{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f049b286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2025-11-30 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b14a76e",
   "metadata": {},
   "source": [
    "Olen käyttänyt Excelin grafiikan perusasioiden opetuksessa työkirjaa https://taanila.fi/kaavio.xlsx. Toistan tässä työkirjan esimerkit vertailun vuoksi Pythonin **matplotlib**-paketilla.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7f01f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecf703e",
   "metadata": {},
   "source": [
    "## Osa 1 - laadukkaista taulukoista laadukkaita kaavioita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1a66359",
   "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>Etelä</th>\n",
       "      <th>Keski</th>\n",
       "      <th>Pohjoinen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1. nelj.</th>\n",
       "      <td>120</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. nelj.</th>\n",
       "      <td>130</td>\n",
       "      <td>102</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3. nelj.</th>\n",
       "      <td>167</td>\n",
       "      <td>120</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4. nelj.</th>\n",
       "      <td>210</td>\n",
       "      <td>180</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Etelä  Keski  Pohjoinen\n",
       "1. nelj.    120     98         89\n",
       "2. nelj.    130    102         95\n",
       "3. nelj.    167    120        108\n",
       "4. nelj.    210    180        120"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# luon dataframen\n",
    "df1 = pd.DataFrame({'Etelä':[120, 130, 167, 210],\n",
    "                    'Keski':[98, 102, 120, 180],\n",
    "                    'Pohjoinen':[89, 95, 108, 120]},\n",
    "                    index = ['1. nelj.', '2. nelj.', '3. nelj.', '4. nelj.'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f676973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203f09216d0>"
      ]
     },
     "execution_count": 4,
     "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='bar', rot=0)    # rot kääntää luokka-akselin nimiöt.\n",
    "\n",
    "plt.yticks([0, 50, 100, 150, 200, 250])    # Arvoakselin asteikon arvot.\n",
    "plt.ylabel('Myynti miljoonaa euroa')    # Arvoakselin otsikko.\n",
    "plt.grid(axis='y')    # Vaakasuuntainen taustaviivoitus.\n",
    "plt.legend(loc=(0.15, -0.2), ncol=3)    # Selitteen sijoittelu ja leveys (3 vierekkäin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f43effdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203f103ce10>"
      ]
     },
     "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": [
    "df1.T.plot(kind='bar', rot=0)    # Äskeinen arvosarjat ja luokat vaihdettuina päittäin.\n",
    "\n",
    "plt.yticks([0, 50, 100, 150, 200, 250])\n",
    "plt.ylabel('Myynti miljoonaa euroa')\n",
    "plt.grid(axis='y')\n",
    "plt.legend(loc=(0, -0.2), ncol=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "daba284c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pylväskaavion sijasta voin käyttää myös viivakaaviota.\n",
    "# style määrittää viivan ja havaintopisteen tyylin.\n",
    "df1.plot(style='-o')\n",
    "\n",
    "plt.yticks([50, 100, 150, 200, 250])\n",
    "plt.ylabel('Myynti miljoonaa euroa')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805159ed",
   "metadata": {},
   "source": [
    "## Osa 2 - vaakapylväskaavio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "095da13a",
   "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>Kotieläimet 1000 kpl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Siipikarjaa</th>\n",
       "      <td>4056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikoja</th>\n",
       "      <td>1483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nautakarjaa</th>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poroja</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lampaita</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hevosia</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Kotieläimet 1000 kpl\n",
       "Siipikarjaa                  4056\n",
       "Sikoja                       1483\n",
       "Nautakarjaa                   915\n",
       "Poroja                        195\n",
       "Lampaita                      122\n",
       "Hevosia                        59"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Kotieläimet 1000 kpl': [4056, 1483, 915, 195, 122, 59]},\n",
    "                    index = ['Siipikarjaa', 'Sikoja', 'Nautakarjaa', 'Poroja', 'Lampaita', 'Hevosia'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "718c5370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(2, 0, '4056'),\n",
       " Text(2, 0, '1483'),\n",
       " Text(2, 0, '915'),\n",
       " Text(2, 0, '195'),\n",
       " Text(2, 0, '122'),\n",
       " Text(2, 0, '59')]"
      ]
     },
     "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": [
    "ax = df2.plot(kind='barh')\n",
    "\n",
    "plt.xticks([0, 1000, 2000, 3000, 4000])\n",
    "plt.grid(axis='x')\n",
    "plt.bar_label(ax.containers[0], padding=2)   # Lukuarvot pylväiden viereen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce6e97a",
   "metadata": {},
   "source": [
    "## Osa 3 - histogrammi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf5b036e",
