{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Dataframe matplotlib-grafiikaksi - perusasiat</h2>\n",
    "\n",
    "<ul><li>Jos teen kaavion suoraan dataframesta (df.plot...), niin käytän pandas-kirjaston matplotlibin pohjalta rakennettua \"viritelmää\" kaavion teosta.\n",
    "    <br>Lue lisää <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html\" target=\"_blank\">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html</a></li>\n",
    "<li>matplotlib.pyplot sisältää komentoja kaavion muotoiluun. Aivan kaikkea näillä komennoilla ei saa tehtyä. \n",
    "    <br>Lue lisää <a href=\"https://matplotlib.org/tutorials/introductory/pyplot.html\" target=\"_blank\">https://matplotlib.org/tutorials/introductory/pyplot.html</a> ja <a href=\"https://matplotlib.org/api/pyplot_summary.html\" target=\"_blank\">https://matplotlib.org/api/pyplot_summary.html</a></li>\n",
    "    <li>Vannoutuneet olio-ohjelmoijat käyttävät mieluummin kaavion luonnin palauttamaa <b>Axes</b>-luokan oliota kaavion muotoiluun.\n",
    "    <br>Lue lisää <a href=\"https://matplotlib.org/api/axes_api.html\" target=\"_blank\">https://matplotlib.org/api/axes_api.html</a></li></ul>\n",
    "    \n",
    "Seuraavassa teen kaaviot suoraan dataframesta ja käytän muotoiluun Axes-oliota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuon kirjastot\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Seuraavan ansiosta kaaviot tulostuvat Jupyter-notebookiin ilman erillistä komentoa\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Myynti 2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Helsinki</th>\n",
       "      <td>1321847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turku</th>\n",
       "      <td>852669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tampere</th>\n",
       "      <td>1032199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oulu</th>\n",
       "      <td>568230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Myynti 2017\n",
       "Helsinki      1321847\n",
       "Turku          852669\n",
       "Tampere       1032199\n",
       "Oulu           568230"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Luon dataframen\n",
    "df1 = pd.DataFrame({'Myynti 2017': [1321847, 852669, 1032199, 568230]},\n",
    "                  index = ['Helsinki', 'Turku', 'Tampere', 'Oulu'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Dataframesta kaavioksi</h3>\n",
