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tonyfast/Airbnb Visualization.ipynb

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Last active August 29, 2015 14:26
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Airbnb Visualization.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"> First, grab the Airbnb data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"!wget http://databits.io/static_content/challenges/airbnb-user-pathways-challenge/airbnb_session_data.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"> What exactly was it again?"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2707463e1f100fe6a1ea204249667128a4cf7a48 airbnb_session_data.txt\r\n"
]
}
],
"source": [
"!sha1sum airbnb_session_data.txt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" <link rel=\"stylesheet\" href=\"http://cdn.pydata.org/bokeh/release/bokeh-0.9.2.min.css\" type=\"text/css\" />\n",
" <script type=\"text/javascript\" src=\"http://cdn.pydata.org/bokeh/release/bokeh-0.9.2.min.js\"></script>\n",
" <script type=\"text/javascript\">\n",
" Bokeh.set_log_level(\"info\");\n",
" </script>\n",
" <div>\n",
" <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span>BokehJS successfully loaded.</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from bokeh.plotting import figure, output_notebook, show, gridplot\n",
"from bokeh.models import ColumnDataSource\n",
"from bokeh.resources import CDN\n",
"output_notebook(resources=CDN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> A little wrapper for ColumnDataSource"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cds(**cols):\n",
" source = ColumnDataSource()\n",
" [source.add(name=name, data=data) for name, data in cols.items()]\n",
" return source"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"> These need to be dates."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"df = pd.read_csv(\"airbnb_session_data.txt\", sep=\"|\")\n",
"df.ds = pd.to_datetime(df.ds)\n",
"df.ts_max = pd.to_datetime(df.ts_max)\n",
"df.ts_min = pd.to_datetime(df.ts_min)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<bokeh.plotting.Figure at 0x7f1348d704e0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = figure(\n",
" x_axis_type = \"datetime\",\n",
" plot_width=900,\n",
" plot_height=600,\n",
")\n",
"s2.scatter(\n",
" 'x', 'y',\n",
" color='color',\n",
" source=cds(\n",
" x=df.ts_min,\n",
" y=df.ts_max - df.ts_min,\n",
" color=[[\"black\", \"red\"][x] for x in df.sent_booking_request]\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Here's the length of impression over time, showing <span style=\"color:red\">booking requests</span>. What happened around 9/8?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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\"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\", \"black\"]}, \"column_names\": [\"x\", \"y\", \"color\"], \"selected\": {\"2d\": {\"indices\": []}, \"1d\": {\"indices\": []}, \"0d\": {\"flag\": false, \"indices\": []}}, \"id\": \"84164547-2a72-4bb3-a2fd-1c3c9a1314dc\", \"doc\": null, \"callback\": null}, \"id\": \"84164547-2a72-4bb3-a2fd-1c3c9a1314dc\"}, {\"type\": \"PreviewSaveTool\", \"attributes\": {\"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"80618569-1d2b-4e59-94a6-5e25ab099e82\"}, \"doc\": null, \"id\": \"99a25206-d8d4-4038-9246-467db02573e8\"}, \"id\": \"99a25206-d8d4-4038-9246-467db02573e8\"}, {\"type\": \"Grid\", \"attributes\": {\"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"80618569-1d2b-4e59-94a6-5e25ab099e82\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"03be173d-39f9-47c9-bbae-6eb894a1d70b\"}, \"doc\": null, \"dimension\": 1, \"id\": \"e55d7aee-cccd-409a-9ca2-fddb039ade00\"}, \"id\": \"e55d7aee-cccd-409a-9ca2-fddb039ade00\"}, {\"type\": \"ResizeTool\", \"attributes\": {\"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"80618569-1d2b-4e59-94a6-5e25ab099e82\"}, \"doc\": null, \"id\": \"2caad532-8337-4a3c-9da9-19d507adda2a\"}, \"id\": \"2caad532-8337-4a3c-9da9-19d507adda2a\"}];\n",
" Bokeh.load_models(all_models);\n",
" var plots = [{'modeltype': 'Plot', 'modelid': '80618569-1d2b-4e59-94a6-5e25ab099e82', 'elementid': '#f8a27fb7-38d8-4e06-88c2-1db8ed26e70d'}];\n",
" for (idx in plots) {\n",
" \tvar plot = plots[idx];\n",
" \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n",
" \tBokeh.logger.info('Realizing plot:')\n",
" \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n",
" \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n",
" \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n",
" \tvar view = new model.default_view({\n",
" \t\tmodel: model,\n",
" \t\tel: plot.elementid\n",
" \t});\n",
" \tBokeh.index[plot.modelid] = view;\n",
" }\n",
" });\n",
" </script>\n",
"<div class=\"plotdiv\" id=\"f8a27fb7-38d8-4e06-88c2-1db8ed26e70d\"></div>\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show(s2)"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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