matplotlib4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2023-12-10 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplolib - osa 4\n",
    "## Värien ja värikarttojen käyttö\n",
    "\n",
    "Tämä on jatkoa sarjan edellisille osille\n",
    "\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib1.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib2.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib3.ipynb\n",
    "\n",
    "Värejä voin vaihtaa yksi kerrallaan värin nimen, RGB-koodin tai HEX-koodin mukaan.\n",
    "\n",
    "* Värien nimiä https://matplotlib.org/3.1.0/gallery/color/named_colors.html\n",
    "* Värien koodeja https://www.w3schools.com/colors/colors_picker.asp\n",
    "\n",
    "Nykyisessä versiossa oletusvärit määritellään HEX-koodeina: https://matplotlib.org/users/dflt_style_changes.html#colors-color-cycles-and-color-maps. Esimerkiksi pylväskaaviossa ensimmäinen arvosarja esitetään ensimmäisen värin mukaisesti, toinen arvosarja toisen värin mukaisesesti jne. HEX-koodin sijasta oletusväreihin voin viitata lyhyemmin 'C0', 'C1', 'C2' jne.\n",
    "\n",
    "Seuraavassa käytän esimerkkinä 5-portaisella asteikolla mitattuja tyytyväisyyksiä johtoon, työtovereihin, työympäristöön, palkkaan ja työtehtäviin. Laadin ensin prosenttitaulukon, johon on koottuna vastaukset prosentteina ja kuvaan ne pinottuna pylväskaaviona. Kaavioiden muotoiluun en tässä kiinnitä huomiota, vaan keskityn väreihin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3587</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "0    1      1   38      1       1.0      22.0    3587      3     3.0       3   \n",
       "1    2      1   29      2       2.0      10.0    2963      1     5.0       2   \n",
       "2    3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "3    4      1   36      2       1.0      14.0    2144      3     3.0       3   \n",
       "4    5      1   24      1       2.0       4.0    2183      2     3.0       2   \n",
       "\n",
       "   palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "0        3        3      NaN      NaN      NaN      NaN  \n",
       "1        1        3      NaN      NaN      NaN      NaN  \n",
       "2        1        3      1.0      NaN      NaN      NaN  \n",
       "3        3        3      1.0      NaN      NaN      NaN  \n",
       "4        1        2      1.0      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('https://taanila.fi/data1.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Erittäin tyytymätön</th>\n",
       "      <th>Jokseenkin tyytymätön</th>\n",
       "      <th>Ei tyytymätön eikä tyytyväinen</th>\n",
       "      <th>Jokseenkin tyytyväinen</th>\n",
       "      <th>Erittäin tyytyväinen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>proportion</th>\n",
       "      <td>8.536585</td>\n",
       "      <td>19.512195</td>\n",
       "      <td>36.585366</td>\n",
       "      <td>28.048780</td>\n",
