├── .gitignore
├── GDELT correlations.ipynb
├── LICENSE
├── README.md
├── analyzing hacker news.ipynb
└── graphs - gdelt correlation countries.ipynb


/.gitignore:
--------------------------------------------------------------------------------
 1 | # Byte-compiled / optimized / DLL files
 2 | __pycache__/
 3 | *.py[cod]
 4 | 
 5 | # C extensions
 6 | *.so
 7 | 
 8 | # Distribution / packaging
 9 | .Python
10 | env/
11 | bin/
12 | build/
13 | develop-eggs/
14 | dist/
15 | eggs/
16 | lib/
17 | lib64/
18 | parts/
19 | sdist/
20 | var/
21 | *.egg-info/
22 | .installed.cfg
23 | *.egg
24 | 
25 | # Installer logs
26 | pip-log.txt
27 | pip-delete-this-directory.txt
28 | 
29 | # Unit test / coverage reports
30 | htmlcov/
31 | .tox/
32 | .coverage
33 | .cache
34 | nosetests.xml
35 | coverage.xml
36 | 
37 | # Translations
38 | *.mo
39 | 
40 | # Mr Developer
41 | .mr.developer.cfg
42 | .project
43 | .pydevproject
44 | 
45 | # Rope
46 | .ropeproject
47 | 
48 | # Django stuff:
49 | *.log
50 | *.pot
51 | 
52 | # Sphinx documentation
53 | docs/_build/
54 | 
55 | 


--------------------------------------------------------------------------------
/GDELT correlations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "metadata": {
  3 |   "name": "",
  4 |   "signature": "sha256:e59be26980c86a31ac9d48f4c2d7103cb8a0b84810433e8cb27d69a296fca9d3"
  5 |  },
  6 |  "nbformat": 3,
  7 |  "nbformat_minor": 0,
  8 |  "worksheets": [
  9 |   {
 10 |    "cells": [
 11 |     {
 12 |      "cell_type": "markdown",
 13 |      "metadata": {},
 14 |      "source": [
 15 |       "By [Felipe Hoffa](https://plus.google.com/+FelipeHoffa/)\n",
 16 |       "([@felipehoffa](http://twitter.com/felipehoffa), [/r/bigquery](http://reddit.com/r/bigquery))\n\n",
 17 |       "\n",
 18 |       "To look further into how to read these results to understand history, look at:\n\n",
 19 |       "* Kalev Leetaru's [GDELT correlations post](http://blog.gdeltproject.org/towards-psychohistory-uncovering-the-patterns-of-world-history-with-google-bigquery/).\n",
 20 |       "* Andrew Gelman's [counterpoint](http://andrewgelman.com/2014/08/15/psychohistory-hype-paradox/).\n",
 21 |       "* Anthony A. Boyles's [counterpoint](http://aaboyles.com/2014/08/access-event-data-in-google-bigquery-from-r/).\n",
 22 |       "\n"
 23 |      ]
 24 |     },
 25 |     {
 26 |      "cell_type": "code",
 27 |      "collapsed": false,
 28 |      "input": [
 29 |       "# Some pre-flight work:\n",
 30 |       "# - Libraries.\n",
 31 |       "# - Patch to handle authentication automatically on Google Compute Engine",
 32 |       "from oauth2client.gce import AppAssertionCredentials\n",
 33 |       "from bigquery_client import BigqueryClient\n",
 34 |       "from pandas.io import gbq\n",
 35 |       "import pandas as pd\n",
 36 |       "\n",
 37 |       "def GetMetadata(path):\n",
 38 |       "  import urllib2\n",
 39 |       "  BASE_PATH = 'http://metadata/computeMetadata/v1/'\n",
 40 |       "  request = urllib2.Request(BASE_PATH + path, headers={'Metadata-Flavor': 'Google'})\n",
 41 |       "  return urllib2.urlopen(request).read()\n",
 42 |       "\n",
 43 |       "credentials = AppAssertionCredentials(scope='https://www.googleapis.com/auth/bigquery')\n",
 44 |       "\n",
 45 |       "client = BigqueryClient(credentials=credentials,\n",
 46 |       "                        api='https://www.googleapis.com',\n",
 47 |       "                        api_version='v2',\n",
 48 |       "                        project_id=GetMetadata('project/project-id'))\n",
 49 |       "\n",
 50 |       "gbq._authenticate = lambda: client"
 51 |      ],
 52 |      "language": "python",
 53 |      "metadata": {},
 54 |      "outputs": [],
 55 |      "prompt_number": 13
 56 |     },
 57 |     {
 58 |      "cell_type": "markdown",
 59 |      "metadata": {},
 60 |      "source": [
 61 |       "How many rows GDELT has for each country?\n",
 62 |       "\n",
 63 |       "We are going to use a view specially defined for that. 'sample_views.country_date_matconf_numarts' counts the number of material conflicts per day per country.\n",
 64 |       "\n",
 65 |       "To see the view definition, go to https://bigquery.cloud.google.com/table/gdelt-bq:sample_views.country_date_matconf_numarts and click on 'Details'."
