{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "title-slide", "slideshow": { "id": "title-slide", "slide_type": "slide" } }, "source": [ "# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum Jülich, 26 February 2019" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## My Motivation\n", "\n", "* I like Python\n", "* I like plotting data\n", "* I like sharing\n", "* I think Pandas is awesome and you should use it too" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Tutorial Setup\n", "\n", "* 60 minutes (we might do this again for some advanced stuff if you want to)\n", "* Alternating between lecture and hands-on\n", "* Direct feedback via https://www.polleverywhere.com/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Please open Jupyter Notebook of this session\n", " - … either on your **local machine** (which has Pandas) \n", " Download here\n", " - … or on the **JSC Jupyter service** at https://jupyter-jsc.fz-juelich.de/\n", " - Either `pip install --user pandas seaborn` once in a shell and `cp $PROJECT_cjsc/herten1/pandas/notebook.ipynb ~/`\n", " - Or \n", " 1. `ln -s $PROJECT_cjsc/herten1/pandas ~/.local/share/jupyter/kernels/` and \n", " 2. `cp $PROJECT_cjsc/herten1/pandas/notebook-with-kernel.ipynb ~/`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<!-- .slide: data-background=\"blue\" -->\n", "\n", "## About Pandas\n", "\n", "<!-- .slide: class=\"slide-with-special-img-positionning\" -->\n", "\n", "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/adorable-animal-animal-photography-1661535.jpg\" />\n", "\n", "* Python package\n", "* For data analysis\n", "* With data structures (multi-dimensional table; time series), operations\n", "* From »**Pan**el **Da**ta« (multi-dimensional time series in economics)\n", "* Since 2008\n", "* https://pandas.pydata.org/\n", "* Install [via PyPI](https://pypi.org/project/pandas/): `pip install pandas`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Pandas and Friends\n", "\n", "* Pandas works great together with other established Python tools\n", " * [Jupyter Notebooks](https://jupyter.org/)\n", " * Plotting with [`matplotlib`](https://matplotlib.org/)\n", " * Modelling with [`statsmodels`](https://www.statsmodels.org/stable/index.html), [`scikit-learn`](https://scikit-learn.org/)\n", " * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }