examples/jupyter/integrations/plotly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demonstrating Plotly Modin Interoperability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Currently Plotly is not completely interoperable with Modin. Each visualization is created with a Modin and then pandas dataframe for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import modin.pandas as pd\n",
    "import pandas\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"notebook\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "modin_df = pd.DataFrame(dict(a=[1,3,2,4], b=[3,2,1,0]))\n",
    "pandas_df = pandas.DataFrame(dict(a=[1,3,2,4], b=[3,2,1,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig2 = px.bar(modin_df)\n",
    "fig2.show()\n",
    "# py.iplot(fig2 , filename='jupyter-basic_bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig2 = px.bar(pandas_df)\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.line(modin_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.line(pandas_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.area(modin_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.area(pandas_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.area(modin_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.area(pandas_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.violin(modin_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.violin(pandas_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.box(modin_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.box(pandas_df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "fig = px.histogram(modin_df, opacity=0.5, orientation='h', nbins=5)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "fig = px.histogram(pandas_df, opacity=0.5, orientation='h', nbins=5)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with Modin df\n",
    "# Example from https://plotly.com/python/mapbox-county-choropleth/#choropleth-map-using-plotlyexpress-and-carto-base-map-no-token-needed\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:\n",
    "    counties = json.load(response)\n",
    "import modin.pandas as pd\n",
    "modin_df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv\",\n",
    "                   dtype={\"fips\": str})\n",
    "fig = px.choropleth(modin_df, geojson=counties, locations='fips', color='unemp',\n",
    "                           color_continuous_scale=\"Viridis\",\n",
    "                           range_color=(0, 12),\n",
    "                           scope=\"usa\",\n",
    "                           labels={'unemp':'unemployment rate'}\n",
    "                          )\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a visualization with pandas df\n",
    "# Example from https://plotly.com/python/mapbox-county-choropleth/#choropleth-map-using-plotlyexpress-and-carto-base-map-no-token-needed\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:\n",
    "    counties = json.load(response)\n",
    "import pandas\n",
    "pandas_df = pandas.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv\",\n",
    "                   dtype={\"fips\": str})\n",
    "\n",
    "fig = px.choropleth(pandas_df, geojson=counties, locations='fips', color='unemp',\n",
    "                           color_continuous_scale=\"Viridis\",\n",
    "                           range_color=(0, 12),\n",
    "                           scope=\"usa\",\n",
    "                           labels={'unemp':'unemployment rate'}\n",
    "                          )\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()\n"
   ]
  }
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