├── LICENSE
├── README.md
├── Section 1
    ├── 2008-election-results.csv
    ├── README.md
    ├── Visualizing the US Elections.ipynb
    ├── Wealth of Nations.ipynb
    ├── nations.json
    └── state_codes.csv
├── Section 2
    ├── NetworkX - Creating and customizing LAN network.ipynb
    ├── NetworkX - Social Network.ipynb
    └── README.md
├── Section 3
    ├── .ipynb_checkpoints
    │   └── Bokeh - NASA Moon Missions-checkpoint.ipynb
    ├── Bokeh - Intro.ipynb
    ├── Bokeh - NASA Moon Missions.ipynb
    ├── README.md
    ├── barplot.html
    ├── manned.csv
    └── weather
    │   ├── data
    │       ├── 2015_weather.csv
    │       └── london.csv
    │   └── main.py
└── Section 4
    ├── README.md
    ├── android1
        └── app.py
    ├── android2
        └── app.py
    ├── walmart1
        └── app.py
    └── walmart2
        └── app.py


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 Packt
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Data-visualization-projects-in-python
 2 | Data visualization projects in python [video], published by Packt
 3 | # Data Visualization in Python by Examples [Video]
 4 | This is the code repository for [Data Visualization in Python by Examples [Video]](https://www.packtpub.com/virtualization-and-cloud/data-visualization-python-examples-video?utm_source=github&utm_medium=repository&utm_campaign=9781788838658), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the video course from start to finish.
 5 | ## About the Video Course
 6 | Some popular Python data visualization tools and techniques today include Data Visualization in Jupyter Notebook with Bloomberg's bqplot library, Programming Graph and Network Data Visualizations, Data Visualizations with Bokeh (a Python library), and building interactive web visualizations using Dash.
 7 | 
 8 | You will start by programming stunning interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Then you will learn how to programmatically create interactive network graphs and visualizations. You will then programmatically visualize data with the interactive Python visualization library, Bokeh.
 9 | 
10 | Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.
11 | 
12 | <H2>What You Will Learn</H2>
13 | <DIV class=book-info-will-learn-text>
14 | <UL>
15 | <LI>* Set up different data visualization tools in Python such as bqplot, NetworkX, Bokeh, and Dash </LI>
16 | <LI>* Plot data in Jupyter Notebook using bqplot </LI>
17 | <LI>* Use bqplot to compare different US Presidential election trends </LI>
18 | <LI>* Analyze the trends in previous and current FIFA rankings </LI>
19 | <LI>* Use the NetworkX library to plot the LAN networks in your office space and organization </LI>
20 | <LI>* Visualize a social network with NetworkX </LI>
21 | <LI>* Plot weather forecasts using Bokeh, for the weather in US and UK </LI>
22 | <LI>* See the frequency of Moon missions undertaken by NASA over the years </LI>
23 | <LI>* Turn dash plots into interactive visualizations for major Android version releases and Walmart's growth </LI></UL></DIV>
24 | 
25 | ## Instructions and Navigation
26 | ### Assumed Knowledge
27 | To fully benefit from the coverage included in this course, you will need:<br/>
28 | Be familiar with the Python programming language
29 | Find your way around command line
30 | ### Technical Requirements
31 | This course has the following software requirements:<br/>
32 | Have access to a Windows, Mac or Linux Computer
33 | Have Python 3.x installed
34 | 
35 | ## Related Products
36 | * [Data Visualization Solutions for Beginners [Video]](https://www.packtpub.com/big-data-and-business-intelligence/data-visualization-solutions-beginners-video?utm_source=github&utm_medium=repository&utm_campaign=9781788395786)
37 | 
38 | * [Advanced Data Visualization Solutions [Video]](https://www.packtpub.com/big-data-and-business-intelligence/advanced-data-visualization-solutions-video?utm_source=github&utm_medium=repository&utm_campaign=9781788838269)
39 | 
40 | * [Data Visualization with Tableau [Video]](https://www.packtpub.com/big-data-and-business-intelligence/data-visualization-tableau-video?utm_source=github&utm_medium=repository&utm_campaign=9781788837330)
41 | 
42 | 


--------------------------------------------------------------------------------
/Section 1/README.md:
--------------------------------------------------------------------------------
1 | Section 1 Code Examples


--------------------------------------------------------------------------------
/Section 1/Visualizing the US Elections.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Visualizing the 2016 General Election Polls"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 20,
 13 |    "metadata": {
 14 |     "collapsed": true
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import pandas as pd\n",
 19 |     "import numpy as np\n",
 20 |     "from __future__ import print_function\n",
 21 |     "from ipywidgets import VBox, HBox\n",
 22 |     "import os\n",
 23 |     "\n",
 24 |     "codes = pd.read_csv(os.path.abspath('state_codes.csv'))"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 21,
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "try:\n",
 34 |     "    from pollster import Pollster\n",
 35 |     "except ImportError:\n",
 36 |     "    print('Pollster not found. Installing Pollster..')\n",
 37 |     "    import pip\n",
 38 |     "    try:\n",
 39 |     "        pip.main(['install', 'pollster==0.1.6'])\n",
 40 |     "    except:\n",
 41 |     "        print(\"The pip installation failed. Please manually install Pollster and re-run this notebook.\")"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 22,
 47 |    "metadata": {
 48 |     "collapsed": true
 49 |    },
 50 |    "outputs": [],
 51 |    "source": [
 52 |     "def get_candidate_data(question):\n",
 53 |     "    clinton, trump, undecided, other = 0., 0., 0., 0.\n",
 54 |     "    for candidate in question['subpopulations'][0]['responses']:\n",
 55 |     "        if candidate['last_name'] == 'Clinton':\n",
 56 |     "            clinton = candidate['value']\n",
 57 |     "        elif candidate['last_name'] == 'Trump':\n",
 58 |     "            trump = candidate['value']\n",
 59 |     "        elif candidate['choice'] == 'Undecided':\n",
 60 |     "            undecided = candidate['value']\n",
 61 |     "        else:\n",
 62 |     "            other = candidate['value']\n",
 63 |     "    return clinton, trump, other, undecided\n",
 64 |     "\n",
 65 |     "def get_row(question, partisan='Nonpartisan', end_date='2016-06-21'):\n",
 66 |     "    # if question['topic'] != '2016-president':\n",
 67 |     "    if ('2016' in question['topic']) and ('Presidential' in question['topic']):\n",
 68 |     "        hillary, donald, other, undecided = get_candidate_data(question)\n",
 69 |     "        return [{'Name': question['name'], 'Partisan': partisan, 'State': question['state'],\n",
 70 |     "                 'Date': np.datetime64(end_date), 'Trump': donald, 'Clinton': hillary, 'Other': other,\n",
 71 |     "                 'Undecided': undecided}]\n",
 72 |     "    else:\n",
 73 |     "        return\n",
 74 |     "\n",
 75 |     "def analyze_polls(polls):\n",
 76 |     "    global data\n",
 77 |     "    for poll in polls:\n",
 78 |     "        for question in poll.questions:\n",
 79 |     "            resp = get_row(question, partisan=poll.partisan, end_date=poll.end_date)\n",
 80 |     "            if resp is not None:\n",
 81 |     "                data = data.append(resp)\n",
 82 |     "    return"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 23,
 88 |    "metadata": {
 89 |     "collapsed": true
 90 |    },
 91 |    "outputs": [],
 92 |    "source": [
 93 |     "try:\n",
 94 |     "    from pollster import Pollster\n",
 95 |     "    pollster = Pollster()\n",
 96 |     "    \n",
 97 |     "    # Getting data from Pollster. This might take a second.\n",
 98 |     "    raw_data = pollster.charts(topic='2016-president')\n",
 99 |     "    \n",
100 |     "    data = pd.DataFrame(columns=['Name', 'Partisan', 'State', 'Date', 'Trump', 'Clinton', 'Other',\n",
101 |     "                     'Undecided'])\n",
102 |     "    \n",
103 |     "    for i in raw_data:\n",
104 |     "        analyze_polls(i.polls())\n",
105 |     "except:\n",
106 |     "    raise ValueError('Please install Pollster and run the functions above')"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": 24,
112 |    "metadata": {
113 |     "collapsed": true
114 |    },
115 |    "outputs": [],
116 |    "source": [
117 |     "def get_state_party(code):\n",
118 |     "    state = codes[codes['FIPS']==code]['USPS'].values[0]\n",
119 |     "    if data[data['State']==state].shape[0] == 0:\n",
120 |     "        return None\n",
121 |     "    polls = data[(data['State']==state) & (data['Trump'] > 0.) & (data['Clinton'] > 0.)].sort_values(by='Date')\n",
122 |     "    if polls.shape[0] == 0:\n",
123 |     "        return None\n",
124 |     "    if (polls.tail(1)['Trump'] > polls.tail(1)['Clinton']).values[0]:\n",
125 |     "        return 'Republican'\n",
126 |     "    else:\n",
127 |     "        return 'Democrat'\n",
128 |     "\n",
129 |     "def get_color_data():\n",
130 |     "    color_data = {}\n",
131 |     "    for i in codes['FIPS']:\n",
132 |     "        color_data[i] = get_state_party(i)\n",
133 |     "    return color_data\n",
134 |     "\n",
135 |     "def get_state_data(code):\n",
136 |     "    state = codes[codes['FIPS']==code]['USPS'].values[0]\n",
137 |     "    if data[data['State']==state].shape[0] == 0:\n",
138 |     "        return None\n",
139 |     "    polls = data[(data['State']==state) & (data['Trump'] > 0.) & (data['Clinton'] > 0.)].sort_values(by='Date')\n",
140 |     "    return polls"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": 25,
146 |    "metadata": {
147 |     "collapsed": true
148 |    },
149 |    "outputs": [],
150 |    "source": [
151 |     "from bqplot import *\n",
152 |     "from ipywidgets import Layout"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 26,
158 |    "metadata": {
159 |     "collapsed": true
160 |    },
161 |    "outputs": [],
162 |    "source": [
163 |     "dt_x = DateScale()\n",
164 |     "sc_y = LinearScale()\n",
165 |     "\n",
166 |     "time_series = Lines(scales={'x': dt_x, 'y': sc_y}, colors=['#E91D0E', '#2aa1ec'], marker='circle')\n",
167 |     "\n",
168 |     "ax_x = Axis(scale=dt_x, label='Date')\n",
169 |     "ax_y = Axis(scale=sc_y, orientation='vertical', label='Percentage')\n",
170 |     "\n",
171 |     "ts_fig = Figure(marks=[time_series], axes=[ax_x, ax_y], title='General Election - State Polls', \n",
172 |     "                layout=Layout(min_width='650px', min_height='400px'))"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": 27,
178 |    "metadata": {
179 |     "collapsed": true
180 |    },
181 |    "outputs": [],
182 |    "source": [
183 |     "sc_geo = AlbersUSA()\n",
184 |     "sc_c1 = OrdinalColorScale(domain=['Democrat', 'Republican'], colors=['#2aa1ec', '#E91D0E'])\n",
185 |     "\n",
186 |     "color_data = get_color_data()\n",
187 |     "\n",
188 |     "map_styles = {'color': color_data,\n",
189 |     "              'scales': {'projection': sc_geo, 'color': sc_c1}, 'colors': {'default_color': 'Grey'}}\n",
190 |     "\n",
191 |     "axis = ColorAxis(scale=sc_c1)\n",
192 |     "\n",
193 |     "states_map = Map(map_data=topo_load('map_data/USStatesMap.json'), tooltip=ts_fig, **map_styles)\n",
194 |     "map_fig = Figure(marks=[states_map], axes=[axis],title='General Election Polls - State Wise')"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 28,
200 |    "metadata": {
201 |     "collapsed": true
202 |    },
203 |    "outputs": [],
204 |    "source": [
205 |     "def hover_callback(name, value):\n",
206 |     "    polls = get_state_data(value['data']['id'])\n",
207 |     "    if polls is None or polls.shape[0] == 0:\n",
208 |     "        time_series.y = [0.]\n",
209 |     "        return\n",
210 |     "    time_series.x, time_series.y = polls['Date'].values.astype(np.datetime64), [polls['Trump'].values, polls['Clinton'].values]\n",
211 |     "    ts_fig.title = str(codes[codes['FIPS']==value['data']['id']]['Name'].values[0]) + ' Polls - Presidential Election'"
212 |    ]
213 |   },
214 |   {
215 |    "cell_type": "code",
216 |    "execution_count": 29,
217 |    "metadata": {
218 |     "collapsed": true
219 |    },
220 |    "outputs": [],
221 |    "source": [
222 |     "states_map.on_hover(hover_callback)"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "code",
227 |    "execution_count": 30,
228 |    "metadata": {
229 |     "collapsed": true,
230 |     "scrolled": false
231 |    },
232 |    "outputs": [],
233 |    "source": [
234 |     "national = data[(data['State']=='US') & (data['Trump'] > 0.) & (data['Clinton'] > 0.)].sort_values(by='Date')\n",
235 |     "\n",
236 |     "dt_x = DateScale()\n",
237 |     "sc_y = LinearScale()\n",
238 |     "\n",
239 |     "clinton_scatter = Scatter(x=national['Date'].values.astype(np.datetime64), y=national['Clinton'],\n",
240 |     "                          scales={'x': dt_x, 'y': sc_y}, \n",
241 |     "                          colors=['#2aa1ec'])\n",
242 |     "\n",
243 |     "trump_scatter = Scatter(x=national['Date'].values.astype(np.datetime64), y=national['Trump'],\n",
244 |     "                        scales={'x': dt_x, 'y': sc_y},\n",
245 |     "                        colors=['#E91D0E'])\n",
246 |     "\n",
247 |     "ax_x = Axis(scale=dt_x, label='Date', tick_format='%b-%Y', num_ticks=8)\n",
248 |     "ax_y = Axis(scale=sc_y, orientation='vertical', label='Percentage')\n",
249 |     "\n",
250 |     "scat_fig = Figure(marks=[clinton_scatter, trump_scatter], axes=[ax_x, ax_y], title='General Election - National Polls')"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "markdown",
255 |    "metadata": {},
256 |    "source": [
257 |     "#### Hover on the map to visualize the poll data for that state."
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 31,
263 |    "metadata": {
264 |     "scrolled": true
265 |    },
266 |    "outputs": [
267 |     {
268 |      "data": {
269 |       "application/vnd.jupyter.widget-view+json": {
270 |        "model_id": "18e10622553546e3b5da4b3b60ba5deb",
271 |        "version_major": 2,
272 |        "version_minor": 0
273 |       },
274 |       "text/plain": [
275 |        "A Jupyter Widget"
276 |       ]
277 |      },
278 |      "metadata": {},
279 |      "output_type": "display_data"
280 |     }
281 |    ],
282 |    "source": [
283 |     "VBox([map_fig, scat_fig])"
284 |    ]
285 |   },
286 |   {
287 |    "cell_type": "markdown",
288 |    "metadata": {},
289 |    "source": [
290 |     "## Visualizing the County Results of the 2008 Elections"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 32,
296 |    "metadata": {
297 |     "collapsed": true
298 |    },
299 |    "outputs": [],
300 |    "source": [
301 |     "county_data = pd.read_csv(os.path.abspath('2008-election-results.csv'))"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": 33,
307 |    "metadata": {
308 |     "collapsed": true
309 |    },
310 |    "outputs": [],
311 |    "source": [
312 |     "winner = np.array(['McCain'] * county_data.shape[0])"
313 |    ]
314 |   },
315 |   {
316 |    "cell_type": "code",
317 |    "execution_count": 34,
318 |    "metadata": {
319 |     "collapsed": true
320 |    },
321 |    "outputs": [],
322 |    "source": [
323 |     "winner[(county_data['Obama'] > county_data['McCain']).values] = 'Obama'"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "code",
328 |    "execution_count": 35,
329 |    "metadata": {
330 |     "collapsed": true,
331 |     "scrolled": true
332 |    },
333 |    "outputs": [],
334 |    "source": [
335 |     "sc_geo_county = AlbersUSA()\n",
336 |     "sc_c1_county = OrdinalColorScale(domain=['McCain', 'Obama'], colors=['Red', 'DeepSkyBlue'])\n",
337 |     "\n",
338 |     "color_data_county = dict(zip(county_data['FIPS'].values.astype(int), list(winner)))\n",
339 |     "\n",
340 |     "map_styles_county = {'color': color_data_county,\n",
341 |     "              'scales': {'projection': sc_geo_county, 'color': sc_c1_county}, 'colors': {'default_color': 'Grey'}}\n",
342 |     "\n",
343 |     "axis_county = ColorAxis(scale=sc_c1_county)\n",
344 |     "\n",
345 |     "county_map = Map(map_data=topo_load('map_data/USCountiesMap.json'), **map_styles_county)\n",
346 |     "county_fig = Figure(marks=[county_map], axes=[axis_county],title='US Elections 2008 - Example',\n",
347 |     "                    layout=Layout(min_width='800px', min_height='550px'))"
348 |    ]
349 |   },
350 |   {
351 |    "cell_type": "code",
352 |    "execution_count": 36,
353 |    "metadata": {
354 |     "collapsed": true
355 |    },
356 |    "outputs": [],
357 |    "source": [
358 |     "names_sc = OrdinalScale(domain=['Obama', 'McCain'])\n",
359 |     "vote_sc_y = LinearScale(min=0, max=100.)\n",
360 |     "\n",
361 |     "names_ax = Axis(scale=names_sc, label='Candidate')\n",
362 |     "vote_ax = Axis(scale=vote_sc_y, orientation='vertical', label='Percentage')\n",
363 |     "\n",
364 |     "vote_bars = Bars(scales={'x': names_sc, 'y': vote_sc_y}, colors=['#2aa1ec', '#E91D0E'])\n",
365 |     "\n",
366 |     "bar_fig = Figure(marks=[vote_bars], axes=[names_ax, vote_ax], title='Vote Margin',\n",
367 |     "                 layout=Layout(min_width='600px', min_height='400px'))"
368 |    ]
369 |   },
370 |   {
371 |    "cell_type": "code",
372 |    "execution_count": 37,
373 |    "metadata": {
374 |     "collapsed": true
375 |    },
376 |    "outputs": [],
377 |    "source": [
378 |     "def county_hover(name, value):\n",
379 |     "    if (county_data['FIPS'] == value['data']['id']).sum() == 0:\n",
380 |     "        bar_fig.title = ''\n",
381 |     "        vote_bars.y = [0., 0.]\n",
382 |     "        return\n",
383 |     "    votes = county_data[county_data['FIPS'] == value['data']['id']]\n",
384 |     "    dem_vote = float(votes['Obama %'].values[0])\n",
385 |     "    rep_vote = float(votes['McCain %'].values[0])\n",
386 |     "    vote_bars.x, vote_bars.y = ['Obama', 'McCain'], [dem_vote, rep_vote]\n",
387 |     "    bar_fig.title = 'Vote % - ' + value['data']['name']\n",
388 |     "    \n",
389 |     "county_map.on_hover(county_hover)\n",
390 |     "county_map.tooltip = bar_fig"
391 |    ]
392 |   },
393 |   {
394 |    "cell_type": "markdown",
395 |    "metadata": {},
396 |    "source": [
397 |     "#### Hover on the map to visualize the voting percentage for each candidate in that county"
398 |    ]
399 |   },
400 |   {
401 |    "cell_type": "code",
402 |    "execution_count": 38,
403 |    "metadata": {
404 |     "scrolled": true
405 |    },
406 |    "outputs": [
407 |     {
408 |      "data": {
409 |       "application/vnd.jupyter.widget-view+json": {
410 |        "model_id": "613b0f78f24e4642bbc98338aec6cbda",
411 |        "version_major": 2,
412 |        "version_minor": 0
413 |       },
414 |       "text/plain": [
415 |        "A Jupyter Widget"
416 |       ]
417 |      },
418 |      "metadata": {},
419 |      "output_type": "display_data"
420 |     }
421 |    ],
422 |    "source": [
423 |     "county_fig"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": null,
429 |    "metadata": {
430 |     "collapsed": true
431 |    },
432 |    "outputs": [],
433 |    "source": []
434 |   }
435 |  ],
436 |  "metadata": {
437 |   "kernelspec": {
438 |    "display_name": "Python 3",
439 |    "language": "python",
440 |    "name": "python3"
441 |   },
442 |   "language_info": {
443 |    "codemirror_mode": {
444 |     "name": "ipython",
445 |     "version": 3
446 |    },
447 |    "file_extension": ".py",
448 |    "mimetype": "text/x-python",
449 |    "name": "python",
450 |    "nbconvert_exporter": "python",
451 |    "pygments_lexer": "ipython3",
452 |    "version": "3.5.2"
453 |   }
454 |  },
455 |  "nbformat": 4,
456 |  "nbformat_minor": 1
457 | }
458 | 


