{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Päivitetty 2025-05-15 / Aki Taanila\n" ] } ], "source": [ "from datetime import datetime\n", "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dummy-muuttujien yhteenveto selittävän muuttujan mukaan\n", "\n", "Dummy-muuttujaksi (binäärinen, dikotominen) kutsutaan muuttujaa, joka saa arvokseen joko 0 (joskus tämän sijasta käytetään tyhjää solua) tai 1. \n", "\n", "Esimerkiksi kyselytutkimuksessa monivalintakysymys, jonka vaihtoehdoista vastaaja saa valita useammankin kuin yhden, koodataan dummy-muuttujiksi: Jokainen kysymyksen vaihtoehto on muuttuja, joka saa arvokseen 1, jos vastaaja on sen valinnut. Muussa tapauksessa arvo on 0 tai tyhjä.\n", "\n", "Usein valintakysymyksiä (saa valita vain yhden vaihtoehdon) kutsutaan virheellisesti monivalintakysymyksiksi. Valintakysmystä ei kannata koodata dummy-muuttujiksi paitsi koneoppimisen malleja käytettäessä." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nrosukupikäperhekoulutuspalveluvpalkkajohtotyötovtyöymppalkkattyötehttyötervlomaosakuntosahieroja
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2313011.07.0198934.01131.0NaNNaNNaN
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4512412.04.0218323.02121.0NaNNaNNaN
...................................................
777812213.00.0159844.0434NaN1.01.0NaN
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82 rows × 16 columns

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" ], "text/plain": [ " nro sukup ikä perhe koulutus palveluv palkka johto työtov työymp \\\n", "0 1 1 38 1 1.0 22.0 3587 3 3.0 3 \n", "1 2 1 29 2 2.0 10.0 2963 1 5.0 2 \n", "2 3 1 30 1 1.0 7.0 1989 3 4.0 1 \n", "3 4 1 36 2 1.0 14.0 2144 3 3.0 3 \n", "4 5 1 24 1 2.0 4.0 2183 2 3.0 2 \n", ".. ... ... ... ... ... ... ... ... ... ... \n", "77 78 1 22 1 3.0 0.0 1598 4 4.0 4 \n", "78 79 1 33 1 1.0 2.0 1638 1 3.0 2 \n", "79 80 1 27 1 2.0 7.0 2612 3 4.0 3 \n", "80 81 1 35 2 2.0 16.0 2808 3 4.0 3 \n", "81 82 2 35 2 3.0 15.0 2183 3 4.0 4 \n", "\n", " palkkat työteht työterv lomaosa kuntosa hieroja \n", "0 3 3 NaN NaN NaN NaN \n", "1 1 3 NaN NaN NaN NaN \n", "2 1 3 1.0 NaN NaN NaN \n", "3 3 3 1.0 NaN NaN NaN \n", "4 1 2 1.0 NaN NaN NaN \n", ".. ... ... ... ... ... ... \n", "77 3 4 NaN 1.0 1.0 NaN \n", "78 1 2 1.0 NaN NaN NaN \n", "79 3 3 1.0 NaN 1.0 NaN \n", "80 3 3 NaN NaN NaN NaN \n", "81 3 4 1.0 NaN NaN NaN \n", "\n", "[82 rows x 16 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel('https://taanila.fi/data1.xlsx')\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Yhteenvetoon otettavien dikotomisten muuttujien nimet\n", "dikot = ['työterv', 'lomaosa', 'kuntosa', 'hieroja']\n", "\n", "# Miesten ja naisten lukumäärät\n", "miehet_n = len(df[df['sukup']==1])\n", "naiset_n = len(df[df['sukup']==2])\n", "\n", "# Miesten ja naisten otsikoinnit\n", "miehet = f'Miehet, n = {miehet_n}'\n", "naiset = f'Naiset, n = {naiset_n}'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lukumäärät taulukkona ja grafiikkana\n", "\n", "Seabornin barplot sopii hyvin yhteenvedon graafiseen esittämiseen, koska kaaviolle voi estimator-parametrilla määrittää laskentamenetelmäksi summan (summaa yhteen muuttujan arvoina olevia ykkösiä)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 työtervlomaosakuntosahieroja
Miehet, n = 633516813
Naiset, n = 1912419
\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Valintojen lukumäärät sukupuolen mukaan\n", "df1 = df.groupby('sukup')[dikot].sum()\n", "\n", "# Riviotsikot (edellä määritellyt miesten ja naisten otsikoinnit)\n", "df1.index = [miehet, naiset]\n", "\n", "# Ulkoasun viimeistely\n", "df1.style.format('{:.0f}')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Lukumäärä')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+t46uWrUqQkNDYW5ujunTpyMoKAg3btzAmjVr0Lp1awDAJ598gk2bNiEhIQGjRo3CpEmTIJVKsWXLFtWFjo0aNUK3bt0QFhaGCRMmAHgVJL766qt8L+7UdsHBwfjss8+wbNkyjB07FsbGxti2bVuRThGXd3oCf8GENMDf3x8rV67kNQJEFcCkSZPg5+fHu92WIxx5oDJ36tQpyGQyBgeiCuDvv/9GdHR0qV6fQGWPIw9U5uLi4mBqagorKytNl0JEpSw5ORkvX75EjRo1NF0KlSCGByIiIhKFpy2IiIhIFIYHIiIiEoXhgYiIiEQpdzeJUiqVkMvlkEgk7/yRKCIqmCAIUCqV0NfX1+obYLHPE5Wcovb7chce5HI5YmJiNF0GUbnh6OioNXcCzQ/7PFHJe1e/L3fh4XVScnR0LPP7rBOVJwqFAjExMVo96gCwzxOVpKL2+3IXHl4PW0qlUh5IiEqAtp8KYJ8nKnnv6vfa/ZGCiIiItA7DAxEREYnC8EBERESiMDwQERGRKAwPREREJArDAxEREYnC8EBERESiMDy8p5CQEHh6eiIkJETTpRAREZUphof3IJPJEBERAaVSiYiICMhkMk2XREREVGYYHt6DXC6HUqkE8L8f5SEiIqooGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRNBoeFAoFAgICMGXKFNW86Oho+Pr6wsnJCR4eHtizZ48GKyQiIqK3aTQ8rF27FpcuXVJNp6WlYejQofDx8cHFixexYMECLFq0CNeuXdNglURERPQmjYWHc+fO4ejRo+jcubNq3tGjR2FlZQV/f3/o6+ujTZs28Pb2RlhYmKbKJCIiorfoa2Kjz58/x/Tp07Fu3Tps3bpVNf/u3buws7NTW9fW1hbh4eGit6FQKIpbZpHbVigUpbo9Ik3Qtde0rtVLpI2K2o/KPDwolUpMnDgRAwYMQOPGjdWWZWVlwcTERG2esbExXrx4IXo7MTExxaqzMNnZ2Xm29XbdRFS2SrPPE5G6Mg8PGzZsgKGhIQICAvIsMzExQUZGhto8mUwGU1NT0dtxdHSEVCp97zoLk5mZmWdbZmZmpbItIk1RKBQ69YZcmn2eqKIoar8v8/Bw4MABPH36FC1btgQA1e9C/P7775g0aRLOnDmjtv69e/fQqFEj0duRSqWldiB5u93S3BYRFQ37IVHZKfMLJqOionD58mVcunQJly5dQrdu3dCtWzdcunQJnp6eSEpKwtatW5Gbm4vz588jMjISvXr1KusyiYiIqABadZOoypUrY/PmzYiKioKrqytmzJiBGTNmoHXr1poujYiIiP5LI9+2eNPixYvVph0dHbFz504NVUNERETvolUjD0RERKT9GB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRKkx4UCiVmi6hSHSlTiIiqrj0NV1AWZFKJJjx02k8fJpW7LaUuTK16SHroyAxMC52ux9WtcR8v3bFboeIiKg0VZjwAAAPn6bhVlxysdvRk+fA8o3pu/GpEPQNi90uERGRLqgwpy2IiIioZDA8EBERkSgMD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0RERCSKRsLDuXPn4OvrixYtWsDNzQ3z5s2DTCYDAERHR8PX1xdOTk7w8PDAnj17NFEiERERFaDMw0NycjKCgoLQt29fXLp0Cfv378eff/6JjRs3Ii0tDUOHDoWPjw8uXryIBQsWYNGiRbh27VpZl0lEREQF0C/rDVpbW+Ps2bMwMzODIAhITU3Fy5cvYW1tjaNHj8LKygr+/v4AgDZt2sDb2xthYWH4+OOPy7pUIiIiykeZhwcAMDMzAwC4u7sjMTERLVu2RM+ePbFy5UrY2dmprWtra4vw8HDR21AoFGrTUqn0/QsuY2/XTqQJuvY61LV6ibRRUfuRRsLDa0ePHkVaWhomTJiAUaNGoVq1ajAxMVFbx9jYGC9evBDddkxMjOr/JiYmcHBwKHa9ZeX27dvIzs7WdBlEOuXNPk9EpUuj4cHY2BjGxsaYOHEifH19ERAQgIyMDLV1ZDIZTE1NRbft6OioU6MNb7K3t9d0CURQKBQ69Yasy32eSFsUtd+XeXi4fPkypk2bhoMHD8LQ0BAAkJOTAwMDA9ja2uLMmTNq69+7dw+NGjUSvR2pVKqzBxJdrZtIk3S5zxPpmjL/toW9vT1kMhmWLVuGnJwcxMXF4bvvvsOXX34JLy8vJCUlYevWrcjNzcX58+cRGRmJXr16lXWZREREVIAyH3kwNTVFaGgoFi5cCDc3N5ibm8Pb2xsjR46EoaEhNm/ejAULFmD16tWwtrbGjBkz0Lp167Iuk4iIiAqgkWsebG1tsXnz5nyXOTo6YufOnWVcERERERUVb09NREREojA8EBERkSgMD0RERCQKwwMRERGJ8l4XTJ4/fx6JiYkQBAEAkJubi9u3b2PGjBklWhwRERFpnyKHh19++QXdunXD/PnzsXPnTtVdHxUKBbKysuDh4VFqRRIREZH2KPJpi//85z8AgF9//RU7duzAypUr4eHhgYsXL+Lrr7+GpaVlqRVJRERE2kPUyAPw6rcmmjdvjmfPnuHGjRvQ09PDN998g88//7zUiiQiIiLtUeSRh6FDhwIAqlevjufPn8PGxgYJCQnIzc2FsbExsrKySq1IIiIi0h5FHnlwdnYGALi7uyMwMBDbtm1Dq1atMG3aNBgZGeHDDz8stSK1jSCRQIAe9CBAgB4ECb+0QkREFUeR3/WCgoIAAOPGjUOPHj1gYGCAWbNmITU1Fffu3cO8efNKrUitI9FHjk1jCNBDjk1jQKLRXzYnIiIqU6Lf9RYvXoyxY8fCzMwM5ubm+OGHH0qjLq2XXccF2XVcNF0GERFRmRM93h4ZGQkTE5PSqIWIiIh0gOiRh169eiE4OBg9e/aEjY0N9PT0VMtq1qxZosURERGR9hEdHrZs2QIA2L17tyo4CIIAPT093Lx5s2SrIyIiIq0jOjwcO3asNOogIiIiHSE6PNSqVSvPPLlcjjt37uS7jIiIiMoX0eHh5MmTCA4OVvthLAAwMjLC1atXS7I2IiIi0kJFDg8tWrTA5cuXsWTJEnTu3BkWFha4ffs2unXrhpCQEHz55ZelWScRERFpiSJ/VXPjxo0AgCdPnmDixIno2rUrUlJS0LlzZyxbtgw7d+4stSKJiIhIexQ5PCQkJAAArK2tIZFIULNmTdy/fx8AYGtri8TExNKpkIiIiLSK6J/ktre3x6pVqwAAVapUwalTp3DhwgXeOIqIiKiCKHJ4eP2T3BMnTsTvv/+OZ8+eYdSoURgxYgQCAwMxcODAUiuSiIiItIfob1s0bNgQhw4dAvDqa5snTpxAVlZWhfpVTSIioorsvX4O8vHjx2pf1czNzcWpU6cQGBhYkrURERGRFipyePjrr7/g7OyMDRs2YMWKFXluTd2sWTOGByIiogqgyNc8DB48GAAQFhaG1atXY/369fD19cX58+fRpUsXODk5lVqRREREpD2KHB6uXLkCAMjIyEDnzp3RuHFjXL9+HVZWVpg+fTqioqJKrUgiIiLSHkUOD3PmzAEAVK1aFZmZmahWrRqePHkCQRBgbW2N9PT00qqRiIiItEiRw8PriyNbtWqFUaNGISMjAw4ODli+fDnWrl2LatWqlVqRVDZCQkLg6emJkJAQTZdCRERarMjhITg4GAAwZcoU1KtXD3K5HNOmTcPvv/+OXbt2Ydq0aaVWJJU+mUyGiIgIKJVKREREQCaTabokIiLSUqK/qmlmZobZs2cDeHWr6sOHD5d4UVT25HI5lEolAECpVEIul2u4IiIi0laiw0Nqaip++uknxMXFqd5sXlu0aFGJFUZERETaSXR4GDNmDP799180b94cEkmRz3oQERFROSE6PERHR+PEiROwsrIqhXKIiIhI24keOqhbty5yc3NLoxYiIiLSAaJHHmbNmoWhQ4fCx8cHlpaWast8fHxKqi4iIiLSUqLDQ3h4OO7cuYMtW7aoXfOgp6fH8EBERFQBiA4PUVFROHDgAGxtbUujHiIiItJyoq95qFy5MurWrVsatRAREZEOED3yMGrUKEydOhWDBg2CpaWl6qe5AaBmzZolWhwRERFpH9HhYcqUKQCAQ4cOqYKDIAjQ09PDzZs3S7Y6IiIi0jqiw8OxY8eKvdFbt27hu+++w40bN2BgYAA3NzdMmTIF1tbWiI6Oxvz583Hv3j1UrlwZw4cPh6+vb7G3SURERCVD9DUPtWrVKvBfUchkMgwePBhOTk74v//7P/zyyy9ITU3FtGnTkJaWpvoa6MWLF7FgwQIsWrQI165dE71jREREVDpEjzw0btxY7TqHNxXltEV8fDwaN26MkSNHQiqVwtDQEF999RUmTZqEo0ePwsrKCv7+/gCANm3awNvbG2FhYfj444/FlkpERESlQHR42L59u9p0cnIyfvzxR/To0aNIj2/QoAFCQ0PV5h05cgRNmjTB3bt3YWdnp7bM1tYW4eHhYsuEQqFQm5ZKpaLb0JS3a9fENhUKhUbqoHdbv349Dhw4gB49emD48OGlth1de/51rV4ibVTUfiQ6PLi4uOSZ5+zsjMDAQPTu3VtUW4IgYOXKlThx4gR27NiB7du3w8TERG0dY2NjvHjxQmyZiImJUf3fxMQEDg4OotvQlNu3byM7O7tMt/n29mJiYvI8F6R5OTk5iIiIgCAIOHDgAJydnWFoaKjpsrTCm32eiEqX6PCQHwsLCyQmJop6TGZmJqZOnYobN25gx44dsLe3h4mJCTIyMtTWk8lkMDU1FV2To6OjTo02vMne3r7Mt5mZmak27ejoCDMzszKvgwqXmZkJQRAAAEqlEg4ODqX2PCkUCp16Q9blPk+kLYra70WHh4iICLXp3NxcHDt2DB999FGR24iNjcWQIUNQs2ZNhIeHw9raGgBgZ2eHM2fOqK177949NGrUSGyZkEqlOnsg0UTdb29Tl/9+5Rmfp4Lxb0FUdkSHh9WrV6tNS6VSNGzYELNnzy7S49PS0vD111+jdevWWLBggdrvY3h6emLJkiXYunUr/P398ddffyEyMhLr1q0TWyYRERGVEtHh4fjx4/nOl8lkRXr8vn37EB8fj8OHDyMqKkpt2ZUrV7B582YsWLAAq1evhrW1NWbMmIHWrVuLLZOIiIhKyXt926J///5q865evYrJkyfjyJEj73z8gAEDMGDAgAKXOzo6YufOnWLLIiIiojIi+iZR69evx759+wAAcrkcy5cvR79+/dC2bdsSL46IiIi0j+iRh02bNmHQoEFISUnBL7/8gvT0dISGhvLUAhERUQUhOjw4ODggNDQUAwYMQJMmTfDTTz/xfgBEREQVSJHDw9q1a9WmW7RogfPnz2PDhg3Q13/VzDfffFOy1REREZHWKXJ4uHDhQp55jo6O+OuvvwCgwN+7ICIiovKlyOHhxx9/LM06iIiISEcU+w6Tb/Lx8SlGKURERKQLin2HybS0NGRnZ8PZ2ZnhgYiIqAIo9h0mBUHADz/8gNTU1JKqiYiIiLSY6JtEvU1PTw+DBg3CgQMHSqIeIiIi0nLFDg8A8PDhQ37bgoiIqIIQfdoiICBALSjk5ubi9u3b6N69e4kWRkRERNpJdHhwdXVVm5ZIJAgMDESnTp1KrCgiIiLSXqLDQ353kVQoFIiNjcWHH35YIkURERGR9iqRax6SkpLQpUuXkmiKiEhrhYSEwNPTEyEhIZouhUijSiQ8AK++skllS1AqNF1CkehKnUSFkclkiIiIgFKpREREBGQymaZLItIY0actCsJvW5Q9PYkUSfumIDfpQbHbyspRqk0nbA2EqWHxs6XBBw3wQc/FxW6HSNPkcjmUylf9RKlUQi6Xa7giIs0psfBAmpGb9AC5CTeL3Y5crgfA5n/TT+8gV5+jSURElFeRw8PFixcLXJacnFwixRAREZH2K3J4CAgIKHQ5T1sQERFVDEUOD7du3SrNOoiIiEhHlNi3LYiIiKhiYHggIiIiURgeiIiISBSGByIiIhKF4YGIiIhEYXggIiIiURgeiIiISBSGByIiIhKF4YGIiIhEYXigci0kJASenp4ICQnRdClERO9FG49jDA9UbslkMkRERECpVCIiIgIymUwjdQgKhUa2K5au1ElUkWjLcext/EluKrfkcjmUSiUAQKlUQi6Xa6QOPakUN+fNw4t//il2W9lvvcFf/fZbmEilxW63Ur16+GjmzGK3Q0QlS1uOY29jeCAqAy/++QeZd+4Wux0ZABj8r9tm3X8AjhcQUVnjaQsiIiISheGBiIiIRGF4IKJyS/Hfc8XaTlfqJHqN1zwQUbkllUgw46fTePg0rdhtKXPVr3Ifsj4KEgPjYrf7YVVLzPdrV+x2iMoSwwMRlWsPn6bhVlxysdvRk+fA8o3pu/GpEPQNi90ukS7iaQsiIiISheGBiIiIRGF4ICIiIlE0Gh6Sk5Ph6emJCxcuqOZFR0fD19cXTk5O8PDwwJ49ezRYIREREb1NY+Hhr7/+wldffYXY2FjVvLS0NAwdOhQ+Pj64ePEiFixYgEWLFuHatWuaKpOIiIjeopHwsH//fkyYMAFjx45Vm3/06FFYWVnB398f+vr6aNOmDby9vREWFqaJMomIiCgfGvmq5ieffAJvb2/o6+urBYi7d+/Czs5ObV1bW1uEh4eL3obirR8QkpbAjweVlbdrL0h53KfS3KZCodBIHbr6PGnib1Uc+dWrq397otfK+jhW1LY1Eh5sbGzynZ+VlQUTExO1ecbGxnjx4oXobcTExKj+b2JiAgcHB9FtaMrt27eRnZ1d6DrlcZ9K2tvbi4mJyfP6Km18nsrOm30e4N+eygdtOI7lR6tuEmViYoKMjAy1eTKZDKampqLbcnR01KlPHW+yt7fXdAklThP7lJmZqTbt6OgIMzOzMq9Dl7z5PCkUijxvyNpMl/s8UD77PRVfWR/HitrvtSo82NnZ4cyZM2rz7t27h0aNGoluSyqV6uyBRFfrLowm9untberya6Ks6PLfR9efX12unUqPth7HtOo+D56enkhKSsLWrVuRm5uL8+fPIzIyEr169dJ0aURERPRfWhUeKleujM2bNyMqKgqurq6YMWMGZsyYgdatW2u6NCIiIvovjZ+2uH37ttq0o6Mjdu7cqaFqiIiI6F20auSBNEeqJ0APAgBAAgFSPUHDFRER6SZBR752W5w6NT7yQNrBSAp0qpWNY3Em6FgrG0aavx6HiEgn6UmluDlvHl7880+x28p+6w3+6rffwqQELpisVK8ePpo5870fz/BAKn62mfCzzXz3ikREVKgX//yDzDt3i92ODAAM/vdWnXX/AbRhXIOnLYiIiEgUhgciogosJCQEnp6eCAkJ0XQppEMYHkirKJTaMCD3brpSJ5UcQSKBAL1X/4ceBInuHz5lMhkiIiKgVCoREREBmUym6ZJIR/CaB9IqUokUwUeC8SjlUbHbUr5Uqk2P3DsSEqPiH/DrV66P2V6zi90O6RiJPnJsGsPw2S3k2DQGJLp/+JTL5VAqX/UTpVIJuVyu4YpIV+j+q5/KnUcpj3Dn2Z3iN5QDGMNYNXkv6R5gWPxmqeLKruOC7Doumi6DSON0f9yNiIiIyhTDAxEREYnC8ECkQ6QA9IRXd//UEwTwXl5EpAkMD0Q6xACAi1KAniDARSnAQNMFEVGFxAsmiXSMl1IJL+W71yMiKi0ceSAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiItJS2npLeoYHIiIiLaWtt6Tn7amJiIi0mDbekp4jD0RERCQKwwMRERGJwvBARKRDBKVC0yUUia7USe+H1zwQEekQPYkUSfumIDfpQbHbyspRP5GesDUQpobF/0xp8EEDfNBzcbHbIe3F8EBEpGNykx4gN+FmsduRy/UA2Pxv+ukd5OoLxW6Xyj+etiAiIiJRGB6IiKhcCQkJgaenJ0JCQjRdSrnF8EDllwQQ8GoIVtAT+GonqgBkMhkiIiKgVCoREREBmUym6ZLKJR5OqfzSBxQNFBD0BCg+VPAKH6IKQC6XQ6l8dSGoUqmEXC7XcEXlEw+nVK7JP5ZD