{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Päivitetty 2023-05-02 / Aki Taanila\n" ] } ], "source": [ "from datetime import datetime\n", "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Datan muunnokset osa 2\n", "\n", "Katso myös Datan muunnokset: http://nbviewer.org/github/taanila/data/blob/main/muunna.ipynb\n", "\n", "Seuraavassa tarkastelen funktion määrittelyä **lambda**-lausekkeena, **apply**-funktiota, logaritmimuunnosta ja standardointia (normittaminen)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 82 entries, 0 to 81\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 palkka 82 non-null int64 \n", " 1 johto 82 non-null int64 \n", " 2 työtov 81 non-null float64\n", " 3 työymp 82 non-null int64 \n", " 4 palkkat 82 non-null int64 \n", " 5 työteht 82 non-null int64 \n", "dtypes: float64(1), int64(5)\n", "memory usage: 4.0 KB\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "df = pd.read_excel('https://taanila.fi/data1.xlsx')[['palkka', 'johto', 'työtov', 'työymp', 'palkkat', 'työteht']]\n", "df.info()" ] }, { "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", "
palkkajohtotyötovtyöymppalkkattyöteht
0358733.0333
1296315.0213
2198934.0113
3214433.0333
4218323.0212
.....................
77159844.0434
78163813.0212
79261234.0333
80280834.0333
81218334.0434
\n", "

82 rows × 6 columns

\n", "
" ], "text/plain": [ " palkka johto työtov työymp palkkat työteht\n", "0 3587 3 3.0 3 3 3\n", "1 2963 1 5.0 2 1 3\n", "2 1989 3 4.0 1 1 3\n", "3 2144 3 3.0 3 3 3\n", "4 2183 2 3.0 2 1 2\n", ".. ... ... ... ... ... ...\n", "77 1598 4 4.0 4 3 4\n", "78 1638 1 3.0 2 1 2\n", "79 2612 3 4.0 3 3 3\n", "80 2808 3 4.0 3 3 3\n", "81 2183 3 4.0 4 3 4\n", "\n", "[82 rows x 6 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 4, "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", "
palkkajohtotyötovtyöymppalkkattyötehtjohto2työtov2työymp2palkkat2työteht2
0358733.033333.0333
1296315.021351.0453
2198934.011332.0553
3214433.033333.0333
4218323.021243.0454
....................................
77159844.043422.0232
78163813.021253.0454
79261234.033332.0333
80280834.033332.0333
81218334.043432.0232
\n", "

82 rows × 11 columns

\n", "
" ], "text/plain": [ " palkka johto työtov työymp palkkat työteht johto2 työtov2 työymp2 \\\n", "0 3587 3 3.0 3 3 3 3 3.0 3 \n", "1 2963 1 5.0 2 1 3 5 1.0 4 \n", "2 1989 3 4.0 1 1 3 3 2.0 5 \n", "3 2144 3 3.0 3 3 3 3 3.0 3 \n", "4 2183 2 3.0 2 1 2 4 3.0 4 \n", ".. ... ... ... ... ... ... ... ... ... \n", "77 1598 4 4.0 4 3 4 2 2.0 2 \n", "78 1638 1 3.0 2 1 2 5 3.0 4 \n", "79 2612 3 4.0 3 3 3 3 2.0 3 \n", "80 2808 3 4.0 3 3 3 3 2.0 3 \n", "81 2183 3 4.0 4 3 4 3 2.0 2 \n", "\n", " palkkat2 työteht2 \n", "0 3 3 \n", "1 5 3 \n", "2 5 3 \n", "3 3 3 \n", "4 5 4 \n", ".. ... ... \n", "77 3 2 \n", "78 5 4 \n", "79 3 3 \n", "80 3 3 \n", "81 3 2 \n", "\n", "[82 rows x 11 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Käännän mielipideasteikollisten muuttujien (1-5) asteikon päinvastaikseksi\n", "# lambda tarkoittaa funktiota; tässä funktio suorittaa laskutoimituksen 6-x, jokaiselle muuttujan arvolle x\n", "\n", "df[['johto2', 'työtov2', 'työymp2', 'palkkat2', 'työteht2']] = \\\n", " df[['johto', 'työtov', 'työymp', 'palkkat', 'työteht']].apply(lambda x:6-x)\n", "df" ] }, { "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", " \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", "
palkkajohtotyötovtyöymppalkkattyötehtjohto2työtov2työymp2palkkat2työteht2palkka_log1palkka_log2
0358733.033333.03338.1850718.185071
1296315.021351.04537.9939587.993958
2198934.011332.05537.5953877.595387
3214433.033333.03337.6704297.670429
4218323.021243.04547.6884557.688455
..........................................
77159844.043422.02327.3765087.376508
78163813.021253.04547.4012317.401231
79261234.033332.03337.8678717.867871
80280834.033332.03337.9402287.940228
81218334.043432.02327.6884557.688455
\n", "