    "\n",
    "- <b>df.plot()</b> luo oletuksena viivakaavion\n",
    "- Muita kaaviolajeja <b>df.plot.bar(), df.plot.barh(), df.plot.hist(), df.plot.scatter(), df.box()</b>\n",
    "- Jos aiot muotoilla kaaviota, niin sijoita se muuttujaan. Kaavion luonti palauttaa <b>Axes</b>-luokan olion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d31a3efac8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Myynnit vaakapylväskaaviona\n",
    "df1.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pientä tuunausta kaavion luonnin yhteyteen (selite piiloon, pylvään leveyden määritys)\n",
    "ax1 = df1.plot.barh(legend=False, width=0.7)\n",
    "\n",
    "# Otsikoita\n",
    "ax1.set_title('Myynnit vuonna 2017')\n",
    "ax1.set_xlabel('Miljoonaa euroa')\n",
    "\n",
    "# Useimpiin määrittelyihin löytyy matplotlib.pyplot-vastine\n",
    "# Otsikot voisin määritellä myös seuraavasti:\n",
    "#plt.title('Myynnit vuonna 2017')\n",
    "#plt.xlabel('Miljoonaa euroa')\n",
    "\n",
    "# Haen x-akselin ticksit ja muotoilen miljooniksi yhdellä desimaalilla\n",
    "ax1.set_xticklabels(['{:.1f}'.format(x/1000000) for x in ax1.get_xticks()])\n",
    "\n",
    "# Taustaviivoitus x-akselin mukaan\n",
    "ax1.grid(axis='x')\n",
    "\n",
    "# Taustaviivoitus pylväiden taakse\n",
    "ax1.set_axisbelow(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Myynti 2017</th>\n",
       "      <th>Myynti 2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Helsinki</th>\n",
       "      <td>1321847</td>\n",
       "      <td>1203434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turku</th>\n",
       "      <td>852669</td>\n",
       "      <td>923450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tampere</th>\n",
       "      <td>1032199</td>\n",
       "      <td>1023563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oulu</th>\n",
       "      <td>568230</td>\n",
       "      <td>542399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Myynti 2017  Myynti 2016\n",
       "Helsinki      1321847      1203434\n",
       "Turku          852669       923450\n",
       "Tampere       1032199      1023563\n",
       "Oulu           568230       542399"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teen uuden dataframen\n",
    "df2 = pd.DataFrame({'Myynti 2017': [1321847, 852669, 1032199, 568230],\n",
    "       'Myynti 2016': [1203434, 923450, 1023563, 542399]},\n",
