{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Päivitetty 2025-05-14 / 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", "sns.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 3, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nrosukupikäperhekoulutuspalveluvpalkkajohtotyötovtyöymppalkkattyötehttyötervlomaosakuntosahieroja
0113811.022.0358733.0333NaNNaNNaNNaN
1212922.010.0296315.0213NaNNaNNaNNaN
2313011.07.0198934.01131.0NaNNaNNaN
3413621.014.0214433.03331.0NaNNaNNaN
4512412.04.0218323.02121.0NaNNaNNaN
...................................................
777812213.00.0159844.0434NaN1.01.0NaN
787913311.02.0163813.02121.0NaNNaNNaN
798012712.07.0261234.03331.0NaN1.0NaN
808113522.016.0280834.0333NaNNaNNaNNaN
818223523.015.0218334.04341.0NaNNaNNaN
\n", "

82 rows × 16 columns

\n", "
" ], "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", "# fillna(0) tarvitaan vain joissain versioissa\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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 4 }