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  1. {
  2. "cells": [
  3. {
  4. "metadata": {},
  5. "cell_type": "markdown",
  6. "source": "# Overview\n## Instuctions\n1. Create a .py file to record script for this exercise\n2. Read text carefully\n3. Copy and run pre-written code below, and finish the script shown as empty or incomplete code blocks in between<br>\n\n### Problem Statement\n\nThe team would like to do analysis on station/region performance data, currently available in CSD Server\nHowever, some data manipulation and aggregation are needed to prepare such a master table\nCreate master table to house data, and manually populate data that's missing in server\nMajor steps in this process include:\n0. (initialization)\n1. Pull data\n2. Create helpful variables\n3. Create a base table\n4. Clean up the server data, aggregation\n5. Insert everything into base table to get master data\n\n### Data information\n\n__sptf_raw__: data pulled from the server, contains historical performance data for each airport and selective parent regions\n\n note 1: each airport has four different levels of region - Subregion2, Subregion, Region and Region2, each row contains performance data for either an airport or a region, but not all regions are displayed.\n \n note 2: Actual and goal columns are calculated by dividing measure_ and goal_ numerator and denominator, and score is assigned based on bucketof gap to goal.\n \n note 3: All region-level data could be calculated by aggregating measure/goal numerators and denominators of subordinate airports/regions\n\n__AO_Structure__: complete region structure for each airport\n\n__metrics_info__: a glossary of performance metrics and relevant information"
  7. },
  8. {
  9. "metadata": {},
  10. "cell_type": "markdown",
  11. "source": "# Let's get started!\n## 0. Initialzation\nChange directory using %cd C://... <br>\nImport the following packages<br>\n- _numpy_<br>\n- _pandas_<br>\n- _pyodbc_<br>\n- _product_ function from _itertools_ package<br>"
  12. },
  13. {
  14. "metadata": {
  15. "trusted": true
  16. },
  17. "cell_type": "code",
  18. "source": "",
  19. "execution_count": null,
  20. "outputs": []
  21. },
  22. {
  23. "metadata": {},
  24. "cell_type": "markdown",
  25. "source": "## 1. Pull data"
  26. },
  27. {
  28. "metadata": {
  29. "trusted": true
  30. },
  31. "cell_type": "code",
  32. "source": "# Set configuration for SQL connection\nSQL_cnxn = pyodbc.connect('Trusted_Connection=yes', driver = '{SQL Server}',server = 'VCLD16GDACOSI01\\\\ACODEV01', database = 'Cust_Serv_Del')\n\n# Pull data by reading and executing local query files, using with ... as ... keyword to ensure files are properly closed after use\nwith open('Base table query.sql','r') as query:\n sptf_raw = pd.read_sql_query(query.read(),SQL_cnxn)",
  33. "execution_count": 3,
  34. "outputs": []
  35. },
  36. {
  37. "metadata": {},
  38. "cell_type": "markdown",
  39. "source": "__Note__<br>\nIt is a good practice to always open file using with...as..., which terminates connection to the file immediately\nafter the operation is complete and thus avoid unwanted result while saving memory space <br><br>\nNow, replicate the query above for AO structure.sql, save the table as AO_Structure"
  40. },
  41. {
  42. "metadata": {
  43. "trusted": true
  44. },
  45. "cell_type": "code",
  46. "source": "",
  47. "execution_count": null,
  48. "outputs": []
  49. },
  50. {
  51. "metadata": {
  52. "trusted": true
  53. },
  54. "cell_type": "code",
  55. "source": "#close connection to database when done\nSQL_cnxn.close() ",
  56. "execution_count": null,
  57. "outputs": []
  58. },
  59. {
  60. "metadata": {},
  61. "cell_type": "markdown",
  62. "source": "## 2. Create helpful variables"
  63. },
  64. {
  65. "metadata": {
  66. "scrolled": true,
  67. "trusted": true
  68. },
  69. "cell_type": "code",