   "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>Henkilöä</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-1600</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601-2100</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101-2600</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601-3100</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101-3600</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601-</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Henkilöä\n",
       "0-1600            5\n",
       "1601-2100        19\n",
       "2101-2600        25\n",
       "2601-3100        20\n",
       "3101-3600         8\n",
       "3601-             5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'Henkilöä': [5, 19, 25, 20, 8, 5]},\n",
    "                    index = ['0-1600', '1601-2100', '2101-2600', '2601-3100', '3101-3600', '3601-'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35092e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 2, '5'),\n",
       " Text(0, 2, '19'),\n",
       " Text(0, 2, '25'),\n",
       " Text(0, 2, '20'),\n",
       " Text(0, 2, '8'),\n",
       " Text(0, 2, '5')]"
      ]
     },
     "execution_count": 10,
     "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": [
    "# width=1: pylväät kiinni toisissaan.\n",
    "ax = df3.plot(kind='bar', legend=False, edgecolor='white', width=1, rot=45)\n",
    "\n",
    "plt.ylabel('Henkilöä')\n",
    "plt.ylim(0, 30)\n",
    "plt.grid(axis='y')\n",
    "ax.bar_label(ax.containers[0], padding=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc52a709",
   "metadata": {},
   "source": [
    "## Osa 4 - mielipiteiden keskiarvoja pylväinä "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ece9b30",
   "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>Tyytyväisyyskeskiarvo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hintataso</th>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valikoiman laajuus</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henkilökunnan palvelualttius</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Tyytyväisyyskeskiarvo\n",
       "Hintataso                                       2.8\n",
       "Valikoiman laajuus                              3.9\n",
       "Henkilökunnan palvelualttius                    4.7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame([2.8, 3.9, 4.7],\n",
    "                    index = ['Hintataso', 'Valikoiman laajuus', 'Henkilökunnan palvelualttius'],\n",
    "                    columns = ['Tyytyväisyyskeskiarvo'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "424719e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(2, 0, '2.8'), Text(2, 0, '3.9'), Text(2, 0, '4.7')]"
      ]
     },
     "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": [
    "ax = df4.plot(kind='barh')\n",
    "\n",
    "plt.xlim(1, 5)\n",
    "plt.xlabel('1=erittäin tyytymätön, 5=erittäin tyytyväinen')\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.grid(axis = 'x')\n",
    "\n",
    "# Lukuarvot pylväiden viereen.\n",
    "ax.bar_label(ax.containers[0], padding=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ac9ebd",
   "metadata": {},
   "source": [
    "## Osa 5 - useita arvosarjoja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ddadf492",
   "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>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tyytymätön</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ei tyytymätön eikä tyytyväinen</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyytyväinen</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Mies  Nainen\n",
       "Tyytymätön                        17       8\n",
       "Ei tyytymätön eikä tyytyväinen    15      15\n",
       "Tyytyväinen                        6      21"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5a = pd.DataFrame({'Mies': [17, 15, 6],\n",
    "                     'Nainen': [8, 15, 21]},\n",
    "                     index = ['Tyytymätön', 'Ei tyytymätön eikä tyytyväinen', 'Tyytyväinen'])\n",
    "df5a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc400ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df5a.plot(kind='bar', rot=0)\n",
    "\n",
    "plt.yticks([0, 5, 10, 15, 20])\n",
    "# Arvoakselin otsikko vaakasuuntaisena, labelpad määrittää etäisyyden y-akselista.\n",
    "plt.ylabel('lkm', rotation=0, labelpad=15)\n",
    "plt.legend(ncol=2)\n",
    "plt.grid(axis='y')\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, padding=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e28b01b4",
   "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>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tyytymätön</th>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ei tyytymätön eikä tyytyväinen</th>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyytyväinen</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.477273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Mies    Nainen\n",
       "Tyytymätön                      0.447368  0.181818\n",
       "Ei tyytymätön eikä tyytyväinen  0.394737  0.340909\n",
       "Tyytyväinen                     0.157895  0.477273"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prosentti-taulukko.\n",
    "df5b = df5a.copy()\n",
    "df5b['Mies'] = df5a['Mies'] / df5a['Mies'].sum()\n",
    "df5b['Nainen'] = df5a['Nainen'] / df5a['Nainen'].sum()\n",
    "df5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7fa25cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# T vaihtaa dataframen rivit ja sarakkeet päittäin.\n",
    "# stacked=True: pinottu pylväskaavio.\n",