    "       index = ['Helsinki', 'Turku', 'Tampere', 'Oulu'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d31a7a43c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Luokka-akselin nimiöiden asennon säätö (rot=0)\n",
    "ax2 = df2.plot.bar(rot=0)\n",
    "\n",
    "# y-akselin otsikko\n",
    "ax2.set_ylabel('Miljoonaa euroa')\n",
    "\n",
    "# Haen y-akselin ticksit ja muotoilen miljooniksi yhdellä desimaalilla\n",
    "ax2.set_yticklabels(['{:.1f}'.format(y/1000000) for y in ax2.get_yticks()])\n",
    "\n",
    "# Selitteeseen 2 saraketta (selitteet vierekkäin)\n",
    "ax2.legend(ncol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Euroa'),\n",
       " Text(0.5, 0, 'Alue'),\n",
       " Text(0.5, 1.0, 'Myynti vuosina 2016 ja 2017')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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as846i/vuu2+T+A033MCcOXM2/Aaooyr17yBpUUQMLVXeFwk1MyujSy65hKlTp244t1Pwz//8z8ybN4/bbrutQj2rHCceM7MyOvvssze7ugDAf/zHf1SgN+2Dr9VmZma5cuIxM7NcOfGYmVmunHjMzCxXnlxgZp3GgLNvbdP2ll7ypSbLdObbIvz85z/fMBuvrq6Op556iuXLl7PLLrts0b74iMfMbAsU3xYBKOttEYqnXo8ZM6bkbLnCbREWL17MD3/4QyZPngyw4bYI8+bN48knn2TWrFk8+eSTQHb5njvuuIO99957k7a++93vUlNTQ01NDT/5yU/47Gc/u8VJB5x4zMy2WGe9LUKx4v3aUk48ZmZbqLPeFqFg9erV3H777Rx33HHNe0Oa4MRjZraFOuttEQpuvvlmRowY0SbDbODEY2bWJgq3Rag/HFX/tgiFI5A8boswZ86cLbotQsHs2bPbbJgNnHjMzNpEZ7wtAsCqVatYuHAhY8eObbJsc3k6tZl1Gs2Z/lwunfG2CAA33XQTo0aNomfPns16H5rDt0Vogm+L0EK+LYLlyLdFaB98WwQzs3bEt0XYnBOPmVkZ+bYIm/PkAjPr0Hy6oLJa8/478ZhZh9WjRw9WrFjh5FMhEcGKFSvo0aNHi+p5qM3MOqyqqipqa2s3XJLG8tejRw+qqqpaVMeJx8w6rK233pqBAwdWuhvWQh5qMzOzXDnxmJlZrpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7Nc+coFZl1JZ74flO8F1WH4iMfMzHJVtsQjqb+kuyU9JekJSWem+C6SFkh6Nj33TnFJulTSEkmLJX2yqK2JqfyzkiYWxYdIeizVuVSSWrsNMzPLRzmPeOqAf4mI/YDhwBmS9gfOBu6MiEHAnek1wBHAoPSYDEyFLIkA5wHDgEOB8wqJJJWZXFRvdIq3aBtmZpafsiWeiHg5Ih5Jy28DTwF7AmOBGanYDODotDwWuDoyfwF2lrQ7cDiwICJWRsQbwAJgdFq3Y0T8ObKbcVxdr62WbMPMzHKSyzkeSQOATwAPArtFxMuQJSdg11RsT+DFomq1KdZYvLZEnFZso35/J0uqllTt+3yYmbWtsiceSTsANwLfjoi3GitaIhatiDfanebUiYgrImJoRAzt169fE02amVlLlDXxSNqaLOlcExF/SOFXC8Nb6fm1FK8F+hdVrwKWNRGvKhFvzTbMzCwn5ZzVJmAa8FRE/LJo1VygMDNtIjCnKD4hzTwbDqxKw2TzgVGSeqdJBaOA+Wnd25KGp21NqNdWS7ZhZmY5KecPSEcAXwMek1STYj8ALgGul3Qa8ALwlbTuNuBIYAmwGjgFICJWSroIeDiVuzAiVqblbwJXAdsB89KDlm7DzMzyU7bEExH3U/qcCsBhJcoHcEYDbU0HppeIVwMHloivaOk2zMwsH75ygZmZ5cqJx8zMcuXEY2ZmuXLiMTOzXDnxmJlZrpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7NclfPq