  70. "source": "prev_month = 5\ncurrent_month = 6\nprev_year = 2017\ncurrent_year = 2018",
  71. "execution_count": 8,
  72. "outputs": []
  73. },
  74. {
  75. "metadata": {},
  76. "cell_type": "markdown",
  77. "source": "use read_csv() from pandas package to load a table named metrics_info.csv, save as a variable called metrics_info, which is shown below"
  78. },
  79. {
  80. "metadata": {
  81. "trusted": true,
  82. "scrolled": true
  83. },
  84. "cell_type": "code",
  85. "source": "metrics_info.head(5)",
  86. "execution_count": 13,
  87. "outputs": [
  88. {
  89. "output_type": "display_data",
  90. "data": {
  91. "application/vnd.jupyter.widget-view+json": {
  92. "version_major": 2,
  93. "version_minor": 0,
  94. "model_id": "6c08c3d54a2e494c920b87a2f2c7c03b"
  95. }
  96. },
  97. "metadata": {}
  98. }
  99. ]
  100. },
  101. {
  102. "metadata": {
  103. "trusted": true
  104. },
  105. "cell_type": "code",
  106. "source": "",
  107. "execution_count": null,
  108. "outputs": []
  109. },
  110. {
  111. "metadata": {},
  112. "cell_type": "markdown",
  113. "source": "## 3. Create a base table"
  114. },
  115. {
  116. "metadata": {
  117. "scrolled": true,
  118. "trusted": true
  119. },
  120. "cell_type": "markdown",
  121. "source": "### Component 1/3: Year & Month combinations\nCreate a dataframe named *base_table_time* that contains all combination of current/previous years and months, then add a new column to the right named \"key\" where each row equals to 1<br>\nHint: use pandas.DataFrame(list(product(...),...), .......) plus other methods<br>\nYour result should look like this:\n"
  122. },
  123. {
  124. "metadata": {
  125. "trusted": true,
  126. "scrolled": true
  127. },
  128. "cell_type": "code",
  129. "source": "base_table_time.tail(8) # example of the last 8 rows of this table",
  130. "execution_count": 12,
  131. "outputs": [
  132. {
  133. "output_type": "display_data",
  134. "data": {
  135. "application/vnd.jupyter.widget-view+json": {
  136. "version_major": 2,
  137. "version_minor": 0,
  138. "model_id": "3361437415ab4613903731d7a761fbe2"
  139. }
  140. },
  141. "metadata": {}
  142. }
  143. ]
  144. },
  145. {
  146. "metadata": {
  147. "trusted": true
  148. },
  149. "cell_type": "code",
  150. "source": "",
  151. "execution_count": null,
  152. "outputs": []
  153. },
  154. {
  155. "metadata": {
  156. "trusted": true
  157. },
  158. "cell_type": "markdown",
  159. "source": "### Component 2/3: stations & regions\nThe following code block creates a table of all station and region names, along with flags "
  160. },
  161. {
  162. "metadata": {
  163. "trusted": true
  164. },
  165. "cell_type": "code",
  166. "source": "base_table_stations = pd.concat([AO_Structure[['Station','Region2']].assign(Category = 'Line Station'),\n\t\t\t\t\t\t\t\t AO_Structure[['Subregion','Region2']].assign(Category = 'Subregion').rename(columns={'Subregion':'Station'}),\n\t\t\t\t\t\t\t\t AO_Structure[['Subregion2','Region2']].assign(Category = 'Subregion2').rename(columns={'Subregion2':'Station'}),\n\t\t\t\t\t\t\t\t AO_Structure[['Region','Region2']].assign(Category = 'Region').rename(columns={'Region':'Station'}),\n\t\t\t\t\t\t\t\t AO_Structure[['Region2']].assign(Category = 'Region2',Station = AO_Structure['Region2']),\n\t\t\t\t\t\t\t\t pd.DataFrame({'Station':'SYSTEM','Region2':'SYSTEM','Category':'SYSTEM'},index=[0])], ignore_index=True, sort=False)\\\n.drop_duplicates(subset='Station')\\\n.query('Station not in [\"UNK\",\"UNKNOWN\"]')\\\n.assign(key=1)\n\nbase_table_stations['Station'] = base_table_stations.apply(\n lambda x: 'INTERNATIONAL' if x.Station == 'INTERNATIONAL LINE STATIONS' else (\n 'DOMESTIC' if x.Station == 'DOMESTIC LINE AND REGIONAL STATIONS' else x.Station)\n ,axis=1)\nbase_table_stations['Category'] = base_table_stations.apply(\n lambda x: 'UNITED GROUND EXRPESS' if x.Station == 'UNITED GROUND EXPRESS' else (\n 'Hub' if x.Station in ['LAX','SFO','IAH','DEN','ORD','IAD','EWR'] else (\n x.Station if x.Station in ['DEN METRO','EWR METRO'] else x.Category))\n ,axis=1)",