    "ax = (df5b * 100).T.plot(kind='barh', stacked=True)\n",
    "\n",
    "plt.xlabel('%')\n",
    "plt.grid(axis='x')\n",
    "plt.legend(loc=(-0.10, 1), ncol=3)\n",
    "\n",
    "# Lukuarvojen lisääminen on tähän kaaviolajiin hieman konstikasta.\n",
    "for bar in ax.patches:\n",
    "    left, bottom, width, height = bar.get_bbox().bounds\n",
    "    if width>0:\n",
    "        label = '{:.0f} %'.format(width)\n",
    "        plt.text(x=left+width/2, y=bottom+height/2, s=label, ha='center', va='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e2ec16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df5a.T.plot(kind='bar', stacked=True, rot=0)\n",
    "\n",
    "plt.ylabel('Lukumäärä')\n",
    "plt.grid(axis='y')\n",
    "plt.legend(loc=(-0.1, 1), ncol=3)\n",
    "for bar in ax.patches:\n",
    "    left, bottom, width, height = bar.get_bbox().bounds\n",
    "    if height>0:\n",
    "        label = '{:.0f}'.format(height)\n",
    "        plt.text(x=left+width/2, y=bottom+height/2, s=label, ha='center', va='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a16ee6",
   "metadata": {},
   "source": [
    "## Osa 6 - kahden arvoakselin kaavio\n",
    "\n",
    "Jos esitettävät arvosarjat ovat eri suuruusluokkaa, niin kahden arvoakselin käyttö on paikallaan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "442f2ad2",
   "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>Indeksi</th>\n",
       "      <th>Myynti</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>85</td>\n",
       "      <td>307954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>83</td>\n",
       "      <td>566829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>116</td>\n",
       "      <td>1241391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>144</td>\n",
       "      <td>2109871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>193</td>\n",
       "      <td>3009299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>184</td>\n",
       "      <td>9184649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>254</td>\n",
       "      <td>24933405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>320</td>\n",
       "      <td>29726136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>405</td>\n",
       "      <td>31719819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>442</td>\n",
       "      <td>15343617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>284</td>\n",
       "      <td>6318198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Indeksi    Myynti\n",
       "1980       85    307954\n",
       "1981       83    566829\n",
       "1982      116   1241391\n",
       "1983      144   2109871\n",
       "1984      193   3009299\n",
       "1985      184   9184649\n",
       "1986      254  24933405\n",
       "1987      320  29726136\n",
       "1988      405  31719819\n",
       "1989      442  15343617\n",
       "1990      284   6318198"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Luon dataframen Helsingin pörssin kehityksestä vuosina 1980 - 1990\n",
    "df6 = pd.DataFrame({'Indeksi':[85,83,116,144,193,184,254,320,405,442,284],\n",
    "                    'Myynti':[307954,566829,1241391,2109871,3009299,9184649,24933405,29726136,31719819,15343617,6318198]},\n",
    "                   index=['1980','1981','1982','1983','1984','1985','1986','1987','1988','1989','1990'])\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5da9a4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Myynti miljoonaa euroa'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Indeksi viivakaaviona.\n",
    "df6['Indeksi'].plot(color='C0', figsize=(10, 6))\n",
    "\n",
    "# Indeksille ja sen akselille käytössä olevien värien ensimmäinen väri 'C0'.\n",
    "plt.ylabel('Indeksi', color='C0')\n",
    "\n",
    "# Toinen kaavio, jolla on oma arvoakseli, mutta yhteinen x-akseli indeksin kanssa.\n",
    "plt.twinx()\n",
    "\n",
    "# Myynnille ja sen akselille käytössä olevien värien toinen väri 'C1'.\n",
    "plt.ylabel('Myynti miljoonaa euroa', color='C1')\n",
    "\n",
    "# Myynti pylväinä.\n",
    "(df6['Myynti'] / 1000000).plot(kind='bar', color='C1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8518e36",
   "metadata": {},
   "source": [
    "## Osa 7 - pylväskaavioita\n",
    "\n",
    "Excelissä tämä osa oli esimerkkinä pylväskaavioihin liittyvistä Excel-ongelmista. Täällä vastaavaa ongelmaa ei esiinny."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35d45e63",
   "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>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>250</td>\n",
       "      <td>260</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II</th>\n",
       "      <td>350</td>\n",
       "      <td>300</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>III</th>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IV</th>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2008  2009  2010\n",
       "I     250   260   270\n",
       "II    350   300   310\n",
       "III   100   200   240\n",
       "IV    140   120   140"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = pd.DataFrame({'2008':[250,350,100,140],\n",
    "                    '2009':[260,300,200,120],\n",
    "                    '2010':[270,310,240,140]},\n",