 66 |      ]
 67 |     },
 68 |     {
 69 |      "cell_type": "code",
 70 |      "collapsed": false,
 71 |      "input": [
 72 |       "query = \"\"\"\n",
 73 |       "SELECT country, SUM(c) AS count\n",
 74 |       "FROM [gdelt-bq:sample_views.country_date_matconf_numarts]\n",
 75 |       "GROUP BY country\n",
 76 |       "ORDER BY count DESC\n",
 77 |       "LIMIT 10\n",
 78 |       "\"\"\"\n",
 79 |       "gbq.read_gbq(query)"
 80 |      ],
 81 |      "language": "python",
 82 |      "metadata": {},
 83 |      "outputs": [
 84 |       {
 85 |        "output_type": "stream",
 86 |        "stream": "stdout",
 87 |        "text": [
 88 |         "\r",
 89 |         "Waiting on bqjob_r3494ef2663a3c45e_00000147cc145563_1 ... (0s) Current status: DONE   \n"
 90 |        ]
 91 |       },
 92 |       {
 93 |        "html": [
 94 |         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 95 |         "<table border=\"1\" class=\"dataframe\">\n",
 96 |         "  <thead>\n",
 97 |         "    <tr style=\"text-align: right;\">\n",
 98 |         "      <th></th>\n",
 99 |         "      <th>country</th>\n",
100 |         "      <th>count</th>\n",
101 |         "    </tr>\n",
102 |         "  </thead>\n",
103 |         "  <tbody>\n",
104 |         "    <tr>\n",
105 |         "      <th>0</th>\n",
106 |         "      <td>         Israel</td>\n",
107 |         "      <td> 13138702</td>\n",
108 |         "    </tr>\n",
109 |         "    <tr>\n",
110 |         "      <th>1</th>\n",
111 |         "      <td>       Pakistan</td>\n",
112 |         "      <td> 11547139</td>\n",
113 |         "    </tr>\n",
114 |         "    <tr>\n",
115 |         "      <th>2</th>\n",
116 |         "      <td>    Afghanistan</td>\n",
117 |         "      <td> 11334579</td>\n",
118 |         "    </tr>\n",
119 |         "    <tr>\n",
120 |         "      <th>3</th>\n",
121 |         "      <td>           Iraq</td>\n",
122 |         "      <td> 10181510</td>\n",
123 |         "    </tr>\n",
124 |         "    <tr>\n",
125 |         "      <th>4</th>\n",
126 |         "      <td>          Syria</td>\n",
127 |         "      <td>  9538310</td>\n",
128 |         "    </tr>\n",
129 |         "    <tr>\n",
130 |         "      <th>5</th>\n",
131 |         "      <td> United Kingdom</td>\n",
132 |         "      <td>  8764009</td>\n",
133 |         "    </tr>\n",
134 |         "    <tr>\n",
135 |         "      <th>6</th>\n",
136 |         "      <td>         Russia</td>\n",
137 |         "      <td>  7569903</td>\n",
138 |         "    </tr>\n",
139 |         "    <tr>\n",
140 |         "      <th>7</th>\n",
141 |         "      <td>          India</td>\n",
142 |         "      <td>  6988340</td>\n",
143 |         "    </tr>\n",
144 |         "    <tr>\n",
145 |         "      <th>8</th>\n",
146 |         "      <td>          Egypt</td>\n",
147 |         "      <td>  6511839</td>\n",
148 |         "    </tr>\n",
149 |         "    <tr>\n",
150 |         "      <th>9</th>\n",
151 |         "      <td>          China</td>\n",
152 |         "      <td>  6268915</td>\n",
153 |         "    </tr>\n",
154 |         "  </tbody>\n",
155 |         "</table>\n",
156 |         "</div>"
157 |        ],
158 |        "metadata": {},
159 |        "output_type": "pyout",
160 |        "prompt_number": 2,
161 |        "text": [
162 |         "          country     count\n",
163 |         "0          Israel  13138702\n",
164 |         "1        Pakistan  11547139\n",
165 |         "2     Afghanistan  11334579\n",
166 |         "3            Iraq  10181510\n",
167 |         "4           Syria   9538310\n",
168 |         "5  United Kingdom   8764009\n",
169 |         "6          Russia   7569903\n",
170 |         "7           India   6988340\n",
171 |         "8           Egypt   6511839\n",
172 |         "9           China   6268915"
173 |        ]
174 |       }
175 |      ],
176 |      "prompt_number": 2
177 |     },
178 |     {
179 |      "cell_type": "markdown",
180 |      "metadata": {},
181 |      "source": [
182 |       "Focusing solely in Egypt. Let's plot the number of material conflicts reported for each day over the last 35 years:"
183 |      ]
184 |     },
185 |     {
186 |      "cell_type": "code",
187 |      "collapsed": false,
188 |      "input": [
189 |       "query = \"\"\"\n",
190 |       "SELECT date, c AS count\n",
191 |       "FROM [gdelt-bq:sample_views.country_date_matconf_numarts]\n",
192 |       "WHERE country='Egypt'\n",
193 |       "ORDER BY date\n",
194 |       "\"\"\"\n",
195 |       "data=gbq.read_gbq(query)\n",
196 |       "data.index=pd.to_datetime(data['date']*1000)\n",
197 |       "x = data['count'].plot()"
198 |      ],
199 |      "language": "python",
200 |      "metadata": {},
201 |      "outputs": [
202 |       {
203 |        "output_type": "stream",
204 |        "stream": "stdout",
205 |        "text": [
206 |         "\r",
207 |         "Waiting on bqjob_r46d7af66323c4ad9_00000147cc18e43f_8 ... (0s) Current status: DONE   \n"
208 |        ]
209 |       },
210 |       {
211 |        "metadata": {},
212 |        "output_type": "display_data",
213 |        "png": 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GPB7Kj8Bu4L4rdgP3y0N5D+Au4DPYDdzPAF7PokWevxCiQxDN81+zxub5V1fDu++mt+++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\nHlu37nhTFdgx2G/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214 |        "text": [
215 |         "<matplotlib.figure.Figure at 0x7feb5c16b990>"
216 |        ]
217 |       }
218 |      ],
219 |      "prompt_number": 9
220 |     },
221 |     {
222 |      "cell_type": "markdown",
223 |      "metadata": {},
224 |      "source": [
225 |       "What if we only focus on the 30 days previous to January 27th, 2011."
