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cldougl/plotly_offline.ipynb

Created June 17, 2016 15:37
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plotly_offline.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotly Offline brings interactive Plotly graphs to the offline (local) environment. \n",
"\n",
"Instead of saving the graphs to a server, your data and graphs will remain in your local system.\n",
"When your ready to share, you can just publish them to the web with an online [Plotly account](https://plot.ly/feed) or to your company's internal [Plotly Enterprise](https://plot.ly/product/enterprise).\n",
"\n",
"To get started with Plotly Offline, upgrade to the `1.9.x` series or higher:"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"! pip install plotly --upgrade"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.12.1\n"
]
}
],
"source": [
"from plotly import __version__\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"\n",
"print __version__ # requires version >= 1.9.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running Plotly offline from Command Line\n",
"You can plot your graphs from a python script from command line. On executing the script, it will open a web browser with your Plotly Graph drawn."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'file:///Users/Chelsea/Repos/documentation/_posts/python/offline/temp-plot.html'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from plotly.graph_objs import Bar, Scatter, Figure, Layout\n",
"\n",
"plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Static Image Export\n",
"The graphs generated from Plotly Offline can also be saved as an Image, as shown below:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import plotly.plotly as py\n",
"# Generate the figure\n",
"\n",
"trace = Bar(x=[1,2,3],y=[4,5,6])\n",
"data = [trace]\n",
"layout = Layout(title='My Plot')\n",
"fig = Figure(data=data,layout=layout)\n",
"\n",
"# Save the figure as a png image:\n",
"py.image.save_as(fig, 'my_plot.png')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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68cO6zFd4x2FUeJfgd+mll7YzzjijnXzyye1BD3pQ+9rXvtZOP/309pa3vGVWfr0QQAAB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\nJz85+0T3BS94wearjj/++Hbccce1u9/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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='my_plot.png') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating Offline Graphs withing Jupyter Notebook\n",
"You can also plot your graphs offline inside a Jupyter Notebook Environment. First you need to initiate the Plotly Notebook mode as below:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"init_notebook_mode(connected=True) # run at the start of every ipython notebook to use plotly.offline\n",
" # this injects the plotly.js source files into the notebook"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"3d3b711d-1760-4f60-ae17-962ed61cf887\" style=\"height: 525; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3d3b711d-1760-4f60-ae17-962ed61cf887\", [{\"y\": [3, 1, 6], \"x\": [1, 2, 3]}], {}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iplot([{\"x\": [1, 2, 3], \"y\": [3, 1, 6]}])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from plotly.graph_objs import *\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<IPython.core.display.HTML object>"
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],
"source": [
"x = np.random.randn(2000)\n",
"y = np.random.randn(2000)\n",
"iplot([Histogram2dContour(x=x, y=y, contours=Contours(coloring='heatmap')),\n",
