├── README.md
├── Time Series operations with Pandas
    ├── 1. Time-Series Basic Operations.ipynb
    ├── 2. Financial Data Analysis with Pandas.ipynb
    ├── 3. Advanced Financial Data Analysis.ipynb
    ├── 4. Datetime Index Methods, Timezones and NA values.ipynb
    └── README.md
└── Trading_Strategies
    ├── Machine_Learning
        └── README.md
    ├── Mean-Reversion
        ├── Mean-Reversion (Bollinger Bands) Backtester class with OOP.ipynb
        ├── Mean-Reversion (Bollinger Bands) Strategy - Design and Backtest.ipynb
        └── README.md
    ├── Momentum
        ├── Momentum (Contrarian) Backtester Class with OOP.ipynb
        ├── Momentum aka Contrarian Strategies - Design and Backtest.ipynb
        └── README.md
    ├── Moving_Average_Crossover
        ├── README.md
        ├── SMA Backtester Class with OOP.ipynb
        └── SMA Strategies - Design and Backtest.ipynb
    └── OOP - Designing a Financial Instrument Class.ipynb


/README.md:
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1 | # Algorithmic_Trading
2 | Python code for fully automated ML and statistical trading strategies
3 | 


--------------------------------------------------------------------------------
/Time Series operations with Pandas/1. Time-Series Basic Operations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "a532fdc9-76db-4e42-941c-7202fbedb180",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# Importing Time Series data from csv files"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "id": "69c808c3-aba0-41ad-897a-31146a16da27",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "import pandas as pd"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 3,
 26 |    "id": "9d9a261a-04f1-4ec6-a27e-dfb5d71eca07",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "temp = pd.read_csv('temp.csv')"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 4,
 36 |    "id": "65778b42-2820-4dfa-a687-1ea634610303",
 37 |    "metadata": {},
 38 |    "outputs": [
 39 |     {
 40 |      "data": {
 41 |       "text/html": [
 42 |        "<div>\n",
 43 |        "<style scoped>\n",
 44 |        "    .dataframe tbody tr th:only-of-type {\n",
 45 |        "        vertical-align: middle;\n",
 46 |        "    }\n",
 47 |        "\n",
 48 |        "    .dataframe tbody tr th {\n",
 49 |        "        vertical-align: top;\n",
 50 |        "    }\n",
 51 |        "\n",
 52 |        "    .dataframe thead th {\n",
 53 |        "        text-align: right;\n",
 54 |        "    }\n",
 55 |        "</style>\n",
 56 |        "<table border=\"1\" class=\"dataframe\">\n",
 57 |        "  <thead>\n",
 58 |        "    <tr style=\"text-align: right;\">\n",
 59 |        "      <th></th>\n",
 60 |        "      <th>datetime</th>\n",
 61 |        "      <th>LA</th>\n",
 62 |        "      <th>NY</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>2013-01-01 00:00:00</td>\n",
 69 |        "      <td>11.7</td>\n",
 70 |        "      <td>-1.1</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>1</th>\n",
 74 |        "      <td>2013-01-01 01:00:00</td>\n",
 75 |        "      <td>10.7</td>\n",
 76 |        "      <td>-1.7</td>\n",
 77 |        "    </tr>\n",
 78 |        "    <tr>\n",
 79 |        "      <th>2</th>\n",
 80 |        "      <td>2013-01-01 02:00:00</td>\n",
 81 |        "      <td>9.9</td>\n",
 82 |        "      <td>-2.0</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>3</th>\n",
 86 |        "      <td>2013-01-01 03:00:00</td>\n",
 87 |        "      <td>9.3</td>\n",
 88 |        "      <td>-2.1</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>4</th>\n",
 92 |        "      <td>2013-01-01 04:00:00</td>\n",
 93 |        "      <td>8.8</td>\n",
 94 |        "      <td>-2.3</td>\n",
 95 |        "    </tr>\n",
 96 |        "  </tbody>\n",
 97 |        "</table>\n",
 98 |        "</div>"
 99 |       ],
100 |       "text/plain": [
101 |        "              datetime    LA   NY\n",
102 |        "0  2013-01-01 00:00:00  11.7 -1.1\n",
103 |        "1  2013-01-01 01:00:00  10.7 -1.7\n",
104 |        "2  2013-01-01 02:00:00   9.9 -2.0\n",
105 |        "3  2013-01-01 03:00:00   9.3 -2.1\n",
106 |        "4  2013-01-01 04:00:00   8.8 -2.3"
107 |       ]
108 |      },
109 |      "execution_count": 4,
110 |      "metadata": {},
111 |      "output_type": "execute_result"
112 |     }
113 |    ],
114 |    "source": [
115 |     "# using head() to get top 5 entires\n",
116 |     "temp.head()"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "code",
121 |    "execution_count": 5,
122 |    "id": "3fba2ca2-f24b-4f62-823b-6f1d9725e6ca",
123 |    "metadata": {},
124 |    "outputs": [],
125 |    "source": [
126 |     "# Parsing datetime into separate columns to make the data more meaningful. Click Shift+Tab for options on any Signature.\n",
127 |     "# read_csv has parse_dates parameter"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": 6,
133 |    "id": "b993b64c-4d7e-471c-afb4-764f9783c6e4",
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "# This step can be done when data is imported from csv. It makes sense to have datetime information as index of dataframe"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 7,
143 |    "id": "c09439bc-a9d6-479e-b395-1ee9411a0531",
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": [
147 |     "temp = pd.read_csv('temp.csv',parse_dates=['datetime'], index_col = 'datetime')"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": 8,
153 |    "id": "ebca83bb-bdcd-46ee-9d34-f75715eab46b",
154 |    "metadata": {},
155 |    "outputs": [
156 |     {
157 |      "data": {
158 |       "text/html": [
159 |        "<div>\n",
160 |        "<style scoped>\n",
161 |        "    .dataframe tbody tr th:only-of-type {\n",
162 |        "        vertical-align: middle;\n",
163 |        "    }\n",
164 |        "\n",
165 |        "    .dataframe tbody tr th {\n",
166 |        "        vertical-align: top;\n",
167 |        "    }\n",
168 |        "\n",
169 |        "    .dataframe thead th {\n",
170 |        "        text-align: right;\n",
171 |        "    }\n",
172 |        "</style>\n",
173 |        "<table border=\"1\" class=\"dataframe\">\n",
174 |        "  <thead>\n",
175 |        "    <tr style=\"text-align: right;\">\n",
176 |        "      <th></th>\n",
177 |        "      <th>LA</th>\n",
178 |        "      <th>NY</th>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>datetime</th>\n",
182 |        "      <th></th>\n",
183 |        "      <th></th>\n",
184 |        "    </tr>\n",
185 |        "  </thead>\n",
186 |        "  <tbody>\n",
187 |        "    <tr>\n",
188 |        "      <th>2013-01-01 00:00:00</th>\n",
189 |        "      <td>11.7</td>\n",
190 |        "      <td>-1.1</td>\n",
191 |        "    </tr>\n",
192 |        "    <tr>\n",
193 |        "      <th>2013-01-01 01:00:00</th>\n",
194 |        "      <td>10.7</td>\n",
195 |        "      <td>-1.7</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>2013-01-01 02:00:00</th>\n",
199 |        "      <td>9.9</td>\n",
200 |        "      <td>-2.0</td>\n",
201 |        "    </tr>\n",
202 |        "    <tr>\n",
203 |        "      <th>2013-01-01 03:00:00</th>\n",
204 |        "      <td>9.3</td>\n",
205 |        "      <td>-2.1</td>\n",
206 |        "    </tr>\n",
207 |        "    <tr>\n",
208 |        "      <th>2013-01-01 04:00:00</th>\n",
209 |        "      <td>8.8</td>\n",
210 |        "      <td>-2.3</td>\n",
211 |        "    </tr>\n",
212 |        "  </tbody>\n",
213 |        "</table>\n",
214 |        "</div>"
215 |       ],
216 |       "text/plain": [
217 |        "                       LA   NY\n",
218 |        "datetime                      \n",
219 |        "2013-01-01 00:00:00  11.7 -1.1\n",
220 |        "2013-01-01 01:00:00  10.7 -1.7\n",
221 |        "2013-01-01 02:00:00   9.9 -2.0\n",
222 |        "2013-01-01 03:00:00   9.3 -2.1\n",
223 |        "2013-01-01 04:00:00   8.8 -2.3"
224 |       ]
225 |      },
226 |      "execution_count": 8,
227 |      "metadata": {},
228 |      "output_type": "execute_result"
229 |     }
230 |    ],
231 |    "source": [
232 |     "temp.head()"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "code",
237 |    "execution_count": 9,
238 |    "id": "55bdacbc-c5d8-45af-99b4-195f05fa0c5e",
239 |    "metadata": {},
240 |    "outputs": [
241 |     {
242 |      "name": "stdout",
243 |      "output_type": "stream",
244 |      "text": [
245 |       "<class 'pandas.core.frame.DataFrame'>\n",
246 |       "DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
247 |       "Data columns (total 2 columns):\n",
248 |       " #   Column  Non-Null Count  Dtype  \n",
249 |       "---  ------  --------------  -----  \n",
250 |       " 0   LA      35062 non-null  float64\n",
251 |       " 1   NY      35064 non-null  float64\n",
252 |       "dtypes: float64(2)\n",
253 |       "memory usage: 821.8 KB\n"
254 |      ]
255 |     }
256 |    ],
257 |    "source": [
258 |     "# Using info to get information about the dataframe\n",
259 |     "temp.info()"
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "code",
264 |    "execution_count": 10,
265 |    "id": "a66a8100-47cd-4f21-8e44-838b5bbc33a7",
266 |    "metadata": {},
267 |    "outputs": [],
268 |    "source": [
269 |     "# as can be seen datetime datatype changed from object to datetime64"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 11,
275 |    "id": "af5fc299-50a3-405b-9a0b-5b13cea02447",
276 |    "metadata": {},
277 |    "outputs": [
278 |     {
279 |      "data": {
280 |       "text/plain": [
281 |        "11.7"
282 |       ]
283 |      },
284 |      "execution_count": 11,
285 |      "metadata": {},
286 |      "output_type": "execute_result"
287 |     }
288 |    ],
289 |    "source": [
290 |     "# finding a particular object at a location\n",
291 |     "temp.iloc[0,0]"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": 12,
297 |    "id": "3e5516e1-fc00-424a-84f6-2fb8bb003a24",
298 |    "metadata": {},
299 |    "outputs": [
300 |     {
301 |      "data": {
302 |       "text/plain": [
303 |        "numpy.float64"
304 |       ]
305 |      },
306 |      "execution_count": 12,
307 |      "metadata": {},
308 |      "output_type": "execute_result"
309 |     }
310 |    ],
311 |    "source": [
312 |     "# Checking datatype \n",
313 |     "type(temp.iloc[0,0])"
314 |    ]
315 |   },
316 |   {
317 |    "cell_type": "markdown",
318 |    "id": "57a7a975-7e15-4885-8427-61528a8b2ee0",
319 |    "metadata": {},
320 |    "source": [
321 |     "# Converting strings to datetime objects with pd.to_datetime"
322 |    ]
323 |   },
324 |   {
325 |    "cell_type": "code",
326 |    "execution_count": 13,
327 |    "id": "35321537-1e77-424c-a7a3-0e6771d93bb8",
328 |    "metadata": {},
329 |    "outputs": [],
330 |    "source": [
331 |     "import pandas as pd"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 14,
337 |    "id": "d6ecbbcf-243d-40cb-928a-951d13dac61d",
338 |    "metadata": {},
339 |    "outputs": [],
340 |    "source": [
341 |     "temp = pd.read_csv('temp.csv')"
342 |    ]
343 |   },
344 |   {
345 |    "cell_type": "code",
346 |    "execution_count": 15,
347 |    "id": "bac93204-2b23-48d6-9772-f836ccbfa791",
348 |    "metadata": {},
349 |    "outputs": [
350 |     {
351 |      "data": {
352 |       "text/html": [
353 |        "<div>\n",
354 |        "<style scoped>\n",
355 |        "    .dataframe tbody tr th:only-of-type {\n",
356 |        "        vertical-align: middle;\n",
357 |        "    }\n",
358 |        "\n",
359 |        "    .dataframe tbody tr th {\n",
360 |        "        vertical-align: top;\n",
361 |        "    }\n",
362 |        "\n",
363 |        "    .dataframe thead th {\n",
364 |        "        text-align: right;\n",
365 |        "    }\n",
366 |        "</style>\n",
367 |        "<table border=\"1\" class=\"dataframe\">\n",
368 |        "  <thead>\n",
369 |        "    <tr style=\"text-align: right;\">\n",
370 |        "      <th></th>\n",
371 |        "      <th>datetime</th>\n",
372 |        "      <th>LA</th>\n",
373 |        "      <th>NY</th>\n",
374 |        "    </tr>\n",
375 |        "  </thead>\n",
376 |        "  <tbody>\n",
377 |        "    <tr>\n",
378 |        "      <th>0</th>\n",
379 |        "      <td>2013-01-01 00:00:00</td>\n",
380 |        "      <td>11.7</td>\n",
381 |        "      <td>-1.1</td>\n",
382 |        "    </tr>\n",
383 |        "    <tr>\n",
384 |        "      <th>1</th>\n",
385 |        "      <td>2013-01-01 01:00:00</td>\n",
386 |        "      <td>10.7</td>\n",
387 |        "      <td>-1.7</td>\n",
388 |        "    </tr>\n",
389 |        "    <tr>\n",
390 |        "      <th>2</th>\n",
391 |        "      <td>2013-01-01 02:00:00</td>\n",
392 |        "      <td>9.9</td>\n",
393 |        "      <td>-2.0</td>\n",
394 |        "    </tr>\n",
395 |        "    <tr>\n",
396 |        "      <th>3</th>\n",
397 |        "      <td>2013-01-01 03:00:00</td>\n",
398 |        "      <td>9.3</td>\n",
399 |        "      <td>-2.1</td>\n",
400 |        "    </tr>\n",
401 |        "    <tr>\n",
402 |        "      <th>4</th>\n",
403 |        "      <td>2013-01-01 04:00:00</td>\n",
404 |        "      <td>8.8</td>\n",
405 |        "      <td>-2.3</td>\n",
406 |        "    </tr>\n",
407 |        "  </tbody>\n",
408 |        "</table>\n",
409 |        "</div>"
410 |       ],
411 |       "text/plain": [
412 |        "              datetime    LA   NY\n",
413 |        "0  2013-01-01 00:00:00  11.7 -1.1\n",
414 |        "1  2013-01-01 01:00:00  10.7 -1.7\n",
415 |        "2  2013-01-01 02:00:00   9.9 -2.0\n",
416 |        "3  2013-01-01 03:00:00   9.3 -2.1\n",
417 |        "4  2013-01-01 04:00:00   8.8 -2.3"
418 |       ]
419 |      },
420 |      "execution_count": 15,
421 |      "metadata": {},
422 |      "output_type": "execute_result"
423 |     }
424 |    ],
425 |    "source": [
426 |     "temp.head()"
427 |    ]
428 |   },
429 |   {
430 |    "cell_type": "code",
431 |    "execution_count": 16,
432 |    "id": "f54e45f0-581b-4b2a-8fd6-d7cca23af31a",
433 |    "metadata": {},
434 |    "outputs": [
435 |     {
436 |      "name": "stdout",
437 |      "output_type": "stream",
438 |      "text": [
439 |       "<class 'pandas.core.frame.DataFrame'>\n",
440 |       "RangeIndex: 35064 entries, 0 to 35063\n",
441 |       "Data columns (total 3 columns):\n",
442 |       " #   Column    Non-Null Count  Dtype  \n",
443 |       "---  ------    --------------  -----  \n",
444 |       " 0   datetime  35064 non-null  object \n",
445 |       " 1   LA        35062 non-null  float64\n",
446 |       " 2   NY        35064 non-null  float64\n",
447 |       "dtypes: float64(2), object(1)\n",
448 |       "memory usage: 821.9+ KB\n"
