Initial commit

.ipynb_checkpoints/Introduction-to-Pandas-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "title-slide",
+    "slideshow": {
+     "id": "title-slide",
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<section class=\"title-slide\">\n",
+    "    <h1><it>Introduction to</it> Data Analysis and Plotting with Pandas</h1>\n",
+    "    <h2>JSC Tutorial</h2>\n",
+    "    <h3>Andreas Herten, Forschungszentrum Jülich, 26 February 2019</h3>\n",
+    "</section>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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": {},
+   "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/\n",
+    "* 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": {},
+   "source": [
+    "## About Pandas\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": {},
+   "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"
+   ]
+  }
+ ],
+ "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
+}

Introduction-to-Pandas.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "title-slide",
+    "slideshow": {
+     "id": "title-slide",
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<section class=\"title-slide\">\n",
+    "    <h1><it>Introduction to</it> Data Analysis and Plotting with Pandas</h1>\n",
+    "    <h2>JSC Tutorial</h2>\n",
+    "    <h3>Andreas Herten, Forschungszentrum Jülich, 26 February 2019</h3>\n",
+    "</section>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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": {},
+   "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/\n",
+    "* 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": {},
+   "source": [
+    "## About Pandas\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": {},
+   "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

custom.css

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+}
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+    background-repeat: no-repeat, no-repeat, no-repeat;
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+}
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+    background: yellow;
+    padding: 5px;
+}*/
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+    background: var(--color-blue);
+    margin-top: 60px;
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