{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import hvplot.pandas  # noqa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "A `bar` plot represents **categorical data** with rectangular bars with heights proportional to the **numerical values** that they represent.\n",
    "The x-axis represents the categories and the y axis represents the numerical value scale.\n",
    "The bars are of equal width which allows for instant comparison of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({\n",
    "    \"framework\": [\"hvPlot\", \"HoloViews\", \"Panel\"], \n",
    "    \"stars\": [700, 2400, 2600]\n",
    "}).hvplot.bar(x=\"framework\", y=\"stars\", color=\"gold\", title=\"Bar Plot of Github Stars\", ylabel=\"⭐\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Let's import some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.autompg import autompg_clean as autompg\n",
    "\n",
    "autompg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define *long form data*, i.e. one row per `yr` categorical value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_long_form = autompg.groupby(\"yr\").mean(numeric_only=True).reset_index()\n",
    "autompg_long_form.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We define a dataset with a *multi index* representing multiple categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_multi_index = autompg.query(\"yr<=80\").groupby(['yr', 'origin']).mean(numeric_only=True)\n",
    "autompg_multi_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define *wide form data*, i.e. multiple columns representing a category like `origin`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_wide = autompg_multi_index.reset_index().pivot(index='yr', columns='origin', values='mpg')\n",
    "autompg_wide.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Bar Plots\n",
    "\n",
    "You can plot **long form data** if you specify the categorical x-value using the `x` argument and the numerical y-value using the `y-argument`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_long_form.hvplot.bar(x=\"yr\", y=\"mpg\", width=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you don't specify the `x` argument, then the index will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_long_form.hvplot.bar(y=\"mpg\", width=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the index is a `MultiIndex`, the x-axis represents the multiple categories included in the index, the outer index level being displayed as the outer category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_multi_index.hvplot.bar(width=1000, rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can instead stack on the y-axis the values of the nested index/category, *origin* in this example, by setting `stacked` to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_multi_index.hvplot.bar(stacked=True, width=1000, legend=\"top_left\", height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot multiple categories on the x-axis when the data is **wide form**, you need to provide a list of columns to `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_wide.hvplot.bar(y=['Asia', 'Europe', 'North America'], width=1000, ylabel=\"mpg\", rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you may also stack the values of the *wide form* data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_wide.hvplot.bar(y=['Asia', 'Europe', 'North America'], ylabel=\"mpg\", stacked=True, width=1000, legend=\"top_left\", height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colorful Bar Plots\n",
    "\n",
    "You can control the `bar` color using the `color` argument. It accepts the name of a column, the name of a color or a list of colors.\n",
    "\n",
    "Here is an example using a single named color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_long_form.hvplot.bar(x=\"yr\", y=\"mpg\", color=\"teal\", width=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example using a list of colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_wide.hvplot.bar(y=['Asia', 'Europe', 'North America'], width=1000, ylabel=\"mpg\", color=[\"#ba2649\", \"#ffa7ca\", \"#1a6b54\"], rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example using the name of a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autompg_long_form.hvplot.bar(y='mpg', color=\"weight\", colorbar=True, clabel=\"Weight\", cmap=\"bmy\", width=1000)"
   ]
  }
 ],
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