82 rows × 13 columns

\n", "
" ], "text/plain": [ " palkka johto työtov työymp palkkat työteht johto2 työtov2 työymp2 \\\n", "0 3587 3 3.0 3 3 3 3 3.0 3 \n", "1 2963 1 5.0 2 1 3 5 1.0 4 \n", "2 1989 3 4.0 1 1 3 3 2.0 5 \n", "3 2144 3 3.0 3 3 3 3 3.0 3 \n", "4 2183 2 3.0 2 1 2 4 3.0 4 \n", ".. ... ... ... ... ... ... ... ... ... \n", "77 1598 4 4.0 4 3 4 2 2.0 2 \n", "78 1638 1 3.0 2 1 2 5 3.0 4 \n", "79 2612 3 4.0 3 3 3 3 2.0 3 \n", "80 2808 3 4.0 3 3 3 3 2.0 3 \n", "81 2183 3 4.0 4 3 4 3 2.0 2 \n", "\n", " palkkat2 työteht2 palkka_log1 palkka_log2 \n", "0 3 3 8.185071 8.185071 \n", "1 5 3 7.993958 7.993958 \n", "2 5 3 7.595387 7.595387 \n", "3 3 3 7.670429 7.670429 \n", "4 5 4 7.688455 7.688455 \n", ".. ... ... ... ... \n", "77 3 2 7.376508 7.376508 \n", "78 5 4 7.401231 7.401231 \n", "79 3 3 7.867871 7.867871 \n", "80 3 3 7.940228 7.940228 \n", "81 3 2 7.688455 7.688455 \n", "\n", "[82 rows x 13 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Teen palkalle logaritmimuunnoksen kahdella vaihtoehtoisella tavalla\n", "\n", "# Tapa 1\n", "df['palkka_log1'] = df['palkka'].apply('log')\n", "\n", "# Tapa 2\n", "df['palkka_log2'] = np.log(df['palkka'])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "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", "
palkkajohtotyötovtyöymppalkkattyötehtjohto2työtov2työymp2palkkat2työteht2palkka_log1palkka_log2palkka_norm1palkka_norm2
0358733.033333.03338.1850718.1850711.2120071.212007
1296315.021351.04537.9939587.9939580.4728060.472806
2198934.011332.05537.5953877.595387-0.681010-0.681010
3214433.033333.03337.6704297.670429-0.497394-0.497394
4218323.021243.04547.6884557.688455-0.451194-0.451194
................................................
77159844.043422.02327.3765087.376508-1.144195-1.144195
78163813.021253.04547.4012317.401231-1.096810-1.096810
79261234.033332.03337.8678717.8678710.0570060.057006
80280834.033332.03337.9402287.9402280.2891910.289191
81218334.043432.02327.6884557.688455-0.451194-0.451194
\n", "

82 rows × 15 columns

\n", "
" ], "text/plain": [ " palkka johto työtov työymp palkkat työteht johto2 työtov2 työymp2 \\\n", "0 3587 3 3.0 3 3 3 3 3.0 3 \n", "1 2963 1 5.0 2 1 3 5 1.0 4 \n", "2 1989 3 4.0 1 1 3 3 2.0 5 \n", "3 2144 3 3.0 3 3 3 3 3.0 3 \n", "4 2183 2 3.0 2 1 2 4 3.0 4 \n", ".. ... ... ... ... ... ... ... ... ... \n", "77 1598 4 4.0 4 3 4 2 2.0 2 \n", "78 1638 1 3.0 2 1 2 5 3.0 4 \n", "79 2612 3 4.0 3 3 3 3 2.0 3 \n", "80 2808 3 4.0 3 3 3 3 2.0 3 \n", "81 2183 3 4.0 4 3 4 3 2.0 2 \n", "\n", " palkkat2 työteht2 palkka_log1 palkka_log2 palkka_norm1 palkka_norm2 \n", "0 3 3 8.185071 8.185071 1.212007 1.212007 \n", "1 5 3 7.993958 7.993958 0.472806 0.472806 \n", "2 5 3 7.595387 7.595387 -0.681010 -0.681010 \n", "3 3 3 7.670429 7.670429 -0.497394 -0.497394 \n", "4 5 4 7.688455 7.688455 -0.451194 -0.451194 \n", ".. ... ... ... ... ... ... \n", "77 3 2 7.376508 7.376508 -1.144195 -1.144195 \n", "78 5 4 7.401231 7.401231 -1.096810 -1.096810 \n", "79 3 3 7.867871 7.867871 0.057006 0.057006 \n", "80 3 3 7.940228 7.940228 0.289191 0.289191 \n", "81 3 2 7.688455 7.688455 -0.451194 -0.451194 \n", "\n", "[82 rows x 15 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Kaksi vaihtoehtoista tapaa standardoida (normittaa)\n", "\n", "# Keskiarvo\n", "ka = df['palkka'].mean()\n", "\n", "# Keskihajonta\n", "# Huom. keskihajonnan kaavassa voi olla jakajana n tai n-1, tässä ddof-parametri määrää jakajaksi n\n", "kh = df['palkka'].std(ddof=0)\n", "\n", "# Tapa 1\n", "df['palkka_norm1'] = (df['palkka']-ka)/kh\n", "\n", "# Tapa 2\n", "df['palkka_norm2'] = df['palkka'].apply(lambda x:(x-ka)/kh)\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lisätietoa\n", "\n", "* https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.apply\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 }