" Scatter(x=x, y=y, mode='markers', marker=Marker(color='white', size=3, opacity=0.3))], show_link=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plotting with Pandas\n",
"\n",
"import pandas as pd\n",
"df = pd.read_csv('https://plot.ly/~etpinard/191.csv')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
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{
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<br>Life Expectancy: 73.952 years <br>GDP per capita: 12057.49928 $ <br>Population: 6.036914 million\", \"Country: Madagascar <br>Life Expectancy: 59.443 years <br>GDP per capita: 1044.770126 $ <br>Population: 19.167654 million\", \"Country: Malawi <br>Life Expectancy: 48.303 years <br>GDP per capita: 759.3499101 $ <br>Population: 13.327079 million\", \"Country: Mali <br>Life Expectancy: 54.467 years <br>GDP per capita: 1042.581557 $ <br>Population: 12.031795 million\", \"Country: Mauritania <br>Life Expectancy: 64.164 years <br>GDP per capita: 1803.151496 $ <br>Population: 3.270065 million\", \"Country: Mauritius <br>Life Expectancy: 72.801 years <br>GDP per capita: 10956.99112 $ <br>Population: 1.250882 million\", \"Country: Morocco <br>Life Expectancy: 71.164 years <br>GDP per capita: 3820.17523 $ <br>Population: 33.757175 million\", \"Country: Mozambique <br>Life Expectancy: 42.082 years <br>GDP per capita: 823.6856205 $ <br>Population: 19.951656 million\", \"Country: Namibia <br>Life Expectancy: 52.906 years <br>GDP per capita: 4811.060429 $ <br>Population: 2.05508 million\", \"Country: Niger <br>Life Expectancy: 56.867 years <br>GDP per capita: 619.6768924 $ <br>Population: 12.894865 million\", \"Country: Nigeria <br>Life Expectancy: 46.859 years <br>GDP per capita: 2013.977305 $ <br>Population: 135.031164 million\", \"Country: Reunion <br>Life Expectancy: 76.442 years <br>GDP per capita: 7670.122558 $ <br>Population: 0.798094 million\", \"Country: Rwanda <br>Life Expectancy: 46.242 years <br>GDP per capita: 863.0884639 $ <br>Population: 8.860588 million\", \"Country: Sao Tome and Principe <br>Life Expectancy: 65.528 years <br>GDP per capita: 1598.435089 $ <br>Population: 0.199579 million\", \"Country: Senegal <br>Life Expectancy: 63.062 years <br>GDP per capita: 1712.472136 $ <br>Population: 12.267493 million\", \"Country: Sierra Leone <br>Life Expectancy: 42.568 years <br>GDP per capita: 862.5407561 $ <br>Population: 6.144562 million\", \"Country: Somalia <br>Life Expectancy: 48.159 years <br>GDP per capita: 926.1410683 $ <br>Population: 9.118773 million\", \"Country: South Africa <br>Life Expectancy: 49.339 years <br>GDP per capita: 9269.657808 $ <br>Population: 43.997828 million\", \"Country: Sudan <br>Life Expectancy: 58.556 years <br>GDP per capita: 2602.394995 $ <br>Population: 42.292929 million\", \"Country: Swaziland <br>Life Expectancy: 39.613 years <br>GDP per capita: 4513.480643 $ <br>Population: 1.133066 million\", \"Country: Tanzania <br>Life Expectancy: 52.517 years <br>GDP per capita: 1107.482182 $ <br>Population: 38.13964 million\", \"Country: Togo <br>Life Expectancy: 58.42 years <br>GDP per capita: 882.9699438 $ <br>Population: 5.701579 million\", \"Country: Tunisia <br>Life Expectancy: 73.923 years <br>GDP per capita: 7092.923025 $ <br>Population: 10.276158 million\", \"Country: Uganda <br>Life Expectancy: 51.542 years <br>GDP per capita: 1056.380121 $ <br>Population: 29.170398 million\", \"Country: Zambia 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13171.63885 $ <br>Population: 16.284741 million\", \"Country: Colombia <br>Life Expectancy: 72.889 years <br>GDP per capita: 7006.580419 $ <br>Population: 