New examples

docs/Examples/05_water_indices_with_spyndex.ipynb

@@ -97,7 +97,7 @@
     }
    ],
    "source": [
-    "data = pd.read_csv(\"assets/gaul_data.csv\")"
+    "data = pd.read_csv(\"https://raw.githubusercontent.com/dkedar7/fast_dash/docs/docs/Examples/assets/gaul_data.csv\")"
    ]
   },
   {

docs/Examples/06_choropleth_maps.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/dkedar7/fast_dash/blob/docs/docs/Examples/06_choropleth_maps.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is optimized to run in Google Colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "import numpy as np\n",
+    "\n",
+    "from urllib.request import urlopen\n",
+    "import json\n",
+    "\n",
+    "from fast_dash import fastdash, Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Counties sample data\n",
+    "with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:\n",
+    "    counties = json.load(response)\n",
+    "\n",
+    "counties_df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv\", dtype={\"fips\": str})\n",
+    "\n",
+    "# US Ag exports sample data\n",
+    "us_ag_exports_df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')\n",
+    "\n",
+    "for col in us_ag_exports_df.columns:\n",
+    "    us_ag_exports_df[col] = us_ag_exports_df[col].astype(str)\n",
+    "\n",
+    "us_ag_exports_df['text'] = us_ag_exports_df['state'] + '<br>' + \\\n",
+    "    'Beef ' + us_ag_exports_df['beef'] + ' Dairy ' + us_ag_exports_df['dairy'] + '<br>' + \\\n",
+    "    'Fruits ' + us_ag_exports_df['total fruits'] + ' Veggies ' + us_ag_exports_df['total veggies'] + '<br>' + \\\n",
+    "    'Wheat ' + us_ag_exports_df['wheat'] + ' Corn ' + us_ag_exports_df['corn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Parsing function docstring is still an experimental feature. To reduce uncertainty, consider setting `about` to `False`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"650\"\n",
+       "            src=\"http://127.0.0.1:8080/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x1de2282eca0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "@fastdash(theme=\"flatly\")\n",
+    "def visualize_states_and_counties(a: int) -> (Graph, Graph):\n",
+    "    \"\"\"\n",
+    "    Fast Dash allows quick and easy visualization of various geographies.\\\n",
+    "    This application plots states and counties using two differnent basemaps.\\\n",
+    "    Reference: https://plotly.com/python/mapbox-county-choropleth/.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Plotly-native graph map\n",
+    "    fig = go.Figure(data=go.Choropleth(\n",
+    "        locations=us_ag_exports_df['code'],\n",
+    "        z=us_ag_exports_df['total exports'].astype(float),\n",
+    "        locationmode='USA-states',\n",
+    "        colorscale='Reds',\n",
+    "        autocolorscale=False,\n",
+    "        text=us_ag_exports_df['text'], # hover text\n",
+    "        marker_line_color='white', # line markers between states\n",
+    "        colorbar_title=\"Millions USD\"\n",
+    "    ))\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title_text='2011 US Agriculture Exports by State<br>(Hover for breakdown)',\n",
+    "        geo = dict(\n",
+    "            scope='usa',\n",
+    "            projection=go.layout.geo.Projection(type = 'albers usa'),\n",
+    "            showlakes=True, # lakes\n",
+    "            lakecolor='rgb(255, 255, 255)'),\n",
+    "    )\n",
+    "   \n",
+    "    plotly_express = fig\n",
+    "\n",
+    "    # Mapbox graph map\n",
+    "    fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=counties_df.fips, z=counties_df.unemp,\n",
+    "                                        colorscale=\"Viridis\", zmin=0, zmax=12, marker_line_width=0))\n",
+    "    fig.update_layout(mapbox_style=\"light\", mapbox_accesstoken=\"...\",\n",
+    "                    mapbox_zoom=3, mapbox_center = {\"lat\": 37.0902, \"lon\": -95.7129})\n",
+    "    fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
+    "    mapbox = fig\n",
+    "    return plotly_express, mapbox"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}