--------------------------------------------------------------------------------
/Section 1/Wealth of Nations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "This is a `bqplot` recreation of Mike Bostock's [Wealth of Nations](https://bost.ocks.org/mike/nations/). This was also done by [Gapminder](http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2013$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2tPLxKvvnNPA;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=194;dataMax=96846$map_y;scale=lin;dataMin=23;dataMax=86$map_s;sma=49;smi=2.65$cd;bd=0$inds=;modified=60). It is originally based on a TED Talk by [Hans Rosling](http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen)."
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "### Import required modules"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 1,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "import pandas as pd\n",
 26 |     "import numpy as np\n",
 27 |     "import os\n",
 28 |     "\n",
 29 |     "from bqplot import (\n",
 30 |     "    LogScale, LinearScale, OrdinalColorScale, ColorAxis,\n",
 31 |     "    Axis, Scatter, Lines, CATEGORY10, Label, Figure, Tooltip\n",
 32 |     ")\n",
 33 |     "\n",
 34 |     "from ipywidgets import HBox, VBox, IntSlider, Play, jslink"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "markdown",
 39 |    "metadata": {},
 40 |    "source": [
 41 |     "### Read in the data"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 2,
 47 |    "metadata": {
 48 |     "collapsed": true,
 49 |     "scrolled": false
 50 |    },
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "data = pd.read_json(os.path.abspath('nations.json'))"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 95,
 59 |    "metadata": {},
 60 |    "outputs": [
 61 |     {
 62 |      "name": "stdout",
 63 |      "output_type": "stream",
 64 |      "text": [
 65 |       "<class 'pandas.core.frame.DataFrame'>\n",
 66 |       "Int64Index: 180 entries, 0 to 179\n",
 67 |       "Data columns (total 5 columns):\n",
 68 |       "income            180 non-null object\n",
 69 |       "lifeExpectancy    180 non-null object\n",
 70 |       "name              180 non-null object\n",
 71 |       "population        180 non-null object\n",
 72 |       "region            180 non-null object\n",
 73 |       "dtypes: object(5)\n",
 74 |       "memory usage: 8.4+ KB\n"
 75 |      ]
 76 |     }
 77 |    ],
 78 |    "source": [
 79 |     "data.info()"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 96,
 85 |    "metadata": {},
 86 |    "outputs": [
 87 |     {
 88 |      "data": {
 89 |       "text/html": [
 90 |        "<div>\n",
 91 |        "<table border=\"1\" class=\"dataframe\">\n",
 92 |        "  <thead>\n",
 93 |        "    <tr style=\"text-align: right;\">\n",
 94 |        "      <th></th>\n",
 95 |        "      <th>income</th>\n",
 96 |        "      <th>lifeExpectancy</th>\n",
 97 |        "      <th>name</th>\n",
 98 |        "      <th>population</th>\n",
 99 |        "      <th>region</th>\n",
100 |        "    </tr>\n",
101 |        "  </thead>\n",
102 |        "  <tbody>\n",
103 |        "    <tr>\n",
104 |        "      <th>0</th>\n",
105 |        "      <td>[[1800, 359.93], [1820, 359.93], [1913, 556.12...</td>\n",
106 |        "      <td>[[1800, 26.98], [1940, 26.98], [1950, 29.22], ...</td>\n",
107 |        "      <td>Angola</td>\n",
108 |        "      <td>[[1800, 1567028], [1820, 1567028], [1940, 3738...</td>\n",
109 |        "      <td>Sub-Saharan Africa</td>\n",
110 |        "    </tr>\n",
111 |        "    <tr>\n",
112 |        "      <th>1</th>\n",
113 |        "      <td>[[1800, 553.72], [1820, 553.72], [1913, 855.53...</td>\n",
114 |        "      <td>[[1800, 31], [1944, 31], [1950, 36.53], [1951,...</td>\n",
115 |        "      <td>Benin</td>\n",
116 |        "      <td>[[1800, 636559], [1820, 636559], [1950, 167266...</td>\n",
117 |        "      <td>Sub-Saharan Africa</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>2</th>\n",
121 |        "      <td>[[1800, 407.36], [1820, 407.36], [1913, 629.4]...</td>\n",
122 |        "      <td>[[1800, 33.6], [1945, 33.6], [1950, 46.82], [1...</td>\n",
123 |        "      <td>Botswana</td>\n",
124 |        "      <td>[[1800, 121000], [1904, 121000], [1911, 125000...</td>\n",
125 |        "      <td>Sub-Saharan Africa</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>3</th>\n",
129 |        "      <td>[[1800, 454.33], [1820, 454.33], [1913, 497.44...</td>\n",
130 |        "      <td>[[1800, 29.2], [1945, 29.2], [1950, 32.89], [1...</td>\n",
131 |        "      <td>Burkina Faso</td>\n",
132 |        "      <td>[[1800, 1665421], [1820, 1665421], [1950, 4376...</td>\n",
133 |        "      <td>Sub-Saharan Africa</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>4</th>\n",
137 |        "      <td>[[1800, 447.59], [1820, 447.59], [1913, 353.82...</td>\n",
138 |        "      <td>[[1800, 31.5], [1945, 31.5], [1950, 38.42], [1...</td>\n",
139 |        "      <td>Burundi</td>\n",
140 |        "      <td>[[1800, 899097], [1820, 899097], [1950, 236252...</td>\n",
141 |        "      <td>Sub-Saharan Africa</td>\n",
142 |        "    </tr>\n",
143 |        "  </tbody>\n",
144 |        "</table>\n",
145 |        "</div>"
146 |       ],
147 |       "text/plain": [
148 |        "                                              income  \\\n",
149 |        "0  [[1800, 359.93], [1820, 359.93], [1913, 556.12...   \n",
150 |        "1  [[1800, 553.72], [1820, 553.72], [1913, 855.53...   \n",
151 |        "2  [[1800, 407.36], [1820, 407.36], [1913, 629.4]...   \n",
152 |        "3  [[1800, 454.33], [1820, 454.33], [1913, 497.44...   \n",
153 |        "4  [[1800, 447.59], [1820, 447.59], [1913, 353.82...   \n",
154 |        "\n",
155 |        "                                      lifeExpectancy          name  \\\n",
156 |        "0  [[1800, 26.98], [1940, 26.98], [1950, 29.22], ...        Angola   \n",
157 |        "1  [[1800, 31], [1944, 31], [1950, 36.53], [1951,...         Benin   \n",
158 |        "2  [[1800, 33.6], [1945, 33.6], [1950, 46.82], [1...      Botswana   \n",
159 |        "3  [[1800, 29.2], [1945, 29.2], [1950, 32.89], [1...  Burkina Faso   \n",
160 |        "4  [[1800, 31.5], [1945, 31.5], [1950, 38.42], [1...       Burundi   \n",
161 |        "\n",
162 |        "                                          population              region  \n",
163 |        "0  [[1800, 1567028], [1820, 1567028], [1940, 3738...  Sub-Saharan Africa  \n",
164 |        "1  [[1800, 636559], [1820, 636559], [1950, 167266...  Sub-Saharan Africa  \n",
165 |        "2  [[1800, 121000], [1904, 121000], [1911, 125000...  Sub-Saharan Africa  \n",
166 |        "3  [[1800, 1665421], [1820, 1665421], [1950, 4376...  Sub-Saharan Africa  \n",
167 |        "4  [[1800, 899097], [1820, 899097], [1950, 236252...  Sub-Saharan Africa  "
168 |       ]
169 |      },
170 |      "execution_count": 96,
171 |      "metadata": {},
172 |      "output_type": "execute_result"
173 |     }
174 |    ],
175 |    "source": [
176 |     "data.head()"
177 |    ]
178 |   },
179 |   {
180 |    "cell_type": "markdown",
181 |    "metadata": {},
182 |    "source": [
183 |     "### Clean data"
184 |    ]
185 |   },
186 |   {
187 |    "cell_type": "code",
188 |    "execution_count": 97,
189 |    "metadata": {
190 |     "collapsed": true
191 |    },
192 |    "outputs": [],
193 |    "source": [
194 |     "def clean_data(data):\n",
195 |     "    for column in ['income', 'lifeExpectancy', 'population']:\n",
196 |     "        data = data.drop(data[data[column].apply(len) <= 4].index)\n",
197 |     "    return data\n",
198 |     "\n",
199 |     "def extrap_interp(data):\n",
200 |     "    data = np.array(data)\n",
201 |     "    x_range = np.arange(1800, 2009, 1.)\n",
202 |     "    y_range = np.interp(x_range, data[:, 0], data[:, 1])\n",
203 |     "    return y_range\n",
204 |     "\n",
205 |     "def extrap_data(data):\n",
206 |     "    for column in ['income', 'lifeExpectancy', 'population']:\n",
207 |     "        data[column] = data[column].apply(extrap_interp)\n",
208 |     "    return data"
209 |    ]
210 |   },
211 |   {
212 |    "cell_type": "code",
213 |    "execution_count": 98,
214 |    "metadata": {
215 |     "collapsed": true
216 |    },
217 |    "outputs": [],
218 |    "source": [
219 |     "data = clean_data(data)\n",
220 |     "data = extrap_data(data)"
221 |    ]
222 |   },
223 |   {
224 |    "cell_type": "markdown",
225 |    "metadata": {},
226 |    "source": [
227 |     "### Prep Data for visualizations"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "code",
232 |    "execution_count": 99,
233 |    "metadata": {
234 |     "collapsed": true,
235 |     "scrolled": true
236 |    },
237 |    "outputs": [],
238 |    "source": [
239 |     "income_min, income_max = np.min(data['income'].apply(np.min)), np.max(data['income'].apply(np.max))\n",
240 |     "life_exp_min, life_exp_max = np.min(data['lifeExpectancy'].apply(np.min)), np.max(data['lifeExpectancy'].apply(np.max))\n",
241 |     "pop_min, pop_max = np.min(data['population'].apply(np.min)), np.max(data['population'].apply(np.max))"
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": 100,
247 |    "metadata": {
248 |     "collapsed": true
249 |    },
250 |    "outputs": [],
251 |    "source": [
252 |     "def get_data(year):\n",
253 |     "    year_index = year - 1800\n",
254 |     "    income = data['income'].apply(lambda x: x[year_index])\n",
255 |     "    life_exp = data['lifeExpectancy'].apply(lambda x: x[year_index])\n",
256 |     "    pop =  data['population'].apply(lambda x: x[year_index])\n",
257 |     "    return income, life_exp, pop"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 101,
263 |    "metadata": {},
264 |    "outputs": [
265 |     {
266 |      "data": {
267 |       "text/plain": [
268 |        "(0       2446.65\n",
269 |        " 1       1307.57\n",
270 |        " 2      10107.99\n",
271 |        " 3       1001.48\n",
272 |        " 4        443.76\n",
273 |        " 5       1803.44\n",
274 |        " 6       2291.29\n",
275 |        " 7       1017.03\n",
276 |        " 8       1090.79\n",
277 |        " 9        292.45\n",
278 |        " 10      3423.52\n",
279 |        " 11      1746.31\n",
280 |        " 12      5240.45\n",
281 |        " 13       741.57\n",
282 |        " 14       512.36\n",
283 |        " 15     12764.05\n",
284 |        " 16      1067.60\n",
285 |        " 17       906.62\n",
286 |        " 18       632.08\n",
287 |        " 19      1318.49\n",
288 |        " 20      1186.64\n",
289 |        " 21       519.49\n",
290 |        " 22      1027.09\n",
291 |        " 23       713.78\n",
292 |        " 24       854.73\n",
293 |        " 25      1540.84\n",
294 |        " 26      8290.95\n",
295 |        " 28       542.81\n",
296 |        " 29      4022.88\n",
297 |        " 30       577.19\n",
298 |        "          ...   \n",
299 |        " 149     1635.22\n",
300 |        " 150    29241.51\n",
301 |        " 151    48020.35\n",
302 |        " 152      833.36\n",
303 |        " 153     3012.12\n",
304 |        " 154     3856.32\n",
305 |        " 155    24659.01\n",
306 |        " 156    29972.38\n",
307 |        " 157     2714.94\n",
308 |        " 158    28559.60\n",
309 |        " 159     1690.00\n",
310 |        " 160    16996.00\n",
311 |        " 161    22512.56\n",
312 |        " 162    10161.49\n",
313 |        " 163     5530.16\n",
314 |        " 164     2017.51\n",
315 |        " 165     1034.45\n",
316 |        " 166    33884.50\n",
317 |        " 167    21895.16\n",
318 |        " 168     1821.68\n",
319 |        " 169     2598.89\n",
320 |        " 170     4090.83\n",
321 |        " 171    36834.85\n",
322 |        " 172     1802.02\n",
323 |        " 173    23525.00\n",
324 |        " 174     5578.40\n",
325 |        " 175     2743.19\n",
326 |        " 177     4886.27\n",
327 |        " 178     1577.53\n",
328 |        " 179     3762.19\n",
329 |        " Name: income, dtype: float64, 0      43.56\n",
330 |        " 1      58.34\n",
331 |        " 2      50.65\n",
332 |        " 3      50.37\n",
333 |        " 4      46.90\n",
334 |        " 5      51.52\n",
335 |        " 6      68.98\n",
336 |        " 7      49.30\n",
337 |        " 8      61.85\n",
338 |        " 9      46.34\n",
339 |        " 10     53.58\n",
340 |        " 11     55.45\n",
341 |        " 12     48.74\n",
342 |        " 13     55.94\n",
343 |        " 14     51.40\n",
344 |        " 15     59.90\n",
345 |        " 16     57.91\n",
346 |        " 17     53.27\n",
347 |        " 18     45.84\n",
348 |        " 19     52.82\n",
349 |        " 20     50.48\n",
350 |        " 21     54.44\n",
351 |        " 22     56.51\n",
352 |        " 23     50.99\n",
353 |        " 24     45.64\n",
354 |        " 25     56.50\n",
355 |        " 26     71.32\n",
356 |        " 28     47.65\n",
357 |        " 29     58.78\n",
358 |        " 30     46.41\n",
359 |        "        ...  \n",
360 |        " 149    66.99\n",
361 |        " 150    79.93\n",
362 |        " 151    75.93\n",
363 |        " 152    57.08\n",
364 |        " 153    71.29\n",
365 |        " 154    67.28\n",
366 |        " 155    72.24\n",
367 |        " 156    80.83\n",
368 |        " 157    67.41\n",
369 |        " 158    81.35\n",
370 |        " 159    66.96\n",
371 |        " 160    75.92\n",
372 |        " 161    79.04\n",
373 |        " 162    72.50\n",
374 |        " 163    67.29\n",
375 |        " 164    64.19\n",
376 |        " 165    59.94\n",
377 |        " 166    73.92\n",
378 |        " 167    78.90\n",
379 |        " 168    57.74\n",
380 |        " 169    69.49\n",
381 |        " 170    69.28\n",
382 |        " 171    77.93\n",
383 |        " 172    62.15\n",
384 |        " 173    76.07\n",
385 |        " 174    68.09\n",
386 |        " 175    56.23\n",
387 |        " 177    70.75\n",
388 |        " 178    72.08\n",
389 |        " 179    67.42\n",
390 |        " Name: lifeExpectancy, dtype: float64, 0      1.044281e+07\n",
391 |        " 1      6.627964e+06\n",
392 |        " 2      1.607069e+06\n",
393 |        " 3      1.130855e+07\n",
394 |        " 4      6.621126e+06\n",
395 |        " 5      1.523367e+07\n",
396 |        " 6      4.013430e+05\n",
397 |        " 7      8.316481e+06\n",
398 |        " 8      5.784000e+05\n",
399 |        " 9      5.202183e+07\n",
400 |        " 10     3.102404e+06\n",
401 |        " 11     1.556339e+07\n",
402 |        " 12     4.732160e+05\n",
403 |        " 13     4.356581e+06\n",
404 |        " 14     6.469005e+07\n",
405 |        " 15     1.235484e+06\n",
406 |        " 16     1.965772e+07\n",
407 |        " 17     8.638858e+06\n",
408 |        " 18     1.278273e+06\n",
409 |        " 19     2.998584e+07\n",
410 |        " 20     2.037961e+06\n",
411 |        " 21     2.693780e+06\n",
412 |        " 22     1.550647e+07\n",
413 |        " 23     1.125816e+07\n",
414 |        " 24     1.007227e+07\n",
415 |        " 25     2.667859e+06\n",
416 |        " 26     1.179368e+06\n",
417 |        " 28     1.776846e+07\n",
418 |        " 29     1.905659e+06\n",
419 |        " 30     1.051611e+07\n",
420 |        "            ...     \n",
421 |        " 149    2.475552e+07\n",
422 |        " 150    1.916462e+07\n",
423 |        " 151    3.363760e+05\n",
424 |        " 152    1.246626e+07\n",
425 |        " 153    1.262645e+09\n",
426 |        " 154    8.324940e+05\n",
427 |        " 155    2.491100e+05\n",
428 |        " 156    6.658720e+06\n",
429 |        " 157    2.062650e+08\n",
430 |        " 158    1.266998e+08\n",
431 |        " 159    2.164768e+07\n",
432 |        " 160    4.735108e+07\n",
433 |        " 161    4.313380e+05\n",
434 |        " 162    2.179329e+07\n",
435 |        " 163    1.077540e+05\n",
436 |        " 164    2.600835e+06\n",
437 |        " 165    4.470224e+07\n",
438 |        " 166    2.018160e+05\n",
439 |        " 167    3.819762e+06\n",
440 |        " 168    4.926984e+06\n",
441 |        " 169    7.973982e+07\n",
442 |        " 170    1.794660e+05\n",
443 |        " 171    4.036753e+06\n",
444 |        " 172    4.661940e+05\n",
445 |        " 173    2.215124e+07\n",
446 |        " 174    6.186293e+07\n",
447 |        " 175    8.465990e+05\n",
448 |        " 177    1.023210e+05\n",
449 |        " 178    7.906041e+07\n",
450 |        " 179    1.896180e+05\n",
451 |        " Name: population, dtype: float64)"
452 |       ]
453 |      },
454 |      "execution_count": 101,
455 |      "metadata": {},
456 |      "output_type": "execute_result"
457 |     }
458 |    ],
459 |    "source": [
460 |     "get_data(2000)"
461 |    ]
462 |   },
463 |   {
464 |    "cell_type": "markdown",
465 |    "metadata": {},
466 |    "source": [
467 |     "### Setting up the visualization elements"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "markdown",
472 |    "metadata": {},
473 |    "source": [
474 |     "#### Tooltip"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 102,
480 |    "metadata": {
481 |     "collapsed": true
482 |    },
483 |    "outputs": [],
484 |    "source": [
485 |     "tt = Tooltip(fields=['name', 'x', 'y'], labels=['Country Name', 'Income per Capita', 'Life Expectancy'])"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "markdown",
490 |    "metadata": {},
491 |    "source": [
492 |     "#### Labels"
493 |    ]
494 |   },
495 |   {
496 |    "cell_type": "code",
497 |    "execution_count": 103,
498 |    "metadata": {
499 |     "collapsed": true
500 |    },
501 |    "outputs": [],
502 |    "source": [
503 |     "initial_year = 1800\n",
504 |     "year_label = Label(x=[0.75], y=[0.10], default_size=46, font_weight='bolder', colors=['orange'],\n",
505 |     "                   text=[str(initial_year)], enable_move=True)"
506 |    ]
507 |   },
508 |   {
509 |    "cell_type": "markdown",
510 |    "metadata": {},
511 |    "source": [
512 |     "#### Axes and Scales"
513 |    ]
514 |   },
515 |   {
516 |    "cell_type": "code",
517 |    "execution_count": 104,
518 |    "metadata": {
519 |     "collapsed": true
520 |    },
521 |    "outputs": [],
522 |    "source": [
523 |     "x_sc = LogScale(min=income_min, max=income_max)\n",
524 |     "y_sc = LinearScale(min=life_exp_min, max=life_exp_max)\n",
525 |     "c_sc = OrdinalColorScale(domain=data['region'].unique().tolist(), colors=CATEGORY10[:6])\n",
526 |     "size_sc = LinearScale(min=pop_min, max=pop_max)"
527 |    ]
528 |   },
529 |   {
530 |    "cell_type": "code",
531 |    "execution_count": 105,
532 |    "metadata": {
533 |     "collapsed": true
534 |    },
535 |    "outputs": [],
536 |    "source": [
537 |     "ax_y = Axis(label='Life Expectancy', scale=y_sc, orientation='vertical', side='left', grid_lines='solid')\n",
538 |     "ax_x = Axis(label='Income per Capita', scale=x_sc, grid_lines='solid')"
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "markdown",
543 |    "metadata": {},
544 |    "source": [
545 |     "#### Scatter mark size and color"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": 106,
551 |    "metadata": {
552 |     "collapsed": true
553 |    },
554 |    "outputs": [],
555 |    "source": [
556 |     "# Start with the first year's data\n",
557 |     "cap_income, life_exp, pop = get_data(initial_year)"
558 |    ]
559 |   },
560 |   {
561 |    "cell_type": "code",
562 |    "execution_count": 107,
563 |    "metadata": {
564 |     "collapsed": true
565 |    },
566 |    "outputs": [],
567 |    "source": [
568 |     "wealth_scat = Scatter(x=cap_income, y=life_exp, color=data['region'], size=pop,\n",
569 |     "                      names=data['name'], display_names=False,\n",
570 |     "                      scales={'x': x_sc, 'y': y_sc, 'color': c_sc, 'size': size_sc},\n",
571 |     "                      default_size=4112, tooltip=tt, animate=True, stroke='Black',\n",
572 |     "                      unhovered_style={'opacity': 0.5})"
573 |    ]
574 |   },
575 |   {
576 |    "cell_type": "code",
577 |    "execution_count": 108,
578 |    "metadata": {
579 |     "collapsed": true
580 |    },
581 |    "outputs": [],
582 |    "source": [
583 |     "nation_line = Lines(x=data['income'][0], y=data['lifeExpectancy'][0], colors=['Gray'],\n",
584 |     "                       scales={'x': x_sc, 'y': y_sc}, visible=False)"
585 |    ]
586 |   },
587 |   {
588 |    "cell_type": "markdown",
589 |    "metadata": {},
590 |    "source": [
591 |     "#### Creating the Figure"
592 |    ]
593 |   },
594 |   {
595 |    "cell_type": "code",
596 |    "execution_count": 109,
597 |    "metadata": {
598 |     "collapsed": true
599 |    },
600 |    "outputs": [],
601 |    "source": [
602 |     "time_interval = 10"
603 |    ]
604 |   },
605 |   {
606 |    "cell_type": "code",
607 |    "execution_count": 110,
608 |    "metadata": {
609 |     "collapsed": true
610 |    },
611 |    "outputs": [],
612 |    "source": [
613 |     "fig = Figure(marks=[wealth_scat, year_label, nation_line], axes=[ax_x, ax_y],\n",
614 |     "             title='Health and Wealth of Nations', animation_duration=time_interval)"
615 |    ]
616 |   },
617 |   {
618 |    "cell_type": "markdown",
619 |    "metadata": {},
620 |    "source": [
621 |     "### Animations"
622 |    ]
623 |   },
624 |   {
625 |    "cell_type": "code",
626 |    "execution_count": 111,
627 |    "metadata": {
628 |     "collapsed": true
629 |    },
630 |    "outputs": [],
631 |    "source": [
632 |     "year_slider = IntSlider(min=1800, max=2008, step=1, description='Year', value=initial_year)"
633 |    ]
634 |   },
635 |   {
636 |    "cell_type": "code",
637 |    "execution_count": 112,
638 |    "metadata": {
639 |     "collapsed": true
640 |    },
641 |    "outputs": [],
642 |    "source": [
643 |     "def hover_changed(change):\n",
644 |     "    if change.new is not None:\n",
645 |     "        nation_line.x = data[data['name'] == wealth_scat.names[change.new]]['income'].values[0]\n",
646 |     "        nation_line.y = data[data['name'] == wealth_scat.names[change.new]]['lifeExpectancy'].values[0]\n",
647 |     "        nation_line.visible = True\n",
648 |     "    else:\n",
649 |     "        nation_line.visible = False\n",
650 |     "        \n",
651 |     "wealth_scat.observe(hover_changed, 'hovered_point')"
652 |    ]
653 |   },
654 |   {
655 |    "cell_type": "code",
656 |    "execution_count": 113,
657 |    "metadata": {
658 |     "collapsed": true
659 |    },
660 |    "outputs": [],
661 |    "source": [
662 |     "def year_changed(change):\n",
663 |     "    wealth_scat.x, wealth_scat.y, wealth_scat.size = get_data(year_slider.value)\n",
664 |     "    year_label.text = [str(year_slider.value)]\n",
665 |     "\n",
666 |     "year_slider.observe(year_changed, 'value')"
667 |    ]
668 |   },
669 |   {
670 |    "cell_type": "markdown",
671 |    "metadata": {},
672 |    "source": [
673 |     "#### Add an animation button"
674 |    ]
675 |   },
676 |   {
677 |    "cell_type": "code",
678 |    "execution_count": 114,
679 |    "metadata": {
680 |     "collapsed": true
681 |    },
682 |    "outputs": [],
683 |    "source": [
684 |     "play_button = Play(min=1800, max=2008, interval=time_interval)\n",
685 |     "jslink((play_button, 'value'), (year_slider, 'value'))"
686 |    ]
687 |   },
688 |   {
689 |    "cell_type": "markdown",
690 |    "metadata": {},
691 |    "source": [
692 |     "### Displaying the visuslization"
693 |    ]
694 |   },
695 |   {
696 |    "cell_type": "code",
697 |    "execution_count": 116,
698 |    "metadata": {
699 |     "scrolled": false
700 |    },
701 |    "outputs": [
702 |     {
703 |      "data": {
704 |       "application/vnd.jupyter.widget-view+json": {
705 |        "model_id": "1a255afeafaf4d1aa28f9b3bd4ebbafe",
706 |        "version_major": 2,
707 |        "version_minor": 0
708 |       },
709 |       "text/plain": [
710 |        "A Jupyter Widget"
711 |       ]
712 |      },
713 |      "metadata": {},
714 |      "output_type": "display_data"
715 |     }
716 |    ],
717 |    "source": [
718 |     "VBox([HBox([play_button, year_slider]), fig])"
719 |    ]
720 |   },
721 |   {
722 |    "cell_type": "code",
723 |    "execution_count": null,
724 |    "metadata": {
725 |     "collapsed": true
726 |    },
727 |    "outputs": [],
728 |    "source": []
729 |   }
730 |  ],
731 |  "metadata": {
732 |   "kernelspec": {
733 |    "display_name": "Python 3",
734 |    "language": "python",
735 |    "name": "python3"
736 |   },
737 |   "language_info": {
738 |    "codemirror_mode": {
739 |     "name": "ipython",
740 |     "version": 3
741 |    },
742 |    "file_extension": ".py",
743 |    "mimetype": "text/x-python",
744 |    "name": "python",
745 |    "nbconvert_exporter": "python",
746 |    "pygments_lexer": "ipython3",
747 |    "version": "3.5.4"
748 |   }
749 |  },
750 |  "nbformat": 4,
751 |  "nbformat_minor": 1
752 | }
753 | 


--------------------------------------------------------------------------------
/Section 1/state_codes.csv:
--------------------------------------------------------------------------------
 1 | Name,FIPS,USPS
 2 | Alabama,1,AL
 3 | Alaska,2,AK
 4 | Arizona,4,AZ
 5 | Arkansas,5,AR
 6 | California,6,CA
 7 | Colorado,8,CO
 8 | Connecticut,9,CT
 9 | Delaware,10,DE
10 | District of Columbia,11,DC
11 | Florida,12,FL
12 | Georgia,13,GA
13 | Hawaii,15,HI
14 | Idaho,16,ID
15 | Illinois,17,IL
16 | Indiana,18,IN
17 | Iowa,19,IA
18 | Kansas,20,KS
19 | Kentucky,21,KY
20 | Louisiana,22,LA
21 | Maine,23,ME
22 | Maryland,24,MD
23 | Massachusetts,25,MA
24 | Michigan,26,MI
25 | Minnesota,27,MN
26 | Mississippi,28,MS
27 | Missouri,29,MO
28 | Montana,30,MT
29 | Nebraska,31,NE
30 | Nevada,32,NV
31 | New Hampshire,33,NH
32 | New Jersey,34,NJ
33 | New Mexico,35,NM
34 | New York,36,NY
35 | North Carolina,37,NC
36 | North Dakota,38,ND
37 | Ohio,39,OH
38 | Oklahoma,40,OK
39 | Oregon,41,OR
40 | Pennsylvania,42,PA
41 | Rhode Island,44,RI
42 | South Carolina,45,SC
43 | South Dakota,46,SD
44 | Tennessee,47,TN
45 | Texas,48,TX
46 | Utah,49,UT
47 | Vermont,50,VT
48 | Virginia,51,VA
49 | Washington,53,WA
50 | West Virginia,54,WV
51 | Wisconsin,55,WI
52 | Wyoming,56,WY
53 | 


--------------------------------------------------------------------------------
/Section 2/README.md:
--------------------------------------------------------------------------------
1 | Section 2 Code Examples


--------------------------------------------------------------------------------
/Section 3/Bokeh - NASA Moon Missions.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "from bokeh.plotting import figure \n",
 12 |     "from bokeh.io import output_notebook, show\n",
 13 |     "from bokeh.models import ColumnDataSource\n",
 14 |     "import pandas as pd"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 13,
 20 |    "metadata": {},
 21 |    "outputs": [
 22 |     {
 23 |      "data": {
 24 |       "text/html": [
 25 |        "\n",
 26 |        "    <div class=\"bk-root\">\n",
 27 |        "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
 28 |        "        <span id=\"11dbdcc8-9e5c-4713-8907-54eed6db732b\">Loading BokehJS ...</span>\n",
 29 |        "    </div>"
 30 |       ]
 31 |      },
 32 |      "metadata": {},
 33 |      "output_type": "display_data"
 34 |     },
 35 |     {
 36 |      "data": {
 37 |       "application/javascript": [
 38 |        "\n",
 39 |        "(function(root) {\n",
 40 |        "  function now() {\n",
 41 |        "    return new Date();\n",
 42 |        "  }\n",
 43 |        "\n",
 44 |        "  var force = true;\n",
 45 |        "\n",
 46 |        "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
 47 |        "    root._bokeh_onload_callbacks = [];\n",
 48 |        "    root._bokeh_is_loading = undefined;\n",
 49 |        "  }\n",
 50 |        "\n",
 51 |        "  var JS_MIME_TYPE = 'application/javascript';\n",
 52 |        "  var HTML_MIME_TYPE = 'text/html';\n",
 53 |        "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
 54 |        "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
 55 |        "\n",
 56 |        "  /**\n",
 57 |        "   * Render data to the DOM node\n",
 58 |        "   */\n",
 59 |        "  function render(props, node) {\n",
 60 |        "    var script = document.createElement(\"script\");\n",
 61 |        "    node.appendChild(script);\n",
 62 |        "  }\n",
 63 |        "\n",
 64 |        "  /**\n",
 65 |        "   * Handle when an output is cleared or removed\n",
 66 |        "   */\n",
 67 |        "  function handleClearOutput(event, handle) {\n",
 68 |        "    var cell = handle.cell;\n",
 69 |        "\n",
 70 |        "    var id = cell.output_area._bokeh_element_id;\n",
 71 |        "    var server_id = cell.output_area._bokeh_server_id;\n",
 72 |        "    // Clean up Bokeh references\n",
 73 |        "    if (id !== undefined) {\n",
 74 |        "      Bokeh.index[id].model.document.clear();\n",
 75 |        "      delete Bokeh.index[id];\n",
 76 |        "    }\n",
 77 |        "\n",
 78 |        "    if (server_id !== undefined) {\n",
 79 |        "      // Clean up Bokeh references\n",
 80 |        "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
 81 |        "      cell.notebook.kernel.execute(cmd, {\n",
 82 |        "        iopub: {\n",
 83 |        "          output: function(msg) {\n",
 84 |        "            var element_id = msg.content.text.trim();\n",
 85 |        "            Bokeh.index[element_id].model.document.clear();\n",
 86 |        "            delete Bokeh.index[element_id];\n",
 87 |        "          }\n",
 88 |        "        }\n",
 89 |        "      });\n",
 90 |        "      // Destroy server and session\n",
 91 |        "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
 92 |        "      cell.notebook.kernel.execute(cmd);\n",
 93 |        "    }\n",
 94 |        "  }\n",
 95 |        "\n",
 96 |        "  /**\n",
 97 |        "   * Handle when a new output is added\n",
 98 |        "   */\n",
 99 |        "  function handleAddOutput(event, handle) {\n",
100 |        "    var output_area = handle.output_area;\n",
101 |        "    var output = handle.output;\n",
102 |        "\n",
103 |        "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
104 |        "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
105 |        "      return\n",
106 |        "    }\n",
107 |        "\n",
108 |        "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
109 |        "\n",
110 |        "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
111 |        "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
112 |        "      // store reference to embed id on output_area\n",
113 |        "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
114 |        "    }\n",
115 |        "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
116 |        "      var bk_div = document.createElement(\"div\");\n",
117 |        "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
118 |        "      var script_attrs = bk_div.children[0].attributes;\n",
119 |        "      for (var i = 0; i < script_attrs.length; i++) {\n",
120 |        "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
121 |        "      }\n",
122 |        "      // store reference to server id on output_area\n",
123 |        "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
124 |        "    }\n",
125 |        "  }\n",
126 |        "\n",
127 |        "  function register_renderer(events, OutputArea) {\n",
128 |        "\n",
129 |        "    function append_mime(data, metadata, element) {\n",
130 |        "      // create a DOM node to render to\n",
131 |        "      var toinsert = this.create_output_subarea(\n",
132 |        "        metadata,\n",
133 |        "        CLASS_NAME,\n",
134 |        "        EXEC_MIME_TYPE\n",
135 |        "      );\n",
136 |        "      this.keyboard_manager.register_events(toinsert);\n",
137 |        "      // Render to node\n",
138 |        "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
139 |        "      render(props, toinsert[0]);\n",
140 |        "      element.append(toinsert);\n",
141 |        "      return toinsert\n",
142 |        "    }\n",
143 |        "\n",
144 |        "    /* Handle when an output is cleared or removed */\n",
145 |        "    events.on('clear_output.CodeCell', handleClearOutput);\n",
146 |        "    events.on('delete.Cell', handleClearOutput);\n",
147 |        "\n",
148 |        "    /* Handle when a new output is added */\n",
149 |        "    events.on('output_added.OutputArea', handleAddOutput);\n",
150 |        "\n",
151 |        "    /**\n",
152 |        "     * Register the mime type and append_mime function with output_area\n",
153 |        "     */\n",
154 |        "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
155 |        "      /* Is output safe? */\n",
156 |        "      safe: true,\n",
157 |        "      /* Index of renderer in `output_area.display_order` */\n",
158 |        "      index: 0\n",
159 |        "    });\n",
160 |        "  }\n",
161 |        "\n",
162 |        "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
163 |        "  if (root.Jupyter !== undefined) {\n",
164 |        "    var events = require('base/js/events');\n",
165 |        "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
166 |        "\n",
167 |        "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
168 |        "      register_renderer(events, OutputArea);\n",
169 |        "    }\n",
170 |        "  }\n",
171 |        "\n",
172 |        "  \n",
173 |        "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
174 |        "    root._bokeh_timeout = Date.now() + 5000;\n",
175 |        "    root._bokeh_failed_load = false;\n",
176 |        "  }\n",
177 |        "\n",
178 |        "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
179 |        "     \"<div style='background-color: #fdd'>\\n\"+\n",
180 |        "     \"<p>\\n\"+\n",
181 |        "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
182 |        "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
183 |        "     \"</p>\\n\"+\n",
184 |        "     \"<ul>\\n\"+\n",
185 |        "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
186 |        "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
187 |        "     \"</ul>\\n\"+\n",
188 |        "     \"<code>\\n\"+\n",
189 |        "     \"from bokeh.resources import INLINE\\n\"+\n",
190 |        "     \"output_notebook(resources=INLINE)\\n\"+\n",
191 |        "     \"</code>\\n\"+\n",
192 |        "     \"</div>\"}};\n",
193 |        "\n",
194 |        "  function display_loaded() {\n",
195 |        "    var el = document.getElementById(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\");\n",
196 |        "    if (el != null) {\n",
197 |        "      el.textContent = \"BokehJS is loading...\";\n",
198 |        "    }\n",
199 |        "    if (root.Bokeh !== undefined) {\n",
200 |        "      if (el != null) {\n",
201 |        "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
202 |        "      }\n",
203 |        "    } else if (Date.now() < root._bokeh_timeout) {\n",
204 |        "      setTimeout(display_loaded, 100)\n",
205 |        "    }\n",
206 |        "  }\n",
207 |        "\n",
208 |        "\n",
209 |        "  function run_callbacks() {\n",
210 |        "    try {\n",
211 |        "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
212 |        "    }\n",
213 |        "    finally {\n",
214 |        "      delete root._bokeh_onload_callbacks\n",
215 |        "    }\n",
216 |        "    console.info(\"Bokeh: all callbacks have finished\");\n",
217 |        "  }\n",
218 |        "\n",
219 |        "  function load_libs(js_urls, callback) {\n",
220 |        "    root._bokeh_onload_callbacks.push(callback);\n",
221 |        "    if (root._bokeh_is_loading > 0) {\n",
222 |        "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
223 |        "      return null;\n",
224 |        "    }\n",
225 |        "    if (js_urls == null || js_urls.length === 0) {\n",
226 |        "      run_callbacks();\n",
227 |        "      return null;\n",
228 |        "    }\n",
229 |        "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
230 |        "    root._bokeh_is_loading = js_urls.length;\n",
231 |        "    for (var i = 0; i < js_urls.length; i++) {\n",
232 |        "      var url = js_urls[i];\n",
233 |        "      var s = document.createElement('script');\n",
234 |        "      s.src = url;\n",
235 |        "      s.async = false;\n",
236 |        "      s.onreadystatechange = s.onload = function() {\n",
237 |        "        root._bokeh_is_loading--;\n",
238 |        "        if (root._bokeh_is_loading === 0) {\n",
239 |        "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
240 |        "          run_callbacks()\n",
241 |        "        }\n",
242 |        "      };\n",
243 |        "      s.onerror = function() {\n",
244 |        "        console.warn(\"failed to load library \" + url);\n",
245 |        "      };\n",
246 |        "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
247 |        "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
248 |        "    }\n",
249 |        "  };var element = document.getElementById(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\");\n",
250 |        "  if (element == null) {\n",
251 |        "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '11dbdcc8-9e5c-4713-8907-54eed6db732b' but no matching script tag was found. \")\n",
252 |        "    return false;\n",
253 |        "  }\n",
254 |        "\n",
255 |        "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n",
256 |        "\n",
257 |        "  var inline_js = [\n",
258 |        "    function(Bokeh) {\n",
259 |        "      Bokeh.set_log_level(\"info\");\n",
260 |        "    },\n",
261 |        "    \n",
262 |        "    function(Bokeh) {\n",
263 |        "      \n",
264 |        "    },\n",
265 |        "    function(Bokeh) {\n",
266 |        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
267 |        "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
268 |        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
269 |        "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
270 |        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
271 |        "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
272 |        "    }\n",
273 |        "  ];\n",
274 |        "\n",
275 |        "  function run_inline_js() {\n",
276 |        "    \n",
277 |        "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
278 |        "      for (var i = 0; i < inline_js.length; i++) {\n",
279 |        "        inline_js[i].call(root, root.Bokeh);\n",
280 |        "      }if (force === true) {\n",
281 |        "        display_loaded();\n",
282 |        "      }} else if (Date.now() < root._bokeh_timeout) {\n",
283 |        "      setTimeout(run_inline_js, 100);\n",
284 |        "    } else if (!root._bokeh_failed_load) {\n",
285 |        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
286 |        "      root._bokeh_failed_load = true;\n",
287 |        "    } else if (force !== true) {\n",
288 |        "      var cell = $(document.getElementById(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\")).parents('.cell').data().cell;\n",
289 |        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
290 |        "    }\n",
291 |        "\n",
292 |        "  }\n",
293 |        "\n",
294 |        "  if (root._bokeh_is_loading === 0) {\n",
295 |        "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
296 |        "    run_inline_js();\n",
297 |        "  } else {\n",
298 |        "    load_libs(js_urls, function() {\n",
299 |        "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
300 |        "      run_inline_js();\n",
301 |        "    });\n",
302 |        "  }\n",
303 |        "}(window));"
304 |       ],
305 |       "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._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    root._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        root._bokeh_is_loading--;\n        if (root._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(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '11dbdcc8-9e5c-4713-8907-54eed6db732b' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"11dbdcc8-9e5c-4713-8907-54eed6db732b\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._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}(window));"
306 |      },
307 |      "metadata": {},
308 |      "output_type": "display_data"
309 |     }
310 |    ],
311 |    "source": [
312 |     "output_notebook()"
313 |    ]
314 |   },
315 |   {
316 |    "cell_type": "code",
317 |    "execution_count": 14,
318 |    "metadata": {
319 |     "collapsed": true
320 |    },
321 |    "outputs": [],
322 |    "source": [
323 |     "manned_data = pd.read_csv(\"manned.csv\")"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "code",
328 |    "execution_count": 15,
329 |    "metadata": {},
330 |    "outputs": [
331 |     {
332 |      "data": {
333 |       "text/html": [
334 |        "<div>\n",
335 |        "<style>\n",
336 |        "    .dataframe thead tr:only-child th {\n",
337 |        "        text-align: right;\n",
338 |        "    }\n",
339 |        "\n",
340 |        "    .dataframe thead th {\n",
341 |        "        text-align: left;\n",
342 |        "    }\n",
343 |        "\n",
344 |        "    .dataframe tbody tr th {\n",
345 |        "        vertical-align: top;\n",
346 |        "    }\n",
347 |        "</style>\n",
348 |        "<table border=\"1\" class=\"dataframe\">\n",
349 |        "  <thead>\n",
350 |        "    <tr style=\"text-align: right;\">\n",
351 |        "      <th></th>\n",
352 |        "      <th>Mission</th>\n",
353 |        "      <th>Date</th>\n",
354 |        "      <th>Landing</th>\n",
355 |        "      <th>Crew Safety</th>\n",
356 |        "    </tr>\n",
357 |        "  </thead>\n",
358 |        "  <tbody>\n",
359 |        "    <tr>\n",
360 |        "      <th>0</th>\n",
361 |        "      <td>Apollo 1</td>\n",
362 |        "      <td>21-Feb-67</td>\n",
363 |        "      <td>0</td>\n",
364 |        "      <td>0</td>\n",
365 |        "    </tr>\n",
366 |        "    <tr>\n",
367 |        "      <th>1</th>\n",
368 |        "      <td>Apollo 7</td>\n",
369 |        "      <td>11-Oct-68</td>\n",
370 |        "      <td>0</td>\n",
371 |        "      <td>1</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>2</th>\n",
375 |        "      <td>Apollo 8</td>\n",
376 |        "      <td>21-Dec-68</td>\n",
377 |        "      <td>0</td>\n",
378 |        "      <td>1</td>\n",
379 |        "    </tr>\n",
380 |        "    <tr>\n",
381 |        "      <th>3</th>\n",
382 |        "      <td>Apollo 9</td>\n",
383 |        "      <td>03-Mar-69</td>\n",
384 |        "      <td>0</td>\n",
385 |        "      <td>1</td>\n",
386 |        "    </tr>\n",
387 |        "    <tr>\n",
388 |        "      <th>4</th>\n",
389 |        "      <td>Apollo 10</td>\n",
390 |        "      <td>18-May-69</td>\n",
391 |        "      <td>0</td>\n",
392 |        "      <td>1</td>\n",
393 |        "    </tr>\n",
394 |        "    <tr>\n",
395 |        "      <th>5</th>\n",
396 |        "      <td>Apollo 11</td>\n",
397 |        "      <td>16-Jul-69</td>\n",
398 |        "      <td>1</td>\n",
399 |        "      <td>1</td>\n",
400 |        "    </tr>\n",
401 |        "    <tr>\n",
402 |        "      <th>6</th>\n",
403 |        "      <td>Apollo 12</td>\n",
404 |        "      <td>14-Nov-69</td>\n",
405 |        "      <td>1</td>\n",
406 |        "      <td>1</td>\n",
407 |        "    </tr>\n",
408 |        "    <tr>\n",
409 |        "      <th>7</th>\n",
410 |        "      <td>Apollo 13</td>\n",
411 |        "      <td>11-Apr-70</td>\n",
412 |        "      <td>0</td>\n",
413 |        "      <td>1</td>\n",
414 |        "    </tr>\n",
415 |        "    <tr>\n",
416 |        "      <th>8</th>\n",
417 |        "      <td>Apollo 14</td>\n",
418 |        "      <td>31-Jan-71</td>\n",
419 |        "      <td>1</td>\n",
420 |        "      <td>1</td>\n",
421 |        "    </tr>\n",
422 |        "    <tr>\n",
423 |        "      <th>9</th>\n",
424 |        "      <td>Apollo 15</td>\n",
425 |        "      <td>26-Jul-71</td>\n",
426 |        "      <td>1</td>\n",
427 |        "      <td>1</td>\n",
428 |        "    </tr>\n",
429 |        "    <tr>\n",
430 |        "      <th>10</th>\n",
431 |        "      <td>Apollo 16</td>\n",
432 |        "      <td>16-Apr-72</td>\n",
433 |        "      <td>1</td>\n",
434 |        "      <td>1</td>\n",
435 |        "    </tr>\n",
436 |        "    <tr>\n",
437 |        "      <th>11</th>\n",
438 |        "      <td>Apollo 17</td>\n",
439 |        "      <td>07-Dec-72</td>\n",
440 |        "      <td>1</td>\n",
441 |        "      <td>1</td>\n",
442 |        "    </tr>\n",
443 |        "  </tbody>\n",
444 |        "</table>\n",
445 |        "</div>"
446 |       ],
447 |       "text/plain": [
448 |        "      Mission       Date  Landing  Crew Safety\n",
449 |        "0    Apollo 1  21-Feb-67        0            0\n",
450 |        "1    Apollo 7  11-Oct-68        0            1\n",
451 |        "2    Apollo 8  21-Dec-68        0            1\n",
452 |        "3    Apollo 9  03-Mar-69        0            1\n",
453 |        "4   Apollo 10  18-May-69        0            1\n",
454 |        "5   Apollo 11  16-Jul-69        1            1\n",
455 |        "6   Apollo 12  14-Nov-69        1            1\n",
456 |        "7   Apollo 13  11-Apr-70        0            1\n",
457 |        "8   Apollo 14  31-Jan-71        1            1\n",
458 |        "9   Apollo 15  26-Jul-71        1            1\n",
459 |        "10  Apollo 16  16-Apr-72        1            1\n",
460 |        "11  Apollo 17  07-Dec-72        1            1"
461 |       ]
462 |      },
463 |      "execution_count": 15,
464 |      "metadata": {},
465 |      "output_type": "execute_result"
466 |     }
467 |    ],
468 |    "source": [
469 |     "manned_data"
470 |    ]
471 |   },
472 |   {
473 |    "cell_type": "code",
474 |    "execution_count": 16,
475 |    "metadata": {
476 |     "collapsed": true
477 |    },
478 |    "outputs": [],
479 |    "source": [
480 |     "source = ColumnDataSource(manned_data)"
481 |    ]
482 |   },
483 |   {
484 |    "cell_type": "code",
485 |    "execution_count": 6,
486 |    "metadata": {},
487 |    "outputs": [
488 |     {
489 |      "data": {
490 |       "text/html": [
491 |        "\n",
492 |        "<div class=\"bk-root\">\n",
493 |        "    <div class=\"bk-plotdiv\" id=\"aa803cfb-fb51-4643-861e-67b1c7413696\"></div>\n",
494 |        "</div>"
495 |       ]
496 |      },
497 |      "metadata": {},
498 |      "output_type": "display_data"
499 |     },
500 |     {
501 |      "data": {
502 |       "application/javascript": [
503 |        "(function(root) {\n",
504 |        "  function embed_document(root) {\n",
505 |        "    \n",
506 |        "  var docs_json = {\"00d814c7-a6f3-4a79-8f02-d34ca6aa7646\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"22a8d90d-d410-4e4c-9ca9-932ebe6445c0\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"57bf78c9-683e-41b0-bdb7-7b1337c2c76d\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"formatter\":{\"id\":\"57bf78c9-683e-41b0-bdb7-7b1337c2c76d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"3560609d-639f-4a34-804f-6d47ea39a421\",\"type\":\"BasicTicker\"}},\"id\":\"b7460ba2-e1d8-4ad3-a4de-e006a89c2dbe\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"Crew Safety\"},\"y\":{\"field\":\"Landing\"}},\"id\":\"d4db77df-59f5-43f5-840b-92b135a56533\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"3560609d-639f-4a34-804f-6d47ea39a421\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"c21f73f5-fa2f-4b3a-97e8-8f1b9650ab54\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":{\"id\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"3560609d-639f-4a34-804f-6d47ea39a421\",\"type\":\"BasicTicker\"}},\"id\":\"d8266439-9d95-4fc3-a57f-50d25f1eb5c8\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"55d1222e-c1a4-4a83-86af-d33af5769e71\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"b59e4593-56b2-4692-8e7a-8dc81bfcb83c\",\"type\":\"BasicTicker\"}},\"id\":\"39444a33-50dc-4ff6-acbf-c3e9a106187e\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"b59e4593-56b2-4692-8e7a-8dc81bfcb83c\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"b59e4593-56b2-4692-8e7a-8dc81bfcb83c\",\"type\":\"BasicTicker\"}},\"id\":\"b77ab58b-44db-47f8-9c3a-271f97816aed\",\"type\":\"Grid\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"77a59e4a-a4cb-4702-811b-75246a8c48ae\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"a0320ba1-1a9d-439c-8f89-7ddb772fbaa6\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"48063221-cc2b-43f3-86fd-85840d3f990f\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"77a59e4a-a4cb-4702-811b-75246a8c48ae\",\"type\":\"BoxAnnotation\"}},\"id\":\"6240ea19-13c6-4221-ba4d-6ca7fbe176b4\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"040013e5-d4ee-4fc2-8a06-524e7f00dffc\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"e43ea212-0324-4c51-b79c-7868eb4673e3\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"2323e3a7-264f-4b91-8005-1dc1be8ea923\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"d680ad3b-32c0-4791-87bc-499cea599729\",\"type\":\"HelpTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"a0320ba1-1a9d-439c-8f89-7ddb772fbaa6\",\"type\":\"PanTool\"},{\"id\":\"48063221-cc2b-43f3-86fd-85840d3f990f\",\"type\":\"WheelZoomTool\"},{\"id\":\"6240ea19-13c6-4221-ba4d-6ca7fbe176b4\",\"type\":\"BoxZoomTool\"},{\"id\":\"e43ea212-0324-4c51-b79c-7868eb4673e3\",\"type\":\"SaveTool\"},{\"id\":\"2323e3a7-264f-4b91-8005-1dc1be8ea923\",\"type\":\"ResetTool\"},{\"id\":\"d680ad3b-32c0-4791-87bc-499cea599729\",\"type\":\"HelpTool\"}]},\"id\":\"eca00644-bd1d-446a-a69e-52bbab301049\",\"type\":\"Toolbar\"},{\"attributes\":{\"below\":[{\"id\":\"b7460ba2-e1d8-4ad3-a4de-e006a89c2dbe\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"39444a33-50dc-4ff6-acbf-c3e9a106187e\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"b7460ba2-e1d8-4ad3-a4de-e006a89c2dbe\",\"type\":\"LinearAxis\"},{\"id\":\"d8266439-9d95-4fc3-a57f-50d25f1eb5c8\",\"type\":\"Grid\"},{\"id\":\"39444a33-50dc-4ff6-acbf-c3e9a106187e\",\"type\":\"LinearAxis\"},{\"id\":\"b77ab58b-44db-47f8-9c3a-271f97816aed\",\"type\":\"Grid\"},{\"id\":\"77a59e4a-a4cb-4702-811b-75246a8c48ae\",\"type\":\"BoxAnnotation\"},{\"id\":\"2d9cd1d6-d3c3-4a25-9975-f27d603326ed\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"9828fa99-5d5c-44e6-840b-4e3a7fa8e0d6\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"eca00644-bd1d-446a-a69e-52bbab301049\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"c21f73f5-fa2f-4b3a-97e8-8f1b9650ab54\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"e473554b-1f33-4dc9-8deb-8a502828f54e\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"040013e5-d4ee-4fc2-8a06-524e7f00dffc\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"22a8d90d-d410-4e4c-9ca9-932ebe6445c0\",\"type\":\"LinearScale\"}},\"id\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"9828fa99-5d5c-44e6-840b-4e3a7fa8e0d6\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"55d1222e-c1a4-4a83-86af-d33af5769e71\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"Crew Safety\"},\"y\":{\"field\":\"Landing\"}},\"id\":\"176089bf-6233-47dc-9166-df45b9bbc11a\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"Mission\",\"Date\",\"Landing\",\"Crew Safety\",\"index\"],\"data\":{\"Crew Safety\":[0,1,1,1,1,1,1,1,1,1,1,1],\"Date\":[\"21-Feb-67\",\"11-Oct-68\",\"21-Dec-68\",\"03-Mar-69\",\"18-May-69\",\"16-Jul-69\",\"14-Nov-69\",\"11-Apr-70\",\"31-Jan-71\",\"26-Jul-71\",\"16-Apr-72\",\"07-Dec-72\"],\"Landing\":[0,0,0,0,0,1,1,0,1,1,1,1],\"Mission\":[\"Apollo 1\",\"Apollo 7\",\"Apollo 8\",\"Apollo 9\",\"Apollo 10\",\"Apollo 11\",\"Apollo 12\",\"Apollo 13\",\"Apollo 14\",\"Apollo 15\",\"Apollo 16\",\"Apollo 17\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11]}},\"id\":\"9f151c5f-e9e2-4bbf-af2c-9a245b238f86\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"e473554b-1f33-4dc9-8deb-8a502828f54e\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"9f151c5f-e9e2-4bbf-af2c-9a245b238f86\",\"type\":\"ColumnDataSource\"}},\"id\":\"33280ea8-adb1-4e81-9adb-a207a7ae2bcf\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"9f151c5f-e9e2-4bbf-af2c-9a245b238f86\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"176089bf-6233-47dc-9166-df45b9bbc11a\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"d4db77df-59f5-43f5-840b-92b135a56533\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"33280ea8-adb1-4e81-9adb-a207a7ae2bcf\",\"type\":\"CDSView\"}},\"id\":\"2d9cd1d6-d3c3-4a25-9975-f27d603326ed\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
507 |        "  var render_items = [{\"docid\":\"00d814c7-a6f3-4a79-8f02-d34ca6aa7646\",\"elementid\":\"aa803cfb-fb51-4643-861e-67b1c7413696\",\"modelid\":\"4529d39d-09b7-4a87-a905-0d57a2bd6b44\"}];\n",
508 |        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
509 |        "\n",
510 |        "  }\n",
511 |        "  if (root.Bokeh !== undefined) {\n",
512 |        "    embed_document(root);\n",
513 |        "  } else {\n",
514 |        "    var attempts = 0;\n",
515 |        "    var timer = setInterval(function(root) {\n",
516 |        "      if (root.Bokeh !== undefined) {\n",
517 |        "        embed_document(root);\n",
518 |        "        clearInterval(timer);\n",
519 |        "      }\n",
520 |        "      attempts++;\n",
521 |        "      if (attempts > 100) {\n",
522 |        "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
523 |        "        clearInterval(timer);\n",
524 |        "      }\n",
525 |        "    }, 10, root)\n",
526 |        "  }\n",
527 |        "})(window);"
528 |       ],
529 |       "application/vnd.bokehjs_exec.v0+json": ""
530 |      },
531 |      "metadata": {
532 |       "application/vnd.bokehjs_exec.v0+json": {
533 |        "id": "4529d39d-09b7-4a87-a905-0d57a2bd6b44"
534 |       }
535 |      },
536 |      "output_type": "display_data"
537 |     }
538 |    ],
539 |    "source": [
540 |     "p = figure()\n",
541 |     "p.circle(x='Crew Safety', y='Landing', source=source)\n",
542 |     "show(p)"
543 |    ]
544 |   },
545 |   {
546 |    "cell_type": "code",
547 |    "execution_count": 17,
548 |    "metadata": {},
549 |    "outputs": [
550 |     {
551 |      "data": {
552 |       "text/html": [
553 |        "\n",
554 |        "<div class=\"bk-root\">\n",
555 |        "    <div class=\"bk-plotdiv\" id=\"ce46d15b-f92f-4436-a2fa-ca188bc8abd3\"></div>\n",
556 |        "</div>"
557 |       ]
558 |      },
559 |      "metadata": {},
560 |      "output_type": "display_data"
561 |     },
562 |     {
563 |      "data": {
564 |       "application/javascript": [
565 |        "(function(root) {\n",
566 |        "  function embed_document(root) {\n",
567 |        "    \n",
568 |        "  var docs_json = {\"360d4179-25e1-44b5-bd92-edabde27b14b\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"4a23fe1f-e3e1-4003-88e8-b40e56761cb8\",\"type\":\"CategoricalScale\"},{\"attributes\":{},\"id\":\"6d2c27aa-4f32-416f-b6fb-02dd7c846312\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"9e0fbbaa-c6dd-4ed4-9851-e3ef6498fa79\",\"type\":\"VBar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"c5b55ed0-181d-48fc-a79f-2a6e363802e7\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"3fd49114-8c6e-43b5-af24-65eabe9e15e7\",\"type\":\"CategoricalTicker\"},{\"attributes\":{},\"id\":\"7447e214-3e7c-4a3e-bd47-e2c2321cf0d5\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\"},\"id\":\"c5338a10-1de9-47b4-8aef-c6969a8c613c\",\"type\":\"Toolbar\"},{\"attributes\":{\"formatter\":{\"id\":\"486e51ff-495c-4ae0-8648-9889d3c2690c\",\"type\":\"CategoricalTickFormatter\"},\"plot\":{\"id\":\"b0a68410-2732-4bdc-b32f-e4696f8860ec\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"3fd49114-8c6e-43b5-af24-65eabe9e15e7\",\"type\":\"CategoricalTicker\"}},\"id\":\"6ad1460c-c066-4090-8e62-c6d3a0219b44\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"top\"],\"data\":{\"top\":[6,6],\"x\":[\"Landed\",\"Didnot 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Missions\"},\"id\":\"9821cd34-957d-498c-8c2a-a834c0383d32\",\"type\":\"Title\"},{\"attributes\":{\"formatter\":{\"id\":\"6d2c27aa-4f32-416f-b6fb-02dd7c846312\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"b0a68410-2732-4bdc-b32f-e4696f8860ec\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"77333a78-e514-468a-bf93-83e40532ffac\",\"type\":\"BasicTicker\"}},\"id\":\"58f1ee6e-61f7-497d-ad5d-b3752c7d480d\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null,\"start\":0},\"id\":\"d109730c-7bea-4f5f-bde9-7f15f3870c1e\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"46d30202-193b-4320-965e-012bef5ff5fb\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"9e0fbbaa-c6dd-4ed4-9851-e3ef6498fa79\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"c5b55ed0-181d-48fc-a79f-2a6e363802e7\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"6763b5b8-bdef-46e6-ba1d-47f273afca2d\",\"type\":\"CDSView\"}},\"id\":\"28e83379-67d3-4114-9bda-b9de940503f8\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"b0a68410-2732-4bdc-b32f-e4696f8860ec\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"77333a78-e514-468a-bf93-83e40532ffac\",\"type\":\"BasicTicker\"}},\"id\":\"16ea0698-6100-4949-ab99-9382b5d59f70\",\"type\":\"Grid\"},{\"attributes\":{\"below\":[{\"id\":\"6ad1460c-c066-4090-8e62-c6d3a0219b44\",\"type\":\"CategoricalAxis\"}],\"left\":[{\"id\":\"58f1ee6e-61f7-497d-ad5d-b3752c7d480d\",\"type\":\"LinearAxis\"}],\"plot_height\":250,\"renderers\":[{\"id\":\"6ad1460c-c066-4090-8e62-c6d3a0219b44\",\"type\":\"CategoricalAxis\"},{\"id\":\"d65959b0-7810-4868-9c55-a3a6a97e7e03\",\"type\":\"Grid\"},{\"id\":\"58f1ee6e-61f7-497d-ad5d-b3752c7d480d\",\"type\":\"LinearAxis\"},{\"id\":\"16ea0698-6100-4949-ab99-9382b5d59f70\",\"type\":\"Grid\"},{\"id\":\"28e83379-67d3-4114-9bda-b9de940503f8\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"9821cd34-957d-498c-8c2a-a834c0383d32\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"c5338a10-1de9-47b4-8aef-c6969a8c613c\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1b3ff668-0bc6-4e52-85ca-9da5075a665d\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"4a23fe1f-e3e1-4003-88e8-b40e56761cb8\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"d109730c-7bea-4f5f-bde9-7f15f3870c1e\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"7447e214-3e7c-4a3e-bd47-e2c2321cf0d5\",\"type\":\"LinearScale\"}},\"id\":\"b0a68410-2732-4bdc-b32f-e4696f8860ec\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"46d30202-193b-4320-965e-012bef5ff5fb\",\"type\":\"ColumnDataSource\"}},\"id\":\"6763b5b8-bdef-46e6-ba1d-47f273afca2d\",\"type\":\"CDSView\"}],\"root_ids\":[\"b0a68410-2732-4bdc-b32f-e4696f8860ec\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
569 |        "  var render_items = [{\"docid\":\"360d4179-25e1-44b5-bd92-edabde27b14b\",\"elementid\":\"ce46d15b-f92f-4436-a2fa-ca188bc8abd3\",\"modelid\":\"b0a68410-2732-4bdc-b32f-e4696f8860ec\"}];\n",
570 |        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
571 |        "\n",
572 |        "  }\n",
573 |        "  if (root.Bokeh !== undefined) {\n",
574 |        "    embed_document(root);\n",
575 |        "  } else {\n",
576 |        "    var attempts = 0;\n",
577 |        "    var timer = setInterval(function(root) {\n",
578 |        "      if (root.Bokeh !== undefined) {\n",
579 |        "        embed_document(root);\n",
580 |        "        clearInterval(timer);\n",
581 |        "      }\n",
582 |        "      attempts++;\n",
583 |        "      if (attempts > 100) {\n",
584 |        "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
585 |        "        clearInterval(timer);\n",
586 |        "      }\n",
587 |        "    }, 10, root)\n",
588 |        "  }\n",
589 |        "})(window);"
590 |       ],
591 |       "application/vnd.bokehjs_exec.v0+json": ""
592 |      },
593 |      "metadata": {
594 |       "application/vnd.bokehjs_exec.v0+json": {
595 |        "id": "b0a68410-2732-4bdc-b32f-e4696f8860ec"
596 |       }
597 |      },
598 |      "output_type": "display_data"
599 |     }
600 |    ],
601 |    "source": [
602 |     "missions = ['Landed', 'Didnot Land']\n",
603 |     "\n",
604 |     "p = figure(x_range=missions, plot_height=250, title=\"Manned Moon Missions\",\n",
605 |     "           toolbar_location=None, tools=\"\")\n",
606 |     "\n",
607 |     "p.vbar(x=missions, top=[6, 6], width=0.9)\n",
608 |     "\n",
609 |     "p.xgrid.grid_line_color = None\n",
610 |     "p.y_range.start = 0\n",
611 |     "\n",
612 |     "show(p)"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 8,
618 |    "metadata": {},
619 |    "outputs": [
620 |     {
621 |      "name": "stderr",
622 |      "output_type": "stream",
623 |      "text": [
624 |       "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\bokeh\\models\\sources.py:138: BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('color', 6), ('counts', 2), ('missions', 2)\n",
625 |       "  \"Current lengths: %s\" % \", \".join(sorted(str((k, len(v))) for k, v in data.items())), BokehUserWarning))\n"
626 |      ]
627 |     },
628 |     {
629 |      "data": {
630 |       "text/html": [
631 |        "\n",
632 |        "<div class=\"bk-root\">\n",
633 |        "    <div class=\"bk-plotdiv\" id=\"918c1778-442d-4466-81f8-056fbd649f52\"></div>\n",
634 |        "</div>"
635 |       ]
636 |      },
637 |      "metadata": {},
638 |      "output_type": "display_data"
639 |     },
640 |     {
641 |      "data": {
642 |       "application/javascript": [
643 |        "(function(root) {\n",
644 |        "  function embed_document(root) {\n",
645 |        "    \n",
646 |        "  var docs_json = {\"d4a1c431-d87b-49db-9753-5cb4efb04633\":{\"roots\":{\"references\":[{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\"},\"id\":\"d2eec5e9-34e8-4f37-ad0d-60b432af601f\",\"type\":\"Toolbar\"},{\"attributes\":{\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"8c526c39-6a0c-497b-8afc-16f5448555f3\",\"type\":\"CategoricalTicker\"}},\"id\":\"ebdbafde-bd13-422c-886a-f0e109d482b3\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"factors\":[\"No crew Fatalities\",\"Crew Fatalities\"]},\"id\":\"1e6f546a-f35c-4c5a-8364-23ebef4e34fb\",\"type\":\"FactorRange\"},{\"attributes\":{},\"id\":\"9260f62e-b47b-46aa-98a4-7af62fdfc0d4\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"callback\":null,\"end\":2},\"id\":\"5076e21b-2fa0-453b-8db4-742f7d80c28b\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"78052152-348e-400b-9c44-c6c9081a27e5\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"below\":[{\"id\":\"cd8b3e7a-9dd1-4592-8903-eb65ce61a089\",\"type\":\"CategoricalAxis\"}],\"left\":[{\"id\":\"f53e21aa-38c5-4143-89a7-bbf05e4276de\",\"type\":\"LinearAxis\"}],\"plot_height\":250,\"renderers\":[{\"id\":\"cd8b3e7a-9dd1-4592-8903-eb65ce61a089\",\"type\":\"CategoricalAxis\"},{\"id\":\"ebdbafde-bd13-422c-886a-f0e109d482b3\",\"type\":\"Grid\"},{\"id\":\"f53e21aa-38c5-4143-89a7-bbf05e4276de\",\"type\":\"LinearAxis\"},{\"id\":\"0bb96623-9a2a-47a2-948f-cbac58160f2a\",\"type\":\"Grid\"},{\"id\":\"ea64171e-688f-45fb-b5ce-efb66f858ac2\",\"type\":\"Legend\"},{\"id\":\"88f0a593-b720-42df-9276-5a50d051d4ba\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"d8ccb6ce-2c52-42c2-8734-a41fc472c3ad\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"d2eec5e9-34e8-4f37-ad0d-60b432af601f\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1e6f546a-f35c-4c5a-8364-23ebef4e34fb\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"9260f62e-b47b-46aa-98a4-7af62fdfc0d4\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"5076e21b-2fa0-453b-8db4-742f7d80c28b\",\"type\":\"Range1d\"},\"y_scale\":{\"id\":\"095e29aa-c4a8-4bbe-8cdc-f842e5460ef3\",\"type\":\"LinearScale\"}},\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"missions\",\"counts\",\"color\"],\"data\":{\"color\":[\"#3288bd\",\"#99d594\",\"#e6f598\",\"#fee08b\",\"#fc8d59\",\"#d53e4f\"],\"counts\":[11,1],\"missions\":[\"No crew Fatalities\",\"Crew Fatalities\"]}},\"id\":\"eb7201ca-2784-47cd-8829-fd0a9696d79c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":null,\"text\":\"Missions Counts\"},\"id\":\"d8ccb6ce-2c52-42c2-8734-a41fc472c3ad\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2f12c91d-38fa-456b-b61a-60fce2ae3b1c\",\"type\":\"BasicTicker\"}},\"id\":\"0bb96623-9a2a-47a2-948f-cbac58160f2a\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2f12c91d-38fa-456b-b61a-60fce2ae3b1c\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"line_color\":{\"field\":\"color\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"missions\"}},\"id\":\"ed06657f-8b23-4af4-9c29-10f1dc8fd96b\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"8c526c39-6a0c-497b-8afc-16f5448555f3\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"78052152-348e-400b-9c44-c6c9081a27e5\",\"type\":\"CategoricalTickFormatter\"},\"plot\":{\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"8c526c39-6a0c-497b-8afc-16f5448555f3\",\"type\":\"CategoricalTicker\"}},\"id\":\"cd8b3e7a-9dd1-4592-8903-eb65ce61a089\",\"type\":\"CategoricalAxis\"},{\"attributes\":{},\"id\":\"8507adf6-fa81-4d16-9ca4-4354b899f7e7\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"missions\"}},\"id\":\"92a59455-bfa0-423e-bf61-8eb57ec2b716\",\"type\":\"VBar\"},{\"attributes\":{\"formatter\":{\"id\":\"8507adf6-fa81-4d16-9ca4-4354b899f7e7\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2f12c91d-38fa-456b-b61a-60fce2ae3b1c\",\"type\":\"BasicTicker\"}},\"id\":\"f53e21aa-38c5-4143-89a7-bbf05e4276de\",\"type\":\"LinearAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"eb7201ca-2784-47cd-8829-fd0a9696d79c\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ed06657f-8b23-4af4-9c29-10f1dc8fd96b\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"92a59455-bfa0-423e-bf61-8eb57ec2b716\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"71faa0fe-5d8c-4b60-8694-2be615188cf6\",\"type\":\"CDSView\"}},\"id\":\"88f0a593-b720-42df-9276-5a50d051d4ba\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"eb7201ca-2784-47cd-8829-fd0a9696d79c\",\"type\":\"ColumnDataSource\"}},\"id\":\"71faa0fe-5d8c-4b60-8694-2be615188cf6\",\"type\":\"CDSView\"},{\"attributes\":{\"label\":{\"field\":\"missions\"},\"renderers\":[{\"id\":\"88f0a593-b720-42df-9276-5a50d051d4ba\",\"type\":\"GlyphRenderer\"}]},\"id\":\"0a8fd78f-e571-443e-ac14-49ff204d2df5\",\"type\":\"LegendItem\"},{\"attributes\":{\"items\":[{\"id\":\"0a8fd78f-e571-443e-ac14-49ff204d2df5\",\"type\":\"LegendItem\"}],\"orientation\":\"horizontal\",\"plot\":{\"id\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"ea64171e-688f-45fb-b5ce-efb66f858ac2\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"095e29aa-c4a8-4bbe-8cdc-f842e5460ef3\",\"type\":\"LinearScale\"}],\"root_ids\":[\"13d6484a-bdee-4343-9d91-02421f98b3ad\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
647 |        "  var render_items = [{\"docid\":\"d4a1c431-d87b-49db-9753-5cb4efb04633\",\"elementid\":\"918c1778-442d-4466-81f8-056fbd649f52\",\"modelid\":\"13d6484a-bdee-4343-9d91-02421f98b3ad\"}];\n",
648 |        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
649 |        "\n",
650 |        "  }\n",
651 |        "  if (root.Bokeh !== undefined) {\n",
652 |        "    embed_document(root);\n",
653 |        "  } else {\n",
654 |        "    var attempts = 0;\n",
655 |        "    var timer = setInterval(function(root) {\n",
656 |        "      if (root.Bokeh !== undefined) {\n",
657 |        "        embed_document(root);\n",
658 |        "        clearInterval(timer);\n",
659 |        "      }\n",
660 |        "      attempts++;\n",
661 |        "      if (attempts > 100) {\n",
662 |        "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
663 |        "        clearInterval(timer);\n",
664 |        "      }\n",
665 |        "    }, 10, root)\n",
666 |        "  }\n",
667 |        "})(window);"
668 |       ],
669 |       "application/vnd.bokehjs_exec.v0+json": ""
670 |      },
671 |      "metadata": {
672 |       "application/vnd.bokehjs_exec.v0+json": {
673 |        "id": "13d6484a-bdee-4343-9d91-02421f98b3ad"
674 |       }
675 |      },
676 |      "output_type": "display_data"
677 |     }
678 |    ],
679 |    "source": [
680 |     "from bokeh.palettes import Spectral6\n",
681 |     "\n",
682 |     "missions = ['No crew Fatalities', 'Crew Fatalities']\n",
683 |     "counts = [11,1]\n",
684 |     "\n",
685 |     "source = ColumnDataSource(data=dict(missions=missions, counts=counts, color=Spectral6))\n",
686 |     "\n",
687 |     "p = figure(x_range=missions, y_range=(0,2), plot_height=250, title=\"Missions Counts\",\n",
688 |     "           toolbar_location=None, tools=\"\")\n",
689 |     "\n",
690 |     "p.vbar(x='missions', top='counts', width=0.9, color='color', legend=\"missions\", source=source)\n",
691 |     "\n",
692 |     "p.xgrid.grid_line_color = None\n",
693 |     "p.legend.orientation = \"horizontal\"\n",
694 |     "p.legend.location = \"top_right\"\n",
695 |     "\n",
696 |     "show(p)"
697 |    ]
698 |   },
699 |   {
700 |    "cell_type": "code",
701 |    "execution_count": 9,
702 |    "metadata": {},
703 |    "outputs": [
704 |     {
705 |      "data": {
706 |       "text/html": [
707 |        "\n",
708 |        "<div class=\"bk-root\">\n",
709 |        "    <div class=\"bk-plotdiv\" id=\"65d8c81f-26e2-49e3-bf54-f709fdad8af9\"></div>\n",
710 |        "</div>"
711 |       ]
712 |      },
713 |      "metadata": {},
714 |      "output_type": "display_data"
715 |     },
716 |     {
717 |      "data": {
718 |       "application/javascript": [
719 |        "(function(root) {\n",
720 |        "  function embed_document(root) {\n",
721 |        "    \n",
722 |        "  var docs_json = {\"90c6632c-9c40-473f-8d2c-56a6b8b7d345\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"2ac6c7aa-2f4d-47a6-bc6c-256db7046a81\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"a60984dc-0c72-4740-86f5-06b8d0c523da\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a60984dc-0c72-4740-86f5-06b8d0c523da\",\"type\":\"BasicTicker\"}},\"id\":\"4368d364-49a6-4943-8e07-0c2a52d8f8e8\",\"type\":\"Grid\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"77b7946e-ea61-4954-b2c7-92a922e99d0d\",\"type\":\"VBar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"4a402c15-690e-4c6e-a365-096db77d9823\",\"type\":\"VBar\"},{\"attributes\":{\"data_source\":{\"id\":\"28c8b65c-fe33-4955-b4c0-912b12c8c495\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"77b7946e-ea61-4954-b2c7-92a922e99d0d\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"4a402c15-690e-4c6e-a365-096db77d9823\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"f7134c8d-33fd-4414-9e58-33de679c7660\",\"type\":\"CDSView\"}},\"id\":\"3e9fc4d2-3aa3-4893-99ca-1bb103dacda9\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"28c8b65c-fe33-4955-b4c0-912b12c8c495\",\"type\":\"ColumnDataSource\"}},\"id\":\"f7134c8d-33fd-4414-9e58-33de679c7660\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"2a5b8f49-0545-4051-9ac5-4df49989bce0\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"callback\":null,\"factors\":[[\"India\",\"2018\"],[\"India\",\"2019\"],[\"India\",\"2021\"],[\"India\",\"2022\"],[\"India\",\"2023\"],[\"USA\",\"2018\"],[\"USA\",\"2019\"],[\"USA\",\"2021\"],[\"USA\",\"2022\"],[\"USA\",\"2023\"],[\"China\",\"2018\"],[\"China\",\"2019\"],[\"China\",\"2021\"],[\"China\",\"2022\"],[\"China\",\"2023\"],[\"Private\",\"2018\"],[\"Private\",\"2019\"],[\"Private\",\"2021\"],[\"Private\",\"2022\"],[\"Private\",\"2023\"],[\"Japan\",\"2018\"],[\"Japan\",\"2019\"],[\"Japan\",\"2021\"],[\"Japan\",\"2022\"],[\"Japan\",\"2023\"]],\"range_padding\":0.1},\"id\":\"3baf40a2-60b5-430e-ad55-6502e8acccde\",\"type\":\"FactorRange\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"counts\"],\"data\":{\"counts\":[1,0,0,0,0,1,10,0,0,1,1,1,0,0,0,1,2,0,0,0,0,2,1,1,0],\"x\":[[\"India\",\"2018\"],[\"India\",\"2019\"],[\"India\",\"2021\"],[\"India\",\"2022\"],[\"India\",\"2023\"],[\"USA\",\"2018\"],[\"USA\",\"2019\"],[\"USA\",\"2021\"],[\"USA\",\"2022\"],[\"USA\",\"2023\"],[\"China\",\"2018\"],[\"China\",\"2019\"],[\"China\",\"2021\"],[\"China\",\"2022\"],[\"China\",\"2023\"],[\"Private\",\"2018\"],[\"Private\",\"2019\"],[\"Private\",\"2021\"],[\"Private\",\"2022\"],[\"Private\",\"2023\"],[\"Japan\",\"2018\"],[\"Japan\",\"2019\"],[\"Japan\",\"2021\"],[\"Japan\",\"2022\"],[\"Japan\",\"2023\"]]}},\"id\":\"28c8b65c-fe33-4955-b4c0-912b12c8c495\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"below\":[{\"id\":\"55bf4f91-1bc0-4a59-9f3d-b307ab2814c5\",\"type\":\"CategoricalAxis\"}],\"left\":[{\"id\":\"1b434cc6-c090-476e-802c-8b254a551569\",\"type\":\"LinearAxis\"}],\"plot_height\":250,\"renderers\":[{\"id\":\"55bf4f91-1bc0-4a59-9f3d-b307ab2814c5\",\"type\":\"CategoricalAxis\"},{\"id\":\"4931ed3f-4e1e-4c22-bcd0-a53777f02494\",\"type\":\"Grid\"},{\"id\":\"1b434cc6-c090-476e-802c-8b254a551569\",\"type\":\"LinearAxis\"},{\"id\":\"4368d364-49a6-4943-8e07-0c2a52d8f8e8\",\"type\":\"Grid\"},{\"id\":\"3e9fc4d2-3aa3-4893-99ca-1bb103dacda9\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"53978f9b-4515-4a49-adba-1839ae9feae8\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"fdaab5f4-937e-441b-9e14-bb9745f11e1e\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"3baf40a2-60b5-430e-ad55-6502e8acccde\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"1f94e4f4-8ca7-4c1b-8f8d-0b1e83b7f210\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"12be2d8b-239f-484e-9948-d34db5f01823\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"6c8cec45-9aec-40c4-816e-ea573373a7bd\",\"type\":\"LinearScale\"}},\"id\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null,\"start\":0},\"id\":\"12be2d8b-239f-484e-9948-d34db5f01823\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":null,\"text\":\"Mission Counts by Year\"},\"id\":\"53978f9b-4515-4a49-adba-1839ae9feae8\",\"type\":\"Title\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\"},\"id\":\"fdaab5f4-937e-441b-9e14-bb9745f11e1e\",\"type\":\"Toolbar\"},{\"attributes\":{\"formatter\":{\"id\":\"2ac6c7aa-2f4d-47a6-bc6c-256db7046a81\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a60984dc-0c72-4740-86f5-06b8d0c523da\",\"type\":\"BasicTicker\"}},\"id\":\"1b434cc6-c090-476e-802c-8b254a551569\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"6c8cec45-9aec-40c4-816e-ea573373a7bd\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1f94e4f4-8ca7-4c1b-8f8d-0b1e83b7f210\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fa7d1e2c-fa77-4551-b86b-e1eaa086123a\",\"type\":\"CategoricalTicker\"}},\"id\":\"4931ed3f-4e1e-4c22-bcd0-a53777f02494\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"2a5b8f49-0545-4051-9ac5-4df49989bce0\",\"type\":\"CategoricalTickFormatter\"},\"major_label_orientation\":1,\"plot\":{\"id\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fa7d1e2c-fa77-4551-b86b-e1eaa086123a\",\"type\":\"CategoricalTicker\"}},\"id\":\"55bf4f91-1bc0-4a59-9f3d-b307ab2814c5\",\"type\":\"CategoricalAxis\"},{\"attributes\":{},\"id\":\"fa7d1e2c-fa77-4551-b86b-e1eaa086123a\",\"type\":\"CategoricalTicker\"}],\"root_ids\":[\"dff097a8-0673-4b38-aed4-c48ccd8659b3\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
723 |        "  var render_items = [{\"docid\":\"90c6632c-9c40-473f-8d2c-56a6b8b7d345\",\"elementid\":\"65d8c81f-26e2-49e3-bf54-f709fdad8af9\",\"modelid\":\"dff097a8-0673-4b38-aed4-c48ccd8659b3\"}];\n",
724 |        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
725 |        "\n",
726 |        "  }\n",
727 |        "  if (root.Bokeh !== undefined) {\n",
728 |        "    embed_document(root);\n",
729 |        "  } else {\n",
730 |        "    var attempts = 0;\n",
731 |        "    var timer = setInterval(function(root) {\n",
732 |        "      if (root.Bokeh !== undefined) {\n",
733 |        "        embed_document(root);\n",
734 |        "        clearInterval(timer);\n",
735 |        "      }\n",
736 |        "      attempts++;\n",
737 |        "      if (attempts > 100) {\n",
738 |        "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
739 |        "        clearInterval(timer);\n",
740 |        "      }\n",
741 |        "    }, 10, root)\n",
742 |        "  }\n",
743 |        "})(window);"
744 |       ],
745 |       "application/vnd.bokehjs_exec.v0+json": ""
746 |      },
747 |      "metadata": {
748 |       "application/vnd.bokehjs_exec.v0+json": {
749 |        "id": "dff097a8-0673-4b38-aed4-c48ccd8659b3"
750 |       }
751 |      },
752 |      "output_type": "display_data"
753 |     }
754 |    ],
755 |    "source": [
756 |     "from bokeh.models import FactorRange\n",
757 |     "\n",
758 |     "countries = ['India', 'USA', 'China', 'Private', 'Japan']\n",
759 |     "years = ['2018', '2019', '2021', '2022', '2023']\n",
760 |     "\n",
761 |     "data = {'countries' : countries,\n",
762 |     "        '2018'   : [1, 1, 1, 1, 0],\n",
763 |     "        '2019'   : [0, 10, 1, 2, 2],\n",
764 |     "        '2021'   : [0, 0, 0, 0, 1],\n",
765 |     "        '2022'   : [0, 0, 0, 0, 1],\n",
766 |     "        '2023'   : [0, 1, 0, 0, 0]}\n",
767 |     "\n",
768 |     "\n",
769 |     "x = [ (country, year) for country in countries for year in years ]\n",
770 |     "counts = sum(zip(data['2018'], data['2019'], data['2021'], data['2022'], data['2023']), ()) # like an hstack\n",
771 |     "\n",
772 |     "source = ColumnDataSource(data=dict(x=x, counts=counts))\n",
773 |     "\n",
774 |     "p = figure(x_range=FactorRange(*x), plot_height=250, title=\"Mission Counts by Year\",\n",
775 |     "           toolbar_location=None, tools=\"\")\n",
776 |     "\n",
777 |     "p.vbar(x='x', top='counts', width=0.9, source=source)\n",
778 |     "\n",
779 |     "p.y_range.start = 0\n",
780 |     "p.x_range.range_padding = 0.1\n",
781 |     "p.xaxis.major_label_orientation = 1\n",
782 |     "p.xgrid.grid_line_color = None\n",
783 |     "\n",
784 |     "show(p)"
785 |    ]
786 |   },
787 |   {
788 |    "cell_type": "code",
789 |    "execution_count": null,
790 |    "metadata": {
791 |     "collapsed": true
792 |    },
793 |    "outputs": [],
794 |    "source": []
795 |   }
796 |  ],
797 |  "metadata": {
798 |   "kernelspec": {
799 |    "display_name": "Python 3",
800 |    "language": "python",
801 |    "name": "python3"
802 |   },
803 |   "language_info": {
804 |    "codemirror_mode": {
805 |     "name": "ipython",
806 |     "version": 3
807 |    },
808 |    "file_extension": ".py",
809 |    "mimetype": "text/x-python",
810 |    "name": "python",
811 |    "nbconvert_exporter": "python",
812 |    "pygments_lexer": "ipython3",
813 |    "version": "3.6.0"
814 |   }
815 |  },
816 |  "nbformat": 4,
817 |  "nbformat_minor": 1
818 | }
819 | 