44.22755 million\", \"Country: Costa Rica <br>Life Expectancy: 78.782 years <br>GDP per capita: 9645.06142 $ <br>Population: 4.133884 million\", \"Country: Cuba <br>Life Expectancy: 78.273 years <br>GDP per capita: 8948.102923 $ <br>Population: 11.416987 million\", \"Country: Dominican Republic <br>Life Expectancy: 72.235 years <br>GDP per capita: 6025.374752 $ <br>Population: 9.319622 million\", \"Country: Ecuador <br>Life Expectancy: 74.994 years <br>GDP per capita: 6873.262326 $ <br>Population: 13.75568 million\", \"Country: El Salvador <br>Life Expectancy: 71.878 years <br>GDP per capita: 5728.353514 $ <br>Population: 6.939688 million\", \"Country: Guatemala <br>Life Expectancy: 70.259 years <br>GDP per capita: 5186.050003 $ <br>Population: 12.572928 million\", \"Country: Haiti <br>Life Expectancy: 60.916 years <br>GDP per capita: 1201.637154 $ <br>Population: 8.502814 million\", \"Country: Honduras <br>Life Expectancy: 70.198 years <br>GDP per capita: 3548.330846 $ <br>Population: 7.483763 million\", \"Country: Jamaica <br>Life Expectancy: 72.567 years <br>GDP per capita: 7320.880262 $ <br>Population: 2.780132 million\", \"Country: Mexico <br>Life Expectancy: 76.195 years <br>GDP per capita: 11977.57496 $ <br>Population: 108.700891 million\", \"Country: Nicaragua <br>Life Expectancy: 72.899 years <br>GDP per capita: 2749.320965 $ <br>Population: 5.675356 million\", \"Country: Panama <br>Life Expectancy: 75.537 years <br>GDP per capita: 9809.185636 $ <br>Population: 3.242173 million\", \"Country: Paraguay <br>Life Expectancy: 71.752 years <br>GDP per capita: 4172.838464 $ <br>Population: 6.667147 million\", \"Country: Peru <br>Life Expectancy: 71.421 years <br>GDP per capita: 7408.905561 $ <br>Population: 28.674757 million\", \"Country: Puerto Rico <br>Life Expectancy: 78.746 years <br>GDP per capita: 19328.70901 $ <br>Population: 3.942491 million\", \"Country: Trinidad and Tobago <br>Life Expectancy: 69.819 years <br>GDP per capita: 18008.50924 $ <br>Population: 1.056608 million\", \"Country: United States <br>Life Expectancy: 78.242 years <br>GDP per capita: 42951.65309 $ <br>Population: 301.139947 million\", \"Country: Uruguay <br>Life Expectancy: 76.384 years <br>GDP per capita: 10611.46299 $ <br>Population: 3.447496 million\", \"Country: Venezuela <br>Life Expectancy: 73.747 years <br>GDP per capita: 11415.80569 $ <br>Population: 26.084662 million\", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"marker\": {\"sizemode\": \"area\", \"sizeref\": 131868, \"size\": [40301927.0, 9119152.0, 190010647.0, 33390141.0, 16284741.0, 44227550.0, 4133884.0, 11416987.0, 9319622.0, 13755680.0, 6939688.0, 12572928.0, 8502814.0, 7483763.0, 2780132.0, 108700891.0, 5675356.0, 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75.53699999999999, 71.752, 71.421, 78.74600000000001, 69.819, 78.242, 76.384, 73.747, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"type\": \"scatter\"}, {\"name\": \"Asia\", \"text\": [\"Country: Afghanistan <br>Life Expectancy: 43.828 years <br>GDP per capita: 974.5803384 $ <br>Population: 31.889923 million\", \"Country: Bahrain <br>Life Expectancy: 75.635 years <br>GDP per capita: 29796.04834 $ <br>Population: 0.708573 million\", \"Country: Bangladesh <br>Life Expectancy: 64.062 years <br>GDP per capita: 1391.253792 $ <br>Population: 150.448339 million\", \"Country: Cambodia <br>Life Expectancy: 59.723 years <br>GDP per capita: 1713.778686 $ <br>Population: 14.131858 million\", \"Country: China <br>Life Expectancy: 72.961 years <br>GDP per capita: 4959.114854 $ <br>Population: 1318.683096 million\", \"Country: Hong Kong, China <br>Life Expectancy: 82.208 years <br>GDP per capita: 39724.97867 $ <br>Population: 6.980412 million\", \"Country: India <br>Life Expectancy: 64.698 years <br>GDP per capita: 2452.210407 $ <br>Population: 1110.396331 million\", \"Country: Indonesia <br>Life Expectancy: 70.65 years <br>GDP per capita: 3540.651564 $ <br>Population: 223.547 million\", \"Country: Iran <br>Life Expectancy: 70.964 years <br>GDP per capita: 11605.71449 $ <br>Population: 69.45357 million\", \"Country: Iraq <br>Life Expectancy: 59.545 years <br>GDP per capita: 4471.061906 $ <br>Population: 27.499638 million\", \"Country: Israel <br>Life Expectancy: 80.745 years <br>GDP per capita: 25523.2771 $ <br>Population: 6.426679 million\", \"Country: Japan <br>Life Expectancy: 82.603 years <br>GDP per capita: 31656.06806 $ <br>Population: 127.467972 million\", \"Country: Jordan <br>Life Expectancy: 72.535 years <br>GDP per capita: 4519.461171 $ <br>Population: 6.053193 million\", \"Country: Korea, Dem. Rep. <br>Life Expectancy: 67.297 years <br>GDP per capita: 1593.06548 $ <br>Population: 23.301725 million\", \"Country: Korea, Rep. <br>Life Expectancy: 78.623 years <br>GDP per capita: 23348.13973 $ <br>Population: 49.04479 million\", \"Country: Kuwait <br>Life Expectancy: 77.588 years <br>GDP per capita: 47306.98978 $ <br>Population: 2.505559 million\", \"Country: Lebanon <br>Life Expectancy: 71.993 years <br>GDP per capita: 10461.05868 $ <br>Population: 3.921278 million\", \"Country: Malaysia <br>Life Expectancy: 74.241 years <br>GDP per capita: 12451.6558 $ <br>Population: 24.821286 million\", \"Country: Mongolia <br>Life Expectancy: 66.803 years <br>GDP per capita: 3095.772271 $ <br>Population: 2.874127 million\", \"Country: Myanmar <br>Life Expectancy: 62.069 years <br>GDP per capita: 944.0 $ <br>Population: 47.76198 million\", \"Country: Nepal <br>Life Expectancy: 63.785 years <br>GDP per capita: 1091.359778 $ <br>Population: 28.90179 million\", \"Country: Oman <br>Life Expectancy: 75.64 years <br>GDP per capita: 22316.19287 $ <br>Population: 3.204897 million\", \"Country: Pakistan <br>Life Expectancy: 65.483 years <br>GDP per capita: 2605.94758 $ <br>Population: 169.270617 million\", \"Country: Philippines <br>Life Expectancy: 71.688 years <br>GDP per capita: 3190.481016 $ <br>Population: 91.077287 million\", \"Country: Saudi Arabia <br>Life Expectancy: 72.777 years <br>GDP per capita: 21654.83194 $ <br>Population: 27.601038 million\", \"Country: Singapore <br>Life Expectancy: 79.972 years <br>GDP per capita: 47143.17964 $ <br>Population: 4.553009 million\", \"Country: Sri Lanka <br>Life Expectancy: 72.396 years <br>GDP per capita: 3970.095407 $ <br>Population: 20.378239 million\", \"Country: Syria <br>Life Expectancy: 74.143 years <br>GDP per capita: 4184.548089 $ <br>Population: 19.314747 million\", \"Country: Taiwan <br>Life Expectancy: 78.4 years <br>GDP per capita: 28718.27684 $ <br>Population: 23.174294 million\", \"Country: Thailand <br>Life Expectancy: 70.616 years <br>GDP per capita: 7458.396327 $ <br>Population: 65.068149 million\", \"Country: Vietnam <br>Life Expectancy: 74.249 years <br>GDP per capita: 2441.576404 $ <br>Population: 85.262356 million\", \"Country: West Bank and Gaza <br>Life Expectancy: 73.422 years <br>GDP per capita: 3025.349798 $ <br>Population: 4.018332 million\", \"Country: Yemen, Rep. <br>Life Expectancy: 62.698 years <br>GDP per capita: 2280.769906 $ <br>Population: 22.211743 million\", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"marker\": {\"sizemode\": \"area\", \"sizeref\": 131868, \"size\": [31889923.0, 708573.0, 150448339.0, 14131858.0, 1318683096.0, 6980412.0, 1110396331.0, 223547000.0, 69453570.0, 27499638.0, 6426679.0, 127467972.0, 6053193.0, 23301725.0, 49044790.0, 2505559.0, 3921278.0, 24821286.0, 2874127.0, 47761980.0, 28901790.0, 3204897.0, 169270617.0, 91077287.0, 27601038.0, 4553009.0, 20378239.0, 19314747.0, 23174294.0, 65068149.0, 85262356.0, 4018332.0, 22211743.