226 |      ]
227 |     },
228 |     {
229 |      "cell_type": "code",
230 |      "collapsed": false,
231 |      "input": [
232 |       "query = \"\"\"\n",
233 |       "  SELECT country, date, c AS count\n",
234 |       "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
235 |       "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
236 |       "  WHERE country='Egypt'\n",
237 |       "  AND date+i*86400000000 = PARSE_UTC_USEC('2011-01-27')\n",
238 |       "\"\"\"\n",
239 |       "data_egypt=gbq.read_gbq(query)\n",
240 |       "data_egypt.index=pd.to_datetime(data_egypt['date']*1000)\n",
241 |       "x = data_egypt['count'].plot()"
242 |      ],
243 |      "language": "python",
244 |      "metadata": {},
245 |      "outputs": [
246 |       {
247 |        "output_type": "stream",
248 |        "stream": "stdout",
249 |        "text": [
250 |         "\r",
251 |         "Waiting on bqjob_r2f64a01b0c420ec3_00000147cc356c4a_11 ... (0s) Current status: DONE   \n"
252 |        ]
253 |       },
254 |       {
255 |        "metadata": {},
256 |        "output_type": "display_data",
257 |        "png": 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mY5DCZFGj4aEqn182XVPxtNSvLxqyNVR6rvmH/cAWHaBaYmGLDshcy4IF0tKo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\ndern3LpVnurbtq08vtYhhxj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258 |        "text": [
259 |         "<matplotlib.figure.Figure at 0x7feb5c5fac90>"
260 |        ]
261 |       }
262 |      ],
263 |      "prompt_number": 28
264 |     },
265 |     {
266 |      "cell_type": "markdown",
267 |      "metadata": {},
268 |      "source": [
269 |       "With that timeline defined, we can look for similar timelines across all years and countries.",
270 |       "\n",
271 |       "To make this work, we used the trick explained on http://stackoverflow.com/questions/24923101/computing-a-moving-maximum-in-bigquery/24943950#24943950."
272 |      ]
273 |     },
274 |     {
275 |      "cell_type": "code",
276 |      "collapsed": false,
277 |      "input": [
278 |       "query = \"\"\"\n",
279 |       "SELECT\n",
280 |       "  STRFTIME_UTC_USEC(a.ending_at, \"%Y-%m-%d\") ending_at1,\n",
281 |       "  STRFTIME_UTC_USEC(b.ending_at-30*86400000000, \"%Y-%m-%d\") starting_at2,\n",
282 |       "  STRFTIME_UTC_USEC(b.ending_at, \"%Y-%m-%d\") ending_at2,\n",
283 |       "  a.country, b.country, CORR(a.c, b.c) corr, COUNT(*) c\n",
284 |       "FROM (\n",
285 |       "  SELECT country, date+i*86400000000 ending_at, c, i\n",
286 |       "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
287 |       "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
288 |       ") b\n",
289 |       "JOIN (\n",
290 |       "  SELECT country, date+i*86400000000 ending_at, c, i\n",
291 |       "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
292 |       "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
293 |       "  WHERE country='Egypt'\n",
294 |       "  AND date+i*86400000000 = PARSE_UTC_USEC('2011-01-27')\n",
295 |       ") a\n",
296 |       "ON a.i=b.i\n",
297 |       "WHERE a.ending_at != b.ending_at\n",
298 |       "GROUP EACH BY ending_at1, ending_at2, starting_at2, a.country, b.country\n",
299 |       "HAVING (c = 30 AND ABS(corr) > 0.254)\n",
300 |       "ORDER BY corr DESC\n",
301 |       "\n",
302 |       "LIMIT 10\n",
303 |       "\"\"\"\n",
304 |       "data=gbq.read_gbq(query)\n",
305 |       "data\n"
306 |      ],
307 |      "language": "python",
308 |      "metadata": {},
309 |      "outputs": [
310 |       {
311 |        "output_type": "stream",
312 |        "stream": "stdout",
313 |        "text": [
314 |         "\r",
315 |         "Waiting on bqjob_r5ccda16b63fbcd4a_00000147cc1a0174_11 ... (3s) Current status: RUNNING "
316 |        ]
317 |       },
318 |       {
319 |        "output_type": "stream",
320 |        "stream": "stdout",
321 |        "text": [
322 |         "\r",
323 |         "Waiting on bqjob_r5ccda16b63fbcd4a_00000147cc1a0174_11 ... (203s) Current status: DONE   \n"
324 |        ]
325 |       },
326 |       {
327 |        "html": [
328 |         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
329 |         "<table border=\"1\" class=\"dataframe\">\n",
330 |         "  <thead>\n",
331 |         "    <tr style=\"text-align: right;\">\n",
332 |         "      <th></th>\n",
333 |         "      <th>ending_at1</th>\n",
334 |         "      <th>starting_at2</th>\n",
335 |         "      <th>ending_at2</th>\n",
336 |         "      <th>a_country</th>\n",
