sns_hist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5505d2e3",
   "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": "99e63c07",
   "metadata": {},
   "source": [
    "# Määrällisen muuttujan luokiteltu jakauma\n",
    "\n",
    "Tässä muistiossa käytän **seaborn**-funktioita **histplot** ja **displot**.\n",
    "\n",
    "Kaavioiden muotoiluun käytän **matplotlib.pyplot**-funktioita. Jos kaipaat lisää muotoiluja, niin lue https://matplotlib.org/stable/tutorials/introductory/pyplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cf278ec",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</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>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 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",
    "tips = sns.load_dataset('tips') # Esimerkkidata ravintolaseurueiden tipeistä\n",
    "tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226fd4d7",
   "metadata": {},
   "source": [
    "## Luokiteltu jakauma\n",
    "\n",
    "**histplot** esittää määrällisen muuttujan luokitellun jakauman histogrammina.\n",
    "\n",
    "Lisätietoa https://seaborn.pydata.org/generated/seaborn.histplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb3f7f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='total_bill', ylabel='Count'>"
      ]
     },
     "execution_count": 3,
     "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",
    "# bins-parametrilla voin määrittää luokkien lukumäärän\n",
    "sns.histplot(data=tips, x='total_bill', bins=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17b4ccd",
   "metadata": {},
   "source": [
    "**bins**-parametrilla voin määrittää luokkien lukumäärien sijasta myös täsmälliset luokkarajat listana. Luokkarajoja tulkitaan seuraavasti:\n",
    "* Luokkien alarajat sisältyvät luokkaan, mutta ylärajat eivät.\n",
    "* Poikkeuksena viimeisen luokan yläraja sisältyy luokkaan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9dbf5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'lukumäärä')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QqVS2i6kREZH0iC78jRs3Ii0tDY899pjsbjlHRKRkogu/qakJSUlJLHsiIpkR3dpjx47FBx984IwsRETkRKL38EeMGIHnnnsOb7zxBrp06WI37/Dhww4LRkREjiW68FeuXInHH38cUVFRsr0rPBGREoku/J9++gmvvfaaM7IQ3XXc3NxcHYHIRvQx/CFDhuDEiRPOyEJ0g646N