449 |      ]
450 |     }
451 |    ],
452 |    "source": [
453 |     "temp.info()"
454 |    ]
455 |   },
456 |   {
457 |    "cell_type": "code",
458 |    "execution_count": 17,
459 |    "id": "88bdfdd0-4c78-4d50-b87d-d05f4b5efbee",
460 |    "metadata": {},
461 |    "outputs": [
462 |     {
463 |      "data": {
464 |       "text/plain": [
465 |        "0        2013-01-01 00:00:00\n",
466 |        "1        2013-01-01 01:00:00\n",
467 |        "2        2013-01-01 02:00:00\n",
468 |        "3        2013-01-01 03:00:00\n",
469 |        "4        2013-01-01 04:00:00\n",
470 |        "                ...         \n",
471 |        "35059    2016-12-31 19:00:00\n",
472 |        "35060    2016-12-31 20:00:00\n",
473 |        "35061    2016-12-31 21:00:00\n",
474 |        "35062    2016-12-31 22:00:00\n",
475 |        "35063    2016-12-31 23:00:00\n",
476 |        "Name: datetime, Length: 35064, dtype: object"
477 |       ]
478 |      },
479 |      "execution_count": 17,
480 |      "metadata": {},
481 |      "output_type": "execute_result"
482 |     }
483 |    ],
484 |    "source": [
485 |     "# Selecting one column from dataframe\n",
486 |     "temp.datetime"
487 |    ]
488 |   },
489 |   {
490 |    "cell_type": "code",
491 |    "execution_count": 18,
492 |    "id": "934d0c54-f354-4f1a-bb85-d408b8f1e436",
493 |    "metadata": {},
494 |    "outputs": [
495 |     {
496 |      "data": {
497 |       "text/plain": [
498 |        "'2013-01-01 00:00:00'"
499 |       ]
500 |      },
501 |      "execution_count": 18,
502 |      "metadata": {},
503 |      "output_type": "execute_result"
504 |     }
505 |    ],
506 |    "source": [
507 |     "temp.datetime[0]"
508 |    ]
509 |   },
510 |   {
511 |    "cell_type": "code",
512 |    "execution_count": 19,
513 |    "id": "5c1ad261-a3f9-4acc-9f5d-d44dbc74f3d4",
514 |    "metadata": {},
515 |    "outputs": [
516 |     {
517 |      "data": {
518 |       "text/plain": [
519 |        "0       2013-01-01 00:00:00\n",
520 |        "1       2013-01-01 01:00:00\n",
521 |        "2       2013-01-01 02:00:00\n",
522 |        "3       2013-01-01 03:00:00\n",
523 |        "4       2013-01-01 04:00:00\n",
524 |        "                ...        \n",
525 |        "35059   2016-12-31 19:00:00\n",
526 |        "35060   2016-12-31 20:00:00\n",
527 |        "35061   2016-12-31 21:00:00\n",
528 |        "35062   2016-12-31 22:00:00\n",
529 |        "35063   2016-12-31 23:00:00\n",
530 |        "Name: datetime, Length: 35064, dtype: datetime64[ns]"
531 |       ]
532 |      },
533 |      "execution_count": 19,
534 |      "metadata": {},
535 |      "output_type": "execute_result"
536 |     }
537 |    ],
538 |    "source": [
539 |     "# using pd.to_datetime to transform datetime column from strings to datetime object\n",
540 |     "pd.to_datetime(temp.datetime)"
541 |    ]
542 |   },
543 |   {
544 |    "cell_type": "code",
545 |    "execution_count": 20,
546 |    "id": "391826fc-57fc-4a4b-8795-01d943069125",
547 |    "metadata": {},
548 |    "outputs": [],
549 |    "source": [
550 |     "# Setting datetime column as index using set_index. We need to first apply the pd.to_datetime \n",
551 |     "# and pass it through df.set_index. Also drop the datetime column\n",
552 |     "# In this same step can overwrite our temp dataframe using temp =\n",
553 |     "temp = temp.set_index(pd.to_datetime(temp.datetime)).drop('datetime', axis = 1)"
554 |    ]
555 |   },
556 |   {
557 |    "cell_type": "code",
558 |    "execution_count": 21,
559 |    "id": "40ce2924-b58f-4c8f-a00c-830b6671cc89",
560 |    "metadata": {},
561 |    "outputs": [
562 |     {
563 |      "data": {
564 |       "text/html": [
565 |        "<div>\n",
566 |        "<style scoped>\n",
567 |        "    .dataframe tbody tr th:only-of-type {\n",
568 |        "        vertical-align: middle;\n",
569 |        "    }\n",
570 |        "\n",
571 |        "    .dataframe tbody tr th {\n",
572 |        "        vertical-align: top;\n",
573 |        "    }\n",
574 |        "\n",
575 |        "    .dataframe thead th {\n",
576 |        "        text-align: right;\n",
577 |        "    }\n",
578 |        "</style>\n",
579 |        "<table border=\"1\" class=\"dataframe\">\n",
580 |        "  <thead>\n",
581 |        "    <tr style=\"text-align: right;\">\n",
582 |        "      <th></th>\n",
583 |        "      <th>LA</th>\n",
584 |        "      <th>NY</th>\n",
585 |        "    </tr>\n",
586 |        "    <tr>\n",
587 |        "      <th>datetime</th>\n",
588 |        "      <th></th>\n",
589 |        "      <th></th>\n",
590 |        "    </tr>\n",
591 |        "  </thead>\n",
592 |        "  <tbody>\n",
593 |        "    <tr>\n",
594 |        "      <th>2013-01-01 00:00:00</th>\n",
595 |        "      <td>11.7</td>\n",
596 |        "      <td>-1.1</td>\n",
597 |        "    </tr>\n",
598 |        "    <tr>\n",
599 |        "      <th>2013-01-01 01:00:00</th>\n",
600 |        "      <td>10.7</td>\n",
601 |        "      <td>-1.7</td>\n",
602 |        "    </tr>\n",
603 |        "    <tr>\n",
604 |        "      <th>2013-01-01 02:00:00</th>\n",
605 |        "      <td>9.9</td>\n",
606 |        "      <td>-2.0</td>\n",
607 |        "    </tr>\n",
608 |        "    <tr>\n",
609 |        "      <th>2013-01-01 03:00:00</th>\n",
610 |        "      <td>9.3</td>\n",
611 |        "      <td>-2.1</td>\n",
612 |        "    </tr>\n",
613 |        "    <tr>\n",
614 |        "      <th>2013-01-01 04:00:00</th>\n",
615 |        "      <td>8.8</td>\n",
616 |        "      <td>-2.3</td>\n",
617 |        "    </tr>\n",
618 |        "  </tbody>\n",
619 |        "</table>\n",
620 |        "</div>"
621 |       ],
622 |       "text/plain": [
623 |        "                       LA   NY\n",
624 |        "datetime                      \n",
625 |        "2013-01-01 00:00:00  11.7 -1.1\n",
626 |        "2013-01-01 01:00:00  10.7 -1.7\n",
627 |        "2013-01-01 02:00:00   9.9 -2.0\n",
628 |        "2013-01-01 03:00:00   9.3 -2.1\n",
629 |        "2013-01-01 04:00:00   8.8 -2.3"
630 |       ]
631 |      },
632 |      "execution_count": 21,
633 |      "metadata": {},
634 |      "output_type": "execute_result"
635 |     }
636 |    ],
637 |    "source": [
638 |     "temp.head()"
639 |    ]
640 |   },
641 |   {
642 |    "cell_type": "code",
643 |    "execution_count": 22,
644 |    "id": "0fb36332-a107-43ae-ad4b-fc43f1d4fbac",
645 |    "metadata": {},
646 |    "outputs": [
647 |     {
648 |      "name": "stdout",
649 |      "output_type": "stream",
650 |      "text": [
651 |       "<class 'pandas.core.frame.DataFrame'>\n",
652 |       "DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
653 |       "Data columns (total 2 columns):\n",
654 |       " #   Column  Non-Null Count  Dtype  \n",
655 |       "---  ------  --------------  -----  \n",
656 |       " 0   LA      35062 non-null  float64\n",
657 |       " 1   NY      35064 non-null  float64\n",
658 |       "dtypes: float64(2)\n",
659 |       "memory usage: 821.8 KB\n"
660 |      ]
661 |     }
662 |    ],
663 |    "source": [
664 |     "temp.info()"
665 |    ]
666 |   },
667 |   {
668 |    "cell_type": "code",
669 |    "execution_count": 23,
670 |    "id": "90ab4711-e01b-4571-95d4-1de56603a2da",
671 |    "metadata": {},
672 |    "outputs": [
673 |     {
674 |      "data": {
675 |       "text/plain": [