337 |         "      <th>b_country</th>\n",
338 |         "      <th>corr</th>\n",
339 |         "      <th>c</th>\n",
340 |         "    </tr>\n",
341 |         "  </thead>\n",
342 |         "  <tbody>\n",
343 |         "    <tr>\n",
344 |         "      <th>0</th>\n",
345 |         "      <td> 2011-01-27</td>\n",
346 |         "      <td> 2013-10-19</td>\n",
347 |         "      <td> 2013-11-18</td>\n",
348 |         "      <td> Egypt</td>\n",
349 |         "      <td>        Chad</td>\n",
350 |         "      <td> 0.875190</td>\n",
351 |         "      <td> 30</td>\n",
352 |         "    </tr>\n",
353 |         "    <tr>\n",
354 |         "      <th>1</th>\n",
355 |         "      <td> 2011-01-27</td>\n",
356 |         "      <td> 2011-05-15</td>\n",
357 |         "      <td> 2011-06-14</td>\n",
358 |         "      <td> Egypt</td>\n",
359 |         "      <td>      Jordan</td>\n",
360 |         "      <td> 0.862724</td>\n",
361 |         "      <td> 30</td>\n",
362 |         "    </tr>\n",
363 |         "    <tr>\n",
364 |         "      <th>2</th>\n",
365 |         "      <td> 2011-01-27</td>\n",
366 |         "      <td> 2005-08-07</td>\n",
367 |         "      <td> 2005-09-06</td>\n",
368 |         "      <td> Egypt</td>\n",
369 |         "      <td>      Turkey</td>\n",
370 |         "      <td> 0.856022</td>\n",
371 |         "      <td> 30</td>\n",
372 |         "    </tr>\n",
373 |         "    <tr>\n",
374 |         "      <th>3</th>\n",
375 |         "      <td> 2011-01-27</td>\n",
376 |         "      <td> 2006-10-03</td>\n",
377 |         "      <td> 2006-11-02</td>\n",
378 |         "      <td> Egypt</td>\n",
379 |         "      <td>       Italy</td>\n",
380 |         "      <td> 0.847174</td>\n",
381 |         "      <td> 30</td>\n",
382 |         "    </tr>\n",
383 |         "    <tr>\n",
384 |         "      <th>4</th>\n",
385 |         "      <td> 2011-01-27</td>\n",
386 |         "      <td> 2006-05-29</td>\n",
387 |         "      <td> 2006-06-28</td>\n",
388 |         "      <td> Egypt</td>\n",
389 |         "      <td>       Syria</td>\n",
390 |         "      <td> 0.846571</td>\n",
391 |         "      <td> 30</td>\n",
392 |         "    </tr>\n",
393 |         "    <tr>\n",
394 |         "      <th>5</th>\n",
395 |         "      <td> 2011-01-27</td>\n",
396 |         "      <td> 2013-05-15</td>\n",
397 |         "      <td> 2013-06-14</td>\n",
398 |         "      <td> Egypt</td>\n",
399 |         "      <td> South Korea</td>\n",
400 |         "      <td> 0.842728</td>\n",
401 |         "      <td> 30</td>\n",
402 |         "    </tr>\n",
403 |         "    <tr>\n",
404 |         "      <th>6</th>\n",
405 |         "      <td> 2011-01-27</td>\n",
406 |         "      <td> 1995-06-11</td>\n",
407 |         "      <td> 1995-07-11</td>\n",
408 |         "      <td> Egypt</td>\n",
409 |         "      <td>        Iraq</td>\n",
410 |         "      <td> 0.842362</td>\n",
411 |         "      <td> 30</td>\n",
412 |         "    </tr>\n",
413 |         "    <tr>\n",
414 |         "      <th>7</th>\n",
415 |         "      <td> 2011-01-27</td>\n",
416 |         "      <td> 2011-10-10</td>\n",
417 |         "      <td> 2011-11-09</td>\n",
418 |         "      <td> Egypt</td>\n",
419 |         "      <td>    Portugal</td>\n",
420 |         "      <td> 0.841235</td>\n",
421 |         "      <td> 30</td>\n",
422 |         "    </tr>\n",
423 |         "    <tr>\n",
424 |         "      <th>8</th>\n",
425 |         "      <td> 2011-01-27</td>\n",
426 |         "      <td> 2002-08-04</td>\n",
427 |         "      <td> 2002-09-03</td>\n",
428 |         "      <td> Egypt</td>\n",
429 |         "      <td>       Sudan</td>\n",
430 |         "      <td> 0.835436</td>\n",
431 |         "      <td> 30</td>\n",
432 |         "    </tr>\n",
433 |         "    <tr>\n",
434 |         "      <th>9</th>\n",
435 |         "      <td> 2011-01-27</td>\n",
436 |         "      <td> 2009-08-07</td>\n",
437 |         "      <td> 2009-09-06</td>\n",
438 |         "      <td> Egypt</td>\n",
439 |         "      <td>     Germany</td>\n",
440 |         "      <td> 0.832848</td>\n",
441 |         "      <td> 30</td>\n",
442 |         "    </tr>\n",
443 |         "  </tbody>\n",
444 |         "</table>\n",
445 |         "</div>"
446 |        ],
447 |        "metadata": {},
448 |        "output_type": "pyout",
449 |        "prompt_number": 12,
450 |        "text": [
451 |         "   ending_at1 starting_at2  ending_at2 a_country    b_country      corr   c\n",