1itgizfTWo0GoTwTnAkIaL38Hv27Ilnn30WQ4YMga+vr928tWvXOiwYEQB4e2ihVqvw5mEDymsaXR1HlJ4+OswbHgiLxeLqKEQAOlD4DQ0NePzxx52RheiWymsa8X1VvatjEMma6MLnXjwRkTyJLvzNmzffct7cuXPvKEyrmpoarFmzBkeOHIHVasUjjzyCVatW4b777kNJSQleffVVlJWVwdfXF7Nnz8bkyZMdsl4ioruZ6MIvLCy0e1xTU4Nz58459DDPvHnz0LVrVxw6dAhqtRpLly7FihUrsH79etvlmRMTE1FUVITU1FQEBwdj4MCBDls/EdHdSHThv/feezdM27t37w2/CDrq9OnTKCkpwbFjx6DX6wEAGRkZuHLlCvLz8+Hj44OkpCQAQFRUFOLj45Gbm8vCJyJqQ4cuj/xL48aNw5o1axzxVDh16hQCAgKwc+dOfPDBB2hsbMSvf/1rLF68GAaDAUFBQXbLBwQEIC8vT/R6xJ450bq8HM+4sFqtLac1CgIEwdVpRLIFlm92ud0NTs7bulKzt3eMQwr/yy+/hJeXlyOeCrW1tTh79iwGDBiAPXv2wGQyYdGiRVi8eDG6desGnU5nt7ynpycaGhpEr6e0tLRD+To6zpV0Oh1CQkLQ0NgIo9Ho6jiimK56t3w1XZVd9gYvFQDAYDCgsVFep5QC8tzWWzH7zYku/NjYWKhUKttjs9mMyspKzJ492yGB3N3dAQDLly+Hh4cH9Ho9FixYgClTpmDixIkwmUx2y5tMJnh7e4teT2hoqKgP81gsFpSWlooeJwWte5heOh30enntJnt6eLZ89fSwHeKTC69rOyeBgYGyurqsnLd1pWZvHdsW0YU/b948u8dqtRoPPvggBgwYIPapbiogIABWqxVmsxkeHh4A/l1YDz30EP785z/bLV9WVobAwEDR69FoNB3aIDo6ThJUKlz3u1oebIHlm12tVstym5Hzts7sNyd6tyMmJgYTJkyw/Tdu3DgMGDAAO3fudEigRx99FL169cKyZctQX1+P6upqbNiwASNGjMCYMWNQWVmJnJwcmM1mFBQUYN++fZg0aZJD1k1EdDcTXfjPPvssamtrbY8rKyuRkpKCrKwshwRyc3PDe++9B41Gg1GjRmHUqFHo3r071qxZA19fX2RnZ+PAgQMYMmQI0tPTkZ6ejsjISIesm4jobib6kM7AgQPx3HPP4U9/+hOOHDmC1atXIzg4GH/9618dFur+++/Hhg0bbjovNDQUO3bscNi6iIiUQnThZ2ZmYunSpXj88cdRX1+Pl19+2XZePBERSVeHTh1Ys2YNoqKiMGDAAEydOtXRmYiIyAnavYffr18/u9MxhWsfKrn+7Jxvv/3WgdGIiMiR2l347777rjNzEBGRk7W78AcPHuzMHERE5GR3/Enb6x0+fPiOAxERkXPc8Sdtq6ursXv3bl6TnohI4kQX/oQJE26YFhcXh9/97ndITk52SCgiInI8h1zRqWfPnvj+++8d8VREROQkovfwi4qK7B6bzWYcOHAA//Ef/+GwUERE5HiiC3/atGl2j1uvlrly5UqHhSIiIsdrd+FfvnwZwM3PxLnVWTtERCQd7S78252OKQgCVCoVP2lLRCRh7S58nmNPRCRv7S78nj17OjMHERE5mXxutElERHeEhU9EpBAsfCIihWDhExEpBAufiEghWPhERArBwiciUggWPhGRQrDwiYgUQrKFb7FYMG3aNCxZssQ2raSkBJMnT0Z4eDhiY2Oxa9cuFyYkIpIXyRb+5s2bUVxcbHtcW1uLlJQUjB8/HkVFRcjMzMTatWtx6tQpF6YkIpIPSRb+8ePHkZ+fj5EjR9qm5efnw8fHB0lJSdBqtYiKikJ8fDxyc3NdmJSISD5E3wDF2aqqqrB8+XJs2bIFOTk5tukGgwFBQUF2ywYEBCAvL69D67FYLB1aXuw4KbBardBoNIAgQBBcnUYkW2D5ZrdarS4OIo6ct3WlZm/vGEkVvtVqRVpaGpKTk9GvXz+7efX19dDpdHbTPD090dDQ0KF1lZaWduo4V9LpdAgJCUFDYyOMRqOr44hiuurd8tV0VXbZG7xa7h9hMBjQ2Njo4jTiyXFbb8XsNyepwt+6dSvc3d1vuI0i0FJadXV1dtNMJhO8vb07tK7Q0NCWvd52slgsKC0tFT1OClr3ML10Ouj18tpN9vTwbPnq6QG9Xu/iNOJ4XdtBCQwMhFotyaOnNyXnbV2p2VvHtkVShb9371789NNPiIiIANBS6ADw97//HYsWLcIXX3xht3xZWRkCAwM7tC6NRtOhDaKj4yRBpYLs7kZpCyzf7Gq1WpbbjJy3dWa/OUntdhw4cABff/01iouLUVxcjDFjxmDMmDEoLi5GXFwcKisrkZOTA7PZjIKCAuzbtw+TJk1ydWwiIlmQVOHfjq+vL7Kzs3HgwAEMGTIE6enpSE9PR2RkpKujERHJgqQO6fxSVlaW3ePQ0FDs2LHDRWmIiORNNnv4RER0Z1j4REQKwcInIlIIFj4RkUKw8ImIFIKFT0SkECx8IiKFYOETESkEC5+ISCFY+ERECsHCJyJSCBY+EZFCsPCJiBSChU9EpBAsfCIihWDhExEpBAufiEghWPhERArBwiciUggWPhGRQrDwiYgUgoVPRKQQLHwiIoVg4RPRDdzc3FwdgZxAkoV/5swZJCcnY/DgwYiOjsaiRYtQXV0NACgpKcHkyZMRHh6O2NhY7Nq1y8VpiW6uq84NVqsAjUbj6iiiaDQahPTv7+oY5ARaVwf4JZPJhOeffx5TpkzB1q1bUV9fj8WLF2PZsmVYt24dUlJSMH/+fCQmJqKoqAipqakIDg7GwIEDXR2dyI63hxZqtQpvHjagvKbR1XHaraePDvOGB8Jisbg6CjmY5Ar/8uXL6NevH1JTU6HRaODu7o7ExEQsWrQI+fn58PHxQVJSEgAgKioK8fHxyM3NZeGTZJXXNOL7qnpXxyCSXuH37dsX27dvt5t28OBB9O/fHwaDAUFBQXbzAgICkJeXJ3o9YvdeWpeX416P1WptOawgCBAEV6cRyRaY2TvNtbBWq9XFQcST88/pnWRv7xjJFf71BEHAxo0b8emnn+L999/Hu+++C51OZ7eMp6cnGhoaRD93aWlphzJ1dJwr6XQ6hISEoKGxEUaj0dVxRDFd9W75arrK7J2kwUsFADAYDGhslM+hqOvJ8ee0lTOzS7bwjUYjli5dim+++Qbvv/8+goODodPpUFdXZ7ecyWSCt7e36OcPDQ0V9cc0i8WC0tJS0eOkoHVPzUung14vp11NwNPDs+Wrpwf0er2L04gj1+xe13aqAgMDoVZL8ryOW5Lzz+mdZG8d2xZJFv6FCxcwc+ZM9OjRA3l5efDz8wMABAUF4YsvvrBbtqysDIGBgaLXodFoOrRBdHScJKhUUKlcHUIkW2Bm7zTXwqrVatlu63L+OXVmdsn9+q6trcUzzzyDhx9+GO+8846t7AEgLi4OlZWVyMnJgdlsRkFBAfbt24dJkya5MDERkTxIbg//ww8/xOXLl/HJJ5/gwIEDdvNOnDiB7OxsZGZmYtOmTfDz80N6ejoiIyM7JRs/jEJEcia5wk9OTkZycvIt54eGhmLHjh2dmOjfQvr3l+XbRDlmJiLHk1zhS5lWo5Hdh2gAIKyXD6YO7u3qGETkYix8keT4IZoePrq2FyKiu57k/mhLRETOwcInIlIIFj4RkUKw8ImIFIKFT0SkECx8IiKFYOETESkEC5+ISCFY+ER0V/nlPTPkxNnX6+InbYnIjlxvvg5cuwF7SIirY3RIZ9w8noVPRHbkevP1Vg0NDfDy8nJ1DNE64+bxLHwiuik5XjdKEFrulqfXC/K66Uwn4TF8IiKFYOETESkEC5+ISCFY+ERECsHCJyJSCBY+EZFCsPCJiBSChU9EpBAsfCIihWDhExEphCwLv6qqCnPmzEFERASGDBmCzMxMNDc3uzoWEZGkybLwFyxYAC8vL3z++efIy8vD8ePHkZOT4+pYRESSJrvC/+GHH/Dll18iLS0NOp0OvXr1wpw5c5Cbm+vqaEREkia7q2UaDAb4+Pjg/vvvt0178MEHcfnyZfzf//0f7rnnntuOFwQBANDU1CTqet9WqxUA0NvHA1qV0IHkrtO9ixYWi4XZO5lcs8s1NwBAENCoV0Hn6Qm5XS6zR1cPWCwWmM1m0ZdIbl2+td9uRXaFX19ff8MdbVofNzQ0tFn4rcX9j3/8o0PrH+IDwKdDQ12oGidPVjN7p5NrdrnmbuUGwHnXlHceI06ePHlHz9Dab7ciu8L38vJCY6P9TRlaH3t7e7c5XqvVIjQ0FGq1GiqZ7QEQEd2MIAiwWq3Qam9f6bIr/MDAQNTU1KCyshLdunUDAJw7dw7du3dHly5d2hyvVqvh7u7u7JhERJIjuz/a9unTB4MGDcKaNWtgNBpx8eJFbNmyBQkJCa6ORkQkaSqhraP8ElRZWYlXXnkFhYWFUKvVGD9+PBYuXCjLmy4TEXUWWRY+ERGJJ7tDOkRE1DEsfCIihWDhExEpBAufiEghWPhtkOOVOaurqxEXF4fCwkLbtJKSEkyePBnh4eGIjY3Frl27XJjwRmfOnEFycjIGDx6M6OhoLFq0CNXV1QCkn/348eOYPHkyHn74YURHRyMjIwMmkwmA9LO3slgsmDZtGpYsWWKbJvXs+/fvR0hICMLDw23/paWlAZB+9pqaGixatAhDhgzBI488gjlz5uCnn34C4OTsAt3W008/Lbz88stCQ0ODcOHCBeHJJ58Utm3b5upYt1RcXCyMGDFCCAoKEgoKCgRBEISamhph8ODBwvvvvy+YzWbh2LFjQnh4uFBSUuLitC0aGxuF6Oho4Y033hCuXr0qVFdXCzNnzhRmzZol+exVVVVCaGiosHv3bsFisQgVFRXCmDFjhDfeeEPy2a+3ceNGoV+/fsLixYsFQZD+NiMIgpCVlSUsWbLkhulyyP70008LqampQm1trVBXVyfMnTtXSElJcXp27uHfhtyuzLlnzx4sXLgQL730kt30/Px8+Pj4ICkpCVqtFlFRUYiPj5fM93H58mX069cPqampcHd3h6+vLxITE1FUVCT57H5+fjh27BgmTpwIlUqFmpoaXL16FX5+fpLP3ur48ePIz8/HyJEjbdPkkL20tBQDBgy4YbrUs58+fRolJSXIysrCPffcA71ej4yMDCxcuNDp2Vn4t9HWlTmlJiYmBocOHcLo0aPtphsMBgQFBdlNCwgIwJkzZzoz3i317dsX27dvt/vg3MGDB9G/f3/JZwcAvV4PAPjNb36D+Ph4+Pv7Y+LEibLIXlVVheXLl+O1116zuyih1LNbrVZ88803+OyzzzBs2DAMHToUK1asQG1treSznzp1CgEBAdi5cyfi4uIQExODdevWwd/f3+nZWfi30daVOaXG39//phdPutn34enpKcnvQRAEbNiwAZ9++imWL18uq+z5+fk4evQo1Go15s+fL/nsVqsVaWlpSE5ORr9+/ezmST17dXU1QkJCMGrUKOzfvx87duzA999/j7S0NMlnr62txdmzZ/H9999jz549+Oijj1BRUYHFixc7PTsL/zbu9MqcUqHT6Wx/RGxlMpkk9z0YjUbMnz8f+/btw/vvv4/g4GDZZAdafjDvv/9+pKWl4fPPP5d89q1bt8Ld3R3Tpk27YZ7Us3fr1g25ublISEiATqdDjx49kJaWhqNHj0IQBElnb7144/Lly6HX69GtWzcsWLAAR44ccXp2Fv5tXH9lzlZirswpFUFBQTAYDHbTysrKEBgY6KJEN7pw4QImTZoEo9GIvLw8BAcHA5B+9q+//hqPP/44mpqabNOamprg5uaGgIAASWffu3cvvvzyS0RERCAiIgIff/wxPv74Y0REREj+dT9z5gx+//vf293wo6mpCWq1GgMHDpR09oCAAFitVpjNZtu01uvYP/TQQ87N7pA//d7Ffvvb3wovvfSSUFdXZztLZ9OmTa6O1abrz9Kprq4WIiIihD/+8Y9CU1OTcPz4cSE8PFw4fvy4i1O2qKmpER577DFhyZIlgsVisZsn9exGo1H4zW9+I6xZs0a4evWqcOnSJSEhIUFYuXKl5LP/0uLFi21n6Ug9+48//iiEhYUJb7/9tmA2m4Xy8nJhypQpwrJlyySfvampSYiLixPmzZsnGI1GoaqqSpg+fbqQmprq9Ows/DZcuXJFmDdvnjB48GAhMjJSyMrKEpqbm10dq03XF74gCMKpU6eExMREITw8XBg+fLiwe/duF6azl52dLQQFBQn/9V//JYSFhdn9JwjSzi4IgmAwGITk5GQhIiJCGDZsmPD6668LV69eFQRB+tmvd33hC4L0sxcWFtryRUZGChkZGYLJZBIEQfrZ//WvfwkLFiwQoqOjhYiICGHRokVCbW2tIAjOzc6rZRIRKQSP4RMRKQQLn4hIIVj4REQKwcInIlIIFj4RkUKw8ImIFIKFT0SkECx86hSXLl1CcHAwLl26dEfP8+GHHyI2NtZBqW7NUXmJpISFT0SkECx8comvv/4a06dPR0xMDEJDQzFx4kScPHkSANDc3IxVq1YhOjoaQ4YMwVNPPYWvvvrqhudoamrCzJkzkZSUBKPRiDfffPOGKz/Gxsbiww8/BABMmzYNr732GpKSkhAeHo4nnngC+/fvb1fe8vJyLFiwAFFRUYiOjsbLL79suyVdYWEhhg4dijfeeANDhgyx3Qqz9YJqS5YswbJlyzB9+nSEhYXhiSeewN///nfbcwcHB9vdjvL6dzG3ey1u9m5n2rRpePPNN23rfeWVVzBr1iyEh4cjLi4Ox48fR0ZGBh555BFER0fbbp/X+o7mo48+wrBhwxAWFoalS5eiuLgYY8eORXh4OJ555hnbbSeNRiPS09MxcuRIhIWF4de//jXeeuutdr2W5DosfOp0JpMJs2fPxqhRo3D06FEUFhaid+/eWL9+PYCWqzieOHECn3zyCY4dO4ZHHnkEq1evvulzCIKAd955x3YTkrbs3LkTy5cvR2FhIUaOHIn//u//xtWrV287xmw249lnn4VGo0F+fj4++eQTAMALL7xgu79xRUUFzp8/j8OHD+Mvf/kLPvvsM2zZssX2HHv27MHUqVNRXFyMWbNmYcGCBTh37lybedvzWtzO7t27MXPmTHz11VcYOHAgnnvuOfTp0wfHjx/HrFmz8Morr9hd6fPIkSPYv38/du7cib179yIjIwPbtm3D4cOH8eOPP+LPf/4zAOD3v/89Ll26hLy8PJw4cQLp6enYsGEDfvjhh3Zno87HwqdO5+bmhr/85S946qmn0NTUhPLycvj4+KCiogJAy3XlW8vk/PnzePHFF/HXv/7VNr6pqQkvvPACKisrsWXLFnh6erZ73aNGjUJISAjc3d0xYcIE1NXVoaqq6rZjiouLcfHiRaxevRpdunTBPffcg9WrV+PMmTM4ffo0AEClUmHlypXQ6/Xo06cPnn/+ebvMjz32GEaPHg2tVovx48djwIAB7Xp30dZr0ZbIyEhERERArVYjMjISXl5emDZtGrRaLYYNG4ampia7y38/++yz0Ol0CAoKgr+/PyZMmID7778ffn5+CAsLQ3l5OQBg3rx52LhxI/R6Pf71r3/Bw8MDAGzvekiabrw9EpGTaTQaFBYWYubMmWhoaEBAQAC0Wq3t2uZPPvkkzGYzdu3ahddffx333nsvXnjhBfz2t78FAFy5cgX9+vXDuXPncPr0aTz88MPtXre/v7/t3613B2u9FvmtVFVVwdfX1+5dhF6vh4+PD8rLy9GtWzd07doVvr6+tvkPPPCAXfn16dPH7jkfeOABXLlypc28bb0WbfHx8bH9W6PR4J577rE9VqlUAOy//9str1arbf+PqqqqkJmZiX/84x/41a9+Zbu3bFuvJbkWC586XUlJCTIyMrBjxw5bUWRnZ+P8+fMAgPPnz6N///4YP348TCYTDhw4gMWLFyMiIgIAcN9992Hbtm1Yv349lixZgo8++gheXl5Qq9U33FSipqbmjvP27NkTP//8M4xGo6306+rq8PPPP8Pf3x+CIKCurg6NjY2229NdunQJPXr0sD1H67uXVpcuXbIdf/9l7p9//tn279u9Fmq12u5wzC/HAv8u9fZq7/IvvvgiYmNj8c4770Cr1eLnn3/Gzp07Ra2LOh8P6VCnq6urg1qtth2KOXnyJN59911beX366aeYO3cuLl26BE9PT/j4+ECr1druMubm5gaVSoUFCxZArVZj3bp1AFpuMH/27FkYDAY0Nzdj+/btDrkXaGhoKAICArBy5UrU1dWhrq4Oq1atQu/evW3vLiwWC9atW4erV6/in//8J9555x0kJCTYnuPQoUM4duwYmpubkZeXh++++w5jxoyx5T548CCam5tx4cIF5OXl2cbd7rV48MEHUVlZiYKCAgiCgL1797br7wKOUFdXB09PT2g0GlRXV+PVV18FALtfXCQ93MOnThcdHY2nnnoKSUlJsFqt+NWvfmU7g6ayshLTp09HRUUFpk6dCqPRiJ49e2LDhg3o3r273fN4eHhg7dq1SEpKwvDhwzFixAgcO3YMM2bMgNVqxfjx4zFo0KA7zqvVarF161ZkZWVh1KhRaGpqwqOPPoo//vGPdjeN79q1K4YPHw4AmDp1Kp5//nnbvIiICGzbtg1z585Fnz598Pbbb6NXr14AgJUrV2Lt2rUYPHgw+vTpg4SEBOTm5gLAbV+L7t27Y/bs2ViyZAnq6+sxYsQIjBo16o6/3/ZYu3Yt1qxZg+zsbHTt2hWjR49GSEgIvvvuO8TExHRKBhKPN0AhukOFhYWYPn06zp49e9P5S5YsAQBkZWV1ZiyiG/CQDhGRQrDwiYgUgod0iIgUgnv4REQKwcInIlIIFj4RkUKw8ImIFIKFT0SkECx8IiKFYOETESkEC5+ISCFY+ERECvH/jaExTPJ6KbUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0, 10, 20, 30, 40, 60]\n",
    "plt.figure(figsize=(4, 3))\n",
    "sns.histplot(data=tips, x='total_bill', bins=bins)\n",
    "\n",
    "plt.xlabel('laskun loppusumma')\n",
