{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Forberedelser til første time\n", "Følg instruksjonene under for å være forberedt til første time. Merk at det er en oppgave nederst på siden som kommer til å ta litt tid. \n", "\n", "## Logge inn på diskusjonsforum\n", "Vi skal bruke et discourse-basert diskusjonsforum som heter [discourse](https://hon2200-discourse.uio.no). Logg inn her før timen. Bruk dette forumet om du lurer på noe faglig eller praktisk i kurset. For kontakt om personlige forhold (f. eks. for fravær eller utsettelser) bruker du mail eller telefon. \n", "\n", "## Opprette en bruker på GitHub\n", "Når dere skal jobbe med programkode i grupper, trenger dere et sted å dele koden deres med hverandre. Standarden for å holde styr på kode er versjonskontrollsystemer slik som Git. Derfor trenger alle en bruker på GitHub. Inne på github kan dere opprette felles prosjekter. Når prosjektet deres skal leveres, kan dere enkelt laste det ned som en zip-fil og levere på Devilry. Dere trenger ikke øve så mye på å *bruke* github enda, men det kan være fint å opprette et *repository* og legge inn en fil der, bare for å komme litt i gang. Vi skal øve mer på dette senere. \n", "\n", "## Datasett\n", "Vi legger ut noen [datasett](https://zenodo.org/record/4494328/) til bruk i dette kurset på Zenodo.\n", "\n", "## Installere JupyterLab\n", "Vi skal bruke Python vha. Jupyter notebook i kurset. For å kunne gjøre datavitenskap trenger vi å installere noen pakker i python-installasjonen. I første omgang ønsker vi at dere skal kjøre JupyterLab på egen maskin. Dette kan installeres på flere forskjellige måter. Vi anbefaler å gjøre en av følgende: \n", "\n", "- Bruke Visual Studio Code med Jupyter-extension\n", "- Dersom du er komfortabel med terminalvinduet, og vet hva `pip install` er for noe, installer JupyterLab med kommandoen `pip install jupyterlab`. Vi anbefaler at du gjør dette i et *virtual environment*, for eksempel med pyenv-virtualenv. \n", "- Om du ikke er komfortabel med terminalen: Installer Anaconda. Der