/jEPHkREJYkjD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0RERCQKwwMRERGJwvBAREREojA8EBERkSgMD0REFZRUT4Def2/hLoEAqV7Z/6KmQqko822+L12qtbRp5R0mnz9/jpkzZ+LPP/+EVCpF9+7dMXnyZOjra2W5REQ6yUgKdKqVjWNxJuhYKxtG0rKvQSqRIvhIMB6lPCqR9pQvlWrTI/eOhMSo+J+T61euj9les4vdTnmhle/GY8aMQbVq1XD69GkkJSVh+PDh2Lp1KwYPHqzp0oiIyhU/20z42WZqtIZHKY9w59mdkmksBzCGsWryXtI9wLBkmqb/0brTFv/88w/+/PNPTJw4ESYmJqhTpw5GjBiBsLAwTZdGRERE0MKRh7t378LKygrVqlVTzWvYsCHi4+ORnp4OCwuLQh8vCK/O2eXk5EAq/d8YnFQqRaPqljCU6pVO4SWgno0FFAoFFIqinVeTSqWQ2thBKdHeWC2tUl/0Ptla28JAYlDKlb2/elb1RO+TScOGgIH27pNJ3bp59un1/1/3KW1VUJ8H2O81RUy/L+k+L7wUEGcUp5puULUB9IyK//yL6fe62ueBovd7PUHLjgwHDhzAihUrcPLkSdW82NhYeHp64tSpU6hevXqhj8/JyUFMTEwpV0lUcTg6OsLQUHvfqNjniUreu/q91o08VKpUCdnZ2WrzXk+bmpq+8/H6+vpwdHSERCKBnp72ftog0naCIECpVGr9hcrs80Qlp6j9XuuOCo0aNUJqaiqSkpLwwQcfAADu37+P6tWrw9zc/J2Pl0gkWv0piYhKFvs8UdnTugsm69evD2dnZyxcuBCZmZl4/Pgx1q1bhy+//FLTpRERERG08JoHAEhKSsLcuXNx4cIFSCQS+Pj4YMKECXkuhiIiIqKyp5XhgYiIiLSX1p22ICIiIu3G8EBERESiMDwQERGRKBUqPLx8+RIJCQnFbufRo0fFL4bKBJ8rYr+vePhclb4KFR78/Pxw9uzZd64XHx8PJycnxMfHY9asWZg1a5Zq2d9//41u3bqVZpkFevLkCezt7fHkyRONbL+0lNZ+hYWFYebMmSXa5vsqbB8PHjyIrl27lsp233wtV1S63O/La58Hyn+/L+99XutuElWaUlJSirRezZo1ceXKFQDA3Llz1ZZlZGQgNze3xGujkpecnKzpEoqke/fu6N69e6m0/eZruaJiv69YdKHfl4c+X2FGHgYOHIj4+HjMnj0bgYGBeZJpUFAQVq1aBQC4dOkS/P390bJlS3h4eGDlypXIycnB48ePMWTIEACAk5MTrly5AkEQsH37dnh5eaFly5bw8/PD9evXVe16eHhg1qxZcHNzg4+PD/z9/bF8+XK1bfv6+iI0NFTU/sTFxWHMmDFo06YN3NzcMH78eDx9+hQAcOHCBXh4eCA0NBRubm5wdnbG8uXLcezYMXh5ecHJyQnffvstcnJyAACJiYkYM2YMPDw80KxZM3Ts2BHh4eFF2pZcLsecOXPg5uYGV1dX+Pn54a+//gLw6janGzduhLe3N1q2bIlWrVph/PjxkMlkhe7b2rVr0a5dO4SFhcHe3l5t2ZQpUzBlyhQAwJo1azBq1ChMmDABLVu2RPv27bFs2TIAwP79+7FhwwZcunQJLVu2BPDqTWTmzJn45JNP4OrqiqCgILXhzTVr1sDd3R0uLi7o1asXjh07ploWHh6Onj17wtXVFU5OTggKCnqvg1RkZCQ+//xzNG/eHIGBgUhMTMS+ffvg4eGhWufGjRsICAhAq1at0LlzZ2zdulX1IzVr1qzBwIED0atXL7i4uODixYuF7tfbn34uX76M/v3745NPPoGjoyN69uyJq1evit4PXVGe+n157vNA+e335bbPCxVIhw4dhL179wqHDh0SWrZsKbx8+VIQBEF49uyZ0KRJEyE2Nla4f/++0LRpU2Hr1q3Cy5cvhUePHgne3t7CvHnzBEEQhPPnzwt2dnaqNnfs2CF8+umnws2bN4WcnBxhz549QsuWLYVnz56pttmjRw8hLS1NSEtLE/bv3y+4u7sLCoVCEARBuHfvntCkSRPV+oV5/PixYGdnJzx8+FDo3LmzMG7cOCE9PV1IS0sTxo0bJ3zxxRdCbm6uqsaFCxcKOTk5wsmTJwU7OzthwIABQmpqqhAbGyu0atVK2L9/vyAIgjB48GBhwoQJwosXLwS5XC5s3rxZ+Pjjj4XMzEwhJyen0G2Fh4cL3bt3F9LS0gS5XC4sX75c8Pb2FgRBEA4dOiS4ubkJDx8+VO2ri4uLsHv37nz36/Hjx8LKlSsFDw8PITY2Ns/fWhAEYfLkycLkyZMFQRCE1atXC/b29sL+/fsFuVwunDx5UrC3txeuXLmiWt6vXz/VY/v16yf0799fePr0qZCdnS0sXrxYcHd3FzIyMoRz584Jbm5uQmJioqBUKoWff/5ZcHV1FXJycoTo6GihWbNmQnR0tCAIgvDvv/8KnTt3FlasWPHO5+ztfRw5cqSQnp4upKamCj4+PsLMmTOFvXv3Ch06dBAEQRASEhIEZ2dnYceOHUJOTo5w9+5dwdPTU/j5559V+9S4cWPh7NmzQmZmppCbm1vofr35t83OzhZcXFyEHTt2CAqFQsjKyhJGjx4t9O3bt8j7oYt0ud+X1z7/5r6V135f3vt8hRl5eFOnTp0gkUhw/PhxAK+SoZOTE+rUqYPIyEjY29vj66+/hqGhIerVq4fx48djz549UCqVedoKCwtDUFAQGjduDAMDA3z55Zdo2LAhDh48qFrHy8sLFhYWsLCwwGeffYasrCxcuHABALBv3z64u7urfsejKC5duoTHjx8jODgY5ubmsLCwQHBwMG7duqX26ScoKAgGBgb45JNPAAB9+/aFpaUl6tSpg0aNGqmS6fz58zF79mwYGBggPj4epqamkMlkSEtLe+e2jI2N8eTJE4SHh+Phw4cYPXq0at/bt2+P8PBw1K9fH8nJyUhJSYGVlRUSExPz3a9Vq1YhNDQUO3bsQJ06dYr0t6hfvz58fHwglUrh7u4OGxubfC+Wevz4Mf7880/MnDkTNjY2MDY2xoQJEyCXy3Hq1CkYGRkhLS0Nu3fvxt9//w1fX1+cO3cOBgYGsLOzwy+//IKPP/4YaWlpePr0KaytrQvcj8IMGzYM5ubmsLS0RLt27RAbG6u2/ODBg2jYsCH8/f1hYGAAW1tbDBo0CGFhYap16tSpgzZt2sDU1BT//vtvofv1JgMDA+zatQt+fn7IyclBXFxcoc9HeaPL/b689nmg/Pf78trnK9Q1D68ZGhqiW7duOHDgAD777DPs378fAwcOBAA8f/48zwu4du3akMlkeP78eZ624uLi8N1332Hp0qWqeXK5HE2bNlVNV61aVfV/Y2NjeHt7IyIiAi4uLjh48CDmzZsnqv7nz5+jcuXKMDMzU80zMzODlZUV4uLiVAekypUrA4Dqtt4WFhaq9SUSiWpY7PHjx/j+++/x6NEj1K9fH/Xq1QMAKJXKd26ra9euyM3NxZ49e7B8+XJUqVIFw4YNQ9++fSEIAlasWIETJ07A2toaH330EXJzcwv8nfi7d+/CysoKkZGRGDp0aJH+FjY2NmrTBgYG+R7sk5KSAEDtuZVKpahRo4ZqP9asWYMff/wRoaGhMDY2RkBAAIYPHw6JRILt27cjMjISlSpVgr29PTIzM9/5e/f5sbKyUqtVoVCoLY+Li8ONGzdUQ67Aq+fhzVuzv/l6etd+NWvWTG3+hQsXMGTIELx48QK2trbQ19d/r/3QRbrc78trnwfKf78vr32+QoYHAOjVqxd69+6NK1eu4MmTJ/Dy8gIA1KpVC0ePHlVbNzY2FoaGhrC0tMzTTvXq1TFq1Ci1K2djY2PVXjBv/0xw79690bdvX3h6ekJPTw/t2rUTVbuLiwtWrVqFzMxMVQfPyMhASkoKbGxsVC+Movw8cW5uLoKCgjBu3Dj4+flBT08P169fV32SqFWrFlJSUgrc1sOHD9GkSRP4+PhAJpMhKioKkydPRsuWLbFjxw7Ex8fj+PHjqsd6e3sXWMuKFSvw6NEjjBo1Cu7u7qrOk5OTo/rVxJSUFNUBUoxatWoBePXcNGrUCACgUCgQHx8PGxsbxMfHo0qVKti0aRNycnJw7tw5fPPNN2jSpAnu3LmDM2fOIDIyUnWQHjZsmOgaiqJ69epwdXXFpk2bVPNSUlKQlZWlmn7zeX3Xfr0pOjoa8+bNw86dO1Vvcps3b8bDhw9LZV+0ka72+/La5wH2e13t8xXqtIWhoSEyMjIAAA4ODrC1tcXcuXPRpUsXmJiYAAC6du2K+/fvY9u2bcjJyUFsbCyWL18Ob29vGBoawsjICABU7fTu3Rvr16/H/fv3AQCnT59G165dcfHixQLraNy4MRo0aICFCxfiiy++EP2DX9bW1rC1tcXs2bORkZGBjIwMzJkzB3Xr1kWLFi1EtZWbmwuZTAZjY2Po6ekhPj4eS5YsUS1zdHQsdFsnTpzAN998gydPnsDY2BhWVlbQ19eHubk5MjMzYWRkBKlUipcvX2Lz5s24c+dOgVetGxgYoEOHDujSpQsmTZqEGjVqQF9fH4cOHQIAnD17FufPny/yvhkZGak+KVStWhXu7u6YP38+nj17BplMhqVLl0KhUKBDhw6IiYnB4MGDcevWLRgaGqJKlSoAXn2Sy8zMhL6+PgwMDCCXy3HgwAGcPn26VK6+9/b2xtWrV3Hw4EHI5XI8ffoUw