Ex2.boxplot.ipynb

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   "source": [
    "<img src=\"https://www.th-koeln.de/img/logo.svg\" style=\"float:right;\" width=\"200\">"
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  {
   "cell_type": "markdown",
   "id": "3efc66f5",
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   "source": [
    "# 2nd exercise: <font color=\"#C70039\">Work with Boxplots for anomaly detection</font>\n",
    "* Course: AML\n",
    "* Lecturer: <a href=\"https://www.gernotheisenberg.de/\">Gernot Heisenberg</a>\n",
    "* Author of notebook: Finn Heydemann\n",
    "* Date:   28.10.2023\n",
    "\n",
    "<img src=\"https://r-graph-gallery.com/img/other/boxplot_explanation.png\" style=\"float: center;\" width=\"800\">\n",
    "<img src=\"https://i2.wp.com/upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Boxplot_vs_PDF.svg/550px-Boxplot_vs_PDF.svg.png?w=578\" style=\"float: center;\" width=\"800\">\n",
    "\n",
    "\n",
    "---------------------------------\n",
    "**GENERAL NOTE 1**: \n",
    "Please make sure you are reading the entire notebook, since it contains a lot of information on your tasks (e.g. regarding the set of certain paramaters or a specific computational trick), and the written mark downs as well as comments contain a lot of information on how things work together as a whole. \n",
    "\n",
    "**GENERAL NOTE 2**: \n",
    "* Please, when commenting source code, just use English language only. \n",
    "* When describing an observation please use English language, too\n",
    "* This applies to all exercises throughout this course.  \n",
    "\n",
    "---------------------\n",
    "\n",
    "### <font color=\"ce33ff\">DESCRIPTION</font>:\n",
    "This notebook allows you for using boxplots to detect anomalies. Try to interpret the boxplot. \n",
    "\n",
    "-------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "### <font color=\"FFC300\">TASKS</font>:\n",
    "The tasks that you need to work on within this notebook are always indicated below as bullet points. \n",
    "If a task is more challenging and consists of several steps, this is indicated as well. \n",
    "Make sure you have worked down the task list and commented your doings. \n",
    "This should be done by using markdown.<br> \n",
    "<font color=red>Make sure you don't forget to specify your name and your matriculation number in the notebook.</font>\n",
    "\n",
    "**YOUR TASKS in this exercise are as follows**:\n",
    "1. import the notebook to Google Colab or use your local machine.\n",
    "2. make sure you specified you name and your matriculation number in the header below my name and date. \n",
    "    * set the date too and remove mine.\n",
    "3. read the entire notebook carefully \n",
    "    * add comments whereever you feel it necessary for better understanding\n",
    "    * run the notebook for the first time. \n",
    "4. take the three data sets from exercize 1 and create a boxplot for each of them\n",
    "5. interpret the visual results and describe them\n",
    "6. how can you test your data for being normally distributed? \n",
    "-----------------------------------------------------------------------------------"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9e37b8a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "np.random.seed(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "259ca2cf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# multiply and add by random numbers to get some real values\n",
    "# randn generates samples from the normal distribution (important - see below)\n",
    "random_data = np.random.randn(50000)  * 20 + 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f60ff6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=random_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97808fcb-a007-4693-b706-f05f894db843",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-35.8617    ,  80.61714225, -35.65068935,  99.17205408,\n",
       "       -35.81992813,  86.42157512, -33.96723483, -41.07528761,\n",
       "        78.34617526,  76.54599588,  74.23899748, -43.067149  ,\n",
       "       -40.3206397 ,  88.65326864, -35.82888076,  76.87877139,\n",
       "       -41.28282712,  94.80497807,  74.58924271,  77.88772383,\n",
       "        82.7009468 ,  84.76686394, -34.78283478, -37.44100501,\n",
       "       -34.20787481,  75.74722896,  74.12249844,  77.87185104,\n",
       "        74.84310423,  76.95564235,  75.54105986, -36.03124433,\n",
       "        76.38174189, -45.0606847 ,  74.34772486, -34.39876182,\n",
       "       -38.28997835,  77.08848163,  74.61690622,  82.64059456,\n",
       "        80.25509136,  76.43577761, -36.89875188, -39.69672273,\n",
       "       -43.81232694, -45.21230192, -33.97837966, -37.02139085,\n",
       "       -34.16678629, -38.50610072,  88.08604551,  79.51578577,\n",
       "       -34.21033755,  77.81616357,  82.36959182,  85.97081047,\n",
       "        82.9796805 , -36.04264086,  78.5366674 , -34.46354787,\n",
       "       -45.89716813, -34.07986795, -41.11266481, -35.21399504,\n",
       "       100.53698089, -44.38264211,  77.15467882,  76.09101895,\n",
       "        82.23783662, -37.73388132,  74.20425715,  92.26554014,\n",
       "       -36.14464871,  87.96231313,  91.21746641, -53.12880199,\n",
       "        75.97638813, -45.64157593,  80.09264253, -37.29267907,\n",
       "        91.21225295, -35.98303297, -43.46923275, -34.08442185,\n",
       "        82.13498657,  82.90737927, -39.86982454, -46.21685124,\n",
       "       -37.15055461,  74.14251134, -41.95653104, -37.58465058,\n",
       "       -36.7891546 , -45.60655194,  75.08540296,  77.38927174,\n",
       "       -38.56479496,  76.871437  ,  76.78295427,  74.63852025,\n",
       "        80.52436989, -35.59292026,  76.12359139, -39.67330888,\n",
       "        74.09865159, -43.30420249,  79.51049288,  74.26327018,\n",