--------------------------------------------------------------------------------
/Section 3/README.md:
--------------------------------------------------------------------------------
1 | Section 3 Code Examples


--------------------------------------------------------------------------------
/Section 3/manned.csv:
--------------------------------------------------------------------------------
 1 | Mission,Date,Landing,Crew Safety
 2 | Apollo 1,21-Feb-67,0,0
 3 | Apollo 7,11-Oct-68,0,1
 4 | Apollo 8,21-Dec-68,0,1
 5 | Apollo 9,03-Mar-69,0,1
 6 | Apollo 10,18-May-69,0,1
 7 | Apollo 11,16-Jul-69,1,1
 8 | Apollo 12,14-Nov-69,1,1
 9 | Apollo 13,11-Apr-70,0,1
10 | Apollo 14,31-Jan-71,1,1
11 | Apollo 15,26-Jul-71,1,1
12 | Apollo 16,16-Apr-72,1,1
13 | Apollo 17,07-Dec-72,1,1
14 | 


--------------------------------------------------------------------------------
/Section 3/weather/data/2015_weather.csv:
--------------------------------------------------------------------------------
   1 | airport,date,actual_max_temp,average_max_temp,record_max_temp,actual_min_temp,average_min_temp,record_min_temp
   2 | BOS,2015-06-10,82,74,96,62,58,45
   3 | BOS,2015-04-28,64,60,93,42,45,30
   4 | SEA,2015-02-03,50,49,63,42,37,8
   5 | SEA,2015-11-28,45,48,59,27,37,17
   6 | SEA,2015-11-27,49,48,58,29,38,19
   7 | AUS,2015-06-26,92,94,106,74,71,62
   8 | BOS,2015-11-15,54,52,78,37,38,17
   9 | BOS,2015-11-26,59,48,67,38,35,12
  10 | SEA,2015-03-23,52,55,68,42,40,28
  11 | BOS,2015-02-08,29,38,66,13,24,-12
  12 | SEA,2015-06-15,86,70,88,61,52,43
  13 | BOS,2015-05-23,65,68,93,44,52,40
  14 | AUS,2015-06-17,87,93,104,67,70,62
  15 | BOS,2015-02-19,30,40,65,10,25,4
  16 | BOS,2015-11-04,60,55,78,49,41,21
  17 | SEA,2015-08-27,85,75,88,58,55,44
  18 | BOS,2015-05-14,69,65,86,49,49,37
  19 | AUS,2015-04-17,74,80,100,65,55,31
  20 | BOS,2015-02-22,39,40,66,28,26,2
  21 | AUS,2015-04-02,83,77,91,67,51,38
  22 | SEA,2015-08-05,74,77,93,54,57,49
  23 | BOS,2015-07-15,82,82,98,67,66,54
  24 | BOS,2015-03-14,39,45,76,31,30,12
  25 | SEA,2015-05-05,58,63,86,45,45,36
  26 | AUS,2015-07-28,99,96,104,73,72,68
  27 | AUS,2015-02-28,37,68,87,31,42,24
  28 | BOS,2015-07-04,72,81,104,62,64,53
  29 | BOS,2015-03-05,39,42,72,18,28,-4
  30 | SEA,2015-12-24,42,45,62,36,35,16
  31 | AUS,2015-02-17,54,65,92,30,40,22
  32 | AUS,2015-05-29,87,90,98,64,67,54
  33 | BOS,2015-10-16,61,61,88,50,46,30
  34 | AUS,2015-02-02,47,63,85,33,37,8
  35 | SEA,2015-12-22,46,45,57,37,35,14
  36 | BOS,2015-08-05,85,81,100,65,66,54
  37 | SEA,2015-06-18,76,70,94,57,52,43
  38 | BOS,2015-06-17,69,77,95,59,60,48
  39 | AUS,2015-12-19,67,63,84,28,36,24
  40 | AUS,2015-06-04,89,91,99,63,69,58
  41 | BOS,2015-06-20,75,78,98,58,61,47
  42 | SEA,2015-05-14,64,65,87,49,47,36
  43 | SEA,2015-11-15,48,51,62,36,40,6
  44 | SEA,2015-05-23,61,66,90,53,49,37
  45 | AUS,2015-08-26,95,95,103,69,70,60
  46 | SEA,2015-09-03,65,74,92,51,54,45
  47 | SEA,2015-01-27,52,48,58,47,37,11
  48 | SEA,2015-09-16,68,71,91,50,52,44
  49 | SEA,2015-04-14,53,58,83,37,42,33
  50 | BOS,2015-10-15,62,62,86,47,47,31
  51 | AUS,2015-06-10,92,92,100,68,70,57
  52 | BOS,2015-10-04,56,66,86,51,51,34
  53 | AUS,2015-06-01,87,90,100,60,68,56
  54 | AUS,2015-10-23,80,80,92,70,54,38
  55 | SEA,2015-08-15,71,77,96,57,56,47
  56 | SEA,2015-10-28,57,56,66,52,44,30
  57 | AUS,2015-09-09,93,92,100,73,67,54
  58 | AUS,2015-08-23,99,96,105,72,71,63
  59 | SEA,2015-07-23,79,77,99,58,56,47
  60 | SEA,2015-01-11,49,47,59,45,37,12
  61 | AUS,2015-07-21,95,96,102,71,72,66
  62 | AUS,2015-02-23,37,66,94,30,41,24
  63 | SEA,2015-09-25,60,68,85,55,50,36
  64 | BOS,2015-07-03,76,81,102,63,64,51
  65 | AUS,2015-02-18,64,65,89,25,40,25
  66 | SEA,2015-03-06,59,52,68,38,38,26
  67 | AUS,2015-01-19,72,62,86,29,36,14
  68 | AUS,2015-07-07,92,94,104,77,71,64
  69 | AUS,2015-02-09,81,64,87,45,38,23
  70 | SEA,2015-03-13,63,53,70,46,39,28
  71 | BOS,2015-03-22,39,47,83,21,33,8
  72 | BOS,2015-09-14,72,73,92,57,58,40
  73 | AUS,2015-07-12,93,95,106,69,72,64
  74 | BOS,2015-08-02,88,81,102,68,66,54
  75 | BOS,2015-09-05,73,76,93,58,61,48
  76 | BOS,2015-04-01,47,50,76,30,36,13
  77 | AUS,2015-12-10,79,64,83,45,38,18
  78 | SEA,2015-11-29,35,48,59,28,37,18
  79 | SEA,2015-10-06,65,63,78,50,48,37
  80 | AUS,2015-01-28,80,62,80,36,37,18
  81 | SEA,2015-12-09,54,46,61,46,36,18
  82 | BOS,2015-06-30,75,80,95,60,64,52
  83 | SEA,2015-05-24,64,66,89,52,49,37
  84 | BOS,2015-09-27,64,68,86,48,53,37
  85 | SEA,2015-01-20,50,48,64,38,37,16
  86 | AUS,2015-06-20,84,93,103,71,71,63
  87 | BOS,2015-01-25,38,36,59,19,22,-5
  88 | SEA,2015-10-16,68,60,70,48,46,35
  89 | SEA,2015-01-31,45,49,61,38,37,0
  90 | BOS,2015-10-21,61,60,82,53,45,30
  91 | SEA,2015-10-09,67,62,79,54,47,35
  92 | AUS,2015-11-23,64,69,87,27,43,27
  93 | SEA,2015-04-04,55,57,75,39,41,31
  94 | AUS,2015-08-07,99,97,106,74,72,70
  95 | BOS,2015-12-22,60,39,62,48,26,1
  96 | BOS,2015-10-03,54,66,85,49,51,35
  97 | AUS,2015-03-29,82,75,95,53,50,30
  98 | AUS,2015-10-28,81,78,89,51,52,33
  99 | BOS,2015-02-04,38,37,64,14,23,-2
 100 | BOS,2015-12-15,61,41,66,48,28,-2
 101 | BOS,2015-05-07,79,63,95,53,47,37
 102 | AUS,2015-03-30,80,76,91,61,50,31
 103 | AUS,2015-10-31,73,77,90,63,51,36
 104 | SEA,2015-01-09,50,46,56,38,37,13
 105 | BOS,2015-02-15,20,39,61,-2,25,-14
 106 | BOS,2015-11-06,73,55,73,62,41,21
 107 | AUS,2015-03-07,60,69,90,34,44,25
 108 | SEA,2015-01-18,57,48,60,45,37,9
 109 | AUS,2015-07-26,96,96,106,70,72,69
 110 | SEA,2015-02-24,52,51,62,36,37,24
 111 | BOS,2015-01-12,38,36,61,33,22,-10
 112 | AUS,2015-10-29,82,78,88,47,52,33
 113 | BOS,2015-08-25,81,78,96,67,64,46
 114 | BOS,2015-01-07,26,36,63,4,23,-2
 115 | AUS,2015-08-29,94,95,108,65,70,62
 116 | AUS,2015-07-08,90,95,105,76,72,68
 117 | SEA,2015-05-09,80,63,81,49,46,38
 118 | BOS,2015-07-24,81,82,98,66,66,54
 119 | BOS,2015-03-25,49,48,78,28,34,10
 120 | AUS,2015-05-16,88,87,97,66,64,48
 121 | AUS,2015-07-19,96,96,106,74,72,64
 122 | AUS,2015-12-06,69,65,83,38,39,19
 123 | AUS,2015-04-25,91,82,93,66,57,42
 124 | AUS,2015-05-25,84,89,100,66,66,58
 125 | BOS,2015-04-06,42,52,82,36,38,20
 126 | BOS,2015-08-16,90,80,97,67,65,47
 127 | BOS,2015-04-13,69,55,86,41,40,20
 128 | AUS,2015-01-21,56,62,81,48,37,17
 129 | AUS,2015-12-20,68,63,84,46,36,24
 130 | SEA,2015-02-13,60,50,60,44,37,18
 131 | SEA,2015-10-12,65,61,72,51,47,40
 132 | AUS,2015-01-30,58,63,88,44,37,0
 133 | AUS,2015-12-31,55,62,82,36,36,18
 134 | SEA,2015-02-06,58,49,64,50,37,18
 135 | BOS,2015-10-01,59,67,90,54,52,36
 136 | BOS,2015-11-10,53,53,78,44,40,25
 137 | SEA,2015-06-07,88,68,90,60,51,44
 138 | AUS,2015-08-13,102,97,103,75,72,59
 139 | AUS,2015-08-24,98,96,107,72,71,61
 140 | BOS,2015-04-22,67,58,88,43,43,26
 141 | SEA,2015-10-30,63,55,66,53,43,30
 142 | SEA,2015-11-03,51,54,74,41,43,29
 143 | AUS,2015-11-11,87,73,86,64,47,26
 144 | AUS,2015-03-26,69,75,91,47,49,26
 145 | BOS,2015-12-12,61,42,68,49,29,2
 146 | SEA,2015-06-12,68,69,85,53,51,38
 147 | AUS,2015-09-07,99,93,102,70,68,50
 148 | BOS,2015-04-09,37,53,85,33,39,24
 149 | SEA,2015-08-22,80,76,86,54,56,45
 150 | AUS,2015-09-12,90,91,102,63,67,56
 151 | AUS,2015-04-10,71,79,93,55,53,34
 152 | SEA,2015-07-01,90,73,94,63,54,45
 153 | SEA,2015-08-19,89,76,89,61,56,48
 154 | AUS,2015-03-11,59,70,92,54,45,20
 155 | AUS,2015-09-29,93,87,99,65,61,45
 156 | SEA,2015-07-10,70,75,90,62,55,46
 157 | BOS,2015-03-11,57,44,67,37,30,9
 158 | AUS,2015-07-27,97,96,104,73,72,67
 159 | AUS,2015-05-19,87,87,97,73,65,46
 160 | SEA,2015-11-30,42,48,59,25,37,21
 161 | AUS,2015-01-04,51,62,80,29,36,15
 162 | SEA,2015-12-21,42,45,56,37,35,12
 163 | AUS,2015-10-11,95,84,95,59,58,43
 164 | SEA,2015-04-22,60,60,78,41,43,35
 165 | AUS,2015-01-15,58,62,78,35,36,17
 166 | AUS,2015-08-28,96,95,110,64,70,61
 167 | BOS,2015-06-07,67,73,97,53,57,45
 168 | SEA,2015-05-31,77,67,90,53,50,35
 169 | BOS,2015-07-09,69,81,99,63,65,53
 170 | AUS,2015-08-19,97,96,106,76,71,65
 171 | BOS,2015-06-12,79,75,96,65,59,46
 172 | AUS,2015-09-21,94,89,102,69,64,54
 173 | SEA,2015-11-25,45,49,59,32,38,18
 174 | BOS,2015-06-25,81,79,97,64,62,48
 175 | BOS,2015-11-17,45,51,76,33,38,14
 176 | SEA,2015-06-20,77,71,88,55,53,43
 177 | SEA,2015-11-12,52,52,60,41,41,13
 178 | BOS,2015-11-20,61,50,74,44,37,15
 179 | SEA,2015-09-19,70,70,82,58,52,40
 180 | AUS,2015-12-29,50,62,80,35,36,20
 181 | SEA,2015-09-04,65,74,87,50,54,42
 182 | AUS,2015-06-22,87,93,103,71,71,64
 183 | BOS,2015-06-22,77,78,97,61,62,46
 184 | SEA,2015-03-25,58,55,64,45,40,31
 185 | BOS,2015-10-22,73,59,83,52,45,28
 186 | BOS,2015-05-25,81,69,92,58,53,40
 187 | AUS,2015-06-15,89,92,105,70,70,62
 188 | BOS,2015-12-06,59,44,72,36,32,8
 189 | SEA,2015-08-29,72,75,91,56,55,44
 190 | AUS,2015-03-09,54,70,86,48,45,29
 191 | BOS,2015-05-16,71,66,93,55,50,34
 192 | AUS,2015-04-19,89,81,99,55,56,40
 193 | BOS,2015-02-24,19,41,70,2,26,-7
 194 | AUS,2015-03-18,78,72,90,60,47,31
 195 | SEA,2015-01-05,54,46,55,49,36,15
 196 | SEA,2015-08-03,83,77,90,63,57,46
 197 | BOS,2015-01-09,30,36,64,19,22,-4
 198 | BOS,2015-07-13,77,82,97,67,66,56
 199 | SEA,2015-07-24,73,77,95,56,56,43
 200 | SEA,2015-05-07,69,63,81,43,46,33
 201 | AUS,2015-02-26,52,67,88,26,42,23
 202 | SEA,2015-02-28,54,51,68,38,38,24
 203 | BOS,2015-03-07,38,43,69,17,29,2
 204 | AUS,2015-06-19,84,93,103,71,70,58
 205 | AUS,2015-02-15,67,65,83,50,39,23
 206 | SEA,2015-03-03,51,52,69,32,38,20
 207 | BOS,2015-03-30,45,50,81,30,35,10
 208 | AUS,2015-02-04,57,63,84,42,38,20
 209 | BOS,2015-07-27,86,82,98,68,66,52
 210 | BOS,2015-03-29,42,49,86,26,35,4
 211 | BOS,2015-09-11,73,74,99,63,59,40
 212 | BOS,2015-08-07,73,81,98,64,66,55
 213 | AUS,2015-10-16,90,82,92,52,56,41
 214 | AUS,2015-04-29,75,83,93,49,59,42
 215 | BOS,2015-09-08,96,75,96,72,60,48
 216 | SEA,2015-10-18,59,59,70,55,45,32
 217 | AUS,2015-10-08,88,85,96,63,58,44
 218 | SEA,2015-04-30,63,61,85,46,44,32
 219 | SEA,2015-05-12,60,64,81,51,47,38
 220 | SEA,2015-12-02,51,47,57,40,37,25
 221 | AUS,2015-10-03,85,86,96,51,60,49
 222 | SEA,2015-05-21,78,66,93,53,48,39
 223 | BOS,2015-11-29,44,47,69,32,34,8
 224 | SEA,2015-06-26,89,72,90,64,53,46
 225 | AUS,2015-06-08,92,91,99,64,69,58
 226 | AUS,2015-06-30,91,94,100,71,71,63
 227 | SEA,2015-01-25,63,48,63,45,37,7
 228 | BOS,2015-01-28,24,36,63,13,22,-5
 229 | SEA,2015-09-14,62,71,93,51,53,46
 230 | SEA,2015-04-16,64,59,81,39,42,34
 231 | SEA,2015-11-02,52,54,72,45,43,31
 232 | BOS,2015-10-13,72,62,87,55,47,30
 233 | SEA,2015-04-03,52,56,71,41,41,32
 234 | BOS,2015-12-09,46,43,70,37,31,-8
 235 | AUS,2015-08-02,98,97,106,67,72,64
 236 | BOS,2015-10-06,64,65,86,48,50,31
 237 | AUS,2015-06-03,89,90,98,62,68,58
 238 | AUS,2015-10-25,65,80,92,58,53,32
 239 | SEA,2015-08-17,81,76,96,57,56,45
 240 | BOS,2015-05-02,50,62,91,39,46,33
 241 | AUS,2015-08-12,103,97,104,74,72,64
 242 | SEA,2015-01-02,42,46,56,32,36,6
 243 | BOS,2015-10-17,56,61,89,40,46,27
 244 | SEA,2015-01-17,56,47,56,38,37,15
 245 | AUS,2015-07-23,98,96,103,74,72,62
 246 | AUS,2015-02-21,78,66,101,55,41,30
 247 | SEA,2015-09-23,69,68,83,47,51,40
 248 | SEA,2015-02-23,55,51,61,33,37,27
 249 | SEA,2015-03-04,55,52,68,31,38,11
 250 | BOS,2016-01-01,41,37,70,33,24,-3
 251 | AUS,2015-07-05,91,94,105,73,71,64
 252 | SEA,2015-03-15,51,54,65,43,39,30
 253 | BOS,2015-03-20,32,46,79,23,32,10
 254 | BOS,2015-09-16,81,72,96,65,57,42
 255 | AUS,2015-07-14,95,95,105,73,72,67
 256 | AUS,2015-04-20,72,81,93,53,56,33
 257 | BOS,2015-09-03,82,76,97,70,62,47
 258 | BOS,2015-04-03,59,51,77,45,37,21
 259 | BOS,2015-06-03,56,72,98,47,56,44
 260 | AUS,2015-12-16,65,63,80,37,37,18
 261 | SEA,2015-10-04,73,64,76,50,48,38
 262 | BOS,2015-04-16,60,56,87,41,41,28
 263 | AUS,2015-12-25,73,63,91,51,36,11
 264 | SEA,2015-10-15,70,60,77,49,46,35
 265 | AUS,2015-09-23,93,89,101,62,63,45
 266 | SEA,2015-07-28,82,77,97,57,57,47
 267 | BOS,2015-01-27,19,36,63,14,22,-6
 268 | BOS,2015-04-30,55,61,86,44,45,31
 269 | SEA,2015-06-02,64,68,94,55,50,40
 270 | SEA,2015-10-29,59,55,65,54,43,31
 271 | AUS,2015-08-14,99,97,104,74,72,62
 272 | AUS,2015-08-22,97,96,103,74,71,65
 273 | BOS,2015-04-27,57,60,92,45,44,33
 274 | AUS,2015-08-05,99,97,105,77,72,64
 275 | SEA,2015-10-24,59,57,72,48,44,33
 276 | AUS,2015-11-16,76,71,84,66,45,26
 277 | SEA,2015-06-28,83,72,91,65,54,45
 278 | SEA,2015-03-29,60,56,78,48,41,24
 279 | BOS,2015-02-02,31,37,55,10,23,-5
 280 | BOS,2015-12-17,52,41,64,39,28,-4
 281 | BOS,2015-05-05,70,63,89,54,47,35
 282 | BOS,2015-10-24,49,59,80,38,44,30
 283 | BOS,2015-02-17,23,39,61,10,25,-11
 284 | AUS,2015-03-05,46,69,93,26,44,17
 285 | AUS,2015-09-19,93,90,101,67,64,47
 286 | BOS,2015-02-28,31,41,63,12,27,2
 287 | AUS,2015-03-14,77,71,93,48,46,29
 288 | BOS,2015-01-14,25,35,69,14,22,-8
 289 | AUS,2015-07-31,101,97,103,77,72,64
 290 | BOS,2015-11-02,64,56,83,51,42,26
 291 | BOS,2015-01-05,50,36,63,17,23,-4
 292 | AUS,2015-05-05,83,84,101,61,60,44
 293 | SEA,2015-12-19,47,45,56,37,35,17
 294 | BOS,2015-07-22,84,82,103,69,66,55
 295 | AUS,2015-05-14,86,86,96,66,63,45
 296 | AUS,2015-01-01,39,62,84,35,36,24
 297 | AUS,2015-04-27,84,83,94,60,58,45
 298 | AUS,2015-05-23,86,88,99,64,66,50
 299 | BOS,2015-04-08,42,53,85,34,38,21
 300 | BOS,2015-08-14,86,80,101,63,65,52
 301 | BOS,2015-07-29,92,82,97,71,66,55
 302 | BOS,2015-04-11,57,54,78,42,39,25
 303 | AUS,2015-01-27,80,62,84,35,37,21
 304 | SEA,2015-02-11,55,50,62,42,37,23
 305 | BOS,2015-10-20,66,60,79,45,45,32
 306 | BOS,2015-11-07,69,54,77,50,40,25
 307 | BOS,2015-11-12,57,53,76,46,39,25
 308 | BOS,2015-09-22,68,70,94,56,55,35
 309 | BOS,2015-11-25,44,48,70,30,35,13
 310 | AUS,2015-11-28,45,67,88,40,41,25
 311 | BOS,2015-04-20,51,57,89,41,42,21
 312 | SEA,2015-09-09,76,73,89,57,53,40
 313 | AUS,2015-03-20,73,73,92,59,48,24
 314 | BOS,2015-05-31,77,71,96,48,55,39
 315 | AUS,2015-11-02,81,77,87,57,50,29
 316 | SEA,2015-06-10,78,69,83,52,51,44
 317 | BOS,2015-05-20,66,67,91,51,51,40
 318 | SEA,2015-08-24,75,76,88,54,55,45
 319 | AUS,2015-09-10,96,92,100,73,67,52
 320 | AUS,2015-04-12,82,79,92,65,54,38
 321 | BOS,2015-02-21,34,40,63,4,26,-1
 322 | BOS,2015-06-04,58,72,96,49,56,44
 323 | AUS,2015-03-13,76,71,91,50,46,25
 324 | AUS,2015-04-07,86,78,95,65,52,28
 325 | SEA,2015-07-12,79,75,97,62,55,47
 326 | BOS,2015-04-05,49,52,84,32,37,11
 327 | AUS,2015-05-02,81,84,96,50,60,47
 328 | BOS,2015-03-17,51,46,70,28,31,10
 329 | SEA,2015-12-10,53,46,66,43,36,16
 330 | AUS,2015-10-06,88,85,95,57,59,42
 331 | BOS,2015-03-02,37,42,66,26,28,0
 332 | AUS,2015-01-06,68,62,84,31,36,20
 333 | AUS,2015-12-09,75,64,81,43,38,23
 334 | AUS,2015-02-12,60,64,83,40,39,16
 335 | AUS,2015-10-13,93,83,98,55,57,39
 336 | AUS,2015-01-13,42,62,78,36,36,18
 337 | BOS,2015-04-18,60,57,94,43,42,26
 338 | AUS,2015-12-30,56,62,82,38,36,19
 339 | SEA,2015-12-26,40,45,62,32,36,22
 340 | BOS,2015-06-14,74,76,96,62,59,46
 341 | BOS,2015-10-30,61,57,78,42,42,28
 342 | BOS,2015-11-08,56,54,74,44,40,25
 343 | SEA,2015-11-23,44,49,58,32,38,10
 344 | BOS,2015-06-27,68,79,96,57,63,52
 345 | SEA,2015-12-29,45,45,54,33,36,8
 346 | BOS,2015-06-09,78,74,96,64,58,47
 347 | BOS,2015-11-19,55,50,75,43,37,14
 348 | SEA,2015-11-10,52,52,63,39,41,30
 349 | BOS,2015-11-22,49,49,74,36,36,9
 350 | SEA,2015-09-06,61,73,90,53,54,45
 351 | AUS,2015-11-18,72,70,83,38,45,26
 352 | SEA,2015-06-24,78,72,92,61,53,45
 353 | SEA,2015-09-13,69,72,87,55,53,39
 354 | SEA,2015-03-27,65,55,71,48,41,26
 355 | AUS,2015-11-09,75,74,88,50,48,26
 356 | SEA,2016-01-01,46,46,58,28,36,10
 357 | BOS,2015-05-27,85,70,96,65,53,39
 358 | AUS,2015-06-13,94,92,102,73,70,57
 359 | AUS,2015-10-19,85,82,93,44,55,42
 360 | BOS,2015-12-04,48,45,70,38,32,2
 361 | BOS,2015-12-28,38,38,63,29,24,-2
 362 | BOS,2015-05-10,89,64,94,59,48,34
 363 | AUS,2015-06-06,90,91,103,66,69,59
 364 | BOS,2015-02-26,22,41,68,17,27,-1
 365 | SEA,2015-08-10,84,77,98,61,56,46
 366 | SEA,2015-11-09,50,52,64,41,41,27
 367 | BOS,2015-05-09,71,64,95,46,48,35
 368 | BOS,2015-10-26,52,58,84,43,43,25
 369 | SEA,2015-07-19,95,77,95,63,56,48
 370 | SEA,2015-01-07,46,46,59,42,37,19
 371 | SEA,2015-08-01,92,77,92,60,57,50
 372 | BOS,2015-07-11,83,82,100,68,65,54
 373 | BOS,2015-03-18,30,46,74,22,32,5
 374 | SEA,2015-01-12,52,47,59,40,37,13
 375 | AUS,2015-02-24,37,67,86,30,41,24
 376 | SEA,2015-10-31,60,55,71,53,43,30
 377 | BOS,2015-03-09,48,43,72,26,29,5
 378 | SEA,2015-03-01,52,52,62,34,38,23
 379 | BOS,2015-09-28,78,68,90,56,53,37
 380 | AUS,2015-07-02,89,94,104,69,71,63
 381 | AUS,2015-02-06,58,63,79,37,38,21
 382 | SEA,2015-03-10,56,53,68,41,39,20
 383 | SEA,2015-07-27,74,77,95,54,57,46
 384 | BOS,2015-09-13,66,74,93,63,59,45
 385 | BOS,2015-08-01,90,81,98,68,66,54
 386 | BOS,2015-08-23,71,79,96,67,64,49
 387 | BOS,2015-08-18,87,80,95,71,65,53
 388 | BOS,2015-12-07,56,44,76,37,31,4
 389 | SEA,2015-05-10,67,64,80,52,46,38
 390 | SEA,2015-12-04,51,47,57,43,36,21
 391 | SEA,2015-11-19,48,50,60,37,39,25
 392 | SEA,2015-05-27,76,67,89,53,49,37
 393 | SEA,2015-12-31,42,46,56,28,36,13
 394 | SEA,2015-01-23,54,48,56,47,37,16
 395 | SEA,2015-06-30,87,73,96,59,54,43
 396 | BOS,2015-09-20,75,71,97,57,56,42
 397 | SEA,2015-04-10,57,57,74,46,42,30
 398 | SEA,2015-11-11,52,52,65,43,41,15
 399 | SEA,2015-05-29,79,67,85,55,50,35
 400 | BOS,2015-10-11,68,63,82,48,48,32
 401 | SEA,2015-04-01,55,56,82,42,41,29
 402 | BOS,2015-05-19,65,67,90,52,51,38
 403 | AUS,2015-10-27,85,79,94,53,52,37
 404 | BOS,2015-11-03,73,56,76,47,41,27
 405 | BOS,2015-02-07,29,37,56,16,23,-5
 406 | BOS,2015-12-18,52,40,61,39,27,-6
 407 | AUS,2015-04-09,86,78,95,65,53,33
 408 | SEA,2015-08-08,77,77,98,60,56,47
 409 | SEA,2015-01-15,46,47,58,34,37,15
 410 | AUS,2015-07-25,96,96,103,68,72,67
 411 | SEA,2015-09-21,65,69,88,49,51,39
 412 | SEA,2015-02-21,54,51,67,42,37,25
 413 | BOS,2015-01-11,33,36,62,15,22,-4
 414 | SEA,2015-11-07,54,53,62,49,42,31
 415 | SEA,2015-09-30,65,66,82,50,49,36
 416 | BOS,2015-07-30,91,82,98,74,66,52
 417 | SEA,2015-03-17,56,54,65,40,40,27
 418 | BOS,2015-03-26,56,48,79,38,34,13
 419 | AUS,2015-05-13,71,86,94,63,63,48
 420 | AUS,2015-07-16,94,95,106,69,72,62
 421 | AUS,2015-12-05,66,65,86,30,39,19
 422 | AUS,2015-04-22,79,81,94,66,56,39
 423 | BOS,2015-09-01,79,77,96,69,62,50
 424 | SEA,2015-04-28,60,61,76,48,44,31
 425 | BOS,2015-08-13,82,80,100,66,65,53
 426 | AUS,2015-12-14,74,64,83,40,37,22
 427 | SEA,2015-10-02,60,65,79,50,49,35
 428 | BOS,2015-04-14,65,55,81,52,40,26
 429 | AUS,2015-12-23,82,63,82,46,36,6
 430 | SEA,2015-02-16,59,50,59,42,37,13
 431 | SEA,2015-05-28,82,67,92,54,50,38
 432 | BOS,2015-06-18,76,77,94,57,60,47
 433 | BOS,2015-01-30,34,36,63,19,22,-5
 434 | BOS,2015-01-21,34,35,68,22,22,0
 435 | SEA,2015-04-19,70,59,74,47,42,33
 436 | AUS,2015-08-16,97,97,104,70,72,59
 437 | AUS,2015-11-27,77,67,87,42,41,22
 438 | SEA,2015-04-08,63,57,70,43,41,29
 439 | BOS,2015-12-26,55,38,61,41,25,-4
 440 | AUS,2015-11-14,67,72,88,55,46,25
 441 | AUS,2015-03-25,80,74,88,61,49,28
 442 | AUS,2015-06-28,90,94,105,73,71,61
 443 | AUS,2015-08-30,92,95,104,70,70,62
 444 | BOS,2015-12-11,58,43,68,43,30,-2
 445 | AUS,2015-11-05,87,75,90,73,49,27
 446 | AUS,2015-09-02,93,94,106,65,69,63
 447 | SEA,2015-11-22,50,49,58,35,39,14
 448 | BOS,2015-02-11,25,38,62,14,24,-4
 449 | BOS,2015-08-28,75,78,99,64,63,47
 450 | AUS,2015-03-03,59,68,85,41,43,19
 451 | AUS,2015-09-17,94,90,105,73,65,50
 452 | SEA,2015-07-04,92,74,91,59,54,43
 453 | AUS,2015-03-16,75,72,85,47,47,32
 454 | BOS,2015-01-16,38,35,64,15,22,-7
 455 | SEA,2015-07-15,79,76,92,58,56,49
 456 | BOS,2015-01-03,37,37,64,22,23,-1
 457 | AUS,2015-05-07,84,85,102,72,61,44
 458 | AUS,2015-10-01,93,86,98,65,61,45
 459 | SEA,2015-12-17,44,45,57,39,35,11
 460 | BOS,2015-07-20,92,82,99,70,66,54
 461 | BOS,2015-06-19,86,77,96,63,61,47
 462 | AUS,2015-01-03,60,62,86,34,36,17
 463 | AUS,2015-12-02,68,66,85,40,40,25
 464 | SEA,2015-10-23,55,57,73,45,45,32
 465 | AUS,2015-05-21,75,88,97,62,65,46
 466 | SEA,2015-04-27,77,61,81,51,44,31
 467 | AUS,2015-01-16,63,62,82,27,36,19
 468 | AUS,2015-05-30,87,90,100,68,68,53
 469 | BOS,2015-06-02,49,72,96,46,55,42
 470 | AUS,2015-01-25,72,62,89,32,37,19
 471 | AUS,2015-08-21,90,96,104,70,71,66
 472 | SEA,2015-02-02,52,49,64,41,37,8
 473 | SEA,2015-11-26,49,48,61,30,38,25
 474 | BOS,2015-06-28,59,80,97,52,63,52
 475 | BOS,2015-11-14,47,52,71,37,39,16
 476 | BOS,2015-08-26,85,78,99,69,63,50
 477 | BOS,2015-11-27,64,48,72,48,35,8
 478 | BOS,2015-12-05,53,45,66,39,32,4
 479 | AUS,2015-03-22,77,74,98,52,48,30
 480 | BOS,2015-09-26,63,69,95,50,54,34
 481 | SEA,2015-03-20,57,54,69,48,40,26
 482 | BOS,2015-02-09,25,38,61,14,24,-18
 483 | SEA,2015-06-16,73,70,88,52,52,42
 484 | BOS,2015-05-22,76,68,93,52,52,39
 485 | SEA,2015-03-31,55,56,75,43,41,30
 486 | BOS,2015-02-18,29,39,66,17,25,-4
 487 | BOS,2015-12-03,52,46,69,41,33,0
 488 | SEA,2015-08-26,83,75,91,57,55,46
 489 | BOS,2015-05-15,67,66,93,48,50,36
 490 | AUS,2015-04-14,77,80,95,55,54,30
 491 | BOS,2015-02-23,33,40,65,4,26,-3
 492 | AUS,2015-04-05,66,77,89,58,52,36
 493 | SEA,2015-08-04,79,77,95,58,57,44
 494 | BOS,2015-07-16,68,82,99,59,66,54
 495 | BOS,2015-03-15,41,45,72,27,31,12
 496 | SEA,2015-05-02,65,62,74,46,45,33
 497 | AUS,2015-07-29,99,96,105,71,72,68
 498 | AUS,2015-10-04,86,86,95,53,60,45
 499 | BOS,2015-03-04,43,42,70,33,28,2
 500 | AUS,2015-01-08,37,62,81,21,36,13
 501 | SEA,2015-12-25,41,45,60,36,35,24
 502 | AUS,2015-02-10,80,64,89,38,38,19
 503 | BOS,2015-09-19,79,71,92,62,56,42
 504 | AUS,2015-10-15,93,83,93,54,56,38
 505 | AUS,2015-09-24,92,88,101,62,63,50
 506 | AUS,2015-01-11,49,62,85,35,36,14
 507 | AUS,2015-11-19,77,70,87,41,44,26
 508 | AUS,2015-02-01,73,63,83,44,37,13
 509 | AUS,2015-11-21,65,69,86,38,43,28
 510 | SEA,2015-02-18,54,50,62,40,37,20
 511 | BOS,2015-06-16,71,76,96,56,60,48
 512 | SEA,2015-02-09,56,49,62,47,37,26
 513 | AUS,2015-12-18,57,63,81,30,37,25
 514 | SEA,2015-11-21,48,50,62,33,39,21
 515 | BOS,2015-06-21,76,78,96,60,61,48
 516 | SEA,2015-05-17,67,65,90,51,48,39
 517 | AUS,2015-06-27,90,94,104,75,71,60
 518 | SEA,2015-12-01,50,47,57,39,37,20
 519 | SEA,2015-11-16,48,51,68,35,40,21
 520 | AUS,2015-09-26,93,88,107,64,62,47
 521 | BOS,2015-10-28,65,57,81,42,43,28
 522 | SEA,2015-06-22,77,71,92,55,53,45
 523 | SEA,2015-01-28,54,48,57,41,37,7
 524 | SEA,2015-09-11,81,72,93,59,53,40
 525 | SEA,2015-04-15,57,58,74,38,42,33
 526 | BOS,2015-05-29,72,70,94,55,54,39
 527 | AUS,2015-06-11,92,92,99,70,70,58
 528 | AUS,2015-12-27,75,62,80,38,36,20
 529 | BOS,2015-05-12,87,65,88,48,49,33
 530 | BOS,2015-10-19,48,60,84,31,45,28
 531 | AUS,2015-10-20,87,81,92,48,55,36
 532 | SEA,2015-08-12,83,77,96,62,56,49
 533 | SEA,2015-01-01,42,46,58,26,36,10
 534 | SEA,2015-07-20,80,77,100,62,56,50
 535 | SEA,2015-01-10,46,47,59,43,37,12
 536 | AUS,2015-02-22,56,66,98,37,41,25