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, \"mode\": \"markers\", \"y\": [974.5803384, 29796.048339999998, 1391.253792, 1713.7786859999999, 4959.1148539999995, 39724.97867, 2452.210407, 3540.6515640000002, 11605.71449, 4471.061906, 25523.2771, 31656.06806, 4519.461171, 1593.06548, 23348.139730000003, 47306.98978, 10461.05868, 12451.6558, 3095.7722710000003, 944.0, 1091.359778, 22316.19287, 2605.94758, 3190.481016, 21654.83194, 47143.179639999995, 3970.0954070000003, 4184.548089, 28718.27684, 7458.3963269999995, 2441.576404, 3025.349798, 2280.769906, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"x\": [43.828, 75.635, 64.062, 59.723, 72.961, 82.208, 64.69800000000001, 70.65, 70.964, 59.545, 80.745, 82.603, 72.535, 67.297, 78.623, 77.58800000000001, 71.993, 74.241, 66.803, 62.068999999999996, 63.785, 75.64, 65.483, 71.688, 72.777, 79.972, 72.396, 74.143, 78.4, 70.616, 74.249, 73.422, 62.698, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"type\": \"scatter\"}, {\"name\": \"Europe\", \"text\": [\"Country: Albania <br>Life Expectancy: 76.423 years <br>GDP per capita: 5937.029526 $ <br>Population: 3.600523 million\", \"Country: Austria <br>Life Expectancy: 79.829 years <br>GDP per capita: 36126.4927 $ <br>Population: 8.199783 million\", \"Country: Belgium <br>Life Expectancy: 79.441 years <br>GDP per capita: 33692.60508 $ <br>Population: 10.392226 million\", \"Country: Bosnia and Herzegovina <br>Life Expectancy: 74.852 years <br>GDP per capita: 7446.298803 $ <br>Population: 4.552198 million\", \"Country: Bulgaria <br>Life Expectancy: 73.005 years <br>GDP per capita: 10680.79282 $ <br>Population: 7.322858 million\", \"Country: Croatia <br>Life Expectancy: 75.748 years <br>GDP per capita: 14619.22272 $ <br>Population: 4.493312 million\", \"Country: Czech Republic <br>Life Expectancy: 76.486 years <br>GDP per capita: 22833.30851 $ <br>Population: 10.228744 million\", \"Country: Denmark <br>Life Expectancy: 78.332 years <br>GDP per capita: 35278.41874 $ <br>Population: 5.46812 million\", \"Country: Finland <br>Life Expectancy: 79.313 years <br>GDP per capita: 33207.0844 $ <br>Population: 5.23846 million\", \"Country: France <br>Life Expectancy: 80.657 years <br>GDP per capita: 30470.0167 $ <br>Population: 61.083916 million\", \"Country: Germany <br>Life Expectancy: 79.406 years <br>GDP per capita: 32170.37442 $ <br>Population: 82.400996 million\", \"Country: Greece <br>Life Expectancy: 79.483 years <br>GDP per capita: 27538.41188 $ <br>Population: 10.70629 million\", \"Country: Hungary <br>Life Expectancy: 73.338 years <br>GDP per capita: 18008.94444 $ <br>Population: 9.956108 million\", \"Country: Iceland <br>Life Expectancy: 81.757 years <br>GDP per capita: 36180.78919 $ <br>Population: 0.301931 million\", \"Country: Ireland <br>Life Expectancy: 78.885 years <br>GDP per capita: 40675.99635 $ <br>Population: 4.109086 million\", \"Country: Italy <br>Life Expectancy: 80.546 years <br>GDP per capita: 28569.7197 $ <br>Population: 58.147733 million\", \"Country: Montenegro <br>Life Expectancy: 74.543 years <br>GDP per capita: 9253.896111 $ <br>Population: 0.684736 million\", \"Country: Netherlands <br>Life Expectancy: 79.762 years <br>GDP per capita: 36797.93332 $ <br>Population: 