1GZt7/ydKt2D8jp/VaV7YFZ2PuIxM7NcOfGYmVmunHjMzCxXTjxmZpYrJx4zM8uVE4+ZmeXKicfMzHLlxGNmZrly4jEzs1w58ZiZWa6ceMzMLFdOPGZmlisnHjMzy5UTj5mZ5cqJx8zMcuXEY2ZmuXLiMTOzXDnxmJlZrpx4zMwsV2VLPJKmS3pN0uNFsfMlvSSpJj2OLFr3fUlLJD0t6fCi+OgUWyLp7KL4QEkPSnpW0nWStknxbdPrJWn9gKa2YWZm+SnnEc9VwOgS8V9FxOD0uA1A0v7AOOCAVOdySd0kdQN+CxwB7A+MT2UBfpraGgS8AZyW4qcBb0TEPsCvUrkGt9HG+2xmZk0oW+KJiHuBlc0sPhaYHRHvR8TzwBLg0PRYEhHPRcRaYDYwVpKAzwM3pPozgKOL2pqRlm8ADkvlG9qGmZnlqBLneKZIWpyG4nqn2J7Ai0VlalOsoXgf4M2IqKsX36SttH5VKt9QW5uRNFlStaTq5cuXt24vzcyspLwTz1TgI8Bg4GXgFymuEmWjFfHWtLV5MOKKiBgaEUP79etXqoiZmbVSroknIl6NiPUR8QHwOzYOddUC/YuKVgHLGom/DuwsqXu9+CZtpfU7kQ35NdSWmZnlqFmJR9JwSQ9LekfSWknrJb3V0o1J2r3o5TFAYcbbXGBcmpE2EBgEPAQ8DAxKM9i2IZscMDciArgb+HKqPxGYU9TWxLT8ZeCuVL6hbZiZWY66N10EgMvIPvT/GxgKTAD2aayCpFnASKCvpFrgPGCkpMFkQ1xLga8DRMQTkq4HngTqgDMiYn1qZwowH+gGTI+IJ9ImvgfMlnQx8L/AtBSfBsyUtITsSGdcU9swM7P8NDfxEBFLJHVLH9ZXSvqfJsqPLxGeViJWKP8j4Ecl4rcBt5WIP0eJWWkRsQb4Sku2YWZm+Wlu4lmdhrpqJP2MbGJAz/J1y8zMOqvmTi74Wio7BXiX7CT9ceXqlJmZdV7NOuKJiL+nI569gBuBpyNiXVl7ZmZmnVKzEo+kkWRXA1hK9nuY/pImpqsTmJmZNVtzz/H8AhgVEU8DSNoXmAUMKVfHzMysc2ruOZ6tC0kHICKeAbYuT5fMzKwza+4RT7WkacDM9PpEYFF5umRmZp1ZcxPPN4EzgG+RneO5F7i8XJ0yM7POq8nEk+5ZMy0iTgJ+Wf4udW4D1lxb6S6U1dJKd8DM2r0mz/GkKxX0K9zh08zMbEs0d6htKfCApLlkPyAFICJ8BGRmZi3S3MSzLD22AnqVrztmZtbZNffKBReUuyNmZtY1NPfKBXdT4m6dEfH5Nu+RmZl1as0davtO0XIPsguE1rV9d8zMrLNr7lBb/R+LPiBpYRn6Y2ZmnVxzh9p2KXq5Fdk12j5Ulh6ZmVmn1tyhtkVk53hENsT2PHBauTplZmadV3OH2gaWuyNmZtY1NHrlAkn/WrT8lXrrflyuTpmZWefV1CVzxhUtf7/eutFt3BczM+sCmko8amC51GszM7MmNZV4ooHlUq/NzMya1NTkgoMlvUV2dLNdWia97lHWnpmZWafUaOKJiG55dcTMzLqGJu/HY2Zm1paa+wNSM7Ou7fydKt2D8jp/VW6b8hGPmZnlyonHzMxy5cRjZma5cuIxM7NcOfGYmVmuyjarTdJ04CjgtYg4MMV2Aa4DBgBLgeMj4g1JAn4DHAmsBk6OiEdSnYnAOanZiyNiRooPAa4CtgNuA86MiGjNNsy6igFrrq10F8pmaaU7YM1WziOeq9j8QqJnA3dGxCDgzvQa4AhgUHpMBqbChkR1HjAMOBQ4T1LvVGdqKluoN7o12zAzs3yVLfFExL3AynrhscCMtDwDOLoofnVk/gLsLGl34HBgQUSsjIg3gAXA6LRux4j4c0QEcHW9tlqyDTMzy1He53h2i4iXAdLzrim+J/BiUbnaFGssXlsi3pptbEbSZEnVkqqXL1/eoh00M7PGtZfJBaVusRCtiLdmG5sHI66IiKERMbRfv35NNGtmZi2Rd+J5tTC8lZ5fS/FaoH9RuSpgWRPxqhLx1mzDzMxylHfimQtMTMsTgTlF8QnKDAdWpWGy+cAoSb3TpIJRwPy07m1Jw9NstQn12mrJNszMLEflnE49CxgJ9JVUSzY77RLgekmnAS8AX0nFbyOb5ryEbKrzKQARsVLSRcDDqdyFEVGYsPBNNk6nnpcetHQbZmaWr7IlnogY38Cqw0qUDeCMBtqZDkwvEa8GDiwRX9HSbZiZWX7ay+QCMzPrIpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxyVbbp1Gbl0Jkv6w++tL91DT7iMTOzXDnxmJlZrpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7NcOfGYmVmunHjMzCxXTjxmZpYrJx4zM8uVE4+ZmeXKicfMzHLlxGNmZrly4jEzs1w58ZiZWa6ceMzMLFdOPGZmlisnHjMzy5UTj5mZ5ap7pTtgZtYRDFhzbaW7UFZLc9yWj3jMzCxXTjxmZpariiQeSUslPSapRlJ1iu0iaYGkZ9Nz7xSXpEslLZG0WNIni9qZmMo/K2liUXxIan9JqqvGtmFmZvmp5BHP5yJicEQMTa/PBu6MiEHAnek1wBHAoPSYDEyFLIkA5wHDgEOB84oSydRUtlBvdBPbMDOznLSnobaxwIy0PAM4uih+dWT+AuwsaXfgcGBBRKyMiDeABcDotG7HiPhzRARwdb22Sm3DzMxyUqnEE8CfJC2SNDnFdouIlwHS864pvifwYlHd2hRrLF5bIt7YNjYhabKkaknVy5cvb+UumplZKZWaTj0iIpZJ2hVYIOmvjZRViVi0It5sEXEFcAXA0KFDW1TXzMwaV5EjnohYlp5fA24iO0fzahomIz2/lorXAv2LqlcBy5qIV5WI08g2zMwsJ7knHkk9JfUqLAOjgMeBuUBhZtpEYE5angtMSLPbhgOr0jDZfGCUpN5pUsEoYH5a97ak4Wk224R6bZXahpmZ5aQSQ227ATelGc7dgWsj4nZJDwPXSzoNeAH4Sip/G3AksARYDZwCEBErJV0EPJzKXRgRK9PyN4GrgO2AeekBcEkD2zAzs5zknngi4jng4BLxFcBhJeIBnNFAW9OB6SXi1cCBzd2GmZnlpz1NpzYzsy7AicfMzHLlxGNmZrly4jEzs1w58ZiZWa6ceMzMLFdOPGZmlisnHjMzy5UTj5mZ5cqJx8zMcuXEY2ZmuXLiMTOzXDnxmJlZrpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7NcOfGYmVmunHjMzCxXTjxmZpYrJx4zM8uVE4+ZmeXKicfMzHLlxGNmZrly4jEzs1w58ZiZWa6ceMzMLFdOPGZmlisnHjMzy1WXTDySRkt6WtISSWdXuj9mZl1Jl0s8kroBvwWOAPYHxkvav7K9MjPrOrpc4gEOBZZExHMRsRaYDYytcJ/MzLoMRUSl+5ArSV8GRkfEpPT6a8CwiJhSVGYyMDm9/CjwdO4dbTt9gdcr3YkOzO9f6/m92zId/f3bOyL6lVrRPe+etAMqEdsk+0bEFcAV+XSnvCRVR8TQSvejo/L713p+77ZMZ37/uuJQWy3Qv+h1FbCsQn0xM+tyumLieRgYJGmgpG2AccDcCvfJzKzL6HJDbRFRJ2kKMB/oBkyPiCcq3K1y6hRDhhXk96/1/N5tmU77/nW5yQVmZlZZXXGozczMKsiJx8zMcuXE0w5Jeqfe65MlXdZEnUbLSBoq6dIm2hgg6fHW1G3PJPWRVJMer0h6qej1Ns1s478kHV3uvlZCW7w/1nySqiTNkfSspL9J+k1T77OkpZL65tXHcutykwu6qoioBqrzrtseRMQKYDCApPOBdyLi35tbX1Kn/n+ype9PuUjqFhHrK92PtiRJwB+AqRExNl3C6wrgR8B3K9q5HPmIp4OR1E/SjZIeTo8RJcp8RdLjkh6VdG+KjZR0S1o+X9J0SfdIek7St0q08WFJ/yvpkOK6nYmkfSTVFL0+W9I5afl+ST9K79+UevV+ImmapK0k1UraOcWHS7oj150oM0k3S1ok6QlJhat9dJf0pqSfS3pE0nxJwyQtTH9PR6ZykyTdlNY/XXhv07qJkh5KR1WXp/ey0O7Fkh4CDk1/fwtTH+ZJ2q1Cb0Vb+TywJiKuBEiJ9SzgVEmnF49aSLpF0sjiyvVHJSR9J31Z6FA69Te5Dmy74g9EYBc2/tboN8CvIuJ+SXuRTQvfr179c4HDI+KlwodiCR8DPgf0Ap6WNLWwQtJHya5hd0pE1NT/4+9CdoyIz0A21JaefwlsC0yKiMi+wHZqEyNipaTtgWpJNwJvAzsBf4qI70q6GTgfOAw4GPhP4LZU/1DgQGAt8HD6AlMHHAN8Kv284Qqy39Ndn9p9JCLOkbQtcDcwJiJel3QicBEbL2fVER0ALCoORMRbkl6gC30ed5kd7WDei4jBhReSTgYKl874ArB/0QfejpJ61av/AHCVpOvJDutLuTUi3gfel/QaUPgm2dCDMyIAAAOwSURBVA+YAxzXyX/f1Byz672+APifiPhmJTpTIWdJGpOWq4CPADVkf6MLUvwxYFVKIo8BA4rqz4+INwAk/RH4R7LPnUPIEhnAdsCLqfxa4Ka0vB/ZB/UdqVw3siuPdGSi3iW6moh3Sk48Hc9WwD9ExHvFweJv3hHxDUnDgC8BNZIGs7n3i5bXs/FvYRXZh8AIoLMnnjo2HW7ukWIF79Yr/xAwVFLvwodpvTZ6lKWXFSLpC8BngOER8Z6k+9m4j2uLin7Axr+nD9j0c6X+