{ "cells": [ { "source": [ "# Deilige asiatiske og indiske retter\n", "\n", "## Introduksjon\n", "\n", "Asiatisk og indisk mat er kjent for sine rike smaker, varierte ingredienser og unike krydderblandinger. I denne guiden vil vi utforske noen populære retter og deres opprinnelse.\n", "\n", "## Populære asiatiske retter\n", "\n", "### Sushi\n", "Sushi er en japansk rett som består av ris krydret med eddik, kombinert med ulike ingredienser som rå fisk, grønnsaker og tang. Det finnes mange varianter, inkludert maki, nigiri og sashimi.\n", "\n", "### Pad Thai\n", "Pad Thai er en klassisk thailandsk nuddelrett laget med risnudler, tofu, reker, egg og en smakfull saus. Den toppes ofte med knuste peanøtter og lime.\n", "\n", "### Dim Sum\n", "Dim Sum er en kinesisk tradisjon som består av små porsjoner mat, ofte servert i dampede kurver. Rettene kan inkludere dumplings, boller og andre småretter.\n", "\n", "## Populære indiske retter\n", "\n", "### Butter Chicken\n", "Butter Chicken, eller \"Murgh Makhani\", er en kremet og smakfull kyllingrett laget med tomatbasert saus, smør og en blanding av aromatiske krydder.\n", "\n", "### Biryani\n", "Biryani er en aromatisk risrett som kan lages med kylling, lam eller grønnsaker. Den er kjent for sin bruk av safran og en kompleks blanding av krydder.\n", "\n", "### Samosa\n", "Samosa er en populær indisk snack som består av en sprø deig fylt med krydret potet, erter og noen ganger kjøtt. Den serveres ofte med chutney.\n", "\n", "## Tips for å lage autentiske retter\n", "\n", "- Bruk ferske ingredienser for å få frem de beste smakene.\n", "- Ikke vær redd for å eksperimentere med krydder, men smak til underveis.\n", "- Følg tradisjonelle oppskrifter som en guide, men tilpass dem etter din egen smak.\n", "\n", "## Oppsummering\n", "\n", "Asiatisk og indisk mat tilbyr en verden av smaker og opplevelser. Enten du liker krydret, søtt eller syrlig, finnes det noe for alle. Utforsk disse rettene og oppdag nye favoritter!\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [], "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": [ "Dette datasettet inkluderer 385 kolonner som indikerer alle slags ingredienser i ulike kjøkken fra et gitt sett med kjøkken.\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": "
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