       "        77.15907871, -42.25263324,  81.59215546,  74.12707741,\n",
       "        77.04719248, -39.68480773,  75.29533039, 103.36235356,\n",
       "        74.02110979,  96.68762042, -40.58687956, -48.7185162 ,\n",
       "       -45.18412137,  81.54158108, -35.09207617,  74.80784467,\n",
       "       -34.72710031, -37.36091861, -37.37803455, -37.08108277,\n",
       "        74.5619158 ,  75.40605486, -39.31399767,  84.55872241,\n",
       "        79.82213434,  77.93003549, -38.64509283, -40.76885172,\n",
       "       -44.08128901,  76.86311843, -42.04329787,  87.71430312,\n",
       "        76.78575604,  88.93912032,  74.72061321, -40.52113315,\n",
       "       -49.02805813, -34.27289958,  86.72050503, -35.4277613 ,\n",
       "       -44.45227837, -58.55028132,  76.51837469, -36.45328824,\n",
       "        87.83885146, -41.81006578, -39.34934736,  75.11321611,\n",
       "        74.45219054,  75.52708945,  75.2745851 , -42.38237155,\n",
       "        91.19937116,  74.29202493,  75.88551312,  93.19531684,\n",
       "       -36.10682579, -41.01728295,  82.94710559,  77.20168456,\n",
       "       -36.92257349, -36.40929274, -37.4039325 ,  86.84102578,\n",
       "        79.74073867, -47.35894572, -35.7353855 , -47.27471768,\n",
       "        77.31043419,  73.82331644, -37.58905227, -44.0528396 ,\n",
       "        77.68210664,  74.35283883,  81.39224117, -64.66329595,\n",
       "       -41.91924536,  76.66955617,  74.13141131,  77.33477115,\n",
       "        86.98293402, -42.78324538, -43.44070545,  83.14254869,\n",
       "       -38.79374341,  76.87828875,  79.26711716,  78.70519455,\n",
       "        80.29722046,  80.97664482, -34.05891632,  79.4440025 ,\n",
       "        79.87234235, -34.78093295,  76.57433111, -37.25081912,\n",
       "       -49.1353069 , -39.30115858,  90.95359817,  85.75049141,\n",
       "        77.99813217,  90.63353727, -52.25624571, -35.14250257,\n",
       "        78.2720295 , -48.42727398,  73.99698456,  92.45148786,\n",
       "       -40.60136533,  77.52783527, -34.38975118, -34.28407191,\n",
       "       -34.66404324,  74.67513853,  79.31507012,  90.66197938,\n",
       "       -40.65200033,  78.05468898,  76.10665342, -39.7258654 ,\n",
       "        85.47272253, -40.59878231, -37.6879442 , -34.50634921,\n",
       "       -53.95783717, -38.31172548, -35.15621329, -40.80036485,\n",
       "        82.47403923, -38.52266653,  82.69560614, -37.56320898,\n",
       "        73.90157419,  77.9433286 , -46.18148127,  86.99084876,\n",
       "        79.03591658,  77.76137077, -35.31903536, -38.68031117,\n",
       "        80.14572971,  82.24721889,  74.49607437,  74.99869464,\n",
       "       -45.83227596,  84.17626694, -36.25476452, -34.63844819,\n",
       "       -36.98596119, -38.7804372 ,  75.30982429, -39.45102341,\n",
       "        84.41938097,  74.4208264 ,  74.26219913,  75.47032111,\n",
       "       -48.12080166, -47.66016306, -37.67850883,  83.03299707,\n",
