{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import *\n",
    "import pandas\n",
    "import requests, requests_cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "requests_cache.install_cache('frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass, field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mimetypes\n",
    "mimetypes = mimetypes.MimeTypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import traitlets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Explorer:\n",
    "    df: pandas.DataFrame = None    \n",
    "        \n",
    "    def __post_init__(self):\n",
    "        self.table = HTML()\n",
    "        self.rows = IntSlider(value=10, start=1, end=min(50, len(self.df)-10))\n",
    "        self.start = IntSlider(start=1, end=len(self.df)-self.rows.value)\n",
    "        traitlets.dlink((self.rows, 'value'), (self.table, 'value'), \n",
    "                        lambda _: df.iloc[self.start.value: self.start.value+self.rows.value]._repr_html_())\n",
    "        traitlets.dlink((self.start, 'value'), (self.table, 'value'), \n",
    "                       lambda _: df.iloc[self.start.value: self.start.value+self.rows.value]._repr_html_())\n",
    "    def _ipython_display_(self):\n",
    "        IPython.display.display(self.start, self.rows, self.table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.util.testing.makeDataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c77c374e66164ff7976c4606d1880c60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fe9d5c713594dceab00bc10cc3ac7dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "133cd48c2df54fb6996cee9a67b8d860",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='<div>\\n<style scoped>\\n    .dataframe tbody tr th:only-of-type {\\n        vertical-align: middle;\\…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Explorer(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}