h2mQfchOj4gf1tted7KEVqgjYHFEfHqLd6b9eAI4rjggaUeyy3itYvO/x/pK/c12OD7H0/H8iaJzDqWSiqSPRMSDEXEu2dVt+9cv04i1wNHABElf3dLOtnOvAHtI6i2pB1mibsytwC+AWyTtkGJLgSFp+bhSlTqwnYCVKekcQHaU0lKjJO2churGkh2N3wEcrzRLS9msur1K1H0S2FPSoancNqkfHdmdwPaSJsCG+4P9ArgKeA4YnM539ScbpqzvVWDX9J5tCxyVT7fblo94Op5vAb+VtJjs3+9e4Bv1yvxc0iCyb4x3Ao8Cn23uBiLiXUlHAQskvUv2TazTiYg1kn5Mdv2+58g+6JqqMzsNbc6R9CWycxu/k/QK2RFRZ3IrMFnSo8BfgQdb0cb9wLVkQ3QzI6IGQNIFZENoWwHryP6GN7lYb0S8r+w2Jpem97w72Yd0hz0ST+cFjwEul/RDsi//twE/IPvS9zzZ0OXjwCMl6q+TdCHZv8XzZP8uHY4vmWNmZZFmwR0YEd+udF+sffFQm5mZ5cpHPGZmlisf8ZiZWa6ceMzMLFdOPGZmlisnHrN2StIxkkLSx9LrklcPN+tonHjM2q/xZL+DGVfpjpi1JSces3YoXRlhBHAaJRKP6t1/qfhKxpJGSfpzunL0fxddZcGsXXDiMWufjgZuj4hngJWSPtmcSukyNOcAX4iIT5LdR+n/lq+bZi3nxGPWPo1n49WxZ6fXzTEc2B94IN1aYyKwd9t3z6z1fK02s3ZGUh+yG4YdKCnIbgcQwOVFxRq6SrGABRHR3ERlljsf8Zi1P18Gro6IvSNiQET0J7sgZFVRmaWUvpLxX4ARkvYBkLS9pH1z7LtZk3zEY9b+jAcuqRe7kewKxgUPUOJKxhGxPN04cFa6bD5k53yeKWeHzVrC12ozM7NceajNzMxy5cRjZma5cuIxM7NcOfGYmVmunHjMzCxXTjxmZpYrJx4zM8vV/wfvMGevQydokAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# legend='reverse' kääntää selitteen järjestyksen\n",
    "# stacked=True pinoaa pylväät päällekkäin\n",
    "# rot=0 määrittää luokka-akselin nimiöiden asennon\n",
    "# width=0.7 määrittää pylväiden leveyden (enintään 1)\n",
    "ax3 = df2.plot.bar(legend='reverse', rot=0, stacked=True, width=0.7)\n",
    "\n",
    "# Otsikoinnit\n",
    "ax3.set(title='Myynti vuosina 2016 ja 2017', xlabel='Alue', ylabel='Euroa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teen uuden dataframen\n",
    "df3 = pd.DataFrame({'Mies':[17,15,6],'Nainen':[8,15,21]},\n",
    "                  index = ['Tyytymätön','Ei tyytymätön eikä tyytyväinen','Tyytyväinen'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d31a8c87b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kaavio ilman tuunausta\n",
    "df3.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rot kääntää luokka-akselin nimiöt 45 asteen kulmaan\n",
    "ax4 = df3.plot.bar(rot=45)\n",
    "\n",
    "# Otsikoita\n",
    "ax4.set_title('Miesten ja naisten vertailua')\n",
    "ax4.set_ylabel('Lukumäärä')\n",
    "\n",
    "# Asetan y-akselin ticksit itse\n",
    "ax4.set_yticks([5, 10, 15, 20])\n",
    "\n",
    "# y-akselin mukainen taustaviivoitus\n",
    "ax4.grid(axis='y')\n",
    "ax4.set_axisbelow(True)\n",
    "\n",
    "# Selite kahteen sarakkeeseen\n",
    "ax4.legend(ncol=2)\n",
    "\n",
