Introduction-to-Pandas.ipynb

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    "# *Introduction to* Data Analysis and Plotting with Pandas\n",
    "## JSC Tutorial\n",
    "\n",
    "Andreas Herten, Forschungszentrum Jülich, 26 February 2019"
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   "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"
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   "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/"
   ]
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    "* 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 ~/`"
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    "## 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 (Python 2, Python 3)\n",
    "* For data analysis\n",
    "* With data structures (multi-dimensional table; time series), operations\n",
    "* Name 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`"
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    "## Pandas Cohabitation\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/), [`plotly`](https://plot.ly/)"
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   "source": [
    "## First Steps"
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    "import pandas"
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   "source": [
    "import pandas as pd"
   ]
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       "'0.24.1'"
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   "source": [
    "pd.__version__"
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       "\u001b[0;31mClass docstring:\u001b[0m\n",
       "    pandas - a powerful data analysis and manipulation library for Python\n",
       "    =====================================================================\n",
       "    \n",
       "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
       "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
       "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
       "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
       "    the broader goal of becoming **the most powerful and flexible open source data\n",
       "    analysis / manipulation tool available in any language**. It is already well on\n",
       "    its way toward this goal.\n",
       "    \n",
       "    Main Features\n",
       "    -------------\n",
       "    Here are just a few of the things that pandas does well:\n",
       "    \n",
       "      - Easy handling of missing data in floating point as well as non-floating\n",
       "        point data.\n",
       "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
       "        higher dimensional objects\n",
       "      - Automatic and explicit data alignment: objects can be explicitly aligned\n",
       "        to a set of labels, or the user can simply ignore the labels and let\n",