 537 | SEA,2015-09-26,65,67,78,50,50,39
 538 | BOS,2015-07-02,80,80,98,64,64,51
 539 | SEA,2015-07-31,94,77,94,64,57,47
 540 | SEA,2015-12-27,40,45,58,35,36,20
 541 | AUS,2015-02-19,70,66,92,29,40,23
 542 | SEA,2015-03-07,62,52,66,39,38,26
 543 | AUS,2015-01-18,70,62,83,32,36,19
 544 | SEA,2015-11-01,54,55,67,48,43,30
 545 | AUS,2015-02-08,75,64,85,50,38,21
 546 | SEA,2015-03-12,64,53,68,49,39,25
 547 | BOS,2015-09-15,84,73,94,62,58,41
 548 | AUS,2015-07-11,93,95,104,65,72,64
 549 | BOS,2015-08-03,90,81,98,68,66,55
 550 | SEA,2015-10-21,61,58,72,47,45,32
 551 | BOS,2015-09-04,72,76,94,62,61,43
 552 | AUS,2015-12-13,77,64,81,48,37,12
 553 | AUS,2015-06-05,88,91,98,62,69,56
 554 | SEA,2015-10-07,61,63,75,57,48,37
 555 | AUS,2015-01-29,76,63,85,44,37,17
 556 | SEA,2015-12-06,55,46,57,45,36,19
 557 | SEA,2015-07-09,84,75,93,58,55,48
 558 | SEA,2015-05-25,60,66,84,52,49,37
 559 | AUS,2015-08-27,96,95,108,67,70,64
 560 | SEA,2015-01-21,45,48,62,31,37,13
 561 | BOS,2015-01-24,34,36,62,31,22,-13
 562 | SEA,2015-04-12,56,58,79,42,42,33
 563 | SEA,2015-01-30,47,49,59,34,37,7
 564 | BOS,2015-12-30,37,37,63,25,24,-15
 565 | AUS,2015-11-22,56,69,87,31,43,31
 566 | SEA,2015-04-07,58,57,78,44,41,29
 567 | AUS,2015-08-06,100,97,104,73,72,68
 568 | BOS,2015-12-21,53,39,62,32,26,-5
 569 | BOS,2015-10-02,54,66,88,50,51,34
 570 | BOS,2015-09-24,74,70,90,58,54,39
 571 | BOS,2015-02-05,36,37,65,7,23,-10
 572 | BOS,2015-05-06,73,63,90,52,47,33
 573 | AUS,2015-10-30,75,78,88,66,51,32
 574 | BOS,2015-02-14,30,39,59,3,25,-3
 575 | BOS,2015-07-18,78,82,98,64,66,54
 576 | SEA,2015-07-06,85,74,94,60,55,45
 577 | SEA,2015-02-27,50,51,70,44,37,20
 578 | BOS,2015-01-13,35,36,63,15,22,-9
 579 | SEA,2015-03-08,63,53,67,39,38,28
 580 | BOS,2015-01-06,18,36,69,15,23,-10
 581 | AUS,2015-05-08,83,85,97,70,61,48
 582 | AUS,2015-07-09,93,95,104,68,72,67
 583 | SEA,2015-03-19,60,54,63,47,40,27
 584 | BOS,2015-03-24,35,48,72,20,33,6
 585 | AUS,2015-05-11,77,86,98,67,62,44
 586 | AUS,2015-07-18,96,95,103,74,72,67
 587 | BOS,2015-08-08,76,81,96,61,66,52
 588 | AUS,2015-12-07,69,65,81,35,39,17
 589 | AUS,2015-04-24,84,82,94,68,57,41
 590 | AUS,2015-05-26,88,89,96,64,67,58
 591 | BOS,2015-04-07,42,53,90,36,38,16
 592 | BOS,2015-08-11,72,80,99,64,65,54
 593 | SEA,2015-05-19,71,65,88,53,48,38
 594 | BOS,2015-04-12,68,54,85,39,40,20
 595 | AUS,2015-01-22,53,62,83,40,37,20
 596 | AUS,2015-12-21,74,63,81,50,36,22
 597 | SEA,2015-02-14,58,50,63,44,37,22
 598 | SEA,2015-10-11,64,61,83,51,47,37
 599 | BOS,2015-04-29,54,61,85,48,45,31
 600 | SEA,2015-02-05,56,49,62,47,37,14
 601 | AUS,2015-06-21,87,93,102,71,71,58
 602 | BOS,2015-01-23,37,36,68,24,22,-6
 603 | SEA,2015-06-06,85,68,90,56,51,42
 604 | AUS,2015-08-10,103,97,107,71,72,63
 605 | SEA,2015-10-08,66,63,80,56,47,39
 606 | AUS,2015-11-25,77,68,85,59,42,23
 607 | BOS,2015-04-23,51,58,86,38,43,29
 608 | AUS,2015-08-09,101,97,106,74,72,66
 609 | AUS,2015-11-12,71,73,87,56,47,26
 610 | AUS,2015-03-27,80,75,96,49,50,30
 611 | BOS,2015-04-24,50,59,84,37,43,28
 612 | BOS,2015-12-13,53,42,63,46,29,4
 613 | SEA,2015-08-30,68,75,88,55,55,45
 614 | SEA,2015-06-13,75,69,94,49,52,42
 615 | AUS,2015-09-04,96,93,110,73,69,58
 616 | BOS,2015-02-13,22,38,66,7,24,-3
 617 | BOS,2015-11-05,76,55,79,50,41,17
 618 | SEA,2015-08-21,72,76,88,58,56,46
 619 | AUS,2015-03-01,43,68,88,35,43,25
 620 | AUS,2015-09-15,92,91,99,69,66,56
 621 | AUS,2015-04-11,70,79,92,59,53,42
 622 | SEA,2015-07-02,93,73,92,64,54,43
 623 | SEA,2015-09-28,70,67,84,49,50,38
 624 | AUS,2015-03-10,64,70,93,47,45,28
 625 | BOS,2015-01-18,51,35,63,20,22,-3
 626 | SEA,2015-07-17,82,76,96,57,56,48
 627 | BOS,2015-08-24,75,79,96,68,64,50
 628 | BOS,2015-01-01,33,37,70,22,24,-3
 629 | AUS,2015-05-01,83,83,95,48,59,42
 630 | BOS,2015-03-12,43,44,71,25,30,7
 631 | SEA,2015-10-19,63,58,72,54,45,28
 632 | SEA,2015-12-15,44,45,63,34,35,19
 633 | AUS,2015-05-18,88,87,95,71,64,51
 634 | AUS,2015-01-05,51,62,78,24,36,14
 635 | SEA,2015-06-27,92,72,92,63,53,45
 636 | AUS,2015-10-10,89,84,93,65,58,43
 637 | SEA,2015-04-21,63,59,76,44,43,29
 638 | AUS,2015-01-14,41,62,79,38,36,11
 639 | SEA,2015-05-30,73,67,87,50,50,36
 640 | SEA,2015-06-19,75,70,92,56,52,43
 641 | BOS,2015-06-11,88,75,98,64,58,46
 642 | BOS,2015-10-09,72,64,87,52,49,32
 643 | SEA,2015-11-24,44,49,59,37,38,14
 644 | SEA,2015-11-06,60,53,65,47,42,30
 645 | BOS,2015-11-16,61,51,76,39,38,12
 646 | SEA,2015-11-13,56,51,62,49,41,14
 647 | AUS,2015-09-30,93,87,97,66,61,46
 648 | BOS,2015-11-21,48,50,74,38,37,10
 649 | AUS,2015-08-25,99,95,104,71,71,61
 650 | SEA,2015-09-05,69,74,88,48,54,44
 651 | BOS,2015-11-30,38,47,69,28,34,-2
 652 | SEA,2015-03-22,53,55,68,43,40,28
 653 | SEA,2015-06-14,82,70,86,53,52,43
 654 | BOS,2015-05-24,83,69,93,51,52,36
 655 | AUS,2015-06-16,81,92,102,72,70,57
 656 | SEA,2015-04-25,56,60,77,42,43,34
 657 | BOS,2015-12-01,45,46,71,32,33,0
 658 | SEA,2015-08-28,74,75,95,60,55,46
 659 | BOS,2015-05-17,67,66,92,52,50,39
 660 | AUS,2015-04-16,79,80,95,63,55,32
 661 | BOS,2015-02-25,35,41,65,14,27,-6
 662 | AUS,2015-09-25,92,88,108,67,63,50
 663 | AUS,2015-04-03,85,77,89,66,51,35
 664 | BOS,2015-10-27,54,58,85,38,43,25
 665 | SEA,2015-08-02,87,77,89,61,57,46
 666 | BOS,2015-01-08,19,36,67,-1,22,-6
 667 | BOS,2015-07-14,82,82,100,65,66,55
 668 | SEA,2015-07-25,70,77,94,58,56,47
 669 | SEA,2015-05-04,63,62,85,45,45,33
 670 | BOS,2015-07-05,83,81,101,61,65,52
 671 | BOS,2015-03-06,24,43,70,9,28,-8
 672 | AUS,2015-02-16,66,65,83,37,40,20
 673 | AUS,2015-05-28,88,89,100,73,67,56
 674 | BOS,2015-03-31,50,50,89,37,36,15
 675 | AUS,2015-02-03,43,63,80,38,38,16
 676 | BOS,2015-03-28,40,49,85,30,35,11
 677 | BOS,2015-08-04,89,81,96,66,66,52
 678 | BOS,2015-08-27,82,78,100,65,63,47
 679 | AUS,2015-04-28,64,83,93,51,58,43
 680 | AUS,2015-09-20,94,89,103,62,64,52
 681 | BOS,2015-06-23,88,78,96,60,62,47
 682 | SEA,2015-05-15,68,65,85,49,47,38
 683 | SEA,2015-06-21,78,71,89,57,53,45
 684 | SEA,2015-11-14,49,51,65,43,40,9
 685 | SEA,2015-05-22,62,66,86,53,49,37
 686 | SEA,2015-09-02,67,74,98,52,55,41
 687 | SEA,2015-01-26,61,48,61,43,37,9
 688 | AUS,2015-06-23,91,93,103,71,71,63
 689 | SEA,2015-09-17,65,70,85,55,52,42
 690 | SEA,2015-04-17,66,59,74,43,42,33
 691 | BOS,2015-10-23,59,59,84,40,44,29
 692 | BOS,2015-10-14,68,62,81,51,47,31
 693 | SEA,2015-04-02,56,56,76,42,41,30
 694 | BOS,2015-10-29,75,57,79,57,43,26
 695 | BOS,2015-12-08,44,44,65,37,31,-4
 696 | BOS,2015-10-05,61,65,87,48,50,28
 697 | AUS,2015-06-02,88,90,100,64,68,56
 698 | AUS,2015-10-22,85,81,92,70,54,41
 699 | BOS,2015-12-20,41,40,63,31,27,-11
 700 | SEA,2015-08-14,65,77,95,59,56,44
 701 | AUS,2015-09-08,97,92,103,74,68,56
 702 | SEA,2015-01-03,41,46,58,35,36,6
 703 | SEA,2015-07-22,75,77,96,57,56,49
 704 | SEA,2015-01-16,53,47,55,42,37,13
 705 | AUS,2015-07-20,96,96,103,68,72,66
 706 | AUS,2015-02-20,73,66,94,52,40,29
 707 | SEA,2015-09-24,72,68,89,52,50,38
 708 | SEA,2015-02-22,53,51,64,38,37,30
 709 | AUS,2015-06-18,88,93,105,72,70,63
 710 | SEA,2015-03-05,56,52,66,37,38,13
 711 | AUS,2015-06-25,91,93,107,68,71,62
 712 | AUS,2015-07-06,92,94,103,76,71,63
 713 | SEA,2015-03-14,57,53,64,49,39,26
 714 | BOS,2015-03-23,33,47,78,18,33,6
 715 | BOS,2015-09-17,89,72,91,64,57,44
 716 | AUS,2015-07-13,95,95,104,72,72,68
 717 | AUS,2015-10-17,87,82,96,54,56,35
 718 | BOS,2015-09-02,91,77,100,66,62,49
 719 | AUS,2015-12-11,81,64,83,47,38,24
 720 | SEA,2015-10-05,74,64,82,49,48,38
 721 | SEA,2015-12-08,60,46,59,50,36,13
 722 | SEA,2015-10-14,59,60,80,50,46,36
 723 | AUS,2015-11-07,63,75,88,57,49,30
 724 | AUS,2015-06-09,94,92,99,68,69,64
 725 | BOS,2015-01-26,29,36,72,11,22,0
 726 | SEA,2015-06-01,61,67,94,53,50,40
 727 | AUS,2015-08-15,97,97,104,74,72,62
 728 | AUS,2015-11-20,77,70,85,42,44,30
 729 | SEA,2015-04-05,62,57,73,37,41,29
 730 | AUS,2015-08-04,98,97,105,71,72,66
 731 | BOS,2015-12-23,56,39,67,45,26,-12
 732 | AUS,2015-11-17,76,71,89,42,45,26
 733 | AUS,2015-03-28,81,75,97,39,50,28
 734 | BOS,2015-02-03,22,37,59,6,23,-5
 735 | BOS,2015-12-14,58,42,66,46,29,0
 736 | BOS,2015-05-04,84,62,90,49,46,33
 737 | AUS,2015-03-31,79,76,89,66,51,33
 738 | AUS,2015-09-01,93,94,107,69,69,63
 739 | SEA,2015-01-08,46,46,54,35,37,18
 740 | BOS,2015-02-16,19,39,60,-3,25,-9
 741 | AUS,2015-03-06,54,69,98,21,44,21
 742 | AUS,2015-09-18,95,90,99,72,65,48
 743 | SEA,2015-01-19,50,48,62,43,37,18
 744 | SEA,2015-07-07,81,74,90,57,55,47
 745 | SEA,2015-02-25,50,51,64,44,37,20
 746 | BOS,2015-01-15,31,35,66,25,22,-12
 747 | AUS,2015-07-30,100,96,105,69,72,67
 748 | BOS,2015-01-04,52,36,64,35,23,-4
 749 | SEA,2015-05-08,75,63,84,47,46,36
 750 | SEA,2015-12-18,48,45,54,40,35,18
 751 | BOS,2015-07-25,69,82,96,62,66,53
 752 | AUS,2015-05-17,83,87,93,65,64,50
 753 | AUS,2015-12-01,57,66,84,47,40,23
 754 | AUS,2015-04-26,84,82,96,69,58,44
 755 | AUS,2015-05-24,82,89,99,62,66,58
 756 | SEA,2015-04-24,54,60,76,43,43,30
 757 | BOS,2015-08-17,91,80,97,71,65,51
 758 | BOS,2015-12-02,51,46,65,39,33,4
 759 | BOS,2015-04-10,55,54,85,36,39,26
 760 | AUS,2015-01-20,80,62,82,33,36,15
 761 | BOS,2015-07-08,88,81,99,69,65,55
 762 | SEA,2015-02-12,62,50,66,49,37,21
 763 | SEA,2015-10-13,62,61,75,49,46,35
 764 | AUS,2015-01-31,62,63,82,43,37,-5
 765 | AUS,2015-09-22,94,89,103,65,63,47
 766 | SEA,2015-02-07,54,49,66,49,37,20
 767 | BOS,2015-11-11,50,53,69,46,39,24
 768 | SEA,2015-06-23,79,71,92,53,53,40
 769 | AUS,2015-08-31,96,94,106,67,70,62
 770 | SEA,2015-07-08,86,75,95,58,55,47
 771 | SEA,2015-09-18,67,70,85,55,52,41
 772 | BOS,2015-04-21,65,58,88,46,42,21
 773 | SEA,2015-10-17,67,59,74,53,46,34
 774 | AUS,2015-11-10,75,73,90,54,48,31
 775 | AUS,2015-03-21,61,73,90,56,48,26
 776 | AUS,2015-11-01,75,77,88,59,51,36
 777 | SEA,2015-06-11,76,69,84,52,51,40
 778 | AUS,2015-09-06,98,93,101,72,68,52
 779 | BOS,2015-12-24,69,39,69,48,26,0
 780 | SEA,2015-08-23,82,76,91,57,56,46
 781 | AUS,2015-09-13,90,91,102,59,66,59
 782 | AUS,2015-04-13,84,79,94,62,54,31
 783 | SEA,2015-08-18,86,76,88,59,56,47
 784 | AUS,2015-03-12,70,71,89,52,46,19
 785 | AUS,2015-09-28,87,87,105,65,62,46
 786 | AUS,2015-04-06,84,78,91,63,52,38
 787 | SEA,2015-07-11,72,75,98,62,55,49
 788 | AUS,2016-01-01,50,62,84,44,36,24
 789 | BOS,2015-11-01,62,56,77,48,42,26
 790 | AUS,2015-05-03,83,84,94,48,60,42
 791 | BOS,2015-03-10,48,44,71,31,29,6
 792 | SEA,2015-05-01,65,62,85,48,44,28
 793 | SEA,2015-12-13,46,46,59,43,35,19
 794 | AUS,2015-10-07,91,85,96,58,59,42
 795 | BOS,2015-03-01,30,42,63,12,27,-1
 796 | AUS,2015-01-07,51,62,80,30,36,12
 797 | SEA,2015-12-20,46,45,57,40,35,14
 798 | AUS,2015-02-13,67,64,83,35,39,23
 799 | BOS,2015-08-20,78,79,98,68,64,52
 800 | SEA,2015-04-23,54,60,72,44,43,31
 801 | AUS,2015-01-12,47,62,80,42,36,12
 802 | BOS,2015-07-26,79,82,96,63,66,56
 803 | BOS,2015-06-06,67,73,100,52,57,45
 804 | BOS,2015-06-13,85,75,98,64,59,46
 805 | BOS,2015-10-31,52,57,81,38,42,27
 806 | BOS,2015-11-09,60,54,74,41,40,24
 807 | AUS,2015-10-09,87,84,93,67,58,39
 808 | BOS,2015-06-24,84,79,95,67,62,46
 809 | BOS,2015-11-18,48,51,73,31,37,14
 810 | BOS,2015-04-25,56,59,83,36,44,27
 811 | BOS,2015-11-23,42,49,77,30,36,11
 812 | SEA,2015-09-07,70,73,94,56,54,45
 813 | BOS,2015-08-31,90,77,97,71,62,47
 814 | SEA,2015-09-12,80,72,85,58,53,44
 815 | SEA,2015-03-24,55,55,68,43,40,30
 816 | AUS,2015-11-08,69,74,90,53,48,33
 817 | BOS,2015-05-26,87,69,97,64,53,39
 818 | AUS,2015-06-14,88,92,109,71,70,60
 819 | SEA,2015-06-05,80,68,92,55,51,41
 820 | AUS,2015-03-08,53,70,88,48,44,26
 821 | BOS,2015-05-11,80,64,91,50,48,33
 822 | AUS,2015-04-18,76,80,99,56,55,35
 823 | BOS,2015-02-27,27,41,64,15,27,-4
 824 | SEA,2015-08-11,86,77,96,62,56,46
 825 | AUS,2015-03-19,80,73,90,57,48,29
 826 | BOS,2015-05-08,71,64,87,46,48,34
 827 | AUS,2015-04-01,81,76,92,66,51,36
 828 | SEA,2015-07-18,92,76,93,64,56,47
 829 | SEA,2015-01-04,51,46,61,38,36,14
 830 | SEA,2015-04-29,61,61,80,45,44,31
 831 | BOS,2015-07-12,89,82,97,72,65,54
 832 | BOS,2015-03-19,34,46,74,19,32,5
 833 | SEA,2015-05-06,62,63,87,45,45,36
 834 | AUS,2015-02-27,37,67,89,31,42,13
 835 | BOS,2015-07-07,84,81,99,65,65,52
 836 | BOS,2015-03-08,42,43,68,24,29,7
 837 | AUS,2015-02-14,77,65,84,40,39,24
 838 | BOS,2015-10-08,62,64,88,51,49,35
 839 | SEA,2015-03-02,52,52,64,40,38,22
 840 | AUS,2015-07-03,90,94,102,73,71,60
 841 | AUS,2015-02-05,52,63,83,39,38,17
 842 | SEA,2015-11-05,53,53,65,46,42,31
 843 | BOS,2015-09-23,68,70,96,54,55,35
 844 | SEA,2015-07-26,72,77,92,57,56,49
 845 | BOS,2015-09-10,77,74,95,64,60,41
 846 | BOS,2015-08-06,82,81,97,65,66,52
 847 | SEA,2015-08-06,77,77,91,59,57,50
 848 | BOS,2015-09-09,93,75,93,72,60,45
 849 | SEA,2015-10-01,70,65,89,49,49,35
 850 | SEA,2015-05-13,54,64,84,50,47,37
 851 | BOS,2015-06-08,76,74,97,53,57,42
 852 | SEA,2015-12-05,50,47,55,43,36,23
 853 | SEA,2015-05-20,74,66,92,51,48,40
 854 | SEA,2015-12-30,42,45,56,30,36,6
 855 | BOS,2015-11-28,60,47,74,41,34,11
 856 | SEA,2015-01-24,58,48,58,52,37,10
 857 | SEA,2015-07-29,90,77,103,58,57,45
 858 | BOS,2015-01-29,32,36,65,10,22,-6
 859 | SEA,2015-06-25,87,72,88,60,53,45
 860 | SEA,2015-09-15,64,71,93,50,52,42
 861 | SEA,2015-04-11,53,58,80,42,42,35
 862 | BOS,2015-10-12,76,63,90,51,48,31
 863 | AUS,2015-08-03,97,97,105,68,72,65
 864 | BOS,2015-10-07,72,65,90,52,49,30
 865 | AUS,2015-10-24,77,80,93,60,53,37
 866 | SEA,2015-08-16,77,77,98,58,56,50
 867 | BOS,2015-12-19,40,40,64,33,27,-10
 868 | BOS,2015-05-03,67,62,92,45,46,31
 869 | AUS,2015-04-08,78,78,92,68,53,34
 870 | SEA,2015-01-14,43,47,59,33,37,8
 871 | AUS,2015-07-22,96,96,102,75,72,66
 872 | SEA,2015-09-22,66,69,92,46,51,37
 873 | SEA,2015-02-20,52,50,62,45,37,25
 874 | BOS,2015-07-31,89,81,98,73,66,54
 875 | BOS,2015-09-30,78,67,87,57,52,36
 876 | AUS,2015-07-04,91,94,103,73,71,63
 877 | SEA,2015-03-16,57,54,66,43,39,29
 878 | BOS,2015-03-21,41,47,83,28,32,7
 879 | AUS,2015-05-12,68,86,97,66,63,48
 880 | AUS,2015-07-15,95,95,105,70,72,66
 881 | AUS,2015-04-21,79,81,92,47,56,44
 882 | BOS,2015-08-22,73,79,93,67,64,47
 883 | BOS,2015-04-02,61,51,75,30,36,19
 884 | BOS,2015-08-12,85,80,101,68,65,53
 885 | AUS,2015-12-17,66,63,85,33,37,21
 886 | BOS,2015-07-28,86,82,99,70,66,54
 887 | BOS,2015-04-17,65,56,93,47,41,26
 888 | AUS,2015-12-24,73,63,82,44,36,14
 889 | SEA,2015-02-17,61,50,58,40,37,23
 890 | AUS,2015-04-30,80,83,93,44,59,44
 891 | BOS,2015-10-25,63,58,80,48,44,26
 892 | BOS,2015-01-31,21,36,62,11,23,-8
 893 | BOS,2015-01-20,39,35,60,27,22,-3
 894 | BOS,2015-09-21,66,71,94,53,56,40
 895 | SEA,2015-06-03,68,68,89,53,50,42
 896 | AUS,2015-08-17,95,96,105,69,72,61
 897 | AUS,2015-11-26,75,68,85,69,42,27
 898 | BOS,2015-04-26,56,59,87,43,44,28
 899 | BOS,2015-12-25,62,38,65,49,25,-8
 900 | AUS,2015-11-15,73,71,89,51,46,28
 901 | SEA,2015-06-29,84,73,93,63,54,45
 902 | SEA,2015-03-28,60,56,73,49,41,26
 903 | BOS,2015-02-01,30,36,66,12,23,-7
 904 | BOS,2015-12-16,48,41,64,38,28,1
 905 | AUS,2015-11-04,80,76,89,55,50,26
 906 | AUS,2015-09-03,92,94,107,68,69,61
 907 | BOS,2015-02-10,30,38,60,14,24,-5
 908 | AUS,2015-03-04,70,69,91,35,43,17
 909 | AUS,2015-09-16,93,90,99,73,65,54
 910 | SEA,2015-07-05,91,74,91,62,55,47
 911 | BOS,2015-06-05,59,73,96,50,56,45
 912 | AUS,2015-03-15,68,72,87,44,46,30
 913 | BOS,2015-01-17,22,35,59,9,22,-4
 914 | SEA,2015-07-14,82,76,91,61,56,50
 915 | BOS,2015-01-02,41,37,66,31,23,-3
 916 | AUS,2015-05-04,84,84,98,59,60,37
 917 | SEA,2015-12-16,43,45,57,37,35,10
 918 | BOS,2015-07-23,82,82,100,67,66,54
 919 | AUS,2015-05-15,76,87,96,64,64,42
 920 | AUS,2015-11-24,66,68,87,44,42,26
 921 | AUS,2015-12-03,63,66,86,34,40,27
 922 | AUS,2015-05-22,78,88,97,62,66,47
 923 | SEA,2015-04-26,60,61,78,40,44,32
 924 | BOS,2015-08-15,91,80,98,68,65,52
 925 | SEA,2015-06-08,87,69,87,58,51,42
 926 | AUS,2015-01-26,74,62,86,34,37,24
 927 | SEA,2015-02-10,55,49,65,47,37,24
 928 | SEA,2015-10-03,67,65,80,52,48,39
 929 | SEA,2015-02-01,49,49,63,40,37,1
 930 | BOS,2015-06-29,75,80,99,53,63,49
 931 | BOS,2015-11-13,59,52,69,46,39,14
 932 | AUS,2015-11-30,55,66,83,45,40,20
 933 | BOS,2015-11-24,44,49,72,29,36,11
 934 | AUS,2015-11-29,47,67,85,43,41,20
 935 | SEA,2015-09-08,73,73,85,56,54,43
 936 | AUS,2015-03-23,79,74,90,46,49,30
 937 | BOS,2015-05-30,88,71,96,58,54,35
 938 | SEA,2015-03-21,56,55,69,47,40,29
 939 | AUS,2015-09-27,93,87,105,63,62,45
 940 | AUS,2015-11-03,82,76,87,58,50,26
 941 | SEA,2015-06-17,77,70,91,52,52,43
 942 | BOS,2015-05-21,67,68,93,45,51,41
 943 | SEA,2015-03-30,64,56,72,51,41,27
 944 | SEA,2015-12-12,48,46,60,42,35,22
 945 | SEA,2015-08-25,78,75,87,54,55,45
 946 | AUS,2015-09-11,91,92,102,72,67,56
 947 | AUS,2015-04-15,80,80,92,49,55,34
 948 | BOS,2015-02-20,20,40,68,5,26,0
 949 | AUS,2015-04-04,68,77,92,56,52,36
 950 | SEA,2015-07-13,78,76,93,61,56,50
 951 | SEA,2015-08-07,83,77,95,60,56,48
 952 | BOS,2015-12-10,58,43,64,43,30,-1
 953 | BOS,2015-07-17,75,82,98,60,66,54
 954 | BOS,2015-03-16,45,45,71,26,31,10
 955 | SEA,2015-05-03,69,62,77,46,45,35
 956 | SEA,2015-12-11,49,46,58,40,35,21
 957 | AUS,2015-11-06,75,75,89,61,49,31
 958 | AUS,2015-10-05,85,85,96,62,59,43
 959 | BOS,2015-03-03,33,42,61,20,28,4
 960 | AUS,2015-01-09,39,62,84,34,36,10
 961 | AUS,2015-12-08,75,65,83,33,38,23
 962 | AUS,2015-02-11,76,64,81,37,39,16
 963 | AUS,2015-10-14,95,83,95,52,57,43
 964 | AUS,2015-01-10,37,62,86,32,36,13
 965 | BOS,2015-08-30,88,77,99,69,63,48
 966 | SEA,2015-10-26,54,56,65,50,44,29
 967 | BOS,2015-04-19,50,57,87,41,42,22
 968 | SEA,2015-02-19,51,50,67,47,37,22
 969 | BOS,2015-06-15,63,76,98,54,60,42
 970 | SEA,2015-02-08,59,49,68,47,37,22
 971 | SEA,2015-11-20,47,50,64,33,39,25
 972 | BOS,2015-06-26,72,79,100,59,63,51
 973 | SEA,2015-05-16,60,65,84,52,47,40
 974 | SEA,2015-11-17,56,51,61,44,40,23
 975 | SEA,2015-10-25,67,57,67,48,44,31
 976 | SEA,2015-12-28,41,45,56,35,36,12
 977 | SEA,2015-09-01,67,74,91,57,55,46
 978 | SEA,2015-01-29,54,49,60,38,37,6
 979 | SEA,2015-09-10,77,72,85,58,53,39
 980 | SEA,2015-03-26,69,55,70,50,40,32
 981 | AUS,2015-10-18,84,82,93,47,55,42
 982 | BOS,2015-05-28,86,70,92,64,54,43
 983 | AUS,2015-06-12,93,92,101,72,70,57
 984 | BOS,2015-09-06,82,76,94,61,61,43
 985 | BOS,2015-12-29,40,37,73,24,24,-17
 986 | BOS,2015-05-13,69,65,87,53,49,38
 987 | AUS,2015-06-07,89,91,98,64,69,59
 988 | AUS,2015-10-21,88,81,92,71,54,36
 989 | SEA,2015-08-13,83,77,92,60,56,44
 990 | AUS,2015-08-20,82,96,105,70,71,67
 991 | SEA,2015-01-06,54,46,57,43,37,14
 992 | BOS,2015-07-10,77,81,101,60,65,53
 993 | SEA,2015-07-21,75,77,97,59,56,48
 994 | SEA,2015-01-13,49,47,56,37,37,11
 995 | AUS,2015-02-25,62,67,89,33,42,23
 996 | SEA,2015-09-27,64,67,89,45,50,35
 997 | BOS,2015-07-01,82,80,98,62,64,50
 998 | SEA,2015-07-30,94,77,96,63,57,48
 999 | BOS,2015-09-18,85,72,90,65,57,43
1000 | BOS,2015-09-29,84,68,88,65,52,34
1001 | AUS,2015-07-01,89,94,103,66,71,66
1002 | AUS,2015-02-07,68,64,84,38,38,19
1003 | SEA,2015-03-11,58,53,65,48,39,23
1004 | BOS,2015-09-12,71,74,93,60,59,43
1005 | AUS,2015-07-10,93,95,103,66,72,65
1006 | BOS,2015-09-07,93,75,102,66,60,43
1007 | BOS,2015-08-19,88,79,92,71,65,51
1008 | AUS,2015-12-12,75,64,88,71,37,22
1009 | SEA,2015-05-11,57,64,87,50,46,35
1010 | AUS,2015-06-24,91,93,104,70,71,65
1011 | SEA,2015-12-07,52,46,58,47,36,18
1012 | SEA,2015-11-18,48,50,59,38,40,29
1013 | SEA,2015-05-26,71,67,89,53,49,40
1014 | SEA,2015-06-04,73,68,91,53,50,41
1015 | SEA,2015-01-22,49,48,60,43,37,19
1016 | SEA,2015-04-13,53,58,75,39,42,33
1017 | AUS,2015-08-18,98,96,104,73,71,64
1018 | BOS,2015-12-31,47,37,65,33,24,-8
1019 | BOS,2015-10-10,61,63,87,48,48,32
1020 | SEA,2015-04-06,57,57,78,44,41,31
1021 | AUS,2015-12-26,78,62,81,73,36,18
1022 | AUS,2015-08-01,97,97,105,68,72,65
1023 | SEA,2015-10-27,61,56,67,46,44,31
1024 | BOS,2015-05-18,58,67,91,49,50,37
1025 | BOS,2015-10-18,47,61,82,33,46,31
1026 | AUS,2015-10-26,79,79,98,58,53,33
1027 | BOS,2015-02-06,21,37,57,5,23,-6
1028 | BOS,2015-05-01,49,61,89,39,46,33
1029 | SEA,2015-08-09,83,77,99,59,56,47
1030 | BOS,2015-08-29,82,78,96,63,63,47
1031 | BOS,2015-07-19,90,82,99,71,66,54
1032 | SEA,2015-11-04,50,54,74,38,42,30
1033 | AUS,2015-07-24,98,96,103,73,72,68
1034 | BOS,2015-07-06,80,81,101,68,65,50
1035 | SEA,2015-09-20,73,69,88,54,51,40
1036 | SEA,2015-02-26,53,51,67,46,37,18
1037 | BOS,2015-01-10,24,36,56,17,22,-3
1038 | SEA,2015-03-09,58,53,67,40,39,28
1039 | AUS,2015-10-12,97,84,97,70,57,41
1040 | AUS,2015-05-09,86,85,98,69,62,47
1041 | SEA,2015-03-18,60,54,70,45,40,29
1042 | BOS,2015-03-27,44,49,79,37,34,16
1043 | AUS,2015-05-10,87,85,94,75,62,49
1044 | AUS,2015-07-17,96,95,105,75,72,66
1045 | BOS,2015-08-09,73,81,99,62,66,53
1046 | AUS,2015-12-04,63,65,84,32,39,23
1047 | SEA,2015-10-20,64,58,69,51,45,29
1048 | AUS,2015-04-23,83,82,97,67,57,42
1049 | AUS,2015-05-27,87,89,100,67,67,59
1050 | BOS,2015-04-04,52,51,77,36,37,17
1051 | BOS,2015-08-10,83,81,101,63,65,53
1052 | AUS,2015-12-15,76,63,81,46,37,19
1053 | SEA,2015-05-18,78,65,89,54,48,37
1054 | BOS,2015-04-15,67,55,82,50,41,28
1055 | AUS,2015-01-23,50,62,89,34,37,18
1056 | AUS,2015-12-22,78,63,80,39,36,16
1057 | SEA,2015-02-15,54,50,65,39,37,13
1058 | SEA,2015-10-10,70,62,77,56,47,40
1059 | SEA,2015-06-09,84,69,96,58,51,44
1060 | SEA,2015-02-04,51,49,63,40,37,7
1061 | BOS,2015-01-22,37,36,66,29,22,-3
1062 | SEA,2015-12-03,60,47,60,46,36,27
1063 | SEA,2015-04-18,66,59,77,47,42,34
1064 | AUS,2015-08-11,103,97,105,68,72,67
1065 | SEA,2015-10-22,61,57,70,48,45,34
1066 | SEA,2015-04-09,63,57,73,43,41,34
1067 | AUS,2015-08-08,100,97,108,74,72,68
1068 | BOS,2015-12-27,50,38,61,38,25,0
1069 | AUS,2015-11-13,65,72,90,57,46,28
1070 | AUS,2015-03-24,80,74,88,51,49,30
1071 | BOS,2015-09-25,66,69,89,55,54,38
1072 | AUS,2015-06-29,92,94,107,71,71,63
1073 | SEA,2015-11-08,52,53,64,46,42,27
1074 | BOS,2015-08-21,83,79,97,67,64,52
1075 | SEA,2015-08-31,66,74,92,61,55,46
1076 | AUS,2015-09-05,99,93,112,74,68,55
1077 | BOS,2015-02-12,30,38,58,16,24,-11
1078 | AUS,2015-12-28,48,62,77,35,36,25
1079 | SEA,2015-08-20,73,76,87,58,56,47
1080 | AUS,2015-03-02,45,68,86,38,43,19
1081 | AUS,2015-09-14,89,91,101,58,66,54
1082 | SEA,2015-07-03,92,74,92,64,54,44
1083 | SEA,2015-09-29,71,66,81,48,49,37
1084 | AUS,2015-03-17,76,72,89,63,47,30
1085 | BOS,2015-01-19,46,35,61,32,22,-6
1086 | SEA,2015-07-16,79,76,98,59,56,50
1087 | AUS,2015-05-06,87,85,98,71,61,45
1088 | BOS,2015-03-13,40,44,73,24,30,3
1089 | SEA,2015-12-14,46,45,59,35,35,22
1090 | BOS,2015-07-21,81,82,102,70,66,52
1091 | AUS,2015-10-02,87,86,97,57,60,47
1092 | AUS,2015-01-02,42,62,81,38,36,16
1093 | SEA,2015-12-23,41,45,58,37,35,9
1094 | AUS,2015-05-20,88,88,99,70,65,45
1095 | SEA,2015-04-20,73,59,74,46,43,30
1096 | AUS,2015-01-17,71,62,80,33,36,21
1097 | AUS,2015-05-31,84,90,100,64,68,51
1098 | BOS,2015-06-01,49,71,96,47,55,41
1099 | AUS,2015-01-24,62,62,82,26,37,14
1100 | 


