{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test notebook Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.utils.capture import capture_output\n",
    "from ipywidgets import widgets\n",
    "\n",
    "from ydata_profiling import ProfileReport\n",
    "from ydata_profiling.utils.cache import cache_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the Titanic Dataset\n",
    "file_name = cache_file(\n",
    "    \"titanic.csv\",\n",
    "    \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n",
    ")\n",
    "df = pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Profiling Report (with progress bar)\n",
    "with capture_output() as out:\n",
    "    profile = ProfileReport(\n",
    "        df,\n",
    "        title=\"Titanic Dataset\",\n",
    "        html={\"style\": {\"full_width\": True}},\n",
    "        progress_bar=True,\n",
    "        lazy=False,\n",
    "    )\n",
    "\n",
    "assert all(\n",
    "    any(v in s.data[\"text/plain\"] for v in [\"%|\", \"FloatProgress\"]) for s in out.outputs\n",
    ")\n",
    "assert len(out.outputs) == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Profiling Report (without progress bar)\n",
    "with capture_output() as out:\n",
    "    profile = ProfileReport(\n",
    "        df,\n",
    "        title=\"Titanic Dataset\",\n",
    "        html={\"style\": {\"full_width\": True}},\n",
    "        progress_bar=False,\n",
    "        lazy=False,\n",
    "    )\n",
    "\n",
    "assert len(out.outputs) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Waiting on issue: https://github.com/computationalmodelling/nbval/issues/136\n",
    "\n",
    "# The Notebook Widgets Interface\n",
    "# with capture_output() as out:\n",
    "#     profile.to_widgets()\n",
    "\n",
    "# assert len(out.outputs) == 2\n",
    "# assert out.outputs[0].data['text/plain'].startswith('Tab(children=(HTML(value=')\n",
    "# assert out.outputs[1].data['text/plain'] == '<IPython.display.HTML object>'\n",
    "# assert 'ydata-profiling' in out.outputs[1].data['text/html']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or use the HTML report in an iframe\n",
    "with capture_output() as out:\n",
    "    profile.to_notebook_iframe()\n",
    "\n",
    "assert len(out.outputs) == 1\n",
    "assert out.outputs[0].data[\"text/plain\"] == \"<IPython.core.display.HTML object>\""
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}