676 |        "DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 01:00:00',\n",
677 |        "               '2013-01-01 02:00:00', '2013-01-01 03:00:00',\n",
678 |        "               '2013-01-01 04:00:00', '2013-01-01 05:00:00',\n",
679 |        "               '2013-01-01 06:00:00', '2013-01-01 07:00:00',\n",
680 |        "               '2013-01-01 08:00:00', '2013-01-01 09:00:00',\n",
681 |        "               ...\n",
682 |        "               '2016-12-31 14:00:00', '2016-12-31 15:00:00',\n",
683 |        "               '2016-12-31 16:00:00', '2016-12-31 17:00:00',\n",
684 |        "               '2016-12-31 18:00:00', '2016-12-31 19:00:00',\n",
685 |        "               '2016-12-31 20:00:00', '2016-12-31 21:00:00',\n",
686 |        "               '2016-12-31 22:00:00', '2016-12-31 23:00:00'],\n",
687 |        "              dtype='datetime64[ns]', name='datetime', length=35064, freq=None)"
688 |       ]
689 |      },
690 |      "execution_count": 23,
691 |      "metadata": {},
692 |      "output_type": "execute_result"
693 |     }
694 |    ],
695 |    "source": [
696 |     "temp.index # now we will get datetime as index"
697 |    ]
698 |   },
699 |   {
700 |    "cell_type": "code",
701 |    "execution_count": 24,
702 |    "id": "cd54c9d3-73f0-47e9-98d5-a697d0df2b7d",
703 |    "metadata": {},
704 |    "outputs": [
705 |     {
706 |      "data": {
707 |       "text/plain": [
708 |        "Timestamp('2013-01-01 00:00:00')"
709 |       ]
710 |      },
711 |      "execution_count": 24,
712 |      "metadata": {},
713 |      "output_type": "execute_result"
714 |     }
715 |    ],
716 |    "source": [
717 |     "# we can check for a single element as well\n",
718 |     "temp.index[0]"
719 |    ]
720 |   },
721 |   {
722 |    "cell_type": "code",
723 |    "execution_count": 25,
724 |    "id": "a7290e6e-ce1b-4e86-89ca-8c86481638bf",
725 |    "metadata": {},
726 |    "outputs": [],
727 |    "source": [
728 |     "# Pandas is very versatile in handling datetime objects. It can handle yyyy-mm-dd, yyyymdd, yyyy/mm/dd, yyyy mm dd, \n",
729 |     "# yyyy-dd-mm, yyyy dd mm, 2015 May 20, May 2015 20, May 20 2015 formats\n",
730 |     "# uppercase or lowercase letters do not make a difference\n",
731 |     "# by default pandas assumes time to be midnight. time formats can be hh, hh:mm, hh:mm:ss"
732 |    ]
733 |   },
734 |   {
735 |    "cell_type": "code",
736 |    "execution_count": 26,
737 |    "id": "cfef6073-f0f7-4448-b46f-6675cb8b6642",
738 |    "metadata": {},
739 |    "outputs": [
740 |     {
741 |      "data": {
742 |       "text/plain": [
743 |        "Timestamp('2015-06-25 10:30:00')"
744 |       ]
745 |      },
746 |      "execution_count": 26,
747 |      "metadata": {},
748 |      "output_type": "execute_result"
749 |     }
750 |    ],
751 |    "source": [
752 |     "pd.to_datetime('2015-06-25 10:30')"
753 |    ]
754 |   },
755 |   {
756 |    "cell_type": "code",
757 |    "execution_count": 27,
758 |    "id": "093847b7-8446-47a1-b29b-7d6bb029af2e",
759 |    "metadata": {},
760 |    "outputs": [
761 |     {
762 |      "data": {
763 |       "text/plain": [
764 |        "DatetimeIndex(['2015-05-30', '2016-02-16'], dtype='datetime64[ns]', freq=None)"
765 |       ]
766 |      },
767 |      "execution_count": 27,
768 |      "metadata": {},
769 |      "output_type": "execute_result"
770 |     }
771 |    ],
772 |    "source": [
773 |     "pd.to_datetime(['may 30 2015', '02-16-2016'])"
774 |    ]
775 |   },
776 |   {
777 |    "cell_type": "code",
778 |    "execution_count": 28,
779 |    "id": "3d818ef0-4af9-4547-a301-5e1757c3920b",
780 |    "metadata": {},
781 |    "outputs": [
782 |     {
783 |      "data": {
784 |       "text/plain": [
785 |        "DatetimeIndex(['2015-05-30', '2016-02-16', 'NaT'], dtype='datetime64[ns]', freq=None)"
786 |       ]
787 |      },
788 |      "execution_count": 28,
789 |      "metadata": {},
790 |      "output_type": "execute_result"
791 |     }
792 |    ],
793 |    "source": [
794 |     "pd.to_datetime(['may 30 2015', '02-16-2016', 'Apple'], errors = 'coerce')"
795 |    ]
796 |   },
797 |   {
798 |    "cell_type": "code",
799 |    "execution_count": 29,
800 |    "id": "cdcefe8b-7c3d-445d-93e0-56adb7a5c497",
801 |    "metadata": {},
802 |    "outputs": [],
803 |    "source": [
804 |     "# We see that apple was changed to NaT which means Not a Time"
805 |    ]
806 |   }
807 |  ],
808 |  "metadata": {
809 |   "kernelspec": {
810 |    "display_name": "Python 3 (ipykernel)",
811 |    "language": "python",
812 |    "name": "python3"
813 |   },
814 |   "language_info": {
815 |    "codemirror_mode": {
816 |     "name": "ipython",
817 |     "version": 3
818 |    },
819 |    "file_extension": ".py",
820 |    "mimetype": "text/x-python",
821 |    "name": "python",
822 |    "nbconvert_exporter": "python",
823 |    "pygments_lexer": "ipython3",
824 |    "version": "3.8.5"
825 |   }
826 |  },
827 |  "nbformat": 4,
828 |  "nbformat_minor": 5
829 | }
830 | 


--------------------------------------------------------------------------------
/Time Series operations with Pandas/4. Datetime Index Methods, Timezones and NA values.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "9e102804",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "# Datetime Index attributes and Methods\n",
   9 |     "Helps in creating customised features for dates and times"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 1,
  15 |    "id": "083408cf",
  16 |    "metadata": {},
  17 |    "outputs": [],
  18 |    "source": [
  19 |     "import pandas as pd"
  20 |    ]
  21 |   },
  22 |   {
  23 |    "cell_type": "code",
  24 |    "execution_count": 2,
  25 |    "id": "a5074038",
  26 |    "metadata": {},
  27 |    "outputs": [],
  28 |    "source": [
  29 |     "# Importing data from csv files\n",
  30 |     "stocks = pd.read_csv('stocks.csv', header = [0,1], index_col = [0], parse_dates = [0])"
  31 |    ]
  32 |   },
  33 |   {
  34 |    "cell_type": "code",
  35 |    "execution_count": 3,
  36 |    "id": "406f8048",
  37 |    "metadata": {},
  38 |    "outputs": [
  39 |     {
  40 |      "data": {
  41 |       "text/html": [
  42 |        "<div>\n",
  43 |        "<style scoped>\n",
  44 |        "    .dataframe tbody tr th:only-of-type {\n",
  45 |        "        vertical-align: middle;\n",
  46 |        "    }\n",
  47 |        "\n",
  48 |        "    .dataframe tbody tr th {\n",
  49 |        "        vertical-align: top;\n",
  50 |        "    }\n",
  51 |        "\n",
  52 |        "    .dataframe thead tr th {\n",
  53 |        "        text-align: left;\n",
  54 |        "    }\n",
  55 |        "\n",
  56 |        "    .dataframe thead tr:last-of-type th {\n",
  57 |        "        text-align: right;\n",
  58 |        "    }\n",
  59 |        "</style>\n",
  60 |        "<table border=\"1\" class=\"dataframe\">\n",
  61 |        "  <thead>\n",
  62 |        "    <tr>\n",
  63 |        "      <th></th>\n",
  64 |        "      <th colspan=\"6\" halign=\"left\">Adj Close</th>\n",
  65 |        "      <th colspan=\"4\" halign=\"left\">Close</th>\n",
  66 |        "      <th>...</th>\n",
  67 |        "      <th colspan=\"4\" halign=\"left\">Open</th>\n",
  68 |        "      <th colspan=\"6\" halign=\"left\">Volume</th>\n",
  69 |        "    </tr>\n",
  70 |        "    <tr>\n",
  71 |        "      <th></th>\n",