  167. "execution_count": null,
  168. "outputs": []
  169. },
  170. {
  171. "metadata": {
  172. "trusted": true
  173. },
  174. "cell_type": "code",
  175. "source": "base_table_stations.head(5) # example",
  176. "execution_count": 14,
  177. "outputs": [
  178. {
  179. "output_type": "display_data",
  180. "data": {
  181. "application/vnd.jupyter.widget-view+json": {
  182. "version_major": 2,
  183. "version_minor": 0,
  184. "model_id": "73cd07aa03a5498e82357af5d39ac1af"
  185. }
  186. },
  187. "metadata": {}
  188. }
  189. ]
  190. },
  191. {
  192. "metadata": {},
  193. "cell_type": "markdown",
  194. "source": "Now, replicate the last line of code in the above block to apply the following change<br><br>\nUpdate *Dom_Intl* column:<br>\nif station is either __SYSTEM__ or __UNITED GROUND EXPRESS__, use station name for Dom_Intl column<br>\nif station name is in one of the seven hubs, use \"Hub\" for Dom_Intl column<br>\nif Region2 is __INTERNATIONAL LINE STATIONS__, use \"International\", otherwise \"Domestic\""
  195. },
  196. {
  197. "metadata": {
  198. "trusted": true
  199. },
  200. "cell_type": "code",
  201. "source": "",
  202. "execution_count": null,
  203. "outputs": []
  204. },
  205. {
  206. "metadata": {},
  207. "cell_type": "markdown",
  208. "source": "### Component 3/3: data points\nCreate a single column dataframe called *base_table_measures* with column name \"variable\" and rows ['Actual','Goal','Score','MEASURE_NUM','MEASURE_DEN','GOAL_NUM','GOAL_DEN']<br>\nalso add another column named \"key\" where each row equals to 1"
  209. },
  210. {
  211. "metadata": {
  212. "trusted": true
  213. },
  214. "cell_type": "code",
  215. "source": "base_table_measures.head(3) # example",
  216. "execution_count": 16,
  217. "outputs": [
  218. {
  219. "output_type": "display_data",
  220. "data": {
  221. "application/vnd.jupyter.widget-view+json": {
  222. "version_major": 2,
  223. "version_minor": 0,
  224. "model_id": "4d537444fadf457396906a68e7ef6126"
  225. }
  226. },
  227. "metadata": {}
  228. }
  229. ]
  230. },
  231. {
  232. "metadata": {
  233. "trusted": true
  234. },
  235. "cell_type": "code",
  236. "source": "",
  237. "execution_count": null,
  238. "outputs": []
  239. },
  240. {
  241. "metadata": {},
  242. "cell_type": "markdown",
  243. "source": "### Stitch base table together\nCreate a table called base_table by joining all base table components together using .merge(table,on='column name'), specifically: <br>\n 1. base_table_time\n 2. *Metric_Name* and *Metric_Type* columsn from metrics_info Attn: you need to add a \"key\" column for join operation to be successful\n 3. base_table_statins Attn: please drop the *Region2* column before joining since it is no longer needed__\n 4. base_table_measures\nFinally, drop the *key* column which is used only for joining <br>"
  244. },
  245. {
  246. "metadata": {
  247. "trusted": true
  248. },
  249. "cell_type": "code",
  250. "source": "",
  251. "execution_count": null,
  252. "outputs": []
  253. },
  254. {
  255. "metadata": {
  256. "trusted": true
  257. },
  258. "cell_type": "code",
  259. "source": "# Remove individual parts no longer used\n%reset_selective -f base_table_\n\nbase_table.head(5) # example",
  260. "execution_count": 17,
  261. "outputs": [
  262. {
  263. "output_type": "display_data",
  264. "data": {
  265. "application/vnd.jupyter.widget-view+json": {
  266. "version_major": 2,
  267. "version_minor": 0,
  268. "model_id": "c4b59c0dfbf047649233b834deed9215"
  269. }
  270. },
  271. "metadata": {}
  272. }
  273. ]
  274. },
  275. {
  276. "metadata": {},
  277. "cell_type": "markdown",
  278. "source": "## 4. Clean up the server data, aggregation"
  279. },
  280. {
  281. "metadata": {},
  282. "cell_type": "markdown",