452 |         "0  2011-01-27   2013-10-19  2013-11-18     Egypt         Chad  0.875190  30\n",
453 |         "1  2011-01-27   2011-05-15  2011-06-14     Egypt       Jordan  0.862724  30\n",
454 |         "2  2011-01-27   2005-08-07  2005-09-06     Egypt       Turkey  0.856022  30\n",
455 |         "3  2011-01-27   2006-10-03  2006-11-02     Egypt        Italy  0.847174  30\n",
456 |         "4  2011-01-27   2006-05-29  2006-06-28     Egypt        Syria  0.846571  30\n",
457 |         "5  2011-01-27   2013-05-15  2013-06-14     Egypt  South Korea  0.842728  30\n",
458 |         "6  2011-01-27   1995-06-11  1995-07-11     Egypt         Iraq  0.842362  30\n",
459 |         "7  2011-01-27   2011-10-10  2011-11-09     Egypt     Portugal  0.841235  30\n",
460 |         "8  2011-01-27   2002-08-04  2002-09-03     Egypt        Sudan  0.835436  30\n",
461 |         "9  2011-01-27   2009-08-07  2009-09-06     Egypt      Germany  0.832848  30"
462 |        ]
463 |       }
464 |      ],
465 |      "prompt_number": 12
466 |     },
467 |     {
468 |      "cell_type": "markdown",
469 |      "metadata": {},
470 |      "source": [
471 |       "Those are the 10 periods that most closely resemble the timeline of material conflicts in Egypt before January 27th, 2011.\n",
472 |       "\n",
473 |       "Let's plot Chad 30 days before October 19th, 2013."
474 |      ]
475 |     },
476 |     {
477 |      "cell_type": "code",
478 |      "collapsed": false,
479 |      "input": [
480 |       "query = \"\"\"\n",
481 |       "  SELECT country, date, c AS count\n",
482 |       "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
483 |       "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
484 |       "  WHERE country='Chad'\n",
485 |       "  AND date+i*86400000000 = PARSE_UTC_USEC('2013-11-18')\n",
486 |       "\"\"\"\n",
487 |       "data_chad=gbq.read_gbq(query)\n",
488 |       "data_chad.index=pd.to_datetime(data_chad['date']*1000)\n",
489 |       "data_chad['count'].plot()"
490 |      ],
491 |      "language": "python",
492 |      "metadata": {},
493 |      "outputs": [
494 |       {
495 |        "output_type": "stream",
496 |        "stream": "stdout",
497 |        "text": [
498 |         "\r",
499 |         "Waiting on bqjob_r39c0b56b485a6249_00000147cc384b90_16 ... (0s) Current status: DONE   \n"
500 |        ]
501 |       },
502 |       {
503 |        "metadata": {},
504 |        "output_type": "pyout",
505 |        "prompt_number": 34,
506 |        "text": [
507 |         "<matplotlib.axes.AxesSubplot at 0x7feb5c1ecbd0>"
508 |        ]
509 |       },
510 |       {
511 |        "metadata": {},
512 |        "output_type": "display_data",
513 |        "png": 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qBAJKSkooKyujvLwciFpad95dlmx9uvPV1eU0a5bZ/rvvDv/6V4RIJPPz+T3vErYed1nY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\neSjV4l8rsAbJYDoaeTudQfR/He6c90S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514 |        "text": [
515 |         "<matplotlib.figure.Figure at 0x7feb5c1f6290>"
516 |        ]
517 |       }
518 |      ],
519 |      "prompt_number": 34
520 |     },
521 |     {
522 |      "cell_type": "markdown",
523 |      "metadata": {},
524 |      "source": [
525 |       "In fact, both periods look remarkably similar (but not necesarily significant, more analysis is needed before reaching any conclusions):"
526 |      ]
527 |     },
528 |     {
529 |      "cell_type": "code",
530 |      "collapsed": false,
531 |      "input": [
532 |       "data_egypt['count'].plot()"
533 |      ],
534 |      "language": "python",
535 |      "metadata": {},
536 |      "outputs": [
537 |       {
538 |        "metadata": {},
539 |        "output_type": "pyout",
540 |        "prompt_number": 33,
541 |        "text": [
542 |         "<matplotlib.axes.AxesSubplot at 0x7feb5cd93890>"
543 |        ]
544 |       },
545 |       {
546 |        "metadata": {},
547 |        "output_type": "display_data",
548 |        "png": 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mY5DCZFGj4aEqn182XVPxtNSvLxqyNVR6rvmH/cAWHaBaYmGLDshcy4IF0tKo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\ndern3LpVnurbtq08vtYhhxj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549 |        "text": [
550 |         "<matplotlib.figure.Figure at 0x7feb5cb6a990>"
551 |        ]
552 |       }
553 |      ],
554 |      "prompt_number": 33
555 |     },
556 |     {
557 |      "cell_type": "markdown",
558 |      "metadata": {},
559 |      "source": [
560 |       "To look further into how to read these results to understand history, look at:\n\n",
