{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Päivitetty 2023-04-30 / Aki Taanila\n" ] } ], "source": [ "from datetime import datetime\n", "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Frekvenssit dikotomisille (dummy) muuttujille\n", "\n", "Dummy-muuttujaksi (binäärinen muuttuja, dikotominen muuttuja) kutsutaan muuttujaa, joka saa arvokseen 1 tai 0 (joskus nollan sijasta käytetään tyhjää). \n", "\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", "\n", "Usein valintakysymyksiä (saa valita vain yhden vaihtoehdon) kutsutaan virheellisesti monivalintakysymyksiksi. Valintakysymystä 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", "\n", "# Grafiikan tyylimääritys\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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3413621.014.0214433.03331.0NaNNaNNaN
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", "dummy = ['työterv', 'lomaosa', 'kuntosa', 'hieroja']" ] }, { "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", " \n", " \n", " \n", " \n", "
 f% vastaajista
työterv4757.3 %
hieroja2226.8 %
lomaosa2024.4 %
kuntosa911.0 %
\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Valintojen lukumäärät sum-funktiolla lukumäärän mukaan järjestettynä\n", "df1 = df[dummy].sum().to_frame('f').sort_values('f', ascending=False)\n", "\n", "# shape[0] antaa datan rivien lukumäärän eli vastaajien lukumäärän\n", "n = df.shape[0]\n", "\n", "# Prosentit\n", "df1['% vastaajista'] = df1['f']/n*100\n", "\n", "# Ulkoasun viimeistely\n", "df1.style.format({'f':'{:.0f}', '% vastaajista':'{:.1f} %'})" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1['f'].plot.bar(width=0.8, rot=0)\n", "\n", "plt.ylabel('lukumäärä')\n", "plt.grid(axis='x')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'lukumäärä')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Voin tehdä kaavion myös seabornin barplot-funktiolla\n", "\n", "# Muuttujien lista frekvenssien mukaisessa järjestyksessä\n", "list = df[dummy].sum().sort_values(ascending=False).index\n", "\n", "# fillna(0) korvaa puuttuvat arvot nollilla\n", "sns.barplot(data=df[list].fillna(0), estimator=sum)\n", "\n", "plt.ylabel('lukumäärä')" ] }, { "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 2 }