{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "URL: http://matplotlib.org/examples/showcase/bachelors_degrees_by_gender.html\n",
    "\n",
    "Most examples work across multiple plotting backends equivalent, this example is also available for:\n",
    "\n",
    "* [Matplotlib - bachelors_degrees_by_gender](../matplotlib/bachelors_degrees_by_gender.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "hv.extension('bokeh', 'matplotlib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "gender_degree_data = pd.read_csv(\"https://datasets.holoviz.org/bachelor_women/v1/percent_bachelors_degrees_women_usa.csv\")\n",
    "\n",
    "title = ('Percentage of Bachelor\\'s degrees conferred to women in '\n",
    "        'the U.S.A. by major (1970-2011)\\n')\n",
    "\n",
    "# These are the colors that will be used in the plot\n",
    "color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',\n",
    "                  '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',\n",
    "                  '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',\n",
    "                  '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']\n",
    "\n",
    "# Offsets for degree labels\n",
    "y_offsets = {'Foreign Languages': 0.5, 'English': -0.5,\n",
    "             'Communications and Journalism': 0.75,\n",
    "             'Art and Performance': -0.25, 'Agriculture': 1.25,\n",
    "             'Social Sciences and History': 0.25, 'Business': -0.75,\n",
    "             'Math and Statistics': 0.75, 'Architecture': -0.75,\n",
    "             'Computer Science': 0.75, 'Engineering': -0.25}\n",
    "\n",
    "# Load the data into a dataframe and us pd.melt to unpivot the degree column\n",
    "df = pd.DataFrame(gender_degree_data)\n",
    "df = pd.melt(df, id_vars='Year', var_name='Degree', value_name='conferred')\n",
    "df['Degree'] = [d.replace('_', ' ').title() for d in df.Degree]\n",
    "\n",
    "# Define a formatter that works for both bokeh and matplotlib\n",
    "def percent_format(x):\n",
    "    try:\n",
    "        return '{:0.0f}%'.format(x)\n",
    "    except:\n",
    "        return '%d%' % x\n",
    "\n",
    "# Define the value dimensions\n",
    "vdim = hv.Dimension('conferred', range=(0, 90))\n",
    "\n",
    "# Define the dataset\n",
    "ds = hv.Dataset(df, vdims=vdim)\n",
    "curves = ds.to(hv.Curve, 'Year', groupby='Degree').overlay().redim(Year=dict(range=(1970, 2030)))\n",
    "\n",
    "# Define a function to get the text annotations\n",
    "max_year = ds['Year'].max()\n",
    "def offset(row):\n",
    "    row['conferred'] += y_offsets.get(row.Degree, 0)\n",
    "    return row\n",
    "label_df = df[df.Year==max_year].apply(offset, axis=1)\n",
    "labels = hv.Labels(label_df, ['Year', 'conferred'], 'Degree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlay = (curves * labels).relabel(title)\n",
    "\n",
    "gridstyle = {'grid_line_dash': [6, 4], 'grid_line_width': 3, 'grid_bounds': (0, 100)}\n",
    "\n",
    "overlay.opts(\n",
    "    opts.Curve(show_frame=False, labelled=[], tools=['hover'],\n",
    "               height=900, width=900, show_legend=False, xticks=[1970, 1980, 1990, 2000, 2010],\n",
    "               color=hv.Cycle(values=color_sequence), line_width=2, show_grid=True, yformatter='%d%%'),\n",
    "    opts.Labels(text_color='Degree', cmap=color_sequence, text_align='left'),\n",
    "    opts.NdOverlay(batched=False, gridstyle=gridstyle))"
   ]
  }
 ],
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