10 - Intro to pandas.ipynb

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    "# Intro to pandas\n",
    "\n",
    "## Resources\n",
    "\n",
    "- [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/).\n",
    "- [Python for Data Analysis by Wes McKinney](http://shop.oreilly.com/product/0636920050896.do): the pandas bible, [but not the best resource to learn pandas if you are just starting with python](http://blog.tomgurion.me/python-readings.html).\n",
    "- [Python Data Science Handbook by Jake Vanderplas](https://github.com/jakevdp/PythonDataScienceHandbook) (chapter 3).\n",
    "- [FiveThirtyEight datasets](https://github.com/fivethirtyeight/data/).\n",
    "- [seaborn documentation](https://seaborn.pydata.org/index.html), and especially its [excelent tutorial](https://seaborn.pydata.org/tutorial.html).\n",
    "- [Choosing the right scikit-learn estimator](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html).\n",
    "\n",
    "## 4 hours agenda\n",
    "\n",
    "- Why?\n",
    "- Organising\n",
    "- Aggregating\n",
    "- **Break**\n",
    "- Visualizing\n",
    "- Analysing\n",
    "- **Break**\n",
    "- Python on EECS servers\n",
    "\n",
    "## Why?\n",
    "\n",
    "### [pandas](https://pandas.pydata.org/)\n",
    "\n",
    "- Very capable alternative to spreadsheets.\n",
    "- Well integrated with the python scientific ecosystem (e.g. based on numpy, seaborn uses it).\n",
    "- Lots of resources.\n",
    "\n",
    "### [seaborn](https://seaborn.pydata.org/)\n",
    "\n",
    "- Easy to use plotting library for statistic applications.\n",
    "- Works well with pandas data structures.\n",
    "- Based on matplotlib, so everything can be customized if needed."
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