sns_box.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc9088bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2023-12-09 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c925e54",
   "metadata": {},
   "source": [
    "## Tilastollisia tunnuslukuja grafiikkana\n",
    "\n",
    "Tässä muistiossa käytän **seaborn**-funktioita **boxplot** ja **catplot**.\n",
    "\n",
    "**boxplot** havainnollistaa määrällisen muuttujan arvojen jakaumaa ja sillä on monta nimeä: box & whisker, ruutu- ja janakaavio, laatikko- ja viiksikaavio, tai laatikko- ja janakaavio. Kaavio esittää viiden luvun yhteenvedon:\n",
    "\n",
    "* pienin arvo\n",
    "* alaneljännes/alakvartiili (arvo, jota pienmpiä on neljäsosa arvoista)\n",
    "* mediaani (arvo, jota pienempiä on puolet arvoista)\n",
    "* yläneljännes/yläkvartiili (arvo, jota pienempiä on kolme neljäsosaa arvoista\n",
    "* suurin arvo\n",
    "\n",
    "Lisäksi kaaviossa esitetään muista arvoista selvästi poikkeavat arvot erillisinä pisteinä, jotta niihin osataan kiinnittää huomiota.\n",
    "\n",
    "Kaavio koostuu laatikosta, jonka reunat vastaavat alaneljännestä ja yläneljännestä. Laatikon sisään on merkitty viivalla mediaanin paikka. Laatikon päistä lähtee janat, joiden päätepisteet vastaavat pienintä ja suurinta arvoa paitsi silloin kun selvästi muista poikkeavia arvoja on merkitty erillisillä pisteillä. Oletuksena erillisinä pisteinä piirretään havainnot, jotka ovat yli 1,5 kvartiilivälin (yläneljännes-alaneljännes) päässä laatikon reunasta.\n",
    "\n",
    "Kaavioiden muotoiluun käytän **matplotlib.pyplot-funktioita**. Jos kaipaat lisää muotoiluja, niin lue https://matplotlib.org/stable/tutorials/introductory/pyplot.html\n",
    "\n",
    "Seabornin boxplotista löydät lisätietoa https://seaborn.pydata.org/generated/seaborn.boxplot.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7193ff8",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</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>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\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",
       "339  Gentoo     Biscoe             NaN            NaN                NaN   \n",
       "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
       "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
       "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
       "343  Gentoo     Biscoe            49.9           16.1              213.0   \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  \n",
       "..           ...     ...  \n",
       "339          NaN     NaN  \n",
       "340       4850.0  Female  \n",
       "341       5750.0    Male  \n",
       "342       5200.0  Female  \n",
       "343       5400.0    Male  \n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "penguins = sns.load_dataset('penguins')    # Tietoja pingviineistä\n",
    "penguins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3370946",
   "metadata": {},
   "source": [
    "## boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5d971bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pingviinien nokan pituus')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3, 2))\n",
    "# showmeans-parametrin ansiosta myös keskiarvo näytetään kaaviossa (kolmio)\n",
    "sns.boxplot(data=penguins, x='bill_length_mm', showmeans=True)\n",
    "\n",
    "plt.title('Pingviinien nokan pituus')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6acefae7",
   "metadata": {},
   "source": [
    "## Useamman muuttujan boxplot\n",
    "\n",
    "Voin esittää useamman muuttujan boxplotin antamalla muuttujat **data**-parametrin arvona."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cab98c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Nokan pituus ja leveys')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "sns.boxplot(data=penguins[['bill_length_mm', 'bill_depth_mm']])\n",
    "\n",
    "plt.title('Nokan pituus ja leveys')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71938527",
   "metadata": {},
   "source": [
    "## boxplot kategorisen muuttujan määräämissä ryhmissä"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf322dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='species', ylabel='bill_length_mm'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "# Kategorisena muuttujana species (alalaji)\n",
    "sns.boxplot(data=penguins, x='species', y='bill_length_mm', showmeans=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbfe3108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='bill_length_mm'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "# Kategorinen muuttuja voidaan sijoittaa myös hue-parametriksi\n",
    "sns.boxplot(data=penguins, x='sex', y='bill_length_mm', hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d1ff96",
   "metadata": {},
   "source": [
    "## catplot\n",
    "\n",
    "**catplot** mahdollistaa määrällisen muuttujan boxplotin tarkastelun kategoristen muuttujien määrittämissä ryhmissä. **kind**-parametrin arvo **'box'** määrittää että kaaviolajina on **boxplot**.\n",
    "\n",
    "Seuraavassa tarkastelen pingviinien nokan pituutta alalajin (**species**) määrittämissä ryhmissä (edellisessä solussa sama tarkastelu oli toisella tavalla toteutettuna).\n",
    "\n",
    "Lisätietoa https://seaborn.pydata.org/generated/seaborn.catplot.html\n",
    "\n",
    "**catplot** tuottaa **Facetgrid**-luokan olion, jolla on omat funktionsa otsikointien ym. ominaisuuksien muotoiluun.\n",
    "Lisätietoa https://seaborn.pydata.org/generated/seaborn.FacetGrid.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0fb271a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26a3c4de350>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=penguins, y='bill_length_mm', x='species', col='sex', kind='box', height=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bf0f41",
   "metadata": {},
   "source": [
    "## N-arvot!\n",
    "\n",
    "Tilastollisten tunnuslukujen laskennassa mukana olleiden arvojen lukumäärä täytyy aina tunnuslukuja käytettäessä laskea ja tunnuslukuja raportoitaessa n-arvojen täytyy olla näkyvillä. Pienten n-arvojen kohdalla tunnuslukuihin täytyy suhtautua varoen.\n",
    "\n",
    "N-arvot voin selvittää **pandas**-funktiolla **count**. Seuraavassa muutama esimerkki."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dadbbc33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species              344\n",
       "island               344\n",
       "bill_length_mm       342\n",
       "bill_depth_mm        342\n",
       "flipper_length_mm    342\n",
       "body_mass_g          342\n",
       "sex                  333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yksittäisten muuttujien n-arvot\n",
    "penguins.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1b5de03",
   "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>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",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adelie</th>\n",
       "      <td>152</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinstrap</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gentoo</th>\n",
       "      <td>124</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "species                                                               \n",
       "Adelie        152             151            151                151   \n",
       "Chinstrap      68              68             68                 68   \n",
       "Gentoo        124             123            123                123   \n",
       "\n",
       "           body_mass_g  sex  \n",
       "species                      \n",
       "Adelie             151  146  \n",
       "Chinstrap           68   68  \n",
       "Gentoo             123  119  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muuttujien n-arvot alalajin mukaan\n",
    "penguins.groupby('species').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e151732",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Adelie</th>\n",
       "      <th>Female</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Chinstrap</th>\n",
       "      <th>Female</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Gentoo</th>\n",
       "      <th>Female</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "species   sex                                                                \n",
       "Adelie    Female      73              73             73                 73   \n",
       "          Male        73              73             73                 73   \n",
       "Chinstrap Female      34              34             34                 34   \n",
       "          Male        34              34             34                 34   \n",
       "Gentoo    Female      58              58             58                 58   \n",
       "          Male        61              61             61                 61   \n",
       "\n",
       "                  body_mass_g  \n",
       "species   sex                  \n",
       "Adelie    Female           73  \n",
       "          Male             73  \n",
       "Chinstrap Female           34  \n",
       "          Male             34  \n",
       "Gentoo    Female           58  \n",
       "          Male             61  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muuttujien n-arvot alalajin ja sukupuolen mukaan\n",
    "penguins.groupby(['species', 'sex']).count()"
   ]
  }
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