  72 |        "      <th>AAPL</th>\n",
  73 |        "      <th>BA</th>\n",
  74 |        "      <th>DIS</th>\n",
  75 |        "      <th>IBM</th>\n",
  76 |        "      <th>KO</th>\n",
  77 |        "      <th>MSFT</th>\n",
  78 |        "      <th>AAPL</th>\n",
  79 |        "      <th>BA</th>\n",
  80 |        "      <th>DIS</th>\n",
  81 |        "      <th>IBM</th>\n",
  82 |        "      <th>...</th>\n",
  83 |        "      <th>DIS</th>\n",
  84 |        "      <th>IBM</th>\n",
  85 |        "      <th>KO</th>\n",
  86 |        "      <th>MSFT</th>\n",
  87 |        "      <th>AAPL</th>\n",
  88 |        "      <th>BA</th>\n",
  89 |        "      <th>DIS</th>\n",
  90 |        "      <th>IBM</th>\n",
  91 |        "      <th>KO</th>\n",
  92 |        "      <th>MSFT</th>\n",
  93 |        "    </tr>\n",
  94 |        "    <tr>\n",
  95 |        "      <th>Date</th>\n",
  96 |        "      <th></th>\n",
  97 |        "      <th></th>\n",
  98 |        "      <th></th>\n",
  99 |        "      <th></th>\n",
 100 |        "      <th></th>\n",
 101 |        "      <th></th>\n",
 102 |        "      <th></th>\n",
 103 |        "      <th></th>\n",
 104 |        "      <th></th>\n",
 105 |        "      <th></th>\n",
 106 |        "      <th></th>\n",
 107 |        "      <th></th>\n",
 108 |        "      <th></th>\n",
 109 |        "      <th></th>\n",
 110 |        "      <th></th>\n",
 111 |        "      <th></th>\n",
 112 |        "      <th></th>\n",
 113 |        "      <th></th>\n",
 114 |        "      <th></th>\n",
 115 |        "      <th></th>\n",
 116 |        "      <th></th>\n",
 117 |        "    </tr>\n",
 118 |        "  </thead>\n",
 119 |        "  <tbody>\n",
 120 |        "    <tr>\n",
 121 |        "      <th>2009-12-31</th>\n",
 122 |        "      <td>6.503574</td>\n",
 123 |        "      <td>41.856789</td>\n",
 124 |        "      <td>28.090706</td>\n",
 125 |        "      <td>92.406113</td>\n",
 126 |        "      <td>18.951757</td>\n",
 127 |        "      <td>23.801456</td>\n",
 128 |        "      <td>7.526072</td>\n",
 129 |        "      <td>54.130001</td>\n",
 130 |        "      <td>32.25</td>\n",
 131 |        "      <td>130.899994</td>\n",
 132 |        "      <td>...</td>\n",
 133 |        "      <td>32.27</td>\n",
 134 |        "      <td>132.410004</td>\n",
 135 |        "      <td>28.790001</td>\n",
 136 |        "      <td>30.980000</td>\n",
 137 |        "      <td>352410800</td>\n",
 138 |        "      <td>2189400</td>\n",
 139 |        "      <td>19651700</td>\n",
 140 |        "      <td>4223400</td>\n",
 141 |        "      <td>10848800</td>\n",
 142 |        "      <td>31929700</td>\n",
 143 |        "    </tr>\n",
 144 |        "    <tr>\n",
 145 |        "      <th>2010-01-04</th>\n",
 146 |        "      <td>6.604801</td>\n",
 147 |        "      <td>43.441975</td>\n",
 148 |        "      <td>27.933924</td>\n",
 149 |        "      <td>93.500313</td>\n",
 150 |        "      <td>18.965061</td>\n",
 151 |        "      <td>24.168472</td>\n",
 152 |        "      <td>7.643214</td>\n",
 153 |        "      <td>56.180000</td>\n",
 154 |        "      <td>32.07</td>\n",
 155 |        "      <td>132.449997</td>\n",
 156 |        "      <td>...</td>\n",
 157 |        "      <td>32.50</td>\n",
 158 |        "      <td>131.179993</td>\n",
 159 |        "      <td>28.580000</td>\n",
 160 |        "      <td>30.620001</td>\n",
 161 |        "      <td>493729600</td>\n",
 162 |        "      <td>6186700</td>\n",
 163 |        "      <td>13700400</td>\n",
 164 |        "      <td>6155300</td>\n",
 165 |        "      <td>13870400</td>\n",
 166 |        "      <td>38409100</td>\n",
 167 |        "    </tr>\n",
 168 |        "    <tr>\n",
 169 |        "      <th>2010-01-05</th>\n",
 170 |        "      <td>6.616219</td>\n",
 171 |        "      <td>44.864773</td>\n",
 172 |        "      <td>27.864237</td>\n",
 173 |        "      <td>92.370834</td>\n",
 174 |        "      <td>18.735643</td>\n",
 175 |        "      <td>24.176279</td>\n",
 176 |        "      <td>7.656428</td>\n",
 177 |        "      <td>58.020000</td>\n",
 178 |        "      <td>31.99</td>\n",
 179 |        "      <td>130.850006</td>\n",
 180 |        "      <td>...</td>\n",
 181 |        "      <td>32.07</td>\n",
 182 |        "      <td>131.679993</td>\n",
 183 |        "      <td>28.424999</td>\n",
 184 |        "      <td>30.850000</td>\n",
 185 |        "      <td>601904800</td>\n",
 186 |        "      <td>8867800</td>\n",
 187 |        "      <td>10307700</td>\n",
 188 |        "      <td>6841400</td>\n",
 189 |        "      <td>23172400</td>\n",
 190 |        "      <td>49749600</td>\n",
 191 |        "    </tr>\n",
 192 |        "    <tr>\n",
 193 |        "      <th>2010-01-06</th>\n",
 194 |        "      <td>6.510980</td>\n",
 195 |        "      <td>46.225727</td>\n",
 196 |        "      <td>27.716166</td>\n",
 197 |        "      <td>91.770790</td>\n",
 198 |        "      <td>18.728989</td>\n",
 199 |        "      <td>24.027906</td>\n",
 200 |        "      <td>7.534643</td>\n",
 201 |        "      <td>59.779999</td>\n",
 202 |        "      <td>31.82</td>\n",
 203 |        "      <td>130.000000</td>\n",
 204 |        "      <td>...</td>\n",
 205 |        "      <td>31.90</td>\n",
 206 |        "      <td>130.679993</td>\n",
 207 |        "      <td>28.174999</td>\n",
 208 |        "      <td>30.879999</td>\n",
 209 |        "      <td>552160000</td>\n",
 210 |        "      <td>8836500</td>\n",
 211 |        "      <td>10709500</td>\n",
 212 |        "      <td>5605300</td>\n",
 213 |        "      <td>19264600</td>\n",
 214 |        "      <td>58182400</td>\n",
 215 |        "    </tr>\n",
 216 |        "    <tr>\n",
 217 |        "      <th>2010-01-07</th>\n",
 218 |        "      <td>6.498945</td>\n",
 219 |        "      <td>48.097031</td>\n",
 220 |        "      <td>27.724878</td>\n",
 221 |        "      <td>91.453102</td>\n",
 222 |        "      <td>18.682440</td>\n",
 223 |        "      <td>23.778025</td>\n",
 224 |        "      <td>7.520714</td>\n",
 225 |        "      <td>62.200001</td>\n",
 226 |        "      <td>31.83</td>\n",
 227 |        "      <td>129.550003</td>\n",
 228 |        "      <td>...</td>\n",
 229 |        "      <td>31.77</td>\n",
 230 |        "      <td>129.869995</td>\n",
 231 |        "      <td>28.165001</td>\n",
 232 |        "      <td>30.629999</td>\n",
 233 |        "      <td>477131200</td>\n",
 234 |        "      <td>14379100</td>\n",
 235 |        "      <td>8202100</td>\n",
 236 |        "      <td>5840600</td>\n",
 237 |        "      <td>13234600</td>\n",
 238 |        "      <td>50559700</td>\n",
 239 |        "    </tr>\n",
 240 |        "  </tbody>\n",
 241 |        "</table>\n",
 242 |        "<p>5 rows × 36 columns</p>\n",
 243 |        "</div>"
 244 |       ],
 245 |       "text/plain": [
 246 |        "           Adj Close                                                         \\\n",
 247 |        "                AAPL         BA        DIS        IBM         KO       MSFT   \n",
 248 |        "Date                                                                          \n",
 249 |        "2009-12-31  6.503574  41.856789  28.090706  92.406113  18.951757  23.801456   \n",
 250 |        "2010-01-04  6.604801  43.441975  27.933924  93.500313  18.965061  24.168472   \n",
