├── hist.png ├── requirements.txt ├── README.md ├── .gitignore ├── 1 - Missing Data.ipynb ├── data ├── btc-eth-prices-original.csv ├── btc-eth-prices-outliers.csv ├── btc-eth-prices.csv ├── eth-price.csv ├── btc-market-price.csv ├── eth-price-full.csv └── btc-market-price-full.csv └── 3 - Cleaning Not Null Values.ipynb /hist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rmotr-curriculum/data-cleaning-rmotr-freecodecamp/HEAD/hist.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==0.22.0 2 | seaborn==0.8.1 3 | numpy==1.14.0 4 | matplotlib==2.2.2 5 | ipython==6.2.1 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | #### We're in the process of adapting these notebooks into interactive projects in [DataWars](https://www.datawars.io/?utm_source=fccrepo&utm_medium=data-cleaning). Sign up now, it's [completely free](https://www.datawars.io/?utm_source=fccrepo&utm_medium=data-cleaning). 3 | 4 | Stay tuned! Have any questions? [Join our Discord](https://discord.gg/DSTe8tY38T) 5 | 6 | --- 7 | 8 | Created by Santiago Basulto. Connect with me on [X](https://x.com/santiagobasulto) or [LinkedIn](https://www.linkedin.com/in/santiagobasulto/) 9 | -------------------------------------------------------------------------------- /.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 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | .ipynb_checkpoints/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | 47 | # Translations 48 | *.mo 49 | *.pot 50 | 51 | # Django stuff: 52 | *.log 53 | 54 | # Sphinx documentation 55 | docs/_build/ 56 | 57 | # PyBuilder 58 | target/ 59 | .DS_Store 60 | -------------------------------------------------------------------------------- /1 - Missing Data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "![rmotr](https://i.imgur.com/jiPp4hj.png)\n", 8 | "
\n", 9 | "\n", 10 | "\n", 12 | "\n", 13 | "# Missing Data" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "![separator2](https://i.imgur.com/4gX5WFr.png)\n", 21 | "\n", 22 | "## Hands on! " 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 1, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "import numpy as np\n", 32 | "import pandas as pd" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "What does \"missing data\" mean? What is a missing value? It depends on the origin of the data and the context it was generated. For example, for a survey, a _`Salary`_ field with an empty value, or a number 0, or an invalid value (a string for example) can be considered \"missing data\". These concepts are related to the values that Python will consider \"Falsy\":" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "falsy_values = (0, False, None, '', [], {})" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "metadata": {}, 54 | "source": [ 55 | "For Python, all the values above are considered \"falsy\":" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "metadata": { 62 | "scrolled": true 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "any(falsy_values)" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "Numpy has a special \"nullable\" value for numbers which is `np.nan`. It's _NaN_: \"Not a number\"" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": null, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "np.nan" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "The `np.nan` value is kind of a virus. Everything that it touches becomes `np.nan`:" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": null, 95 | "metadata": {}, 96 | "outputs": [], 97 | "source": [ 98 | "3 + np.nan" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": null, 104 | "metadata": {}, 105 | "outputs": [], 106 | "source": [ 107 | "a = np.array([1, 2, 3, np.nan, np.nan, 4])" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "a.sum()" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": null, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "a.mean()" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "This is better than regular `None` values, which in the previous examples would have raised an exception:" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": null, 138 | "metadata": {}, 139 | "outputs": [], 140 | "source": [ 141 | "3 + None" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "For a numeric array, the `None` value is replaced by `np.nan`:" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "a = np.array([1, 2, 3, np.nan, None, 4], dtype='float')" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "a" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "As we said, `np.nan` is like a virus. If you have any `nan` value in an array and you try to perform an operation on it, you'll get unexpected results:" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": null, 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [ 182 | "a = np.array([1, 2, 3, np.nan, np.nan, 4])" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": null, 188 | "metadata": {}, 189 | "outputs": [], 190 | "source": [ 191 | "a.mean()" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": null, 197 | "metadata": {}, 198 | "outputs": [], 199 | "source": [ 200 | "a.sum()" 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": {}, 206 | "source": [ 207 | "Numpy also supports an \"Infinite\" type:" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": null, 213 | "metadata": {}, 214 | "outputs": [], 215 | "source": [ 216 | "np.inf" 217 | ] 218 | }, 219 | { 220 | "cell_type": "markdown", 221 | "metadata": {}, 222 | "source": [ 223 | "Which also behaves as a virus:" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": null, 229 | "metadata": {}, 230 | "outputs": [], 231 | "source": [ 232 | "3 + np.inf" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": {}, 239 | "outputs": [], 240 | "source": [ 241 | "np.inf / 3" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "np.inf / np.inf" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [ 259 | "b = np.array([1, 2, 3, np.inf, np.nan, 4], dtype=np.float)" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": null, 265 | "metadata": {}, 266 | "outputs": [], 267 | "source": [ 268 | "b.sum()" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "![separator1](https://i.imgur.com/ZUWYTii.png)\n", 276 | "\n", 277 | "### Checking for `nan` or `inf`\n", 278 | "\n", 279 | "There are two functions: `np.isnan` and `np.isinf` that will perform the desired checks:" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": null, 285 | "metadata": {}, 286 | "outputs": [], 287 | "source": [ 288 | "np.isnan(np.nan)" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "metadata": {}, 295 | "outputs": [], 296 | "source": [ 297 | "np.isinf(np.inf)" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "And the joint operation can be performed with `np.isfinite`." 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": null, 310 | "metadata": {}, 311 | "outputs": [], 312 | "source": [ 313 | "np.isfinite(np.nan), np.isfinite(np.inf)" 314 | ] 315 | }, 316 | { 317 | "cell_type": "markdown", 318 | "metadata": {}, 319 | "source": [ 320 | "`np.isnan` and `np.isinf` also take arrays as inputs, and return boolean arrays as results:" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": null, 326 | "metadata": {}, 327 | "outputs": [], 328 | "source": [ 329 | "np.isnan(np.array([1, 2, 3, np.nan, np.inf, 4]))" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": {}, 336 | "outputs": [], 337 | "source": [ 338 | "np.isinf(np.array([1, 2, 3, np.nan, np.inf, 4]))" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": null, 344 | "metadata": { 345 | "scrolled": true 346 | }, 347 | "outputs": [], 348 | "source": [ 349 | "np.isfinite(np.array([1, 2, 3, np.nan, np.inf, 4]))" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": {}, 355 | "source": [ 356 | "_Note: It's not so common to find infinite values. From now on, we'll keep working with only `np.nan`_" 357 | ] 358 | }, 359 | { 360 | "cell_type": "markdown", 361 | "metadata": {}, 362 | "source": [ 363 | "![separator1](https://i.imgur.com/ZUWYTii.png)\n", 364 | "\n", 365 | "### Filtering them out\n", 366 | "\n", 367 | "Whenever you're trying to perform an operation with a Numpy array and you know there might be missing values, you'll need to filter them out before proceeding, to avoid `nan` propagation. We'll use a combination of the previous `np.isnan` + boolean arrays for this purpose:" 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": null, 373 | "metadata": {}, 374 | "outputs": [], 375 | "source": [ 376 | "a = np.array([1, 2, 3, np.nan, np.nan, 4])" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [ 385 | "a[~np.isnan(a)]" 386 | ] 387 | }, 388 | { 389 | "cell_type": "markdown", 390 | "metadata": {}, 391 | "source": [ 392 | "Which is equivalent to:" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": null, 398 | "metadata": {}, 399 | "outputs": [], 400 | "source": [ 401 | "a[np.isfinite(a)]" 402 | ] 403 | }, 404 | { 405 | "cell_type": "markdown", 406 | "metadata": {}, 407 | "source": [ 408 | "And with that result, all the operation can be now performed:" 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": null, 414 | "metadata": {}, 415 | "outputs": [], 416 | "source": [ 417 | "a[np.isfinite(a)].sum()" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "scrolled": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "a[np.isfinite(a)].mean()" 429 | ] 430 | }, 431 | { 432 | "cell_type": "markdown", 433 | "metadata": {}, 434 | "source": [ 435 | "![separator2](https://i.imgur.com/4gX5WFr.png)" 436 | ] 437 | } 438 | ], 439 | "metadata": { 440 | "kernelspec": { 441 | "display_name": "Python 3", 442 | "language": "python", 443 | "name": "python3" 444 | }, 445 | "language_info": { 446 | "codemirror_mode": { 447 | "name": "ipython", 448 | "version": 3 449 | }, 450 | "file_extension": ".py", 451 | "mimetype": "text/x-python", 452 | "name": "python", 453 | "nbconvert_exporter": "python", 454 | "pygments_lexer": "ipython3", 455 | "version": "3.8.1" 456 | } 457 | }, 458 | "nbformat": 4, 459 | "nbformat_minor": 4 460 | } 461 | -------------------------------------------------------------------------------- /data/btc-eth-prices-original.csv: -------------------------------------------------------------------------------- 1 | Timestamp,Bitcoin,Ether 2 | 2017-04-02,1099.169125,48.55 3 | 2017-04-03,1141.813,44.13 4 | 2017-04-04,1141.6003625,44.43 5 | 2017-04-05,1133.0793142857142,44.9 6 | 2017-04-06,1196.3079375,43.23 7 | 2017-04-07,1190.45425,42.31 8 | 2017-04-08,1181.1498375,44.37 9 | 2017-04-09,1208.8005,43.72 10 | 2017-04-10,1207.744875,43.74 11 | 2017-04-11,1226.6170375,43.74 12 | 2017-04-12,1218.92205,46.38 13 | 2017-04-13,1180.0237125,49.97 14 | 2017-04-14,1185.2600571428572,47.32 15 | 2017-04-15,1184.8806714285713,48.89 16 | 2017-04-16,1186.9274125,48.22 17 | 2017-04-17,1205.634875,47.94 18 | 2017-04-18,1216.1867428571427,49.88 19 | 2017-04-19,1217.9300875,47.88 20 | 2017-04-20,1241.686325,49.36 21 | 2017-04-21,1258.3614125,48.27 22 | 2017-04-22,1261.311225,48.41 23 | 2017-04-23,1257.9881125,48.75 24 | 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"4/3/2017","1491177600","44.13" 4 | "4/4/2017","1491264000","44.43" 5 | "4/5/2017","1491350400","44.90" 6 | "4/6/2017","1491436800","43.23" 7 | "4/7/2017","1491523200","42.31" 8 | "4/8/2017","1491609600","44.37" 9 | "4/9/2017","1491696000","43.72" 10 | "4/10/2017","1491782400","43.74" 11 | "4/11/2017","1491868800","43.74" 12 | "4/12/2017","1491955200","46.38" 13 | "4/13/2017","1492041600","49.97" 14 | "4/14/2017","1492128000","47.32" 15 | "4/15/2017","1492214400","48.89" 16 | "4/16/2017","1492300800","48.22" 17 | "4/17/2017","1492387200","47.94" 18 | "4/18/2017","1492473600","49.88" 19 | "4/19/2017","1492560000","47.88" 20 | "4/20/2017","1492646400","49.36" 21 | "4/21/2017","1492732800","48.27" 22 | "4/22/2017","1492819200","48.41" 23 | "4/23/2017","1492905600","48.75" 24 | "4/24/2017","1492992000","49.94" 25 | "4/25/2017","1493078400","50.09" 26 | "4/26/2017","1493164800","53.28" 27 | "4/27/2017","1493251200","63.14" 28 | "4/28/2017","1493337600","72.42" 29 | "4/29/2017","1493424000","69.83" 30 | "4/30/2017","1493510400","79.83" 31 | "5/1/2017","1493596800","77.53" 32 | "5/2/2017","1493683200","77.25" 33 | "5/3/2017","1493769600","80.37" 34 | "5/4/2017","1493856000","94.55" 35 | "5/5/2017","1493942400","90.79" 36 | "5/6/2017","1494028800","94.82" 37 | "5/7/2017","1494115200","90.46" 38 | "5/8/2017","1494201600","88.39" 39 | "5/9/2017","1494288000","86.27" 40 | "5/10/2017","1494374400","87.83" 41 | "5/11/2017","1494460800","88.20" 42 | "5/12/2017","1494547200","85.15" 43 | "5/13/2017","1494633600","87.96" 44 | "5/14/2017","1494720000","88.72" 45 | "5/15/2017","1494806400","90.32" 46 | "5/16/2017","1494892800","87.80" 47 | "5/17/2017","1494979200","86.98" 48 | "5/18/2017","1495065600","95.88" 49 | "5/19/2017","1495152000","124.38" 50 | "5/20/2017","1495238400","123.06" 51 | "5/21/2017","1495324800","148.00" 52 | "5/22/2017","1495411200","160.39" 53 | "5/23/2017","1495497600","169.50" 54 | "5/24/2017","1495584000","193.03" 55 | 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81 | "6/20/2017","1497916800","350.53" 82 | "6/21/2017","1498003200","325.30" 83 | "6/22/2017","1498089600","320.97" 84 | "6/23/2017","1498176000","326.85" 85 | "6/24/2017","1498262400","304.54" 86 | "6/25/2017","1498348800","279.36" 87 | "6/26/2017","1498435200","253.68" 88 | "6/27/2017","1498521600","286.14" 89 | "6/28/2017","1498608000","315.86" 90 | "6/29/2017","1498694400","292.90" 91 | "6/30/2017","1498780800","280.68" 92 | "7/1/2017","1498867200","261.00" 93 | "7/2/2017","1498953600","283.99" 94 | "7/3/2017","1499040000","276.41" 95 | "7/4/2017","1499126400","269.05" 96 | "7/5/2017","1499212800","266.00" 97 | "7/6/2017","1499299200","265.88" 98 | "7/7/2017","1499385600","240.94" 99 | "7/8/2017","1499472000","245.67" 100 | "7/9/2017","1499558400","237.72" 101 | "7/10/2017","1499644800","205.76" 102 | "7/11/2017","1499731200","190.55" 103 | "7/12/2017","1499817600","224.15" 104 | "7/13/2017","1499904000","205.41" 105 | "7/14/2017","1499990400","197.14" 106 | 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"8/9/2017","1502236800","295.28" 132 | "8/10/2017","1502323200","298.28" 133 | "8/11/2017","1502409600","309.32" 134 | "8/12/2017","1502496000","308.02" 135 | "8/13/2017","1502582400","296.62" 136 | "8/14/2017","1502668800","299.16" 137 | "8/15/2017","1502755200","286.52" 138 | "8/16/2017","1502841600","301.38" 139 | "8/17/2017","1502928000","300.30" 140 | "8/18/2017","1503014400","292.62" 141 | "8/19/2017","1503100800","293.02" 142 | "8/20/2017","1503187200","298.20" 143 | "8/21/2017","1503273600","321.85" 144 | "8/22/2017","1503360000","313.37" 145 | "8/23/2017","1503446400","317.40" 146 | "8/24/2017","1503532800","325.28" 147 | "8/25/2017","1503619200","330.06" 148 | "8/26/2017","1503705600","332.86" 149 | "8/27/2017","1503792000","347.88" 150 | "8/28/2017","1503878400","347.66" 151 | "8/29/2017","1503964800","372.35" 152 | "8/30/2017","1504051200","383.86" 153 | "8/31/2017","1504137600","388.33" 154 | "9/1/2017","1504224000","391.42" 155 | "9/2/2017","1504310400","351.03" 156 | "9/3/2017","1504396800","352.45" 157 | "9/4/2017","1504483200","303.70" 158 | "9/5/2017","1504569600","317.94" 159 | "9/6/2017","1504656000","338.92" 160 | "9/7/2017","1504742400","335.37" 161 | "9/8/2017","1504828800","306.72" 162 | "9/9/2017","1504915200","303.79" 163 | "9/10/2017","1505001600","299.21" 164 | "9/11/2017","1505088000","297.95" 165 | "9/12/2017","1505174400","294.10" 166 | "9/13/2017","1505260800","275.84" 167 | "9/14/2017","1505347200","223.14" 168 | "9/15/2017","1505433600","259.57" 169 | "9/16/2017","1505520000","254.49" 170 | "9/17/2017","1505606400","258.40" 171 | "9/18/2017","1505692800","297.53" 172 | "9/19/2017","1505779200","283.00" 173 | "9/20/2017","1505865600","283.56" 174 | "9/21/2017","1505952000","257.77" 175 | "9/22/2017","1506038400","262.94" 176 | "9/23/2017","1506124800","286.14" 177 | "9/24/2017","1506211200","282.60" 178 | "9/25/2017","1506297600","294.89" 179 | "9/26/2017","1506384000","288.64" 180 | "9/27/2017","1506470400","309.97" 181 | 