{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### New to Plotly?\n", "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Version Check\n", "Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.6.1'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly\n", "plotly.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### United States Choropleth Map" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')\n", "\n", "for col in df.columns:\n", " df[col] = df[col].astype(str)\n", "\n", "scl = [\n", " [0.0, 'rgb(242,240,247)'],\n", " [0.2, 'rgb(218,218,235)'],\n", " [0.4, 'rgb(188,189,220)'],\n", " [0.6, 'rgb(158,154,200)'],\n", " [0.8, 'rgb(117,107,177)'],\n", " [1.0, 'rgb(84,39,143)']\n", "]\n", "\n", "df['text'] = df['state'] + '
' + \\\n", " 'Beef ' + df['beef'] + ' Dairy ' + df['dairy'] + '
' + \\\n", " 'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '
' + \\\n", " 'Wheat ' + df['wheat'] + ' Corn ' + df['corn']\n", "\n", "data = [go.Choropleth(\n", " colorscale = scl,\n", " autocolorscale = False,\n", " locations = df['code'],\n", " z = df['total exports'].astype(float),\n", " locationmode = 'USA-states',\n", " text = df['text'],\n", " marker = go.choropleth.Marker(\n", " line = go.choropleth.marker.Line(\n", " color = 'rgb(255,255,255)',\n", " width = 2\n", " )),\n", " colorbar = go.choropleth.ColorBar(\n", " title = \"Millions USD\")\n", ")]\n", "\n", "layout = go.Layout(\n", " title = go.layout.Title(\n", " text = '2011 US Agriculture Exports by State
(Hover for breakdown)'\n", " ),\n", " geo = go.layout.Geo(\n", " scope = 'usa',\n", " projection = go.layout.geo.Projection(type = 'albers usa'),\n", " showlakes = True,\n", " lakecolor = 'rgb(255, 255, 255)'),\n", ")\n", "\n", "fig = go.Figure(data = data, layout = layout)\n", "py.iplot(fig, filename = 'd3-cloropleth-map')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### World Choropleth Map" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import pandas as pd\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')\n", "\n", "data = [go.Choropleth(\n", " locations = df['CODE'],\n", " z = df['GDP (BILLIONS)'],\n", " text = df['COUNTRY'],\n", " colorscale = [\n", " [0, \"rgb(5, 10, 172)\"],\n", " [0.35, \"rgb(40, 60, 190)\"],\n", " [0.5, \"rgb(70, 100, 245)\"],\n", " [0.6, \"rgb(90, 120, 245)\"],\n", " [0.7, \"rgb(106, 137, 247)\"],\n", " [1, \"rgb(220, 220, 220)\"]\n", " ],\n", " autocolorscale = False,\n", " reversescale = True,\n", " marker = go.choropleth.Marker(\n", " line = go.choropleth.marker.Line(\n", " color = 'rgb(180,180,180)',\n", " width = 0.5\n", " )),\n", " colorbar = go.choropleth.ColorBar(\n", " tickprefix = '$',\n", " title = 'GDP
Billions US$'),\n", ")]\n", "\n", "layout = go.Layout(\n", " title = go.layout.Title(\n", " text = '2014 Global GDP'\n", " ),\n", " geo = go.layout.Geo(\n", " showframe = False,\n", " showcoastlines = False,\n", " projection = go.layout.geo.Projection(\n", " type = 'equirectangular'\n", " )\n", " ),\n", " annotations = [go.layout.Annotation(\n", " x = 0.55,\n", " y = 0.1,\n", " xref = 'paper',\n", " yref = 'paper',\n", " text = 'Source: \\\n", " CIA World Factbook',\n", " showarrow = False\n", " )]\n", ")\n", "\n", "fig = go.Figure(data = data, layout = layout)\n", "py.iplot(fig, filename = 'd3-world-map')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Choropleth Inset Map" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')\n", "df.head()\n", "\n", "cases = []\n", "colors = ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']\n", "months = {6:'June',7:'July',8:'Aug',9:'Sept'}\n", "\n", "for i in range(6,10)[::-1]:\n", " cases.append(go.Scattergeo(\n", " lon = df[ df['Month'] == i ]['Lon'], #-(max(range(6,10))-i),\n", " lat = df[ df['Month'] == i ]['Lat'],\n", " text = df[ df['Month'] == i ]['Value'],\n", " name = months[i],\n", " marker = go.scattergeo.Marker(\n", " size = df[ df['Month'] == i ]['Value']/50,\n", " color = colors[i-6],\n", " line = go.scattergeo.marker.Line(width = 0)\n", " ),\n", " ) )\n", "\n", "cases[0]['text'] = df[ df['Month'] == 9 ]['Value'].map('{:.0f}'.format).astype(str)+' '+\\\n", " df[ df['Month'] == 9 ]['Country']\n", "cases[0]['mode'] = 'markers+text'\n", "cases[0]['textposition'] = 'bottom center'\n", "\n", "inset = [\n", " go.Choropleth(\n", " locationmode = 'country names',\n", " locations = df[ df['Month'] == 9 ]['Country'],\n", " z = df[ df['Month'] == 9 ]['Value'],\n", " text = df[ df['Month'] == 9 ]['Country'],\n", " colorscale = [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],\n", " autocolorscale = False,\n", " showscale = False,\n", " geo = 'geo2'\n", " ),\n", " go.Scattergeo(\n", " lon = [21.0936],\n", " lat = [7.1881],\n", " text = ['Africa'],\n", " mode = 'text',\n", " showlegend = False,\n", " geo = 'geo2'\n", " )\n", "]\n", "\n", "layout = go.Layout(\n", " title = go.layout.Title(\n", " text = 'Ebola cases reported by month in West Africa 2014
\\\n", "Source: \\\n", "HDX'),\n", " geo = go.layout.Geo(\n", " resolution = 50,\n", " scope = 'africa',\n", " showframe = False,\n", " showcoastlines = True,\n", " showland = True,\n", " landcolor = \"rgb(229, 229, 229)\",\n", " countrycolor = \"rgb(255, 255, 255)\" ,\n", " coastlinecolor = \"rgb(255, 255, 255)\",\n", " projection = go.layout.geo.Projection(\n", " type = 'equirectangular'\n", " ),\n", " lonaxis = go.layout.geo.Lonaxis(\n", " range= [ -15.0, -5.0 ]\n", " ),\n", " lataxis = go.layout.geo.Lataxis(\n", " range= [ 0.0, 12.0 ]\n", " ),\n", " domain = go.layout.geo.Domain(\n", " x = [ 0, 1 ],\n", " y = [ 0, 1 ]\n", " )\n", " ),\n", " geo2 = go.layout.Geo(\n", " scope = 'africa',\n", " showframe = False,\n", " showland = True,\n", " landcolor = \"rgb(229, 229, 229)\",\n", " showcountries = False,\n", " domain = go.layout.geo.Domain(\n", " x = [ 0, 0.6 ],\n", " y = [ 0, 0.6 ]\n", " ),\n", " bgcolor = 'rgba(255, 255, 255, 0.0)',\n", " ),\n", " legend = go.layout.Legend(\n", " traceorder = 'reversed'\n", " )\n", ")\n", "\n", "fig = go.Figure(layout=layout, data=cases+inset)\n", "py.iplot(fig, filename='West Africa Ebola cases 2014')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Full County Choropleths\n", "For the full county choropleth doc page checkout https://plotly.com/python/county-choropleth/" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The draw time for this plot will be slow for clients without much RAM.\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.figure_factory as ff\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv')\n", "df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(lambda x: str(x).zfill(2))\n", "df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(lambda x: str(x).zfill(3))\n", "df_sample['FIPS'] = df_sample['State FIPS Code'] + df_sample['County FIPS Code']\n", "\n", "colorscale = [\"#f7fbff\", \"#ebf3fb\", \"#deebf7\", \"#d2e3f3\", \"#c6dbef\", \"#b3d2e9\", \"#9ecae1\",\n", " \"#85bcdb\", \"#6baed6\", \"#57a0ce\", \"#4292c6\", \"#3082be\", \"#2171b5\", \"#1361a9\",\n", " \"#08519c\", \"#0b4083\", \"#08306b\"\n", "]\n", "endpts = list(np.linspace(1, 12, len(colorscale) - 1))\n", "fips = df_sample['FIPS'].tolist()\n", "values = df_sample['Unemployment Rate (%)'].tolist()\n", "\n", "fig = ff.create_choropleth(\n", " fips = fips, values = values, scope = ['usa'],\n", " binning_endpoints = endpts, colorscale = colorscale,\n", " show_state_data = False,\n", " show_hover = True, centroid_marker = {\n", " 'opacity': 0\n", " },\n", " asp = 2.9,\n", " title = 'USA by Unemployment %',\n", " legend_title = '% unemployed'\n", ")\n", "py.iplot(fig, filename = 'choropleth_full_usa')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dash Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Dash](https://plotly.com/products/dash/) is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its [source code](https://github.com/plotly/simple-example-chart-apps/tree/master/dash-choroplethplot) can easily be deployed to a PaaS." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-choroplethplot/\", width=\"100%\", height=\"950px\", frameBorder=\"0\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-choroplethplot/code\", width=\"100%\", height=500, frameBorder=\"0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reference\n", "See https://plotly.com/python/reference/#choropleth for more information and chart attribute options!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/plotly/publisher.git\n", " Cloning https://github.com/plotly/publisher.git to /private/var/folders/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-req-build-yyuryex2\n", "Building wheels for collected packages: publisher\n", " Building wheel for publisher (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /private/var/folders/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-ephem-wheel-cache-m3irlu81/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.13\n", " Uninstalling publisher-0.13:\n", " Successfully uninstalled publisher-0.13\n", "Successfully installed publisher-0.13\n", "\u001b[33mYou are using pip version 19.0.3, however version 19.1.1 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "from IPython.display import display, HTML\n", "\n", "display(HTML(''))\n", "display(HTML(''))\n", "\n", "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", "import publisher\n", "publisher.publish(\n", " 'Choropleth_maps.ipynb', 'python/choropleth-maps/', 'Choropleth Maps',\n", " 'How to make choropleth maps in Python with Plotly.',\n", " title = 'Python Choropleth Maps | Plotly',\n", " has_thumbnail='true', thumbnail='thumbnail/choropleth.jpg', \n", " language='python',\n", " display_as='maps', order=1, ipynb='~notebook_demo/55')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }