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Created February 1, 2016 13:48
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Introduction to Bokeh
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level_5.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Level 5: Interactive Visualizations\n",
"\n",
"Welcome to the last level of this data science tutorial! So far, we have gathered, explored, and modeled weather data. However, one key part is left: visualizing and interacting with data. In this level, we will generate interactive visualizations using the `bokeh` module in Python. The best part of `bokeh` is that we can integrate it easily with web apps that are powered by Python.\n",
"\n",
"First, let's import our data from previous levels."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"index int64\n",
"month int64\n",
"day int64\n",
"year int64\n",
"mean_temperature int64\n",
"max_temperature int64\n",
"min_temperature int64\n",
"dew_point int64\n",
"average_humidity int64\n",
"maximum_humidity int64\n",
"minimum_humidity int64\n",
"precipitation float64\n",
"wind_speed float64\n",
"max_wind_speed float64\n",
"max_gust_speed float64\n",
"dtype: object\n"
]
},
{
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>year</th>\n",
" <th>mean_temperature</th>\n",
" <th>max_temperature</th>\n",
" <th>min_temperature</th>\n",
" <th>dew_point</th>\n",
" <th>average_humidity</th>\n",
" <th>maximum_humidity</th>\n",
" <th>minimum_humidity</th>\n",
" <th>precipitation</th>\n",
" <th>wind_speed</th>\n",
" <th>max_wind_speed</th>\n",
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"text/plain": [
" index month day year mean_temperature max_temperature \\\n",
"0 0 1 1 2013 33 40 \n",
"1 1 1 2 2013 28 33 \n",
"2 2 1 3 2013 28 32 \n",
"3 3 1 4 2013 34 37 \n",
"4 4 1 5 2013 37 42 \n",
"\n",
" min_temperature dew_point average_humidity maximum_humidity \\\n",
"0 26 22 54 64 \n",
"1 22 11 48 57 \n",
"2 24 14 56 68 \n",
"3 30 19 56 63 \n",
"4 32 19 48 56 \n",
"\n",
" minimum_humidity precipitation wind_speed max_wind_speed max_gust_speed \n",
"0 44 0 7 15 26 \n",
"1 39 0 6 15 22 \n",
"2 43 0 5 13 20 \n",
"3 48 0 8 18 28 \n",
"4 39 0 7 17 26 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"data = pd.read_csv('clean_weather_data.csv')\n",
"print(data.dtypes)\n",
"data[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool, let's start using `bokeh`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" <script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
" window._bokeh_onload_callbacks = [];\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.0.min.js'];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }\n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\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 bokeh.io, bokeh.plotting, bokeh.models\n",
"bokeh.io.output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code gets the necessary modules loaded and tells `bokeh` that we want output directly in the notebook. (Feel free to ignore that line if not running as a notebook.)\n",
"\n",
"Now, let's get a basic plot set up with `bokeh`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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"\n",
"\n",
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" \n",
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" return new Date();\n",
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" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
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" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
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" };\n",
" s.onerror = function() {\n",
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" }\n",
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" return false;\n",
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" function(Bokeh) {\n",
" Bokeh.$(function() {\n",
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" Bokeh.embed.embed_items(docs_json, render_items);\n",
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"metadata": {},
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"source": [
"p = bokeh.plotting.figure()\n",
"p.circle(data.mean_temperature, data.dew_point)\n",
"bokeh.plotting.show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What an interesting interactive plot! Let's add some titles and axis labels."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
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"source": [
"q = bokeh.plotting.figure()\n",
"q.circle(data.mean_temperature, data.dew_point)\n",
"q.title = 'My Interactive Visualization'\n",
"q.xaxis.axis_label = 'Average Temperature'\n",
"q.yaxis.axis_label = 'Dew Point'\n",
"bokeh.plotting.show(q)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The great thing about `bokeh` is that most of the interactive functionality comes out of the box; all you have to do is specify the kind of plot that you want. Now, let's try a histogram."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" window._bokeh_onload_callbacks = [];\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
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" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"1fff7d3b-ad4c-4f6a-9c0a-be3609b37fc1\");\n",
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" }var js_urls = [];\n",
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" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
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" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
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"source": [
"hist_plot = bokeh.plotting.figure(title = 'Histogram of Average Temperature')\n",
"hist, edges = np.histogram(data.mean_temperature, bins = 25)\n",
"hist_plot.quad(top = hist, bottom = 0, left = edges[:-1], right = edges[1:], line_color = \"#000000\")\n",
"hist_plot.xaxis.axis_label = \"Average Temperature\"\n",
"hist_plot.yaxis.axis_label = \"Frequency\"\n",
"bokeh.plotting.show(hist_plot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To compare with our previous plotting tool, Seaborn, it takes a little bit more work to get `bokeh` plots together, but the upside is that we get interactivity for free, letting us explore which parts of the plot are most interesting to us. Another interesting bit of functionality is the ability to \"tab\" your plots, let's check it out."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
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},
"outputs": [
{
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"source": [
"line_data = np.linspace(data.mean_temperature.min(), data.mean_temperature.max())\n",
"\n",
"first = bokeh.plotting.figure()\n",
"first.circle(data.mean_temperature, data.max_temperature)\n",
"first.line(line_data, line_data)\n",
"tab1 = bokeh.models.Panel(child = first, title = 'First Plot')\n",
"\n",
"second = bokeh.plotting.figure()\n",
"second.circle(data.mean_temperature, data.min_temperature)\n",
"second.line(line_data, line_data)\n",
"tab2 = bokeh.models.Panel(child = second, title = 'Second Plot')\n",
"\n",
"tabs = bokeh.models.Tabs(tabs = [tab1, tab2])\n",
"bokeh.plotting.show(tabs)"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another cool aspect of `bokeh` is the ability to include widgets. Let's use a widget to interactively change the size of the points in our scatterplot.\n",
"\n",
"Instead of specifying the data directly, we'll use the name of the column in a data source we create."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" Bokeh.$(function() {\n",
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" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" });\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }\n",
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" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
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" }\n",
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"</script>"
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"metadata": {},
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{
"data": {
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}
],
"source": [
"start_value = 5\n",
"point_size = [start_value for i in range(len(data))]\n",
"\n",
"source = bokeh.models.ColumnDataSource(data = dict(x = data.mean_temperature, y = data.dew_point, s = point_size))\n",
"\n",
"plot = bokeh.plotting.figure()\n",
"plot.circle('x', 'y', size = 's', source = source)\n",
"\n",
"callback = bokeh.models.CustomJS(args = dict(source = source), code=\"\"\"\n",
" var data = source.get('data');\n",
" var size = cb_obj.get('value');\n",
" x = data['x'];\n",
" y = data['y'];\n",
" s = data['s'];\n",
" for (i = 0; i < x.length; i++) {\n",
" s[i] = size;\n",
" }\n",
" source.trigger('change');\n",
" \"\"\")\n",
"\n",
"size = bokeh.models.Slider(start = 1, end = 10, value = start_value, step = .05, title = \"Size\", callback = callback)\n",
"\n",
"layout = bokeh.io.vform(size, plot)\n",
"\n",
"bokeh.plotting.show(layout)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can leverage the above functionality to create cool interactions with our users.\n",
"\n",
"One last flashy bit of `bokeh` is the tool tip option, which allows us to display more information about the point when someone hovers over the point. Let's try using it on our scatterplot."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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" run_callbacks();\n",
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" s.onerror = function() {\n",
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10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]}, \"column_names\": [\"month\", \"day\", \"y\", \"x\", \"year\"]}, \"type\": \"ColumnDataSource\", \"id\": \"95465252-8543-48f8-a4be-44d8db7f6a69\"}, {\"attributes\": {\"plot\": {\"id\": \"e8bfe266-4bc6-488d-9412-de3159e32bc8\", \"type\": \"Plot\", \"subtype\": \"Figure\"}}, \"type\": \"PreviewSaveTool\", \"id\": \"4e613903-5541-4166-a4ce-adeb8568168d\"}]}, \"version\": \"0.11.0\"}};\n",
" var render_items = [{\"docid\": \"d4769322-5af4-46b2-94d0-c7f7a84ea4c7\", \"elementid\": \"0990ac5d-6d26-4edb-b55d-7c0aa355a8d3\", \"notebook_comms_target\": \"a4d7d1ad-65cb-462a-8275-9a8000980b1f\", \"modelid\": \"9e7c2529-8b55-48e4-8ec3-3e95815a2a46\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" });\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }\n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<bokeh.io._CommsHandle at 0x10c067940>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from bokeh.plotting import figure, output_file, show, ColumnDataSource\n",
"from bokeh.models import HoverTool\n",
"\n",
"tooltip_source = bokeh.plotting.ColumnDataSource(data = dict(x = data.mean_temperature, y = data.dew_point,\\\n",
" month = data.month, day = data.day, year = data.year))\n",
"\n",
"hover = bokeh.models.HoverTool(\n",
" tooltips = [\n",
" (\"Date\", \"@month-@day-@year\"),\n",
" (\"Average Temperature\", \"@x\"),\n",
" (\"Dew Point\", \"@y\")\n",
" ]\n",
" )\n",
"\n",
"fig = figure(tools = [hover], title = \"Hover over the dots!\")\n",
"\n",
"fig.circle('x', 'y', source = tooltip_source)\n",
"\n",
"show(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Awesome, we created a nice plot that shows us useful information when we hover over it.\n",
"\n",
"Thanks for completing this curriculum and taking your first steps towards being a data scientist!"
]
}
],
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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