 251 |        "2010-01-05  6.616219  44.864773  27.864237  92.370834  18.735643  24.176279   \n",
 252 |        "2010-01-06  6.510980  46.225727  27.716166  91.770790  18.728989  24.027906   \n",
 253 |        "2010-01-07  6.498945  48.097031  27.724878  91.453102  18.682440  23.778025   \n",
 254 |        "\n",
 255 |        "               Close                                ...   Open              \\\n",
 256 |        "                AAPL         BA    DIS         IBM  ...    DIS         IBM   \n",
 257 |        "Date                                                ...                      \n",
 258 |        "2009-12-31  7.526072  54.130001  32.25  130.899994  ...  32.27  132.410004   \n",
 259 |        "2010-01-04  7.643214  56.180000  32.07  132.449997  ...  32.50  131.179993   \n",
 260 |        "2010-01-05  7.656428  58.020000  31.99  130.850006  ...  32.07  131.679993   \n",
 261 |        "2010-01-06  7.534643  59.779999  31.82  130.000000  ...  31.90  130.679993   \n",
 262 |        "2010-01-07  7.520714  62.200001  31.83  129.550003  ...  31.77  129.869995   \n",
 263 |        "\n",
 264 |        "                                     Volume                               \\\n",
 265 |        "                   KO       MSFT       AAPL        BA       DIS      IBM   \n",
 266 |        "Date                                                                       \n",
 267 |        "2009-12-31  28.790001  30.980000  352410800   2189400  19651700  4223400   \n",
 268 |        "2010-01-04  28.580000  30.620001  493729600   6186700  13700400  6155300   \n",
 269 |        "2010-01-05  28.424999  30.850000  601904800   8867800  10307700  6841400   \n",
 270 |        "2010-01-06  28.174999  30.879999  552160000   8836500  10709500  5605300   \n",
 271 |        "2010-01-07  28.165001  30.629999  477131200  14379100   8202100  5840600   \n",
 272 |        "\n",
 273 |        "                                \n",
 274 |        "                  KO      MSFT  \n",
 275 |        "Date                            \n",
 276 |        "2009-12-31  10848800  31929700  \n",
 277 |        "2010-01-04  13870400  38409100  \n",
 278 |        "2010-01-05  23172400  49749600  \n",
 279 |        "2010-01-06  19264600  58182400  \n",
 280 |        "2010-01-07  13234600  50559700  \n",
 281 |        "\n",
 282 |        "[5 rows x 36 columns]"
 283 |       ]
 284 |      },
 285 |      "execution_count": 3,
 286 |      "metadata": {},
 287 |      "output_type": "execute_result"
 288 |     }
 289 |    ],
 290 |    "source": [
 291 |     "stocks.head()"
 292 |    ]
 293 |   },
 294 |   {
 295 |    "cell_type": "code",
 296 |    "execution_count": 4,
 297 |    "id": "7c28e981",
 298 |    "metadata": {},
 299 |    "outputs": [],
 300 |    "source": [
 301 |     "# Creating a new dataframe containing only closing prices by copying the close columns\n",
 302 |     "close = stocks.loc[:, 'Close'].copy()"
 303 |    ]
 304 |   },
 305 |   {
 306 |    "cell_type": "code",
 307 |    "execution_count": 5,
 308 |    "id": "032279cb",
 309 |    "metadata": {},
 310 |    "outputs": [
 311 |     {
 312 |      "data": {
 313 |       "text/html": [
 314 |        "<div>\n",
 315 |        "<style scoped>\n",
 316 |        "    .dataframe tbody tr th:only-of-type {\n",
 317 |        "        vertical-align: middle;\n",
 318 |        "    }\n",
 319 |        "\n",
 320 |        "    .dataframe tbody tr th {\n",
 321 |        "        vertical-align: top;\n",
 322 |        "    }\n",
 323 |        "\n",
 324 |        "    .dataframe thead th {\n",
 325 |        "        text-align: right;\n",
 326 |        "    }\n",
 327 |        "</style>\n",
 328 |        "<table border=\"1\" class=\"dataframe\">\n",
 329 |        "  <thead>\n",
 330 |        "    <tr style=\"text-align: right;\">\n",
 331 |        "      <th></th>\n",
 332 |        "      <th>AAPL</th>\n",
 333 |        "      <th>BA</th>\n",
 334 |        "      <th>DIS</th>\n",
 335 |        "      <th>IBM</th>\n",
 336 |        "      <th>KO</th>\n",
 337 |        "      <th>MSFT</th>\n",
 338 |        "    </tr>\n",
 339 |        "    <tr>\n",
 340 |        "      <th>Date</th>\n",
 341 |        "      <th></th>\n",
 342 |        "      <th></th>\n",
 343 |        "      <th></th>\n",
 344 |        "      <th></th>\n",
 345 |        "      <th></th>\n",
 346 |        "      <th></th>\n",
 347 |        "    </tr>\n",
 348 |        "  </thead>\n",
 349 |        "  <tbody>\n",
 350 |        "    <tr>\n",
 351 |        "      <th>2009-12-31</th>\n",
 352 |        "      <td>7.526072</td>\n",
 353 |        "      <td>54.130001</td>\n",
 354 |        "      <td>32.25</td>\n",
 355 |        "      <td>130.899994</td>\n",
 356 |        "      <td>28.500000</td>\n",
 357 |        "      <td>30.480000</td>\n",
 358 |        "    </tr>\n",
 359 |        "    <tr>\n",
 360 |        "      <th>2010-01-04</th>\n",
 361 |        "      <td>7.643214</td>\n",
 362 |        "      <td>56.180000</td>\n",
 363 |        "      <td>32.07</td>\n",
 364 |        "      <td>132.449997</td>\n",
 365 |        "      <td>28.520000</td>\n",
 366 |        "      <td>30.950001</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>2010-01-05</th>\n",
 370 |        "      <td>7.656428</td>\n",
 371 |        "      <td>58.020000</td>\n",
 372 |        "      <td>31.99</td>\n",
 373 |        "      <td>130.850006</td>\n",
 374 |        "      <td>28.174999</td>\n",
 375 |        "      <td>30.959999</td>\n",
 376 |        "    </tr>\n",
 377 |        "    <tr>\n",
 378 |        "      <th>2010-01-06</th>\n",
 379 |        "      <td>7.534643</td>\n",
 380 |        "      <td>59.779999</td>\n",
 381 |        "      <td>31.82</td>\n",
 382 |        "      <td>130.000000</td>\n",
 383 |        "      <td>28.165001</td>\n",
 384 |        "      <td>30.770000</td>\n",
 385 |        "    </tr>\n",
 386 |        "    <tr>\n",
 387 |        "      <th>2010-01-07</th>\n",
 388 |        "      <td>7.520714</td>\n",
 389 |        "      <td>62.200001</td>\n",
 390 |        "      <td>31.83</td>\n",
 391 |        "      <td>129.550003</td>\n",
 392 |        "      <td>28.094999</td>\n",
 393 |        "      <td>30.450001</td>\n",
 394 |        "    </tr>\n",
 395 |        "  </tbody>\n",
 396 |        "</table>\n",
 397 |        "</div>"
 398 |       ],
 399 |       "text/plain": [
 400 |        "                AAPL         BA    DIS         IBM         KO       MSFT\n",
 401 |        "Date                                                                    \n",
 402 |        "2009-12-31  7.526072  54.130001  32.25  130.899994  28.500000  30.480000\n",
 403 |        "2010-01-04  7.643214  56.180000  32.07  132.449997  28.520000  30.950001\n",
 404 |        "2010-01-05  7.656428  58.020000  31.99  130.850006  28.174999  30.959999\n",
 405 |        "2010-01-06  7.534643  59.779999  31.82  130.000000  28.165001  30.770000\n",
 406 |        "2010-01-07  7.520714  62.200001  31.83  129.550003  28.094999  30.450001"
 407 |       ]
 408 |      },
 409 |      "execution_count": 5,
 410 |      "metadata": {},
 411 |      "output_type": "execute_result"
 412 |     }
 413 |    ],
 414 |    "source": [
 415 |     "close.head()"
 416 |    ]
 417 |   },
 418 |   {
 419 |    "cell_type": "code",
 420 |    "execution_count": 6,