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9 | 2017-04-10 00:00:00,1207.744875 10 | 2017-04-11 00:00:00,1226.6170375 11 | 2017-04-12 00:00:00,1218.92205 12 | 2017-04-13 00:00:00,1180.0237125 13 | 2017-04-14 00:00:00,1185.2600571428572 14 | 2017-04-15 00:00:00,1184.8806714285713 15 | 2017-04-16 00:00:00,1186.9274125 16 | 2017-04-17 00:00:00,1205.634875 17 | 2017-04-18 00:00:00,1216.1867428571427 18 | 2017-04-19 00:00:00,1217.9300875 19 | 2017-04-20 00:00:00,1241.6863250000001 20 | 2017-04-21 00:00:00,1258.3614125 21 | 2017-04-22 00:00:00,1261.311225 22 | 2017-04-23 00:00:00,1257.9881125 23 | 2017-04-24 00:00:00,1262.902775 24 | 2017-04-25 00:00:00,1279.4146875000001 25 | 2017-04-26 00:00:00,1309.109875 26 | 2017-04-27 00:00:00,1345.3539125 27 | 2017-04-28 00:00:00,1331.2944285714286 28 | 2017-04-29 00:00:00,1334.9790375 29 | 2017-04-30 00:00:00,1353.0045 30 | 2017-05-01 00:00:00,1417.1728125 31 | 2017-05-02 00:00:00,1452.0762875 32 | 2017-05-03 00:00:00,1507.5768571428573 33 | 2017-05-04 00:00:00,1508.292125 34 | 2017-05-05 00:00:00,1533.3350714285714 35 | 2017-05-06 00:00:00,1560.4102 36 | 2017-05-07 00:00:00,1535.8684285714285 37 | 2017-05-08 00:00:00,1640.619225 38 | 2017-05-09 00:00:00,1721.2849714285715 39 | 2017-05-10 00:00:00,1762.88625 40 | 2017-05-11 00:00:00,1820.9905625 41 | 2017-05-12 00:00:00,1720.4785 42 | 2017-05-13 00:00:00,1771.9200125 43 | 2017-05-14 00:00:00,1776.3165 44 | 2017-05-15 00:00:00,1723.1269375 45 | 2017-05-16 00:00:00,1739.031975 46 | 2017-05-17 00:00:00,1807.4850625 47 | 2017-05-18 00:00:00,1899.0828875 48 | 2017-05-19 00:00:00,1961.5204875 49 | 2017-05-20 00:00:00,2052.9097875 50 | 2017-05-21 00:00:00,2046.5344625 51 | 2017-05-22 00:00:00,2090.6623125 52 | 2017-05-23 00:00:00,2287.7102875 53 | 2017-05-24 00:00:00,2379.1938333333333 54 | 2017-05-25 00:00:00,2387.2062857142855 55 | 2017-05-26 00:00:00,2211.976857142857 56 | 2017-05-27 00:00:00,2014.0529625 57 | 2017-05-28 00:00:00,2192.9808 58 | 2017-05-29 00:00:00,2275.9307 59 | 2017-05-30 00:00:00,2239.2053428571426 60 | 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00:00:00,6020.371683333334 204 | 2017-10-22 00:00:00,5983.184550000001 205 | 2017-10-23 00:00:00,5876.079866666667 206 | 2017-10-24 00:00:00,5505.827766666666 207 | 2017-10-25 00:00:00,5669.622533333334 208 | 2017-10-26 00:00:00,5893.138416666666 209 | 2017-10-27 00:00:00,5772.504983333333 210 | 2017-10-28 00:00:00,5776.6969500000005 211 | 2017-10-29 00:00:00,6155.43402 212 | 2017-10-30 00:00:00,6105.87422 213 | 2017-10-31 00:00:00,6388.645166666666 214 | 2017-11-01 00:00:00,6665.306683333333 215 | 2017-11-02 00:00:00,7068.020100000001 216 | 2017-11-03 00:00:00,7197.72006 217 | 2017-11-04 00:00:00,7437.543316666666 218 | 2017-11-05 00:00:00,7377.012366666667 219 | 2017-11-06 00:00:00,6989.071666666667 220 | 2017-11-07 00:00:00,7092.127233333333 221 | 2017-11-08 00:00:00,7415.878250000001 222 | 2017-11-09 00:00:00,7158.03706 223 | 2017-11-10 00:00:00,6719.39785 224 | 2017-11-11 00:00:00,6362.851033333333 225 | 2017-11-12 00:00:00,5716.301583333334 226 | 2017-11-13 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2018-01-24 00:00:00,11282.258333333333 299 | 2018-01-25 00:00:00,11214.44 300 | 2018-01-26 00:00:00,10969.815 301 | 2018-01-27 00:00:00,11524.776666666667 302 | 2018-01-28 00:00:00,11765.71 303 | 2018-01-29 00:00:00,11212.654999999999 304 | 2018-01-30 00:00:00,10184.061666666666 305 | 2018-01-31 00:00:00,10125.013333333334 306 | 2018-02-01 00:00:00,9083.258333333333 307 | 2018-02-02 00:00:00,8901.901666666667 308 | 2018-02-03 00:00:00,9076.678333333333 309 | 2018-02-04 00:00:00,8400.648333333333 310 | 2018-02-05 00:00:00,6838.816666666667 311 | 2018-02-06 00:00:00,7685.633333333334 312 | 2018-02-07 00:00:00,8099.958333333333 313 | 2018-02-08 00:00:00,8240.536666666667 314 | 2018-02-09 00:00:00,8535.516666666668 315 | 2018-02-10 00:00:00,8319.876566184 316 | 2018-02-11 00:00:00,8343.455 317 | 2018-02-12 00:00:00,8811.343333333332 318 | 2018-02-13 00:00:00,8597.7675 319 | 2018-02-14 00:00:00,9334.633333333333 320 | 2018-02-15 00:00:00,9977.154 321 | 2018-02-16 00:00:00,10127.161666666667 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-------------------------------------------------------------------------------- 1 | "Date(UTC)","UnixTimeStamp","Value" 2 | "7/30/2015","1438214400","0.00" 3 | "7/31/2015","1438300800","0.00" 4 | "8/1/2015","1438387200","0.00" 5 | "8/2/2015","1438473600","0.00" 6 | "8/3/2015","1438560000","0.00" 7 | "8/4/2015","1438646400","0.00" 8 | "8/5/2015","1438732800","0.00" 9 | "8/6/2015","1438819200","0.00" 10 | "8/7/2015","1438905600","3.00" 11 | "8/8/2015","1438992000","1.20" 12 | "8/9/2015","1439078400","1.20" 13 | "8/10/2015","1439164800","0.00" 14 | "8/11/2015","1439251200","0.99" 15 | "8/12/2015","1439337600","1.29" 16 | "8/13/2015","1439424000","1.88" 17 | "8/14/2015","1439510400","1.79" 18 | "8/15/2015","1439596800","1.79" 19 | "8/17/2015","1439769600","1.30" 20 | "8/16/2015","1439683200","1.37" 21 | "8/18/2015","1439856000","1.36" 22 | "8/19/2015","1439942400","1.24" 23 | "8/20/2015","1440028800","1.52" 24 | "8/21/2015","1440115200","1.44" 25 | "8/22/2015","1440201600","1.40" 26 | "8/23/2015","1440288000","1.35" 27 | "8/24/2015","1440374400","1.24" 28 | "8/25/2015","1440460800","1.27" 29 | "8/26/2015","1440547200","1.18" 30 | "8/27/2015","1440633600","1.14" 31 | "8/28/2015","1440720000","1.30" 32 | "8/29/2015","1440806400","1.18" 33 | "8/30/2015","1440892800","1.32" 34 | "8/31/2015","1440979200","1.31" 35 | "9/1/2015","1441065600","1.36" 36 | "9/2/2015","1441152000","1.14" 37 | "9/3/2015","1441238400","1.23" 38 | "9/4/2015","1441324800","1.35" 39 | "9/5/2015","1441411200","1.37" 40 | "9/6/2015","1441497600","1.34" 41 | "9/7/2015","1441584000","1.28" 42 | "9/8/2015","1441670400","1.26" 43 | "9/9/2015","1441756800","1.21" 44 | "9/10/2015","1441843200","1.19" 45 | "9/11/2015","1441929600","0.92" 46 | "9/12/2015","1442016000","1.15" 47 | "9/13/2015","1442102400","0.89" 48 | "9/14/2015","1442188800","0.96" 49 | "9/15/2015","1442275200","0.95" 50 | "9/16/2015","1442361600","0.94" 51 | "9/17/2015","1442448000","0.88" 52 | "9/18/2015","1442534400","0.85" 53 | "9/19/2015","1442620800","0.89" 54 | "9/20/2015","1442707200","0.89" 55 | "9/21/2015","1442793600","0.94" 56 | "9/22/2015","1442880000","0.81" 57 | "9/23/2015","1442966400","0.91" 58 | "9/24/2015","1443052800","0.80" 59 | "9/25/2015","1443139200","0.68" 60 | "9/26/2015","1443225600","0.77" 61 | "9/27/2015","1443312000","0.70" 62 | "9/28/2015","1443398400","0.60" 63 | "9/29/2015","1443484800","0.68" 64 | "9/30/2015","1443571200","0.71" 65 | "10/1/2015","1443657600","0.65" 66 | "10/2/2015","1443744000","0.66" 67 | "10/3/2015","1443830400","0.68" 68 | "10/4/2015","1443916800","0.61" 69 | "10/5/2015","1444003200","0.61" 70 | "10/6/2015","1444089600","0.65" 71 | "10/7/2015","1444176000","0.60" 72 | "10/8/2015","1444262400","0.62" 73 | "10/9/2015","1444348800","0.64" 74 | "10/10/2015","1444435200","0.64" 75 | "10/11/2015","1444521600","0.60" 76 | "10/12/2015","1444608000","0.63" 77 | "10/13/2015","1444694400","0.60" 78 | "10/14/2015","1444780800","0.50" 79 | "10/15/2015","1444867200","0.56" 80 | "10/16/2015","1444953600","0.53" 81 | "10/17/2015","1445040000","0.52" 82 | "10/18/2015","1445126400","0.51" 83 | "10/19/2015","1445212800","0.50" 84 | "10/20/2015","1445299200","0.44" 85 | "10/22/2015","1445472000","0.61" 86 | "10/21/2015","1445385600","0.42" 87 | "10/23/2015","1445558400","0.56" 88 | "10/24/2015","1445644800","0.56" 89 | "10/25/2015","1445731200","0.62" 90 | "10/26/2015","1445817600","0.71" 91 | "10/27/2015","1445904000","0.83" 92 | "10/28/2015","1445990400","0.99" 93 | "10/29/2015","1446076800","1.12" 94 | "10/30/2015","1446163200","1.14" 95 | "10/31/2015","1446249600","0.87" 96 | "11/1/2015","1446336000","0.99" 97 | "11/2/2015","1446422400","0.99" 98 | "11/3/2015","1446508800","1.06" 99 | "11/4/2015","1446595200","0.80" 100 | "11/5/2015","1446681600","0.88" 101 | "11/6/2015","1446768000","0.99" 102 | "11/7/2015","1446854400","0.93" 103 | "11/8/2015","1446940800","1.00" 104 | "11/9/2015","1447027200","1.00" 105 | "11/10/2015","1447113600","0.90" 106 | "11/11/2015","1447200000","0.75" 107 | "11/12/2015","1447286400","0.88" 108 | "11/13/2015","1447372800","0.90" 109 | "11/14/2015","1447459200","0.88" 110 | "11/15/2015","1447545600","0.92" 111 | "11/16/2015","1447632000","0.93" 112 | "11/17/2015","1447718400","1.00" 113 | "11/18/2015","1447804800","1.00" 114 | "11/19/2015","1447891200","0.94" 115 | "11/20/2015","1447977600","0.92" 116 | "11/21/2015","1448064000","0.96" 117 | "11/22/2015","1448150400","0.97" 118 | "11/23/2015","1448236800","0.92" 119 | "11/24/2015","1448323200","0.91" 120 | "11/25/2015","1448409600","0.87" 121 | "11/26/2015","1448496000","0.86" 122 | "11/27/2015","1448582400","0.88" 123 | "11/28/2015","1448668800","0.91" 124 | "11/29/2015","1448755200","0.87" 125 | "11/30/2015","1448841600","0.88" 126 | "12/1/2015","1448928000","0.87" 127 | "12/3/2015","1449100800","0.83" 128 | "12/2/2015","1449014400","0.82" 129 | "12/4/2015","1449187200","0.84" 130 | "12/5/2015","1449273600","0.87" 131 | "12/6/2015","1449360000","0.83" 132 | "12/7/2015","1449446400","0.79" 133 | "12/8/2015","1449532800","0.81" 134 | "12/9/2015","1449619200","0.81" 135 | "12/10/2015","1449705600","0.84" 136 | "12/11/2015","1449792000","0.90" 137 | "12/12/2015","1449878400","0.96" 138 | "12/13/2015","1449964800","0.92" 139 | "12/14/2015","1450051200","0.99" 140 | "12/15/2015","1450137600","1.00" 141 | "12/16/2015","1450224000","0.80" 142 | "12/17/2015","1450310400","0.94" 143 | "12/18/2015","1450396800","0.90" 144 | "12/19/2015","1450483200","0.89" 145 | "12/20/2015","1450569600","1.07" 146 | "12/21/2015","1450656000","0.91" 147 | "12/22/2015","1450742400","0.88" 148 | "12/23/2015","1450828800","0.87" 149 | "12/24/2015","1450915200","0.87" 150 | "12/25/2015","1451001600","0.88" 151 | "12/26/2015","1451088000","0.85" 152 | "12/27/2015","1451174400","0.92" 153 | "12/28/2015","1451260800","0.87" 154 | "12/29/2015","1451347200","0.86" 155 | "12/30/2015","1451433600","0.89" 156 | "12/31/2015","1451520000","0.95" 157 | "1/1/2016","1451606400","0.92" 158 | "1/2/2016","1451692800","0.95" 159 | "1/3/2016","1451779200","0.96" 160 | "1/4/2016","1451865600","0.95" 161 | "1/5/2016","1451952000","0.94" 162 | "1/6/2016","1452038400","0.95" 163 | "1/7/2016","1452124800","0.94" 164 | "1/8/2016","1452211200","0.99" 165 | "1/9/2016","1452297600","0.99" 166 | "1/10/2016","1452384000","1.00" 167 | "1/11/2016","1452470400","1.08" 168 | "1/12/2016","1452556800","1.22" 169 | "1/13/2016","1452643200","1.14" 170 | "1/14/2016","1452729600","1.16" 171 | "1/15/2016","1452816000","1.20" 172 | "1/16/2016","1452902400","1.22" 173 | "1/17/2016","1452988800","1.31" 174 | "1/18/2016","1453075200","1.47" 175 | "1/19/2016","1453161600","1.22" 176 | "1/20/2016","1453248000","1.54" 177 | "1/21/2016","1453334400","1.54" 178 | "1/22/2016","1453420800","1.52" 179 | "1/23/2016","1453507200","2.03" 180 | "1/24/2016","1453593600","2.10" 181 | "1/25/2016","1453680000","2.50" 182 | "1/26/2016","1453766400","2.30" 183 | "1/27/2016","1453852800","2.42" 184 | "1/28/2016","1453939200","2.55" 185 | "1/29/2016","1454025600","2.41" 186 | "1/30/2016","1454112000","2.44" 187 | "1/31/2016","1454198400","2.20" 188 | "2/1/2016","1454284800","2.17" 189 | "2/2/2016","1454371200","2.45" 190 | "2/3/2016","1454457600","2.53" 191 | "2/4/2016","1454544000","2.57" 192 | "2/5/2016","1454630400","2.56" 193 | "2/6/2016","1454716800","2.53" 194 | "2/7/2016","1454803200","3.00" 195 | "2/8/2016","1454889600","3.16" 196 | "2/9/2016","1454976000","3.76" 197 | "2/10/2016","1455062400","4.35" 198 | "2/11/2016","1455148800","6.38" 199 | "2/13/2016","1455321600","5.22" 200 | "2/12/2016","1455235200","5.27" 201 | "2/14/2016","1455408000","5.20" 202 | "2/15/2016","1455494400","5.22" 203 | "2/16/2016","1455580800","4.25" 204 | "2/17/2016","1455667200","3.86" 205 | "2/18/2016","1455753600","4.36" 206 | "2/19/2016","1455840000","4.45" 207 | "2/20/2016","1455926400","4.37" 208 | "2/21/2016","1456012800","4.63" 209 | "2/22/2016","1456099200","5.60" 210 | "2/23/2016","1456185600","5.70" 211 | "2/24/2016","1456272000","6.23" 212 | "2/25/2016","1456358400","5.93" 213 | "2/26/2016","1456444800","6.03" 214 | "2/27/2016","1456531200","6.31" 215 | "2/28/2016","1456617600","6.50" 216 | "2/29/2016","1456704000","6.35" 217 | "3/1/2016","1456790400","7.59" 218 | "3/2/2016","1456876800","8.70" 219 | "3/3/2016","1456963200","9.35" 220 | "3/4/2016","1457049600","9.96" 221 | "3/5/2016","1457136000","11.00" 222 | "3/6/2016","1457222400","10.98" 223 | "3/7/2016","1457308800","9.50" 224 | "3/8/2016","1457395200","9.88" 225 | "3/9/2016","1457481600","11.55" 226 | "3/10/2016","1457568000","11.11" 227 | "3/11/2016","1457654400","11.25" 228 | "3/12/2016","1457740800","13.25" 229 | "3/13/2016","1457827200","15.00" 230 | "3/14/2016","1457913600","12.50" 231 | "3/15/2016","1458000000","13.09" 232 | "3/16/2016","1458086400","12.92" 233 | "3/17/2016","1458172800","11.14" 234 | "3/18/2016","1458259200","10.75" 235 | "3/19/2016","1458345600","10.55" 236 | "3/20/2016","1458432000","10.06" 237 | "3/21/2016","1458518400","11.97" 238 | "3/22/2016","1458604800","10.96" 239 | "3/23/2016","1458691200","12.29" 240 | "3/24/2016","1458777600","11.13" 241 | "3/25/2016","1458864000","10.69" 242 | "3/26/2016","1458950400","11.00" 243 | "3/27/2016","1459036800","10.50" 244 | "3/28/2016","1459123200","11.58" 245 | "3/29/2016","1459209600","11.73" 246 | "3/30/2016","1459296000","11.88" 247 | "3/31/2016","1459382400","11.41" 248 | "4/1/2016","1459468800","11.63" 249 | "4/2/2016","1459555200","11.61" 250 | "4/3/2016","1459641600","11.58" 251 | "4/4/2016","1459728000","11.10" 252 | "4/5/2016","1459814400","10.39" 253 | "4/6/2016","1459900800","10.79" 254 | "4/7/2016","1459987200","10.08" 255 | "4/8/2016","1460073600","9.74" 256 | "4/9/2016","1460160000","9.16" 257 | "4/10/2016","1460246400","8.80" 258 | "4/11/2016","1460332800","8.72" 259 | "4/12/2016","1460419200","7.53" 260 | "4/13/2016","1460505600","8.02" 261 | "4/14/2016","1460592000","8.48" 262 | "4/15/2016","1460678400","8.22" 263 | "4/16/2016","1460764800","8.48" 264 | "4/17/2016","1460851200","9.45" 265 | "4/18/2016","1460937600","8.92" 266 | "4/19/2016","1461024000","8.77" 267 | "4/20/2016","1461110400","8.54" 268 | "4/21/2016","1461196800","8.15" 269 | "4/22/2016","1461283200","7.83" 270 | "4/23/2016","1461369600","8.31" 271 | "4/24/2016","1461456000","8.00" 272 | "4/25/2016","1461542400","7.43" 273 | "4/26/2016","1461628800","7.50" 274 | "4/27/2016","1461715200","7.77" 275 | "4/28/2016","1461801600","7.30" 276 | "4/29/2016","1461888000","7.51" 277 | "4/30/2016","1461974400","8.83" 278 | "5/1/2016","1462060800","8.76" 279 | "5/2/2016","1462147200","10.03" 280 | "5/3/2016","1462233600","9.37" 281 | "5/4/2016","1462320000","9.43" 282 | "5/5/2016","1462406400","9.79" 283 | "5/6/2016","1462492800","9.27" 284 | "5/7/2016","1462579200","9.30" 285 | "5/8/2016","1462665600","9.44" 286 | "5/9/2016","1462752000","9.32" 287 | "5/10/2016","1462838400","9.39" 288 | "5/11/2016","1462924800","9.97" 289 | "5/12/2016","1463011200","10.10" 290 | "5/13/2016","1463097600","10.48" 291 | "5/14/2016","1463184000","10.14" 292 | "5/15/2016","1463270400","9.94" 293 | "5/16/2016","1463356800","11.04" 294 | "5/17/2016","1463443200","12.26" 295 | "5/18/2016","1463529600","13.29" 296 | "5/19/2016","1463616000","14.49" 297 | "5/20/2016","1463702400","13.73" 298 | "5/21/2016","1463788800","13.95" 299 | "5/22/2016","1463875200","14.21" 300 | "5/23/2016","1463961600","13.45" 301 | "5/24/2016","1464048000","12.62" 302 | "5/25/2016","1464134400","12.53" 303 | "5/26/2016","1464220800","12.37" 304 | "5/28/2016","1464393600","11.56" 305 | "5/27/2016","1464307200","11.11" 306 | "5/29/2016","1464480000","12.28" 307 | "5/30/2016","1464566400","12.48" 308 | "5/31/2016","1464652800","13.85" 309 | "6/1/2016","1464739200","13.83" 310 | "6/2/2016","1464825600","13.78" 311 | "6/3/2016","1464912000","13.78" 312 | "6/4/2016","1464998400","13.66" 313 | "6/5/2016","1465084800","13.85" 314 | "6/6/2016","1465171200","13.96" 315 | "6/7/2016","1465257600","14.41" 316 | "6/8/2016","1465344000","14.44" 317 | "6/9/2016","1465430400","14.49" 318 | "6/10/2016","1465516800","13.97" 319 | "6/11/2016","1465603200","14.01" 320 | "6/12/2016","1465689600","15.57" 321 | "6/13/2016","1465776000","17.55" 322 | "6/14/2016","1465862400","18.70" 323 | "6/15/2016","1465948800","18.30" 324 | "6/16/2016","1466035200","20.61" 325 | "6/17/2016","1466121600","15.49" 326 | "6/18/2016","1466208000","11.36" 327 | "6/19/2016","1466294400","12.33" 328 | "6/20/2016","1466380800","11.70" 329 | "6/21/2016","1466467200","12.71" 330 | "6/22/2016","1466553600","13.21" 331 | "6/24/2016","1466726400","14.25" 332 | "6/23/2016","1466640000","13.58" 333 | "6/25/2016","1466812800","14.28" 334 | "6/26/2016","1466899200","13.82" 335 | "6/27/2016","1466985600","14.04" 336 | "6/28/2016","1467072000","12.15" 337 | "6/29/2016","1467158400","12.76" 338 | "6/30/2016","1467244800","12.40" 339 | "7/1/2016","1467331200","12.23" 340 | "7/2/2016","1467417600","12.04" 341 | "7/3/2016","1467504000","11.85" 342 | "7/4/2016","1467590400","11.34" 343 | "7/5/2016","1467676800","10.45" 344 | "7/6/2016","1467763200","10.51" 345 | "7/7/2016","1467849600","10.07" 346 | "7/8/2016","1467936000","11.30" 347 | "7/9/2016","1468022400","10.92" 348 | "7/10/2016","1468108800","10.97" 349 | "7/11/2016","1468195200","10.58" 350 | "7/12/2016","1468281600","10.54" 351 | "7/13/2016","1468368000","10.44" 352 | "7/14/2016","1468454400","11.55" 353 | "7/15/2016","1468540800","11.88" 354 | "7/16/2016","1468627200","11.59" 355 | "7/17/2016","1468713600","11.19" 356 | "7/18/2016","1468800000","11.03" 357 | "7/19/2016","1468886400","11.63" 358 | "7/20/2016","1468972800","12.54" 359 | "7/21/2016","1469059200","12.66" 360 | "7/22/2016","1469145600","14.82" 361 | "7/23/2016","1469232000","14.40" 362 | "7/24/2016","1469318400","12.63" 363 | "7/25/2016","1469404800","13.84" 364 | "7/26/2016","1469491200","12.08" 365 | "7/27/2016","1469577600","13.05" 366 | "7/28/2016","1469664000","12.87" 367 | "7/29/2016","1469750400","12.87" 368 | "7/30/2016","1469836800","12.57" 369 | "7/31/2016","1469923200","11.86" 370 | "8/1/2016","1470009600","11.04" 371 | "8/2/2016","1470096000","8.30" 372 | "8/3/2016","1470182400","10.42" 373 | "8/4/2016","1470268800","11.21" 374 | "8/5/2016","1470355200","11.05" 375 | "8/6/2016","1470441600","10.95" 376 | "8/7/2016","1470528000","10.98" 377 | "8/8/2016","1470614400","11.29" 378 | "8/10/2016","1470787200","12.22" 379 | "8/9/2016","1470700800","12.22" 380 | "8/11/2016","1470873600","11.68" 381 | "8/12/2016","1470960000","11.78" 382 | "8/13/2016","1471046400","11.56" 383 | "8/14/2016","1471132800","11.21" 384 | "8/15/2016","1471219200","11.21" 385 | "8/16/2016","1471305600","11.17" 386 | "8/17/2016","1471392000","10.77" 387 | "8/18/2016","1471478400","10.77" 388 | "8/19/2016","1471564800","10.71" 389 | "8/20/2016","1471651200","11.28" 390 | "8/21/2016","1471737600","11.14" 391 | "8/22/2016","1471824000","11.07" 392 | "8/23/2016","1471910400","11.01" 393 | "8/24/2016","1471996800","11.01" 394 | "8/25/2016","1472083200","11.35" 395 | "8/26/2016","1472169600","11.26" 396 | "8/27/2016","1472256000","11.19" 397 | "8/28/2016","1472342400","10.99" 398 | "8/29/2016","1472428800","10.95" 399 | "8/30/2016","1472515200","11.21" 400 | "8/31/2016","1472601600","11.55" 401 | "9/1/2016","1472688000","12.21" 402 | "9/2/2016","1472774400","12.08" 403 | "9/3/2016","1472860800","11.85" 404 | "9/4/2016","1472947200","11.71" 405 | "9/5/2016","1473033600","11.75" 406 | "9/6/2016","1473120000","11.70" 407 | "9/7/2016","1473206400","11.59" 408 | "9/8/2016","1473292800","11.39" 409 | "9/9/2016","1473379200","11.72" 410 | "9/10/2016","1473465600","12.05" 411 | "9/11/2016","1473552000","11.64" 412 | "9/12/2016","1473638400","11.89" 413 | "9/13/2016","1473724800","11.92" 414 | "9/14/2016","1473811200","11.97" 415 | "9/15/2016","1473897600","11.96" 416 | "9/16/2016","1473984000","12.61" 417 | "9/17/2016","1474070400","12.83" 418 | "9/18/2016","1474156800","12.39" 419 | "9/19/2016","1474243200","12.93" 420 | "9/20/2016","1474329600","14.72" 421 | "9/21/2016","1474416000","13.72" 422 | "9/22/2016","1474502400","13.11" 423 | "9/23/2016","1474588800","13.36" 424 | "9/24/2016","1474675200","12.91" 425 | "9/25/2016","1474761600","13.05" 426 | "9/26/2016","1474848000","12.89" 427 | "9/27/2016","1474934400","13.09" 428 | "9/29/2016","1475107200","13.17" 429 | "9/28/2016","1475020800","13.30" 430 | "9/30/2016","1475193600","13.24" 431 | "10/1/2016","1475280000","13.21" 432 | "10/2/2016","1475366400","13.23" 433 | "10/3/2016","1475452800","13.45" 434 | "10/4/2016","1475539200","13.32" 435 | "10/5/2016","1475625600","13.09" 436 | "10/6/2016","1475712000","12.87" 437 | "10/7/2016","1475798400","12.68" 438 | "10/8/2016","1475884800","12.24" 439 | "10/9/2016","1475971200","12.06" 440 | "10/10/2016","1476057600","11.74" 441 | "10/11/2016","1476144000","11.75" 442 | "10/12/2016","1476230400","11.77" 443 | "10/13/2016","1476316800","12.02" 444 | "10/14/2016","1476403200","11.90" 445 | "10/15/2016","1476489600","11.96" 446 | "10/16/2016","1476576000","11.93" 447 | "10/17/2016","1476662400","11.98" 448 | "10/18/2016","1476748800","12.50" 449 | "10/19/2016","1476835200","11.98" 450 | "10/20/2016","1476921600","12.05" 451 | "10/21/2016","1477008000","12.07" 452 | "10/22/2016","1477094400","12.06" 453 | "10/23/2016","1477180800","11.95" 454 | "10/24/2016","1477267200","11.93" 455 | "10/25/2016","1477353600","11.38" 456 | "10/26/2016","1477440000","11.50" 457 | "10/27/2016","1477526400","11.43" 458 | "10/28/2016","1477612800","11.08" 459 | "10/29/2016","1477699200","10.39" 460 | "10/30/2016","1477785600","11.22" 461 | "10/31/2016","1477872000","10.91" 462 | "11/1/2016","1477958400","10.75" 463 | "11/2/2016","1478044800","10.82" 464 | "11/3/2016","1478131200","10.86" 465 | "11/4/2016","1478217600","11.13" 466 | "11/5/2016","1478304000","11.11" 467 | "11/6/2016","1478390400","10.97" 468 | "11/7/2016","1478476800","10.90" 469 | "11/8/2016","1478563200","10.86" 470 | "11/9/2016","1478649600","10.64" 471 | "11/10/2016","1478736000","10.52" 472 | "11/11/2016","1478822400","10.29" 473 | "11/12/2016","1478908800","9.96" 474 | "11/13/2016","1478995200","10.13" 475 | "11/15/2016","1479168000","10.22" 476 | "11/14/2016","1479081600","10.00" 477 | "11/16/2016","1479254400","10.01" 478 | "11/17/2016","1479340800","9.95" 479 | "11/18/2016","1479427200","9.53" 480 | "11/19/2016","1479513600","9.70" 481 | "11/20/2016","1479600000","9.57" 482 | "11/21/2016","1479686400","9.56" 483 | "11/22/2016","1479772800","9.84" 484 | "11/23/2016","1479859200","9.78" 485 | "11/24/2016","1479945600","9.22" 486 | "11/25/2016","1480032000","9.39" 487 | "11/26/2016","1480118400","9.34" 488 | "11/27/2016","1480204800","8.91" 489 | "11/28/2016","1480291200","8.66" 490 | "11/29/2016","1480377600","8.18" 491 | "11/30/2016","1480464000","8.59" 492 | "12/1/2016","1480550400","8.44" 493 | "12/2/2016","1480636800","7.65" 494 | "12/3/2016","1480723200","7.90" 495 | "12/4/2016","1480809600","7.54" 496 | "12/5/2016","1480896000","6.69" 497 | "12/6/2016","1480982400","7.61" 498 | "12/7/2016","1481068800","8.35" 499 | "12/8/2016","1481155200","8.30" 500 | "12/9/2016","1481241600","8.52" 501 | "12/10/2016","1481328000","8.09" 502 | "12/11/2016","1481414400","8.20" 503 | "12/12/2016","1481500800","8.45" 504 | "12/13/2016","1481587200","8.40" 505 | "12/14/2016","1481673600","8.23" 506 | "12/15/2016","1481760000","7.76" 507 | "12/16/2016","1481846400","7.85" 508 | "12/17/2016","1481932800","7.66" 509 | "12/18/2016","1482019200","7.89" 510 | "12/19/2016","1482105600","7.61" 511 | "12/20/2016","1482192000","7.59" 512 | "12/21/2016","1482278400","7.87" 513 | "12/22/2016","1482364800","7.64" 514 | "12/23/2016","1482451200","7.16" 515 | "12/24/2016","1482537600","7.23" 516 | "12/25/2016","1482624000","7.19" 517 | "12/26/2016","1482710400","7.21" 518 | "12/27/2016","1482796800","7.15" 519 | "12/28/2016","1482883200","7.57" 520 | "12/29/2016","1482969600","8.21" 521 | "12/30/2016","1483056000","8.16" 522 | "12/31/2016","1483142400","8.05" 523 | "1/1/2017","1483228800","8.14" 524 | "1/2/2017","1483315200","8.33" 525 | "1/3/2017","1483401600","9.59" 526 | "1/4/2017","1483488000","10.88" 527 | "1/5/2017","1483574400","10.20" 528 | "1/6/2017","1483660800","10.07" 529 | "1/7/2017","1483747200","9.78" 530 | "1/8/2017","1483833600","10.27" 531 | "1/9/2017","1483920000","10.20" 532 | "1/10/2017","1484006400","10.55" 533 | "1/11/2017","1484092800","9.83" 534 | "1/12/2017","1484179200","9.81" 535 | "1/13/2017","1484265600","9.78" 536 | "1/14/2017","1484352000","9.78" 537 | "1/15/2017","1484438400","9.88" 538 | "1/16/2017","1484524800","9.59" 539 | "1/17/2017","1484611200","10.14" 540 | "1/18/2017","1484697600","10.19" 541 | "1/19/2017","1484784000","10.43" 542 | "1/20/2017","1484870400","10.60" 543 | "1/21/2017","1484956800","10.91" 544 | "1/22/2017","1485043200","10.71" 545 | "1/23/2017","1485129600","10.78" 546 | "1/24/2017","1485216000","10.51" 547 | "1/25/2017","1485302400","10.51" 548 | "1/26/2017","1485388800","10.65" 549 | "1/27/2017","1485475200","10.51" 550 | "1/28/2017","1485561600","10.54" 551 | "1/29/2017","1485648000","10.47" 552 | "1/30/2017","1485734400","10.62" 553 | "1/31/2017","1485820800","10.71" 554 | "2/1/2017","1485907200","10.71" 555 | "2/2/2017","1485993600","10.78" 556 | "2/3/2017","1486080000","10.95" 557 | "2/4/2017","1486166400","11.32" 558 | "2/5/2017","1486252800","11.22" 559 | "2/6/2017","1486339200","11.32" 560 | "2/7/2017","1486425600","11.45" 561 | "2/8/2017","1486512000","11.39" 562 | "2/9/2017","1486598400","10.94" 563 | "2/10/2017","1486684800","11.34" 564 | "2/11/2017","1486771200","11.43" 565 | "2/12/2017","1486857600","11.42" 566 | "2/13/2017","1486944000","11.39" 567 | "2/14/2017","1487030400","13.00" 568 | "2/15/2017","1487116800","12.97" 569 | "2/16/2017","1487203200","12.95" 570 | "2/17/2017","1487289600","12.72" 571 | "2/18/2017","1487376000","12.83" 572 | "2/19/2017","1487462400","12.82" 573 | "2/20/2017","1487548800","12.52" 574 | "2/21/2017","1487635200","12.77" 575 | "2/22/2017","1487721600","12.69" 576 | "2/23/2017","1487808000","13.13" 577 | "2/24/2017","1487894400","13.11" 578 | "2/25/2017","1487980800","13.57" 579 | "2/26/2017","1488067200","14.59" 580 | "2/27/2017","1488153600","15.55" 581 | "2/28/2017","1488240000","16.07" 582 | "3/1/2017","1488326400","17.55" 583 | "3/2/2017","1488412800","19.08" 584 | "3/3/2017","1488499200","19.48" 585 | "3/4/2017","1488585600","18.61" 586 | "3/5/2017","1488672000","19.22" 587 | "3/6/2017","1488758400","19.75" 588 | "3/7/2017","1488844800","18.91" 589 | "3/8/2017","1488931200","16.54" 590 | "3/9/2017","1489017600","17.71" 591 | "3/10/2017","1489104000","19.13" 592 | "3/11/2017","1489190400","21.45" 593 | "3/12/2017","1489276800","23.31" 594 | "3/13/2017","1489363200","28.45" 595 | "3/14/2017","1489449600","28.58" 596 | "3/15/2017","1489536000","35.18" 597 | "3/16/2017","1489622400","45.51" 598 | "3/17/2017","1489708800","44.48" 599 | "3/18/2017","1489795200","34.00" 600 | "3/19/2017","1489881600","43.12" 601 | "3/20/2017","1489968000","42.51" 602 | "3/21/2017","1490054400","42.67" 603 | "3/22/2017","1490140800","41.65" 604 | "3/23/2017","1490227200","43.20" 605 | "3/24/2017","1490313600","53.19" 606 | "3/25/2017","1490400000","50.62" 607 | "3/26/2017","1490486400","50.63" 608 | "3/27/2017","1490572800","49.06" 609 | "3/28/2017","1490659200","50.25" 610 | "3/29/2017","1490745600","53.07" 611 | "3/30/2017","1490832000","51.91" 612 | "3/31/2017","1490918400","49.91" 613 | "4/1/2017","1491004800","50.60" 614 | "4/2/2017","1491091200","48.55" 615 | "4/3/2017","1491177600","44.13" 616 | "4/4/2017","1491264000","44.43" 617 | 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"2/22/2018","1519257600","804.63" 938 | "2/23/2018","1519344000","854.70" 939 | "2/24/2018","1519430400","833.49" 940 | "2/25/2018","1519516800","840.28" 941 | "2/26/2018","1519603200","867.62" 942 | "2/27/2018","1519689600","871.58" 943 | "2/28/2018","1519776000","851.50" 944 | "3/1/2018","1519862400","869.87" 945 | "3/2/2018","1519948800","855.60" 946 | "3/3/2018","1520035200","855.65" 947 | "3/4/2018","1520121600","864.83" 948 | "3/5/2018","1520208000","849.42" 949 | "3/6/2018","1520294400","815.69" 950 | "3/7/2018","1520380800","751.13" 951 | "3/8/2018","1520467200","698.83" 952 | "3/9/2018","1520553600","726.92" 953 | "3/10/2018","1520640000","682.30" 954 | "3/11/2018","1520726400","720.36" 955 | "3/12/2018","1520812800","697.02" 956 | "3/13/2018","1520899200","689.96" 957 | "3/14/2018","1520985600","613.15" 958 | "3/15/2018","1521072000","610.56" 959 | "3/16/2018","1521158400","600.53" 960 | "3/17/2018","1521244800","549.79" 961 | "3/18/2018","1521331200","537.38" 962 | "3/19/2018","1521417600","555.55" 963 | "3/20/2018","1521504000","557.57" 964 | "3/21/2018","1521590400","559.91" 965 | "3/22/2018","1521676800","539.89" 966 | "3/23/2018","1521763200","543.83" 967 | "3/24/2018","1521849600","520.16" 968 | "3/25/2018","1521936000","523.01" 969 | "3/26/2018","1522022400","486.25" 970 | "3/27/2018","1522108800","448.78" 971 | "3/28/2018","1522195200","445.93" 972 | "3/29/2018","1522281600","383.90" 973 | "3/30/2018","1522368000","393.82" 974 | "3/31/2018","1522454400","394.07" 975 | "4/1/2018","1522540800","378.85" 976 | -------------------------------------------------------------------------------- /3 - Cleaning Not Null Values.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "![rmotr](https://i.imgur.com/jiPp4hj.png)\n", 8 | "
\n", 9 | "\n", 10 | "\n", 12 | "\n", 13 | "# Cleaning not-null values\n", 14 | "\n", 15 | "After dealing with many datasets I can tell you that \"missing data\" is not such a big deal. The best thing that can happen is to clearly see values like `np.nan`. The only thing you need to do is just use methods like `isnull` and `fillna`/`dropna` and pandas will take care of the rest.\n", 16 | "\n", 17 | "But sometimes, you can have invalid values that are not just \"missing data\" (`None`, or `nan`). For example:" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "![separator2](https://i.imgur.com/4gX5WFr.png)\n", 25 | "\n", 26 | "## Hands on!" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 1, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "import numpy as np\n", 36 | "import pandas as pd" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 2, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/html": [ 47 | "
\n", 48 | "\n", 61 | "\n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | "
SexAge
0M29
1F30
2F24
3D290
4?25
\n", 97 | "
