2 - Plot data with plotly.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT DATA WITH PLOTLY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necesary to use plotly in a jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "pio.renderers.default = 'iframe'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data from previous dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"out.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **plotly.express** module (usually imported as px) contains functions that can create entire figures at once.   \n",
    "Plotly Express is a built-in part of the plotly library, and is the recommended starting point for creating most common figures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://plotly.com/python/plotly-express/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BAR and HISTOGRAM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.bar(df, x=\"Type\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.histogram(df, x=\"Type\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some times COLOR could be used as 3rd dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.histogram(df, x=\"Age\",color='Gender')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.histogram(df, x='Lunch Box',color='Food')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can personalize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.histogram(df, x=\"Lunch Box\", color=\"Food\",\n",
    "            title=\"PyDay Lunch Boxes\",\n",
    "            width=600, height=300,\n",
    "            labels={\"Lunch Box\": \"Catering needs\"},\n",
    "            category_orders={ # replaces default order by column name\n",
    "                \"Lunch Box\": [\"No, thanks\", \"Yes, please\"]\n",
    "            },\n",
    "            color_discrete_map={\n",
    "                \"Vegetarian\": \"Aquamarine\",\n",
    "                \"Lactose free\": \"MediumBlue\",\n",
    "                \"No special needs\": \"Grey\",\n",
    "                \"Vegan\": \"ForestGreen\",\n",
    "                \"Other\": \"FireBrick\"\n",
    "            })\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINE/ TIMELINE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(df, x='Date', y=df.index)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(df, x='Date')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SCATTER:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(df,x='Date', color='Type')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(df,x='Date', color='Type',marginal_x=\"histogram\", marginal_y=\"rug\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PIE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.pie(df, values=df.index,names='Age')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= px.pie(df, values=df.index,names='Type')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADVANCED:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.sunburst(df, path=['Type', 'Gender', 'Age'], values=df.index)\n",
    "fig.show()"
   ]
  }
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