    "                   index=['I','II','III','IV'])\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8fd9e998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203f17d0e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xMZo3b15te2hoqJYuXaqgoCAjhwEAAEzKULBZsWKF3nzzTU2cOFFXXHGFCgsLlZmZKYvFookTJ7rUx/jx4zV+/PgLto8ZM8bpVNQvJSQk1AYdAACAnzMUbFatWqWXX37Z6U6Pvn376oEHHnA52AAAAHiKoWtsiouL1aOH8x04PXr00IkTJ9xZEwAAQJ0YCjbdunXTxx9/7LTs448/Vrdu3dxaFAAAQF0YOhU1efJkPfTQQ9qwYYO6du2qwsJCffrpp8rIyPBUfQAAAC4zNGMzZMgQZWZmyt/fX3v27FGbNm2UlZWlm2++2VP1AQDcqLrG/a+68ESfQF0ZmrFJTU3VM888owEDBniqHgCAB1ktVs34coYOlh10S3/d23ZX+iBjD2kFPMlQsMnNzZW/v7+nagEANICDZQe1t3Svt8sAPMJQsLnttts0depUxcfHq0OHDk4P5bv++uvdXhwAAIARhoLNypUrJUmff/6503IfHx/t3Uv6BwAA3mUo2OTl5XmqDgAAgHozFGyKioou2NalS5d6FwMAAFAfhoJNbGysfHx85HA4JMnpGhtORQEAAG8zFGw+/fRTp69LS0uVmZmp3/zmN24tCgAAoC4MBZvLL7/8nK+ffPJJjRw5UrfffrtbCwMAADDK0JOHL+TkyZPu6AYAAKBeDM3Y/PnPf3b6uqqqSl999ZX69OnjzpoAAADqxFCwycnJcfraarUqKipKkyZNcmtRAAAAdWEo2Lz11lueqgMAAKDeDF9jc+DAAT355JOaMmWKjh8/Xvs0YgAAAG8zFGy+/vprJScn6/jx49qyZYsqKir00ksvacmSJZ6qDwAAwGWGgs2zzz6r5557TosXL5bVatVll12mJUuW6J133vFUfQAAAC4zFGy+//57DRo0SNL/PXW4d+/eKisrc39lAAAABhkKNl26dNG3337rtGz37t267LLL3FoUAABAXRi6K2rSpElKTU3VnXfeqaqqKi1dulRvvfWWHnnkEU/VBwAA4DJDwWb48OEKDAxUVlaWunTpom3btmn27NkaOnSop+oDAABwmaFgI0mDBw/W4MGDPVELAABAvbjlXVEAAACNAcEGAACYBsEGAACYhluCzZkzZ9zRDQAAQL0Yuni4sLBQL730ko4cOaKamhpJUlVVlf75z39q27ZtHikQAADAVYZmbGbPnq0ffvhBrVu31pkzZxQeHq78/Hz97ne/81R9AAAALjMUbP7xj3/opZde0uTJk9W6dWvNmTNHzz77rLZu3eqp+gAAAFxmKNjYbDa1bdtWV155pfbv3y9JGjRokA4ePOiR4gAAAIwwFGyuvPJKffHFF2rVqpVqamp06NAhHTlyhIuHAQBAo2Do4uGJEydq6tSp+uijjzR69GiNGTNGVqtVsbGxnqoPAADAZYaCTWxsrDZt2qSQkBBNnjxZv/rVr2S32zVixAgPlQcAAOA6w++K6tSpU+1/33rrrTpz5oz279+vnj17urUwAAAAowwFm88//1x/+MMfdOTIETkcjv/rxNdXu3fvdntxAAAARhgKNv/zP/+jW265RW3atNG+fft022236aWXXlJSUpKn6gMAAHCZobuiDh06pOnTp2v48OE6fvy4brnlFi1evFjvvvuuoZ3m5eVp/Pjx6tevnwYOHKjHHntMpaWlkqSdO3cqOTlZUVFRio2NVXZ2ttO2a9asUVxcnPr06aNRo0YpNzfX0L4BAIB5GQo2ISEhslgs6tKliw4cOCBJCg0N1eHDh13uo6KiQhMmTFBUVJT+3//7f/roo4904sQJzZo1S2VlZZo4caJGjBih7du3a+HChVq0aJF27dolScrJydGCBQuUnp6u7du36/bbb1dqaqpOnz5t5DAAAIBJGQo2EREReuGFFyRJ7dq10xdffKGcnBy1aNHC5T6KiorUo0cPpaWlyd/fX8HBwRo9erS2b9+uTZs2KSgoSGPHjpWvr69iYmIUHx+vrKwsSVJ2draGDx+u6Oho+fn5ady4cQoODta6deuMHAYAADApQ9fYTJ8+XVOnTtUdd9yhqVOnavLkyaqpqdFjjz3mch/du3dXZmam07KNGzfq2muvVX5+vsLDw53aQkNDtXr1aklSQUGBEhMTz2nPy8szchiSpOrqalVXVxverr6sVmuD77Ox8Mbn7W2MNxobT31PMt7wNFe/xwwFm6uvvlp/+9vfJEmXX365Nm/erPLycl111VXGK5TkcDj0/PPPa/PmzVq5cqVWrFghm83mtE5AQIBOnTolSSovL79ouxG7du1Sy5Yt61R3XdlstmZ9W/y+ffua1WlDxrt5jbcn+Pn5qVfPa2Tx9fN2KZfEeMPTXP1db/g5NsXFxSosLHS63bukpETXX3+9oX7sdrtmzpypPXv2aOXKlYqIiJDNZtOPP/7otF5FRYVatWol6adfFBUVFee0BwcHGz0MRUZGKjAw0PB2qLuIiAhvl4AGxHi7h8Vqld6bIJXsr39noUOk38ytfz/nwXjD0+x2u0vrGQo2b731ltLT08+ZDvLx8dHevXtd7qewsFD33XefunTpotWrVyskJESSFB4erq+//tpp3YKCAoWFhUmSwsLClJ+ff077oEGDjByGpJ+mY5vzaQJv4PNuXhhvNyrZL/1nZ/37aR9+6XXqiPGGp7n6PWbo4uHly5dr7ty5+sc//qG8vLzaf0ZCTVlZme655x717dtXr7/+em2okaS4uDiVlJRo2bJlqqqq0rZt27R27dra62qSkpK0du1abdu2TVVVVVq2bJmOHTumuLg4I4cBAABMytCMTWlpqZKTk2WxGMpDTt5//30VFRVp/fr12rBhg1Nbbm6u3njjDS1cuFAZGRkKCQnRnDlzNGDAAElSTEyM5s2bp/nz5+vIkSMKDQ3V0qVLFRQUVOd6AACAeRgKNv369VNOTo5iYmLqvMPx48dr/PjxF2zv3bu3Vq1adcH2hIQEJSQk1Hn/AADAvAwFm06dOmnSpEnq37+/2rdv79S2aNEitxYGAABglKFgU1lZqeHDh3uqFgAAgHoxFGyYlQEAAI2Z4auAly9frltvvVXXXXedhgwZoldffdXpmTYAAADeYmjGZvny5XrzzTc1ceJEXXHFFSosLFRmZqYsFosmTpzoqRoBAABcYijYrFq1Si+//LLTY+L79u2rBx54gGADAAC8ztCpqOLiYvXo0cNpWY8ePXTixAl31gQAAFAnhoJNt27d9PHHHzst+/jjj9WtWze3FgUAAFAXhk5FTZ48WQ899JA2bNigrl27qrCwUJ9++qkyMjI8VR8AAIDLDM3YDBkyRJmZmfL399eePXvUpk0bZWVl6eabb/ZUfQAAAC4zNGMjSQMGDFBYWJh++OEHdejQQZdddpkn6gIAADDMULCx2+167LHHtHnz5tplMTExev7559WmTRu3FwcAAGCEoVNRixcv1qlTp/TRRx/pu+++0wcffKCamho988wznqoPAADAZYaCzebNm7V48WJdffXVatGihcLDw/XMM8/ok08+8VR9AAAALjMUbE6fPq3WrVs7LWvTpo1qamrcWhQAAEBdGAo21113nV544YXad0M5HA698MIL6t27t0eKAwAAMMLQxcPTpk1TSkqKPvzwQ11++eX64Ycf5OPjozfffNNT9QEAALjMULAJDw/Xxo0b9cknn6i0tFSXX365Bg8erMDAQE/VBwAA4DLDz7EJCgpSUlKSJ2oBAACoF5eCTd++ffXtt9+qR48e8vHxOe86FotF7dq10+TJkzVmzBi3FgkAAOAKl4LNkiVLJEnLly+/YLCpqanR/v379dxzzxFsAACAV7gUbH79619Lkvr373/R9aKiorRz5876VwUAAFAHLgWb2NjYC87UnPXpp5+qRYsWWrx4sVsKAwAAMMqlYDNlypRLBhsAAABvcynYjBo1ytN1AAAA1JtLwWbixIlasmSJUlJSLjhzs2LFCrcWBgAAYJRLwSY6OlrSpS8eBgAA8CaXgs2kSZMk/XStDQAAQGNl6MnDhw4d0quvvqoffvjhnDd6cyoKAAB4m6Fg8/DDD8vf318DBgyQxWLoxeAAAAAeZyjYHDhwQFu3blVAQICn6gEAAKgzQ9MuPXr00OHDhz1VCwAAQL0YmrGZM2eOxo0bp1tuuUVt2rRxauPCYgAA4G2Ggs2LL76oU6dOac+ePU7X2PBUYgAA0BgYCjY5OTn6+OOP1b59e0/VAwAAUGeGrrHp2LGjWrRo4alaAAAA6sXQjM3vf/97TZ48WXfffbfatm3rdArq+uuvd3txAAAARhgKNnPnzpUkbd++3Wm5j4+P9u7d676qAAAA6sBQsMnLy/NUHQAAAPXG44MBAIBpeD3YlJaWKi4uTjk5ObXL5s2bp169eikqKqr23zvvvFPbvmbNGsXFxalPnz4aNWqUcnNzvVE6AABoZAydinK3HTt2aMaMGSosLHRavnv3bi1YsEAjR448Z5ucnBwtWLBAS5cuVWRkpLKyspSamqrNmzfLZrM1VOkAAKAR8tqMzZo1azRt2jQ9/PDDTssrKyu1f/9+9erV67zbZWdna/jw4YqOjpafn5/GjRun4OBgrVu3riHKBgAAjZhLMzY7duxQdHT0OXdD/ZzR271vvPFGxcfHy9fX1ync5OXl6cyZM8rIyNCOHTvUunVrJSYmasKECbJYLCooKFBiYqJTX6GhoVzYDAAAXAs29913n7799lulpKSct70ut3t36NDhvMt//PFH9evXTykpKXr22We1d+9epaWlyWKxaMKECSovLz/nlFNAQIBOnTplaP/V1dWqrq42tI07WK3WBt9nY+GNz9vbGG/UV1P5HmK84Wmufo+5FGy+/fZbSQ1zu/fAgQM1cODA2q8jIyN1zz33aN26dZowYYJsNpsqKiqctqmoqFBwcLCh/ezatUstW7Z0S82ustls6tmzZ4PuszHZt2+fTp8+7e0yGgzj3bzG2xOa0vcQ4w1Pc3UCw9DFwyNGjNBf//rXc5bHxsbqs88+M9LVBX3yyScqKSnRmDFjapdVVlYqICBAkhQWFqb8/HynbQoKCjRo0CBD+4mMjFRgYGD9C4bLIiIivF0CGhDj3bww3vA0u93u0nqXDDaFhYV65ZVXJP0UIGbOnHnOjn45g1IfDodDixYtUrdu3TRgwAB99913WrFiRe1+k5KSlJaWpmHDhik6OlpZWVk6duyY4uLiDO3HarU2mSles+Dzbl4Y7+aF8Yanufo9dslgc+WVVyo4OFjHjx8/b3tISIiee+45Y9VdRFxcnGbOnKn58+fryJEjat++vR544AElJCRIkmJiYjRv3rza9tDQUC1dulRBQUFuqwEAADRNLp2KeuyxxyRJXbt21eTJk91exL59+5y+HjNmjNOpqF9KSEioDToAAABnGbrGZvLkySouLlZhYaEcDodTG2/3BgAA3mYo2Lz11ltKT08/55Yr3u4NAAAaA0PBZvny5Zo7d64SExPl6+vVtzEAAACcw1A6KS0tVXJysiwWr787EwAA4ByGEkq/fv2c3sINAADQmBiasenUqZMmTZqk/v37q3