" 98 | ], 99 | "text/plain": [ 100 | " Sex Age\n", 101 | "0 M 29\n", 102 | "1 F 30\n", 103 | "2 F 24\n", 104 | "3 D 290\n", 105 | "4 ? 25" 106 | ] 107 | }, 108 | "execution_count": 2, 109 | "metadata": {}, 110 | "output_type": "execute_result" 111 | } 112 | ], 113 | "source": [ 114 | "df = pd.DataFrame({\n", 115 | " 'Sex': ['M', 'F', 'F', 'D', '?'],\n", 116 | " 'Age': [29, 30, 24, 290, 25],\n", 117 | "})\n", 118 | "df" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "The previous `DataFrame` doesn't have any \"missing value\", but clearly has invalid data. `290` doesn't seem like a valid age, and `D` and `?` don't correspond with any known sex category. How can you clean these not-missing, but clearly invalid values then?\n", 126 | "\n", 127 | "### Finding Unique Values\n", 128 | "\n", 129 | "The first step to clean invalid values is to **notice** them, then **identify** them and finally handle them appropriately (remove them, replace them, etc). Usually, for a \"categorical\" type of field (like Sex, which only takes values of a discrete set `('M', 'F')`), we start by analyzing the variety of values present. For that, we use the `unique()` method:" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 3, 135 | "metadata": {}, 136 | "outputs": [ 137 | { 138 | "data": { 139 | "text/plain": [ 140 | "array(['M', 'F', 'D', '?'], dtype=object)" 141 | ] 142 | }, 143 | "execution_count": 3, 144 | "metadata": {}, 145 | "output_type": "execute_result" 146 | } 147 | ], 148 | "source": [ 149 | "df['Sex'].unique()" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 4, 155 | "metadata": {}, 156 | "outputs": [ 157 | { 158 | "data": { 159 | "text/plain": [ 160 | "F 2\n", 161 | "D 1\n", 162 | "? 1\n", 163 | "M 1\n", 164 | "Name: Sex, dtype: int64" 165 | ] 166 | }, 167 | "execution_count": 4, 168 | "metadata": {}, 169 | "output_type": "execute_result" 170 | } 171 | ], 172 | "source": [ 173 | "df['Sex'].value_counts()" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "Clearly if you see values like `'D'` or `'?'`, it'll immediately raise your attention. Now, what to do with them? Let's say you picked up the phone, called the survey company and they told you that `'D'` was a typo and it should actually be `F`. You can use the `replace` function to replace these values:" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 5, 186 | "metadata": { 187 | "scrolled": true 188 | }, 189 | "outputs": [ 190 | { 191 | "data": { 192 | "text/plain": [ 193 | "0 M\n", 194 | "1 F\n", 195 | "2 F\n", 196 | "3 F\n", 197 | "4 ?\n", 198 | "Name: Sex, dtype: object" 199 | ] 200 | }, 201 | "execution_count": 5, 202 | "metadata": {}, 203 | "output_type": "execute_result" 204 | } 205 | ], 206 | "source": [ 207 | "df['Sex'].replace('D', 'F')" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "It can accept a dictionary of values to replace. For example, they also told you that there might be a few `'N's`, that should actually be `'M's`:" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 6, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/plain": [ 225 | "0 M\n", 226 | "1 F\n", 227 | "2 F\n", 228 | "3 F\n", 229 | "4 ?\n", 230 | "Name: Sex, dtype: object" 231 | ] 232 | }, 233 | "execution_count": 6, 234 | "metadata": {}, 235 | "output_type": "execute_result" 236 | } 237 | ], 238 | "source": [ 239 | "df['Sex'].replace({'D': 'F', 'N': 'M'})" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "If you have many columns to replace, you could apply it at \"DataFrame level\":" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 7, 252 | "metadata": { 253 | "scrolled": true 254 | }, 255 | "outputs": [ 256 | { 257 | "data": { 258 | "text/html": [ 259 | "
\n", 260 | "\n", 273 | "\n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | "
SexAge
0M29
1F30
2F24
3F29
4?25
\n", 309 | "
" 310 | ], 311 | "text/plain": [ 312 | " Sex Age\n", 313 | "0 M 29\n", 314 | "1 F 30\n", 315 | "2 F 24\n", 316 | "3 F 29\n", 317 | "4 ? 25" 318 | ] 319 | }, 320 | "execution_count": 7, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "df.replace({\n", 327 | " 'Sex': {\n", 328 | " 'D': 'F',\n", 329 | " 'N': 'M'\n", 330 | " },\n", 331 | " 'Age': {\n", 332 | " 290: 29\n", 333 | " }\n", 334 | "})" 335 | ] 336 | }, 337 | { 338 | "cell_type": "markdown", 339 | "metadata": {}, 340 | "source": [ 341 | "In the previous example, I explicitly replaced 290 with 29 (assuming it was just an extra 0 entered at data-entry phase). But what if you'd like to remove all the extra 0s from the ages columns? (example, `150 > 15`, `490 > 49`).\n", 342 | "\n", 343 | "The first step would be to just set the limit of the \"not possible\" age. Is it 100? 120? Let's say that anything above 100 isn't credible for **our** dataset. We can then combine boolean selection with the operation:" 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": 8, 349 | "metadata": { 350 | "scrolled": true 351 | }, 352 | "outputs": [ 353 | { 354 | "data": { 355 | "text/html": [ 356 | "
\n", 357 | "\n", 370 | "\n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | "
SexAge
3D290
\n", 386 | "
" 387 | ], 388 | "text/plain": [ 389 | " Sex Age\n", 390 | "3 D 290" 391 | ] 392 | }, 393 | "execution_count": 8, 394 | "metadata": {}, 395 | "output_type": "execute_result" 396 | } 397 | ], 398 | "source": [ 399 | "df[df['Age'] > 100]" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "And we can now just divide by 10:" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": 9, 412 | "metadata": { 413 | "scrolled": true 414 | }, 415 | "outputs": [], 416 | "source": [ 417 | "df.loc[df['Age'] > 100, 'Age'] = df.loc[df['Age'] > 100, 'Age'] / 10" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": 10, 423 | "metadata": { 424 | "scrolled": true 425 | }, 426 | "outputs": [ 427 | { 428 | "data": { 429 | "text/html": [ 430 | "
\n", 431 | "\n", 444 | "\n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | "
SexAge
0M29.0
1F30.0
2F24.0
3D29.0
4?25.0
\n", 480 | "
" 481 | ], 482 | "text/plain": [ 483 | " Sex Age\n", 484 | "0 M 29.0\n", 485 | "1 F 30.0\n", 486 | "2 F 24.0\n", 487 | "3 D 29.0\n", 488 | "4 ? 25.0" 489 | ] 490 | }, 491 | "execution_count": 10, 492 | "metadata": {}, 493 | "output_type": "execute_result" 494 | } 495 | ], 496 | "source": [ 497 | "df" 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "metadata": {}, 503 | "source": [ 504 | "![separator1](https://i.imgur.com/ZUWYTii.png)\n", 505 | "\n", 506 | "### Duplicates\n", 507 | "\n", 508 | "Checking duplicate values is extremely simple. It'll behave differently between Series and DataFrames. Let's start with Series. As an example, let's say we're throwing a fancy party and we're inviting Ambassadors from Europe. But can only invite one ambassador per country. This is our original list, and as you can see, both the UK and Germany have duplicated ambassadors:" 509 | ] 510 | }, 511 | { 512 | "cell_type": "code", 513 | "execution_count": 11, 514 | "metadata": {}, 515 | "outputs": [], 516 | "source": [ 517 | "ambassadors = pd.Series([\n", 518 | " 'France',\n", 519 | " 'United Kingdom',\n", 520 | " 'United Kingdom',\n", 521 | " 'Italy',\n", 522 | " 'Germany',\n", 523 | " 'Germany',\n", 524 | " 'Germany',\n", 525 | "], index=[\n", 526 | " 'Gérard Araud',\n", 527 | " 'Kim Darroch',\n", 528 | " 'Peter Westmacott',\n", 529 | " 'Armando Varricchio',\n", 530 | " 'Peter Wittig',\n", 531 | " 'Peter Ammon',\n", 532 | " 'Klaus Scharioth '\n", 533 | "])" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 12, 539 | "metadata": {}, 540 | "outputs": [ 541 | { 542 | "data": { 543 | "text/plain": [ 544 | "Gérard Araud France\n", 545 | "Kim Darroch United Kingdom\n", 546 | "Peter Westmacott United Kingdom\n", 547 | "Armando Varricchio Italy\n", 548 | "Peter Wittig Germany\n", 549 | "Peter Ammon Germany\n", 550 | "Klaus Scharioth Germany\n", 551 | "dtype: object" 552 | ] 553 | }, 554 | "execution_count": 12, 555 | "metadata": {}, 556 | "output_type": "execute_result" 557 | } 558 | ], 559 | "source": [ 560 | "ambassadors" 561 | ] 562 | }, 563 | { 564 | "cell_type": "markdown", 565 | "metadata": {}, 566 | "source": [ 567 | "The two most important methods to deal with duplicates are `duplicated` (that will tell you which values are duplicates) and `drop_duplicates` (which will just get rid of duplicates):" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 13, 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "data": { 577 | "text/plain": [ 578 | "Gérard Araud False\n", 579 | "Kim Darroch False\n", 580 | "Peter Westmacott True\n", 581 | "Armando Varricchio False\n", 582 | "Peter Wittig False\n", 583 | "Peter Ammon True\n", 584 | "Klaus Scharioth True\n", 585 | "dtype: bool" 586 | ] 587 | }, 588 | "execution_count": 13, 589 | "metadata": {}, 590 | "output_type": "execute_result" 591 | } 592 | ], 593 | "source": [ 594 | "ambassadors.duplicated()" 595 | ] 596 | }, 597 | { 598 | "cell_type": "markdown", 599 | "metadata": {}, 600 | "source": [ 601 | "In this case `duplicated` didn't consider `'Kim Darroch'`, the first instance of the United Kingdom or `'Peter Wittig'` as duplicates. That's because, by default, it'll consider the first occurrence of the value as not-duplicate. You can change this behavior with the `keep` parameter:" 602 | ] 603 | }, 604 | { 605 | "cell_type": "code", 606 | "execution_count": 14, 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "data": { 611 | "text/plain": [ 612 | "Gérard Araud False\n", 613 | "Kim Darroch True\n", 614 | "Peter Westmacott False\n", 615 | "Armando Varricchio False\n", 616 | "Peter Wittig True\n", 617 | "Peter Ammon True\n", 618 | "Klaus Scharioth False\n", 619 | "dtype: bool" 620 | ] 621 | }, 622 | "execution_count": 14, 623 | "metadata": {}, 624 | "output_type": "execute_result" 625 | } 626 | ], 627 | "source": [ 628 | "ambassadors.duplicated(keep='last')" 629 | ] 630 | }, 631 | { 632 | "cell_type": "markdown", 633 | "metadata": {}, 634 | "source": [ 635 | "In this case, the result is \"flipped\", `'Kim Darroch'` and `'Peter Wittig'` (the first ambassadors of their countries) are considered duplicates, but `'Peter Westmacott'` and `'Klaus Scharioth'` are not duplicates. You can also choose to mark all of them as duplicates with `keep=False`:" 636 | ] 637 | }, 638 | { 639 | "cell_type": "code", 640 | "execution_count": 15, 641 | "metadata": {}, 642 | "outputs": [ 643 | { 644 | "data": { 645 | "text/plain": [ 646 | "Gérard Araud False\n", 647 | "Kim Darroch True\n", 648 | "Peter Westmacott True\n", 649 | "Armando Varricchio False\n", 650 | "Peter Wittig True\n", 651 | "Peter Ammon True\n", 652 | "Klaus Scharioth True\n", 653 | "dtype: bool" 654 | ] 655 | }, 656 | "execution_count": 15, 657 | "metadata": {}, 658 | "output_type": "execute_result" 659 | } 660 | ], 661 | "source": [ 662 | "ambassadors.duplicated(keep=False)" 663 | ] 664 | }, 665 | { 666 | "cell_type": "markdown", 667 | "metadata": {}, 668 | "source": [ 669 | "A similar method is `drop_duplicates`, which just excludes the duplicated values and also accepts the `keep` parameter:" 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": 16, 675 | "metadata": {}, 676 | "outputs": [ 677 | { 678 | "data": { 679 | "text/plain": [ 680 | "Gérard Araud France\n", 681 | "Kim Darroch United Kingdom\n", 682 | "Armando Varricchio Italy\n", 683 | "Peter Wittig Germany\n", 684 | "dtype: object" 685 | ] 686 | }, 687 | "execution_count": 16, 688 | "metadata": {}, 689 | "output_type": "execute_result" 690 | } 691 | ], 692 | "source": [ 693 | "ambassadors.drop_duplicates()" 694 | ] 695 | }, 696 | { 697 | "cell_type": "code", 698 | "execution_count": 17, 699 | "metadata": {}, 700 | "outputs": [ 701 | { 702 | "data": { 703 | "text/plain": [ 704 | "Gérard Araud France\n", 705 | "Peter Westmacott United Kingdom\n", 706 | "Armando Varricchio Italy\n", 707 | "Klaus Scharioth Germany\n", 708 | "dtype: object" 709 | ] 710 | }, 711 | "execution_count": 17, 712 | "metadata": {}, 713 | "output_type": "execute_result" 714 | } 715 | ], 716 | "source": [ 717 | "ambassadors.drop_duplicates(keep='last')" 718 | ] 719 | }, 720 | { 721 | "cell_type": "code", 722 | "execution_count": 18, 723 | "metadata": { 724 | "scrolled": true 725 | }, 726 | "outputs": [ 727 | { 728 | "data": { 729 | "text/plain": [ 730 | "Gérard Araud France\n", 731 | "Armando Varricchio Italy\n", 732 | "dtype: object" 733 | ] 734 | }, 735 | "execution_count": 18, 736 | "metadata": {}, 737 | "output_type": "execute_result" 738 | } 739 | ], 740 | "source": [ 741 | "ambassadors.drop_duplicates(keep=False)" 742 | ] 743 | }, 744 | { 745 | "cell_type": "markdown", 746 | "metadata": {}, 747 | "source": [ 748 | "### Duplicates in DataFrames\n", 749 | "\n", 750 | "Conceptually speaking, duplicates in a DataFrame happen at \"row\" level. Two rows with exactly the same values are considered to be duplicates:" 751 | ] 752 | }, 753 | { 754 | "cell_type": "code", 755 | "execution_count": 19, 756 | "metadata": {}, 757 | "outputs": [], 758 | "source": [ 759 | "players = pd.DataFrame({\n", 760 | " 'Name': [\n", 761 | " 'Kobe Bryant',\n", 762 | " 'LeBron James',\n", 763 | " 'Kobe Bryant',\n", 764 | " 'Carmelo Anthony',\n", 765 | " 'Kobe Bryant',\n", 766 | " ],\n", 767 | " 'Pos': [\n", 768 | " 'SG',\n", 769 | " 'SF',\n", 770 | " 'SG',\n", 771 | " 'SF',\n", 772 | " 'SF'\n", 773 | " ]\n", 774 | "})" 775 | ] 776 | }, 777 | { 778 | "cell_type": "code", 779 | "execution_count": 20, 780 | "metadata": {}, 781 | "outputs": [ 782 | { 783 | "data": { 784 | "text/html": [ 785 | "
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NamePos
0Kobe BryantSG
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2Kobe BryantSG
3Carmelo AnthonySF
4Kobe BryantSF
\n", 835 | "
" 836 | ], 837 | "text/plain": [ 838 | " Name Pos\n", 839 | "0 Kobe Bryant SG\n", 840 | "1 LeBron James SF\n", 841 | "2 Kobe Bryant SG\n", 842 | "3 Carmelo Anthony SF\n", 843 | "4 Kobe Bryant SF" 844 | ] 845 | }, 846 | "execution_count": 20, 847 | "metadata": {}, 848 | "output_type": "execute_result" 849 | } 850 | ], 851 | "source": [ 852 | "players" 853 | ] 854 | }, 855 | { 856 | "cell_type": "markdown", 857 | "metadata": {}, 858 | "source": [ 859 | "In the previous DataFrame, we clearly see that Kobe is duplicated; but he appears with two different positions. What does `duplicated` say?" 860 | ] 861 | }, 862 | { 863 | "cell_type": "code", 864 | "execution_count": 21, 865 | "metadata": {}, 866 | "outputs": [ 867 | { 868 | "data": { 869 | "text/plain": [ 870 | "0 False\n", 871 | "1 False\n", 872 | "2 True\n", 873 | "3 False\n", 874 | "4 False\n", 875 | "dtype: bool" 876 | ] 877 | }, 878 | "execution_count": 21, 879 | "metadata": {}, 880 | "output_type": "execute_result" 881 | } 882 | ], 883 | "source": [ 884 | "players.duplicated()" 885 | ] 886 | }, 887 | { 888 | "cell_type": "markdown", 889 | "metadata": {}, 890 | "source": [ 891 | "Again, conceptually, \"duplicated\" means \"all the column values should be duplicates\". We can customize this with the `subset` parameter:" 892 | ] 893 | }, 894 | { 895 | "cell_type": "code", 896 | "execution_count": 22, 897 | "metadata": {}, 898 | "outputs": [ 899 | { 900 | "data": { 901 | "text/plain": [ 902 | "0 False\n", 903 | "1 False\n", 904 | "2 True\n", 905 | "3 False\n", 906 | "4 True\n", 907 | "dtype: bool" 908 | ] 909 | }, 910 | "execution_count": 22, 911 | "metadata": {}, 912 | "output_type": "execute_result" 913 | } 914 | ], 915 | "source": [ 916 | "players.duplicated(subset=['Name'])" 917 | ] 918 | }, 919 | { 920 | "cell_type": "markdown", 921 | "metadata": {}, 922 | "source": [ 923 | "And the same rules of `keep` still apply:" 924 | ] 925 | }, 926 | { 927 | "cell_type": "code", 928 | "execution_count": null, 929 | "metadata": {}, 930 | "outputs": [], 931 | "source": [ 932 | "players.duplicated(subset=['Name'], keep='last')" 933 | ] 934 | }, 935 | { 936 | "cell_type": "markdown", 937 | "metadata": {}, 938 | "source": [ 939 | "`drop_duplicates` takes the same parameters:" 940 | ] 941 | }, 942 | { 943 | "cell_type": "code", 944 | "execution_count": null, 945 | "metadata": {}, 946 | "outputs": [], 947 | "source": [ 948 | "players.drop_duplicates()" 949 | ] 950 | }, 951 | { 952 | "cell_type": "code", 953 | "execution_count": null, 954 | "metadata": {}, 955 | "outputs": [], 956 | "source": [ 957 | "players.drop_duplicates(subset=['Name'])" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": null, 963 | "metadata": { 964 | "scrolled": true 965 | }, 966 | "outputs": [], 967 | "source": [ 968 | "players.drop_duplicates(subset=['Name'], keep='last')" 969 | ] 970 | }, 971 | { 972 | "cell_type": "markdown", 973 | "metadata": {}, 974 | "source": [ 975 | "![separator1](https://i.imgur.com/ZUWYTii.png)\n", 976 | "\n", 977 | "### Text Handling\n", 978 | "\n", 979 | "Cleaning text values can be incredibly hard. Invalid text values involves, 99% of the time, mistyping, which is completely unpredictable and doesn't follow any pattern. Thankfully, it's not so common these days, where data-entry tasks have been replaced by machines. Still, let's explore the most common cases:\n", 980 | "\n", 981 | "### Splitting Columns\n", 982 | "\n", 983 | "The result of a survey is loaded and this is what you get:" 984 | ] 985 | }, 986 | { 987 | "cell_type": "code", 988 | "execution_count": 23, 989 | "metadata": {}, 990 | "outputs": [], 991 | "source": [ 992 | "df = pd.DataFrame({\n", 993 | " 'Data': [\n", 994 | " '1987_M_US _1',\n", 995 | " '1990?_M_UK_1',\n", 996 | " '1992_F_US_2',\n", 997 | " '1970?_M_ IT_1',\n", 998 | " '1985_F_I T_2'\n", 999 | "]})" 1000 | ] 1001 | }, 1002 | { 1003 | "cell_type": "code", 1004 | "execution_count": 24, 1005 | "metadata": {}, 1006 | "outputs": [ 1007 | { 1008 | "data": { 1009 | "text/html": [ 1010 | "
