{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis\n",
    "\n",
    "In 1977, John Tukey, one of the great statisticians and mathematicians of all time, published a book entitled *Exploratory Data Analysis*. In it, he laid out general principles on how researchers should handle their first encounters with their data, before formal statistical inference. Most of us spend a lot of time doing exploratory data analysis, or EDA, without really knowing it. Mostly, EDA involves a graphical exploration of a data set.\n",
    "\n",
    "We start off with a few wise words from John Tukey himself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Useful EDA advice from John Tukey\n",
    "\n",
    "- \"Exploratory data analysis can never be the whole story, but nothing else can serve as a foundation stone—as the first step.\"\n",
    "\n",
    "<br/>\n",
    "\n",
    "- \"In exploratory data analysis there can be no substitute for flexibility; for adapting what is calculated—and what we hope plotted—both to the needs of the situation and the clues that the data have already provided.\"\n",
    "\n",
    "<br />\n",
    "\n",
    "- \"There is no excuse for failing to plot and look.\"\n",
    "\n",
    "<br />\n",
    "\n",
    "- \"There is often no substitute for the detective's microscope - - or for the enlarging graphs.\"\n",
    "\n",
    "<br />\n",
    "\n",
    "- \"Graphs force us to note the unexpected; nothing could be more important.\"\n",
    "\n",
    "<br />\n",
    "\n",
    "- \"'Exploratory data analysis' is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The tools of EDA\n",
    "\n",
    "Being able to load in a data set and quickly start exploring it graphically enables you to _think_ about your data set instead being mired in the mechanics of producing a plot. In the notebooks that follow in this lesson, we will learn how to use the Python-based tools for EDA. In particular, we will learn how to use [Pandas](https://pandas.pydata.org) to keep the data set organized and accessible, and [Bokeh](https://docs.bokeh.org/en/latest/) and [HoloViews](https://holoviews.org/) to make interactive graphics.\n",
    "\n",
    "Along the way, we will learn key concepts of data organization and display. Importantly, we will learn about **tidy data**, **split-apply-combine**, and how to **plot all of your data**.\n",
    "\n",
    "Before we march on this trajectory, though, we need to learn a bit about [Numpy](http://numpy.org/) and [Scipy](http://scipy.org/), which form the foundation upon which much of these tools are built."
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}