python_tut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "19e6c794",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'DataFrame' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-5a66d4f9bdf6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\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[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m(\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[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns   \n",
    "\n",
    "df = pd.read_csv(\"./data/Kursmaterialien/data/Diamonds/diamonds.csv.bz2\") \n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bde09eb2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carat', ylabel='price'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zkZjVKs9bsWqVVyKJiJdL9P7zSNc2Mi6XdCHQDnw1IrYCE0nXODI6krLdyXbPcjMzK6N8+0iKTtJBwF8BP02KbgDeB8wCNgDfzxya4/ToozzXe82X1C6pvbOzs5Cwzcysh4olEuBTwOMRsREgIjZGxN6ISAE3ke4TgXRNY3LWeZOA15LySTnK9xMRN0ZEW0S0tbS0FPljmJk1tkomkgvIataSNCFr37nA08n2fcA8SQdLOgqYBjwWERuAHZJOTUZrXQjcW57QzcwsI9/O9qKSNAz4OPDFrOL/LWkW6eapdZl9EbFK0mLgGWAPcFlE7E3OuRS4FRhKupPdHe1mZmWW1/DfeuLhv2Zm/VeMtbbMzMxyciIxM7OCOJGYmVlBnEjMzKwgTiRmZlYQJxIzMyuIE4mZmRXEicTMzAriRGJmZgVxIjEzs4I4kZiZWUGcSMzMrCAVWf3XrFZlnpu+cXsX40f5uelm4ERilrdUKrh/1etcuXglXbtTNA9pYuH5s5gz43AnE2tobtoyy9O6Lbu6kwhA1+4UVy5eybotuyocmVllOZGY5Wnj9q7uJJLRtTvFph1dJX3fVCpY27mTZS9uZm3nTlKpxnqGkFU/N22Z5Wn8qGaahzTtk0yahzQxbmRzyd7TzWlWC1wjMctT65jhLDx/Fs1D0v9tMr/UW8cML9l7ujnNaoFrJGZ5amoSc2YcznELZrNpRxfjRpZ+1FZfzWlTW0aU7H3N+sOJxKwfmprE1JYRZfslXonmNLP+ctOWWRWrRHOaWX9VpEYiaR2wA9gL7ImINkmHAXcDrcA64PyI2Jocfw1wcXL8goj4dVJ+MnArMBRYAlwRER7SYhVXrImLlWhOM+uvSjZtfTQiNmf9fDXwYER8V9LVyc9XSZoOzANmAEcAv5V0TETsBW4A5gPLSSeSOcDScn4Iq18DTQa9jbT6xPHjWb/1rX5fr9zNaWb9VU19JGcDpyfbtwG/B65Kyu+KiHeAlyStAU5JajWjImIZgKTbgXNwIrEiKGTYba6RVtfdv5rde1Ncdc+THsZrdadSfSQB/EbSCknzk7LxEbEBIPl3XFI+EXgl69yOpGxist2z3KxghQy7zTXS6qyZE7uTSH+vZ1btKpVITouIk4BPAZdJ+kgfx+b6cy36KN//AtJ8Se2S2js7O/sfrTWcQmaxZ0ZaZRvUREVmxZuVQ0USSUS8lvy7CfgFcAqwUdIEgOTfTcnhHcDkrNMnAa8l5ZNylOd6vxsjoi0i2lpaWor5UaxO5UoG+Q67zTXS6i+PPGzA1zOrdmVPJJKGSxqZ2QY+ATwN3AdclBx2EXBvsn0fME/SwZKOAqYBjyXNXzsknSpJwIVZ55gVpJBht5mRVksWzOau+R9gyYLZfHDqGA/jtbqlco+WlTSVdC0E0p39P4mIf5A0BlgMTAHWA38dEW8k5/w98LfAHuDLEbE0KW/jveG/S4EvHWj4b1tbW7S3txf9c1n92bMnxaoN29iwrYsJo5uZMWE0gwcP/G+vzCgwD+O1WiRpRUS05dzXaNMunEgsHwMdteUHX1m96iuReGa7WQ4DGbWVST5zFz3CBTc9ytxFj3D/qte97LvVPScSsxwGMmrLK/Vao3IiMcthIKO2KvXgK7NKcyIxy2Ego7YKGTJsVsuqaYkUs6IoRof3QBZLzCSfnh30HuJr9c