       "      <td>7.317073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>työtov</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>19.753086</td>\n",
       "      <td>43.209877</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>työymp</th>\n",
       "      <td>10.975610</td>\n",
       "      <td>10.975610</td>\n",
       "      <td>36.585366</td>\n",
       "      <td>28.048780</td>\n",
       "      <td>13.414634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>palkkat</th>\n",
       "      <td>40.243902</td>\n",
       "      <td>23.170732</td>\n",
       "      <td>23.170732</td>\n",
       "      <td>12.195122</td>\n",
       "      <td>1.219512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>työteht</th>\n",
       "      <td>6.097561</td>\n",
       "      <td>18.292683</td>\n",
       "      <td>35.365854</td>\n",
       "      <td>30.487805</td>\n",
       "      <td>9.756098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Erittäin tyytymätön  Jokseenkin tyytymätön  \\\n",
       "proportion             8.536585              19.512195   \n",
       "työtov                      NaN               3.703704   \n",
       "työymp                10.975610              10.975610   \n",
       "palkkat               40.243902              23.170732   \n",
       "työteht                6.097561              18.292683   \n",
       "\n",
       "            Ei tyytymätön eikä tyytyväinen  Jokseenkin tyytyväinen  \\\n",
       "proportion                       36.585366               28.048780   \n",
       "työtov                           19.753086               43.209877   \n",
       "työymp                           36.585366               28.048780   \n",
       "palkkat                          23.170732               12.195122   \n",
       "työteht                          35.365854               30.487805   \n",
       "\n",
       "            Erittäin tyytyväinen  \n",
       "proportion              7.317073  \n",
       "työtov                 33.333333  \n",
       "työymp                 13.414634  \n",
       "palkkat                 1.219512  \n",
       "työteht                 9.756098  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taulukon laskeminen\n",
    "df1 = df['johto'].value_counts(normalize=True).sort_index().to_frame()\n",
    "df1['työtov'] = df['työtov'].value_counts(sort=False, normalize=True)\n",
    "df1['työymp'] = df['työymp'].value_counts(sort=False, normalize=True)\n",
    "df1['palkkat'] = df['palkkat'].value_counts(sort=False, normalize=True)\n",
    "df1['työteht'] = df['työteht'].value_counts(sort=False, normalize=True)\n",
    "df1.index = ['Erittäin tyytymätön', 'Jokseenkin tyytymätön', 'Ei tyytymätön eikä tyytyväinen', \n",
    "               'Jokseenkin tyytyväinen', 'Erittäin tyytyväinen']\n",
    "df1 = df1.T * 100\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaavio oletusväreillä\n",
    "\n",
    "Jos en määritä värejä, niin käytössä ovat 5 ensimmäistä oletusväriä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oletusvärien HEX-koodit:\n",
      " ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Viisi ensimmäistä oletusväriä')"
      ]
     },
     "execution_count": 5,
     "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": [
    "print('Oletusvärien HEX-koodit:\\n', plt.rcParams['axes.prop_cycle'].by_key()['color'])\n",
    "\n",
    "t = np.arange(5)\n",
    "cmap = plt.get_cmap('tab10')\n",
    "for i in range(5):\n",
    "    plt.plot(t, i*(t+1), color=cmap(i), linestyle='-')\n",
    "    plt.plot(t, i*(t+1)+.3, color=cmap(i), linestyle=':')\n",
    "plt.title('Viisi ensimmäistä oletusväriä')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kaavio oletusväreillä')"
      ]
     },
     "execution_count": 6,
     "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": [
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False)\n",
    "plt.title('Kaavio oletusväreillä')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Omat värit listana\n",
    "\n",
    "Voin määrittää käytettävät värit listana käyttäen värien nimiä, RGB-koodeja tai HEX-koodeja. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "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": [
    "colors = ['darkred', 'red', 'gray', 'blue', 'darkblue']\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, color=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Värikartan käyttö</h2>\n",
    "\n",
    "Matplotlibin värikarttoja: https://matplotlib.org/stable/tutorials/colors/colormaps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "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": [
    "# Esimerkki sequential-tyyppisestä värikartasta\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, colormap='Greens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "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": [
    "# Esimerkki diverging-tyyppisestä värikartasta\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, colormap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7BLQh1RPm2T2CrbKysuwegStWAAAAphBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhhBWAAAAhrTLsHI4HNq2bZvy8vIUGRlp9zgAAACSJKedPzwpKUlDhgzRqlWrmvW4qqoqde3aVfX19Zo4cWLLDAcAANBMtobV9fJ4PP4/h4WF2TgJAADA/7PtrcCMjAwVFhZq9erVcjgccjgccjqdWrlyZcC6w4cPKygoSCdOnJAkVVRUaPLkyYqIiJDL5VJKSorOnDkjSSovL1dQUJD+/Oc/B5xj7dq16tu3ryzL0u7du+VwOPT73/9eQ4cOVVhYmMaPH6+zZ8/qrbfe0sCBA+VyuTRt2jR98cUX/nMkJSUpOztb2dnZioyMVLdu3bRo0SJZltXCOwUAANoL28Jq9erVSkxM1MyZM1VVVaWqqiotXbpUubm5AevWr1+v0aNH66677pJlWZoyZYrOnz+vwsJCFRQU6MSJE0pNTZUkRUdHKzk5ucE5cnNzlZGRIYfD4T+2ZMkS/eIXv9CePXtUWVmplJQUrVq1Sps2bdJvf/tbFRQUaO3atQHneeWVV+R0OlVUVKQ1a9YoJydHL7/8cqPPsaamRj6fL+AGAAA6LtvCyu12KyQkROHh4fJ4PPJ4PMrMzNTRo0dVXFwsSbp06ZI2btyozMxMSdKuXbt08OBBbdq0SfHx8brvvvu0YcMGFRYWau/evZKkxx57TPn5+aqpqZEkHThwQKWlpfrnf/7ngJ//zDPPaOTIkRo6dKgeffRRFRYWat26dRo6dKhGjx6tH/7wh3rnnXcCHhMVFaWcnBz1799faWlpmj17tnJychp9jsuXL5fb7fbfoqKijO0fAABoe9rUvwrs2bOnJk2apPXr10uSduzYoYsXL+qhhx6SJJWVlSkqKiogUGJjYxUZGamysjJJ0pQpU+R0OvXmm29K+vKK17hx4xQdHR3ws+Li4vx/7tGjh8LDwxUTExNw7OzZswGPGTFiRMBVr8TERB0/flx1dXVXfD4LFy6U1+v13yorK5u7JQAAoB1pU2ElfXnFafPmzbpw4YJyc3OVmpqq8PBwSZJlWQFhc9lXj4eEhGj69OnKzc1VbW2tNm3a5L/i9VWdOnXy/9nhcAT8/fKx+vr6G3ouoaGhcrlcATcAANBx2fqvAkNCQhpc7Zk4caI6d+6sdevW6a233tK7777rvy82NlYVFRWqrKz0X7U6cuSIvF6vBg4c6F/32GOPafDgwfrlL3+pS5cu6cEHHzQy74cfftjg7/369VNwcLCR8wMAgPbN1itW0dHRKioqUnl5uc6dO6f6+noFBwcrIyNDCxcu1N13363ExET/+uTkZMXFxSktLU379u1TcXGx0tPTNXbsWCUkJPjXDRw4UCNGjNBTTz2ladOmGftKhsrKSs2fP19Hjx5Vfn6+1q5dqzlz5hg5NwAAaP9sDasFCxYoODhYsbGx6t69uyoqKiRJjz76qGpraxu8hXf5G9e7du2qMWPGKDk5WTExMdqyZUuDczd2jhuRnp6uCxcuaPjw4XriiSc0e/ZsZWVlGTs/AABo3xxWG/wipg8++EBJSUk6deqUevTocV3nePbZZ7V582YdOnTIyEzX+y3xX+Xz+eR2u1Xm6aUuQW3u423XZdZLcdde1I5sL91p9whAs+SXDbB7BLQh1RPm2T2CrVrqYsfl12+v13vNz0u3qW9er6mpUWVlpRYvXqyUlJTriqrPP/9cZWVlWrt2rZYtW9YCUwIAAFxZm7pskp+fr/79+8vr9eq55567rnNkZ2dr1KhRGjt2rNG3AQEAAK6lTV2xysjIUEZGxg2dIy8vT3l5eUbm+ardu3cbPycAAOhY2tQVKwAAgPaMsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADCEsAIAADDEYVmWZfcQNwufzye32y2v1yuXy2X3OAAAoAma8/rNFSsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDnHYPcDNK3fFDdQrvdEPn2F6609A0aKr8sgF2jwC0G9UT5tk9Am5CWVlZdo/AFSsAAABTCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDCCsAAABDOmRYORwObdu2TXl5eYqMjLR7HAAAcJNo02GVlJSkuXPnNvtxVVVV+u53v6vU1FQdO3Ys4L4lS5ZoyJAhZgYEAAD4CqfdA7QEj8fj/3NYWJiNkwAAgJtJm71ilZGRocLCQq1evVoOh0MOh0NOp1MrV64MWHf48GEFBQXpxIkTkqSKigpNnjxZERERcrlcSklJ0ZkzZyRJeXl5Wrp0qQ4cOOA/Z15e3jUfd/ToUTkcDn300UcBP/vFF19UdHS0LMtq4d0AAADtQZsNq9WrVysxMVEzZ85UVVWVqqqqtHTpUuXm5gasW79+vUaPHq277rpLlmVpypQpOn/+vAoLC1VQUKATJ04oNTVVkpSamqonn3xSgwYN8p8zNTX1mo/r37+/4uPj9eqrrwb87E2bNunhhx+Ww+G44nOoqamRz+cLuAEAgI6rzYaV2+1WSEiIwsPD5fF45PF4lJmZqaNHj6q4uFiSdOnSJW3cuFGZmZmSpF27dungwYPatGmT4uPjdd9992nDhg0qLCzU3r17FRYWpoiICDmdTv85w8LCrvk4SUpLS9OmTZv88x07dkwlJSV65JFHGn0Oy5cvl9vt9t+ioqJacMcAAIDd2mxYXUnPnj01adIkrV+/XpK0Y8cOXbx4UQ899JAkqaysTFFRUQEBExsbq8jISJWVlTV63qY8burUqfr444/14YcfSpJeffVVDRkyRLGxsY2ed+HChfJ6vf5bZWXl9T95AADQ5rWrsJKkxx57TJs3b9aFCxeUm5ur1NRUhYeHS5Isy7ri23KNHb/W/V893rNnT40bN85/1So/P/+qV6skKTQ0VC6XK+AGAAA6rjYdViEhIaqrqws4NnHiRHXu3Fnr1q3TW2+95X8bUPryKlNFRUXAlaEjR47I6/Vq4MCBjZ6zKY+Tvnw7cMuWLfrTn/6kEydOaOrUqUafLwAAaN/adFhFR0erqKhI5eXlOnfunOrr6xUcHKyMjAwtXLhQd999txITE/3rk5OTFRcXp7S0NO3bt0/FxcVKT0/X2LFjlZCQ4D/nyZMnVVpaqnPnzqmmpqZJj5OkBx98UD6fT7NmzdK4cePUu3fvVt8TAADQdrXpsFqwYIGCg4MVGxur7t27q6KiQpL06KOPqra2NuBqlfT/37jetWtXjRkzRsnJyYqJidGWLVv8a37wgx/oO9/5jsaNG6fu3bsrPz+/SY+TJJfLpe9973s6cOCA0tLSWn4DAABAu9KmvyD0nnvu0Z/+9KcGx6uqquR0OpWent7gvj59+ui///u/Gz1naGiofvOb3zT7cZe99tpr11wDAABuTm06rL6upqZGlZWVWrx4sVJSUtSjRw+7RwIAAPBr028Ffl1+fr769+8vr9er5557zu5xAAAAArSrsMrIyFBdXZ1KSkr44DgAAGhz2lVYAQAAtGWEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCGEFQAAgCEOy7Isu4e4Wfh8Prndbnm9XrlcLrvHAQAATdCc12+uWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABhCWAEAABjitHuAm9En/QeqOqjtNu2sl+LsHuGmsr10p90jAM2SXzbA7hHQhlRPmGf3CH5ZWVl2j8AVKwAAAFMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAEMIKwAAAENuyrDavXu3HA6HPvvsM7tHAQAAHUiHD6ukpCTNnTs34Ni3vvUtVVVVye122zMUAADokFo0rGpra1vy9Fd16dKlRu8LCQmRx+ORw+FoxYkAAEBH16ywSkpKUnZ2trKzsxUZGalu3bpp0aJFsixLkhQdHa1nnnlGGRkZcrvdmjlzpiTp9ddf16BBgxQaGqro6Gi98MILAeeNjo7WsmXL9PDDDysiIkK9evXS2rVrA9ZUVFRo8uTJioiIkMvlUkpKis6cOeO/f8mSJRoyZIjWr1+vmJgYhYaGasaMGSosLNTq1avlcDjkcDhUXl5+xbcCmzLjz3/+c2VmZqpLly7q06ePfvWrXzVn+wAAQAfX7CtWr7zyipxOp4qKirRmzRrl5OTo5Zdf9t///PPPa/DgwSopKdHixYtVUlKilJQUTZ06VYcOHdKSJUu0ePFi5eXlBZz3+eefV1xcnPbt26eFCxdq3rx5KigokCRZlqUpU6bo/PnzKiwsVEFBgU6cOKHU1NSAc/z1r3/Va6+9ptdff12lpaVas2aNEhMTNXPmTFVVVamqqkpRUVENnlNTZ3zhhReUkJCg/fv360c/+pFmzZqljz76qNG9qqmpkc/nC7gBAICOy9ncB0RFRSknJ0cOh0P9+/fXoUOHlJOT4786NX78eC1YsMC/Pi0tTRMmTNDixYslSffcc4+OHDmi559/XhkZGf51I0eO1NNPP+1f88EHHygnJ0ff/va3tWvXLh08eFAnT570h9GGDRs0aNAg7d27V9/85jclffnW44YNG9S9e3f/eUNCQhQeHi6Px9Poc3rxxRebNOPEiRP1ox/9SJL01FNPKScnR7t379aAAQOueN7ly5dr6dKlTdpXAADQ/jX7itWIESMCPpuUmJio48ePq66uTpKUkJAQsL6srEwjR44MODZy5MiAx1w+z1clJiaqrKzMf46oqKiAq02xsbGKjIz0r5Gkvn37BkRVUzV1xri4OP+fHQ6HPB6Pzp492+h5Fy5cKK/X679VVlY2ezYAANB+NPuK1bV07tw54O+WZTX4kPjlz2Rdy+XHXekcVzr+9Z/dVE2dsVOnTg3mq6+vb/S8oaGhCg0Nva6ZAABA+9PsK1Yffvhhg7/369dPwcHBV1wfGxur999/P+DYnj17dM899wQ85krnvfwWW2xsrCoqKgKu+Bw5ckRer1cDBw686rwhISEBV51uZEYAAICraXZYVVZWav78+Tp69Kjy8/O1du1azZkzp9H1Tz75pN5++20tW7ZMx44d0yuvvKJf/OIXAZ/DkqQPPvhAzz33nI4dO6aXXnpJW7du9Z83OTlZcXFxSktL0759+1RcXKz09HSNHTu2wVuPXxcdHa2ioiKVl5fr3LlzV7zC1NQZAQAArqbZYZWenq4LFy5o+PDheuKJJzR79mxlZWU1un7YsGF67bXXtHnzZg0ePFj/8R//oZ/97GcBHwqXvoybkpISDR06VMuWLdMLL7ygBx54QNKXb7lt27ZNXbt21ZgxY5ScnKyYmBht2bLlmvMuWLBAwcHBio2NVffu3VVRUXHdMwIAAFyNw2rqB5705fdYDRkyRKtWrTI6RHR0tObOndvgG9I7Gp/PJ7fbrTJPL3UJartfej/rpbhrL4Ix20t32j0C0Cz5ZVf+l9C4OVVPmGf3CH5Xu9BzIy6/fnu9Xrlcrquubbuv7gAAAO0MYQUAAGBIs75uYffu3S0yRHl5eYucFwAAoDVxxQoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQwgoAAMAQh2VZlt1D3Cx8Pp/cbre8Xq9cLpfd4wAAgCZozus3V6wAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMIawAAAAMcdo9wM3EsixJks/ns3kSAADQVJdfty+/jl8NYdWKPv30U0lSVFSUzZMAAIDmqq6ultvtvuoawqoV3XrrrZKkioqKa/5iYJbP51NUVJQqKyvlcrnsHuemwb7bh723B/tun5bce8uyVF1drV69el1zLWHVioKCvvxIm9vt5v/gbOJyudh7G7Dv9mHv7cG+26el9r6pF0T48DoAAIAhhBUAAIAhhFUrCg0N1U9/+lOFhobaPcpNh723B/tuH/beHuy7fdrK3juspvzbQQAAAFwTV6wAAAAMIawAAAAMIawAAAAMIawAAAAMIaxa0S9/+UvdeeeduuWWWxQfH6/33nvP7pE6lOXLl+ub3/ymunTpottvv11TpkzR0aNHA9ZYlqUlS5aoV69eCgsLU1JSkv7yl7/YNHHHtHz5cjkcDs2dO9d/jH1vOX//+9/1yCOPqFu3bgoPD9eQIUNUUlLiv5+9N+8f//iHFi1apDvvvFNhYWGKiYnRz372M9XX1/vXsO9mvPvuu/re976nXr16yeFwaNu2bQH3N2Wfa2pqNHv2bN12223q3Lmzvv/97+vUqVMtN7SFVrF582arU6dO1q9//WvryJEj1pw5c6zOnTtbH3/8sd2jdRgPPPCAlZubax0+fNgqLS21Jk2aZPXp08f6/PPP/WtWrFhhdenSxXr99detQ4cOWampqVbPnj0tn89n4+QdR3FxsRUdHW3FxcVZc+bM8R9n31vG+fPnrb59+1oZGRlWUVGRdfLkSWvXrl3WX//6V/8a9t68Z555xurWrZu1Y8cO6+TJk9bWrVutiIgIa9WqVf417LsZO3futH7yk59Yr7/+uiXJevPNNwPub8o+P/7441bv3r2tgoICa9++fda4ceOse++91/rHP/7RIjMTVq1k+PDh1uOPPx5wbMCAAdbTTz9t00Qd39mzZy1JVmFhoWVZllVfX295PB5rxYoV/jUXL1603G639Z//+Z92jdlhVFdXW/369bMKCgqssWPH+sOKfW85Tz31lDVq1KhG72fvW8akSZOszMzMgGMPPvig9cgjj1iWxb63lK+HVVP2+bPPPrM6depkbd682b/m73//uxUUFGT97ne/a5E5eSuwFdTW1qqkpET3339/wPH7779fe/bssWmqjs/r9Ur6///x65MnT+r06dMBv4fQ0FCNHTuW34MBTzzxhCZNmqTk5OSA4+x7y9m+fbsSEhL00EMP6fbbb9fQoUP161//2n8/e98yRo0apbffflvHjh2TJB04cEDvv/++Jk6cKIl9by1N2eeSkhJdunQpYE2vXr00ePDgFvtd8D/C3ArOnTunuro69ejRI+B4jx49dPr0aZum6tgsy9L8+fM1atQoDR48WJL8e32l38PHH3/c6jN2JJs3b9a+ffu0d+/eBvex7y3nb3/7m9atW6f58+fr3//931VcXKx//dd/VWhoqNLT09n7FvLUU0/J6/VqwIABCg4OVl1dnZ599llNmzZNEv/Nt5am7PPp06cVEhKirl27NljTUq+/hFUrcjgcAX+3LKvBMZiRnZ2tgwcP6v33329wH78HsyorKzVnzhz94Q9/0C233NLoOvbdvPr6eiUkJOjnP/+5JGno0KH6y1/+onXr1ik9Pd2/jr03a8uWLdq4caM2bdqkQYMGqbS0VHPnzlWvXr00Y8YM/zr2vXVczz635O+CtwJbwW233abg4OAGdXz27NkGpY0bN3v2bG3fvl3vvPOO7rjjDv9xj8cjSfweDCspKdHZs2cVHx8vp9Mpp9OpwsJCrVmzRk6n07+37Lt5PXv2VGxsbMCxgQMHqqKiQhL/zbeUf/u3f9PTTz+tqVOn6hvf+IamT5+uefPmafny5ZLY99bSlH32eDyqra3V//7v/za6xjTCqhWEhIQoPj5eBQUFAccLCgr0rW99y6apOh7LspSdna033nhDf/zjH3XnnXcG3H/nnXfK4/EE/B5qa2tVWFjI7+EGTJgwQYcOHVJpaan/lpCQoLS0NJWWliomJoZ9byEjR45s8JUix44dU9++fSXx33xL+eKLLxQUFPjyGRwc7P+6Bfa9dTRln+Pj49WpU6eANVVVVTp8+HDL/S5a5CPxaODy1y3813/9l3XkyBFr7ty5VufOna3y8nK7R+swZs2aZbndbmv37t1WVVWV//bFF1/416xYscJyu93WG2+8YR06dMiaNm0a/wS6BXz1XwVaFvveUoqLiy2n02k9++yz1vHjx61XX33VCg8PtzZu3Ohfw96bN2PGDKt3797+r1t44403rNtuu8368Y9/7F/DvptRXV1t7d+/39q/f78lyXrxxRet/fv3+7+qqCn7/Pjjj1t33HGHtWvXLmvfvn3W+PHj+bqFjuKll16y+vbta4WEhFjDhg3zfw0AzJB0xVtubq5/TX19vfXTn/7U8ng8VmhoqDVmzBjr0KFD9g3dQX09rNj3lvM///M/1uDBg63Q0FBrwIAB1q9+9auA+9l783w+nzVnzhyrT58+1i233GLFxMRYP/nJT6yamhr/GvbdjHfeeeeK/399xowZlmU1bZ8vXLhgZWdnW7feeqsVFhZm/dM//ZNVUVHRYjM7LMuyWuZaGAAAwM2Fz1gBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAYQlgBAAAY8n+4LRkX8xU/cgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Esimerkki qualitative-tyyppisestä värikartasta\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, colormap='Set1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Värikartan värialueen supistaminen</h2>\n",
    "\n",
    "Esimerkkikaavioissamme värikartasta poimitaan tasaisin välein viisi väriä. Voin supistaa värikarttaa rajaamalla alkuperäisen värialueen 0 - 1 suppeammaksi, esimerkiksi 0.2 - 0.8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "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": [
    "# Värikartan inferno alueelta 0.2 - 0.8 viisi väriä\n",
    "colors = plt.cm.inferno(np.linspace(0.2, 0.8, 5))\n",
    "\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "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": [
    "# Jos valitsen viiden sijasta kymmenen väriä, niin viiden värin kuvioon otetaan vain viisi ensimmäistä väriä\n",
    "colors = plt.cm.inferno(np.linspace(0.3, 0.7, 10))\n",
    "\n",
    "df1.plot(kind='barh', stacked=True, width=0.8, legend=False, color=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lisätietoa\n",
    "\n",
    "Seuraava osa https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib5.ipynb käsittelee kaavioiden oletuspatrametreja, kuten fontteja ja fonttikokoja"
   ]
  }
 ],
 "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
}