følger JupyterLab med, og fungerer fint. \n", "\n", "\n", "## Installere python-pakker og sjekke at de fungerer\n", "Enten kjøre følgende kommando i terminalen: \n", "```\n", "pip install pandas numpy matplotlib\n", "```\n", "eller følgende kommando inne i JupyterLab:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "!pip install -q pandas numpy matplotlib" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n", "
" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd \n", "file_name = \"https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv\"\n", "df = pd.read_csv(file_name)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etter kommandoen over skal du se en tabell med data fra Iris-datasettet. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "plt.scatter(df[\"sepal_length\"], df[\"sepal_width\"])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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duTM0MEBRqxvA0aT1ytjMRkmaJeneFF0ukbSi/ePVkmaYmWUeHhB/HbW66/c3y+njWt1rN9eHHRqAiEj3NvW/SfpHSW0pHh8paZckOecOS2qSdEKmwQG5gFrdAHrTazI2s4slveOc25TpFzOz68ys2syqKbmGgYJa3QB6k84r4+mSZpvZTkk/l3SemT3Yo0+9pNGSZGaDJA2RtK/nQM65u51zZc65suHDh2cUOBAXqWpyU6sbQIdek7Fz7jbn3Cjn3BhJl0t6xjl3ZY9uj0v6evvHl7b3YesPQIla3UUF+d3aqNUNoKs+l8M0syWSqp1zj0u6T9IDZvaGpPeUSNoARK1uAL2zsF7AlpWVuerq6lC+NgAAYTCzTc65sp7tVOACACBkJGMAAEJGMgYAIGQkYwAAQtbn1dRAGBavrdXDG3ep1Tnlm+mKqaO1dE5J2GEBQEZIxoiNxWtr9eCLb3cetzrXeUxCBhBn3KZGbDy8cZdXOwDEBckYsdGa4m/iU7UDQFyQjBEb+Sl25UzVDgBxQTJGbFwxdbRXOwDEBQu4EBsdi7RYTQ0g11CbGgCAfkJtagAAIopkDABAyEjGAACEjGQMAEDISMYAAISMP21C2r52zwv63ZvvdR5P/8yn9NC1Z4cYUXjWbq7XsvU7tGd/s04aWqRFM8drzqSRYYcFDChVdVWqrKlU48FGFQ8uVvnkcs0aNyuy4x4Nr4yRlp6JWJJ+9+Z7+to9L4QUUXjWbq7XbY/Vqn5/s5yk+v3Nuu2xWq3dXB92aMCAUVVXpYrnK9RwsEFOTg0HG1TxfIWq6qoiOW5vSMZIS89E3Ft7Llu2foeaD7V2a2s+1Kpl63eEFBEw8FTWVKqltaVbW0triyprKiM5bm9IxoCnPfubvdoBZF/jwUav9rDH7Q3JGPB00tAir3YA2Vc8uNirPexxe0MyRlqmf+ZTXu25bNHM8SoqyO/WVlSQr0Uzx4cUETDwlE8uV2F+Ybe2wvxClU8uj+S4vSEZIy0PXXv2EYl3oK6mnjNppO78SolGDi2SSRo5tEh3fqWE1dRAP5o1bpYqzqnQiMEjZDKNGDxCFedUZLzqOahxe8NGEQAA9BM2igAAIKJIxgAAhIxkDABAyEjGAACEjNrUSFtQ9Zh9xqUmNIBcRDJGWjrqMXeUgeyoxywpo2ToM25QMQBA2LhNjbQEVY/ZZ1xqQgPIVSRjpCWoesw+41ITGkCuIhkjLUHVY/YZl5rQAHIVyRhpCaoes8+41IQGkKtYwIW0dCyQyvZKZp9xg4oBAMJGbWoAAPoJtakBAIgokjEAACEjGQMAEDKSMQAAIWM1dZbEsWZyHGMGkJ6quipV1lSq8WCjigcXq3xyuWaNmxV2WEiBZJwFcayZHMeYAaSnqq5KFc9XqKW1RZLUcLBBFc9XSBIJOaK4TZ0FcayZHMeYAaSnsqayMxF3aGltUWVNZUgRoTck4yyIY83kOMYMID2NBxu92hE+knEWxLFmchxjBpCe4sHFXu0IH8k4C+JYMzmOMQNIT/nkchXmF3ZrK8wvVPnk8pAiQm9YwJUFcayZHMeYAaSnY5EWq6njg9rUAAD0E2pTAwAQUSRjAABCRjIGACBkJGMAAELWazI2s0Iz+72ZbTWzl83sfyfps8DM9prZlvZ/fxtMuAAA5J50/rTpI0nnOec+MLMCSc+Z2S+dcy/26LfSOXdT9kNEUBavrdXDG3ep1Tnlm+mKqaO1dE5JVvpHZROKqMQBAEfTazJ2ib99+qD9sKD9Xzh/D4WsWby2Vg+++HbncatzncfJEqxP/6hsQhGVOACgN2m9Z2xm+Wa2RdI7kp5yzm1M0u2rZrbNzFab2ehsBonse3jjrsDao7IJRVTiAIDepJWMnXOtzrkzJI2SdJaZTezR5QlJY5xzpZKekrQi2Thmdp2ZVZtZ9d69ezMIG5lqTVHsJRvtUdmEIipxAEBvvFZTO+f2S/q1pC/3aN/nnPuo/fBeSVNSfP7dzrky51zZ8OHD+xAusiXfLLD2qGxCEZU4AKA36aymHm5mQ9s/LpJ0vqRXe/QZ0eVwtqTtWYwRAbhiavJ3ErLRHpVNKKISBwD0Jp3V1CMkrTCzfCWS9yrn3DozWyKp2jn3uKSFZjZb0mFJ70laEFTAyI6ORVfpro726R+VTSiiEgcA9IaNIgAA6CdsFAEAQESRjAEACBnJGACAkJGMAQAIWTqrqZGGIGsg+9aQDmpcnzkGdT6COhextW2VtGGJ1LRbGjJKmnGHVHpZVoauqqtSZU2lGg82qnhwsconl2vWuFkZ9wVwJJJxFgRZA9m3hnRQ4/rMMajzEdS5iK1tq6QnFkqH2iuKNe1KHEsZJ+SquipVPF+hltYWSVLDwQZVPF8hSUckWZ++AJLjNnUWBFkD2bdWdFDj+swxqPMR1LmIrQ1LPk7EHQ41J9ozVFlT2ZlcO7S0tqiypjKjvgCSIxlnQZA1kH1rRQc1rs8cgzofQZ2L2Gra7dfuofFgY9rtPn0BJEcyzoIgayD71ooOalyfOQZ1PoI6F7E1ZJRfu4fiwcVpt/v0BZAcyTgLgqyB7FsrOqhxfeYY1PkI6lzE1ow7pIIev+AUFCXaM1Q+uVyF+YXd2grzC1U+uTyjvgCSYwFXFgRZA9m3hnRQ4/rMMajzEdS5iK2ORVoBrKbuWHiVzgppn74AkqM2NQAA/YTa1AAARBTJGACAkJGMAQAIGckYAICQsZp6APOtHx1k/W3EW9Wzt6uybo0a86TiNql83FzNOvf7YYd1VEtfXKpHXntEba5NeZaneafO0+Jpi8MOCwMUyXiA8q0fHWT9bcRb1bO3q+KtNWrJTxRfaciXKt5aI0mRTchLX1yqlTtWdh63ubbOYxIywsBt6gHKt350kPW3EW+VdWvUkte9ClpLnqmybk1IEfXukdce8WoHgkYyHqB860cHWX8b8daY4qdIqvYoaHNtXu1A0CL8dEGQfOtHB1l/G/FWnCJ/pWqPgjxL/qMvVTsQNL7zBijf+tFB1t9GvJWPm6vCtu6V/ArbnMrHzQ0pot7NO3WeVzsQNBZwDVC+9aODrL+NeOtYpBWn1dQdi7RYTY2ooDY1AAD9hNrUAABEFMkYAICQkYwBAAgZyRgAgJCRjAEACNmA+9OmoDY78B138dpaPbxxl1qdU76Zrpg