       "        78.18464751,  75.34171294, -36.98426248, -46.34572896,\n",
       "       -43.94127801,  76.23971068, -34.08308692, -44.88889348,\n",
       "        75.36273255,  82.73490952,  83.57335544, -36.62496027,\n",
       "        73.85844051, -33.93766999,  75.72339019, -39.66076026,\n",
       "        80.0340636 ,  78.89243874, -39.88268155,  80.77274131,\n",
       "       -37.25289797,  73.89505015, -52.50413207,  75.01576701,\n",
       "       -36.86932899, -49.69745467,  77.11012485,  75.7783091 ,\n",
       "        77.98008641,  80.22051939, -61.76977168, -35.99473753,\n",
       "       -41.57467214, -37.01407673, -37.31450019,  76.44535736,\n",
       "       -35.34890278,  74.55129507, -58.8628464 , -51.19738386,\n",
       "        78.76972805,  75.60342332, -34.408634  , -56.11928396,\n",
       "       -36.16808082, -48.85042291, -38.6027919 , -52.64159592,\n",
       "       -34.58919861,  79.42685188,  74.4909021 , -40.85801593,\n",
       "        90.00512514, -35.85217866, -44.15080376,  81.07575084,\n",
       "        74.43680601,  88.42082611, -48.97515467, -41.87410602,\n",
       "        81.91272478,  84.69303765, -40.93984642, -35.22769246,\n",
       "       -52.61561504, -34.72573462, -49.39834453,  84.48319182,\n",
       "       -36.15276168,  79.96406554,  78.51670098,  80.41393381,\n",
       "       -34.54292521,  89.04319371, -35.02476011,  83.11545349,\n",
       "        75.84397277, -35.34727853,  86.3948526 ,  74.15549321,\n",
       "        75.43073823, -54.34260433, -53.88408955, -60.83360878,\n",
       "        75.7614544 , -38.16842492, -45.50295147, -38.81306884,\n",
       "       -35.08762697,  74.04412014, -42.8411709 ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_outliers(data): \n",
    "    q25, q75 = np.quantile(data, 0.25), np.quantile(data, 0.75)\n",
    "    iqr = q75 - q25\n",
    "    u_bound, l_bound = q75 + 1.5 * iqr, q25 - 1.5 * iqr\n",
    "    return data[np.logical_or(data < l_bound, data > u_bound)]\n",
    "\n",
    "get_outliers(random_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c5e4246-b721-42a1-9cc6-3bcacb264281",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "data = pd.read_csv(\"data/exercise_1/StudentsPerformance.csv\")[\"math score\"]\n",
    "sns.boxplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f9d2b60-a751-4b35-9e20-bbc21ebf4de0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17     18\n",
       "59      0\n",
       "145    22\n",
       "338    24\n",
       "466    26\n",
       "787    19\n",
       "842    23\n",
       "980     8\n",
       "Name: math score, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_outliers(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a27393a-b21a-478e-9e3e-9c996cd2aa2f",
   "metadata": {},
   "source": [
