{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e8cff9a",
   "metadata": {},
   "source": [
    "# Goal: Recreate Gapminder's Bubble Chart*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0592be0b",
   "metadata": {},
   "source": [
    "![Gapminder](Gapminder.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7205f9df",
   "metadata": {},
   "source": [
    "*As much as possible..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51e5440",
   "metadata": {},
   "source": [
    "See https://www.gapminder.org/tools/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea83b75",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f2b16b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc574529",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "088f9d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Gapminder-data.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3323617",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9515c150",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info = pd.read_csv('Gapminder-info.csv', sep=',', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c27a468d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d096bd8f",
   "metadata": {},
   "source": [
    "## Create the bubble chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "872e455c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e5629a50",
   "metadata": {},
   "source": [
    "## Add animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed99d63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2a7ea93d",
   "metadata": {},
   "source": [
    "## Use Dash"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2851266c",
   "metadata": {},
   "source": [
    "- Dash html components: https://dash.plotly.com/dash-html-components\n",
    "- Dash core components: https://dash.plotly.com/dash-core-components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c9e9d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc, callback, Output, Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a6a5520",
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = ['Income', 'Life expectancy', 'Fertility', 'Child mortality']\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "app = Dash(__name__, external_stylesheets=external_stylesheets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a670d1c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
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 "nbformat": 4,
 "nbformat_minor": 5
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