379k5tixYtcmthAAA0ZtU1DlktPo26z+qaalkt7n3GkCf6dCdDwaayslLDhw/3VC0AADQZVouPHlyVq4Ji156IeymhHQP1wpgot/R1ltVi1YwvZ+hg2UG39Ne9bXelD0p3S1+eYvh2765du3qqFgAAmpSCYrv2FJ30dhkXdbDsoPaWNp87lw1dYzNs2DClpKTogw8+cOtrFAAAANzBULD5/PPPdfPNN+v111/XjTfeqCeeeEK5ubmeqg0AAMAQQ8Gmffv2uvfee/Xhhx9qxYoVatOmjWbMmKFhw4YpMzNTpaWlnqoTAADgkur0QJozZ86oqKhIRUVFOnbsmGw2m3bu3KlbbrlFa9ascXeNAAAALjF08fB3332nDz74QOvXr5ePj4/i4+O1cuVK9ejRQ5L08ccfa/bs2Ro5cqRHigUAALgYQ8Fm7NixGjhwoP7whz8oNjZWfn5+Tu3XXHONYmNj3VogAACAqwy/BLNv374XbL/iiiuUnt64728HAADmZegam7vvvpvbvQEAQKPF7d4AAMA0uN0bAACYBrd7AwAA0+B2bwAAYBrc7g0AAEzDULDZvHmzOnbseMF2bvcGAADe5FKw+fOf/3zJdaZMmVLvYgAAAOrD5WDTunVrXXPNNXI4HOe0+/j4uL0wAAAAo1wKNo8//rjef/99FRcXKzk5WSNGjFC7du08XRsAAIAhLt3uPX78eK1du1ZPP/20CgsLddtttyktLU2ff/65ampqPF0jAACm1yGwhVRT7e0ymjxDFw9HRkYqMjJSM2fO1IYNG/Tmm29q3rx5SkhI0COPPOKpGgEAML02Nl/JYpXemyCV7K9/h6FDpN/MrX8/TYyhYHNWQECA4uLiVFVVpeXLl2vZsmUEGwAA3KFkv/SfnfXvp314/ftoggwHmy1btui9997TZ599pquuukpjxozRbbfd5onaAAAADHEp2PzrX//SmjVr9MEHH6iqqkq33XabVq1apYiICE/XBwAA4DKXgs2wYcMUHBys+Ph43XTTTfL19dXJkye1ffv22nWuv/56jxUJAADgCpeCjcPhUGlpqZYvX67ly5ef0+7j46O9e/e6vTgAAAAjXAo2eXl5nq4DAACg3lx6jg0AAEBTQLABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACmQbABAACm4fVgU1paqri4OOXk5NQu27lzp5KTkxUVFaXY2FhlZ2c7bbNmzRrFxcWpT58+GjVqlHJzcxu6bAAA0Ah5Ndjs2LFDo0ePVmFhYe2ysrIyTZw4USNGjND27du1cOFCLVq0SLt27ZIk5eTkaMGCBUpPT9f27dt1++23KzU1VadPn/bWYQAAgEbCa8FmzZo1mjZtmh5++GGn5Zs2bVJQUJDGjh0rX19fxcTEKD4+XllZWZKk7OxsDR8+XNHR0fLz89O4ceMUHBysdevWeeMwAABAI+LSu6I84cYbb1R8fLx8fX2dwk1+fr7Cw8Od1g0NDdXq1aslSQUFBUpMTDyn3ej7rKqrq1VdXV3H6uvOarU2+D4bC2983t7GeKO+msr3UHMc76YyNp7gjfF2dZ9eCzYdOnQ47/Ly8nLZbDanZQEBATp16pRL7a7atWuXWrZsaWib+rLZbOrZs2eD7rMx2bdvX7M6Zch4N6/x9vPz0zU9r5Wfb/P8Zdfcxpv/vxt+vF39Pe+1YHMhNptNP/74o9OyiooKtWrVqra9oqLinPbg4GBD+4mMjFRgYGD9ioUhERER3i4BDag5jrfVatWDq3JVUGx3S383RXTQ9KE93NKXpzXH8W7OvDHedrtr/181umATHh6ur7/+2mlZQUGBwsLCJElhYWHKz88/p33QoEGG9mO1Wpv1NKI38Hk3L811vAuK7dpTdNItfV3doZVb+mkIzXW8mytvjLer+/T67d6/FBcXp5KSEi1btkxVVVXatm2b1q5dW3tdTVJSktauXatt27apqqpKy5Yt07FjxxQXF+flygEAgLc1uhmb4OBgvfHGG1q4cKEyMjIUEhKiOXPmaMCAAZKkmJgYzZs3T/Pnz9eRI0cUGhqqpUuXKigoyLuFAwAAr2sUwWbfvn1OX/fu3VurVq264PoJCQlKSEjwdFkAAKCJaXSnogAAAOqKYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyDYAMAAEyjUQabdevWqWfPnoqKiqr9N336dEnSzp07lZycrKioKMXGxio7O9vL1QIAgMbC19sFnM/u3buVkJCgRYsWOS0vKyvTxIkTNXXqVI0ePVrbt29XWlqaIiIiFBkZ6aVqAQBAY9EoZ2x2796tXr16nbN806ZNCgoK0tixY+Xr66uYmBjFx8crKyvLC1UCAIDGptHN2NTU1GjPnj2y2WzKzMxUdXW1Bg8erGnTpik/P1/h4eFO64eGhmr16tWG91NdXa3q6mp3le0yq9Xa4PtsLLzxeXsb4928MN7NC+PdOPfZ6IJNaWmpevbsqaFDhyojI0PHjx/X448/runTp6tDhw6y2WxO6wcEBOjUqVOG97Nr1y61bNnSXWW7xGazqWfPng26z8Zk3759On36tLfLaDCMN+PdnDDezYs3xtvV3/WNLti0b9/e6dSSzWbT9OnTdccdd2jUqFGqqKhwWr+iokKtWrUyvJ/IyEgFBgbWu164LiIiwtsloAEx3s0L4928eGO87Xa7S+s1umCTl5enjz76SI8++qh8fHwkSZWVlbJYLIqMjNTy5cud1i8oKFBYWJjh/Vit1mY9jegNfN7NC+PdvDDezYs3xtvVfTa6i4eDgoKUlZWlzMxMnTlzRkVFRXrmmWc0cuRIDR06VCUlJVq2bJmqqqq0bds2rV27VomJid4uGwAANAKNLth07txZr732mj799FP169dPiYmJ6t27t+bOnavg4GC98cYb2rBhg/r37685c+Zozpw5GjBggLfLBgAAjUCjOxUlSf369dOqVavO29a7d+8LtgEAgOat0c3YAAAA1BXBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmAbBBgAAmEaTDDbHjh3T5MmT9etf/1r9+/fXwoULdebMGW+XBQAAvKxJBpuHHnpILVu21FdffaXVq1dr69atWrZsmbfLAgAAXtbkgs3333+vv//975o+fbpsNpu6du2qyZMnKysry9ulAQAAL2tywSY/P19BQUHq1KlT7bKrr75aRUVFOnnypBcrAwAA3ubr7QKMKi8vl81mc1p29utTp06pTZs2F93e4XBIksrKylRdXe2ZIi/CarXqV20sqqn0c0t/nWyS3W6XArtLITX177DFZZLdrq7+XVXd0j2fT1f/rrLb7V75vL2N8W5eGO/mhfFuWHa7XdL//R6/EB/HpdZoZD7++GPNmTNHOTk5tcv27dun22+/Xd98841at2590e0PHz6swYMHe7pMAADgAV988YU6d+58wfYmN2MTFhamEydOqKSkRO3bt5ckHThwQJ07d75kqJGkjh076osvvlCrVq3k4+Pj6XIBAIAbOBwOlZeXq2PHjhddr8nN2EjSXXfdpc6dO+uPf/yjjh8/rtTUVA0dOlQPPPCAt0sDAABe1CSDTUlJif74xz8qJydHFotFI0aM0LRp02S1Wr1dGgAA8KImGWwAAADOp8nd7g0AAHAhBBsAAGAaBBsAAGAaBBsAAGAaBJtmIiUlRS+++KK3y4CH/XycIyIinB5kCXO50Fjn5OQoIiLCm6XBzVJSUvT000+rX79+WrFixXnXefTRRzV58uQGrqxxItgAANDI2Ww2JSYmKjs7+5y20tJSbdy4UXfffbcXKmt8CDYAADQBd911lwoKCvTdd985LV+9erWuuuoqDRgwwDuFNTIEGwAAmoCuXbtq8ODBevfdd2uX1dTU6J133mG25mcINgAANBF333231q9fX/um66+++kp2u13x8fFerqzxINgAANBE3HDDDbrsssv00UcfSZL+8pe/aPTo0QoICPByZY0HwQYAgCZk7Nixys7O1n/+8x99/fXXuuuuu7xdUqNCsAEAoAkZOXKkvv/+ez3//PMaMmSIOnfu7O2SGhWCDQAATUjLli01atQo/fWvf1VKSoq3y2l0eLs3AAAwDWZsAACAaRBsAACAaRBsAACAaRBsAACAaRBsAACAaRBsAACAaRBsAACAaRBsAACAaRBsAJzj3nvv1ZQpU87b9u677+qGG25QZWWlR/YdFRWlb775pt79vP/++4qNjZUkffPNN4qKiqptKyoqUlRUlIqKivTqq69qwoQJ9d4fgMbB19sFAGh8UlJSNGXKFB09elQdOnRwanv77bc1ZswY+fv7e2Tfubm5bu/z17/+tVO/Xbp0qf36/vvvd/v+AHgPMzYAzjF48GB16dJFa9ascVr+3XffKT8/Xy1atKidDTkrJSVFL774oiSppqZGS5Ys0ZAhQxQdHa2kpCR99dVXtetu3LhRw4cPV3R0tIYNG6aXX365ti0iIkI5OTmSpNjYWL322msaMWKEoqKiNGLECG3btq123cLCQt1///3q37+/br75Zj333HPnnUnKyclRRERE7defffaZxowZo5iYGF133XX63e9+p3/961+SfprpufPOO/Xkk09qwIABiomJ0ezZs1VVVSVJmjFjhubOnav7779fUVFR+s1vfqMVK1bU9m232/XHP/5RgwcPVkxMjB5++GGVlJTUtr/44osaPHiw+vXrp8TERH366aeuDQoAlxBsAJzDYrHorrvuUnZ2tn7+Orm3335bv/3tb8+Zxfmll156SVlZWXrhhReUk5Oje++9V5MnT9auXbtUUVGh6dOna+7cudqxY4cWL16spUuXateuXeft67333tMLL7ygLVu2qEePHpo/f74k6dSpUxo3bpzCwsL05Zdf6i9/+Yu2bNlSG64u5PDhw3rwwQc1ceJEbd26VZ9//rkcDodeeuml2nW+/fZbtWvXTl999ZVee+01rVu3Tps2baptf//995WSkqLt27frvvvuU3p6uo4cOSJJmjVrlr7//nu9//77+uSTTxQYGKgpU6bI4XBo27Zteuedd5Sdna2cnBwlJyc7hSYA9UewAXBeSUlJKikpqZ0hOXHihNavX6+77777ktu+9957mjhxoq699lr5+vrq1ltvVWxsrFavXi1JCggI0OrVq7V161ZdffXV2rFjhyIjIy9YR7du3WSz2RQfH187s/L555+rsrJSjzzyiFq0aKHLLrtMDz74oLKysi5aW0hIiP72t78pNjZWdrtdhw8fVnBwcG0wOVvf/fffLz8/P0VGRioiIkL//Oc/a9v79++vgQMHytfXV4mJiaqurlZhYaGOHTumjRs3avbs2WrXrp1atWqlWbNmaffu3dqzZ49atGihsrIyvfvuu/rf//1fJScna+vWrfLz87vkZwrANVxjA+C8Wrdurdtvv13Z2dmKiYnRe++9p549eyoyMlIFBQUX3bakpERdu3