{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot.pandas  # noqa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`scatter` plots are a good first way to plot data with non continuous axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.iris import flowers as df\n",
    "\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.hvplot.scatter(x='sepal_length', y='sepal_width', by='species', \n",
    "                  legend='top', height=400, width=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for most other types of hvPlot plots, you can add fields to the hover display using the `hover_cols` argument. It can also take \"all\" as input to show all fields. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.hvplot.scatter(x='sepal_length', y='sepal_width', by='species', \n",
    "                  legend='top', height=400, width=400,\n",
    "                  hover_cols=[\"species\", \"sepal_length\", \"sepal_width\", \"petal_width\"])"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}