 421 |    "id": "95a5a441",
 422 |    "metadata": {},
 423 |    "outputs": [
 424 |     {
 425 |      "name": "stdout",
 426 |      "output_type": "stream",
 427 |      "text": [
 428 |       "<class 'pandas.core.frame.DataFrame'>\n",
 429 |       "DatetimeIndex: 2289 entries, 2009-12-31 to 2019-02-05\n",
 430 |       "Data columns (total 6 columns):\n",
 431 |       " #   Column  Non-Null Count  Dtype  \n",
 432 |       "---  ------  --------------  -----  \n",
 433 |       " 0   AAPL    2289 non-null   float64\n",
 434 |       " 1   BA      2289 non-null   float64\n",
 435 |       " 2   DIS     2289 non-null   float64\n",
 436 |       " 3   IBM     2289 non-null   float64\n",
 437 |       " 4   KO      2289 non-null   float64\n",
 438 |       " 5   MSFT    2289 non-null   float64\n",
 439 |       "dtypes: float64(6)\n",
 440 |       "memory usage: 125.2 KB\n"
 441 |      ]
 442 |     }
 443 |    ],
 444 |    "source": [
 445 |     "close.info()"
 446 |    ]
 447 |   },
 448 |   {
 449 |    "cell_type": "code",
 450 |    "execution_count": 8,
 451 |    "id": "4ddc9c22",
 452 |    "metadata": {},
 453 |    "outputs": [
 454 |     {
 455 |      "data": {
 456 |       "text/plain": [
 457 |        "DatetimeIndex(['2009-12-31', '2010-01-04', '2010-01-05', '2010-01-06',\n",
 458 |        "               '2010-01-07', '2010-01-08', '2010-01-11', '2010-01-12',\n",
 459 |        "               '2010-01-13', '2010-01-14',\n",
 460 |        "               ...\n",
 461 |        "               '2019-01-23', '2019-01-24', '2019-01-25', '2019-01-28',\n",
 462 |        "               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01',\n",
 463 |        "               '2019-02-04', '2019-02-05'],\n",
 464 |        "              dtype='datetime64[ns]', name='Date', length=2289, freq=None)"
 465 |       ]
 466 |      },
 467 |      "execution_count": 8,
 468 |      "metadata": {},
 469 |      "output_type": "execute_result"
 470 |     }
 471 |    ],
 472 |    "source": [
 473 |     "# Taking a closer look at datetime index\n",
 474 |     "close.index"
 475 |    ]
 476 |   },
 477 |   {
 478 |    "cell_type": "code",
 479 |    "execution_count": 9,
 480 |    "id": "c6944cbd",
 481 |    "metadata": {},
 482 |    "outputs": [],
 483 |    "source": [
 484 |     "# Suppose we want to insert additional information regarding day of the week or quarter for more clarity, we can do that"
 485 |    ]
 486 |   },
 487 |   {
 488 |    "cell_type": "code",
 489 |    "execution_count": 10,
 490 |    "id": "b5ffa149",
 491 |    "metadata": {},
 492 |    "outputs": [
 493 |     {
 494 |      "data": {
 495 |       "text/plain": [
 496 |        "Int64Index([31,  4,  5,  6,  7,  8, 11, 12, 13, 14,\n",
 497 |        "            ...\n",
 498 |        "            23, 24, 25, 28, 29, 30, 31,  1,  4,  5],\n",
 499 |        "           dtype='int64', name='Date', length=2289)"
 500 |       ]
 501 |      },
 502 |      "execution_count": 10,
 503 |      "metadata": {},
 504 |      "output_type": "execute_result"
 505 |     }
 506 |    ],
 507 |    "source": [
 508 |     "# To get day number of the month in Datetime index\n",
 509 |     "close.index.day"
 510 |    ]
 511 |   },
 512 |   {
 513 |    "cell_type": "code",
 514 |    "execution_count": 11,
 515 |    "id": "91c85630",
 516 |    "metadata": {},
 517 |    "outputs": [
 518 |     {
 519 |      "data": {
 520 |       "text/plain": [
 521 |        "Int64Index([12,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
 522 |        "            ...\n",
 523 |        "             1,  1,  1,  1,  1,  1,  1,  2,  2,  2],\n",
 524 |        "           dtype='int64', name='Date', length=2289)"
 525 |       ]
 526 |      },
 527 |      "execution_count": 11,
 528 |      "metadata": {},
 529 |      "output_type": "execute_result"
 530 |     }
 531 |    ],
 532 |    "source": [
 533 |     "# To get the month of the year in Datetime index, 1 being January, 12 being December\n",
 534 |     "close.index.month"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 13,
 540 |    "id": "e5aae555",
 541 |    "metadata": {},
 542 |    "outputs": [
 543 |     {
 544 |      "data": {
 545 |       "text/plain": [
 546 |        "Int64Index([2009, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010,\n",
 547 |        "            ...\n",
 548 |        "            2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019],\n",
 549 |        "           dtype='int64', name='Date', length=2289)"
 550 |       ]
 551 |      },
 552 |      "execution_count": 13,
 553 |      "metadata": {},
 554 |      "output_type": "execute_result"
 555 |     }
 556 |    ],
 557 |    "source": [
 558 |     "# To get year in Datetime index\n",
 559 |     "close.index.year"
 560 |    ]
 561 |   },
 562 |   {
 563 |    "cell_type": "code",
 564 |    "execution_count": 14,
 565 |    "id": "45ab07eb",
 566 |    "metadata": {},
 567 |    "outputs": [
 568 |     {
 569 |      "data": {
 570 |       "text/plain": [
 571 |        "Index(['Thursday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',\n",
 572 |        "       'Monday', 'Tuesday', 'Wednesday', 'Thursday',\n",
 573 |        "       ...\n",
 574 |        "       'Wednesday', 'Thursday', 'Friday', 'Monday', 'Tuesday', 'Wednesday',\n",
 575 |        "       'Thursday', 'Friday', 'Monday', 'Tuesday'],\n",
 576 |        "      dtype='object', name='Date', length=2289)"
 577 |       ]
 578 |      },
 579 |      "execution_count": 14,
 580 |      "metadata": {},
 581 |      "output_type": "execute_result"
 582 |     }
 583 |    ],
 584 |    "source": [
 585 |     "# To get name of the day in Datetime index\n",
 586 |     "close.index.day_name()"
 587 |    ]
 588 |   },
 589 |   {
 590 |    "cell_type": "code",
 591 |    "execution_count": 21,
 592 |    "id": "0120df4b",
 593 |    "metadata": {},
 594 |    "outputs": [
 595 |     {
 596 |      "data": {
 597 |       "text/plain": [
 598 |        "Index(['December', 'January', 'January', 'January', 'January', 'January',\n",
 599 |        "       'January', 'January', 'January', 'January',\n",
 600 |        "       ...\n",
 601 |        "       'January', 'January', 'January', 'January', 'January', 'January',\n",
 602 |        "       'January', 'February', 'February', 'February'],\n",
 603 |        "      dtype='object', name='Date', length=2289)"
 604 |       ]
 605 |      },
 606 |      "execution_count": 21,
 607 |      "metadata": {},
 608 |      "output_type": "execute_result"
 609 |     }
 610 |    ],
 611 |    "source": [
 612 |     "# To get the month name in Datetime index\n",
 613 |     "close.index.month_name()"
 614 |    ]
 615 |   },
 616 |   {
 617 |    "cell_type": "code",
 618 |    "execution_count": 22,
 619 |    "id": "eb1aadb9",
 620 |    "metadata": {},
 621 |    "outputs": [
 622 |     {
 623 |      "data": {
 624 |       "text/plain": [
 625 |        "Int64Index([3, 0, 1, 2, 3, 4, 0, 1, 2, 3,\n",
 626 |        "            ...\n",
 627 |        "            2, 3, 4, 0, 1, 2, 3, 4, 0, 1],\n",
 628 |        "           dtype='int64', name='Date', length=2289)"
 629 |       ]
 630 |      },
 631 |      "execution_count": 22,
 632 |      "metadata": {},
 633 |      "output_type": "execute_result"
 634 |     }
 635 |    ],
 636 |    "source": [
 637 |     "# To get weekday as integer representation in Datetime index, Monday being 0 and Friday being 4\n",
