{ "cells": [ { "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://astro-discourse.uio.no/c/hon2200-24v/). 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](mailto:eirik.gramstad@fys.uio.no). \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](https://github.com/). 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 i [Canvas](https://uio.instructure.com/). 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](https://www.anaconda.com/download). 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": 1, "metadata": {}, "outputs": [], "source": [ "!pip install -q pandas numpy matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test oppsettet med å se på data av ulike typer Iris\n", "| setosa | versicolor | virginica |\n", "| :-: | :-: | :-: |\n", "| \"drawing\"|\"drawing\"|\"drawing\" |\n" ] }, { "cell_type": "code", "execution_count": 2, "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", "
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": 2, "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": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'sepal width')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "plt.scatter(df[\"sepal_length\"], df[\"sepal_width\"])\n", "plt.xlabel(\"sepal length\")\n", "plt.ylabel(\"sepal width\")" ] }, { "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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", "text/plain": [ "
" ] }, "metadata": {}, "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[\"petal_length\"], label=species)\n", "plt.xlabel(\"sepal length\")\n", "plt.ylabel(\"petal length\")\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. " ] } ], "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": "3.11.3" }, "vscode": { "interpreter": { "hash": "60609e9a68f2ae74d5fb06acf53186e92fd9e2025bb150d86d559b15aa0b3a5d" } } }, "nbformat": 4, "nbformat_minor": 4 }