{ "cells": [ { "source": [ "# Herkullisia aasialaisia ja intialaisia ruokia\n", "\n", "## Johdanto\n", "\n", "Aasialainen ja intialainen keittiö ovat tunnettuja monipuolisista mauistaan ja ainutlaatuisista mausteistaan. Näissä ruoissa yhdistyvät usein makeat, suolaiset, happamat ja tuliset maut, jotka tekevät niistä vastustamattomia.\n", "\n", "[!NOTE] Tämä opas tarjoaa yleiskatsauksen joihinkin suosituimpiin aasialaisiin ja intialaisiin ruokiin sekä vinkkejä niiden valmistamiseen kotona.\n", "\n", "---\n", "\n", "## Suosittuja aasialaisia ruokia\n", "\n", "### Sushi\n", "Sushi on japanilainen ruokalaji, joka koostuu etikalla maustetusta riisistä, merenelävistä, vihanneksista ja joskus hedelmistä. Se tarjoillaan usein soijakastikkeen, wasabin ja inkiväärin kanssa.\n", "\n", "### Pad Thai\n", "Pad Thai on thaimaalainen paistettu nuudeliruoka, joka sisältää yleensä riisinuudeleita, kananmunia, tofua, katkarapuja, pähkinöitä ja tamarindikastiketta.\n", "\n", "### Dim Sum\n", "Dim Sum on kiinalainen kokoelma pieniä annoksia, kuten höyrytettyjä nyyttejä, kevätrullia ja täytettyjä pullia. Se tarjoillaan usein teen kanssa.\n", "\n", "---\n", "\n", "## Suosittuja intialaisia ruokia\n", "\n", "### Butter Chicken\n", "Butter Chicken on kermaisen tomaattikastikkeen kanssa tarjoiltava kananruoka, joka on yksi tunnetuimmista intialaisista ruoista.\n", "\n", "### Biryani\n", "Biryani on mausteinen riisiruoka, joka valmistetaan usein lampaan-, kanan- tai kasvisvaihtoehdolla. Se on erityisen suosittu Pohjois-Intiassa.\n", "\n", "### Samosa\n", "Samosa on rapea, kolmionmuotoinen taikinanyytti, joka on täytetty mausteisella peruna- ja hernetäytteellä. Se on suosittu välipala.\n", "\n", "---\n", "\n", "## Vinkkejä kotikokeille\n", "\n", "[!TIP] Käytä tuoreita mausteita, kuten inkivääriä, valkosipulia ja korianteria, saadaksesi autenttisen maun.\n", "\n", "[!IMPORTANT] Muista tasapainottaa maut. Esimerkiksi, jos ruoka on liian tulista, voit lisätä jogurttia tai kookosmaitoa lieventämään makua.\n", "\n", "[!CAUTION] Ole varovainen käsitellessäsi tulisia chilejä. Käytä käsineitä ja vältä koskemasta silmiin.\n", "\n", "---\n", "\n", "## Yhteenveto\n", "\n", "Aasialainen ja intialainen keittiö tarjoavat loputtomasti mahdollisuuksia kokeilla uusia makuja ja ruokia. Olitpa sitten aloittelija tai kokenut kokki, näiden ruokien valmistaminen kotona voi olla hauskaa ja palkitsevaa.\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Asenna Imblearn, joka mahdollistaa SMOTE:n. Tämä on Scikit-learn-paketti, joka auttaa käsittelemään epätasapainoista dataa luokittelua suoritettaessa. (https://imbalanced-learn.org/stable/)\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n", "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n", "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n", "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n", "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n", "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n", "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n", "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install imblearn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import numpy as np\n", "from imblearn.over_sampling import SMOTE" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../../data/cuisines.csv')" ] }, { "source": [ "Tämä tietoaineisto sisältää 385 saraketta, jotka osoittavat kaikenlaisia ainesosia eri keittiöissä annetusta keittiöiden joukosta.\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n", "0 65 indian 0 0 0 0 0 \n", "1 66 indian 1 0 0 0 0 \n", "2 67 indian 0 0 0 0 0 \n", "3 68 indian 0 0 0 0 0 \n", "4 69 indian 0 0 0 0 0 \n", "\n", " apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n", "0 0 0 0 ... 0 0 0 \n", "1 0 0 0 ... 0 0 0 \n", "2 0 0 0 ... 0 0 0 \n", "3 0 0 0 ... 0 0 0 \n", "4 0 0 0 ... 0 0 0 \n", "\n", " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", "0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 1 0 \n", "\n", "[5 rows x 385 columns]" ], "text/html": "
\n | Unnamed: 0 | \ncuisine | \nalmond | \nangelica | \nanise | \nanise_seed | \napple | \napple_brandy | \napricot | \narmagnac | \n... | \nwhiskey | \nwhite_bread | \nwhite_wine | \nwhole_grain_wheat_flour | \nwine | \nwood | \nyam | \nyeast | \nyogurt | \nzucchini | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n65 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
1 | \n66 | \nindian | \n1 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
2 | \n67 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3 | \n68 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
4 | \n69 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n1 | \n0 | \n
5 rows × 385 columns
\n\n | almond | \nangelica | \nanise | \nanise_seed | \napple | \napple_brandy | \napricot | \narmagnac | \nartemisia | \nartichoke | \n... | \nwhiskey | \nwhite_bread | \nwhite_wine | \nwhole_grain_wheat_flour | \nwine | \nwood | \nyam | \nyeast | \nyogurt | \nzucchini | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
1 | \n1 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
2 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
4 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n1 | \n0 | \n
5 rows × 380 columns
\n\n | almond | \nangelica | \nanise | \nanise_seed | \napple | \napple_brandy | \napricot | \narmagnac | \nartemisia | \nartichoke | \n... | \nwhiskey | \nwhite_bread | \nwhite_wine | \nwhole_grain_wheat_flour | \nwine | \nwood | \nyam | \nyeast | \nyogurt | \nzucchini | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
1 | \n1 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
2 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
4 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n1 | \n0 | \n
5 rows × 380 columns
\n\n | cuisine | \nalmond | \nangelica | \nanise | \nanise_seed | \napple | \napple_brandy | \napricot | \narmagnac | \nartemisia | \n... | \nwhiskey | \nwhite_bread | \nwhite_wine | \nwhole_grain_wheat_flour | \nwine | \nwood | \nyam | \nyeast | \nyogurt | \nzucchini | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
1 | \nindian | \n1 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
2 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
4 | \nindian | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n1 | \n0 | \n
... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
3990 | \nthai | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3991 | \nthai | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3992 | \nthai | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3993 | \nthai | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3994 | \nthai | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n... | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
3995 rows × 381 columns
\n