    "plt.ylabel('lukumäärä')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08085de5",
   "metadata": {},
   "source": [
    "## Usean muuttujan histogrammit samaan kuvioon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d28d375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'juomaraha')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lista muuttujista\n",
    "muuttujat = ['total_bill', 'tip']\n",
    "\n",
    "# Kuvio, joka sisältää muuttujat-listan määrittämän määrän kaavioita\n",
    "fig, axs = plt.subplots(nrows=1, ncols=len(muuttujat), figsize=(8, 3))\n",
    "\n",
    "# Käydään muuttujat-lista läpi: i=järjestysnumero, muuttuja=muuttujan nimi\n",
    "for i, muuttuja in enumerate(muuttujat):\n",
    "    sns.histplot(data=tips, x=muuttuja, ax=axs[i])\n",
    "    axs[i].set_ylabel('')\n",
    "    \n",
    "axs[0].set_ylabel('lukumäärä')    # ylabel vain ensimmäiselle kaaviolle\n",
    "axs[0].set_xlabel('laskun loppusumma')\n",
    "axs[1].set_xlabel('juomaraha')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2283cbc2",
   "metadata": {},
   "source": [
    "## displot\n",
    "\n",
    "**displot** mahdollistaa histogrammien tarkastelun muiden kategoristen muuttujien määrittämissä ryhmissä. **kind**-parametrin arvo **'hist'** määrittää että kaaviolajina on **histplot**.\n",
    "\n",
    "Seuraavassa tarkastelen tipin (**tip**) suuruutta sukupuolen (**sex**) ja tupakoinnin (**smoker**) määrittämissä ryhmissä.\n",
    "\n",
    "Lisätietoa https://seaborn.pydata.org/generated/seaborn.displot.html\n",
    "\n",
    "**displot** 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": 6,
   "id": "8c6e28ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'tupakoimaton nainen')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=tips, x='tip', row='sex', col='smoker', kind='hist', bins=8, height=2.5)\n",
    "\n",
    "g.set_axis_labels('juomaraha', 'lukumäärä')\n",
    "\n",
    "axes = g.axes.flatten()    # Litistetään 2x2 kaaviomatriisin kaaviot listaksi\n",
    "axes[0].set_title('tupakoiva mies')\n",
    "axes[1].set_title('tupakoimaton mies')\n",
    "axes[2].set_title('tupakoiva nainen')\n",
    "axes[3].set_title('tupakoimaton nainen')"
   ]
  }
 ],
 "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": 5
}