\n",
    "main = pd.concat([main,pd.get_dummies(main['nationality'])],axis=1)\n",
    "print(main.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultId               int64\nraceId                 int64\ndriverId               int64\nconstructorId          int64\nnumber               float64\ngrid                   int64\npositionOrder          int64\nnationality           object\ncircuitId              int64\nyear                   int64\nlat                  float64\nlng                  float64\npodium                  bool\nAmerican               uint8\nAmerican-Italian       uint8\nArgentine              uint8\nArgentine-Italian      uint8\nAustralian             uint8\nAustrian               uint8\nBelgian                uint8\nBrazilian              uint8\nBritish                uint8\nCanadian               uint8\nChilean                uint8\nColombian              uint8\nCzech                  uint8\nDanish                 uint8\nDutch                  uint8\nEast German            uint8\nFinnish                uint8\nFrench                 uint8\nGerman                 uint8\nHungarian              uint8\nIndian                 uint8\nIndonesian             uint8\nIrish                  uint8\nItalian                uint8\nJapanese               uint8\nLiechtensteiner        uint8\nMalaysian              uint8\nMexican                uint8\nMonegasque             uint8\nNew Zealander          uint8\nPolish                 uint8\nPortuguese             uint8\nRhodesian              uint8\nRussian                uint8\nSouth African          uint8\nSpanish                uint8\nSwedish                uint8\nSwiss                  uint8\nThai                   uint8\nUruguayan              uint8\nVenezuelan             uint8\ndtype: object\n"
     ]
    }
   ],
   "source": [
    "print(main.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21c34117b38>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "main.dropna()\n",
    "\n",
    "X = main.drop(['resultId','raceId','driverId','constructorId','positionOrder','nationality','circuitId'], axis=1)\n",
    "Y = main['podium']\n",
    "\n",
    "X.boxplot(figsize=(40,20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23771\n"
     ]
    }
   ],
   "source": [
    "time = []\n",
    "for index, row in main.iterrows():\n",
    "    qualrow = qualifying[(qualifying.raceId == row.raceId) & (qualifying.driverId == row.driverId)]\n",
    "    if len(qualrow)<1:\n",
    "        time.append(1000000)\n",
    "    else:\n",
    "        time.append(float(qualrow.time))\n",
    "\n",
    "print(len(time))\n",
    "\n",
    "main['qualifying_time'] = time\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   resultId  raceId  driverId  constructorId  number  grid  positionOrder  \\\n0         1      18         1              1    22.0     1              1   \n1         2      18         2              2     3.0     5              2   \n2         3      18         3              3     7.0     7              3   \n3         4      18         4              4     5.0    11              4   \n4         5      18         5              1    23.0     3              5   \n\n  nationality  circuitId  year       ...         Rhodesian  Russian  \\\n0     British          1  2008       ...                 0        0   \n1      German          1  2008       ...                 0        0   \n2      German          1  2008       ...                 0        0   \n3     Spanish          1  2008       ...                 0        0   \n4     Finnish          1  2008       ...                 0        0   \n\n   South African  Spanish  Swedish  Swiss  Thai  Uruguayan  Venezuelan  \\\n0              0        0        0      0     0          0           0   \n1              0        0        0      0     0          0           0   \n2              0        0        0      0     0          0           0   \n3              0        1        0      0     0          0           0   \n4              0        0        0      0     0          0           0   \n\n   qualifying_time  \n0          85200.0  \n1          85500.0  \n2          86100.0  \n3        1000000.0  \n4          85500.0  \n\n[5 rows x 55 columns]\n"
     ]
    }
   ],
   "source": [
    "print(main.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   resultId  raceId  driverId  constructorId  number  grid  positionOrder  \\\n0         1      18         1              1    22.0     1              1   \n1         2      18         2              2     3.0     5              2   \n2         3      18         3              3     7.0     7              3   \n3         4      18         4              4     5.0    11              4   \n4         5      18         5              1    23.0     3              5   \n\n  nationality  circuitId  year       ...         Rhodesian  Russian  \\\n0     British          1  2008       ...                 0        0   \n1      German          1  2008       ...                 0        0   \n2      German          1  2008       ...                 0        0   \n3     Spanish          1  2008       ...                 0        0   \n4     Finnish          1  2008       ...                 0        0   \n\n   South African  Spanish  Swedish  Swiss  Thai  Uruguayan  Venezuelan  \\\n0              0        0        0      0     0          0           0   \n1              0        0        0      0     0          0           0   \n2              0        0        0      0     0          0           0   \n3              0        1        0      0     0          0           0   \n4              0        0        0      0     0          0           0   \n\n   qualifying_time  \n0          85200.0  \n1          85500.0  \n2          86100.0  \n3        1000000.0  \n4          85500.0  \n\n[5 rows x 55 columns]\n"
     ]
    }
   ],
   "source": [
    "main.sort_values(by=['year'])\n",
    "print(main.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "main.to_csv('final.csv',encoding='ANSI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86200.0\n"
     ]
    }
   ],
   "source": [
    "print(main.qualifying_time.iloc[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}