    "As can be seen 8 students are worse the then lower limit which makes them outliers. These are the same students as in exercise one plus 5 more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "225f7baa-1a0a-420e-aa24-12500520e4dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_63548/3201813435.py:1: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = pd.read_csv(\"data/exercise_1/city_temperature.csv\")\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/exercise_1/city_temperature.csv\")\n",
    "data.drop(data[data[\"AvgTemperature\"] == -99].index, inplace=True)\n",
    "data[\"AvgTemperature\"] = (data[\"AvgTemperature\"] - 32) * 5/9 \n",
    "data[\"date\"] = pd.to_datetime(data[[\"Year\", \"Month\", \"Day\"]])\n",
    "data = data.set_index(\"date\").groupby([\"City\", pd.Grouper(freq=\"D\")])[\"AvgTemperature\"].mean()\n",
    "data = data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b7bd29de-a4ee-4592-b8a2-a8b9b24dcfc5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[data[\"City\"] == \"Brasilia\"][\"AvgTemperature\"].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0c74fca4-08b7-42f8-8c2d-3bba0ff89e50",
   "metadata": {
    "tags": []
   },
   "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>City</th>\n",
       "      <th>date</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405901</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>1995-06-08</td>\n",
       "      <td>15.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406288</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>1996-06-29</td>\n",
       "      <td>13.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406289</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>14.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406608</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>1997-05-15</td>\n",
       "      <td>16.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406632</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>1997-06-08</td>\n",
       "      <td>15.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414742</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>2019-10-20</td>\n",
       "      <td>28.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414754</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>27.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414764</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>27.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414765</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>2019-11-12</td>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414766</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            City       date  AvgTemperature\n",
       "405901  Brasilia 1995-06-08       15.722222\n",
       "406288  Brasilia 1996-06-29       13.666667\n",
       "406289  Brasilia 1996-06-30       14.333333\n",
       "406608  Brasilia 1997-05-15       16.111111\n",
       "406632  Brasilia 1997-06-08       15.944444\n",
       "...          ...        ...             ...\n",
       "414742  Brasilia 2019-10-20       28.555556\n",
       "414754  Brasilia 2019-11-01       27.944444\n",
