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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 1,
  6. "metadata": {
  7. "collapsed": false
  8. },
  9. "outputs": [],
  10. "source": [
  11. "%matplotlib inline\n",
  12. "import datetime\n",
  13. "import matplotlib.pyplot as plt\n",
  14. "import matplotlib as mpl\n",
  15. "import numpy as np\n",
  16. "import pandas as pd\n",
  17. "import dask.dataframe as dd\n",
  18. "import collections\n",
  19. "\n",
  20. "mpl.rcParams['axes.labelsize'] = 20\n",
  21. "mpl.rcParams['axes.titlesize'] = 24\n",
  22. "mpl.rcParams['xtick.labelsize'] = 16\n",
  23. "mpl.rcParams['ytick.labelsize'] = 16\n",
  24. "mpl.rcParams['legend.fontsize'] = 16"
  25. ]
  26. },
  27. {
  28. "cell_type": "code",
  29. "execution_count": 2,
  30. "metadata": {
  31. "collapsed": false
  32. },
  33. "outputs": [
  34. {
  35. "data": {
  36. "text/html": [
  37. "<div>\n",
  38. "<table border=\"1\" class=\"dataframe\">\n",
  39. " <thead>\n",
  40. " <tr style=\"text-align: right;\">\n",
  41. " <th></th>\n",
  42. " <th>execution_time</th>\n",
  43. " <th>available_bikes</th>\n",
  44. " <th>available_docks</th>\n",
  45. " <th>id</th>\n",
  46. " <th>last_communication_time</th>\n",
  47. " <th>lat</th>\n",
  48. " <th>lon</th>\n",
  49. " <th>st_address</th>\n",
  50. " <th>station_name</th>\n",
  51. " <th>status_key</th>\n",
  52. " <th>status_value</th>\n",
  53. " <th>test_station</th>\n",
  54. " <th>total_docks</th>\n",
  55. " </tr>\n",
  56. " </thead>\n",
  57. " <tbody>\n",
  58. " <tr>\n",
  59. " <th>0</th>\n",
  60. " <td>2016-09-18 16:59:51</td>\n",
  61. " <td>3</td>\n",
  62. " <td>36</td>\n",
  63. " <td>72</td>\n",
  64. " <td>2016-09-18 16:58:36</td>\n",
  65. " <td>40.767272</td>\n",
  66. " <td>-73.993929</td>\n",
  67. " <td>W 52 St & 11 Ave</td>\n",
  68. " <td>W 52 St & 11 Ave</td>\n",
  69. " <td>1</td>\n",
  70. " <td>In Service</td>\n",
  71. " <td>f</td>\n",
  72. " <td>39</td>\n",
  73. " </tr>\n",
  74. " <tr>\n",
  75. " <th>1</th>\n",
  76. " <td>2016-09-18 16:59:51</td>\n",
  77. " <td>12</td>\n",
  78. " <td>19</td>\n",
  79. " <td>79</td>\n",
  80. " <td>2016-09-18 16:57:02</td>\n",
  81. " <td>40.719116</td>\n",
  82. " <td>-74.006667</td>\n",
  83. " <td>Franklin St & W Broadway</td>\n",
  84. " <td>Franklin St & W Broadway</td>\n",
  85. " <td>1</td>\n",
  86. " <td>In Service</td>\n",
  87. " <td>f</td>\n",
  88. " <td>33</td>\n",
  89. " </tr>\n",
  90. " <tr>\n",
  91. " <th>2</th>\n",
  92. " <td>2016-09-18 16:59:51</td>\n",
  93. " <td>22</td>\n",
  94. " <td>5</td>\n",
  95. " <td>82</td>\n",
  96. " <td>2016-09-18 16:59:26</td>\n",
  97. " <td>40.711174</td>\n",
  98. " <td>-74.000165</td>\n",
  99. " <td>St James Pl & Pearl St</td>\n",
  100. " <td>St James Pl & Pearl St</td>\n",
  101. " <td>1</td>\n",
  102. " <td>In Service</td>\n",
  103. " <td>f</td>\n",
  104. " <td>27</td>\n",
  105. " </tr>\n",
  106. " <tr>\n",
  107. " <th>3</th>\n",
  108. " <td>2016-09-18 16:59:51</td>\n",
  109. " <td>2</td>\n",
  110. " <td>58</td>\n",
  111. " <td>83</td>\n",
  112. " <td>2016-09-18 16:57:32</td>\n",
  113. " <td>40.683826</td>\n",
  114. " <td>-73.976323</td>\n",
  115. " <td>Atlantic Ave & Fort Greene Pl</td>\n",
  116. " <td>Atlantic Ave & Fort Greene Pl</td>\n",
  117. " <td>1</td>\n",
  118. " <td>In Service</td>\n",
  119. " <td>f</td>\n",
  120. " <td>62</td>\n",
  121. " </tr>\n",
  122. " <tr>\n",