Z1WnbFFVcoLy9PAQEBevvtt/Xyyy/r0Ucfld1u19ChQzVnzhy1bdv2nL7at29f+9++vr61M0g//PCDSktLdf3119e2OxwOVVVV6dixYxeszc/PTx999JFWrVolHx8fhYeHy263y9f3/34ctmvXTj4+Pk7b/Hzm6uczVmdDSU1NjX744QdJ0h133OG0T6vVqn//+9/67W9/qxdffFFvvfWWMjMzFRAQoJSUFKWmpspi4e9MwB0INgAuKCUlRSNHjtTx48f17rvvaurUqZJ+OlX1y2tZjh8/Xvvfl19+uQ4dOuTUfujQIXXs2FF2u13FxcVavHixJGnv3r165JFH9Oqrr+rxxx93ubbOnTvryiuv1IYNG2qX2e12HTt2TCEhIRfcbv369Vq5cqXefvttdevWTZK0YMEC7d+/3+V9X0inTp1q9/Hz8FNQUKCuXbuqqKhI7dq10+uvv67Kykpt3bpVU6ZM0bXXXqubbrqp3vsHwKkoABcRGhqq6Ohopaen6/Tp07rlllskSVdffXXtaSqHw6EPPvhABw4cqN0uOTlZS5Ys0Z49e1RdXa3169frs88+08iRI1VeXq777rtPa9eulcPhUMeOHWWxWBQcHGyotptvvlnl5eXKzMxUZWWlTp48qccff1wPP/yw02zLL/3444+yWCwKCAiQw+HQl19+qb/+9a9uuc6lU6dOuummm7Rw4UIdP35cVVVVeuWVV5SUlKSTJ09q9+7dmjBhgvLy8uTv76927dpJkuFjB3BhzNgAuKjf/e53SktL00MPPVR72qV3795KTU3VjBkzVF5eriFDhmjo0KG124wfP141NTV6+OGHdfToUXXr1k3PPvus+vXrJ0nKyMjQ888/r7lz5yogIEC33nqrxo0bZ6iuwMBALVu2TOnp6crMzFRNTY369++vV1555aLbjRw5Ujt27NBtt90mi8Wiq666Svfee69WrFjhlmfzPP3001q8eLFGjBghu92usLAwZWZmqkOHDho6dKj+9a9/KTU1VcePH1e7du00a9YsXXfddfXeL4Cf+Dh+fuIYAExo69atuvfee7V3715vlwLAwzgVBcDUKioqlJ+fX3vaB4C5cSoKgKnNnj1bX375pR566CFvlwKgAXAqCgAAmAanogAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGkQbAAAgGn4ersAQJKqq6tVVVXl7TIANFH+/v6yWPhbHQQbeJnD4dDhw4d14sQJb5cCoAmzWCy66qqr5O/v7+1S4GU+DofD4e0i0Hz95z//0YkTJ9SxY0e1bNlSPj4+3i4JQBNTU1OjoqIi+fn56corr+TnSDPHjA28prq6ujbUtGvXztvlAGjCOnTooKKiIp05c0Z+fn7eLgdexAlJeM3Za2patmzp5UoANHVnT0FVV1d7uRJ4G8EGXse0MYD64ucIziLYAAAA0yDYoFGqrmm4a9rrsq+8vDyNHz9e/fr108CBA/XYY4+ptLRUkrRz504lJycrKipKsbGxys7Odtp2zZo1iouLU58+fTRq1Cjl5ubWtpWWlurhhx9W//791b9/f02ePFlFRUX1O8C6qmngKf067M9T4/Djjz9qzpw5iomJUf/+/TVz5kydPHmyfsdXR9UNPA5G91efMTjrzTffVEpKinMd1dV66qmndMMNNygqKkqpqakqLi6u20GhWeGuKHhNRUWF/vnPf+qqq65SQEDAOe0PrspVQbHdozWEdgzUC2OiDG1TUVGhIUOG6I477tD999+v8vJyPf7447JYLHrqqad0yy23aOrUqRo9erS2b9+utLQ0LVu2TJGRkcrJyVFqaqqWLl2qyMhIZWVl6dVXX9XmzZtls9n00EMPyWq1asGCBZKk2bNnq7S0VMuXL/fE4V/aexOkkv2e30/7cCkx09AmnhyHKVOm6MiRI8rIyFCbNm00d+5cFRcX66233vLQB3BxM76coYNlBz2+n+5tuyt9ULrL69dnDCTp1KlTysjI0Jtvvql+/fo5fb5//vOftWnTJr322mtq3bq1nnjiCZWXl2vJkiUXrOViP0/QfHBXFBqtgmK79hR556/kiykqKlKPHj2UlpYmq9Uqf39/jR49Wo899pg2bdqkoKAgjR07VpIUExOj+Ph4ZWVlKTIyUtnZ2Ro+fLiio6MlSePGjdM777yjdevWKTExUQcOHFBYWJjO/r1hsVhks9m8dqwq2S/9Z6f39n8RnhqHW2+9VZ9++qlWrFihyy67TJI0c+ZMDRw4UAcOHNDVV1/d4Md6sOyg9pbubfD9Xkp9xkCSEhISFBkZqTvvvFMHDhxw6js7O1vTpk2rHYPZs2frxhtv1KFDh9S1a9eGPVA0KZyKAgzq3r27MjMzZbVaa5dt3LhR1157rfLz8xUeHu60fmhoqPLy8iRJBQUFF21PTU3V5s2bFR0drejoaP3jH/+onb2BM0+NQ01NjRwOh1OgPPtE24MHPT9r0pTUZwwk6a233tLixYvPedzDjz/+qMOHDztt3759e7Vt21b79u3z0NHALAg2QD04HA4999xz2rx5s2bPnq3y8vJzZlgCAgJ06tQpSbpke01NjUaPHq2cnBxt2bJF3bt310MPPdQgx9KUuXMcWrVqpYEDB+q5557T0aNHZbfb9fTTT8tqtaqioqLBjqmpMToGktS5c+fz9lVeXi7p3EdBBAQE1LYBF0KwAerIbrdr6tSpWrt2rVauXKmIiAjZbLZzfvlVVFSoVatWknTR9qNHj2rGjBn6/e9/r7Zt2yokJETz58/XN998w1+pF+HucZCkZ555RsHBwUpISFBiYqL69Omj1q1bq02bNg1zUE1MXcbgYs4GotOnT9dpezRvBBugDgoLC5WYmCi73a7Vq1crIiJCkhQeHq78/HyndQsKChQWFiZJCgsLu2D70aNHVVVVpcrKyto2X9+fLoPjSarn54lxkKSjR4/qiSee0JYtW7Rx40Zdf/31KisrU69evRrgqJqWuo7BxbRt21adOnVSQUFB7bKjR4/qxIkT55zeAn6JYAMYVFZWpnvuuUd9+/bV66+/rpCQkNq2uLg4lZSUaNmyZaqqqtK2bdu0du1aJSYmSpKSkpK0du1abdu2TVVVVVq2bJmOHTumuLg4hYaGqmvXrlq4cKHsdrvsdrv+9Kc/KTIyUr/61a+8dLSNl6fGQfppxiY9PV2VlZU6cuSI/vCHP2j48OG8+uMX6jMGlzJq1Ci98sorOnToUO3/C/369dOVV17pqcOBSXBXFBqt0I6BjXIf77//voqKirR+/Xpt2LDBqS03N1dvvPGGFi5cqIyMDIWEhGjOnDkaMGCApJ/uDJk3b57mz5+vI0eOKDQ0VEuXLlVQUJAk6fXXX9dTTz2lIUOGyGKxaMCAAXrppZdqL15tcO0b6K/jOuzHk+Pw5JNPau7cubrhhhvk5+enYcOG6bHHHqv3YdZV97bdG+V+6jMGl5KWlqYzZ85o7NixKi8vV//+/fX8888bqg/NE8+xgddc7LkT1TUOWS0N84j0htxXk1JTLVmsl16vqe6viaiuqZa1AT+Xht6fu/AcG5zFqSg0Sg0ZNAg1F9DQv9ya4C/ThtDQIaMphhrg5wg2AADANAg2AADANAg2AADANAg2AADANAg28DpuzANQX/wcwVkEG3jN2afp/vzdMQBQF2ef2P3zF3KieeIBffAaq9WqoKAgFRcXS/rphXc+Ptx6DcCYmpoaHT16VC1btqx9DQmaL74D4FVn3+57NtwAQF1YLBZdeeWV/HEEnjyMxqG6ulpVVVXeLgNAE+Xv7++9V4+gUSHYAAAA0yDeAgAA0yDYAAAA0yDYAAAA0yDYAAAA0yDYAAAA0yDYAAAA0yDYAAAA0/j/TfU73EHGmmkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.plot(kind='bar', rot=0)\n",
    "\n",
    "plt.xlabel('Vuosineljännes')\n",
    "plt.ylabel('Myynti miljoonaa euroa')\n",
    "plt.grid(axis='y')\n",
    "plt.legend(loc=(0.2, -0.25), ncol=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1968e5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203f28902d0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df7.T.plot(kind='bar', rot=0)    # T vaihtaa arvosarjat ja luokat päittäin.\n",
    "\n",
    "plt.xlabel('Vuosi')\n",
    "plt.ylabel('Myynti miljoonaa euroa')\n",
    "plt.grid(axis='y')\n",
    "plt.legend(loc=(0.2, -0.25), ncol=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6d609e",
   "metadata": {},
   "source": [
    "## Osa 8 - ympyräkaavio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d948dbc",
   "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>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nordea</th>\n",
       "      <td>0.25910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OP</th>\n",
       "      <td>0.22920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sampo</th>\n",
       "      <td>0.14446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEB Gyllenberg</th>\n",
       "      <td>0.05700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evli</th>\n",
       "      <td>0.05190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muut</th>\n",
       "      <td>0.25820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      %\n",
       "Nordea          0.25910\n",
       "OP              0.22920\n",
       "Sampo           0.14446\n",