    "# Tallennan kaavion tai tarkkaan ottaen tallenna kuvion (figure), jonka sisällä kaavio on\n",
    "# Kuvioon (figure) pääsen käsiksi plt.gcf() -funktiolla (get current figure)\n",
    "# Ilman lisäparametria bbox_inches='tight' kuvion reunoilta voi jäädä osia tallentumatta\n",
    "plt.gcf().savefig('testi1.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tällä selvitän tarjolla olevat kaavion tallennusmuodot\n",
    "plt.figure().canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Usean kaavion kuviot</h3>\n",
    "\n",
    "- Kaavio on aina <b>figure</b> (kuvio) -olion sisällä. \n",
    "- <b>Figure</b>-olion sisään voin sijoittaa useita kaavioita <b>plt.subplots()</b>-funktiolla\n",
    "- <b>plt.subplots()</b> palauttaa <b>figure</b>-olion ja kokoelman <b>Axes</b>-olioista."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2x2 kaaviomatriisi\n",
    "# sharex- ja sharey-lisäparametreilla asetan yhteiset akselit\n",
    "fig, ax5 = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "\n",
    "# Figuren otsikko\n",
    "fig.suptitle('Miesten ja naisten vertailua', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Vasemman yläkulman kaavio\n",
    "df3['Mies'].plot.barh(ax=ax5[0,0])\n",
    "\n",
    "# Oikean yläkulman kaavio; värinä väripaletin toinen väri (C1)\n",
    "df3['Nainen'].plot.barh(color='C1', ax=ax5[0,1])\n",
    "\n",
    "# Vasemman alakulman kaavio\n",
    "df3.plot.barh(legend='reverse', ax=ax5[1,0])\n",
    "\n",
    "# Oikean alakulman kaavio\n",
    "df3.plot.barh(legend=False, stacked=True, ax=ax5[1,1])\n",
    "\n",
    "# Kaaviot kiinni toisiinsa\n",
    "fig.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "# Tallennan kuvion\n",
    "# Ilman lisäparametria bbox_inches='tight' kuvion reunoilta voi jäädä osia tallentumatta\n",
    "fig.savefig('testi2.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Valmiit tyylit</h3>\n",
    "\n",
    "Valmista tyyliä käyttämällä saat nopeasti tuunattua grafiikan ulkoasua.\n",
    "\n",
    "Tyylin voit ottaa käyttöön <b>plt.style.use('tyylin nimi')</b> -komennolla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tarjolla olevien tyylien listaus\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d31ab367f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Esimerkkinä ggplot-tyyli\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "df2.plot.barh()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}