  123. " <th>4</th>\n",
  124. " <td>2016-09-18 16:59:51</td>\n",
  125. " <td>1</td>\n",
  126. " <td>38</td>\n",
  127. " <td>116</td>\n",
  128. " <td>2016-09-18 16:59:05</td>\n",
  129. " <td>40.741776</td>\n",
  130. " <td>-74.001497</td>\n",
  131. " <td>W 17 St & 8 Ave</td>\n",
  132. " <td>W 17 St & 8 Ave</td>\n",
  133. " <td>1</td>\n",
  134. " <td>In Service</td>\n",
  135. " <td>f</td>\n",
  136. " <td>39</td>\n",
  137. " </tr>\n",
  138. " </tbody>\n",
  139. "</table>\n",
  140. "</div>"
  141. ],
  142. "text/plain": [
  143. " execution_time available_bikes available_docks id \\\n",
  144. "0 2016-09-18 16:59:51 3 36 72 \n",
  145. "1 2016-09-18 16:59:51 12 19 79 \n",
  146. "2 2016-09-18 16:59:51 22 5 82 \n",
  147. "3 2016-09-18 16:59:51 2 58 83 \n",
  148. "4 2016-09-18 16:59:51 1 38 116 \n",
  149. "\n",
  150. " last_communication_time lat lon \\\n",
  151. "0 2016-09-18 16:58:36 40.767272 -73.993929 \n",
  152. "1 2016-09-18 16:57:02 40.719116 -74.006667 \n",
  153. "2 2016-09-18 16:59:26 40.711174 -74.000165 \n",
  154. "3 2016-09-18 16:57:32 40.683826 -73.976323 \n",
  155. "4 2016-09-18 16:59:05 40.741776 -74.001497 \n",
  156. "\n",
  157. " st_address station_name status_key \\\n",
  158. "0 W 52 St & 11 Ave W 52 St & 11 Ave 1 \n",
  159. "1 Franklin St & W Broadway Franklin St & W Broadway 1 \n",
  160. "2 St James Pl & Pearl St St James Pl & Pearl St 1 \n",
  161. "3 Atlantic Ave & Fort Greene Pl Atlantic Ave & Fort Greene Pl 1 \n",
  162. "4 W 17 St & 8 Ave W 17 St & 8 Ave 1 \n",
  163. "\n",
  164. " status_value test_station total_docks \n",
  165. "0 In Service f 39 \n",
  166. "1 In Service f 33 \n",
  167. "2 In Service f 27 \n",
  168. "3 In Service f 62 \n",
  169. "4 In Service f 39 "
  170. ]
  171. },
  172. "execution_count": 3,
  173. "metadata": {},
  174. "output_type": "execute_result"
  175. }
  176. ],
  177. "source": [
  178. "ddf = dd.read_csv('dat_20160918_20170214.csv', **{'sep': '|'})\n",
  179. "ddf.head()"
  180. ]
  181. },
  182. {
  183. "cell_type": "code",
  184. "execution_count": 3,
  185. "metadata": {
  186. "collapsed": false
  187. },
  188. "outputs": [],
  189. "source": [
  190. "howard, macdougal = dd.compute(ddf.query('station_name == \"Howard St & Centre St\"'),\n",
  191. " ddf.query('station_name == \"MacDougal St & Prince St\"'))"
  192. ]
  193. },
  194. {
  195. "cell_type": "code",
  196. "execution_count": 4,
  197. "metadata": {
  198. "collapsed": false
  199. },
  200. "outputs": [],
  201. "source": [
  202. "def cleanup(df):\n",
  203. " df = df.set_index('execution_time', drop=True)\n",
  204. " df.index = pd.to_datetime(df.index)\n",
  205. " df.sort_index(inplace=True)\n",
  206. " return df\n",
  207. "\n",
  208. "howard = cleanup(howard)\n",
  209. "macdougal = cleanup(macdougal)"
  210. ]
  211. }
  212. ],
  213. "metadata": {
  214. "anaconda-cloud": {},
  215. "kernelspec": {
  216. "display_name": "Python [Root]",
  217. "language": "python",
  218. "name": "Python [Root]"
  219. },
  220. "language_info": {
  221. "codemirror_mode": {
  222. "name": "ipython",
  223. "version": 3
  224. },
  225. "file_extension": ".py",
  226. "mimetype": "text/x-python",
  227. "name": "python",
  228. "nbconvert_exporter": "python",
  229. "pygments_lexer": "ipython3",
  230. "version": "3.5.2"
  231. }
  232. },
  233. "nbformat": 4,
  234. "nbformat_minor": 0
  235. }
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