       "SEB Gyllenberg  0.05700\n",
       "Evli            0.05190\n",
       "Muut            0.25820"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.DataFrame([0.2591,0.2292,0.14446,0.0570,0.0519,0.2582],\n",
    "                   index=['Nordea','OP','Sampo','SEB Gyllenberg','Evli','Muut'],\n",
    "                   columns=['%'])\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6687d660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "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": [
    "# autopct muotoilee prosenttiluvut, muotoilukoodit hieman hankalia.\n",
    "df8['%'].plot(kind='pie', autopct='%1.1f %%')\n",
    "\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f1f158",
   "metadata": {},
   "source": [
    "## Osa 9 - aikasarjojen vertailu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0231b72",
   "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>Etelä</th>\n",
       "      <th>Keski</th>\n",
       "      <th>Pohjoinen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2009</th>\n",
       "      <td>120</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.2009</th>\n",
       "      <td>130</td>\n",
       "      <td>102</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2009</th>\n",
       "      <td>167</td>\n",
       "      <td>120</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.2009</th>\n",
       "      <td>210</td>\n",
       "      <td>180</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2010</th>\n",
       "      <td>130</td>\n",
       "      <td>103</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.2010</th>\n",
       "      <td>135</td>\n",
       "      <td>109</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2010</th>\n",
       "      <td>172</td>\n",
       "      <td>140</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.2010</th>\n",
       "      <td>213</td>\n",
       "      <td>189</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2011</th>\n",
       "      <td>134</td>\n",
       "      <td>104</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Etelä  Keski  Pohjoinen\n",
       "1.2009    120     98         89\n",
       "2.2009    130    102         95\n",
       "3.2009    167    120        108\n",
       "4.2009    210    180        120\n",
       "1.2010    130    103         88\n",
       "2.2010    135    109         99\n",
       "3.2010    172    140        110\n",
       "4.2010    213    189        123\n",
       "1.2011    134    104         88"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = pd.DataFrame({'Etelä':[120,130,167,210,130,135,172,213,134],\n",
    "                    'Keski':[98,102,120,180,103,109,140,189,104],\n",
    "                    'Pohjoinen':[89,95,108,120,88,99,110,123,88]},\n",
    "                   index=['1.2009','2.2009','3.2009','4.2009','1.2010','2.2010','3.2010','4.2010','1.2011'])\n",
    "df9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96542084",
   "metadata": {},
   "source": [
    "Tässä vaihdan tyylimäärittelyksi **'seaborn-whitegrid'**, jossa taustaviivoitukset ovat valmiina.\n",
    "\n",
    "Huomaa, miten indeksin arvot tulevat automaattisesti kaavion vaaka-akselille.\n",
    "\n",
    "**style**-parametri määrittää viivan ja havaintopisteiden tyylin. Lisätietoa: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f021036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "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.set_style('whitegrid')\n",
    "\n",
    "df9.plot(style='-o', rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc7ce0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "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": [
    "df9.plot(kind='bar', rot=45)    # Edellinen kaavio pylväinä."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71500b1b",
   "metadata": {},
   "source": [
    "Pylväskaavio ei ole havainnollinen aikasarjojen vertailussa (vertaa viivakaavioon)!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50586f8c",
   "metadata": {},
   "source": [
    "## Osa 10 - viivakaavio ja hajontakaavio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6baccc34",
   "metadata": {},
   "source": [
    "Pandas-kirjaston **to_datetime**-funktio osaa muuntaa eri tavoin esitettyjä aikatietoja oikeiksi aikaleimoiksi.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c0ee8e36",
   "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>Kysyntä</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-03</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-03</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-03</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-03</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Kysyntä\n",
       "2011-06-03        5\n",
       "2011-07-03        9\n",
       "2011-08-03        8\n",
       "2011-09-03       10\n",
       "2011-10-03       11"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10a = pd.DataFrame({'Kysyntä':[5, 9, 8, 10, 11]})\n",
    "df10a.index = pd.to_datetime(['6.3.2011', '7.3.2011', '8.3.2011', '9.3.2011', '10.3.2011'])\n",
    "df10a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e84a433a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "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": [
    "df10a['Kysyntä'].plot(style='-o', rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f6f1818",
   "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>Joukkoliikenne</th>\n",