\n", 1011 | "\n", 1024 | "\n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | " \n", 1030 | " \n", 1031 | " \n", 1032 | " \n", 1033 | " \n", 1034 | " \n", 1035 | " \n", 1036 | " \n", 1037 | " \n", 1038 | " \n", 1039 | " \n", 1040 | " \n", 1041 | " \n", 1042 | " \n", 1043 | " \n", 1044 | " \n", 1045 | " \n", 1046 | " \n", 1047 | " \n", 1048 | " \n", 1049 | " \n", 1050 | " \n", 1051 | " \n", 1052 | " \n", 1053 | "
Data
01987_M_US _1
11990?_M_UK_1
21992_F_US_2
31970?_M_ IT_1
41985_F_I T_2
\n", 1054 | "
" 1055 | ], 1056 | "text/plain": [ 1057 | " Data\n", 1058 | "0 1987_M_US _1\n", 1059 | "1 1990?_M_UK_1\n", 1060 | "2 1992_F_US_2\n", 1061 | "3 1970?_M_ IT_1\n", 1062 | "4 1985_F_I T_2" 1063 | ] 1064 | }, 1065 | "execution_count": 24, 1066 | "metadata": {}, 1067 | "output_type": "execute_result" 1068 | } 1069 | ], 1070 | "source": [ 1071 | "df" 1072 | ] 1073 | }, 1074 | { 1075 | "cell_type": "markdown", 1076 | "metadata": {}, 1077 | "source": [ 1078 | "You know that the single columns represent the values \"year, Sex, Country and number of children\", but it's all been grouped in the same column and separated by an underscore. Pandas has a convenient method named `split` that we can use in these situations:" 1079 | ] 1080 | }, 1081 | { 1082 | "cell_type": "code", 1083 | "execution_count": 26, 1084 | "metadata": {}, 1085 | "outputs": [ 1086 | { 1087 | "data": { 1088 | "text/plain": [ 1089 | "0 [1987, M, US , 1]\n", 1090 | "1 [1990?, M, UK, 1]\n", 1091 | "2 [1992, F, US, 2]\n", 1092 | "3 [1970?, M, IT, 1]\n", 1093 | "4 [1985, F, I T, 2]\n", 1094 | "Name: Data, dtype: object" 1095 | ] 1096 | }, 1097 | "execution_count": 26, 1098 | "metadata": {}, 1099 | "output_type": "execute_result" 1100 | } 1101 | ], 1102 | "source": [ 1103 | "df['Data'].str.split('_')" 1104 | ] 1105 | }, 1106 | { 1107 | "cell_type": "code", 1108 | "execution_count": 27, 1109 | "metadata": {}, 1110 | "outputs": [ 1111 | { 1112 | "data": { 1113 | "text/html": [ 1114 | "
\n", 1115 | "\n", 1128 | "\n", 1129 | " \n", 1130 | " \n", 1131 | " \n", 1132 | " \n", 1133 | " \n", 1134 | " \n", 1135 | " \n", 1136 | " \n", 1137 | " \n", 1138 | " \n", 1139 | " \n", 1140 | " \n", 1141 | " \n", 1142 | " \n", 1143 | " \n", 1144 | " \n", 1145 | " \n", 1146 | " \n", 1147 | " \n", 1148 | " \n", 1149 | " \n", 1150 | " \n", 1151 | " \n", 1152 | " \n", 1153 | " \n", 1154 | " \n", 1155 | " \n", 1156 | " \n", 1157 | " \n", 1158 | " \n", 1159 | " \n", 1160 | " \n", 1161 | " \n", 1162 | " \n", 1163 | " \n", 1164 | " \n", 1165 | " \n", 1166 | " \n", 1167 | " \n", 1168 | " \n", 1169 | " \n", 1170 | " \n", 1171 | " \n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | "
0123
01987MUS1
11990?MUK1
21992FUS2
31970?MIT1
41985FI T2
\n", 1176 | "
" 1177 | ], 1178 | "text/plain": [ 1179 | " 0 1 2 3\n", 1180 | "0 1987 M US 1\n", 1181 | "1 1990? M UK 1\n", 1182 | "2 1992 F US 2\n", 1183 | "3 1970? M IT 1\n", 1184 | "4 1985 F I T 2" 1185 | ] 1186 | }, 1187 | "execution_count": 27, 1188 | "metadata": {}, 1189 | "output_type": "execute_result" 1190 | } 1191 | ], 1192 | "source": [ 1193 | "df['Data'].str.split('_', expand=True)" 1194 | ] 1195 | }, 1196 | { 1197 | "cell_type": "code", 1198 | "execution_count": 28, 1199 | "metadata": {}, 1200 | "outputs": [], 1201 | "source": [ 1202 | "df = df['Data'].str.split('_', expand=True)" 1203 | ] 1204 | }, 1205 | { 1206 | "cell_type": "code", 1207 | "execution_count": 29, 1208 | "metadata": {}, 1209 | "outputs": [], 1210 | "source": [ 1211 | "df.columns = ['Year', 'Sex', 'Country', 'No Children']" 1212 | ] 1213 | }, 1214 | { 1215 | "cell_type": "markdown", 1216 | "metadata": {}, 1217 | "source": [ 1218 | "You can also check which columns contain a given value with the `contains` method:" 1219 | ] 1220 | }, 1221 | { 1222 | "cell_type": "code", 1223 | "execution_count": 30, 1224 | "metadata": { 1225 | "scrolled": true 1226 | }, 1227 | "outputs": [ 1228 | { 1229 | "data": { 1230 | "text/html": [ 1231 | "
\n", 1232 | "\n", 1245 | "\n", 1246 | " \n", 1247 | " \n", 1248 | " \n", 1249 | " \n", 1250 | " \n", 1251 | " \n", 1252 | " \n", 1253 | " \n", 1254 | " \n", 1255 | " \n", 1256 | " \n", 1257 | " \n", 1258 | " \n", 1259 | " \n", 1260 | " \n", 1261 | " \n", 1262 | " \n", 1263 | " \n", 1264 | " \n", 1265 | " \n", 1266 | " \n", 1267 | " \n", 1268 | " \n", 1269 | " \n", 1270 | " \n", 1271 | " \n", 1272 | " \n", 1273 | " \n", 1274 | " \n", 1275 | " \n", 1276 | " \n", 1277 | " \n", 1278 | " \n", 1279 | " \n", 1280 | " \n", 1281 | " \n", 1282 | " \n", 1283 | " \n", 1284 | " \n", 1285 | " \n", 1286 | " \n", 1287 | " \n", 1288 | " \n", 1289 | " \n", 1290 | " \n", 1291 | " \n", 1292 | "
YearSexCountryNo Children
01987MUS1
11990?MUK1
21992FUS2
31970?MIT1
41985FI T2
\n", 1293 | "
" 1294 | ], 1295 | "text/plain": [ 1296 | " Year Sex Country No Children\n", 1297 | "0 1987 M US 1\n", 1298 | "1 1990? M UK 1\n", 1299 | "2 1992 F US 2\n", 1300 | "3 1970? M IT 1\n", 1301 | "4 1985 F I T 2" 1302 | ] 1303 | }, 1304 | "execution_count": 30, 1305 | "metadata": {}, 1306 | "output_type": "execute_result" 1307 | } 1308 | ], 1309 | "source": [ 1310 | "df" 1311 | ] 1312 | }, 1313 | { 1314 | "cell_type": "code", 1315 | "execution_count": 31, 1316 | "metadata": {}, 1317 | "outputs": [ 1318 | { 1319 | "data": { 1320 | "text/plain": [ 1321 | "0 False\n", 1322 | "1 True\n", 1323 | "2 False\n", 1324 | "3 True\n", 1325 | "4 False\n", 1326 | "Name: Year, dtype: bool" 1327 | ] 1328 | }, 1329 | "execution_count": 31, 1330 | "metadata": {}, 1331 | "output_type": "execute_result" 1332 | } 1333 | ], 1334 | "source": [ 1335 | "df['Year'].str.contains('\\?')" 1336 | ] 1337 | }, 1338 | { 1339 | "cell_type": "markdown", 1340 | "metadata": {}, 1341 | "source": [ 1342 | "[`contains`](http://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.Series.str.contains.html) takes a regex/pattern as first value, so we need to escape the `?` symbol as it has a special meaning for these patterns. Regular letters don't need escaping:" 1343 | ] 1344 | }, 1345 | { 1346 | "cell_type": "code", 1347 | "execution_count": 32, 1348 | "metadata": {}, 1349 | "outputs": [ 1350 | { 1351 | "data": { 1352 | "text/plain": [ 1353 | "0 True\n", 1354 | "1 True\n", 1355 | "2 True\n", 1356 | "3 False\n", 1357 | "4 False\n", 1358 | "Name: Country, dtype: bool" 1359 | ] 1360 | }, 1361 | "execution_count": 32, 1362 | "metadata": {}, 1363 | "output_type": "execute_result" 1364 | } 1365 | ], 1366 | "source": [ 1367 | "df['Country'].str.contains('U')" 1368 | ] 1369 | }, 1370 | { 1371 | "cell_type": "markdown", 1372 | "metadata": {}, 1373 | "source": [ 1374 | "Removing blank spaces (like in `'US '` or `'I T'` can be achieved with `strip` (`lstrip` and `rstrip` also exist) or just `replace`:" 1375 | ] 1376 | }, 1377 | { 1378 | "cell_type": "code", 1379 | "execution_count": 33, 1380 | "metadata": { 1381 | "scrolled": true 1382 | }, 1383 | "outputs": [ 1384 | { 1385 | "data": { 1386 | "text/plain": [ 1387 | "0 US\n", 1388 | "1 UK\n", 1389 | "2 US\n", 1390 | "3 IT\n", 1391 | "4 I T\n", 1392 | "Name: Country, dtype: object" 1393 | ] 1394 | }, 1395 | "execution_count": 33, 1396 | "metadata": {}, 1397 | "output_type": "execute_result" 1398 | } 1399 | ], 1400 | "source": [ 1401 | "df['Country'].str.strip()" 1402 | ] 1403 | }, 1404 | { 1405 | "cell_type": "code", 1406 | "execution_count": 34, 1407 | "metadata": {}, 1408 | "outputs": [ 1409 | { 1410 | "data": { 1411 | "text/plain": [ 1412 | "0 US\n", 1413 | "1 UK\n", 1414 | "2 US\n", 1415 | "3 IT\n", 1416 | "4 IT\n", 1417 | "Name: Country, dtype: object" 1418 | ] 1419 | }, 1420 | "execution_count": 34, 1421 | "metadata": {}, 1422 | "output_type": "execute_result" 1423 | } 1424 | ], 1425 | "source": [ 1426 | "df['Country'].str.replace(' ', '')" 1427 | ] 1428 | }, 1429 | { 1430 | "cell_type": "markdown", 1431 | "metadata": {}, 1432 | "source": [ 1433 | "As we said, `replace` and `contains` take regex patterns, which can make it easier to replace values in bulk:" 1434 | ] 1435 | }, 1436 | { 1437 | "cell_type": "code", 1438 | "execution_count": 35, 1439 | "metadata": { 1440 | "scrolled": true 1441 | }, 1442 | "outputs": [ 1443 | { 1444 | "data": { 1445 | "text/plain": [ 1446 | "0 1987\n", 1447 | "1 1990\n", 1448 | "2 1992\n", 1449 | "3 1970\n", 1450 | "4 1985\n", 1451 | "Name: Year, dtype: object" 1452 | ] 1453 | }, 1454 | "execution_count": 35, 1455 | "metadata": {}, 1456 | "output_type": "execute_result" 1457 | } 1458 | ], 1459 | "source": [ 1460 | "df['Year'].str.replace(r'(?P\\d{4})\\?', lambda m: m.group('year'))" 1461 | ] 1462 | }, 1463 | { 1464 | "cell_type": "markdown", 1465 | "metadata": {}, 1466 | "source": [ 1467 | "But, be warned:\n", 1468 | "\n", 1469 | "> Some people, when confronted with a problem, think \"I know, I'll use regular expressions.\" Now they have two problems." 1470 | ] 1471 | }, 1472 | { 1473 | "cell_type": "markdown", 1474 | "metadata": {}, 1475 | "source": [ 1476 | "As you can see, all these string/text-related operations are applied over the `str` attribute of the series. That's because they have a special place in Series handling and you can read more about it [here](https://pandas.pydata.org/pandas-docs/stable/text.html)." 1477 | ] 1478 | }, 1479 | { 1480 | "cell_type": "markdown", 1481 | "metadata": {}, 1482 | "source": [ 1483 | "![separator2](https://i.imgur.com/4gX5WFr.png)" 1484 | ] 1485 | } 1486 | ], 1487 | "metadata": { 1488 | "kernelspec": { 1489 | "display_name": "Python 3", 1490 | "language": "python", 1491 | "name": "python3" 1492 | }, 1493 | "language_info": { 1494 | "codemirror_mode": { 1495 | "name": "ipython", 1496 | "version": 3 1497 | }, 1498 | "file_extension": ".py", 1499 | "mimetype": "text/x-python", 1500 | "name": "python", 1501 | "nbconvert_exporter": "python", 1502 | "pygments_lexer": "ipython3", 1503 | "version": "3.8.1" 1504 | } 1505 | }, 1506 | "nbformat": 4, 1507 | "nbformat_minor": 4 1508 | } 1509 | -------------------------------------------------------------------------------- /data/btc-market-price-full.csv: 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00:00:00,0.0 102 | 2009-07-24 00:00:00,0.0 103 | 2009-07-26 00:00:00,0.0 104 | 2009-07-28 00:00:00,0.0 105 | 2009-07-30 00:00:00,0.0 106 | 2009-08-01 00:00:00,0.0 107 | 2009-08-03 00:00:00,0.0 108 | 2009-08-05 00:00:00,0.0 109 | 2009-08-07 00:00:00,0.0 110 | 2009-08-09 00:00:00,0.0 111 | 2009-08-11 00:00:00,0.0 112 | 2009-08-13 00:00:00,0.0 113 | 2009-08-15 00:00:00,0.0 114 | 2009-08-17 00:00:00,0.0 115 | 2009-08-19 00:00:00,0.0 116 | 2009-08-21 00:00:00,0.0 117 | 2009-08-23 00:00:00,0.0 118 | 2009-08-25 00:00:00,0.0 119 | 2009-08-27 00:00:00,0.0 120 | 2009-08-29 00:00:00,0.0 121 | 2009-08-31 00:00:00,0.0 122 | 2009-09-02 00:00:00,0.0 123 | 2009-09-04 00:00:00,0.0 124 | 2009-09-06 00:00:00,0.0 125 | 2009-09-08 00:00:00,0.0 126 | 2009-09-10 00:00:00,0.0 127 | 2009-09-12 00:00:00,0.0 128 | 2009-09-14 00:00:00,0.0 129 | 2009-09-16 00:00:00,0.0 130 | 2009-09-18 00:00:00,0.0 131 | 2009-09-20 00:00:00,0.0 132 | 2009-09-22 00:00:00,0.0 133 | 2009-09-24 00:00:00,0.0 134 | 2009-09-26 00:00:00,0.0 135 | 2009-09-28 00:00:00,0.0 136 | 2009-09-30 00:00:00,0.0 137 | 2009-10-02 00:00:00,0.0 138 | 2009-10-04 00:00:00,0.0 139 | 2009-10-06 00:00:00,0.0 140 | 2009-10-08 00:00:00,0.0 141 | 2009-10-10 00:00:00,0.0 142 | 2009-10-12 00:00:00,0.0 143 | 2009-10-14 00:00:00,0.0 144 | 2009-10-16 00:00:00,0.0 145 | 2009-10-18 00:00:00,0.0 146 | 2009-10-20 00:00:00,0.0 147 | 2009-10-22 00:00:00,0.0 148 | 2009-10-24 00:00:00,0.0 149 | 2009-10-26 00:00:00,0.0 150 | 2009-10-28 00:00:00,0.0 151 | 2009-10-30 00:00:00,0.0 152 | 2009-11-01 00:00:00,0.0 153 | 2009-11-03 00:00:00,0.0 154 | 2009-11-05 00:00:00,0.0 155 | 2009-11-07 00:00:00,0.0 156 | 2009-11-09 00:00:00,0.0 157 | 2009-11-11 00:00:00,0.0 158 | 2009-11-13 00:00:00,0.0 159 | 2009-11-15 00:00:00,0.0 160 | 2009-11-17 00:00:00,0.0 161 | 2009-11-19 00:00:00,0.0 162 | 2009-11-21 00:00:00,0.0 163 | 2009-11-23 00:00:00,0.0 164 | 2009-11-25 00:00:00,0.0 165 | 2009-11-27 00:00:00,0.0 166 | 2009-11-29 00:00:00,0.0 167 | 2009-12-01 00:00:00,0.0 168 | 2009-12-03 00:00:00,0.0 169 | 2009-12-05 00:00:00,0.0 170 | 2009-12-07 00:00:00,0.0 171 | 2009-12-09 00:00:00,0.0 172 | 2009-12-11 00:00:00,0.0 173 | 2009-12-13 00:00:00,0.0 174 | 2009-12-15 00:00:00,0.0 175 | 2009-12-17 00:00:00,0.0 176 | 2009-12-19 00:00:00,0.0 177 | 2009-12-21 00:00:00,0.0 178 | 2009-12-23 00:00:00,0.0 179 | 2009-12-25 00:00:00,0.0 180 | 2009-12-27 00:00:00,0.0 181 | 2009-12-29 00:00:00,0.0 182 | 2009-12-31 00:00:00,0.0 183 | 2010-01-02 00:00:00,0.0 184 | 2010-01-04 00:00:00,0.0 185 | 2010-01-06 00:00:00,0.0 186 | 2010-01-08 00:00:00,0.0 187 | 2010-01-10 00:00:00,0.0 188 | 2010-01-12 00:00:00,0.0 189 | 2010-01-14 00:00:00,0.0 190 | 2010-01-16 00:00:00,0.0 191 | 2010-01-18 00:00:00,0.0 192 | 2010-01-20 00:00:00,0.0 193 | 2010-01-22 00:00:00,0.0 194 | 2010-01-24 00:00:00,0.0 195 | 2010-01-26 00:00:00,0.0 196 | 2010-01-28 00:00:00,0.0 197 | 2010-01-30 00:00:00,0.0 198 | 2010-02-01 00:00:00,0.0 199 | 2010-02-03 00:00:00,0.0 200 | 2010-02-05 00:00:00,0.0 201 | 2010-02-07 00:00:00,0.0 202 | 2010-02-09 00:00:00,0.0 203 | 2010-02-11 00:00:00,0.0 204 | 2010-02-13 00:00:00,0.0 205 | 2010-02-15 00:00:00,0.0 206 | 2010-02-17 00:00:00,0.0 207 | 2010-02-19 00:00:00,0.0 208 | 2010-02-21 00:00:00,0.0 209 | 2010-02-23 00:00:00,0.0 210 | 2010-02-25 00:00:00,0.0 211 | 2010-02-27 00:00:00,0.0 212 | 2010-03-01 00:00:00,0.0 213 | 2010-03-03 00:00:00,0.0 214 | 2010-03-05 00:00:00,0.0 215 | 2010-03-07 00:00:00,0.0 216 | 2010-03-09 00:00:00,0.0 217 | 2010-03-11 00:00:00,0.0 218 | 2010-03-13 00:00:00,0.0 219 | 2010-03-15 00:00:00,0.0 220 | 2010-03-17 00:00:00,0.0 221 | 2010-03-19 00:00:00,0.0 222 | 2010-03-21 00:00:00,0.0 223 | 2010-03-23 00:00:00,0.0 224 | 2010-03-25 00:00:00,0.0 225 | 2010-03-27 00:00:00,0.0 226 | 2010-03-29 00:00:00,0.0 227 | 2010-03-31 00:00:00,0.0 228 | 2010-04-02 00:00:00,0.0 229 | 2010-04-04 00:00:00,0.0 230 | 2010-04-06 00:00:00,0.0 231 | 2010-04-08 00:00:00,0.0 232 | 2010-04-10 00:00:00,0.0 233 | 2010-04-12 00:00:00,0.0 234 | 2010-04-14 00:00:00,0.0 235 | 2010-04-16 00:00:00,0.0 236 | 2010-04-18 00:00:00,0.0 237 | 2010-04-20 00:00:00,0.0 238 | 2010-04-22 00:00:00,0.0 239 | 2010-04-24 00:00:00,0.0 240 | 2010-04-26 00:00:00,0.0 241 | 2010-04-28 00:00:00,0.0 242 | 2010-04-30 00:00:00,0.0 243 | 2010-05-02 00:00:00,0.0 244 | 2010-05-04 00:00:00,0.0 245 | 2010-05-06 00:00:00,0.0 246 | 2010-05-08 00:00:00,0.0 247 | 2010-05-10 00:00:00,0.0 248 | 2010-05-12 00:00:00,0.0 249 | 2010-05-14 00:00:00,0.0 250 | 2010-05-16 00:00:00,0.0 251 | 2010-05-18 00:00:00,0.0 252 | 2010-05-20 00:00:00,0.0 253 | 2010-05-22 00:00:00,0.0 254 | 2010-05-24 00:00:00,0.0 255 | 2010-05-26 00:00:00,0.0 256 | 2010-05-28 00:00:00,0.0 257 | 2010-05-30 00:00:00,0.0 258 | 2010-06-01 00:00:00,0.0 259 | 2010-06-03 00:00:00,0.0 260 | 2010-06-05 00:00:00,0.0 261 | 2010-06-07 00:00:00,0.0 262 | 2010-06-09 00:00:00,0.0 263 | 2010-06-11 00:00:00,0.0 264 | 2010-06-13 00:00:00,0.0 265 | 2010-06-15 00:00:00,0.0 266 | 2010-06-17 00:00:00,0.0 267 | 2010-06-19 00:00:00,0.0 268 | 2010-06-21 00:00:00,0.0 269 | 2010-06-23 00:00:00,0.0 270 | 2010-06-25 00:00:00,0.0 271 | 2010-06-27 00:00:00,0.0 272 | 2010-06-29 00:00:00,0.0 273 | 2010-07-01 00:00:00,0.0 274 | 2010-07-03 00:00:00,0.0 275 | 2010-07-05 00:00:00,0.0 276 | 2010-07-07 00:00:00,0.0 277 | 2010-07-09 00:00:00,0.0 278 | 2010-07-11 00:00:00,0.0 279 | 2010-07-13 00:00:00,0.0 280 | 2010-07-15 00:00:00,0.0 281 | 2010-07-17 00:00:00,0.0 282 | 2010-07-19 00:00:00,0.0 283 | 2010-07-21 00:00:00,0.0 284 | 2010-07-23 00:00:00,0.0 285 | 2010-07-25 00:00:00,0.0 286 | 2010-07-27 00:00:00,0.0 287 | 2010-07-29 00:00:00,0.0 288 | 2010-07-31 00:00:00,0.0 289 | 2010-08-02 00:00:00,0.0 290 | 2010-08-04 00:00:00,0.0 291 | 2010-08-06 00:00:00,0.0 292 | 2010-08-08 00:00:00,0.0 293 | 2010-08-10 00:00:00,0.0 294 | 2010-08-12 00:00:00,0.0 295 | 2010-08-14 00:00:00,0.0 296 | 2010-08-16 00:00:00,0.0 297 | 2010-08-18 00:00:00,0.074 298 | 2010-08-20 00:00:00,0.0667 299 | 2010-08-22 00:00:00,0.0664 300 | 2010-08-24 00:00:00,0.066889 301 | 2010-08-26 00:00:00,0.066499 302 | 2010-08-28 00:00:00,0.065 303 | 2010-08-30 00:00:00,0.069 304 | 2010-09-01 00:00:00,0.0649 305 | 2010-09-03 00:00:00,0.0634 306 | 2010-09-05 00:00:00,0.0629 307 | 2010-09-07 00:00:00,0.06185 308 | 2010-09-09 00:00:00,0.0624 309 | 2010-09-11 00:00:00,0.062 310 | 2010-09-13 00:00:00,0.06201 311 | 2010-09-15 00:00:00,0.175 312 | 2010-09-17 00:00:00,0.0609 313 | 2010-09-19 00:00:00,0.062599 314 | 2010-09-21 00:00:00,0.0633 315 | 2010-09-23 00:00:00,0.063 316 | 2010-09-25 00:00:00,0.0624 317 | 2010-09-27 00:00:00,0.062206 318 | 2010-09-29 00:00:00,0.06219 319 | 2010-10-01 00:00:00,0.061999 320 | 2010-10-03 00:00:00,0.0614 321 | 2010-10-05 00:00:00,0.06301 322 | 2010-10-07 00:00:00,0.0638 323 | 2010-10-09 00:00:00,0.12001 324 | 2010-10-11 00:00:00,0.1301 325 | 2010-10-13 00:00:00,0.095 326 | 2010-10-15 00:00:00,0.119 327 | 2010-10-17 00:00:00,0.1045 328 | 2010-10-19 00:00:00,0.1024 329 | 2010-10-21 00:00:00,0.109 330 | 2010-10-23 00:00:00,0.10901 331 | 2010-10-25 00:00:00,0.15 332 | 2010-10-27 00:00:00,0.19 333 | 2010-10-29 00:00:00,0.191 334 | 2010-10-31 00:00:00,0.199 335 | 2010-11-02 00:00:00,0.1955 336 | 2010-11-04 00:00:00,0.23601 337 | 2010-11-06 00:00:00,0.29 338 | 2010-11-08 00:00:00,0.37 339 | 2010-11-10 00:00:00,0.2667 340 | 2010-11-12 00:00:00,0.251 341 | 2010-11-14 00:00:00,0.299 342 | 2010-11-16 00:00:00,0.2827 343 | 2010-11-18 00:00:00,0.289999 344 | 2010-11-20 00:00:00,0.289 345 | 2010-11-22 00:00:00,0.282 346 | 2010-11-24 00:00:00,0.28299 347 | 2010-11-26 00:00:00,0.289 348 | 2010-11-28 00:00:00,0.28461 349 | 2010-11-30 00:00:00,0.275 350 | 2010-12-02 00:00:00,0.25 351 | 2010-12-04 00:00:00,0.2589 352 | 2010-12-06 00:00:00,0.225 353 | 2010-12-08 00:00:00,0.2477 354 | 2010-12-10 00:00:00,0.204 355 | 2010-12-12 00:00:00,0.228 356 | 2010-12-14 00:00:00,0.23 357 | 2010-12-16 00:00:00,0.2459 358 | 2010-12-18 00:00:00,0.249 359 | 2010-12-20 00:00:00,0.275 360 | 2010-12-22 00:00:00,0.267 361 | 2010-12-24 00:00:00,0.25 362 | 2010-12-26 00:00:00,0.269999 363 | 2010-12-28 00:00:00,0.28 364 | 2010-12-30 00:00:00,0.299999 365 | 2011-01-01 00:00:00,0.299998 366 | 2011-01-03 00:00:00,0.299998 367 | 2011-01-05 00:00:00,0.298998 368 | 2011-01-07 00:00:00,0.322 369 | 2011-01-09 00:00:00,0.322998 370 | 2011-01-11 00:00:00,0.329 371 | 2011-01-13 00:00:00,0.405 372 | 2011-01-15 00:00:00,0.4 373 | 2011-01-17 00:00:00,0.4 374 | 2011-01-19 00:00:00,0.3401 375 | 2011-01-21 00:00:00,0.44 376 | 2011-01-23 00:00:00,0.4443 377 | 2011-01-25 00:00:00,0.425 378 | 2011-01-27 00:00:00,0.4174 379 | 2011-01-29 00:00:00,0.446 380 | 2011-01-31 00:00:00,0.5 381 | 2011-02-02 00:00:00,0.840099 382 | 2011-02-04 00:00:00,0.88 383 | 2011-02-06 00:00:00,0.92 384 | 2011-02-08 00:00:00,0.9 385 | 2011-02-10 00:00:00,1.1 386 | 2011-02-12 00:00:00,1.0899 387 | 2011-02-14 00:00:00,1.08 388 | 2011-02-16 00:00:00,1.05019 389 | 2011-02-18 00:00:00,1.05019 390 | 2011-02-20 00:00:00,0.954896 391 | 2011-02-22 00:00:00,0.869499 392 | 2011-02-24 00:00:00,0.9499 393 | 2011-02-26 00:00:00,0.988567 394 | 2011-02-28 00:00:00,0.949231 395 | 2011-03-02 00:00:00,0.9498 396 | 2011-03-04 00:00:00,0.9392 397 | 2011-03-06 00:00:00,0.910445 398 | 2011-03-08 00:00:00,0.9072 399 | 2011-03-10 00:00:00,0.87 400 | 2011-03-12 00:00:00,0.9197 401 | 2011-03-14 00:00:00,0.9 402 | 2011-03-16 00:00:00,0.89 403 | 2011-03-18 00:00:00,0.850617 404 | 2011-03-20 00:00:00,0.79 405 | 2011-03-22 00:00:00,0.799646 406 | 2011-03-24 00:00:00,0.9 407 | 2011-03-26 00:00:00,0.905 408 | 2011-03-28 00:00:00,0.8575 409 | 2011-03-30 00:00:00,0.795 410 | 2011-04-01 00:00:00,0.79997 411 | 2011-04-03 00:00:00,0.7998 412 | 2011-04-05 00:00:00,0.71 413 | 2011-04-07 00:00:00,0.7677 414 | 2011-04-09 00:00:00,0.7676 415 | 2011-04-11 00:00:00,0.781 416 | 2011-04-13 00:00:00,1.0 417 | 2011-04-15 00:00:00,1.08999 418 | 2011-04-17 00:00:00,1.085 419 | 2011-04-19 00:00:00,1.19 420 | 2011-04-21 00:00:00,1.1979 421 | 2011-04-23 00:00:00,1.549 422 | 2011-04-25 00:00:00,1.701 423 | 2011-04-27 00:00:00,1.95 424 | 2011-04-29 00:00:00,2.7 425 | 2011-05-01 00:00:00,4.09 426 | 2011-05-03 00:00:00,3.49 427 | 2011-05-05 00:00:00,3.5 428 | 2011-05-07 00:00:00,3.7 429 | 2011-05-09 00:00:00,3.937 430 | 2011-05-11 00:00:00,6.065 431 | 2011-05-13 00:00:00,8.45 432 | 2011-05-15 00:00:00,8.55 433 | 2011-05-17 00:00:00,8.38901 434 | 2011-05-19 00:00:00,7.34 435 | 2011-05-21 00:00:00,6.6036 436 | 2011-05-23 00:00:00,7.45 437 | 2011-05-25 00:00:00,7.51 438 | 2011-05-27 00:00:00,8.92 439 | 2011-05-29 00:00:00,8.4992 440 | 2011-05-31 00:00:00,9.4998 441 | 2011-06-02 00:00:00,10.57 442 | 2011-06-04 00:00:00,17.41 443 | 2011-06-06 00:00:00,19.23 444 | 2011-06-08 00:00:00,31.9099 445 | 2011-06-10 00:00:00,35.0 446 | 2011-06-12 00:00:00,24.99 447 | 2011-06-14 00:00:00,20.99 448 | 2011-06-16 00:00:00,19.96 449 | 2011-06-18 00:00:00,17.2 450 | 2011-06-20 00:00:00,17.35 451 | 2011-06-22 00:00:00,15.05 452 | 2011-06-24 00:00:00,16.7501 453 | 2011-06-26 00:00:00,17.51001 454 | 2011-06-28 00:00:00,17.52 455 | 2011-06-30 00:00:00,17.5 456 | 2011-07-02 00:00:00,16.49 457 | 2011-07-04 00:00:00,15.85 458 | 2011-07-06 00:00:00,16.5 459 | 2011-07-08 00:00:00,15.64276 460 | 2011-07-10 00:00:00,15.68 461 | 2011-07-12 00:00:00,14.63988 462 | 2011-07-14 00:00:00,14.1 463 | 2011-07-16 00:00:00,14.1 464 | 2011-07-18 00:00:00,13.6901 465 | 2011-07-20 00:00:00,14.04 466 | 2011-07-22 00:00:00,13.9389 467 | 2011-07-24 00:00:00,13.979999 468 | 2011-07-26 00:00:00,14.4 469 | 2011-07-28 00:00:00,14.0 470 | 2011-07-30 00:00:00,13.8 471 | 2011-08-01 00:00:00,13.5501 472 | 2011-08-03 00:00:00,13.1 473 | 2011-08-05 00:00:00,11.55 474 | 2011-08-07 00:00:00,9.8 475 | 2011-08-09 00:00:00,12.1 476 | 2011-08-11 00:00:00,10.4959 477 | 2011-08-13 00:00:00,10.05 478 | 2011-08-15 00:00:00,11.89 479 | 2011-08-17 00:00:00,11.3 480 | 2011-08-19 00:00:00,11.81 481 | 2011-08-21 00:00:00,11.59 482 | 2011-08-23 00:00:00,11.49 483 | 2011-08-25 00:00:00,10.96554 484 | 2011-08-27 00:00:00,9.11 485 | 2011-08-29 00:00:00,9.4811 486 | 2011-08-31 00:00:00,9.0 487 | 2011-09-02 00:00:00,8.4989 488 | 2011-09-04 00:00:00,8.5939999988 489 | 2011-09-06 00:00:00,7.65713 490 | 2011-09-08 00:00:00,7.31509 491 | 2011-09-10 00:00:00,6.208920000199999 492 | 2011-09-12 00:00:00,7.081 493 | 2011-09-14 00:00:00,5.990000000899999 494 | 2011-09-16 00:00:00,5.23 495 | 2011-09-18 00:00:00,5.0 496 | 2011-09-20 00:00:00,6.795 497 | 2011-09-22 00:00:00,5.83001 498 | 2011-09-24 00:00:00,5.66 499 | 2011-09-26 00:00:00,5.46014 500 | 2011-09-28 00:00:00,4.989 501 | 2011-09-30 00:00:00,5.35 502 | 2011-10-02 00:00:00,5.16 503 | 2011-10-04 00:00:00,5.03 504 | 2011-10-06 00:00:00,4.93 505 | 2011-10-08 00:00:00,4.594 506 | 2011-10-10 00:00:00,4.25937 507 | 2011-10-12 00:00:00,4.20612 508 | 2011-10-14 00:00:00,4.11455 509 | 2011-10-16 00:00:00,3.9009000002 510 | 2011-10-18 00:00:00,2.9 511 | 2011-10-20 00:00:00,2.42 512 | 2011-10-22 00:00:00,3.3 513 | 2011-10-24 00:00:00,3.2 514 | 2011-10-26 00:00:00,2.876 515 | 2011-10-28 00:00:00,3.255 516 | 2011-10-30 00:00:00,3.65026 517 | 2011-11-01 00:00:00,3.35 518 | 2011-11-03 00:00:00,3.299 519 | 2011-11-05 00:00:00,3.21 520 | 2011-11-07 00:00:00,3.0289 521 | 2011-11-09 00:00:00,3.114 522 | 2011-11-11 00:00:00,3.0 523 | 2011-11-13 00:00:00,3.099 524 | 2011-11-15 00:00:00,2.69 525 | 2011-11-17 00:00:00,2.60031 526 | 2011-11-19 00:00:00,2.3 527 | 2011-11-21 00:00:00,2.29 528 | 2011-11-23 00:00:00,2.38 529 | 2011-11-25 00:00:00,2.56 530 | 2011-11-27 00:00:00,2.4965 531 | 2011-11-29 00:00:00,2.981 532 | 2011-12-01 00:00:00,3.14 533 | 2011-12-03 00:00:00,3.12999 534 | 2011-12-05 00:00:00,2.93 535 | 2011-12-07 00:00:00,3.082 536 | 2011-12-09 00:00:00,3.039 537 | 2011-12-11 00:00:00,3.38 538 | 2011-12-13 00:00:00,3.3 539 | 2011-12-15 00:00:00,3.1933 540 | 2011-12-17 00:00:00,3.23 541 | 2011-12-19 00:00:00,3.70036 542 | 2011-12-21 00:00:00,4.11 543 | 2011-12-23 00:00:00,3.95 544 | 2011-12-25 00:00:00,4.3897 545 | 2011-12-27 00:00:00,4.06 546 | 2011-12-29 00:00:00,4.33 547 | 2011-12-31 00:00:00,4.995 548 | 2012-01-02 00:00:00,5.4999 549 | 2012-01-04 00:00:00,5.6063 550 | 2012-01-06 00:00:00,7.22 551 | 2012-01-08 00:00:00,7.2 552 | 2012-01-10 00:00:00,6.89 553 | 2012-01-12 00:00:00,6.997 554 | 2012-01-14 00:00:00,6.75 555 | 2012-01-16 00:00:00,7.18888 556 | 2012-01-18 00:00:00,6.95 557 | 2012-01-20 00:00:00,6.58 558 | 2012-01-22 00:00:00,6.39 559 | 2012-01-24 00:00:00,6.515 560 | 2012-01-26 00:00:00,6.2 561 | 2012-01-28 00:00:00,5.74815 562 | 2012-01-30 00:00:00,5.6 563 | 2012-02-01 00:00:00,5.638 564 | 2012-02-03 00:00:00,6.148 565 | 2012-02-05 00:00:00,5.9625 566 | 2012-02-07 00:00:00,5.70999 567 | 2012-02-09 00:00:00,5.8 568 | 2012-02-11 00:00:00,6.0 569 | 2012-02-13 00:00:00,5.72 570 | 2012-02-15 00:00:00,4.88 571 | 2012-02-17 00:00:00,4.76998 572 | 2012-02-19 00:00:00,4.33333 573 | 2012-02-21 00:00:00,4.4354 574 | 2012-02-23 00:00:00,4.92481 575 | 2012-02-25 00:00:00,5.07 576 | 2012-02-27 00:00:00,5.1 577 | 2012-02-29 00:00:00,4.9 578 | 2012-03-02 00:00:00,4.98888 579 | 2012-03-04 00:00:00,4.9 580 | 2012-03-06 00:00:00,5.05 581 | 2012-03-08 00:00:00,5.0 582 | 2012-03-10 00:00:00,4.94 583 | 2012-03-12 00:00:00,4.95915 584 | 2012-03-14 00:00:00,5.4444 585 | 2012-03-16 00:00:00,5.4 586 | 2012-03-18 00:00:00,5.37998 587 | 2012-03-20 00:00:00,4.98 588 | 2012-03-22 00:00:00,4.88 589 | 2012-03-24 00:00:00,4.74896 590 | 2012-03-26 00:00:00,4.73793 591 | 2012-03-28 00:00:00,4.84592 592 | 2012-03-30 00:00:00,4.83 593 | 2012-04-01 00:00:00,4.929 594 | 2012-04-03 00:00:00,5.01 595 | 2012-04-05 00:00:00,4.94486 596 | 2012-04-07 00:00:00,4.98 597 | 2012-04-09 00:00:00,4.8 598 | 2012-04-11 00:00:00,4.8941 599 | 2012-04-13 00:00:00,4.94464 600 | 2012-04-15 00:00:00,4.98389 601 | 2012-04-17 00:00:00,5.02207 602 | 2012-04-19 00:00:00,5.19 603 | 2012-04-21 00:00:00,5.48 604 | 2012-04-23 00:00:00,5.21799 605 | 2012-04-25 00:00:00,5.16 606 | 2012-04-27 00:00:00,5.1372 607 | 2012-04-29 00:00:00,5.0184 608 | 2012-05-01 00:00:00,5.0 609 | 2012-05-03 00:00:00,5.184 610 | 2012-05-05 00:00:00,5.1495 611 | 2012-05-07 00:00:00,5.0947 612 | 2012-05-09 00:00:00,5.09609 613 | 2012-05-11 00:00:00,5.03188 614 | 2012-05-13 00:00:00,4.99888 615 | 2012-05-15 00:00:00,5.09 616 | 2012-05-17 00:00:00,5.1345 617 | 2012-05-19 00:00:00,5.14291 618 | 2012-05-21 00:00:00,5.1475 619 | 2012-05-23 00:00:00,5.16 620 | 2012-05-25 00:00:00,5.15 621 | 2012-05-27 00:00:00,5.148 622 | 2012-05-29 00:00:00,5.15889 623 | 2012-05-31 00:00:00,5.19 624 | 2012-06-02 00:00:00,5.279 625 | 2012-06-04 00:00:00,5.2785 626 | 2012-06-06 00:00:00,5.47 627 | 2012-06-08 00:00:00,5.66 628 | 2012-06-10 00:00:00,5.62 629 | 2012-06-12 00:00:00,5.7 630 | 2012-06-14 00:00:00,5.95 631 | 2012-06-16 00:00:00,6.599 632 | 2012-06-18 00:00:00,6.4668 633 | 2012-06-20 00:00:00,6.64999 634 | 2012-06-22 00:00:00,6.79962 635 | 2012-06-24 00:00:00,6.5915 636 | 2012-06-26 00:00:00,6.4514 637 | 2012-06-28 00:00:00,6.66897 638 | 2012-06-30 00:00:00,6.693 639 | 2012-07-02 00:00:00,6.75 640 | 2012-07-04 00:00:00,6.55 641 | 2012-07-06 00:00:00,6.73449 642 | 2012-07-08 00:00:00,6.87 643 | 2012-07-10 00:00:00,7.239 644 | 2012-07-12 00:00:00,7.32 645 | 2012-07-14 00:00:00,7.68891 646 | 2012-07-16 00:00:00,8.2899 647 | 2012-07-18 00:00:00,9.39899 648 | 2012-07-20 00:00:00,9.23355 649 | 2012-07-22 00:00:00,9.0 650 | 2012-07-24 00:00:00,8.96 651 | 2012-07-26 00:00:00,8.9 652 | 2012-07-28 00:00:00,8.93 653 | 2012-07-30 00:00:00,9.15 654 | 2012-08-01 00:00:00,9.54 655 | 2012-08-03 00:00:00,11.1188 656 | 2012-08-05 00:00:00,11.1869 657 | 2012-08-07 00:00:00,11.04 658 | 2012-08-09 00:00:00,12.0 659 | 2012-08-11 00:00:00,11.59788 660 | 2012-08-13 00:00:00,11.86999 661 | 2012-08-15 00:00:00,12.67 662 | 2012-08-17 00:00:00,15.4 663 | 2012-08-19 00:00:00,12.0 664 | 2012-08-21 00:00:00,10.29999 665 | 2012-08-23 00:00:00,10.25 666 | 2012-08-25 00:00:00,10.25 667 | 2012-08-27 00:00:00,12.14999 668 | 2012-08-29 00:00:00,11.20999 669 | 2012-08-31 00:00:00,10.8359 670 | 2012-09-02 00:00:00,10.18999 671 | 2012-09-04 00:00:00,10.5934 672 | 2012-09-06 00:00:00,11.17 673 | 2012-09-08 00:00:00,11.21 674 | 2012-09-10 00:00:00,11.127 675 | 2012-09-12 00:00:00,11.3789 676 | 2012-09-14 00:00:00,11.75 677 | 2012-09-16 00:00:00,11.99 678 | 2012-09-18 00:00:00,12.09 679 | 2012-09-20 00:00:00,12.68666 680 | 2012-09-22 00:00:00,12.4433 681 | 2012-09-24 00:00:00,12.29888 682 | 2012-09-26 00:00:00,12.28 683 | 2012-09-28 00:00:00,12.444 684 | 2012-09-30 00:00:00,12.47499 685 | 2012-10-02 00:00:00,12.88 686 | 2012-10-04 00:00:00,13.0899 687 | 2012-10-06 00:00:00,12.89999 688 | 2012-10-08 00:00:00,12.08 689 | 2012-10-10 00:00:00,12.15 690 | 2012-10-12 00:00:00,12.15 691 | 2012-10-14 00:00:00,12.0 692 | 2012-10-16 00:00:00,11.99 693 | 2012-10-18 00:00:00,11.95998 694 | 2012-10-20 00:00:00,11.85 695 | 2012-10-22 00:00:00,11.81 696 | 2012-10-24 00:00:00,11.82 697 | 2012-10-26 00:00:00,11.09988 698 | 2012-10-28 00:00:00,10.61 699 | 2012-10-30 00:00:00,10.85 700 | 2012-11-01 00:00:00,11.279 701 | 2012-11-03 00:00:00,10.60097 702 | 2012-11-05 00:00:00,10.9 703 | 2012-11-07 00:00:00,11.21603 704 | 2012-11-09 00:00:00,11.07 705 | 2012-11-11 00:00:00,10.939 706 | 2012-11-13 00:00:00,11.129 707 | 2012-11-15 00:00:00,11.114 708 | 2012-11-17 00:00:00,11.8 709 | 2012-11-19 00:00:00,11.79998 710 | 2012-11-21 00:00:00,11.784 711 | 2012-11-23 00:00:00,12.43 712 | 2012-11-25 00:00:00,12.6 713 | 2012-11-27 00:00:00,12.52999 714 | 2012-11-29 00:00:00,12.599 715 | 2012-12-01 00:00:00,12.68778 716 | 2012-12-03 00:00:00,12.67901 717 | 2012-12-05 00:00:00,13.5 718 | 2012-12-07 00:00:00,13.68 719 | 2012-12-09 00:00:00,13.53 720 | 2012-12-11 00:00:00,13.63999 721 | 2012-12-13 00:00:00,13.79989 722 | 2012-12-15 00:00:00,13.7722 723 | 2012-12-17 00:00:00,13.498 724 | 2012-12-19 00:00:00,13.399 725 | 2012-12-21 00:00:00,13.6475 726 | 2012-12-23 00:00:00,13.48547 727 | 2012-12-25 00:00:00,13.45 728 | 2012-12-27 00:00:00,13.47 729 | 2012-12-29 00:00:00,13.67 730 | 2012-12-31 00:00:00,13.59 731 | 2013-01-02 00:00:00,13.4 732 | 2013-01-04 00:00:00,13.48986 733 | 2013-01-06 00:00:00,13.52999 734 | 2013-01-08 00:00:00,13.83 735 | 2013-01-10 00:00:00,14.32 736 | 2013-01-12 00:00:00,14.34999 737 | 2013-01-14 00:00:00,14.3 738 | 2013-01-16 00:00:00,14.689 739 | 2013-01-18 00:00:00,15.985 740 | 2013-01-20 00:00:00,15.89 741 | 2013-01-22 00:00:00,17.59 742 | 2013-01-24 00:00:00,19.18999 743 | 2013-01-26 00:00:00,17.61926 744 | 2013-01-28 00:00:00,18.45 745 | 2013-01-30 00:00:00,19.7 746 | 2013-02-01 00:00:00,21.3 747 | 2013-02-03 00:00:00,20.68 748 | 2013-02-05 00:00:00,20.79 749 | 2013-02-07 00:00:00,22.15 750 | 2013-02-09 00:00:00,23.69997 751 | 2013-02-11 00:00:00,24.1955 752 | 2013-02-13 00:00:00,26.09999 753 | 2013-02-15 00:00:00,26.99898 754 | 2013-02-17 00:00:00,25.6083 755 | 2013-02-19 00:00:00,28.78999 756 | 2013-02-21 00:00:00,29.80012 757 | 2013-02-23 00:00:00,29.27998 758 | 2013-02-25 00:00:00,30.25001 759 | 2013-02-27 00:00:00,31.40181 760 | 2013-03-01 00:00:00,34.87799 761 | 2013-03-03 00:00:00,34.10007 762 | 2013-03-05 00:00:00,40.04 763 | 2013-03-07 00:00:00,42.49271 764 | 2013-03-09 00:00:00,46.01112 765 | 2013-03-11 00:00:00,47.7397 766 | 2013-03-13 00:00:00,46.79999 767 | 2013-03-15 00:00:00,47.25005 768 | 2013-03-17 00:00:00,47.4419 769 | 2013-03-19 00:00:00,57.76 770 | 2013-03-21 00:00:00,73.8 771 | 2013-03-23 00:00:00,63.0 772 | 2013-03-25 00:00:00,73.88798 773 | 2013-03-27 00:00:00,88.9 774 | 2013-03-29 00:00:00,89.05 775 | 2013-03-31 00:00:00,92.50001 776 | 2013-04-02 00:00:00,108.73 777 | 2013-04-04 00:00:00,131.99899 778 | 2013-04-06 00:00:00,142.49765 779 | 2013-04-08 00:00:00,184.0 780 | 2013-04-10 00:00:00,198.0 781 | 2013-04-12 00:00:00,76.488 782 | 2013-04-14 00:00:00,91.0 783 | 2013-04-16 00:00:00,91.0 784 | 2013-04-18 00:00:00,96.5 785 | 2013-04-20 00:00:00,123.86 786 | 2013-04-22 00:00:00,123.515 787 | 2013-04-24 00:00:00,153.20019 788 | 2013-04-26 00:00:00,135.601 789 | 2013-04-28 00:00:00,135.98999 790 | 2013-04-30 00:00:00,139.109 791 | 2013-05-02 00:00:00,105.00003 792 | 2013-05-04 00:00:00,111.98979 793 | 2013-05-06 00:00:00,120.94999 794 | 2013-05-08 00:00:00,113.95001 795 | 2013-05-10 00:00:00,117.68 796 | 2013-05-12 00:00:00,114.32 797 | 2013-05-14 00:00:00,119.0 798 | 2013-05-16 00:00:00,115.09024 799 | 2013-05-18 00:00:00,123.74995 800 | 2013-05-20 00:00:00,122.65001 801 | 2013-05-22 00:00:00,122.64 802 | 2013-05-24 00:00:00,131.5 803 | 2013-05-26 00:00:00,134.04701 804 | 2013-05-28 00:00:00,128.321 805 | 2013-05-30 00:00:00,130.99589 806 | 2013-06-01 00:00:00,129.19945 807 | 2013-06-03 00:00:00,120.00002 808 | 2013-06-05 00:00:00,122.4051 809 | 2013-06-07 00:00:00,110.29501 810 | 2013-06-09 00:00:00,98.47045 811 | 2013-06-11 00:00:00,108.0 812 | 2013-06-13 00:00:00,109.0 813 | 2013-06-15 00:00:00,101.96998 814 | 2013-06-17 00:00:00,100.52521 815 | 2013-06-19 00:00:00,107.835 816 | 2013-06-21 00:00:00,111.0 817 | 2013-06-23 00:00:00,107.68389 818 | 2013-06-25 00:00:00,103.86665 819 | 2013-06-27 00:00:00,102.79001 820 | 2013-06-29 00:00:00,95.85301 821 | 2013-07-01 00:00:00,90.998 822 | 2013-07-03 00:00:00,82.8 823 | 2013-07-05 00:00:00,67.85844 824 | 2013-07-07 00:00:00,70.942 825 | 2013-07-09 00:00:00,76.32791 826 | 2013-07-11 00:00:00,87.15 827 | 2013-07-13 00:00:00,95.995 828 | 2013-07-15 00:00:00,98.37516 829 | 2013-07-17 00:00:00,97.97 830 | 2013-07-19 00:00:00,93.41082 831 | 2013-07-21 00:00:00,88.93999 832 | 2013-07-23 00:00:00,95.396 833 | 2013-07-25 00:00:00,96.2 834 | 2013-07-27 00:00:00,95.20461 835 | 2013-07-29 00:00:00,101.04202 836 | 2013-07-31 00:00:00,104.95555 837 | 2013-08-02 00:00:00,105.5 838 | 2013-08-04 00:00:00,104.67459 839 | 2013-08-06 00:00:00,106.88378 840 | 2013-08-08 00:00:00,101.9 841 | 2013-08-10 00:00:00,102.61802 842 | 2013-08-12 00:00:00,104.99498 843 | 2013-08-14 00:00:00,100.03 844 | 2013-08-16 00:00:00,98.51 845 | 2013-08-18 00:00:00,99.02 846 | 2013-08-20 00:00:00,104.59 847 | 2013-08-22 00:00:00,111.01 848 | 2013-08-24 00:00:00,108.79 849 | 2013-08-26 00:00:00,111.8 850 | 2013-08-28 00:00:00,119.25 851 | 2013-08-30 00:00:00,124.79 852 | 2013-09-01 00:00:00,130.82 853 | 2013-09-03 00:00:00,130.45 854 | 2013-09-05 00:00:00,124.51 855 | 2013-09-07 00:00:00,120.15 856 | 2013-09-09 00:00:00,121.66 857 | 2013-09-11 00:00:00,124.1 858 | 2013-09-13 00:00:00,128.29 859 | 2013-09-15 00:00:00,125.46 860 | 2013-09-17 00:00:00,127.56 861 | 2013-09-19 00:00:00,124.1 862 | 2013-09-21 00:00:00,123.5 863 | 2013-09-23 00:00:00,123.02 864 | 2013-09-25 00:00:00,124.18 865 | 2013-09-27 00:00:00,125.6 866 | 2013-09-29 00:00:00,128.1 867 | 2013-10-01 00:00:00,127.11 868 | 2013-10-03 00:00:00,116.95 869 | 2013-10-05 00:00:00,121.51 870 | 2013-10-07 00:00:00,123.6 871 | 2013-10-09 00:00:00,126.08 872 | 2013-10-11 00:00:00,126.84 873 | 2013-10-13 00:00:00,129.74 874 | 2013-10-15 00:00:00,139.24 875 | 2013-10-17 00:00:00,143.33 876 | 2013-10-19 00:00:00,165.05 877 | 2013-10-21 00:00:00,177.07 878 | 2013-10-23 00:00:00,199.95 879 | 2013-10-25 00:00:00,172.81 880 | 2013-10-27 00:00:00,189.06 881 | 2013-10-29 00:00:00,202.09 882 | 2013-10-31 00:00:00,201.61 883 | 2013-11-02 00:00:00,204.15 884 | 2013-11-04 00:00:00,225.1 885 | 2013-11-06 00:00:00,258.23 886 | 2013-11-08 00:00:00,325.56 887 | 2013-11-10 00:00:00,296.91 888 | 2013-11-12 00:00:00,353.95 889 | 2013-11-14 00:00:00,416.5 890 | 2013-11-16 00:00:00,433.53 891 | 2013-11-18 00:00:00,583.16 892 | 2013-11-20 00:00:00,595.0 893 | 2013-11-22 00:00:00,761.0 894 | 2013-11-24 00:00:00,833.16 895 | 2013-11-26 00:00:00,844.9 896 | 2013-11-28 00:00:00,1009.0 897 | 2013-11-30 00:00:00,1119.96 898 | 2013-12-02 00:00:00,992.27 899 | 2013-12-04 00:00:00,1151.0 900 | 2013-12-06 00:00:00,894.0 901 | 2013-12-08 00:00:00,722.99 902 | 2013-12-10 00:00:00,936.98 903 | 2013-12-12 00:00:00,845.75 904 | 2013-12-14 00:00:00,864.7 905 | 2013-12-16 00:00:00,709.0 906 | 2013-12-18 00:00:00,576.16 907 | 2013-12-20 00:00:00,660.0 908 | 2013-12-22 00:00:00,637.0 909 | 2013-12-24 00:00:00,647.27 910 | 2013-12-26 00:00:00,734.42 911 | 2013-12-28 00:00:00,701.61 912 | 2013-12-30 00:00:00,739.1 913 | 2014-01-01 00:00:00,746.9 914 | 2014-01-03 00:00:00,806.21 915 | 2014-01-05 00:00:00,896.0 916 | 2014-01-07 00:00:00,867.38 917 | 2014-01-09 00:00:00,809.17 918 | 2014-01-11 00:00:00,891.85 919 | 2014-01-13 00:00:00,807.83 920 | 2014-01-15 00:00:00,847.7 921 | 2014-01-17 00:00:00,793.0 922 | 2014-01-19 00:00:00,833.0 923 | 2014-01-21 00:00:00,822.56 924 | 2014-01-23 00:00:00,813.02 925 | 2014-01-25 00:00:00,806.0 926 | 2014-01-27 00:00:00,777.0 927 | 2014-01-29 00:00:00,803.6 928 | 2014-01-31 00:00:00,802.5 929 | 2014-02-02 00:00:00,819.57 930 | 2014-02-04 00:00:00,809.05 931 | 2014-02-06 00:00:00,778.0 932 | 2014-02-08 00:00:00,707.0 933 | 2014-02-10 00:00:00,665.0 934 | 2014-02-12 00:00:00,667.01 935 | 2014-02-14 00:00:00,672.1 936 | 2014-02-16 00:00:00,623.5 937 | 2014-02-18 00:00:00,626.77 938 | 2014-02-20 00:00:00,582.7 939 | 2014-02-22 00:00:00,603.98 940 | 2014-02-24 00:00:00,545.0 941 | 2014-02-26 00:00:00,585.38 942 | 2014-02-28 00:00:00,577.97 943 | 2014-03-02 00:00:00,559.88 944 | 2014-03-04 00:00:00,677.61 945 | 2014-03-06 00:00:00,657.02 946 | 2014-03-08 00:00:00,609.0 947 | 2014-03-10 00:00:00,621.99 948 | 2014-03-12 00:00:00,642.1 949 | 2014-03-14 00:00:00,636.47 950 | 2014-03-16 00:00:00,634.94 951 | 2014-03-18 00:00:00,627.0 952 | 2014-03-20 00:00:00,590.0 953 | 2014-03-22 00:00:00,557.0 954 | 2014-03-24 00:00:00,572.0 955 | 2014-03-26 00:00:00,585.7 956 | 2014-03-28 00:00:00,503.0 957 | 2014-03-30 00:00:00,449.02 958 | 2014-04-01 00:00:00,479.51 959 | 2014-04-03 00:00:00,446.31 960 | 2014-04-05 00:00:00,451.84 961 | 2014-04-07 00:00:00,447.74 962 | 2014-04-09 00:00:00,443.1 963 | 2014-04-11 00:00:00,427.99 964 | 2014-04-13 00:00:00,405.0 965 | 2014-04-15 00:00:00,489.91 966 | 2014-04-17 00:00:00,495.0 967 | 2014-04-19 00:00:00,499.9 968 | 2014-04-21 00:00:00,496.5 969 | 2014-04-23 00:00:00,487.3 970 | 2014-04-25 00:00:00,459.15 971 | 2014-04-27 00:00:00,440.1 972 | 2014-04-29 00:00:00,447.7 973 | 2014-05-01 00:00:00,460.01 974 | 2014-05-03 00:00:00,434.5 975 | 2014-05-05 00:00:00,427.83 976 | 2014-05-07 00:00:00,446.65 977 | 2014-05-09 00:00:00,452.02 978 | 2014-05-11 00:00:00,435.0 979 | 2014-05-13 00:00:00,438.95 980 | 2014-05-15 00:00:00,448.99 981 | 2014-05-17 00:00:00,449.08 982 | 2014-05-19 00:00:00,446.42 983 | 2014-05-21 00:00:00,494.87 984 | 2014-05-23 00:00:00,527.47 985 | 2014-05-25 00:00:00,575.0 986 | 2014-05-27 00:00:00,581.87 987 | 2014-05-29 00:00:00,568.0 988 | 2014-05-31 00:00:00,620.45 989 | 2014-06-02 00:00:00,631.49 990 | 2014-06-04 00:00:00,644.66 991 | 2014-06-06 00:00:00,655.75 992 | 2014-06-08 00:00:00,652.0 993 | 2014-06-10 00:00:00,649.89 994 | 2014-06-12 00:00:00,617.0 995 | 2014-06-14 00:00:00,557.92 996 | 2014-06-16 00:00:00,595.0 997 | 2014-06-18 00:00:00,604.6 998 | 2014-06-20 00:00:00,593.33 999 | 2014-06-22 00:00:00,596.08 1000 | 2014-06-24 00:00:00,585.54 1001 | 2014-06-26 00:00:00,569.0 1002 | 2014-06-28 00:00:00,597.08 1003 | 2014-06-30 00:00:00,620.0 1004 | 2014-07-02 00:00:00,654.0 1005 | 2014-07-04 00:00:00,635.49 1006 | 2014-07-06 00:00:00,634.49 1007 | 2014-07-08 00:00:00,623.7 1008 | 2014-07-10 00:00:00,619.78 1009 | 2014-07-12 00:00:00,631.98 1010 | 2014-07-14 00:00:00,622.8 1011 | 2014-07-16 00:00:00,619.48 1012 | 2014-07-18 00:00:00,629.86 1013 | 2014-07-20 00:00:00,623.77 1014 | 2014-07-22 00:00:00,621.03 1015 | 2014-07-24 00:00:00,601.18 1016 | 2014-07-26 00:00:00,595.88 1017 | 2014-07-28 00:00:00,586.96 1018 | 2014-07-30 00:00:00,573.48 1019 | 2014-08-01 00:00:00,601.94 1020 | 2014-08-03 00:00:00,589.79 1021 | 2014-08-05 00:00:00,580.21 1022 | 2014-08-07 00:00:00,587.24 1023 | 2014-08-09 00:00:00,588.61 1024 | 2014-08-11 00:00:00,578.97 1025 | 2014-08-13 00:00:00,550.14 1026 | 2014-08-15 00:00:00,498.16 1027 | 2014-08-17 00:00:00,491.88 1028 | 2014-08-19 00:00:00,487.5 1029 | 2014-08-21 00:00:00,525.78 1030 | 2014-08-23 00:00:00,503.88 1031 | 2014-08-25 00:00:00,503.79 1032 | 2014-08-27 00:00:00,514.84 1033 | 2014-08-29 00:00:00,511.99 1034 | 2014-08-31 00:00:00,477.98 1035 | 2014-09-02 00:00:00,483.65 1036 | 2014-09-04 00:00:00,486.0 1037 | 2014-09-06 00:00:00,481.09 1038 | 2014-09-08 00:00:00,476.61 1039 | 2014-09-10 00:00:00,485.03 1040 | 2014-09-12 00:00:00,470.43 1041 | 2014-09-14 00:00:00,475.49 1042 | 2014-09-16 00:00:00,468.0 1043 | 2014-09-18 00:00:00,428.19 1044 | 2014-09-20 00:00:00,415.56 1045 | 2014-09-22 00:00:00,400.98 1046 | 2014-09-24 00:00:00,431.91 1047 | 2014-09-26 00:00:00,405.14 1048 | 2014-09-28 00:00:00,380.0 1049 | 2014-09-30 00:00:00,382.67 1050 | 2014-10-02 00:00:00,375.85 1051 | 2014-10-04 00:00:00,336.0 1052 | 2014-10-06 00:00:00,331.2 1053 | 2014-10-08 00:00:00,346.26 1054 | 2014-10-10 00:00:00,358.97 1055 | 2014-10-12 00:00:00,364.83 1056 | 2014-10-14 00:00:00,411.89 1057 | 2014-10-16 00:00:00,378.02 1058 | 2014-10-18 00:00:00,389.68 1059 | 2014-10-20 00:00:00,384.95 1060 | 2014-10-22 00:00:00,385.1 1061 | 2014-10-24 00:00:00,358.46 1062 | 2014-10-26 00:00:00,355.43 1063 | 2014-10-28 00:00:00,354.94 1064 | 2014-10-30 00:00:00,337.0 1065 | 2014-11-01 00:00:00,327.39 1066 | 2014-11-03 00:00:00,325.81 1067 | 2014-11-05 00:00:00,341.99 1068 | 2014-11-07 00:00:00,342.26 1069 | 2014-11-09 00:00:00,356.34 1070 | 2014-11-11 00:00:00,365.04 1071 | 2014-11-13 00:00:00,406.56 1072 | 2014-11-15 00:00:00,373.14 1073 | 2014-11-17 00:00:00,378.48 1074 | 2014-11-19 00:00:00,374.77 1075 | 2014-11-21 00:00:00,356.9 1076 | 2014-11-23 00:00:00,362.99 1077 | 2014-11-25 00:00:00,381.03 1078 | 2014-11-27 00:00:00,372.62 1079 | 2014-11-29 00:00:00,377.22 1080 | 2014-12-01 00:00:00,381.72 1081 | 2014-12-03 00:00:00,378.25 1082 | 2014-12-05 00:00:00,373.98 1083 | 2014-12-07 00:00:00,375.73 1084 | 2014-12-09 00:00:00,350.29 1085 | 2014-12-11 00:00:00,356.1 1086 | 2014-12-13 00:00:00,350.31 1087 | 2014-12-15 00:00:00,352.15 1088 | 2014-12-17 00:00:00,318.2 1089 | 2014-12-19 00:00:00,317.63 1090 | 2014-12-21 00:00:00,326.93 1091 | 2014-12-23 00:00:00,336.96 1092 | 2014-12-25 00:00:00,319.31 1093 | 2014-12-27 00:00:00,314.45 1094 | 2014-12-29 00:00:00,314.49 1095 | 2014-12-31 00:00:00,317.4 1096 | 2015-01-02 00:00:00,316.15 1097 | 2015-01-04 00:00:00,270.93 1098 | 2015-01-06 00:00:00,276.8 1099 | 2015-01-08 00:00:00,276.8 1100 | 2015-01-10 00:00:00,278.0 1101 | 2015-01-12 00:00:00,270.0 1102 | 2015-01-14 00:00:00,176.5 1103 | 2015-01-16 00:00:00,205.35 1104 | 2015-01-18 00:00:00,211.18 1105 | 2015-01-20 00:00:00,212.99 1106 | 2015-01-22 00:00:00,233.9 1107 | 2015-01-24 00:00:00,244.64 1108 | 2015-01-26 00:00:00,271.47 1109 | 2015-01-28 00:00:00,249.25 1110 | 2015-01-30 00:00:00,230.11 1111 | 2015-02-01 00:00:00,220.72 1112 | 2015-02-03 00:00:00,236.51 1113 | 2015-02-05 00:00:00,217.37 1114 | 2015-02-07 00:00:00,225.16 1115 | 2015-02-09 00:00:00,220.39 1116 | 2015-02-11 00:00:00,221.99 1117 | 2015-02-13 00:00:00,239.94 1118 | 2015-02-15 00:00:00,243.99 1119 | 2015-02-17 00:00:00,244.0 1120 | 2015-02-19 00:00:00,241.59 1121 | 2015-02-21 00:00:00,245.66 1122 | 2015-02-23 00:00:00,237.09 1123 | 2015-02-25 00:00:00,238.59 1124 | 2015-02-27 00:00:00,254.0 1125 | 2015-03-01 00:00:00,247.56 1126 | 2015-03-03 00:00:00,275.8 1127 | 2015-03-05 00:00:00,265.63 1128 | 2015-03-07 00:00:00,275.83 1129 | 2015-03-09 00:00:00,287.48 1130 | 2015-03-11 00:00:00,293.29 1131 | 2015-03-13 00:00:00,289.51 1132 | 2015-03-15 00:00:00,283.57 1133 | 2015-03-17 00:00:00,287.02 1134 | 2015-03-19 00:00:00,260.62 1135 | 2015-03-21 00:00:00,257.83 1136 | 2015-03-23 00:00:00,266.07 1137 | 2015-03-25 00:00:00,245.68 1138 | 2015-03-27 00:00:00,248.63 1139 | 2015-03-29 00:00:00,244.05 1140 | 2015-03-31 00:00:00,242.92 1141 | 2015-04-02 00:00:00,252.44 1142 | 2015-04-04 00:00:00,253.77 1143 | 2015-04-06 00:00:00,254.7 1144 | 2015-04-08 00:00:00,245.89 1145 | 2015-04-10 00:00:00,235.71 1146 | 2015-04-12 00:00:00,236.76 1147 | 2015-04-14 00:00:00,216.0 1148 | 2015-04-16 00:00:00,229.62 1149 | 2015-04-18 00:00:00,222.32 1150 | 2015-04-20 00:00:00,224.63 1151 | 2015-04-22 00:00:00,237.34 1152 | 2015-04-24 00:00:00,230.93 1153 | 2015-04-26 00:00:00,218.42 1154 | 2015-04-28 00:00:00,222.66 1155 | 2015-04-30 00:00:00,235.13 1156 | 2015-05-02 00:00:00,234.13 1157 | 2015-05-04 00:00:00,236.17 1158 | 2015-05-06 00:00:00,235.79 1159 | 2015-05-08 00:00:00,244.72 1160 | 2015-05-10 00:00:00,239.35 1161 | 2015-05-12 00:00:00,241.51 1162 | 2015-05-14 00:00:00,238.32 1163 | 2015-05-16 00:00:00,236.63 1164 | 2015-05-18 00:00:00,236.45 1165 | 2015-05-20 00:00:00,234.6 1166 | 2015-05-22 00:00:00,240.5 1167 | 2015-05-24 00:00:00,240.46 1168 | 2015-05-26 00:00:00,235.94 1169 | 2015-05-28 00:00:00,236.48 1170 | 2015-05-30 00:00:00,231.12 1171 | 2015-06-01 00:00:00,222.4 1172 | 2015-06-03 00:00:00,226.29 1173 | 2015-06-05 00:00:00,224.15 1174 | 2015-06-07 00:00:00,222.6 1175 | 2015-06-09 00:00:00,230.1 1176 | 2015-06-11 00:00:00,229.0 1177 | 2015-06-13 00:00:00,230.22 1178 | 2015-06-15 00:00:00,236.46 1179 | 2015-06-17 00:00:00,249.96 1180 | 2015-06-19 00:00:00,246.45 1181 | 2015-06-21 00:00:00,242.47 1182 | 2015-06-23 00:00:00,243.92 1183 | 2015-06-25 00:00:00,241.92 1184 | 2015-06-27 00:00:00,248.5 1185 | 2015-06-29 00:00:00,253.8 1186 | 2015-07-01 00:00:00,257.85 1187 | 2015-07-03 00:00:00,255.1 1188 | 2015-07-05 00:00:00,270.92 1189 | 2015-07-07 00:00:00,267.26 1190 | 2015-07-09 00:00:00,270.68 1191 | 2015-07-11 00:00:00,293.5 1192 | 2015-07-13 00:00:00,289.9 1193 | 2015-07-15 00:00:00,290.95 1194 | 2015-07-17 00:00:00,277.99 1195 | 2015-07-19 00:00:00,276.09 1196 | 2015-07-21 00:00:00,278.97 1197 | 2015-07-23 00:00:00,276.64 1198 | 2015-07-25 00:00:00,287.12 1199 | 2015-07-27 00:00:00,290.79 1200 | 2015-07-29 00:00:00,290.7 1201 | 2015-07-31 00:00:00,284.78 1202 | 2015-08-02 00:00:00,280.69 1203 | 2015-08-04 00:00:00,285.0 1204 | 2015-08-06 00:00:00,277.1 1205 | 2015-08-08 00:00:00,269.89 1206 | 2015-08-10 00:00:00,264.07 1207 | 2015-08-12 00:00:00,267.89 1208 | 2015-08-14 00:00:00,266.99 1209 | 2015-08-16 00:00:00,257.47 1210 | 2015-08-18 00:00:00,253.91 1211 | 2015-08-20 00:00:00,233.23 1212 | 2015-08-22 00:00:00,226.04 1213 | 2015-08-24 00:00:00,213.24 1214 | 2015-08-26 00:00:00,228.84 1215 | 2015-08-28 00:00:00,234.17 1216 | 2015-08-30 00:00:00,226.47 1217 | 2015-09-01 00:00:00,228.32 1218 | 2015-09-03 00:00:00,227.42 1219 | 2015-09-05 00:00:00,234.67 1220 | 2015-09-07 00:00:00,241.31 1221 | 2015-09-09 00:00:00,240.08 1222 | 2015-09-11 00:00:00,239.93 1223 | 2015-09-13 00:00:00,229.86 1224 | 2015-09-15 00:00:00,230.0 1225 | 2015-09-17 00:00:00,234.0 1226 | 2015-09-19 00:00:00,230.71 1227 | 2015-09-21 00:00:00,226.56 1228 | 2015-09-23 00:00:00,229.95 1229 | 2015-09-25 00:00:00,235.79 1230 | 2015-09-27 00:00:00,232.85 1231 | 2015-09-29 00:00:00,237.68 1232 | 2015-10-01 00:00:00,238.98 1233 | 2015-10-03 00:00:00,239.17 1234 | 2015-10-05 00:00:00,239.22 1235 | 2015-10-07 00:00:00,245.25 1236 | 2015-10-09 00:00:00,244.78 1237 | 2015-10-11 00:00:00,248.08 1238 | 2015-10-13 00:00:00,249.58 1239 | 2015-10-15 00:00:00,254.73 1240 | 2015-10-17 00:00:00,269.07 1241 | 2015-10-19 00:00:00,264.16 1242 | 2015-10-21 00:00:00,269.1 1243 | 2015-10-23 00:00:00,277.43 1244 | 2015-10-25 00:00:00,295.01 1245 | 2015-10-27 00:00:00,294.73 1246 | 2015-10-29 00:00:00,310.36 1247 | 2015-10-31 00:00:00,310.92 1248 | 2015-11-02 00:00:00,340.69 1249 | 2015-11-04 00:00:00,437.05 1250 | 2015-11-06 00:00:00,388.38 1251 | 2015-11-08 00:00:00,381.51 1252 | 2015-11-10 00:00:00,353.58 1253 | 2015-11-12 00:00:00,331.6 1254 | 2015-11-14 00:00:00,331.9 1255 | 2015-11-16 00:00:00,329.32 1256 | 2015-11-18 00:00:00,334.46 1257 | 2015-11-20 00:00:00,320.62 1258 | 2015-11-22 00:00:00,322.33 1259 | 2015-11-24 00:00:00,321.1 1260 | 2015-11-26 00:00:00,348.0 1261 | 2015-11-28 00:00:00,353.88 1262 | 2015-11-30 00:00:00,372.53 1263 | 2015-12-02 00:00:00,360.98 1264 | 2015-12-04 00:00:00,360.88 1265 | 2015-12-06 00:00:00,390.04 1266 | 2015-12-08 00:00:00,394.99 1267 | 2015-12-10 00:00:00,415.64 1268 | 2015-12-12 00:00:00,426.06 1269 | 2015-12-14 00:00:00,445.6 1270 | 2015-12-16 00:00:00,454.43 1271 | 2015-12-18 00:00:00,458.95 1272 | 2015-12-20 00:00:00,434.98 1273 | 2015-12-22 00:00:00,435.98 1274 | 2015-12-24 00:00:00,453.95 1275 | 2015-12-26 00:00:00,417.99 1276 | 2015-12-28 00:00:00,423.98 1277 | 2015-12-30 00:00:00,428.0 1278 | 2016-01-01 00:00:00,432.33 1279 | 2016-01-03 00:00:00,428.13 1280 | 2016-01-05 00:00:00,431.9 1281 | 2016-01-07 00:00:00,453.71 1282 | 2016-01-09 00:00:00,450.15 1283 | 2016-01-11 00:00:00,447.11 