6WkvnlGQchw82foiobaukDUukpt3SkFHSjDuk0svCjiprfDaVqKqrUmVNpRoPNqp4cLHKJ5dr1rhZ/Ryxfxxe/QO63lE5d0jPgErGQW124Dvu4rW1evDFtzuPW53rPO6vhMzGDxG1bZX0xELpUHuZ0aZdiWMpJxKyz6YSVXVVqni+Qi2tLYm+BxtU8XxFom8/JhXfOLz6B3S9o3LukL4BdZs6qM0OfMd9eOMur/YgsPFDRG1Y8vEP5g6HmhPtOcBnU4nKmsrOZNLZt7VFlTWVgcaYaRxe/QO63lE5d0jfgErGQW124Dtua4pCK6nag8DGDxHVtNuvPWZ8NpVoPNiYvG+K9qD4xuHVHtD1jsq5Q/oGVDIOarMD33Hzzbzag8DGDxE1ZJRfe8z4bCpRPLg4ed8U7UHxjcOrPaDrHZVzh/QNqGQc1GYHvuNeMXW0V3sQ2PghombcIRX0+IWooCjRngN8NpUon1yuwvzC7n3zC1U+uTzQGDONw6t/QNc7KucO6RtQC7iC2uzAd9yORVphrqZm44eI6li0k6OrqX02lehYaBT2imDfOLz6B3S9o3LukD42igAAoJ+wUQQAABFFMgYAIGQkYwAAQkYyBgAgZANqNXWQgqzz7DP21+55Qb97873O4+mf+ZQeuvbsrMQBZENUaiZXrb5ClU1b1Jifr+LWVpUPOUOzLn04K2MvXbdAj7xbrTYlXvHMO7FMiy9enpWxkZt4ZZwFHXWe6/c3y+njOs9rN9f369g9E7Ek/e7N9/S1e17IOA4gGzpqJjccbJCT66yZXFVX1b9xrL5CFQe2qWHQIDkzNQwapIoD21S1+oqMx166boFWvlutNjPJTG1mWvlutZauW5B54MhZJOMsCLLOs8/YPRNxb+1Af4tKzeTKpi1qyev+468lL0+VTVsyHvuRd6ulntX0zBLtQAok4ywIss4zNaSRS6JSM7kxP9+r3UeKip8p2wGJZJwVQdZ5poY0cklUaiYXt7Z6tftI9UOVH7Y4Gr4/siDIOs8+Y0//zKeSjpGqHehvUamZXD7kDBW2dX+tWtjWpvIhZ2Q89rwTy6SelQ2dS7QDKZCMs2DOpJG68yslGjm0SCZp5NAi3fmVkqyspvYZ+6Frzz4i8bKaGlEya9wsVZxToRGDR8hkGjF4hCrOqej/etOXPqyK40o14vBhmXMacfiwKo4rzcpq6sUXL9f8E8uU55zknPKc03xWU6MX1KYGAKCfUJsaAICIIhkDABAykjEAACEjGQMAELJea1ObWaGk30g6pr3/aufc93r0OUbSf0qaImmfpPnOuZ1ZjzYJ35rQQdaQDsritbV6eOMutTqnfDNdMXW0ls4pSdrXZ35xPBfatkrasERq2i0NGSXNuEMqvSw7Y6/7jrRpueRaJcuXpiyQLv5hv8dR9eztqqxbo8Y8qbhNKh83V7PO/X7yvh51nn3GjYpr11+rFxtf7DyeVjxN98y8J/UneFyXoM5zkLyud0Ri9uEbcxznmEqvq6nNzCQNds59YGYFkp6TVO6ce7FLnxsklTrnrjezyyXNdc7NP9q42VhN3VG3uWu5yKKC/JR/+uPbPwoWr63Vgy++fUT7ldNOPiIh+8wvjudC21ZJTyyUDnWpPlZQJP3NjzJPhOu+I1Xfd2R72TePTMgBxlH17O2qeGuNWvI+LqdY2OZUMfbIRNFR57lrecnC/MKkfyrkM25U9EzEHVImZI/rEtR5DpLX9Y5IzD58Y47jHKUMVlO7hA/aDwva//XM4JdIWtH+8WpJM9qTeKB8a0IHWUM6KA9v3JV2u8/84ngutGFJ9x+0UuJ4w5LMx960PP32AOOorOueICSpJc9UWbfmyL4edZ59xo2KZIn4aO0+1yWo8xwkr+sdkZh9+MYcxzkeTVrvGZtZvpltkfSOpKeccxt7dBkpaZckOecOS2qSdEKSca4zs2ozq967d29GgUv+dZvjWOe5NcWdi2TtPvOL47lQ026/dh8uRRnEZO0BxtGY4hmZrN2nzrPPuLHlcV2COs9B8rreEYnZh2/McZzj0aT1VHTOtTrnzpA0StJZZjaxL1/MOXe3c67MOVc2fPjwvgzRjW/d5jjWec5PcYMhWbvP/OJ4LjRklF+7D0uxQUCy9gDjKE6xm0Cydp86zz7jxpbHdQnqPAfJ63pHJGYfvjHHcY5H4/V7sXNuv6RfS/pyj4fqJY2WJDMbJGmIEgu5AuVbEzrIGtJBuWLq6LTbfeYXx3OhGXck3gPsqqAo0Z6pKQvSbw8wjvJxc1XY1v2uR2GbU/m4uUf29ajz7DNuVEwrnubV7nNdgjrPQfK63hGJ2YdvzHGc49Gks5p6uKRDzrn9ZlYk6XxJ/9Kj2+OSvi7pBUmXSnrG9UOdzY6FRumuCPbtHwUdi7TSWU3tM784novORThBrGLuWKSVzmrqAOPoWDyUzirfjkUq6awm9Rk3Ku6ZeY/famqP6xLUeQ6S1/WOSMw+fGOO4xyPJp3V1KVKLM7KV+KV9Crn3BIzWyKp2jn3ePufPz0gaZKk9yRd7pyrO9q41KYGAAw0qVZT9/rK2Dm3TYkk27P9ji4ft0ial2mQAAAMRLm0lhIAgFgiGQMAEDKSMQAAISMZAwAQsl4XcOWaWG6OgP7hs/lDkBtW+AgyZp9NF3wK9gd57qJyXWIolzZdiKMBlYx7bo5Qv79Ztz1WK0kk5IGu5yYDTbsSx9KRP8x9+gYpyJg9+vcs2N9wsEEVz1dI0pE/zIM8d1G5LjHkdQ0RiAF1mzqWmyOgf/hs/hDkhhU+gozZZ9MFn4L9QZ67qFyXGMq1TRfiaEAl41hujoD+4bP5Q5AbVvgIMmafTRd8CvYHee6icl1iKNc2XYijAZWMY7k5AvqHz+YPQW5Y4SPImH02XfAp2B/kuYvKdYmhXNt0IY4GVDKO5eYI6B8+mz8EuWGFjyBj9tl0wadgf5DnLirXJYZybdOFOBpQC7hiuTkC+ofP5g9BbljhI8iYfTZd8CnYH+S5i8p1iaFc23QhjnrdKCIobBQBABhoUm0UMaBuUwMAEEUkYwAAQkYyBgAgZCRjAABCNqBWUwNH41Wbd913pE3LJdcqWb40ZYF08Q+zM3ZQAozZp+/SF5fqkdceUZtrU57lad6p87R42uLkQUSl1nRU4oiISHw/RyiObCAZA/KszbvuO1L1fR8fu9aPj5Mkt0jU/Q0wZp++S19cqpU7VnYet7m2zuMjEnJUak1HJY6IiMT3c4TiyBZuUwPyrM27aXnyQVK0R6Lub4Ax+/R95LVHkn69pO1RqTUdlTgiIhLfzxGKI1tIxoA8a/O61iPbjtIeibq/Acbs07fNtSXtm7Q9KrWmoxJHRETi+zlCcWQLyRiQZ21eyz+y7Sjtkaj7G2DMPn3zLPmPnKTtUak1HZU4IiIS388RiiNbSMaAPGvzTlmQfJAU7ZGo+xtgzD595506L+nXS9oelVrTUYkjIiLx/RyhOLKFBVyAPGvzdix4SnNlciTq/gYYs0/fjkVaaa2mjkqt6ajEERGR+H6OUBzZQm1qAAD6CbWpAQCIKJIxAAAhIxkDABAykjEAACFjNTWCEcdavgHGvPThC/XIR7vUpsRvwPOOGa3FV/wy84Ejcp6DqmMNDBQkY2RfHGv5Bhjz0ocv1MqPdklmkqQ2KXH88IWZJeSInOeg6lgDAwm3qZF9cazlG2DMj3RJxJ3MEu2ZiMh5DqqONTCQkIyRfXGs5RtgzMmrMaduT1tEznNQdayBgYRkjOyLYy3fAGNO9STL+MkXkfMcVB1rYCAhGSP74ljLN8CY5x0zWupZ6c65RHsmInKeg6pjDQwkLOBC9sWxlm+AMS++4pdSEKupI3Keg6pjDQwk1KYGAKCfUJsaAICIIhkDABAykjEAACEjGQMAEDKSMQAAISMZI7dtWyXdNVGqGJr4f9uqcMYNKg4PVXVVumD1BSpdUaoLVl+gqrqqfo8ByKZc+p7m74yRu4LaSMF33Ahs6MAGDcg1ufY9zStj5K6gNlLwHTcCGzqwQQNyTa59T5OMkbuC2kjBd9wIbOjABg3INbn2PU0yRu4KaiMF33EjsKEDGzQg1+Ta9zTJGLkrqI0UfMeNwIYObNCAXJNr39Ms4ELuCmojBd9xI7ChAxs0INfk2vc0G0UAANBP2CgCAICIIhkDABAykjEAACHrNRmb2Wgz+7WZvWJmL5vZEUvVzOxcM2sysy3t//pvmSgAADGXzmrqw5L+3jlXY2bHSdpkZk85517p0e+3zrmLsx8ifFTVVQW3unDbqlBXBPeJT8wefQM9zzFU9eztqqxbo8Y8qbhNKh83V7PO/X7YYR0V1xBR0msyds41SGpo//iAmW2XNFJSz2SMkAVaqzUC9ZW9+cTs0TfXauJmqurZ21Xx1hq15JskqSFfqnhrjSRFNiFzDRE1Xu8Zm9kYSZMkbUzy8NlmttXMfmlmE7IRHPwEWqs1AvWVvfnE7NE312riZqqybo1a8qxbW0ueqbJuTUgR9Y5riKhJu+iHmX1S0qOSbnHOvd/j4RpJpzjnPjCziyStlfTZJGNcJ+k6STr55JP7GjNSCLRWawTqK3vzidmjb67VxM1UY4pf6VO1RwHXEFGT1tPFzAqUSMQPOece6/m4c+5959wH7R//QlKBmZ2YpN/dzrky51zZ8OHDMwwdPQVaqzUC9ZW9+cTs0TfXauJmqrjNrz0KuIaImnRWU5uk+yRtd879MEWf4vZ+MrOz2sfdl81A0btAa7VGoL6yN5+YPfrmWk3cTJWPm6vCtu6V/ArbnMrHzQ0pot5xDRE16dymni7pKkm1Zralve2fJJ0sSc65n0q6VNK3zeywpGZJl7uw6mwOYIHWao1AfWVvPjF79M21mriZ6likFafV1FxDRA21qQEA6CfUpgYAIKJIxgAAhIxkDABAyEjGAACEjGQMAEDI0q7AhZiI42YOQVr3HWnTcsm1SpYvTVkgXZz0z+UBIDQk41wSx80cgrTuO1L1fR8fu9aPj0nIACKE29S5JI6bOQRp03K/dgAICck4l8RxM4cguVa/dgAICck4l8RxM4cgWb5fOwCEhGScS+K4mUOQpizwaweAkJCMc0npZdLf/EgaMlqSJf7/mx8NzMVbUmKRVtk3P34lbPmJYxZvAYgYNooAAKCfsFEEAAARRTIGACBkJGMAAEJGMgYAIGSUwzyKtZvrtWz9Du3Z36yThhZp0czxmjNpZNhhZc9AqGM9EOYYBZxnICMk4xTWbq7XbY/VqvlQolpT/f5m3fZYrSTlRkIeCHWsB8Ico4DzDGSM29QpLFu/ozMRd2g+1Kpl63eEFFGWDYQ61gNhjlHAeQYyRjJOYc/+Zq/22BkIdawHwhyjgPMMZIxknMJJQ4u82mNnINSxHghzjALOM5AxknEKi2aOV1FB9w0FigrytWjm+JAiyrKBUMd6IMwxCjjPQMZYwJVCxyKtnF1N3bGwJpdXwA6EOUYB5xnIGLWpAQDoJ9SmBgAgokjGAACEjGQMAEDISMYAAISM1dRAjFU9e7sq69aoMU8qbpPKx83VrHO/n53BqTcN9BuSMRBTVc/eroq31qgl3yRJDflSxVtrJCnzhEy9aaBfcZsaiKnKujVqybNubS15psq6NZkPTr1poF+RjIGYakzx7E3V7oV600C/IhkDMVXc5tfuhXrTQL8iGQMxVT5urgrbulfQK2xzKh83N/PBqTcN9CsWcAEx1bFIK5DV1NSbBvoVtakBAOgn1KYGACCiSMYAAISMZAwAQMhIxgAAhIxkDABAyEjGAACEjGQMAEDISMYAAISMZAwAQMhIxgAAhIxkDABAyEjGAACEjGQMAEDISMYAAISMZAwAQMhIxgAAhKzXZGxmo83s12b2ipm9bGblSfqYmf3IzN4ws21mNjmYcAEAyD3pvDI+LOnvnXOnSZom6UYzO61Hnwslfbb933WSfpLVKBGMbaukuyZKFUMT/29bFXZEADAg9ZqMnXMNzrma9o8PSNouaWSPbpdI+k+X8KKkoWY2IuvRInu2rZKeWCg17ZLkEv8/sZCEDAAh8HrP2MzGSJokaWOPh0ZK2tXleLeOTNiIkg1LpEPN3dsONSfaAQD9Ku1kbGaflPSopFucc+/35YuZ2XVmVm1m1Xv37u3LEMiWpt1+7