561 |       "* Kalev Leetaru's [GDELT correlations post](http://blog.gdeltproject.org/towards-psychohistory-uncovering-the-patterns-of-world-history-with-google-bigquery/).\n",
562 |       "* Andrew Gelman's [counterpoint](http://andrewgelman.com/2014/08/15/psychohistory-hype-paradox/).\n",
563 |       "* Anthony A. Boyles's [counterpoint](http://aaboyles.com/2014/08/access-event-data-in-google-bigquery-from-r/).\n",
564 |       "\n",
565 |       "\n",
566 |       "By [Felipe Hoffa](https://plus.google.com/+FelipeHoffa/)\n",
567 |       "([@felipehoffa](http://twitter.com/felipehoffa), [/r/bigquery](http://reddit.com/r/bigquery))"
568 |      ]
569 |     }
570 |    ],
571 |    "metadata": {}
572 |   }
573 |  ]
574 | }
575 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | notebooks
2 | =========
3 | 
4 | 
5 | This is not an official Google product
6 | 


--------------------------------------------------------------------------------
/graphs - gdelt correlation countries.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "metadata": {
  3 |   "name": "",
  4 |   "signature": "sha256:bcee04e1b7faec925f93e1c3cf3a9e1639dfa794f2f1b9465e006911ac63acb8"
  5 |  },
  6 |  "nbformat": 3,
  7 |  "nbformat_minor": 0,
  8 |  "worksheets": [
  9 |   {
 10 |    "cells": [
 11 |     {
 12 |      "cell_type": "code",
 13 |      "collapsed": false,
 14 |      "input": [
 15 |       "%pylab inline\n",
 16 |       "from pandas.io import gbq\n",
 17 |       "import networkx as nx"
 18 |      ],
 19 |      "language": "python",
 20 |      "metadata": {},
 21 |      "outputs": [
 22 |       {
 23 |        "output_type": "stream",
 24 |        "stream": "stdout",
 25 |        "text": [
 26 |         "Populating the interactive namespace from numpy and matplotlib\n"
 27 |        ]
 28 |       }
 29 |      ],
 30 |      "prompt_number": 92
 31 |     },
 32 |     {
 33 |      "cell_type": "code",
 34 |      "collapsed": false,
 35 |      "input": [
 36 |       "query = \"\"\"\n",
 37 |       "SELECT CORR(a.c,b.c) corr, a.Actor1CountryCode, b.Actor1CountryCode, COUNT(*) count\n",
 38 |       "FROM\n",
 39 |       "(SELECT Actor1CountryCode, SQLDATE, COUNT(*) c\n",
 40 |       "FROM [gdelt-bq:full.events] \n",
 41 |       "GROUP EACH BY 1,2\n",
 42 |       "HAVING c > 30\n",
 43 |       ") a\n",
 44 |       "JOIN EACH (\n",
 45 |       "SELECT Actor1CountryCode, SQLDATE, COUNT(*) c\n",
 46 |       "FROM [gdelt-bq:full.events] \n",
 47 |       "GROUP EACH BY 1,2\n",
 48 |       "HAVING c > 30\n",
 49 |       ") b\n",
 50 |       "ON a.SQLDATE = b.SQLDATE\n",
 51 |       "WHERE a.Actor1CountryCode > b.Actor1CountryCode\n",
 52 |       "GROUP EACH BY 2, 3\n",
 53 |       "HAVING count > 100\n",
 54 |       "AND corr > 0.85\n",
 55 |       "ORDER BY 1 DESC\n",
 56 |       "\"\"\"\n",
 57 |       "countries = gbq.read_gbq(query)"
 58 |      ],
 59 |      "language": "python",
 60 |      "metadata": {},
 61 |      "outputs": [
 62 |       {
 63 |        "output_type": "stream",
 64 |        "stream": "stdout",
 65 |        "text": [
 66 |         "\r",
 67 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (0s) Current status: RUNNING "
 68 |        ]
 69 |       },
 70 |       {
 71 |        "output_type": "stream",
 72 |        "stream": "stdout",
 73 |        "text": [
 74 |         "\r",
 75 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (1s) Current status: RUNNING "
 76 |        ]
 77 |       },
 78 |       {
 79 |        "output_type": "stream",
 80 |        "stream": "stdout",
 81 |        "text": [
 82 |         "\r",
 83 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (2s) Current status: RUNNING "
 84 |        ]
 85 |       },
 86 |       {
 87 |        "output_type": "stream",
 88 |        "stream": "stdout",
 89 |        "text": [
 90 |         "\r",
 91 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (3s) Current status: RUNNING "
 92 |        ]
 93 |       },
 94 |       {
 95 |        "output_type": "stream",
 96 |        "stream": "stdout",
 97 |        "text": [
 98 |         "\r",
 99 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (4s) Current status: RUNNING "
100 |        ]
101 |       },
102 |       {
103 |        "output_type": "stream",
104 |        "stream": "stdout",
105 |        "text": [
106 |         "\r",
107 |         "Waiting on bqjob_r33880feb8302103a_000001462948da54_13 ... (4s) Current status: DONE   \n"
108 |        ]
109 |       }
110 |      ],
111 |      "prompt_number": 90
112 |     },
113 |     {
114 |      "cell_type": "code",
115 |      "collapsed": false,
116 |      "input": [
117 |       "G = nx.from_edgelist(countries[[u'a_Actor1CountryCode', u'b_Actor1CountryCode']].get_values())\n",
118 |       "nx.draw(G)"
119 |      ],
120 |      "language": "python",
121 |      "metadata": {},
122 |      "outputs": [
123 |       {
124 |        "metadata": {},
125 |        "output_type": "display_data",
126 |        "png": 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GwFBXF99++22a/gqi8EnLtwhQKBTYtm0bmtaogWUAFiDpNKsCwHwAoaleO5F9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127 |        "text": [
128 |         "<matplotlib.figure.Figure at 0x42ba210>"
129 |        ]
130 |       }
131 |      ],
132 |      "prompt_number": 91
133 |     },
134 |     {
135 |      "cell_type": "code",
136 |      "collapsed": false,
137 |      "input": [
138 |       "x = countries[[u'a_Actor1CountryCode', u'b_Actor1CountryCode', 'corr']]\n",
139 |       "x\n"
140 |      ],
141 |      "language": "python",
142 |      "metadata": {},
143 |      "outputs": [
144 |       {
145 |        "html": [
146 |         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
147 |         "<table border=\"1\" class=\"dataframe\">\n",
148 |         "  <thead>\n",
149 |         "    <tr style=\"text-align: right;\">\n",
150 |         "      <th></th>\n",
151 |         "      <th>a_Actor1CountryCode</th>\n",
152 |         "      <th>b_Actor1CountryCode</th>\n",
153 |         "      <th>corr</th>\n",
154 |         "    </tr>\n",
155 |         "  </thead>\n",
156 |         "  <tbody>\n",
157 |         "    <tr>\n",
158 |         "      <th>0 </th>\n",
159 |         "      <td> USA</td>\n",
160 |         "      <td> CAN</td>\n",
161 |         "      <td> 0.951479</td>\n",
162 |         "    </tr>\n",
163 |         "    <tr>\n",
164 |         "      <th>1 </th>\n",
165 |         "      <td> USA</td>\n",
166 |         "      <td> GBR</td>\n",
167 |         "      <td> 0.947686</td>\n",
168 |         "    </tr>\n",
169 |         "    <tr>\n",
170 |         "      <th>2 </th>\n",
171 |         "      <td> GBR</td>\n",
172 |         "      <td> AUS</td>\n",
173 |         "      <td> 0.941443</td>\n",
174 |         "    </tr>\n",
175 |         "    <tr>\n",
176 |         "      <th>3 </th>\n",
177 |         "      <td> GBR</td>\n",
178 |         "      <td> CAN</td>\n",
179 |         "      <td> 0.941280</td>\n",
180 |         "    </tr>\n",
181 |         "    <tr>\n",
182 |         "      <th>4 </th>\n",
183 |         "      <td> CAN</td>\n",
184 |         "      <td> AUS</td>\n",
185 |         "      <td> 0.938083</td>\n",
186 |         "    </tr>\n",
187 |         "    <tr>\n",
188 |         "      <th>5 </th>\n",
189 |         "      <td> USA</td>\n",
190 |         "      <td> AUS</td>\n",
191 |         "      <td> 0.933343</td>\n",
192 |         "    </tr>\n",
193 |         "    <tr>\n",
194 |         "      <th>6 </th>\n",
195 |         "      <td> GBR</td>\n",
196 |         "      <td> CHN</td>\n",
197 |         "      <td> 0.931016</td>\n",
198 |         "    </tr>\n",
199 |         "    <tr>\n",
200 |         "      <th>7 </th>\n",
201 |         "      <td> GBR</td>\n",
202 |         "      <td> DEU</td>\n",
203 |         "      <td> 0.924600</td>\n",
204 |         "    </tr>\n",
205 |         "    <tr>\n",
206 |         "      <th>8 </th>\n",
207 |         "      <td> ZAF</td>\n",
208 |         "      <td> AFR</td>\n",
209 |         "      <td> 0.920514</td>\n",
210 |         "    </tr>\n",
211 |         "    <tr>\n",
212 |         "      <th>9 </th>\n",
213 |         "      <td> USA</td>\n",
214 |         "      <td> CHN</td>\n",
215 |         "      <td> 0.918271</td>\n",
216 |         "    </tr>\n",
217 |         "    <tr>\n",
218 |         "      <th>10</th>\n",
219 |         "      <td> GBR</td>\n",
220 |         "      <td> FRA</td>\n",
221 |         "      <td> 0.917056</td>\n",
222 |         "    </tr>\n",
223 |         "    <tr>\n",
224 |         "      <th>11</th>\n",
225 |         "      <td> GBR</td>\n",
226 |         "      <td> EUR</td>\n",
227 |         "      <td> 0.914234</td>\n",
228 |         "    </tr>\n",
229 |         "    <tr>\n",
230 |         "      <th>12</th>\n",
231 |         "      <td> USA</td>\n",
232 |         "      <td> IND</td>\n",
233 |         "      <td> 0.911673</td>\n",
234 |         "    </tr>\n",
235 |         "    <tr>\n",
236 |         "      <th>13</th>\n",
237 |         "      <td> CHN</td>\n",
238 |         "      <td> CAN</td>\n",
239 |         "      <td> 0.910226</td>\n",
240 |         "    </tr>\n",
241 |         "    <tr>\n",
242 |         "      <th>14</th>\n",
243 |         "      <td> CHN</td>\n",
244 |         "      <td> AUS</td>\n",
245 |         "      <td> 0.908378</td>\n",
246 |         "    </tr>\n",
247 |         "    <tr>\n",
248 |         "      <th>15</th>\n",
249 |         "      <td> GBR</td>\n",
250 |         "      <td> AFR</td>\n",
251 |         "      <td> 0.906980</td>\n",
252 |         "    </tr>\n",
253 |         "    <tr>\n",
254 |         "      <th>16</th>\n",
255 |         "      <td> EUR</td>\n",
256 |         "      <td> DEU</td>\n",
257 |         "      <td> 0.906792</td>\n",
258 |         "    </tr>\n",
259 |         "    <tr>\n",
260 |         "      <th>17</th>\n",
261 |         "      <td> USA</td>\n",
262 |         "      <td> DEU</td>\n",
263 |         "      <td> 0.906668</td>\n",
264 |         "    </tr>\n",
265 |         "    <tr>\n",
266 |         "      <th>18</th>\n",
267 |         "      <td> EUR</td>\n",
268 |         "      <td> CAN</td>\n",
269 |         "      <td> 0.906349</td>\n",
270 |         "    </tr>\n",
271 |         "    <tr>\n",
272 |         "      <th>19</th>\n",
273 |         "      <td> USA</td>\n",
274 |         "      <td> EUR</td>\n",
275 |         "      <td> 0.904783</td>\n",
276 |         "    </tr>\n",
277 |         "    <tr>\n",
278 |         "      <th>20</th>\n",
279 |         "      <td> USA</td>\n",
280 |         "      <td> FRA</td>\n",
281 |         "      <td> 0.904513</td>\n",
282 |         "    </tr>\n",
283 |         "  </tbody>\n",
284 |         "</table>\n",
285 |         "<p>21 rows \u00d7 3 columns</p>\n",
286 |         "</div>"
287 |        ],
288 |        "metadata": {},
289 |        "output_type": "pyout",
290 |        "prompt_number": 80,
291 |        "text": [
292 |         "   a_Actor1CountryCode b_Actor1CountryCode      corr\n",
293 |         "0                  USA                 CAN  0.951479\n",
294 |         "1                  USA                 GBR  0.947686\n",
295 |         "2                  GBR                 AUS  0.941443\n",
296 |         "3                  GBR                 CAN  0.941280\n",
297 |         "4                  CAN                 AUS  0.938083\n",
298 |         "5                  USA                 AUS  0.933343\n",
299 |         "6                  GBR                 CHN  0.931016\n",
300 |         "7                  GBR                 DEU  0.924600\n",
301 |         "8                  ZAF                 AFR  0.920514\n",
302 |         "9                  USA                 CHN  0.918271\n",
303 |         "10                 GBR                 FRA  0.917056\n",
304 |         "11                 GBR                 EUR  0.914234\n",
305 |         "12                 USA                 IND  0.911673\n",
306 |         "13                 CHN                 CAN  0.910226\n",
307 |         "14                 CHN                 AUS  0.908378\n",
308 |         "15                 GBR                 AFR  0.906980\n",
309 |         "16                 EUR                 DEU  0.906792\n",
310 |         "17                 USA                 DEU  0.906668\n",
311 |         "18                 EUR                 CAN  0.906349\n",
312 |         "19                 USA                 EUR  0.904783\n",
313 |         "20                 USA                 FRA  0.904513\n",
314 |         "\n",
315 |         "[21 rows x 3 columns]"
316 |        ]
317 |       }
318 |      ],
319 |      "prompt_number": 80
320 |     },
321 |     {
322 |      "cell_type": "code",
323 |      "collapsed": false,
324 |      "input": [
325 |       "G = nx.Graph()\n",
326 |       "G.add_weighted_edges_from(countries[[u'a_Actor1CountryCode', u'b_Actor1CountryCode', 'corr']].get_values())\n",
327 |       "nx.draw_spring(G)"
328 |      ],
329 |      "language": "python",
330 |      "metadata": {},
331 |      "outputs": [
332 |       {
333 |        "metadata": {},
334 |        "output_type": "display_data",
335 |        "png": 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asY9e69atcezYMQQEBODhw4f4v//7vw/6UiRu+bKSICIYKJUl3w5AV6gOP6cAEOnpYdq0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336 |        "text": [
337 |         "<matplotlib.figure.Figure at 0x3e16f10>"
338 |        ]
339 |       }
340 |      ],
341 |      "prompt_number": 94
342 |     }
343 |    ],
344 |    "metadata": {}
345 |   }
346 |  ]
347 | }


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