  283. "source": "Complete the following tasks to clean the raw data (sptf_raw) and save as a new variable called sptf_cleaned\n1. drop rows where *Station* column is NaN, hint: __dropna()__\n2. since the metric names in server is different from final metric name used, we need to swap metric name by left joining *Metric_Name, Metric_Type and Name_in_Server* columns from metrics_info, and drop *Name in Server* column after use.\n3. remove additional rows if *Year, Month, Metric_Name, Station* columns are duplicated\n4. since the *Month* column has string datatype in server, we need to convert it to integer, hint: __astype()__\n5. database server also has all *Actual and Goal* columns in percentage numbers except for APU and MBR, we need to convert them to decimals, hint: __use apply() with a function similar to that in base_table_stations__\n6. create a new variable called sptf_cleaned_long (Good read: wide vs long data format), hint: __melt()__"
  284. },
  285. {
  286. "metadata": {
  287. "trusted": true
  288. },
  289. "cell_type": "code",
  290. "source": "# example of sptf_cleaned_long\nsptf_cleaned_long.head(8)",
  291. "execution_count": 18,
  292. "outputs": [
  293. {
  294. "output_type": "display_data",
  295. "data": {
  296. "application/vnd.jupyter.widget-view+json": {
  297. "version_major": 2,
  298. "version_minor": 0,
  299. "model_id": "afbbecf0fdbd4ca39190da39b0d9db3e"
  300. }
  301. },
  302. "metadata": {}
  303. }
  304. ]
  305. },
  306. {
  307. "metadata": {
  308. "trusted": true
  309. },
  310. "cell_type": "code",
  311. "source": "",
  312. "execution_count": 9,
  313. "outputs": []
  314. },
  315. {
  316. "metadata": {},
  317. "cell_type": "markdown",
  318. "source": "Now we would like to get data for all regions, which only exist partially in server. Start by preparing a table of only stations for aggregation as shown below<br>\nYou need to join sptf_cleaned with AO_Structure table, and drop *Actual, Goal and Score* columns<br><br>\nHint: to select only stations and not regions, use logical condition *Station*.str.len() == 3"
  319. },
  320. {
  321. "metadata": {
  322. "trusted": true
  323. },
  324. "cell_type": "code",
  325. "source": "temp_stations.head(5)",
  326. "execution_count": 19,
  327. "outputs": [
  328. {
  329. "output_type": "display_data",
  330. "data": {
  331. "application/vnd.jupyter.widget-view+json": {
  332. "version_major": 2,
  333. "version_minor": 0,
  334. "model_id": "76aac5f50c9246dbbee519e2524e389d"
  335. }
  336. },
  337. "metadata": {}
  338. }
  339. ]
  340. },
  341. {
  342. "metadata": {
  343. "trusted": true
  344. },
  345. "cell_type": "code",
  346. "source": "",
  347. "execution_count": 10,
  348. "outputs": []
  349. },
  350. {
  351. "metadata": {},
  352. "cell_type": "markdown",
  353. "source": "Then, use a for loop to aggregate the table at 4 different levels, i.e. Subregion, Subregion2, Region, Region2<br>\nComplete the following code block"
  354. },
  355. {
  356. "metadata": {
  357. "trusted": true
  358. },
  359. "cell_type": "code",
  360. "source": "# Create an empty variable to store aggregated tables from for loop\ntemp_aggr = []\n\n# For loop\nfor ? in ?:\n temp = x.groupby().aggregation()....\n temp_aggr.append(temp)\n \n# Convert the result from for loop into a dataframe\ntemp_aggr = pd.concat(temp_aggr)",
  361. "execution_count": null,
  362. "outputs": []
  363. },
  364. {
  365. "metadata": {},
  366. "cell_type": "markdown",
  367. "source": "Also do the following to aggregated table\n1. drop duplicates in *Year, Month, Metric_Name, Station* columns\n2. convert the table to long format\n3. If station name is 'INTERNATIONAL LINE STATIONS', change to INTERNATIONAL, same for DOMESTIC LINE AND REGIONAL STATIONS\n\n<br> Final table should look like:\n"
  368. },
  369. {
  370. "metadata": {
  371. "trusted": true
  372. },
  373. "cell_type": "code",