6jtqyuVPrRtB7ia/XKo7asYVS6wzuzUu+pU8cytWWEk4g1BCcSqyvu8DYrP/eRWEmVe4KeH01rVn6ukVjJDGSCXioVrO3cybIXN7O2c2e/J/P50bRm5efOdiuZtZ07mbvokf1qB0sWzM75tL9M4rnu/tWcNXMig5rgL488jA9OHdOvNa6qocPbS6VYvemrs91NW1YyffVX5Eok67bs4rr7V/O5tikseuiF7lFX1312Jp+eeUTev4gr/WjaSo8cMys3N21ZyfR3gt7G7V2cNXNidxKBdOK56p4na2qZkUqPHDMrNycSK5n+9FekUsGwgwbXxZMEPXLMGo2btqxk8p0dnt03cvWc42t+1JVHjlmjcY3ESiqfCXqZpqCXt7zNDb9fw38/a3pNj7ryyDFrNK6RWMVlNwU9+ep2Oh9aw8UfnsrMiaOYNn5kzY14Gsg6XWa1zDUSq7ienfIbtnVx87+tZdr4kTW7zIiXSrFG4kTSwAqd/Fesa7kpyKy2uWmrQRVzrkOh13JTkFltc42kQRVzrkMxruWmILPaVfZEImmypN9JWi1plaQrkvJvSXpV0srkNTfrnGskrZH0nKRPZpWfLOmpZN8iSf7tk6diznUY6LWK2bRmZpVTiaatPcBXI+JxSSOBFZIeSPZdHxH/mH2wpOnAPGAGcATwW0nHRMRe4AZgPrAcWALMAZaW6XPUtGLOdRjItbyMiFn9KHuNJCI2RMTjyfYOYDUwsY9Tzgbuioh3IuIlYA1wiqQJwKiIWBbplSdvB84pbfT1o5gd3Lmu9Z1zT6BJ9FrL8DIiZvWjop3tklqBvwAeBU4DLpd0IdBOutaylXSSWZ51WkdStjvZ7llueShmB3fmWsd+aTarX9/O8xt38L1fP8fWt97ttZbR3wUdzax6VayzXdII4B7gyxGxnXQz1fuAWcAG4PuZQ3OcHn2U53qv+ZLaJbV3dnYWGnrdKGYHd1OTkOBrP32CRQ+uYcO2rj5rGf1d0NHMqldFEomkIaSTyI8j4ucAEbExIvZGRAq4CTglObwDmJx1+iTgtaR8Uo7y/UTEjRHRFhFtLS0txf0w1q0/ne6eO2JWP8retJWMrLoZWB0RC7PKJ0TEhuTHc4Gnk+37gJ9IWki6s30a8FhE7JW0Q9KppJvGLgR+UK7PYfsbP6qZI8cM5ayZE8mMn/vXJ17NWcvw3BGz+lGJPpLTgC8AT0lamZR9HbhA0izSzVPrgC8CRMQqSYuBZ0iP+LosGbEFcClwKzCU9Ggtj9iqoCmHDuNLZ0zjG798unsk1v885/1MOXRYzuMr/QAqMysOP2rXiqa/j9Y1s9rR16N2PbO9wZRyEqAf6GTWmLzWVgMp9SRAP9DJrDG5RlLjsmsYL27aybrNvdc2Sj0J0COxzBqTayQ1LFcN45tnTeeux9bz/Kad+9U2Sj0J0COxzBqTayQ1LFcN49u/eoZLTj+aQ4cdtF9toxyTAL2Kr1njcSKpYb3VMJ59fTufOWnSfh3dbnoys1Jw01YN661ze28KRjYP3q+24aYnMysF10iqUL5DdFvHDOd/nXvCPjWMBWdM41dPvsr0CSNz1jbc9GRmxeYaSZXpzxDdpiYxduQQFp4/i2df387eFNzdvp7PtU3h3b0pP9vDzMrCNZIq098huqOaD+a6+1ezNwUSnDVzIne3r6dlxMFOImZWFq6RVEgqFazbsouN27sYP+q9vor+DtGdMWFUzvWtZkwYXa6PYmYNzomkAvpqvurv7PDBg5s458SJTBs3gte3dXH46GZmTBjN4MGubJpZeTiRFKhnzWLKocNYv/Wt/Woa2XprvjpuwWymHDqM7503kxc27SQVMEhw9LgRva6gC+lkcuLkQzlxcq+HmJmVjBPJAOzZk2LVhm1s2vEOww8ezNMdb5ICIuCIQ4ay4c23uW3Zy70+arav5qsmwYZtXdz48Np9Zqs/u3E70yeMdr+HmVUdJ5I8ZNc6Dh/VzIr1W/fpk/jKx45h2JAmvrP02e6yBWdM447lL3fXNLL7N/pqvtq4/R0WPvD8frPV539kKuvfeNsjscys6rgh/QAy/RlzFz3CBTc9yi9WvtqdRCD9i/763z7P5l3v7lO26KEXumeXv7Hrne55IU+88ibb3n6X6z47M+cM813v7slZW0kFRV1g0cysWFwjOYCe/RmpoNdf9D3LJDhyzFBefbOL/3zzY/vUVh569nVu/EIbQwZpn76UIw8bnrO2ElHcBRbNzIrFNZIDyNWfkWvhw+EHDdqvrEnwP84+gavueXK/2soHprYw/452xo9q3meG+VFj918Pa8EZ0/j54x1+toeZVSXXSA6gZ3/GPSs6uOLMafzTgy901zCuOHMax4wf0X1c85AmvnPuCZw05RA2bMvdsS7lrmFk1sM69kuzWf36dp7fuIM7lr/Xce8FFs2s2jiRHEBmxdxM89bWt95l2JBBXP7Ro+nakyICbl/2Ml/9xDFc/tGjeXdvijOPG8cJEw+hqUmkgl6bqnqrYTQ1ifeNG8FRY4czfcIoPvS+MV5g0cyqlhPJAfRcMXfokEEsuOvPvLzl7e5jmoc08dLmt7j539ay8PxZ3UkE9k9Emaaqu9vXH7CGkVlg0X0iZlbNFJF7ZdlaIWkO8E/AIOBfIuK7fR3f1tYW7e3tA36/XLPSr/vsTCYe0sxhww/OWWvIHj487KBB7N6b6vVYM7NqJGlFRLTl3FfLiUTSIOB54ONAB/An4IKIeKa3cwpNJPBeYvAzPcysUfSVSGq9aesUYE1ErAWQdBdwNtBrIikGNzmZmb2n1of/TgReyfq5IykzM7MyqfVEkqs9ab+2OknzJbVLau/s7CxDWGZmjaPWE0kHkL3m7STgtZ4HRcSNEdEWEW0tLS1lC87MrBHUeiL5EzBN0lGSDgLmAfdVOCYzs4ZS053tEbFH0uXAr0kP/70lIlZVOCwzs4ZS08N/B0JSJ/ByHoeOBTaXOJxiqqV4aylWqK14HWvp1FK8pYj1yIjI2TfQcIkkX5LaexszXY1qKd5aihVqK17HWjq1FG+5Y631PhIzM6swJxIzMyuIE0nvbqx0AP1US/HWUqxQW/E61tKppXjLGqv7SMzMrCCukZiZWUEaMpFImiPpOUlrJF2dY//nJT2ZvP4o6cSsfeskPSVppaTClhEuTqynS9qWxLNS0jfzPbcCsf63rDiflrRX0mHJvnJ/r7dI2iTp6V72S9Ki5LM8KemkrH1l/V7zjLea7tkDxVo192ye8VbTfTtZ0u8krZa0StIVOY4p/70bEQ31Ij1x8UVgKnAQ8AQwvccxHwIOTbY/BTyatW8dMLaKYj0d+NVAzi13rD2O/zTwUCW+1+T9PgKcBDzdy/65wFLS67mdmrkHyv299iPeqrhn84y1Ku7ZfOPtcWyl79sJwEnJ9kjSj9Ho+Tuh7PduI9ZIupeej4h3gczS890i4o8RsTX5cTnpNbwq4YCxlujcgejv+10A3FnCePoUEQ8Db/RxyNnA7ZG2HDhE0gTK/73mFW8V3bP5fLe9qcrvtodK37cbIuLxZHsHsJr9Vzwv+73biImkv0vPX0w6u2cE8BtJKyTNL0F82fKN9YOSnpC0VNKMfp5bLHm/n6RhwBzgnqzicn6v+ejt89TCowsqec/mqxru2X6ptvtWUivwF8CjPXaV/d6t6bW2BiivpecBJH2U9H/KD2cVnxYRr0kaBzwg6dnkL5pSyCfWx0kvXbBT0lzgl8C0PM8tpv6836eBf4+I7L8Cy/m95qO3z1Pu77VfquCezUe13LP9VTX3raQRpBPalyNie8/dOU4p6b3biDWSvJaelzQT+Bfg7IjYkimPiNeSfzcBvyBdXaxYrBGxPSJ2JttLgCGSxuZzbrljzTKPHs0DZf5e89Hb5yn395q3KrlnD6iK7tn+qor7VtIQ0knkxxHx8xyHlP/eLVcnUbW8SNfC1gJH8V6H04wex0wB1gAf6lE+HBiZtf1HYE6FYz2c9+YDnQKsJ/2XxwHPLXesyXGjSbdHD6/U95r1vq303iH8H9m3w/Kx/nzOCsRbFfdsnrFWxT2bb7zVdN8m39PtwP/p45iy37sN17QVvSw9L+mSZP8/A98ExgA/kgSwJ9ILoI0HfpGUDQZ+EhH3VzjW84BLJe0B3gbmRfquKesS+3nGCnAu8JuI2JV1elm/VwBJd5IePTRWUgdwLTAkK9YlpEe/rAHeAv6mr89ZyljzjLcq7tk8Y62Ke7Yf8UKV3LfAacAXgKckrUzKvk76D4mK3bue2W5mZgVpxD4SMzMrIicSMzMriBOJmZkVxInEzMwK4kRiZmYFcSIxqyGSWiX9p0rHYZbNicSsykjqa35XK+BEYlXF80jMSkjShcDXSK9p9CSwGPgG6ZnFW4DPR8RGSd8CjiCdKDaTnmR2B+kZ0wCXR8QfJS0HjgdeAm6LiOvL92nMcnMiMSuRZFXbn5Ne2G9z8jCkAN6MiJD0X4DjI+KrSSL5NPDhiHg7WWk2FRFdkqYBd0ZEm6TTga9FxFkV+VBmOTTcEilmZXQG8LOI2AwQEW9IOgG4O3k+xEGkaxYZ90XE28n2EOCHkmYBe4Fjyhe2Wf+4j8SsdMT+y3T/APhhRJwAfBFoztqXvY7TV4CNwIlAG+mkY1aVnEjMSudB4HxJYwCSpq3RwKvJ/ov6OHc0sCEiUqQX6RuUlO8g/YhVs6rhRGJWIsnKqv8A/EHSE8BC4FvATyU9QrpTvTc/Ai5KOteP4b3aypOkV8l9QtJXSha8WT+4s93MzAriGomZmRXEicTMzAriRGJmZgVxIjEzs4I4kZiZWUGcSMzMrCBOJGZmVhAnEjMzK8j/B4C6FuAe/MWdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = \"carat\", y = \"price\", data = df.sample(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d31b2a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.23]\n",