       "414764  Brasilia 2019-11-11       27.611111\n",
       "414765  Brasilia 2019-11-12       28.500000\n",
       "414766  Brasilia 2019-11-13       27.777778\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = get_outliers(data[data[\"City\"] == \"Brasilia\"][\"AvgTemperature\"]).index\n",
    "data.loc[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a8908fae-4764-45c6-bd7f-6c9903b8bf2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGdCAYAAADT1TPdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXiklEQVR4nO3df2xV9f348dcVx23R0mmqLYSqbOKiIboNFlKcA5fQ2RkyMTH6ISGQqNEhM4QsJoxkVhPo4g/CIpHMZFH2B9N/5rZEa2iyDTWoAaLfbZqZuLC1GXREJG3HaKtwvn/wtR8ryOQ7L+dVfDySk/See3p9ka65z73vOT2VoiiKAABI6pyyBwAAOBWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqZ1b9gD/rWPHjsW+ffuioaEhKpVK2eMAAJ9CURQxNDQU06dPj3POOfXayYSPlX379kVra2vZYwAA/x/6+vpixowZpzxmwsdKQ0NDRBz/x06dOrXkaQCAT2NwcDBaW1vH3sdPZcLHyocf/UydOlWsAMAE82lO4XCCLQCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGoT/t5A8FkqiiKGh4fLHoM4/rMYGRmJiIhqtfqp7h9C7dXV1flZcMaJFfiI4eHh6OjoKHsMSKu7uzvq6+vLHoPPGR8DAQCpWVmBj6irq4vu7u6yxyCOr3ItWbIkIiKeffbZqKurK3kiIsLPgVKIFfiISqViiTuhuro6Pxf4HPMxEACQmlgBAFITKwBAamIFAEhNrAAAqdU0VrZs2RJXX311TJ06NaZOnRptbW3jLgstiiI6Oztj+vTpUV9fHwsXLow333yzliMBABNMTWNlxowZ8ZOf/CR2794du3fvjm9/+9vxve99byxIHnroodi4cWNs3rw5du3aFS0tLbFo0aIYGhqq5VgAwARS01hZvHhxfPe7340rrrgirrjiili/fn2cf/758eqrr0ZRFLFp06ZYt25d3HzzzTF79uzYunVr/Pvf/45t27bVciwAYAI5Y+esHD16NJ5++uk4fPhwtLW1xd69e6O/vz/a29vHjqlWq7FgwYLYuXPnmRoLAEiu5n/B9k9/+lO0tbXF8PBwnH/++fHss8/GVVddNRYkzc3N445vbm6Ov//975/4eiMjI2N3Yo2IGBwcrM3gAEAKNV9Z+cpXvhJvvPFGvPrqq/H9738/li9fHm+99dbY8x+/1XhRFKe8/XhXV1c0NjaOba2trTWbHQAoX81jZfLkyXH55ZfH3Llzo6urK6655pr46U9/Gi0tLRER0d/fP+74AwcOnLDa8lFr166NgYGBsa2vr6+m8wMA5Trjf2elKIoYGRmJmTNnRktLS/T09Iw9Nzo6Gjt27Ij58+d/4vdXq9WxS6E/3ACAs1dNz1n50Y9+FB0dHdHa2hpDQ0Px9NNPxx/+8Id44YUXolKpxOrVq2PDhg0xa9asmDVrVmzYsCGmTJkSS5cureVYAMAEUtNY+ec//xnLli2L/fv3R2NjY1x99dXxwgsvxKJFiyIi4r777osjR47EypUr49ChQzFv3rzYvn17NDQ01HIsAGACqRRFUZQ9xH9jcHAwGhsbY2BgwEdCcBY5cuRIdHR0REREd3d31NfXlzwR8Fk6nfdv9wYCAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQ2rllD0BEURQxPDxc9hiQykd/J/x+wInq6uqiUqmUPcYZIVYSGB4ejo6OjrLHgLSWLFlS9giQTnd3d9TX15c9xhnhYyAAIDUrK8n866v/E8U5fiwQRRFx7IPjX59zbsTnZLkbTqVy7IM4/41flj3GGeddMZninHMjJn2h7DEgicllDwCpFGUPUBIfAwEAqdU0Vrq6uuIb3/hGNDQ0xMUXXxw33XRTvP322+OOKYoiOjs7Y/r06VFfXx8LFy6MN998s5ZjAQATSE1jZceOHXHPPffEq6++Gj09PfHBBx9Ee3t7HD58eOyYhx56KDZu3BibN2+OXbt2RUtLSyxatCiGhoZqORoAMEHU9JyVF154YdzjJ598Mi6++OLYs2dPfOtb34qiKGLTpk2xbt26uPnmmyMiYuvWrdHc3Bzbtm2Lu+66q5bjAQATwBk9Z2VgYCAiIi688MKIiNi7d2/09/dHe3v72DHVajUWLFgQO3fuPOlrjIyMxODg4LgNADh7nbFYKYoi1qxZE9/85jdj9uzZERHR398fERHNzc3jjm1ubh577uO6urqisbFxbGttba3t4ABAqc5YrKxatSr++Mc/xi9/eeL14R//c8FFUXzinxBeu3ZtDAwMjG19fX01mRcAyOGM/J2VH/zgB/Hb3/42XnzxxZgxY8bY/paWlog4vsIybdq0sf0HDhw4YbXlQ9VqNarVam0HBgDSqOnKSlEUsWrVqvjVr34Vv/vd72LmzJnjnp85c2a0tLRET0/P2L7R0dHYsWNHzJ8/v5ajAQATRE1XVu65557Ytm1b/OY3v4mGhoax81AaGxujvr4+KpVKrF69OjZs2BCzZs2KWbNmxYYNG2LKlCmxdOnSWo4GAEwQNY2VLVu2RETEwoULx+1/8sknY8WKFRERcd9998WRI0di5cqVcejQoZg3b15s3749GhoaajkaADBB1DRWiuI/38WgUqlEZ2dndHZ21nIUAGCCcm8gACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASO3csgcgoiiK/31w9P3yBgEgt4+8R4x77zjLiZUERkZGxr5u+D9PlzgJABPFyMhITJkypewxzggfAwEAqVlZSaBarY59PXTNbRGTvlDiNACkdfT9sRX4j753nO3ESgKVSuV/H0z6glgB4D8a995xlvMxEACQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkFpNY+XFF1+MxYsXx/Tp06NSqcSvf/3rcc8XRRGdnZ0xffr0qK+vj4ULF8abb75Zy5EAgAmmprFy+PDhuOaaa2Lz5s0nff6hhx6KjRs3xubNm2PXrl3R0tISixYtiqGhoVqOBQBMIDW9N1BHR0d0dHSc9LmiKGLTpk2xbt26uPnmmyMiYuvWrdHc3Bzbtm2Lu+66q5ajAQATRGnnrOzduzf6+/ujvb19bF+1Wo0FCxbEzp07P/H7RkZGYnBwcNwGAJy9SouV/v7+iIhobm4et7+5uXnsuZPp6uqKxsbGsa21tbWmcwIA5Sr9aqCP3+K6KIpT3vZ67dq1MTAwMLb19fXVekQAoEQ1PWflVFpaWiLi+ArLtGnTxvYfOHDghNWWj6pWq1GtVms+HwCQQ2krKzNnzoyWlpbo6ekZ2zc6Oho7duyI+fPnlzUWAJBMTVdW/vWvf8U777wz9njv3r3xxhtvxIUXXhiXXHJJrF69OjZs2BCzZs2KWbNmxYYNG2LKlCmxdOnSWo4FAEwgNY2V3bt3x/XXXz/2eM2aNRERsXz58njqqafivvvuiyNHjsTKlSvj0KFDMW/evNi+fXs0NDTUciwAYAKpaawsXLgwiqL4xOcrlUp0dnZGZ2dnLccAACaw0q8GAgA4FbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmdW/YAjFc59kEUZQ8BGRRFxLEPjn99zrkRlUq580AClQ9/Jz5nxEoy57/xy7JHAIBUfAwEAKRmZSWBurq66O7uLnsMSGV4eDiWLFkSERHPPvts1NXVlTwR5PJ5+p0QKwlUKpWor68vewxIq66uzu8IfI6l+Bjo8ccfj5kzZ0ZdXV3MmTMnXnrppbJHAgCSKD1WnnnmmVi9enWsW7cuXn/99bjuuuuio6Mjent7yx4NAEig9FjZuHFj3H777XHHHXfElVdeGZs2bYrW1tbYsmVL2aMBAAmUGiujo6OxZ8+eaG9vH7e/vb09du7cedLvGRkZicHBwXEbAHD2KjVW3n333Th69Gg0NzeP29/c3Bz9/f0n/Z6urq5obGwc21pbW8/EqABASUr/GCji+NUwH1UUxQn7PrR27doYGBgY2/r6+s7EiABASUq9dLmpqSkmTZp0wirKgQMHTlht+VC1Wo1qtXomxgMAEih1ZWXy5MkxZ86c6OnpGbe/p6cn5s+fX9JUAEAmpf9RuDVr1sSyZcti7ty50dbWFk888UT09vbG3XffXfZoAEACpcfKrbfeGgcPHowHH3ww9u/fH7Nnz47nn38+Lr300rJHAwASKD1WIiJWrlwZK1euLHsMACChFFcDAQB8ErECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqNY2V9evXx/z582PKlCnxxS9+8aTH9Pb2xuLFi+O8886LpqamuPfee2N0dLSWYwEAE8i5tXzx0dHRuOWWW6KtrS1+/vOfn/D80aNH48Ybb4yLLrooXn755Th48GAsX748iqKIxx57rJajAQATRE1j5YEHHoiIiKeeeuqkz2/fvj3eeuut6Ovri+nTp0dExKOPPhorVqyI9evXx9SpU2s5HgAwAZR6zsorr7wSs2fPHguViIjvfOc7MTIyEnv27Dnp94yMjMTg4OC4DQA4e5UaK/39/dHc3Dxu3wUXXBCTJ0+O/v7+k35PV1dXNDY2jm2tra1nYlQAoCSnHSudnZ1RqVROue3evftTv16lUjlhX1EUJ90fEbF27doYGBgY2/r6+k73nwAATCCnfc7KqlWr4rbbbjvlMZdddtmneq2WlpZ47bXXxu07dOhQvP/++yesuHyoWq1GtVr9VK8PAEx8px0rTU1N0dTU9Jn8x9va2mL9+vWxf//+mDZtWkQcP+m2Wq3GnDlzPpP/BgAwsdX0aqDe3t547733ore3N44ePRpvvPFGRERcfvnlcf7550d7e3tcddVVsWzZsnj44Yfjvffeix/+8Idx5513uhIIAIiIGsfKj3/849i6devY46997WsREfH73/8+Fi5cGJMmTYrnnnsuVq5cGddee23U19fH0qVL45FHHqnlWADABFIpiqIoe4j/xuDgYDQ2NsbAwIDVGDiLHDlyJDo6OiIioru7O+rr60ueCPgsnc77t3sDAQCpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEhNrAAAqYkVACA1sQIApCZWAIDUxAoAkJpYAQBSEysAQGpiBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQWs1i5W9/+1vcfvvtMXPmzKivr48vf/nLcf/998fo6Oi443p7e2Px4sVx3nnnRVNTU9x7770nHAMAfH6dW6sX/stf/hLHjh2Ln/3sZ3H55ZfHn//857jzzjvj8OHD8cgjj0RExNGjR+PGG2+Miy66KF5++eU4ePBgLF++PIqiiMcee6xWowEAE0jNYuWGG26IG264Yezxl770pXj77bdjy5YtY7Gyffv2eOutt6Kvry+mT58eERGPPvporFixItavXx9Tp06t1XgAwARxRs9ZGRgYiAsvvHDs8SuvvBKzZ88eC5WIiO985zsxMjISe/bsOelrjIyMxODg4LgNADh71Wxl5eP++te/xmOPPRaPPvro2L7+/v5obm4ed9wFF1wQkydPjv7+/pO+TldXVzzwwAM1nZXPr6IoYnh4uOwxiBj3c/AzyaOuri4qlUrZY/A5c9qx0tnZ+R9jYdeuXTF37tyxx/v27YsbbrghbrnllrjjjjvGHXuy/9EXRfGJvwxr166NNWvWjD0eHByM1tbW0/knwCcaHh6Ojo6OssfgY5YsWVL2CPw/3d3dUV9fX/YYfM6cdqysWrUqbrvttlMec9lll419vW/fvrj++uujra0tnnjiiXHHtbS0xGuvvTZu36FDh+L9998/YcXlQ9VqNarV6umODQBMUKcdK01NTdHU1PSpjv3HP/4R119/fcyZMyeefPLJOOec8afItLW1xfr162P//v0xbdq0iDh+0m21Wo05c+ac7mjwX6urq4vu7u6yxyCOr7COjIxExPH/k+Kjhxzq6urKHoHPoUpRFEUtXnjfvn2xYMGCuOSSS+IXv/hFTJo0aey5lpaWiDh+6fJXv/rVaG5ujocffjjee++9WLFiRdx0002f+tLlwcHBaGxsjIGBAVcPAcAEcTrv3zU7wXb79u3xzjvvxDvvvBMzZswY99yHfTRp0qR47rnnYuXKlXHttddGfX19LF26dOzSZgCAmq2snClWVgBg4jmd92/3BgIAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCkJlYAgNTECgCQmlgBAFITKwBAamIFAEitZjcyPFM+vLXR4OBgyZMAAJ/Wh+/bn+YWhRM+VoaGhiIiorW1teRJAIDTNTQ0FI2Njac8ZsLfdfnYsWOxb9++aGhoiEqlUvY4wGdocHAwWltbo6+vz13V4SxTFEUMDQ3F9OnT45xzTn1WyoSPFeDsdTq3kAfOXk6wBQBSEysAQGpiBUirWq3G/fffH9VqtexRgBI5ZwUASM3KCgCQmlgBAFITKwBAamIFAEhNrABpPf744zFz5syoq6uLOXPmxEsvvVT2SEAJxAqQ0jPPPBOrV6+OdevWxeuvvx7XXXdddHR0RG9vb9mjAWeYS5eBlObNmxdf//rXY8uWLWP7rrzyyrjpppuiq6urxMmAM83KCpDO6Oho7NmzJ9rb28ftb29vj507d5Y0FVAWsQKk8+6778bRo0ejubl53P7m5ubo7+8vaSqgLGIFSKtSqYx7XBTFCfuAs59YAdJpamqKSZMmnbCKcuDAgRNWW4Czn1gB0pk8eXLMmTMnenp6xu3v6emJ+fPnlzQVUJZzyx4A4GTWrFkTy5Yti7lz50ZbW1s88cQT0dvbG3fffXfZowFnmFgBUrr11lvj4MGD8eCDD8b+/ftj9uzZ8fzzz8ell15a9mjAGebvrAAAqTlnBQBITawAAKmJFQAgNbECAKQmVgCA1MQKAJCaWAEAUhMrAEBqYgUASE2sAACpiRUAIDWxAgCk9n8BYLtakORfj0YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[data[\"City\"] == \"Pyongyang\"][\"AvgTemperature\"].to_numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "338f9fd3-4f28-492b-ab8e-229d27cd6d60",
   "metadata": {},
   "source": [
    "Again no outliers in Pyongyang because the data is not normally distributed. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4fb247-2a9d-4631-be5c-f3141cbf7f03",
   "metadata": {},
   "source": [
    "## Check for Normal Distribution: Draw Histogram and visually check if normal curve is close\n",
    "\n",
    "Other statistical methods such as Kolmogorov-Smirnov, Shapiro-Wilk lead to hard facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3485a7e-2a3c-4173-9cf4-d22313a0d79d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "p_data = data[data[\"City\"] == \"Brasilia\"][\"AvgTemperature\"]\n",
    "xmin, xmax = p_data.min() - 2, p_data.max() + 2\n",
    "mu, std = p_data.mean(), p_data.std()\n",
    "weights = np.ones_like(p_data)/float(len(p_data))\n",
    "x = np.linspace(xmin, xmax, 100) \n",
    "p = norm.pdf(x, mu, std) \n",
    "plt.hist(p_data, bins=20, weights=weights)\n",
    "plt.plot(x, p)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be9c793a-9d2d-419e-814f-a6e435164d75",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb3701af-70b5-4636-81c6-e4ab570f32a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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