--------------------------------------------------------------------------------
/Section 3/weather/data/london.csv:
--------------------------------------------------------------------------------
  1 | airport	date	max_temp	min_temp
  2 | Heathrow	1948-1-01	8.9	3.3
  3 | Heathrow	1948-2-01	7.9	2.2
  4 | Heathrow	1948-3-01	14.2	3.8
  5 | Heathrow	1948-4-01	15.4	5.1
  6 | Heathrow	1948-5-01	18.1	6.9
  7 | Heathrow	1948-6-01	19.1	10.3
  8 | Heathrow	1948-7-01	21.7	12
  9 | Heathrow	1948-8-01	20.8	11.7
 10 | Heathrow	1948-9-01	19.6	10.2
 11 | Heathrow	1948-10-01	14.9	6
 12 | Heathrow	1948-11-01	10.8	4.6
 13 | Heathrow	1948-12-01	8.8	3.8
 14 | Heathrow	1949-1-01	8.5	1.8
 15 | Heathrow	1949-2-01	10.4	0.6
 16 | Heathrow	1949-3-01	9.3	1.2
 17 | Heathrow	1949-4-01	16.2	6
 18 | Heathrow	1949-5-01	17.1	6.8
 19 | Heathrow	1949-6-01	22	10.5
 20 | Heathrow	1949-7-01	25.1	12.9
 21 | Heathrow	1949-8-01	23.9	12.5
 22 | Heathrow	1949-9-01	22.8	13.3
 23 | Heathrow	1949-10-01	17	8.6
 24 | Heathrow	1949-11-01	10.2	2.9
 25 | Heathrow	1949-12-01	9.2	2.9
 26 | Heathrow	1950-1-01	7.1	1.7
 27 | Heathrow	1950-2-01	9.9	2.4
 28 | Heathrow	1950-3-01	12.3	3.5
 29 | Heathrow	1950-4-01	12.9	4.2
 30 | Heathrow	1950-5-01	17.2	7.6
 31 | Heathrow	1950-6-01	23.6	12.3
 32 | Heathrow	1950-7-01	21.6	12.9
 33 | Heathrow	1950-8-01	21.9	12.1
 34 | Heathrow	1950-9-01	17.6	10.2
 35 | Heathrow	1950-10-01	14	6.1
 36 | Heathrow	1950-11-01	9.3	3.3
 37 | Heathrow	1950-12-01	3.8	-1
 38 | Heathrow	1951-1-01	7.3	1.5
 39 | Heathrow	1951-2-01	7	0.8
 40 | Heathrow	1951-3-01	8.4	1.7
 41 | Heathrow	1951-4-01	12.3	3
 42 | Heathrow	1951-5-01	15	7
 43 | Heathrow	1951-6-01	20.5	10
 44 | Heathrow	1951-7-01	23	12.9
 45 | Heathrow	1951-8-01	20.2	12.3
 46 | Heathrow	1951-9-01	19.2	11.3
 47 | Heathrow	1951-10-01	14.6	6
 48 | Heathrow	1951-11-01	11.8	6.2
 49 | Heathrow	1951-12-01	8.9	3.1
 50 | Heathrow	1952-1-01	6.2	0.4
 51 | Heathrow	1952-2-01	6.7	-0.3
 52 | Heathrow	1952-3-01	10.6	3.8
 53 | Heathrow	1952-4-01	15.6	5.8
 54 | Heathrow	1952-5-01	19.6	9.4
 55 | Heathrow	1952-6-01	20.9	10.9
 56 | Heathrow	1952-7-01	23.3	13.4
 57 | Heathrow	1952-8-01	21.6	13.3
 58 | Heathrow	1952-9-01	16.4	7.6
 59 | Heathrow	1952-10-01	13.2	5.7
 60 | Heathrow	1952-11-01	7.5	1.6
 61 | Heathrow	1952-12-01	5.8	-0.2
 62 | Heathrow	1953-1-01	5.6	0.6
 63 | Heathrow	1953-2-01	7.5	1
 64 | Heathrow	1953-3-01	11.1	1
 65 | Heathrow	1953-4-01	13.1	4.3
 66 | Heathrow	1953-5-01	18.7	8.9
 67 | Heathrow	1953-6-01	19.8	10.8
 68 | Heathrow	1953-7-01	21	12.1
 69 | Heathrow	1953-8-01	22.6	12.6
 70 | Heathrow	1953-9-01	19.5	9.8
 71 | Heathrow	1953-10-01	14.7	7.1
 72 | Heathrow	1953-11-01	11.3	5.2
 73 | Heathrow	1953-12-01	10	5.1
 74 | Heathrow	1954-1-01	5.9	0.3
 75 | Heathrow	1954-2-01	6.1	0.4
 76 | Heathrow	1954-3-01	10.4	3
 77 | Heathrow	1954-4-01	13.2	2.8
 78 | Heathrow	1954-5-01	16.8	7.6
 79 | Heathrow	1954-6-01	18.6	10.7
 80 | Heathrow	1954-7-01	19.2	11.7
 81 | Heathrow	1954-8-01	19.8	11.5
 82 | Heathrow	1954-9-01	18	9.6
 83 | Heathrow	1954-10-01	16.2	8.9
 84 | Heathrow	1954-11-01	11.2	3.6
 85 | Heathrow	1954-12-01	9.7	3.4
 86 | Heathrow	1955-1-01	5.6	0.4
 87 | Heathrow	1955-2-01	5	-0.8
 88 | Heathrow	1955-3-01	7.9	0.2
 89 | Heathrow	1955-4-01	15.2	5.1
 90 | Heathrow	1955-5-01	15.5	6.4
 91 | Heathrow	1955-6-01	20	11.1
 92 | Heathrow	1955-7-01	24.4	13.1
 93 | Heathrow	1955-8-01	24.3	13.6
 94 | Heathrow	1955-9-01	19.9	9.9
 95 | Heathrow	1955-10-01	13.5	5.3
 96 | Heathrow	1955-11-01	10.7	4.2
 97 | Heathrow	1955-12-01	9.5	2.3
 98 | Heathrow	1956-1-01	7	0.5
 99 | Heathrow	1956-2-01	2.9	-3.6
100 | Heathrow	1956-3-01	11	2.7
101 | Heathrow	1956-4-01	12.1	2.4
102 | Heathrow	1956-5-01	19.1	7.1
103 | Heathrow	1956-6-01	18.5	9.6
104 | Heathrow	1956-7-01	20.9	12.8
105 | Heathrow	1956-8-01	18.8	10.9
106 | Heathrow	1956-9-01	19.2	11.7
107 | Heathrow	1956-10-01	13.8	6
108 | Heathrow	1956-11-01	9.3	2.5
109 | Heathrow	1956-12-01	8.2	3.5
110 | Heathrow	1957-1-01	8.7	2.7
111 | Heathrow	1957-2-01	9	2.9
112 | Heathrow	1957-3-01	13.9	5.7
113 | Heathrow	1957-4-01	14.2	5.2
114 | Heathrow	1957-5-01	16.2	6.5
115 | Heathrow	1957-6-01	23.6	10.7
116 | Heathrow	1957-7-01	22.5	13.8
117 | Heathrow	1957-8-01	21.1	12.5
118 | Heathrow	1957-9-01	17.6	10.1
119 | Heathrow	1957-10-01	15.5	7.7
120 | Heathrow	1957-11-01	9.4	4.3
121 | Heathrow	1957-12-01	7.6	1
122 | Heathrow	1958-1-01	6.8	0.9
123 | Heathrow	1958-2-01	8.9	1.9
124 | Heathrow	1958-3-01	8.1	1.1
125 | Heathrow	1958-4-01	12.3	3.8
126 | Heathrow	1958-5-01	17.3	7.8
127 | Heathrow	1958-6-01	19.4	10.7
128 | Heathrow	1958-7-01	21.7	12.9
129 | Heathrow	1958-8-01	20.8	13.1
130 | Heathrow	1958-9-01	20	12.1
131 | Heathrow	1958-10-01	14.9	8.3
132 | Heathrow	1958-11-01	9.7	4.4
133 | Heathrow	1958-12-01	8	2.7
134 | Heathrow	1959-1-01	5.7	-1.1
135 | Heathrow	1959-2-01	7.4	1.2
136 | Heathrow	1959-3-01	11.9	4.4
137 | Heathrow	1959-4-01	14.2	6.3
138 | Heathrow	1959-5-01	18.7	8
139 | Heathrow	1959-6-01	22.1	11.1
140 | Heathrow	1959-7-01	24.7	13.3
141 | Heathrow	1959-8-01	24.2	13.7
142 | Heathrow	1959-9-01	22.7	10.6
143 | Heathrow	1959-10-01	17.8	8.5
144 | Heathrow	1959-11-01	10.8	3.5
145 | Heathrow	1959-12-01	9.3	3
146 | Heathrow	1960-1-01	6.9	1.8
147 | Heathrow	1960-2-01	7.9	1.6
148 | Heathrow	1960-3-01	10.2	4.5
149 | Heathrow	1960-4-01	14.3	4.6
150 | Heathrow	1960-5-01	18.4	9.3
151 | Heathrow	1960-6-01	22.1	12.1
152 | Heathrow	1960-7-01	20.1	12.4
153 | Heathrow	1960-8-01	20.3	11.8
154 | Heathrow	1960-9-01	18.5	10.5
155 | Heathrow	1960-10-01	14.2	8.2
156 | Heathrow	1960-11-01	11.2	4.5
157 | Heathrow	1960-12-01	6.9	2.1
158 | Heathrow	1961-1-01	6.9	1.2
159 | Heathrow	1961-2-01	10.3	4.9
160 | Heathrow	1961-3-01	13.9	2.9
161 | Heathrow	1961-4-01	15	7.1
162 | Heathrow	1961-5-01	16.8	7.4
163 | Heathrow	1961-6-01	21.7	10.5
164 | Heathrow	1961-7-01	22.1	12.1
165 | Heathrow	1961-8-01	21.7	12.6
166 | Heathrow	1961-9-01	20.9	12
167 | Heathrow	1961-10-01	15.6	7.6
168 | Heathrow	1961-11-01	9.9	3.5
169 | Heathrow	1961-12-01	6.8	0.2
170 | Heathrow	1962-1-01	7.9	1.2
171 | Heathrow	1962-2-01	7.6	1.4
172 | Heathrow	1962-3-01	7.4	-0.6
173 | Heathrow	1962-4-01	12.7	4.7
174 | Heathrow	1962-5-01	15	7.1
175 | Heathrow	1962-6-01	20.4	9.4
176 | Heathrow	1962-7-01	20.6	12.1
177 | Heathrow	1962-8-01	20	11.6
178 | Heathrow	1962-9-01	17.8	9.6
179 | Heathrow	1962-10-01	15.7	7.1
180 | Heathrow	1962-11-01	9.1	3.2
181 | Heathrow	1962-12-01	5	-1.1
182 | Heathrow	1963-1-01	0.8	-4.6
183 | Heathrow	1963-2-01	2.8	-2.2
184 | Heathrow	1963-3-01	10.7	3
185 | Heathrow	1963-4-01	13.6	5.7
186 | Heathrow	1963-5-01	16	6.9
187 | Heathrow	1963-6-01	20.8	11.3
188 | Heathrow	1963-7-01	21.1	11.9
189 | Heathrow	1963-8-01	19.8	11.7
190 | Heathrow	1963-9-01	18	10.1
191 | Heathrow	1963-10-01	14.8	8.2
192 | Heathrow	1963-11-01	11.8	6
193 | Heathrow	1963-12-01	5.4	0
194 | Heathrow	1964-1-01	5.8	0.4
195 | Heathrow	1964-2-01	7.6	2
196 | Heathrow	1964-3-01	7.6	2.6
197 | Heathrow	1964-4-01	12.8	5.4
198 | Heathrow	1964-5-01	19.6	9.8
199 | Heathrow	1964-6-01	19.3	11.2
200 | Heathrow	1964-7-01	22.8	13.5
201 | Heathrow	1964-8-01	22.3	12.7
202 | Heathrow	1964-9-01	21.1	10.6
203 | Heathrow	1964-10-01	13.7	5.3
204 | Heathrow	1964-11-01	11.2	5.3
205 | Heathrow	1964-12-01	7.1	0.9
206 | Heathrow	1965-1-01	6.6	1
207 | Heathrow	1965-2-01	5.9	0.9
208 | Heathrow	1965-3-01	10.5	2.2
209 | Heathrow	1965-4-01	13.4	4.8
210 | Heathrow	1965-5-01	16.8	8.6
211 | Heathrow	1965-6-01	19.7	11
212 | Heathrow	1965-7-01	19.3	11.7
213 | Heathrow	1965-8-01	20.8	11.9
214 | Heathrow	1965-9-01	17.4	9.2
215 | Heathrow	1965-10-01	16.2	7.5
216 | Heathrow	1965-11-01	8.4	2.2
217 | Heathrow	1965-12-01	8.4	1.7
218 | Heathrow	1966-1-01	5.3	1
219 | Heathrow	1966-2-01	9.3	4.4
220 | Heathrow	1966-3-01	10.9	2.6
221 | Heathrow	1966-4-01	12.2	5.4
222 | Heathrow	1966-5-01	17	7.5
223 | Heathrow	1966-6-01	21.8	12.2
224 | Heathrow	1966-7-01	20.1	12.2
225 | Heathrow	1966-8-01	20.6	11.6
226 | Heathrow	1966-9-01	19.6	10.8
227 | Heathrow	1966-10-01	14.7	8.6
228 | Heathrow	1966-11-01	8.8	2.9
229 | Heathrow	1966-12-01	9.3	2.2
230 | Heathrow	1967-1-01	7.3	2.1
231 | Heathrow	1967-2-01	9.3	2.6
232 | Heathrow	1967-3-01	11.6	3.7
233 | Heathrow	1967-4-01	12.5	4.4
234 | Heathrow	1967-5-01	15.6	7.8
235 | Heathrow	1967-6-01	20.1	10.3
236 | Heathrow	1967-7-01	23.5	13.8
237 | Heathrow	1967-8-01	21.5	12.7
238 | Heathrow	1967-9-01	18.5	11.1
239 | Heathrow	1967-10-01	15	8.8
240 | Heathrow	1967-11-01	9.7	2.6
241 | Heathrow	1967-12-01	7.2	1.3
242 | Heathrow	1968-1-01	7.2	1.3
243 | Heathrow	1968-2-01	5.7	0.2
244 | Heathrow	1968-3-01	11.1	3.5
245 | Heathrow	1968-4-01	13.7	4.4
246 | Heathrow	1968-5-01	15.4	7
247 | Heathrow	1968-6-01	20.6	11.5
248 | Heathrow	1968-7-01	20.7	12.6
249 | Heathrow	1968-8-01	20.2	12.6
250 | Heathrow	1968-9-01	18.8	11.6
251 | Heathrow	1968-10-01	16.5	10.7
252 | Heathrow	1968-11-01	9.5	4.8
253 | Heathrow	1968-12-01	5.4	1
254 | Heathrow	1969-1-01	8.9	3.4
255 | Heathrow	1969-2-01	4.9	-0.9
256 | Heathrow	1969-3-01	8	1.3
257 | Heathrow	1969-4-01	13.6	3.9
258 | Heathrow	1969-5-01	17.1	8.6
259 | Heathrow	1969-6-01	20.6	10.2
260 | Heathrow	1969-7-01	23.9	13.9
261 | Heathrow	1969-8-01	21.8	13.6
262 | Heathrow	1969-9-01	19.6	11.7
263 | Heathrow	1969-10-01	18.2	9.9
264 | Heathrow	1969-11-01	9.6	3.3
265 | Heathrow	1969-12-01	6.1	1.4
266 | Heathrow	1970-1-01	7.1	2.1
267 | Heathrow	1970-2-01	7.2	0.8
268 | Heathrow	1970-3-01	8.1	0.7
269 | Heathrow	1970-4-01	11.4	3.7
270 | Heathrow	1970-5-01	19.2	9.2
271 | Heathrow	1970-6-01	23.6	12.7
272 | Heathrow	1970-7-01	21.3	12.3
273 | Heathrow	1970-8-01	22.3	13
274 | Heathrow	1970-9-01	20.3	11.4
275 | Heathrow	1970-10-01	15.7	8.1
276 | Heathrow	1970-11-01	11.9	5.5
277 | Heathrow	1970-12-01	7	1.7
278 | Heathrow	1971-1-01	7.8	2.8
279 | Heathrow	1971-2-01	8.6	1.9
280 | Heathrow	1971-3-01	9.1	2.3
281 | Heathrow	1971-4-01	12.2	4.8
282 | Heathrow	1971-5-01	18.1	8.2
283 | Heathrow	1971-6-01	17.8	10.1
284 | Heathrow	1971-7-01	24.1	13.8
285 | Heathrow	1971-8-01	21.3	13.5
286 | Heathrow	1971-9-01	20.7	10.2
287 | Heathrow	1971-10-01	17.2	8.1
288 | Heathrow	1971-11-01	10.4	3
289 | Heathrow	1971-12-01	9.3	4.5
290 | Heathrow	1972-1-01	6.9	2.1
291 | Heathrow	1972-2-01	7.9	2.6
292 | Heathrow	1972-3-01	12.4	3.3
293 | Heathrow	1972-4-01	12.9	5.7
294 | Heathrow	1972-5-01	16	7.6
295 | Heathrow	1972-6-01	17.5	9
296 | Heathrow	1972-7-01	22.3	12.7
297 | Heathrow	1972-8-01	21.8	12.1
298 | Heathrow	1972-9-01	17.3	9
299 | Heathrow	1972-10-01	15.6	8.5
300 | Heathrow	1972-11-01	9.9	3.2
301 | Heathrow	1972-12-01	9.4	3.5
302 | Heathrow	1973-1-01	7.3	2
303 | Heathrow	1973-2-01	8.1	0.7
304 | Heathrow	1973-3-01	11.5	2.2
305 | Heathrow	1973-4-01	12.5	3.9
306 | Heathrow	1973-5-01	16.9	8.6
307 | Heathrow	1973-6-01	21.9	11.6
308 | Heathrow	1973-7-01	21.4	12.7
309 | Heathrow	1973-8-01	23.7	13.8
310 | Heathrow	1973-9-01	20.7	11.6
311 | Heathrow	1973-10-01	13.9	6.1
312 | Heathrow	1973-11-01	10.3	2.5
313 | Heathrow	1973-12-01	8.6	2.1
314 | Heathrow	1974-1-01	9.6	3.6
315 | Heathrow	1974-2-01	9.2	3
316 | Heathrow	1974-3-01	10.3	2.8
317 | Heathrow	1974-4-01	14	4.7
318 | Heathrow	1974-5-01	17	7.5
319 | Heathrow	1974-6-01	20.1	10.6
320 | Heathrow	1974-7-01	20.9	12.5
321 | Heathrow	1974-8-01	21.5	12.1
322 | Heathrow	1974-9-01	17.5	9.5
323 | Heathrow	1974-10-01	11	5
324 | Heathrow	1974-11-01	10.6	5
325 | Heathrow	1974-12-01	11	5.6
326 | Heathrow	1975-1-01	10.3	4.5
327 | Heathrow	1975-2-01	9.3	1.9
328 | Heathrow	1975-3-01	8.4	2.8
329 | Heathrow	1975-4-01	13.3	5
330 | Heathrow	1975-5-01	15.1	6.9
331 | Heathrow	1975-6-01	21.8	10.4
332 | Heathrow	1975-7-01	24.1	14.1
333 | Heathrow	1975-8-01	25.9	15
334 | Heathrow	1975-9-01	19.5	10.6
335 | Heathrow	1975-10-01	14.4	7
336 | Heathrow	1975-11-01	10.1	3.2
337 | Heathrow	1975-12-01	7.6	1.5
338 | Heathrow	1976-1-01	8.8	3
339 | Heathrow	1976-2-01	7.8	2.3
340 | Heathrow	1976-3-01	9.5	2
341 | Heathrow	1976-4-01	13.7	4.3
342 | Heathrow	1976-5-01	19.3	8.9
343 | Heathrow	1976-6-01	25.5	13.7
344 | Heathrow	1976-7-01	26.6	14.9
345 | Heathrow	1976-8-01	25.1	13.2
346 | Heathrow	1976-9-01	19	11.2
347 | Heathrow	1976-10-01	14.9	8.8
348 | Heathrow	1976-11-01	9.9	3.5
349 | Heathrow	1976-12-01	5.6	-0.2
350 | Heathrow	1977-1-01	6.1	0.8
351 | Heathrow	1977-2-01	9.6	3.4
352 | Heathrow	1977-3-01	11.2	4.5
353 | Heathrow	1977-4-01	12.2	4.1
354 | Heathrow	1977-5-01	16.4	7.2
355 | Heathrow	1977-6-01	17.4	9.6
356 | Heathrow	1977-7-01	22.4	12.7
357 | Heathrow	1977-8-01	20.3	13
358 | Heathrow	1977-9-01	18	10.7
359 | Heathrow	1977-10-01	16.3	9
360 | Heathrow	1977-11-01	10.4	4.6
361 | Heathrow	1977-12-01	9.2	4.1
362 | Heathrow	1978-1-01	6.7	0.7
363 | Heathrow	1978-2-01	6.2	0.7
364 | Heathrow	1978-3-01	11.3	3.5
365 | Heathrow	1978-4-01	10.7	3.9
366 | Heathrow	1978-5-01	17.2	7.9
367 | Heathrow	1978-6-01	19.7	10.7
368 | Heathrow	1978-7-01	20.5	12.7
369 | Heathrow	1978-8-01	20.7	12.1
370 | Heathrow	1978-9-01	20.1	10.8
371 | Heathrow	1978-10-01	17.1	8.4
372 | Heathrow	1978-11-01	12.6	6.2
373 | Heathrow	1978-12-01	7.8	2.6
374 | Heathrow	1979-1-01	3.8	-2.6
375 | Heathrow	1979-2-01	4.5	-0.4
376 | Heathrow	1979-3-01	9.2	2
377 | Heathrow	1979-4-01	12.7	4.8
378 | Heathrow	1979-5-01	15.9	7.2
379 | Heathrow	1979-6-01	19	11.1
380 | Heathrow	1979-7-01	22.5	13.4
381 | Heathrow	1979-8-01	21	12.2
382 | Heathrow	1979-9-01	19.6	10.3
383 | Heathrow	1979-10-01	16.4	8.8
384 | Heathrow	1979-11-01	10.8	3.5
385 | Heathrow	1979-12-01	9.2	3.9
386 | Heathrow	1980-1-01	6	-0.1
387 | Heathrow	1980-2-01	9.9	2.9
388 | Heathrow	1980-3-01	9	2.4
389 | Heathrow	1980-4-01	14	5.4
390 | Heathrow	1980-5-01	17.3	7.4
391 | Heathrow	1980-6-01	19.8	10.9
392 | Heathrow	1980-7-01	19.7	11.9
393 | Heathrow	1980-8-01	21.9	13.3
394 | Heathrow	1980-9-01	20.1	12
395 | Heathrow	1980-10-01	14	5.8
396 | Heathrow	1980-11-01	9.3	4.5
397 | Heathrow	1980-12-01	8.9	2.7
398 | Heathrow	1981-1-01	8.1	2
399 | Heathrow	1981-2-01	7.1	-0.1
400 | Heathrow	1981-3-01	11.8	6.5
401 | Heathrow	1981-4-01	12.9	4.9
402 | Heathrow	1981-5-01	16.1	8.4
403 | Heathrow	1981-6-01	18.5	10.4
404 | Heathrow	1981-7-01	22	12.8
405 | Heathrow	1981-8-01	22.8	13
406 | Heathrow	1981-9-01	20.3	11.3
407 | Heathrow	1981-10-01	12.9	5.8
408 | Heathrow	1981-11-01	11.5	5.1
409 | Heathrow	1981-12-01	4.4	-1.5
410 | Heathrow	1982-1-01	6.9	0.4
411 | Heathrow	1982-2-01	8.4	2.4
412 | Heathrow	1982-3-01	11.1	2.5
413 | Heathrow	1982-4-01	14.3	5
414 | Heathrow	1982-5-01	18	7.6
415 | Heathrow	1982-6-01	21.5	12.8
416 | Heathrow	1982-7-01	22.7	13.4
417 | Heathrow	1982-8-01	22.2	12.9
418 | Heathrow	1982-9-01	20.8	11.1
419 | Heathrow	1982-10-01	14.1	7.8
420 | Heathrow	1982-11-01	11.4	6.2
421 | Heathrow	1982-12-01	8.2	1.9
422 | Heathrow	1983-1-01	10	4.3
423 | Heathrow	1983-2-01	5.5	-0.5
424 | Heathrow	1983-3-01	10.8	3
425 | Heathrow	1983-4-01	12.4	3.6
426 | Heathrow	1983-5-01	15.6	7.5
427 | Heathrow	1983-6-01	20.8	11.4
428 | Heathrow	1983-7-01	27.6	16
429 | Heathrow	1983-8-01	24.5	13.8
430 | Heathrow	1983-9-01	19.3	11.3
431 | Heathrow	1983-10-01	15.1	7.6
432 | Heathrow	1983-11-01	11.3	5.5
433 | Heathrow	1983-12-01	9.2	2.7
434 | Heathrow	1984-1-01	8	1.4
435 | Heathrow	1984-2-01	7.4	1.3
436 | Heathrow	1984-3-01	8.9	2.5
437 | Heathrow	1984-4-01	14.6	3.9
438 | Heathrow	1984-5-01	14.9	6.8
439 | Heathrow	1984-6-01	21.3	11.3
440 | Heathrow	1984-7-01	24.2	12.9
441 | Heathrow	1984-8-01	24.4	14.2
442 | Heathrow	1984-9-01	18.6	11
443 | Heathrow	1984-10-01	15.8	8.5
444 | Heathrow	1984-11-01	12.2	6.4
445 | Heathrow	1984-12-01	8.7	2.8
446 | Heathrow	1985-1-01	4.1	-1.8
447 | Heathrow	1985-2-01	6.3	0.1
448 | Heathrow	1985-3-01	9.4	1.3
449 | Heathrow	1985-4-01	14	5.3
450 | Heathrow	1985-5-01	16.4	8.2
451 | Heathrow	1985-6-01	18.5	9.9
452 | Heathrow	1985-7-01	22.9	13.2
453 | Heathrow	1985-8-01	20.3	11.9
454 | Heathrow	1985-9-01	20.6	11.4
455 | Heathrow	1985-10-01	16.1	8.8
456 | Heathrow	1985-11-01	8.1	1.2
457 | Heathrow	1985-12-01	10	5.2
458 | Heathrow	1986-1-01	7.2	1
459 | Heathrow	1986-2-01	1.7	-2.7
460 | Heathrow	1986-3-01	10.1	1.8
461 | Heathrow	1986-4-01	10.9	3.2
462 | Heathrow	1986-5-01	16.3	7.9
463 | Heathrow	1986-6-01	21.8	11.4
464 | Heathrow	1986-7-01	22.6	13.3
465 | Heathrow	1986-8-01	20	11.5
466 | Heathrow	1986-9-01	17.4	8.1
467 | Heathrow	1986-10-01	16.4	8.5
468 | Heathrow	1986-11-01	12.1	5.2
469 | Heathrow	1986-12-01	9.7	3.2
470 | Heathrow	1987-1-01	3.6	-1
471 | Heathrow	1987-2-01	7.7	1.4
472 | Heathrow	1987-3-01	8.5	1.4
473 | Heathrow	1987-4-01	15.8	6.7
474 | Heathrow	1987-5-01	16.3	7
475 | Heathrow	1987-6-01	18.6	10.6
476 | Heathrow	1987-7-01	21.8	13.2
477 | Heathrow	1987-8-01	21.8	12.5
478 | Heathrow	1987-9-01	19.3	11.4
479 | Heathrow	1987-10-01	15	7.7
480 | Heathrow	1987-11-01	9.8	4.2
481 | Heathrow	1987-12-01	8.9	4.1
482 | Heathrow	1988-1-01	8.8	3.1
483 | Heathrow	1988-2-01	8.7	1.8
484 | Heathrow	1988-3-01	10.7	3.7
485 | Heathrow	1988-4-01	13.5	5.1
486 | Heathrow	1988-5-01	18	9.1
487 | Heathrow	1988-6-01	19.7	11.3
488 | Heathrow	1988-7-01	20	12.2
489 | Heathrow	1988-8-01	21.8	12.4
490 | Heathrow	1988-9-01	18.8	10.8
491 | Heathrow	1988-10-01	15.5	8.5
492 | Heathrow	1988-11-01	9.9	1.8
493 | Heathrow	1988-12-01	10.4	4.8
494 | Heathrow	1989-1-01	9.5	3.1
495 | Heathrow	1989-2-01	10.2	2.6
496 | Heathrow	1989-3-01	12.9	4.5
497 | Heathrow	1989-4-01	11.5	4.3
498 | Heathrow	1989-5-01	21	10
499 | Heathrow	1989-6-01	22.1	11.4
500 | Heathrow	1989-7-01	25.8	14.8
501 | Heathrow	1989-8-01	24.2	13.1
502 | Heathrow	1989-9-01	20.7	12.6
503 | Heathrow	1989-10-01	17.1	9.7
504 | Heathrow	1989-11-01	10.9	3.7
505 | Heathrow	1989-12-01	9.4	4.2
506 | Heathrow	1990-1-01	10.2	4.4
507 | Heathrow	1990-2-01	11.7	5.3
508 | Heathrow	1990-3-01	13.6	4.8
509 | Heathrow	1990-4-01	14.6	4.2
510 | Heathrow	1990-5-01	20.5	9
511 | Heathrow	1990-6-01	19.5	10.8
512 | Heathrow	1990-7-01	24.7	13.2
513 | Heathrow	1990-8-01	26	14.7
514 | Heathrow	1990-9-01	19.7	10
515 | Heathrow	1990-10-01	16.9	9.6
516 | Heathrow	1990-11-01	10.7	5.1
517 | Heathrow	1990-12-01	7.6	2.5
518 | Heathrow	1991-1-01	7.3	1.8
519 | Heathrow	1991-2-01	5.4	-1.3
520 | Heathrow	1991-3-01	12.5	5.3
521 | Heathrow	1991-4-01	13.1	4.8
522 | Heathrow	1991-5-01	15.7	7.7
523 | Heathrow	1991-6-01	17.8	9.7
524 | Heathrow	1991-7-01	23.2	14.4
525 | Heathrow	1991-8-01	24.1	14.4
526 | Heathrow	1991-9-01	21.4	11.5
527 | Heathrow	1991-10-01	14.6	7.6
528 | Heathrow	1991-11-01	10.6	4.2
529 | Heathrow	1991-12-01	8.3	1.7
530 | Heathrow	1992-1-01	7.5	2
531 | Heathrow	1992-2-01	9.5	2.3
532 | Heathrow	1992-3-01	11.8	4.9
533 | Heathrow	1992-4-01	14	5.6
534 | Heathrow	1992-5-01	21	10.2
535 | Heathrow	1992-6-01	22.5	12.3
536 | Heathrow	1992-7-01	22.5	14.2
537 | Heathrow	1992-8-01	21.7	13.6
538 | Heathrow	1992-9-01	18.9	11
539 | Heathrow	1992-10-01	12.5	5.3
540 | Heathrow	1992-11-01	12	4.8
541 | Heathrow	1992-12-01	7.8	1.6
542 | Heathrow	1993-1-01	10.3	3.5
543 | Heathrow	1993-2-01	7.2	2.5
544 | Heathrow	1993-3-01	11.8	3.3
545 | Heathrow	1993-4-01	14.6	6.8
546 | Heathrow	1993-5-01	18	8.8
547 | Heathrow	1993-6-01	21.8	12.2
548 | Heathrow	1993-7-01	21.6	12.6
549 | Heathrow	1993-8-01	21.6	11.7
550 | Heathrow	1993-9-01	17.5	9.8
551 | Heathrow	1993-10-01	13	7
552 | Heathrow	1993-11-01	8.8	2.4
553 | Heathrow	1993-12-01	9.2	3.6
554 | Heathrow	1994-1-01	9.3	2.8
555 | Heathrow	1994-2-01	7.7	1.2
556 | Heathrow	1994-3-01	12.3	5.1
557 | Heathrow	1994-4-01	13.3	5.1
558 | Heathrow	1994-5-01	16	8.2
559 | Heathrow	1994-6-01	21.5	11.2
560 | Heathrow	1994-7-01	26.2	15.2
561 | Heathrow	1994-8-01	23	13.8
562 | Heathrow	1994-9-01	17.8	10.8
563 | Heathrow	1994-10-01	15.4	7.7
564 | Heathrow	1994-11-01	13.6	8.4
565 | Heathrow	1994-12-01	10.7	4
566 | Heathrow	1995-1-01	8.9	2.2
567 | Heathrow	1995-2-01	10.7	4.4
568 | Heathrow	1995-3-01	11.2	2
569 | Heathrow	1995-4-01	15	6.1
570 | Heathrow	1995-5-01	19	8.2
571 | Heathrow	1995-6-01	20.7	10.8
572 | Heathrow	1995-7-01	26.3	15.2
573 | Heathrow	1995-8-01	27	15.6
574 | Heathrow	1995-9-01	19.2	10.7
575 | Heathrow	1995-10-01	18.3	10.2
576 | Heathrow	1995-11-01	12	5.1
577 | Heathrow	1995-12-01	5.8	1
578 | Heathrow	1996-1-01	7.2	3.1
579 | Heathrow	1996-2-01	6.8	0.1
580 | Heathrow	1996-3-01	8.6	2.1
581 | Heathrow	1996-4-01	14.3	5.5
582 | Heathrow	1996-5-01	15.1	5.9
583 | Heathrow	1996-6-01	22.8	11.3
584 | Heathrow	1996-7-01	24.2	13.2
585 | Heathrow	1996-8-01	23.1	13.4
586 | Heathrow	1996-9-01	19.2	10.7
587 | Heathrow	1996-10-01	16.6	8.9
588 | Heathrow	1996-11-01	10.5	2.7
589 | Heathrow	1996-12-01	6.2	1.4
590 | Heathrow	1997-1-01	5.5	0.2
591 | Heathrow	1997-2-01	10.8	4
592 | Heathrow	1997-3-01	13.8	5.3
593 | Heathrow	1997-4-01	15.6	5.4
594 | Heathrow	1997-5-01	18.7	8.5
595 | Heathrow	1997-6-01	20.2	12.2
596 | Heathrow	1997-7-01	23.5	13.9
597 | Heathrow	1997-8-01	25.8	16.6
598 | Heathrow	1997-9-01	20.9	11.8
599 | Heathrow	1997-10-01	15.9	7.5
600 | Heathrow	1997-11-01	12.5	6.2
601 | Heathrow	1997-12-01	9.5	4.2
602 | Heathrow	1998-1-01	9.2	3.5
603 | Heathrow	1998-2-01	11.6	3.5
604 | Heathrow	1998-3-01	12.1	5.7
605 | Heathrow	1998-4-01	12.7	5.7
606 | Heathrow	1998-5-01	19.6	10.2
607 | Heathrow	1998-6-01	19.9	12
608 | Heathrow	1998-7-01	21.4	13
609 | Heathrow	1998-8-01	23.5	12.7
610 | Heathrow	1998-9-01	20.4	12.7
611 | Heathrow	1998-10-01	14.7	8.6
612 | Heathrow	1998-11-01	9.6	3.4
613 | Heathrow	1998-12-01	9.7	3.8
614 | Heathrow	1999-1-01	9.4	3.8
615 | Heathrow	1999-2-01	8.8	2.8
616 | Heathrow	1999-3-01	12.2	5
617 | Heathrow	1999-4-01	14.9	6.3
618 | Heathrow	1999-5-01	19.1	10.2
619 | Heathrow	1999-6-01	20.3	11
620 | Heathrow	1999-7-01	24.9	14.3
621 | Heathrow	1999-8-01	22.8	14
622 | Heathrow	1999-9-01	21.3	13.2
623 | Heathrow	1999-10-01	15.6	7.9
624 | Heathrow	1999-11-01	11.3	5.9
625 | Heathrow	1999-12-01	8.7	2.4
626 | Heathrow	2000-1-01	8.6	2.4
627 | Heathrow	2000-2-01	10.4	3.8
628 | Heathrow	2000-3-01	12.1	4.9
629 | Heathrow	2000-4-01	12.9	5.4
630 | Heathrow	2000-5-01	18	9.6
631 | Heathrow	2000-6-01	21	12.8
632 | Heathrow	2000-7-01	21	12.8
633 | Heathrow	2000-8-01	23.2	14
634 | Heathrow	2000-9-01	20.1	12.5
635 | Heathrow	2000-10-01	14.7	8.5
636 | Heathrow	2000-11-01	11	4.5
637 | Heathrow	2000-12-01	9	4.6
638 | Heathrow	2001-1-01	7.1	1.8
639 | Heathrow	2001-2-01	9.2	2.8
640 | Heathrow	2001-3-01	9.5	3.8
641 | Heathrow	2001-4-01	12.8	5.4
642 | Heathrow	2001-5-01	18.8	9.1
643 | Heathrow	2001-6-01	20.8	11.3
644 | Heathrow	2001-7-01	24.3	14.5
645 | Heathrow	2001-8-01	23.5	14.5
646 | Heathrow	2001-9-01	18.1	10.8
647 | Heathrow	2001-10-01	17.6	11.3
648 | Heathrow	2001-11-01	11.3	4.4
649 | Heathrow	2001-12-01	7.3	1.4
650 | Heathrow	2002-1-01	9.5	3.5
651 | Heathrow	2002-2-01	11.3	4.8
652 | Heathrow	2002-3-01	12.4	5.1
653 | Heathrow	2002-4-01	15.7	6.1
654 | Heathrow	2002-5-01	17.3	9.4
655 | Heathrow	2002-6-01	20.1	11.6
656 | Heathrow	2002-7-01	22.3	13.5
657 | Heathrow	2002-8-01	23.3	14.7
658 | Heathrow	2002-9-01	20.3	11.5
659 | Heathrow	2002-10-01	15.3	8.4
660 | Heathrow	2002-11-01	12.4	7
661 | Heathrow	2002-12-01	9	5.2
662 | Heathrow	2003-1-01	8	2.3
663 | Heathrow	2003-2-01	8.4	1.8
664 | Heathrow	2003-3-01	13.6	4.4
665 | Heathrow	2003-4-01	15.7	6.3
666 | Heathrow	2003-5-01	18.2	9
667 | Heathrow	2003-6-01	23.3	13.3
668 | Heathrow	2003-7-01	24.2	14.7
669 | Heathrow	2003-8-01	26.4	15.7
670 | Heathrow	2003-9-01	21.9	10.8
671 | Heathrow	2003-10-01	14.5	6.4
672 | Heathrow	2003-11-01	12.6	6.7
673 | Heathrow	2003-12-01	8.8	3.2
674 | Heathrow	2004-1-01	8.8	3.1
675 | Heathrow	2004-2-01	8.7	3.8
676 | Heathrow	2004-3-01	10.8	3.9
677 | Heathrow	2004-4-01	14.9	6.3
678 | Heathrow	2004-5-01	18.2	9.5
679 | Heathrow	2004-6-01	22.1	12.6
680 | Heathrow	2004-7-01	22.7	13.3
681 | Heathrow	2004-8-01	23.8	15.1
682 | Heathrow	2004-9-01	20.8	12.5
683 | Heathrow	2004-10-01	15.3	9.1
684 | Heathrow	2004-11-01	11.2	6.2
685 | Heathrow	2004-12-01	8.8	2.9
686 | Heathrow	2005-1-01	9.5	3.8
687 | Heathrow	2005-2-01	7.3	2.5
688 | Heathrow	2005-3-01	11.5	4.5
689 | Heathrow	2005-4-01	14.6	6.2
690 | Heathrow	2005-5-01	17.7	8.5
691 | Heathrow	2005-6-01	22.8	12.9
692 | Heathrow	2005-7-01	23.3	14.1
693 | Heathrow	2005-8-01	23.2	13
694 | Heathrow	2005-9-01	21.4	12.7
695 | Heathrow	2005-10-01	17.8	11.3
696 | Heathrow	2005-11-01	10.7	3.5
697 | Heathrow	2005-12-01	8.2	1.9
698 | Heathrow	2006-1-01	7.6	2.9
699 | Heathrow	2006-2-01	7.2	1.7
700 | Heathrow	2006-3-01	9.4	2.8
701 | Heathrow	2006-4-01	14.1	6
702 | Heathrow	2006-5-01	18	9.8
703 | Heathrow	2006-6-01	23.7	12.9
704 | Heathrow	2006-7-01	28.2	16.7
705 | Heathrow	2006-8-01	22.2	13.5
706 | Heathrow	2006-9-01	22.7	14.5
707 | Heathrow	2006-10-01	17.8	11.2
708 | Heathrow	2006-11-01	12.7	5.4
709 | Heathrow	2006-12-01	9.7	4.5
710 | Heathrow	2007-1-01	10.5	5.1
711 | Heathrow	2007-2-01	9.9	3.9
712 | Heathrow	2007-3-01	12.5	4.4
713 | Heathrow	2007-4-01	18.9	7.7
714 | Heathrow	2007-5-01	17.9	9.7
715 | Heathrow	2007-6-01	21.2	12.6
716 | Heathrow	2007-7-01	21.4	13.1
717 | Heathrow	2007-8-01	21.5	12.9
718 | Heathrow	2007-9-01	19.9	11.4
719 | Heathrow	2007-10-01	15.4	8.5
720 | Heathrow	2007-11-01	11.4	4.6
721 | Heathrow	2007-12-01	8.9	3.1
722 | Heathrow	2008-1-01	10.4	4.7
723 | Heathrow	2008-2-01	11	2
724 | Heathrow	2008-3-01	10.6	3.7
725 | Heathrow	2008-4-01	13.7	5.2
726 | Heathrow	2008-5-01	19.8	10.5
727 | Heathrow	2008-6-01	20.8	11.9
728 | Heathrow	2008-7-01	22.8	13.7
729 | Heathrow	2008-8-01	21.5	14.2
730 | Heathrow	2008-9-01	18.5	11.2
731 | Heathrow	2008-10-01	15	6.8
732 | Heathrow	2008-11-01	10.7	5.2
733 | Heathrow	2008-12-01	7.3	1.7
734 | Heathrow	2009-1-01	6.8	0.3
735 | Heathrow	2009-2-01	7.8	2.1
736 | Heathrow	2009-3-01	12.9	3.7
737 | Heathrow	2009-4-01	16.1	7.2
738 | Heathrow	2009-5-01	19.1	9.4
739 | Heathrow	2009-6-01	22.4	12.2
740 | Heathrow	2009-7-01	23	13.7
741 | Heathrow	2009-8-01	23.9	14.1
742 | Heathrow	2009-9-01	20.5	12
743 | Heathrow	2009-10-01	16.3	9.3
744 | Heathrow	2009-11-01	12.6	7.4
745 | Heathrow	2009-12-01	7	1.3
746 | Heathrow	2010-1-01	4.5	-0.3
747 | Heathrow	2010-2-01	6.9	1.7
748 | Heathrow	2010-3-01	11.1	3.7
749 | Heathrow	2010-4-01	15.8	5.6
750 | Heathrow	2010-5-01	17.3	7.7
751 | Heathrow	2010-6-01	23.5	12.1
752 | Heathrow	2010-7-01	25	15.1
753 | Heathrow	2010-8-01	21.6	13.2
754 | Heathrow	2010-9-01	19.4	11.2
755 | Heathrow	2010-10-01	15.2	8.3
756 | Heathrow	2010-11-01	9.1	4
757 | Heathrow	2010-12-01	3.9	-1.5
758 | Heathrow	2011-1-01	7.4	2.8
759 | Heathrow	2011-2-01	10.2	4.8
760 | Heathrow	2011-3-01	12.3	3.8
761 | Heathrow	2011-4-01	19.7	8.6
762 | Heathrow	2011-5-01	19.4	9.4
763 | Heathrow	2011-6-01	20.7	11
764 | Heathrow	2011-7-01	21.7	12.6
765 | Heathrow	2011-8-01	21.8	13.4
766 | Heathrow	2011-9-01	21.3	12.4
767 | Heathrow	2011-10-01	18.1	10.1
768 | Heathrow	2011-11-01	13.6	7.3
769 | Heathrow	2011-12-01	9.9	3.8
770 | Heathrow	2012-1-01	9.8	3.4
771 | Heathrow	2012-2-01	8	1.3
772 | Heathrow	2012-3-01	14.7	4.7
773 | Heathrow	2012-4-01	13.3	4.9
774 | Heathrow	2012-5-01	18.2	9.7
775 | Heathrow	2012-6-01	19.4	11.6
776 | Heathrow	2012-7-01	21.3	13.2
777 | Heathrow	2012-8-01	23.5	14.3
778 | Heathrow	2012-9-01	20	10.3
779 | Heathrow	2012-10-01	14.2	8
780 | Heathrow	2012-11-01	11	4.6
781 | Heathrow	2012-12-01	9	2.6
782 | Heathrow	2013-1-01	6.5	2
783 | Heathrow	2013-2-01	6.7	1.2
784 | Heathrow	2013-3-01	6.9	1.2
785 | Heathrow	2013-4-01	13.5	4.7
786 | Heathrow	2013-5-01	16.4	7.7
787 | Heathrow	2013-6-01	20.3	11.2
788 | Heathrow	2013-7-01	27	15.2
789 | Heathrow	2013-8-01	24.3	14.3
790 | Heathrow	2013-9-01	19.7	11.1
791 | Heathrow	2013-10-01	17	10.6
792 | Heathrow	2013-11-01	10.4	4.7
793 | Heathrow	2013-12-01	10.2	3.5
794 | Heathrow	2014-1-01	10	3.8
795 | Heathrow	2014-2-01	10.6	4.4
796 | Heathrow	2014-3-01	14.1	4.4
797 | Heathrow	2014-4-01	16.1	7.5
798 | Heathrow	2014-5-01	18	9.8
799 | Heathrow	2014-6-01	22.1	12.5
800 | Heathrow	2014-7-01	25.8	15
801 | Heathrow	2014-8-01	21.7	12.7
802 | Heathrow	2014-9-01	21.5	12.8
803 | Heathrow	2014-10-01	17.6	11
804 | Heathrow	2014-11-01	12.5	6.9
805 | Heathrow	2014-12-01	9.2	3
806 | Heathrow	2015-1-01	8.8	1.6
807 | Heathrow	2015-2-01	8	1.8
808 | Heathrow	2015-3-01	11.6	4.1
809 | Heathrow	2015-4-01	16.3	6
810 | Heathrow	2015-5-01	17.6	8.8
811 | Heathrow	2015-6-01	22.2	11.4
812 | Heathrow	2015-7-01	23.7	13.8
813 | Heathrow	2015-8-01	22.2	14.1
814 | Heathrow	2015-9-01	18.6	10.2
815 | Heathrow	2015-10-01	15.8	9.3
816 | Heathrow	2015-11-01	13.4	8
817 | Heathrow	2015-12-01	13.7	8.9
818 | Heathrow	2016-1-01	9.5	3
819 | Heathrow	2016-2-01	9.4	2.9
820 | Heathrow	2016-3-01	10.7	3.2
821 | Heathrow	2016-4-01	13.5	4.9
822 | Heathrow	2016-5-01	19	9.7
823 | Heathrow	2016-6-01	20.7	12.7
824 | Heathrow	2016-7-01	24	14.5
825 | Heathrow	2016-8-01	24.7	14.6
826 | Heathrow	2016-9-01	22.4	13.7
827 | Heathrow	2016-10-01	15.9	8.7
828 | Heathrow	2016-11-01	10.5	3.8
829 | Heathrow	2016-12-01	10.2	3.4
830 | Heathrow	2017-1-01	7.6	0.7
831 | Heathrow	2017-2-01	10	4.4
832 | Heathrow	2017-3-01	14.1	6.6
833 | Heathrow	2017-4-01	15.8	5.9
834 | Heathrow	2017-5-01	19.8	10.4
835 | Heathrow	2017-6-01	24	13.9
836 | Heathrow	2017-7-01	23.8	14.9
837 | Heathrow	2017-8-01	22	13.5
838 | Heathrow	2017-9-01	19.2	11
839 | Heathrow	2017-10-01	17.1	10.3
840 | Heathrow	2017-11-01	11.1	4.5
841 | Heathrow	2017-12-01	8.7	2.9
842 | Heathrow	2018-1-01	9.7	3.8
843 | Heathrow	2018-2-01	6.7	0.6
844 | 


