{ "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!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### US map small multiples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import pandas as pd\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv')\n", "df.head()\n", "\n", "data = []\n", "layout = dict(\n", " title = 'New Walmart Stores per year 1962-2006
\\\n", "Source: \\\n", "University of Minnesota',\n", " # showlegend = False,\n", " autosize = False,\n", " width = 1000,\n", " height = 900,\n", " hovermode = False,\n", " legend = dict(\n", " x=0.7,\n", " y=-0.1,\n", " bgcolor=\"rgba(255, 255, 255, 0)\",\n", " font = dict( size=11 ),\n", " )\n", ")\n", "years = df['YEAR'].unique()\n", "\n", "for i in range(len(years)):\n", " geo_key = 'geo'+str(i+1) if i != 0 else 'geo'\n", " lons = list(df[ df['YEAR'] == years[i] ]['LON'])\n", " lats = list(df[ df['YEAR'] == years[i] ]['LAT'])\n", " # Walmart store data\n", " data.append(\n", " dict(\n", " type = 'scattergeo',\n", " showlegend=False,\n", " lon = lons,\n", " lat = lats,\n", " geo = geo_key,\n", " name = years[i],\n", " marker = dict(\n", " color = \"rgb(0, 0, 255)\",\n", " opacity = 0.5\n", " )\n", " )\n", " )\n", " # Year markers\n", " data.append(\n", " dict(\n", " type = 'scattergeo',\n", " showlegend = False,\n", " lon = [-78],\n", " lat = [47],\n", " geo = geo_key,\n", " text = [years[i]],\n", " mode = 'text',\n", " )\n", " )\n", " layout[geo_key] = dict(\n", " scope = 'usa',\n", " showland = True,\n", " landcolor = 'rgb(229, 229, 229)',\n", " showcountries = False,\n", " domain = dict( x = [], y = [] ),\n", " subunitcolor = \"rgb(255, 255, 255)\",\n", " )\n", "\n", "\n", "def draw_sparkline( domain, lataxis, lonaxis ):\n", " ''' Returns a sparkline layout object for geo coordinates '''\n", " return dict(\n", " showland = False,\n", " showframe = False,\n", " showcountries = False,\n", " showcoastlines = False,\n", " domain = domain,\n", " lataxis = lataxis,\n", " lonaxis = lonaxis,\n", " bgcolor = 'rgba(255,200,200,0.0)'\n", " )\n", "\n", "# Stores per year sparkline\n", "layout['geo44'] = draw_sparkline({'x':[0.6,0.8], 'y':[0,0.15]}, \\\n", " {'range':[-5.0, 30.0]}, {'range':[0.0, 40.0]} )\n", "data.append(\n", " dict(\n", " type = 'scattergeo',\n", " mode = 'lines',\n", " lat = list(df.groupby(by=['YEAR']).count()['storenum']/1e1),\n", " lon = range(len(df.groupby(by=['YEAR']).count()['storenum']/1e1)),\n", " line = dict( color = \"rgb(0, 0, 255)\" ),\n", " name = \"New stores per year
Peak of 178 stores per year in 1990\",\n", " geo = 'geo44',\n", " )\n", ")\n", "\n", "# Cumulative sum sparkline\n", "layout['geo45'] = draw_sparkline({'x':[0.8,1], 'y':[0,0.15]}, \\\n", " {'range':[-5.0, 50.0]}, {'range':[0.0, 50.0]} )\n", "data.append(\n", " dict(\n", " type = 'scattergeo',\n", " mode = 'lines',\n", " lat = list(df.groupby(by=['YEAR']).count().cumsum()['storenum']/1e2),\n", " lon = range(len(df.groupby(by=['YEAR']).count()['storenum']/1e1)),\n", " line = dict( color = \"rgb(214, 39, 40)\" ),\n", " name =\"Cumulative sum
3176 stores total in 2006\",\n", " geo = 'geo45',\n", " )\n", ")\n", "\n", "z = 0\n", "COLS = 5\n", "ROWS = 9\n", "for y in reversed(range(ROWS)):\n", " for x in range(COLS):\n", " geo_key = 'geo'+str(z+1) if z != 0 else 'geo'\n", " layout[geo_key]['domain']['x'] = [float(x)/float(COLS), float(x+1)/float(COLS)]\n", " layout[geo_key]['domain']['y'] = [float(y)/float(ROWS), float(y+1)/float(ROWS)]\n", " z=z+1\n", " if z > 42:\n", " break\n", "\n", "fig = { 'data':data, 'layout':layout }\n", "py.iplot( fig, filename='US Walmart growth', height=900, width=1000 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reference\n", "See https://plotly.com/python/reference/#scattergeo for more information and chart attribute options!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "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 /var/folders/ld/6cl3s_l50wd40tdjq2b03jxh0000gp/T/pip-3nPpfj-build\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.10\n", " Uninstalling publisher-0.10:\n", " Successfully uninstalled publisher-0.10\n", " Running setup.py install for publisher ... \u001b[?25l-\b \b\\\b \bdone\n", "\u001b[?25hSuccessfully installed publisher-0.10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from nbconvert instead.\n", " \"You should import from nbconvert instead.\", ShimWarning)\n", "/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", " warnings.warn('Did you \"Save\" this notebook before running this command? '\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", " 'map-subplots.ipynb', ' python/map-subplots-and-small-multiples/', ' Python Map Subplots and Map Small Multiples| Plotly',\n", " 'How to make map subplots and map small multiples in Python.',\n", " title = 'Python Map Subplots and Map Small Multiples | plotly',\n", " name = 'Map Subplots',\n", " has_thumbnail='true', thumbnail='thumbnail/map-subplots.jpg', \n", " language='python', page_type='example_index'\n", " display_as='multiple_axes', order=5,\n", " ipynb= '~notebook_demo/59') " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }