{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotly Library Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look under the hood of the Plotly library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference between `plotly.graph_obj` and `plotly.express`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `plotly.graph_obj` \n",
    "\n",
    "* Low-level syntax\n",
    "* Complex interface, data in the form of a dictionary\n",
    "* Complete control of all the details\n",
    "* Analogous to the `matplotlib` library\n",
    "\n",
    "### `plotly.express`\n",
    "\n",
    "* High-level syntax\n",
    "* Easy interface, data should be in a Pandas DataFrame\n",
    "* No control over some details\n",
    "* Analogous to the `seaborn` library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Gapminder-mini.csv', sep=',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df, \n",
    "    x='Income', \n",
    "    y='Life expectancy',\n",
    "    color='Region'\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotly's internal representation of a static scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dictionary with two parts:\n",
    "* data (contains all data-related information)\n",
    "* layout (contains all other information)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = {\n",
    "    'data': [{'hovertemplate': 'Region=Europe<br>Income=%{x}<br>Life expectancy=%{y}<extra></extra>',\n",
    "              'legendgroup': 'Europe',\n",
    "              'marker': {'color': '#636efa', 'symbol': 'circle'},\n",
    "              'mode': 'markers',\n",
    "              'name': 'Europe',\n",
    "              'orientation': 'v',\n",
    "              'showlegend': True,\n",
    "              'type': 'scatter',\n",
    "              'x': [34735., 35373., 35739., 35816., 36084.],\n",
    "              'xaxis': 'x',\n",
    "              'y': [82.97, 83.17, 83.33, 83.49, 83.64],\n",
    "              'yaxis': 'y'},\n",
    "             {'hovertemplate': 'Region=Africa<br>Income=%{x}<br>Life expectancy=%{y}<extra></extra>',\n",
    "              'legendgroup': 'Africa',\n",
    "              'marker': {'color': '#EF553B', 'symbol': 'circle'},\n",
    "              'mode': 'markers',\n",
    "              'name': 'Africa',\n",
    "              'orientation': 'v',\n",
    "              'showlegend': True,\n",
    "              'type': 'scatter',\n",
    "              'x': [12246., 12233., 12143., 12039., 11986.],\n",
    "              'xaxis': 'x',\n",
    "              'y': [64.44, 66.31, 66.64, 66.93, 67.19],\n",
    "              'yaxis': 'y'}],\n",
    "    'layout': {'legend': {'title': {'text': 'Region'}, 'tracegroupgap': 0},\n",
    "               'margin': {'t': 60},\n",
    "               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Income'}},\n",
    "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Life expectancy'}}}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure(f)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotly's internal representation of an animated scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df, \n",
    "    x='Income', \n",
    "    y='Life expectancy',\n",
    "    color='Region',\n",
    "    animation_frame='Year'\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more complex dictionary with three parts:\n",
    "* data (contains all data-related information for the initial frame)\n",
    "* frames (contains all data-related information for each frame (including the first one))\n",
    "* layout (contains all other information, including slider and buttons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}