16.570613 million\", \"Country: Norway <br>Life Expectancy: 80.196 years <br>GDP per capita: 49357.19017 $ <br>Population: 4.627926 million\", \"Country: Poland <br>Life Expectancy: 75.563 years <br>GDP per capita: 15389.92468 $ <br>Population: 38.518241 million\", \"Country: Portugal <br>Life Expectancy: 78.098 years <br>GDP per capita: 20509.64777 $ <br>Population: 10.642836 million\", \"Country: Romania <br>Life Expectancy: 72.476 years <br>GDP per capita: 10808.47561 $ <br>Population: 22.276056 million\", \"Country: Serbia <br>Life Expectancy: 74.002 years <br>GDP per capita: 9786.534714 $ <br>Population: 10.150265 million\", \"Country: Slovak Republic <br>Life Expectancy: 74.663 years <br>GDP per capita: 18678.31435 $ <br>Population: 5.447502 million\", \"Country: Slovenia <br>Life Expectancy: 77.926 years <br>GDP per capita: 25768.25759 $ <br>Population: 2.009245 million\", \"Country: Spain <br>Life Expectancy: 80.941 years <br>GDP per capita: 28821.0637 $ <br>Population: 40.448191 million\", \"Country: Sweden <br>Life Expectancy: 80.884 years <br>GDP per capita: 33859.74835 $ <br>Population: 9.031088 million\", \"Country: Switzerland <br>Life Expectancy: 81.701 years <br>GDP per capita: 37506.41907 $ <br>Population: 7.554661 million\", \"Country: Turkey <br>Life Expectancy: 71.777 years <br>GDP per capita: 8458.276384 $ <br>Population: 71.158647 million\", \"Country: United Kingdom <br>Life Expectancy: 79.425 years <br>GDP per capita: 33203.26128 $ <br>Population: 60.776238 million\", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"marker\": {\"sizemode\": \"area\", \"sizeref\": 131868, \"size\": [3600523.0, 8199783.0, 10392226.0, 4552198.0, 7322858.0, 4493312.0, 10228744.0, 5468120.0, 5238460.0, 61083916.0, 82400996.0, 10706290.0, 9956108.0, 301931.0, 4109086.0, 58147733.0, 684736.0, 16570613.0, 4627926.0, 38518241.0, 10642836.0, 22276056.0, 10150265.0, 5447502.0, 2009245.0, 40448191.0, 9031088.0, 7554661.0, 71158647.0, 60776238.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, \"mode\": \"markers\", \"y\": [5937.029525999999, 36126.4927, 33692.60508, 7446.298803, 10680.79282, 14619.222719999998, 22833.30851, 35278.41874, 33207.0844, 30470.0167, 32170.37442, 27538.41188, 18008.94444, 36180.789189999996, 40675.99635, 28569.7197, 9253.896111, 36797.93332, 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],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iplot({\n",
" 'data': [\n",
" Scatter(x=df[continent+'_Life Expentancy [in years]'],\n",
" y=df[continent+'_Gross Domestic Product per Capita [in USD of the year 2000]'],\n",
" text=df[continent+'_text'],\n",
" marker=Marker(size=df[continent+'_marker.size'], sizemode='area', sizeref=131868,),\n",
" mode='markers',\n",
" name=continent) for continent in ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']\n",
" ],\n",
" 'layout': Layout(xaxis=XAxis(title='Life Expectancy'), yaxis=YAxis(title='GDP per Capita', type='log'))\n",
"}, show_link=False)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cufflinks as cf"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kevintest/891.embed\" height=\"587px\" width=\"1116px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iplot(cf.datagen.lines().iplot(asFigure=True,\n",
" kind='scatter',xTitle='Dates',yTitle='Returns',title='Returns'))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kevintest/893.embed\" height=\"587px\" width=\"1116px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iplot(cf.datagen.heatmap(20,20).iplot(asFigure=True,\n",
