{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forelesning 3: Statistisk analyse\n",
    "\n",
    "I denne forelesningen skal vi se på statistisk analyse og hvordan vi kan bruke data til å lage modeller.\n",
    "- gjøre statistiske operasjoner på data (pandas og numpy)\n",
    "- tolke statistiske størrelser og visualiseringer (som boksplott)\n",
    "- gjøre og tolke regresjon (numpy)\n",
    "\n",
    "<img src=\"https://www.uio.no/studier/emner/matnat/ifi/IN-KJM1900/h21/diverse/pingviner.png\" width=\"500\"/>\n",
    "<img src=\"https://www.uio.no/studier/emner/matnat/ifi/IN-KJM1900/h21/diverse/pingvin.png\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    MALE  \n",
       "1       3800.0  FEMALE  \n",
       "2       3250.0  FEMALE  \n",
       "3          NaN     NaN  \n",
       "4       3450.0  FEMALE  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pingviner = pd.read_csv(\"https://www.uio.no/studier/emner/matnat/ifi/IN-KJM1900/h21/datafiler/penguings.txt\")\n",
    "pingviner.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "luffelengde = pingviner[\"flipper_length_mm\"]\n",
    "gjennomsnitt = luffelengde.mean()\n",
    "median = luffelengde.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.921930</td>\n",
       "      <td>17.151170</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.459584</td>\n",
       "      <td>1.974793</td>\n",
       "      <td>14.061714</td>\n",
       "      <td>801.954536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.100000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.225000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.450000</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>4050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>4750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.600000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>6300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "count      342.000000     342.000000         342.000000   342.000000\n",
       "mean        43.921930      17.151170         200.915205  4201.754386\n",
       "std          5.459584       1.974793          14.061714   801.954536\n",
       "min         32.100000      13.100000         172.000000  2700.000000\n",
       "25%         39.225000      15.600000         190.000000  3550.000000\n",
       "50%         44.450000      17.300000         197.000000  4050.000000\n",
       "75%         48.500000      18.700000         213.000000  4750.000000\n",
       "max         59.600000      21.500000         231.000000  6300.000000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pingviner.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.407885953173359"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kons = [2,3,4,4,5,2,3,6]\n",
    "\n",
    "snitt = np.mean(kons)\n",
    "avvik = np.std(kons, ddof = 1)\n",
    "# pandas: snitt = kons.mean()\n",
    "avvik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.407885953173359"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kons2 = pd.Series(kons)\n",
    "kons2.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fil = \"https://www.uio.no/studier/emner/matnat/ifi/IN-KJM1900/h20/datafiler/vin.csv\"\n",
    "\n",
    "vin = pd.read_csv(fil)\n",
    "vin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pH-en er: 3.31 +- 0.15\n"
     ]
    }
   ],
   "source": [
    "pH = vin[\"pH\"]\n",
    "snitt_pH = pH.mean()\n",
    "avvik_pH = pH.std()\n",
    "\n",
    "print(f\"pH-en er: {snitt_pH:.2f} +- {avvik_pH:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Luffelengde: 197.0 +- 23.0 mm\n"
     ]
    }
   ],
   "source": [
    "Q1, Q2, Q3 = luffelengde.quantile([0.25, 0.50, 0.75])\n",
    "IQR = Q3 - Q1\n",
    "print(f\"Luffelengde: {Q2} +- {IQR} mm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='quality', ylabel='alcohol'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = vin, x = \"quality\", y = \"alcohol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kons = [2,3,4,4,5,2,3,6,9]\n",
    "sns.boxplot(y = kons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresjon\n",
    "\n",
    "|     Temperatur (°C)     |     NH$_3$, løselighet     (g/100 mL H$_2$O)     |     NaCl, Løselighet      (g/100 mL H$_2$O)     |\n",
    "|-------------------------|--------------------------------------------|----------------------------------------------|\n",
    "|     0                   |     88.5                                   |     35.7                                     |\n",
    "|     20                  |     56.0                                   |     35.9                                     |\n",
    "|     40                  |     34.0                                   |     36.4                                     |\n",
    "|     60                  |     20.0                                   |     37.1                                     |\n",
    "|     80                  |     11.0                                   |     38.0                                     |\n",
    "|     100                 |     7.0                                    |     39.2                                     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# La oss gjøre en regresjon\n",
    "T = [0, 20, 40, 60, 80, 100]\n",
    "sol_NaCl = [35.7, 35.9, 36.4, 37.1, 38, 39.2]\n",
    "\n",
    "a, b = np.polyfit(T, sol_NaCl, 1)\n",
    "\n",
    "x = np.linspace(0,120,1000)\n",
    "y = a*x + b\n",
    "\n",
    "plt.scatter(T, sol_NaCl, color = \"firebrick\", label = \"Datapunkter\")\n",
    "plt.plot(x, y, color = \"navy\", label = \"Regresjonskurve\")\n",
    "plt.xlabel(\"Temperatur (C)\")\n",
    "plt.ylabel(\"Løselighet (g/100 mL vann)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menti: https://www.menti.com/2sikjeo4ga"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit 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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}