1284 | 2016-01-13 00:00:00,429.57 1285 | 2016-01-15 00:00:00,391.62 1286 | 2016-01-17 00:00:00,385.45 1287 | 2016-01-19 00:00:00,378.66 1288 | 2016-01-21 00:00:00,408.0 1289 | 2016-01-23 00:00:00,388.5 1290 | 2016-01-25 00:00:00,387.09 1291 | 2016-01-27 00:00:00,394.45 1292 | 2016-01-29 00:00:00,377.26 1293 | 2016-01-31 00:00:00,376.86 1294 | 2016-02-02 00:00:00,373.48 1295 | 2016-02-04 00:00:00,385.06 1296 | 2016-02-06 00:00:00,373.04 1297 | 2016-02-08 00:00:00,375.8 1298 | 2016-02-10 00:00:00,378.98 1299 | 2016-02-12 00:00:00,381.46 1300 | 2016-02-14 00:00:00,402.38 1301 | 2016-02-16 00:00:00,406.69 1302 | 2016-02-18 00:00:00,418.97 1303 | 2016-02-20 00:00:00,439.48 1304 | 2016-02-22 00:00:00,438.5 1305 | 2016-02-24 00:00:00,423.1 1306 | 2016-02-26 00:00:00,424.34 1307 | 2016-02-28 00:00:00,433.47 1308 | 2016-03-01 00:00:00,431.87 1309 | 2016-03-03 00:00:00,421.44 1310 | 2016-03-05 00:00:00,401.0 1311 | 2016-03-07 00:00:00,411.94 1312 | 2016-03-09 00:00:00,413.1 1313 | 2016-03-11 00:00:00,421.22 1314 | 2016-03-13 00:00:00,412.44 1315 | 2016-03-15 00:00:00,414.78 1316 | 2016-03-17 00:00:00,418.64 1317 | 2016-03-19 00:00:00,405.59 1318 | 2016-03-21 00:00:00,410.49 1319 | 2016-03-23 00:00:00,417.8 1320 | 2016-03-25 00:00:00,416.44 1321 | 2016-03-27 00:00:00,426.8 1322 | 2016-03-29 00:00:00,414.0 1323 | 2016-03-31 00:00:00,416.94 1324 | 2016-04-02 00:00:00,418.07 1325 | 2016-04-04 00:00:00,418.99 1326 | 2016-04-06 00:00:00,421.09 1327 | 2016-04-08 00:00:00,422.2 1328 | 2016-04-10 00:00:00,421.63 1329 | 2016-04-12 00:00:00,425.89 1330 | 2016-04-14 00:00:00,423.76 1331 | 2016-04-16 00:00:00,429.43 1332 | 2016-04-18 00:00:00,427.28 1333 | 2016-04-20 00:00:00,442.34 1334 | 2016-04-22 00:00:00,447.25 1335 | 2016-04-24 00:00:00,459.4 1336 | 2016-04-26 00:00:00,467.98 1337 | 2016-04-28 00:00:00,450.0 1338 | 2016-04-30 00:00:00,447.17 1339 | 2016-05-02 00:00:00,443.9 1340 | 2016-05-04 00:00:00,447.37 1341 | 2016-05-06 00:00:00,460.91 1342 | 2016-05-08 00:00:00,457.82 1343 | 2016-05-10 00:00:00,450.0 1344 | 2016-05-12 00:00:00,454.7 1345 | 2016-05-14 00:00:00,455.65 1346 | 2016-05-16 00:00:00,454.88 1347 | 2016-05-18 00:00:00,452.9 1348 | 2016-05-20 00:00:00,441.25 1349 | 2016-05-22 00:00:00,438.0 1350 | 2016-05-24 00:00:00,445.34 1351 | 2016-05-26 00:00:00,452.49 1352 | 2016-05-28 00:00:00,522.43 1353 | 2016-05-30 00:00:00,525.15 1354 | 2016-06-01 00:00:00,539.47 1355 | 2016-06-03 00:00:00,568.0 1356 | 2016-06-05 00:00:00,574.02 1357 | 2016-06-07 00:00:00,577.54 1358 | 2016-06-09 00:00:00,575.2941375 1359 | 2016-06-11 00:00:00,594.4399875 1360 | 2016-06-13 00:00:00,702.0665 1361 | 2016-06-15 00:00:00,692.289625 1362 | 2016-06-17 00:00:00,743.9164 1363 | 2016-06-19 00:00:00,760.8168 1364 | 2016-06-21 00:00:00,665.9586125 1365 | 2016-06-23 00:00:00,624.542 1366 | 2016-06-25 00:00:00,664.9068875 1367 | 2016-06-27 00:00:00,646.7030125 1368 | 2016-06-29 00:00:00,637.959625 1369 | 2016-07-01 00:00:00,675.1863375 1370 | 2016-07-03 00:00:00,658.4175 1371 | 2016-07-05 00:00:00,666.4855875 1372 | 2016-07-07 00:00:00,636.3588625 1373 | 2016-07-09 00:00:00,651.8649125 1374 | 2016-07-11 00:00:00,647.9837 1375 | 2016-07-13 00:00:00,657.4524875 1376 | 2016-07-15 00:00:00,664.57145 1377 | 2016-07-17 00:00:00,675.45905 1378 | 2016-07-19 00:00:00,672.8432 1379 | 2016-07-21 00:00:00,664.882075 1380 | 2016-07-23 00:00:00,655.8469125 1381 | 2016-07-25 00:00:00,654.6954499999999 1382 | 2016-07-27 00:00:00,654.9810375 1383 | 2016-07-29 00:00:00,656.8876124999999 1384 | 2016-07-31 00:00:00,628.015375 1385 | 2016-08-02 00:00:00,515.0618999999999 1386 | 2016-08-04 00:00:00,577.3157714285715 1387 | 2016-08-06 00:00:00,588.2312714285714 1388 | 2016-08-08 00:00:00,592.6975 1389 | 2016-08-10 00:00:00,592.1899 1390 | 2016-08-12 00:00:00,587.7556 1391 | 2016-08-14 00:00:00,570.9764142857142 1392 | 2016-08-16 00:00:00,581.1185875 1393 | 2016-08-18 00:00:00,576.0360875 1394 | 2016-08-20 00:00:00,583.2078625 1395 | 2016-08-22 00:00:00,588.9727625 1396 | 2016-08-24 00:00:00,582.8479875 1397 | 2016-08-26 00:00:00,581.07245 1398 | 2016-08-28 00:00:00,576.534 1399 | 2016-08-30 00:00:00,578.616575 1400 | 2016-09-01 00:00:00,572.818375 1401 | 2016-09-03 00:00:00,600.8860875 1402 | 2016-09-05 00:00:00,608.10975 1403 | 2016-09-07 00:00:00,614.4637625 1404 | 2016-09-09 00:00:00,625.0708999999999 1405 | 2016-09-11 00:00:00,608.15855 1406 | 2016-09-13 00:00:00,610.928125 1407 | 2016-09-15 00:00:00,610.386125 1408 | 2016-09-17 00:00:00,608.006875 1409 | 2016-09-19 00:00:00,610.1965 1410 | 2016-09-21 00:00:00,598.884875 1411 | 2016-09-23 00:00:00,604.2225 1412 | 2016-09-25 00:00:00,601.7472375 1413 | 2016-09-27 00:00:00,605.9622125 1414 | 2016-09-29 00:00:00,606.36375 1415 | 2016-10-01 00:00:00,614.8238 1416 | 2016-10-03 00:00:00,611.8511 1417 | 2016-10-05 00:00:00,612.3522875 1418 | 2016-10-07 00:00:00,617.21035 1419 | 2016-10-09 00:00:00,615.65675 1420 | 2016-10-11 00:00:00,639.30995 1421 | 2016-10-13 00:00:00,635.9650875 1422 | 2016-10-15 00:00:00,637.9498571428572 1423 | 2016-10-17 00:00:00,638.1833875 1424 | 2016-10-19 00:00:00,629.253675 1425 | 2016-10-21 00:00:00,631.9242125 1426 | 2016-10-23 00:00:00,653.0028625 1427 | 2016-10-25 00:00:00,655.3199500000001 1428 | 2016-10-27 00:00:00,682.2239625 1429 | 2016-10-29 00:00:00,714.89545 1430 | 2016-10-31 00:00:00,702.0015125 1431 | 2016-11-02 00:00:00,733.336125 1432 | 2016-11-04 00:00:00,703.6940875 1433 | 2016-11-06 00:00:00,712.00325 1434 | 2016-11-08 00:00:00,708.974875 1435 | 2016-11-10 00:00:00,713.690125 1436 | 2016-11-12 00:00:00,703.718 1437 | 2016-11-14 00:00:00,706.467875 1438 | 2016-11-16 00:00:00,736.9143750000001 1439 | 2016-11-18 00:00:00,747.5217625 1440 | 2016-11-20 00:00:00,729.066625 1441 | 2016-11-22 00:00:00,748.749375 1442 | 2016-11-24 00:00:00,737.450375 1443 | 2016-11-26 00:00:00,733.673 1444 | 2016-11-28 00:00:00,733.0521249999999 1445 | 2016-11-30 00:00:00,742.6967625 1446 | 2016-12-02 00:00:00,772.43725 1447 | 2016-12-04 00:00:00,764.81625 1448 | 2016-12-06 00:00:00,756.6215 1449 | 2016-12-08 00:00:00,769.72975 1450 | 2016-12-10 00:00:00,773.40125 1451 | 2016-12-12 00:00:00,777.006875 1452 | 2016-12-14 00:00:00,774.897 1453 | 2016-12-16 00:00:00,781.5683750000001 1454 | 2016-12-18 00:00:00,788.40575 1455 | 2016-12-20 00:00:00,793.0901375 1456 | 2016-12-22 00:00:00,860.599875 1457 | 2016-12-24 00:00:00,891.6126125 1458 | 2016-12-26 00:00:00,897.335375 1459 | 2016-12-28 00:00:00,967.480375 1460 | 2016-12-30 00:00:00,952.156375 1461 | 2017-01-01 00:00:00,997.729875 1462 | 2017-01-03 00:00:00,1023.141875 1463 | 2017-01-05 00:00:00,994.674875 1464 | 2017-01-07 00:00:00,896.830375 1465 | 2017-01-09 00:00:00,894.18025 1466 | 2017-01-11 00:00:00,785.2237375 1467 | 2017-01-13 00:00:00,826.2956625 1468 | 2017-01-15 00:00:00,822.2076 1469 | 2017-01-17 00:00:00,903.84575 1470 | 2017-01-19 00:00:00,895.798875 1471 | 2017-01-21 00:00:00,920.4479 1472 | 2017-01-23 00:00:00,922.0736125 1473 | 2017-01-25 00:00:00,893.045625 1474 | 2017-01-27 00:00:00,919.27975 1475 | 2017-01-29 00:00:00,915.933 1476 | 2017-01-31 00:00:00,964.706075 1477 | 2017-02-02 00:00:00,1007.6137125 1478 | 2017-02-04 00:00:00,1030.9994125 1479 | 2017-02-06 00:00:00,1024.01375 1480 | 2017-02-08 00:00:00,1052.3766125 1481 | 2017-02-10 00:00:00,999.1034999999999 1482 | 2017-02-12 00:00:00,1000.604625 1483 | 2017-02-14 00:00:00,1011.78025 1484 | 2017-02-16 00:00:00,1035.2081249999999 1485 | 2017-02-18 00:00:00,1056.6371374999999 1486 | 2017-02-20 00:00:00,1084.7550125 1487 | 2017-02-22 00:00:00,1123.2231875 1488 | 2017-02-24 00:00:00,1174.86625 1489 | 2017-02-26 00:00:00,1175.0497500000001 1490 | 2017-02-28 00:00:00,1187.5652857142857 1491 | 2017-03-02 00:00:00,1259.4108166666667 1492 | 2017-03-04 00:00:00,1267.0272 1493 | 2017-03-06 00:00:00,1275.197375 1494 | 2017-03-08 00:00:00,1157.3933 1495 | 2017-03-10 00:00:00,1098.6171285714286 1496 | 2017-03-12 00:00:00,1227.494625 1497 | 2017-03-14 00:00:00,1245.3707857142856 1498 | 2017-03-16 00:00:00,1180.945657142857 1499 | 2017-03-18 00:00:00,952.2323625 1500 | 2017-03-20 00:00:00,1049.0844875 1501 | 2017-03-22 00:00:00,1028.7268625 1502 | 2017-03-24 00:00:00,941.9197142857143 1503 | 2017-03-26 00:00:00,956.7863125 1504 | 2017-03-28 00:00:00,1046.127625 1505 | 2017-03-30 00:00:00,1037.90455 1506 | 2017-04-01 00:00:00,1086.9295714285713 1507 | 2017-04-03 00:00:00,1141.813 1508 | 2017-04-05 00:00:00,1133.0793142857142 1509 | 2017-04-07 00:00:00,1190.45425 1510 | 2017-04-09 00:00:00,1208.8005 1511 | 2017-04-11 00:00:00,1226.6170375 1512 | 2017-04-13 00:00:00,1180.0237125 1513 | 2017-04-15 00:00:00,1184.8806714285713 1514 | 2017-04-17 00:00:00,1205.634875 1515 | 2017-04-19 00:00:00,1217.9300875 1516 | 2017-04-21 00:00:00,1258.3614125 1517 | 2017-04-23 00:00:00,1257.9881125 1518 | 2017-04-25 00:00:00,1279.4146875000001 1519 | 2017-04-27 00:00:00,1345.3539125 1520 | 2017-04-29 00:00:00,1334.9790375 1521 | 2017-05-01 00:00:00,1417.1728125 1522 | 2017-05-03 00:00:00,1507.5768571428573 1523 | 2017-05-05 00:00:00,1533.3350714285714 1524 | 2017-05-07 00:00:00,1535.8684285714285 1525 | 2017-05-09 00:00:00,1721.2849714285715 1526 | 2017-05-11 00:00:00,1820.9905625 1527 | 2017-05-13 00:00:00,1771.9200125 1528 | 2017-05-15 00:00:00,1723.1269375 1529 | 2017-05-17 00:00:00,1807.4850625 1530 | 2017-05-19 00:00:00,1961.5204875 1531 | 2017-05-21 00:00:00,2046.5344625 1532 | 2017-05-23 00:00:00,2287.7102875 1533 | 2017-05-25 00:00:00,2387.2062857142855 1534 | 2017-05-27 00:00:00,2014.0529625 1535 | 2017-05-29 00:00:00,2275.9307 1536 | 2017-05-31 00:00:00,2285.9339142857143 1537 | 2017-06-02 00:00:00,2446.142414285714 1538 | 2017-06-04 00:00:00,2516.173142857143 1539 | 2017-06-06 00:00:00,2883.3136966371426 1540 | 2017-06-08 00:00:00,2792.9991875 1541 | 2017-06-10 00:00:00,2845.3728571428574 1542 | 2017-06-12 00:00:00,2657.6750625 1543 | 2017-06-14 00:00:00,2447.0415625 1544 | 2017-06-16 00:00:00,2464.9598142857144 1545 | 2017-06-18 00:00:00,2507.389252144286 1546 | 2017-06-20 00:00:00,2754.97825 1547 | 2017-06-22 00:00:00,2727.2880125 1548 | 2017-06-24 00:00:00,2589.1648875 1549 | 2017-06-26 00:00:00,2436.4510571428573 1550 | 2017-06-28 00:00:00,2585.349185714286 1551 | 2017-06-30 00:00:00,2477.641375 1552 | 2017-07-02 00:00:00,2501.191342857143 1553 | 2017-07-04 00:00:00,2599.7298375 1554 | 2017-07-06 00:00:00,2609.96775 1555 | 2017-07-08 00:00:00,2562.1306624999997 1556 | 2017-07-10 00:00:00,2366.1701428571428 1557 | 2017-07-12 00:00:00,2385.7485714285717 1558 | 2017-07-14 00:00:00,2190.947833333333 1559 | 2017-07-16 00:00:00,1931.2143 1560 | 2017-07-18 00:00:00,2320.12225 1561 | 2017-07-20 00:00:00,2898.1884166666664 1562 | 2017-07-22 00:00:00,2807.609857142857 1563 | 2017-07-24 00:00:00,2751.821028571429 1564 | 2017-07-26 00:00:00,2495.028585714286 1565 | 2017-07-28 00:00:00,2781.636583333333 1566 | 2017-07-30 00:00:00,2745.955416666666 1567 | 2017-08-01 00:00:00,2710.4130666666665 1568 | 2017-08-03 00:00:00,2794.117716666666 1569 | 2017-08-05 00:00:00,3218.1150166666666 1570 | 2017-08-07 00:00:00,3407.2268333333336 1571 | 2017-08-09 00:00:00,3357.326316666667 1572 | 2017-08-11 00:00:00,3632.5066666666667 1573 | 2017-08-13 00:00:00,4125.54802 1574 | 2017-08-15 00:00:00,4217.028328571429 1575 | 2017-08-17 00:00:00,4328.725716666667 1576 | 2017-08-19 00:00:00,4222.662214285714 1577 | 2017-08-21 00:00:00,4043.722 1578 | 2017-08-23 00:00:00,4174.95 1579 | 2017-08-25 00:00:00,4363.05445 1580 | 2017-08-27 00:00:00,4354.308333333333 1581 | 2017-08-29 00:00:00,4607.98545 1582 | 2017-08-31 00:00:00,4748.255 1583 | 2017-09-02 00:00:00,4580.387479999999 1584 | 2017-09-04 00:00:00,4344.0983166666665 1585 | 2017-09-06 00:00:00,4641.822016666666 1586 | 2017-09-08 00:00:00,4310.750183333334 1587 | 2017-09-10 00:00:00,4329.955 1588 | 2017-09-12 00:00:00,4219.036616666667 1589 | 2017-09-14 00:00:00,3319.6299999999997 1590 | 2017-09-16 00:00:00,3763.62604 1591 | 2017-09-18 00:00:00,4093.316666666667 1592 | 2017-09-20 00:00:00,3977.5616666666665 1593 | 2017-09-22 00:00:00,3637.5025499999997 1594 | 2017-09-24 00:00:00,3703.0406500000004 1595 | 2017-09-26 00:00:00,3910.3073833333333 1596 | 2017-09-28 00:00:00,4201.98905 1597 | 2017-09-30 00:00:00,4335.368316666667 1598 | 2017-10-02 00:00:00,4386.88375 1599 | 2017-10-04 00:00:00,4225.175 1600 | 2017-10-06 00:00:00,4345.6033333333335 1601 | 2017-10-08 00:00:00,4602.280883333334 1602 | 2017-10-10 00:00:00,4782.28 1603 | 2017-10-12 00:00:00,5325.130683333333 1604 | 2017-10-14 00:00:00,5739.438733333333 1605 | 2017-10-16 00:00:00,5711.205866666667 1606 | 2017-10-18 00:00:00,5546.176100000001 1607 | 2017-10-20 00:00:00,5979.45984 1608 | 2017-10-22 00:00:00,5983.184550000001 1609 | 2017-10-24 00:00:00,5505.827766666666 1610 | 2017-10-26 00:00:00,5893.138416666666 1611 | 2017-10-28 00:00:00,5776.6969500000005 1612 | 2017-10-30 00:00:00,6105.87422 1613 | 2017-11-01 00:00:00,6665.306683333333 1614 | 2017-11-03 00:00:00,7197.72006 1615 | 2017-11-05 00:00:00,7377.012366666667 1616 | 2017-11-07 00:00:00,7092.127233333333 1617 | 2017-11-09 00:00:00,7158.03706 1618 | 2017-11-11 00:00:00,6362.851033333333 1619 | 2017-11-13 00:00:00,6550.227533333334 1620 | 2017-11-15 00:00:00,7301.42992 1621 | 2017-11-17 00:00:00,7786.884366666666 1622 | 2017-11-19 00:00:00,8007.654066666667 1623 | 2017-11-21 00:00:00,8059.8 1624 | 2017-11-23 00:00:00,8148.95 1625 | 2017-11-25 00:00:00,8707.407266666667 1626 | 2017-11-27 00:00:00,9718.29505 1627 | 2017-11-29 00:00:00,9879.328333333333 1628 | 2017-12-01 00:00:00,10883.912 1629 | 2017-12-03 00:00:00,11332.622 1630 | 2017-12-05 00:00:00,11878.433333333334 1631 | 2017-12-07 00:00:00,16501.971666666668 1632 | 2017-12-09 00:00:00,15142.834152123332 1633 | 2017-12-11 00:00:00,16762.116666666665 1634 | 2017-12-13 00:00:00,16808.366666666665 1635 | 2017-12-15 00:00:00,17771.899999999998 1636 | 2017-12-17 00:00:00,19289.785 1637 | 2017-12-19 00:00:00,17737.111666666668 1638 | 2017-12-21 00:00:00,16047.51 1639 | 2017-12-23 00:00:00,15360.261666666667 1640 | 2017-12-25 00:00:00,14119.028333333334 1641 | 2017-12-27 00:00:00,15589.321666666665 1642 | 2017-12-29 00:00:00,14640.14 1643 | 2017-12-31 00:00:00,14165.574999999999 1644 | 2018-01-02 00:00:00,15005.856666666667 1645 | 2018-01-04 00:00:00,15199.355000000001 1646 | 2018-01-06 00:00:00,17319.198 1647 | 2018-01-08 00:00:00,15265.906666666668 1648 | 2018-01-10 00:00:00,15126.398333333333 1649 | 2018-01-12 00:00:00,13912.882000000001 1650 | 2018-01-14 00:00:00,13852.92 1651 | 2018-01-16 00:00:00,11180.998333333331 1652 | 2018-01-18 00:00:00,11345.423333333332 1653 | 2018-01-20 00:00:00,12950.793333333333 1654 | 2018-01-22 00:00:00,10544.593333333332 1655 | 2018-01-24 00:00:00,11282.258333333333 1656 | 2018-01-26 00:00:00,10969.815 1657 | 2018-01-28 00:00:00,11765.71 1658 | 2018-01-30 00:00:00,10184.061666666666 1659 | 2018-02-01 00:00:00,9083.258333333333 1660 | 2018-02-03 00:00:00,9076.678333333333 1661 | 2018-02-05 00:00:00,6838.816666666667 1662 | 2018-02-07 00:00:00,8099.958333333333 1663 | 2018-02-09 00:00:00,8535.516666666668 1664 | 2018-02-11 00:00:00,8343.455 1665 | 2018-02-13 00:00:00,8597.7675 1666 | 2018-02-15 00:00:00,9977.154 1667 | 2018-02-17 00:00:00,10841.991666666667 1668 | 2018-02-19 00:00:00,11110.964999999998 1669 | 2018-02-21 00:00:00,10532.791666666666 1670 | 2018-02-23 00:00:00,10162.116666666667 1671 | 2018-02-25 00:00:00,9696.593333333332 1672 | 2018-02-27 00:00:00,10763.883333333333 1673 | 2018-03-01 00:00:00,11009.381666666668 1674 | 2018-03-03 00:00:00,11326.948333333334 1675 | 2018-03-05 00:00:00,11595.54 1676 | 2018-03-07 00:00:00,10118.058 1677 | 2018-03-09 00:00:00,9089.278333333334 1678 | 2018-03-11 00:00:00,9761.396666666666 1679 | 2018-03-13 00:00:00,9154.699999999999 1680 | 2018-03-15 00:00:00,8358.121666666666 1681 | 2018-03-17 00:00:00,7993.674643641666 1682 | 2018-03-19 00:00:00,8412.033333333333 1683 | 2018-03-21 00:00:00,8947.753333333334 1684 | 2018-03-23 00:00:00,8686.826666666666 1685 | 2018-03-25 00:00:00,8617.296666666667 1686 | 2018-03-27 00:00:00,7876.195 1687 | 2018-03-29 00:00:00,7172.28 1688 | 2018-03-31 00:00:00,6935.48 1689 | --------------------------------------------------------------------------------