       "      <th>Henkilöauto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>12.5</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>13.3</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>13.1</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>12.9</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>12.8</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>12.8</td>\n",
       "      <td>59.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>12.9</td>\n",
       "      <td>60.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>13.0</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>13.1</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>13.3</td>\n",
       "      <td>63.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>13.6</td>\n",
       "      <td>63.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>13.2</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Joukkoliikenne  Henkilöauto\n",
       "1980-01-01            12.5         34.8\n",
       "1990-01-01            13.3         51.2\n",
       "2000-01-01            13.1         55.7\n",
       "2001-01-01            12.9         57.0\n",
       "2002-01-01            12.8         58.3\n",
       "2003-01-01            12.8         59.6\n",
       "2004-01-01            12.9         60.9\n",
       "2005-01-01            13.0         61.9\n",
       "2006-01-01            13.1         62.5\n",
       "2007-01-01            13.3         63.8\n",
       "2008-01-01            13.6         63.4\n",
       "2009-01-01            13.2         64.3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10b = pd.DataFrame({'Joukkoliikenne':[12.5, 13.3, 13.1, 12.9, 12.8, 12.8, 12.9, 13.0, 13.1, 13.3, 13.6, 13.2],\n",
    "                      'Henkilöauto':[34.8, 51.2, 55.7, 57.0, 58.3, 59.6, 60.9, 61.9, 62.5, 63.8, 63.4, 64.3]})\n",
    "df10b.index = pd.to_datetime(['1980','1990','2000','2001','2002','2003','2004',\n",
    "                              '2005','2006','2007','2008','2009'], format='%Y')\n",
    "df10b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32223960",
   "metadata": {},
   "source": [
    "Huomaa, että alkupäässä havaintojen väli on eri mittainen kuin loppupäässä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68da3287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 70.0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df10b.plot(style='-o', legend='reverse', rot=45, figsize=(12, 6))\n",
    "\n",
    "plt.xticks(df10b.index)\n",
    "plt.ylim(0, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cca5747a",
   "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>Markkinointi (10000 euroa)</th>\n",
       "      <th>Liikevaihto (miljoonaa euroa)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.9</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.3</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.0</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82.9</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.5</td>\n",
       "      <td>39.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>96.3</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99.1</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102.3</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105.0</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>103.9</td>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.7</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>126.3</td>\n",
       "      <td>64.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Markkinointi (10000 euroa)  Liikevaihto (miljoonaa euroa)\n",
       "0                         72.9                           23.2\n",
       "1                         77.3                           29.2\n",
       "2                         78.0                           35.4\n",
       "3                         82.9                           34.7\n",
       "4                         89.5                           39.8\n",
       "5                         96.3                           45.6\n",
       "6                         99.1                           50.2\n",
       "7                        102.3                           55.0\n",
       "8                        105.0                           53.8\n",
       "9                        103.9                           56.7\n",
       "10                       111.7                           58.9\n",
       "11                       126.3                           64.4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10c = pd.DataFrame({'Markkinointi (10000 euroa)':[72.9, 77.3, 78.0, 82.9, 89.5, 96.3, \n",
    "                                                    99.1, 102.3, 105.0, 103.9, 111.7, 126.3],\n",
    "                      'Liikevaihto (miljoonaa euroa)':[23.2, 29.2, 35.4, 34.7, 39.8, 45.6,\n",
    "                                                       50.2, 55.0, 53.8, 56.7, 58.9, 64.4]})\n",
    "df10c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c93c6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Markkinointi (10000 euroa)', ylabel='Liikevaihto (miljoonaa euroa)'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df10c.plot(x='Markkinointi (10000 euroa)', y='Liikevaihto (miljoonaa euroa)', kind='scatter')    # Hajontakaavio."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a2af1c-4122-48b9-9211-03421de02bd3",
   "metadata": {},
   "source": [
    "## Lisätietoa \n",
    "\n",
    "https://tilastoapu.wordpress.com/python"
   ]
  }
 ],
 "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}