      " [0.21]\n",
      " [0.23]\n",
      " ...\n",
      " [0.7 ]\n",
      " [0.86]\n",
      " [0.75]]\n"
     ]
    }
   ],
   "source": [
    "xs = df['carat'].to_numpy().reshape(-1, 1)\n",
    "print(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76403b95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(53940,)\n"
     ]
    }
   ],
   "source": [
    "ys = df[\"price\"].to_numpy()\n",
    "print(ys.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c6c30ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7756.42561797]\n",
      "-2256.3605800453947\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(xs, ys)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9471af24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75307.8955996546\n"
     ]
    }
   ],
   "source": [
    "def get_price(carat):\n",
    "    return 7756.42561797 * carat - 2256.3605800453947\n",
    "\n",
    "print(get_price(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10f0ad4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carat', ylabel='price'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x_pred = np.array([3, 0])\n",
    "y_pred = model.predict(x_pred.reshape(-1,1))\n",
    "\n",
    "ax = sns.lineplot(x = x_pred,y = y_pred, color=\"red\")\n",
    "sns.scatterplot(x = \"carat\", y = \"price\", data = df.sample(50), ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eda7e196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5500.06503792, 36525.7675098 ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([1,5]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "beba37ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05d3568c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10125.98767084 -1026.85685415]\n",
      "1737.9497379839036\n"
     ]
    }
   ],
   "source": [
    "df['x']\n",
    "\n",
    "x_train = df[[\"carat\", \"x\"]]\n",
    "y_train = df['price']\n",
    "\n",
    "\n",
    "model2.fit(x_train, y_train)\n",
    "\n",
    "print(model2.coef_)\n",
    "print(model2.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f00fcef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred = np.array([\n",
    "    [0.5, 5]\n",
    "    [0.25, 3]\n",
    "])"
   ]
  }
 ],
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