4YNw+LFi/Nd/1379aaMjAxIJBIYGxsDAK5evYrt27erLqIrr8pDvy+vfR5gv9fVPl+hwsOXX36JFStWYMKECQCAnj174u+//0avXr1U69SuXRuhoaE4cuQI2rZtCz8/P7i5uam+821nZwdnZ2e0a9cOp06dQmBgIHx8fDBixAg4OTlhwYIFmDVrFjp27FhoLb1790Z8fPx7/dS4VCrFhg0bIJfL4eXlhQ4dOiA3NxdbtmyBvr64waRKlSph4cKFCAkJgZOTE/r37w83Nzd88MEHuHPnDvT19QvdVv/+/fHpp5+iT58+aN68OZYsWYIVK1agevXqGDNmDGQyGdq2bQsPDw9cvXoVPXr0wJ07dwqtafr06UhOTsbOnTsxbdo0rFu3Di1atMCOHTvQs2fPIu9bhw4dkJqaCmdnZ6Snp+P7779HnTp18MUXX6Bt27a4ffs2tm3bBisrK3h5eWHgwIEYPnw4mjdvjtGjR2PatGlo1qwZBg4ciBo1aqBDhw5o164dDh48CD8/v3fux/uoVasWQkNDsWvXLrRt2xY9evRAgwYNCjyQACh0v97k5uYGPz8/+Pv7o1WrVggODkZAQACSk5NVQ6HlUXno9+W9zwMVt9/rap+v0L+qeezYMSxduhSHDx/WdClEpeLx48fo1KkTTp48iRo1ami6HK3Afk/lWVn1+Qp5zUNKSgoSEhKwfv169O3bV9PlEJWKly9f4vbt25BKpXk+kVRE7PdU3pVln69Qpy1eu379Ovr06QMbGxv06dNH0+UQlYq1a9di0qRJGDJkiOrcfkXGfk/lXVn2+Qp92oKIiIjEq5AjD0RERPT+GB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJRGB6IiIhIFIYHIiIiEoXhgYiIiERheCAiIiJR/h/f6A7IpIVDXgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Luodaan kahden kaavion kuvio\n", "fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(6, 3))\n", "\n", "# Puuttuvien arvojen korvaaminen arvolla 0 (fillna(0)) on välttämätön vain joissain versioissa\n", "sns.barplot(data=df[dikot][df['sukup']==1].fillna(0), ax=axs[0], estimator=sum)\n", "sns.barplot(data=df[dikot][df['sukup']==2].fillna(0), ax=axs[1], estimator=sum)\n", "\n", "axs[0].set_title(miehet)\n", "axs[1].set_title(naiset)\n", "axs[0].set_ylabel('Lukumäärä')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prosenttiyhteenveto" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 työtervlomaosakuntosahieroja
Miehet, n = 6355.6 %25.4 %12.7 %20.6 %
Naiset, n = 1963.2 %21.1 %5.3 %47.4 %
\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Valintojen lukumäärät sukupuolen mukaan\n", "df1 = df.groupby('sukup')[dikot].sum()\n", "\n", "# Prosenttien laskenta: iloc[0] viittaa df1:n ensimmäiseen riviin ja iloc[1] toiseen riviin\n", "df1.iloc[0] = df1.iloc[0]/miehet_n*100\n", "df1.iloc[1] = df1.iloc[1]/naiset_n*100\n", "\n", "# Riviotsikot\n", "df1.index = [miehet, naiset]\n", "\n", "# Prosenttilukujen ulkoasun viimeistely\n", "df1.style.format('{:.1f} %')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lisätietoa\n", "\n", "Data-analytiikka Pythonilla 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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 4 }