" kind='heatmap',colorscale='spectral',title='Cufflinks - Heatmap'))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kevintest/895.embed\" height=\"800px\" width=\"100%\"></iframe>"
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"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')\n",
"df_airports.head()\n",
"\n",
"df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')\n",
"df_flight_paths.head()\n",
"\n",
"airports = [ dict(\n",
" type = 'scattergeo',\n",
" locationmode = 'USA-states',\n",
" lon = df_airports['long'],\n",
" lat = df_airports['lat'],\n",
" hoverinfo = 'text',\n",
" text = df_airports['airport'],\n",
" mode = 'markers',\n",
" marker = dict( \n",
" size=2, \n",
" color='rgb(255, 0, 0)',\n",
" line = dict(\n",
" width=3,\n",
" color='rgba(68, 68, 68, 0)'\n",
" )\n",
" ))]\n",
" \n",
"flight_paths = []\n",
"for i in range( len( df_flight_paths ) ):\n",
" flight_paths.append(\n",
" dict(\n",
" type = 'scattergeo',\n",
" locationmode = 'USA-states',\n",
" lon = [ df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i] ],\n",
" lat = [ df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i] ],\n",
" mode = 'lines',\n",
" line = dict(\n",
" width = 1,\n",
" color = 'red',\n",
" ),\n",
" opacity = float(df_flight_paths['cnt'][i])/float(df_flight_paths['cnt'].max()),\n",
" )\n",
" )\n",
" \n",
"layout = dict(\n",
" title = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',\n",
" showlegend = False, \n",
" height = 800,\n",
" geo = dict(\n",
" scope='north america',\n",
" projection=dict( type='azimuthal equal area' ),\n",
" showland = True,\n",
" landcolor = 'rgb(243, 243, 243)',\n",
" countrycolor = 'rgb(204, 204, 204)',\n",
" ),\n",
" )\n",
" \n",
"fig = dict( data=flight_paths + airports, layout=layout )\n",
"\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kevintest/897.embed\" height=\"900px\" width=\"1000px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import plotly.plotly as py # all methods in plotly.plotly will communicate with a Plotly Cloud or Plotly Enterprise\n",
"\n",
"# get_figure downloads a figure from plot.ly or Plotly Enterprise. \n",
"# You need to provide credentials to download figures: https://plot.ly/python/getting-started/\n",
"fig = py.get_figure('https://plot.ly/~jackp/8715', raw=True)\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<link href=\"//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700\" rel=\"stylesheet\" type=\"text/css\" />"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<link rel=\"stylesheet\" type=\"text/css\" href=\"http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"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/j3/gt_q6y096cjfts4q8zq2dm2c0000gn/T/pip-QVMrFo-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\n"
]
}
],
"source": [
"from IPython.display import display, HTML\n",
"\n",
"display(HTML('<link href=\"//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700\" rel=\"stylesheet\" type=\"text/css\" />'))\n",
"display(HTML('<link rel=\"stylesheet\" type=\"text/css\" href=\"http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css\">'))\n",
"\n",
"! pip install git+https://github.com/plotly/publisher.git --upgrade\n",
" \n",
"import publisher\n",
"publisher.publish(\n",
" 'plotly_offline.ipynb', 'python/offline/', 'Plotly Offline for IPython Notebooks',\n",
" 'How to use Plotly offline inside IPython notebooks',\n",
" title= 'Plotly Offline for IPython Notebooks',\n",
" name = 'Offline Plots in Plotly',\n",
" has_thumbnail='False', \n",
" language='python', page_type='example_index')"
]
},
{
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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