  374. "source": "temp_aggr.head(10)",
  375. "execution_count": 20,
  376. "outputs": [
  377. {
  378. "output_type": "display_data",
  379. "data": {
  380. "application/vnd.jupyter.widget-view+json": {
  381. "version_major": 2,
  382. "version_minor": 0,
  383. "model_id": "8e9a6135d0f04692aef91034930944a3"
  384. }
  385. },
  386. "metadata": {}
  387. }
  388. ]
  389. },
  390. {
  391. "metadata": {
  392. "trusted": true
  393. },
  394. "cell_type": "code",
  395. "source": "",
  396. "execution_count": null,
  397. "outputs": []
  398. },
  399. {
  400. "metadata": {},
  401. "cell_type": "markdown",
  402. "source": "## 5. Insert everything into base table to get master data\nAt this point, we have three major tables: __base_table__, __sptf_cleaned_long__ and __temp_aggr__, where __temp_aggr__ and __sptf_cleaned_long__ have overlaps but overall different data points<br>\nWe would like to merge them into the base table with the following steps\n1. left join __sptf_cleaned_long__ and __temp_aggr__ to __base_table__, only adding *value* column, store in a new variable called __data_long__\n2. Because aggregation returns 0 if there's nothing to aggregate, these values should be coerce to NaN<br>\nForce *value* column from __temp_aggr__ table to be NaN if: Metric_Type is either Safe or Efficient AND *Category* column of a station is not Line Station\n3. If *value* column from __sptf_cleaned_long__ table is NaN, replace with *value* column from __temp_aggr__ table\n4. Drop *value* column from __temp_aggr__ table, keep that of __sptf_cleaned_long__ table and rename column to *value*\n5. create a new data table named __data_wide__ that is the wide format version of __data_long__"
  403. },
  404. {
  405. "metadata": {
  406. "trusted": true
  407. },
  408. "cell_type": "code",
  409. "source": "",
  410. "execution_count": null,
  411. "outputs": []
  412. },
  413. {
  414. "metadata": {},
  415. "cell_type": "markdown",
  416. "source": "The final table should look like this"
  417. },
  418. {
  419. "metadata": {
  420. "trusted": true
  421. },
  422. "cell_type": "code",
  423. "source": "data_w.query('Year == 2018 and Month == 6 and Metric_Name == \"MBR\" and Station == \"SYSTEM\"')",
  424. "execution_count": 21,
  425. "outputs": [
  426. {
  427. "output_type": "display_data",
  428. "data": {
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  441. "kernelspec": {
  442. "name": "python3",
  443. "display_name": "Python 3",
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  458. "nbTranslate": {
  459. "hotkey": "alt-t",
  460. "sourceLang": "en",
  461. "targetLang": "fr",
  462. "displayLangs": [
  463. "*"
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  465. "langInMainMenu": true,
  466. "useGoogleTranslate": true
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  468. "toc": {
  469. "nav_menu": {},
  470. "number_sections": false,
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  481. "varInspector": {
  482. "window_display": false,
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  488. "kernels_config": {
  489. "python": {
  490. "library": "var_list.py",
  491. "delete_cmd_prefix": "del ",
  492. "delete_cmd_postfix": "",
  493. "varRefreshCmd": "print(var_dic_list())"
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  495. "r": {
  496. "library": "var_list.r",
  497. "delete_cmd_prefix": "rm(",
  498. "delete_cmd_postfix": ") ",
  499. "varRefreshCmd": "cat(var_dic_list()) "
  500. }
  501. },
  502. "types_to_exclude": [
  503. "module",
  504. "function",
  505. "builtin_function_or_method",
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  507. "_Feature"
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  511. "left": "1038px",
  512. "top": "76px",
  513. "width": "350px",
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  524. "9a529850ecb84ff08bbf2ed16ceef2a2": {
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