--------------------------------------------------------------------------------
/Section 3/weather/main.py:
--------------------------------------------------------------------------------
 1 | from os.path import join, dirname
 2 | import datetime
 3 | 
 4 | import pandas as pd
 5 | from scipy.signal import savgol_filter
 6 | 
 7 | from bokeh.io import curdoc
 8 | from bokeh.layouts import row, column
 9 | from bokeh.models import ColumnDataSource, DataRange1d, Select
10 | from bokeh.palettes import Blues4
11 | from bokeh.plotting import figure
12 | 
13 | STATISTICS = ['record_min_temp', 'actual_min_temp', 'average_min_temp', 'average_max_temp', 'actual_max_temp', 'record_max_temp']
14 | 
15 | def get_dataset(src, name, distribution):
16 |     df = src[src.airport == name].copy()
17 |     del df['airport']
18 |     df['date'] = pd.to_datetime(df.date)
19 |     # timedelta here instead of pd.DateOffset to avoid pandas bug < 0.18 (Pandas issue #11925)
20 |     df['left'] = df.date - datetime.timedelta(days=0.5)
21 |     df['right'] = df.date + datetime.timedelta(days=0.5)
22 |     df = df.set_index(['date'])
23 |     df.sort_index(inplace=True)
24 |     if distribution == 'Smoothed':
25 |         window, order = 51, 3
26 |         for key in STATISTICS:
27 |             df[key] = savgol_filter(df[key], window, order)
28 | 
29 |     return ColumnDataSource(data=df)
30 | 
31 | def make_plot(source, title):
32 |     plot = figure(x_axis_type="datetime", plot_width=800, tools="", toolbar_location=None)
33 |     plot.title.text = title
34 | 
35 |     plot.quad(top='record_max_temp', bottom='record_min_temp', left='left', right='right',
36 |               color=Blues4[2], source=source, legend="Record")
37 |     plot.quad(top='average_max_temp', bottom='average_min_temp', left='left', right='right',
38 |               color=Blues4[1], source=source, legend="Average")
39 |     plot.quad(top='actual_max_temp', bottom='actual_min_temp', left='left', right='right',
40 |               color=Blues4[0], alpha=0.5, line_color="black", source=source, legend="Actual")
41 | 
42 |     # fixed attributes
43 |     plot.xaxis.axis_label = None
44 |     plot.yaxis.axis_label = "Temperature (F)"
45 |     plot.axis.axis_label_text_font_style = "bold"
46 |     plot.x_range = DataRange1d(range_padding=0.0)
47 |     plot.grid.grid_line_alpha = 0.3
48 | 
49 |     return plot
50 | 
51 | def update_plot(attrname, old, new):
52 |     city = city_select.value
53 |     plot.title.text = "Weather data for " + cities[city]['title']
54 | 
55 |     src = get_dataset(df, cities[city]['airport'], distribution_select.value)
56 |     source.data.update(src.data)
57 | 
58 | city = 'Austin'
59 | distribution = 'Discrete'
60 | 
61 | cities = {
62 |     'Austin': {
63 |         'airport': 'AUS',
64 |         'title': 'Austin, TX',
65 |     },
66 |     'Boston': {
67 |         'airport': 'BOS',
68 |         'title': 'Boston, MA',
69 |     },
70 |     'Seattle': {
71 |         'airport': 'SEA',
72 |         'title': 'Seattle, WA',
73 |     }
74 | }
75 | 
76 | city_select = Select(value=city, title='City', options=sorted(cities.keys()))
77 | distribution_select = Select(value=distribution, title='Distribution', options=['Discrete', 'Smoothed'])
78 | 
79 | df = pd.read_csv(join(dirname(__file__), 'data/2015_weather.csv'))
80 | source = get_dataset(df, cities[city]['airport'], distribution)
81 | plot = make_plot(source, "Weather data for " + cities[city]['title'])
82 | 
83 | city_select.on_change('value', update_plot)
84 | distribution_select.on_change('value', update_plot)
85 | 
86 | controls = column(city_select, distribution_select)
87 | 
88 | curdoc().add_root(row(plot, controls))
89 | curdoc().title = "Weather"
90 | 


--------------------------------------------------------------------------------
/Section 4/README.md:
--------------------------------------------------------------------------------
1 | # Data-visualization-projects-in-python
2 | Data visualization projects in python [video], published by Packt
3 | 


--------------------------------------------------------------------------------
/Section 4/android1/app.py:
--------------------------------------------------------------------------------
 1 | # -*- coding: utf-8 -*-
 2 | import dash
 3 | import dash_core_components as dcc
 4 | import dash_html_components as html
 5 | 
 6 | app = dash.Dash()
 7 | 
 8 | app.layout = html.Div(children=[
 9 |     html.H1(children='Android Data Visualization'),
10 | 
11 |     html.Div(children='''
12 |         Android versions marketshare data visusliation using Dash.
13 |     '''),
14 | 
15 |     dcc.Graph(
16 |         id='example-graph',
17 |         figure={
18 |             'data': [
19 |                 {'x': ['Gingerbread', 'Ice Cream Sandwich', 'Jelly Bean', 'Kitkat',
20 |                  'Lollipop', 'Marshmallow','Nougat', 'Oreo'],
21 |                  'y': [0.3, 0.4, 5, 12, 24.6, 28.10, 28.5, 1.1],
22 |                  'type': 'bar'}
23 |             ],
24 |             'layout': {
25 |                 'title': 'Android Data Visualization'
26 |             }
27 |         }
28 |     )
29 | ])
30 | 
31 | if __name__ == '__main__':
32 |     app.run_server(debug=True)
33 | 


--------------------------------------------------------------------------------
/Section 4/android2/app.py:
--------------------------------------------------------------------------------
 1 | # -*- coding: utf-8 -*-
 2 | import dash
 3 | import dash_core_components as dcc
 4 | import dash_html_components as html
 5 | 
 6 | app = dash.Dash()
 7 | 
 8 | colors = {
 9 |     'background': '#111111',
10 |     'text': '#7FDBFF'
11 | }
12 | 
13 | app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
14 |     html.H1(
15 |         children='Android Data Visualization',
16 |         style={
17 |             'textAlign': 'center',
18 |             'color': colors['text']
19 |         }
20 |     ),
21 | 
22 |     html.Div(children='Android versions\' marketshare data visualization using Dash.', style={
23 |         'textAlign': 'center',
24 |         'color': colors['text']
25 |     }),
26 | 
27 |     dcc.Graph(
28 |         id='example-graph',
29 |         figure={
30 |             'data': [
31 |                 {'x': ['Gingerbread', 'Ice Cream Sandwich', 'Jelly Bean', 'Kitkat',
32 |                  'Lollipop', 'Marshmallow','Nougat', 'Oreo'],
33 |                  'y': [0.3, 0.4, 5, 12, 24.6, 28.10, 28.5, 1.1],
34 |                  'type': 'bar'}
35 |             ],
36 |             'layout': {
37 |                 'title': 'Android Data Visualization',
38 |                 'plot_bgcolor': colors['background'],
39 |                 'paper_bgcolor': colors['background'],
40 |                 'font': {
41 |                     'color': colors['text']
42 |                     }
43 |             }
44 |         }
45 |     )
46 | ])
47 | 
48 | if __name__ == '__main__':
49 |     app.run_server(debug=True)
50 | 


--------------------------------------------------------------------------------
/Section 4/walmart1/app.py:
--------------------------------------------------------------------------------
 1 | import dash
 2 | import dash_core_components as dcc
 3 | import dash_html_components as html
 4 | import plotly.graph_objs as go
 5 | import pandas as pd
 6 | 
 7 | app = dash.Dash()
 8 | 
 9 | df = pd.read_csv(
10 |     'https://raw.githubusercontent.com/'
11 |     'plotly/datasets/master/'
12 |     '1962_2006_walmart_store_openings.csv')
13 | 
14 | app.layout = html.Div([
15 |     html.H1('Walmart Store Openings'),
16 |     html.Div(id='text-content'),
17 |     dcc.Graph(id='map', figure={
18 |         'data': [{
19 |             'lat': df['LAT'],
20 |             'lon': df['LON'],
21 |             'marker': {
22 |                 'color': df['YEAR'],
23 |                 'size': 8,
24 |                 'opacity': 0.6
25 |             },
26 |             'customdata': df['storenum'],
27 |             'type': 'scattermapbox'
28 |         }],
29 |         'layout': {
30 |             'mapbox': {
31 |                 'accesstoken': 'pk.eyJ1IjoiY2hyaWRkeXAiLCJhIjoiY2ozcGI1MTZ3MDBpcTJ3cXR4b3owdDQwaCJ9.8jpMunbKjdq1anXwU5gxIw'
32 |             },
33 |             'hovermode': 'closest',
34 |             'margin': {'l': 0, 'r': 0, 'b': 0, 't': 0}
35 |         }
36 |     })
37 | ])
38 | 
39 | 
40 | @app.callback(
41 |     dash.dependencies.Output('text-content', 'children'),
42 |     [dash.dependencies.Input('map', 'hoverData')])
43 | def update_text(hoverData):
44 |     s = df[df['storenum'] == hoverData['points'][0]['customdata']]
45 |     return html.H3(
46 |         'The {}, {} {} opened in {}'.format(
47 |             s.iloc[0]['STRCITY'],
48 |             s.iloc[0]['STRSTATE'],
49 |             s.iloc[0]['type_store'],
50 |             s.iloc[0]['YEAR']
51 |         )
52 |     )
53 | 
54 | app.css.append_css({
55 |     'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'
56 | })
57 | 
58 | if __name__ == '__main__':
59 |     app.run_server(debug=True)
60 | 


--------------------------------------------------------------------------------
/Section 4/walmart2/app.py:
--------------------------------------------------------------------------------
 1 | import dash
 2 | import dash_html_components as html
 3 | import dash_core_components as dcc
 4 | 
 5 | import pandas as pd
 6 | 
 7 | df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv')
 8 | 
 9 | app = dash.Dash()
10 | app.scripts.config.serve_locally=True
11 | app.css.append_css({"external_url": "https://codepen.io/chriddyp/pen/dZVMbK.css"})
12 | 
13 | 
14 | app.layout = html.Div([
15 |     html.H1('Dash App'),
16 |     html.Div(id='text-element'),
17 |     dcc.Dropdown(
18 |         id='dropdown',
19 |         options=[
20 |             {'label': i, 'value': i}
21 |             for i in df.YEAR.unique()
22 |         ],
23 |         value=2001
24 |     ),
25 |     dcc.Graph(
26 |         id='my-graph',
27 |         figure={
28 |             'data': [
29 |                 {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar'}
30 |             ]
31 |         }
32 |     )
33 | ])
34 | 
35 | @app.callback(
36 |     dash.dependencies.Output('text-element', 'children'),
37 |     [dash.dependencies.Input('dropdown', 'value')])
38 | def update_text(new_year):
39 |     dff = df[df.YEAR == new_year]
40 |     return 'There were {} stores in {}'.format(
41 |         len(dff),
42 |         new_year
43 |     )
44 | 
45 | @app.callback(
46 |     dash.dependencies.Output('my-graph', 'figure'),
47 |     [dash.dependencies.Input('dropdown', 'value')])
48 | def update_graph(new_value):
49 |     dff = df[df.YEAR == new_value]
50 |     return {
51 |         'data': [{
52 |             'lat': dff.LAT,
53 |             'lon': dff.LON,
54 |             'type': 'scattermapbox',
55 |         }],
56 |         'layout': {'mapbox': {'accesstoken': 'pk.eyJ1IjoiY2hyaWRkeXAiLCJhIjoiY2oyY2M4YW55MDF1YjMzbzhmemIzb290NiJ9.sT6pncHLXLgytVEj21q43A'}}
57 |     }
58 | 
59 | if __name__ == '__main__':
60 |     app.run_server(debug=True)
61 | 


--------------------------------------------------------------------------------