QCAwKSVjM2sQIlE/JBz7rEkXeolje5yPKq9rRvn3N3OuTLnXNnw4cP7Ei+yZcgov3YAQGDSWU1tku6TtN0598MU3R6XdHX7quppkpqccw1ZjBPZNuMOqaCoe1tBUaIdANCvBqXRZ7qkqyTVmtmW9rZ/knSyJDnnfirpF5IukvSGpA8lXZP1SJFdpZcl/t+wJHFresioRCLuaAcA9BtzzoXyhcvKylx1dXUoXxsAgDCY2SbnXFnPdipwAQAQMpIxAAAhIxkDABAykjEAACEjGQMAEDKSMQAAISMZAwAQMpIxAAAhIxkDABAykjEAACELrRymme2V9MdQvri/EyW9G3YQAcr1+Um5P0fmF3+5Psdcn5+U3hxPcc4dsW1haMk4TsysOlkt0VyR6/OTcn+OzC/+cn2OuT4/KbM5cpsaAICQkYwBAAgZyTg9d4cdQMByfX5S7s+R+cVfrs8x1+cnZTBH3jMGACBkvDIGACBkJOMezCzfzDab2bokjy0ws71mtqX939+GEWNfmdlOM6ttj706yeNmZj8yszfMbJuZTQ4jzkykMcdzzaypyzW8I4w4+8rMhprZajN71cy2m9nZPR6P9TVMY35xv37ju8S+xczeN7NbevSJ7TVMc35xv4Z/Z2Yvm9lLZvawmRX2ePwYM1vZfv02mtmYdMYdFEi08VYuabuk41M8vtI5d1M/xpNtX3TOpfo7uAslfbb931RJP2n/P26ONkdJ+q1z7uJ+iya7KiU96Zy71Mw+IenYHo/H/Rr2Nj8pxtfPObdD0hlS4hd/SfWS1vToFttrmOb8pJheQzMbKWmhpNOcc81mtkrS5ZKWd+n2TUl/ds79pZldLulfJM3vbWxeGXdhZqMkzZJ0b9ixhOQSSf/pEl6UNNTMRoQdFBLMbIikv5Z0nyQ55/7HObe/R7fYXsM055dLZkh60znXs/hRbK9hD6nmF3eDJBWZ2SAlflnc0+PxSyStaP94taQZZma9DUoy7u7fJP2jpLaj9Plq+62j1WY2un/Cyhon6VdmtsnMrkvy+EhJu7oc725vi5Pe5ihJZ5vZVjP7pZlN6M/gMjRW0l5JP2t/K+VeMxvco0+cr2E685Pie/16ulzSw0na43wNu0o1Pymm19A5Vy/pB5LeltQgqck596se3Tqvn3PusKQmSSf0NjbJuJ2ZXSzpHefcpqN0e0LSGOdcqaSn9PFvP3Hxv5xzk5W4DXajmf112AEFoLc51ihRju50Sf9X0tp+ji8TgyRNlvQT59wkSQcl3RpuSFmVzvzifP06td+Cny3pkbBjCUIv84vtNTSzYUq88h0r6SRJg83symyMTTL+2HRJs81sp6SfSzrPzB7s2sE5t88591H74b2SpvRviJlp/61Ozrl3lHgf56weXeoldX21P6q9LTZ6m6Nz7n3n3AftH/9CUoGZndjvgfbNbkm7nXMb249XK5G8uorzNex1fjG/fl1dKKnGOfenJI/F+Rp2SDm/mF/DL0l6yzm31zl3SNJjks7p0afz+rXfyh4iaV9vA5OM2znnbnPOjXLOjVHi9sozzrluv/H0eN9mthILvWLBzAab2XEdH0u6QNJLPbo9Lunq9tWc05S4BdPQz6H2WTpzNLPijvdvzOwsJZ4DvT5RosA51yhpl5mNb2+aIemVHt1iew3TmV+cr18PVyj1LdzYXsMuUs4v5tfwbUnTzOzY9jnM0JF54HFJX2//+FIlckmvBT1YTd0LM1siqdo597ikhWY2W9JhSe9JWhBmbJ4+LWlN+3NgkKT/55x70syulyTn3E8l/ULSRZLekPShpGtCirWv0pnjpZK+bWaHJTVLujydJ0qE3CzpofbbgHWSrsmxa9jb/OJ+/Tp+UTxf0re6tOXMNUxjfrG9hs65jWa2Wolb7YclbZZ0d488cZ+kB8zsDSXyxOXpjE0FLgAAQsZtagAAQkYyBgAgZCRjAABCRjIGACBkJGMAAEJGMgYAIGQkYwAAQkYyBgAgZP8fq+WwwIztB9UAAAAASUVORK5CYII=", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "for species, s_df in df.groupby(\"species\"):\n", " ax.scatter(s_df[\"sepal_length\"], s_df[\"sepal_width\"], label=species)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Om alt dette fungerer, og du får opp noe som ligner på figurene over, er du klar for undervisningstime. " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Data-dreven beslutning" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "I fjor vår startet semesteret med antallsbegrensninger og barene stengte kl 23. Hele familier var uker i karantene. Man fryktet at omicron ville overbelaste helsevesenet. Men hadde vi noe å frykte? Les artiklene under og gjør deg opp en mening (ja / nei) om hvorvidt alle forskriftsfestede koronatiltak burde vært fjernet ved nyttår 2022. Vurder også om du synes det er nok data tilgjengelig for å ta en beslutning, og om samfunnet kunne gjort noe for å skaffe et bedre datagrunnlag. \n", "\n", "- [Europe must come together to confront omicron](https://www.bmj.com/content/376/bmj.o90)\n", "- [COVID-19 will continue but the end of the pandemic is near](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(22)00100-3/fulltext)\n", "- [Covid-19 vaccines and treatments: we must have raw data, now](https://www.bmj.com/content/376/bmj.o102)\n", "- [Effectiveness of COVID-19 vaccines against the Omicron variant of concern, medArxiv preprint](https://www.medrxiv.org/content/10.1101/2021.12.14.21267615v1.full.pdf) (Ikke les hele denne, bare det du trenger)\n", "\n", "Rådata fra UK: \n", "- https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronaviruscovid19infectionsurveypilot/19january2022\n", "- https://coronavirus.data.gov.uk/details/healthcare" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "hon2200", "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": "3.9.9 (main, Jan 13 2022, 20:39:55) \n[Clang 13.0.0 (clang-1300.0.29.30)]" }, "vscode": { "interpreter": { "hash": "60609e9a68f2ae74d5fb06acf53186e92fd9e2025bb150d86d559b15aa0b3a5d" } } }, "nbformat": 4, "nbformat_minor": 4 }