{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot.pandas  # noqa\n",
    "\n",
    "# hvplot.extension(\"matplotlib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`hist` is often a good way to start looking at continuous data to get a sense of the distribution. Similar methods include [`kde`](kde.ipynb) (also available as `density`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.autompg import autompg_clean\n",
    "\n",
    "autompg_clean.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_clean.hvplot.hist(\"weight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using `by` the plots are overlaid by default. To create subplots instead, use `subplots=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_clean.hvplot.hist(\"weight\", by=\"origin\", subplots=True, width=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also plot histograms of *datetime* data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.sampledata.commits import data as commits\n",
    "\n",
    "commits = commits.reset_index().sort_values(\"datetime\")\n",
    "commits.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "commits.hvplot.hist(\n",
    "    \"datetime\",\n",
    "    bin_range=(pd.Timestamp('2012-11-30'), pd.Timestamp('2017-05-01')),\n",
    "    bins=54,   \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to plot the distribution of a categorical column you can calculate the distribution using Pandas' method `value_counts` and plot it using `.hvplot.bar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_clean[\"mfr\"].value_counts().hvplot.bar(invert=True, flip_yaxis=True, height=500)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}