 638 |     "close.index.weekday"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "code",
 643 |    "execution_count": 23,
 644 |    "id": "c90edebe",
 645 |    "metadata": {},
 646 |    "outputs": [
 647 |     {
 648 |      "data": {
 649 |       "text/plain": [
 650 |        "Int64Index([4, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
 651 |        "            ...\n",
 652 |        "            1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
 653 |        "           dtype='int64', name='Date', length=2289)"
 654 |       ]
 655 |      },
 656 |      "execution_count": 23,
 657 |      "metadata": {},
 658 |      "output_type": "execute_result"
 659 |     }
 660 |    ],
 661 |    "source": [
 662 |     "# To get quarter information in the Datetime index\n",
 663 |     "close.index.quarter"
 664 |    ]
 665 |   },
 666 |   {
 667 |    "cell_type": "code",
 668 |    "execution_count": 24,
 669 |    "id": "df0acc13",
 670 |    "metadata": {},
 671 |    "outputs": [
 672 |     {
 673 |      "data": {
 674 |       "text/plain": [
 675 |        "Int64Index([31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
 676 |        "            ...\n",
 677 |        "            31, 31, 31, 31, 31, 31, 31, 28, 28, 28],\n",
 678 |        "           dtype='int64', name='Date', length=2289)"
 679 |       ]
 680 |      },
 681 |      "execution_count": 24,
 682 |      "metadata": {},
 683 |      "output_type": "execute_result"
 684 |     }
 685 |    ],
 686 |    "source": [
 687 |     "# To find total days of a month in the Datetime index. This is not very helpful really\n",
 688 |     "close.index.days_in_month"
 689 |    ]
 690 |   },
 691 |   {
 692 |    "cell_type": "code",
 693 |    "execution_count": 28,
 694 |    "id": "3122216c",
 695 |    "metadata": {},
 696 |    "outputs": [
 697 |     {
 698 |      "name": "stderr",
 699 |      "output_type": "stream",
 700 |      "text": [
 701 |       "C:\\Users\\anujk\\AppData\\Local\\Temp/ipykernel_15812/1502405383.py:2: FutureWarning: weekofyear and week have been deprecated, please use DatetimeIndex.isocalendar().week instead, which returns a Series.  To exactly reproduce the behavior of week and weekofyear and return an Index, you may call pd.Int64Index(idx.isocalendar().week)\n",
 702 |       "  close.index.week\n"
 703 |      ]
 704 |     },
 705 |     {
 706 |      "data": {
 707 |       "text/plain": [
 708 |        "Int64Index([53,  1,  1,  1,  1,  1,  2,  2,  2,  2,\n",
 709 |        "            ...\n",
 710 |        "             4,  4,  4,  5,  5,  5,  5,  5,  6,  6],\n",
 711 |        "           dtype='int64', name='Date', length=2289)"
 712 |       ]
 713 |      },
 714 |      "execution_count": 28,
 715 |      "metadata": {},
 716 |      "output_type": "execute_result"
 717 |     }
 718 |    ],
 719 |    "source": [
 720 |     "# To find the week number of the year in the Datetime index\n",
 721 |     "close.index.week"
 722 |    ]
 723 |   },
 724 |   {
 725 |    "cell_type": "code",
 726 |    "execution_count": 29,
 727 |    "id": "06a2fa6e",
 728 |    "metadata": {},
 729 |    "outputs": [
 730 |     {
 731 |      "name": "stderr",
 732 |      "output_type": "stream",
 733 |      "text": [
 734 |       "C:\\Users\\anujk\\AppData\\Local\\Temp/ipykernel_15812/3635336598.py:1: FutureWarning: weekofyear and week have been deprecated, please use DatetimeIndex.isocalendar().week instead, which returns a Series.  To exactly reproduce the behavior of week and weekofyear and return an Index, you may call pd.Int64Index(idx.isocalendar().week)\n",
 735 |       "  close.index.weekofyear\n"
 736 |      ]
 737 |     },
 738 |     {
 739 |      "data": {
 740 |       "text/plain": [
 741 |        "Int64Index([53,  1,  1,  1,  1,  1,  2,  2,  2,  2,\n",
 742 |        "            ...\n",
 743 |        "             4,  4,  4,  5,  5,  5,  5,  5,  6,  6],\n",
 744 |        "           dtype='int64', name='Date', length=2289)"
 745 |       ]
 746 |      },
 747 |      "execution_count": 29,
 748 |      "metadata": {},
 749 |      "output_type": "execute_result"
 750 |     }
 751 |    ],
 752 |    "source": [
 753 |     "# Week of the year reproduces the same result\n",
 754 |     "close.index.weekofyear"
 755 |    ]
 756 |   },
 757 |   {
 758 |    "cell_type": "code",
 759 |    "execution_count": 30,
 760 |    "id": "f068c1a4",
 761 |    "metadata": {},
 762 |    "outputs": [
 763 |     {
 764 |      "data": {
 765 |       "text/plain": [
 766 |        "array([ True, False, False, ..., False, False, False])"
 767 |       ]
 768 |      },
 769 |      "execution_count": 30,
 770 |      "metadata": {},
 771 |      "output_type": "execute_result"
 772 |     }
 773 |    ],
 774 |    "source": [
 775 |     "# To check whether the month has ended\n",
 776 |     "close.index.is_month_end"
 777 |    ]
 778 |   },
 779 |   {
 780 |    "cell_type": "code",
 781 |    "execution_count": 31,
 782 |    "id": "381bb5f0",
 783 |    "metadata": {},
 784 |    "outputs": [],
 785 |    "source": [
 786 |     "# Creating new columns containing information about the Day name and the Quarter in the dataframe\n",
 787 |     "close['Day'] = stocks.index.day_name()\n",
 788 |     "close['Quarter'] = stocks.index.quarter"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 32,
 794 |    "id": "25a1b1d4",
 795 |    "metadata": {},
 796 |    "outputs": [
 797 |     {
 798 |      "data": {
 799 |       "text/html": [
 800 |        "<div>\n",
 801 |        "<style scoped>\n",
 802 |        "    .dataframe tbody tr th:only-of-type {\n",
 803 |        "        vertical-align: middle;\n",
 804 |        "    }\n",
 805 |        "\n",
 806 |        "    .dataframe tbody tr th {\n",
 807 |        "        vertical-align: top;\n",
 808 |        "    }\n",
 809 |        "\n",
 810 |        "    .dataframe thead th {\n",
 811 |        "        text-align: right;\n",
 812 |        "    }\n",
 813 |        "</style>\n",
 814 |        "<table border=\"1\" class=\"dataframe\">\n",
 815 |        "  <thead>\n",
 816 |        "    <tr style=\"text-align: right;\">\n",
 817 |        "      <th></th>\n",
 818 |        "      <th>AAPL</th>\n",
 819 |        "      <th>BA</th>\n",
 820 |        "      <th>DIS</th>\n",
 821 |        "      <th>IBM</th>\n",
 822 |        "      <th>KO</th>\n",
 823 |        "      <th>MSFT</th>\n",
 824 |        "      <th>Day</th>\n",
 825 |        "      <th>Quarter</th>\n",
 826 |        "    </tr>\n",
 827 |        "    <tr>\n",
 828 |        "      <th>Date</th>\n",
 829 |        "      <th></th>\n",
 830 |        "      <th></th>\n",
 831 |        "      <th></th>\n",
 832 |        "      <th></th>\n",
 833 |        "      <th></th>\n",
 834 |        "      <th></th>\n",
 835 |        "      <th></th>\n",
 836 |        "      <th></th>\n",
 837 |        "    </tr>\n",
 838 |        "  </thead>\n",
 839 |        "  <tbody>\n",
 840 |        "    <tr>\n",
 841 |        "      <th>2009-12-31</th>\n",
 842 |        "      <td>7.526072</td>\n",
 843 |        "      <td>54.130001</td>\n",
 844 |        "      <td>32.25</td>\n",
 845 |        "      <td>130.899994</td>\n",
 846 |        "      <td>28.500000</td>\n",
 847 |        "      <td>30.480000</td>\n",
 848 |        "      <td>Thursday</td>\n",
 849 |        "      <td>4</td>\n",
 850 |        "    </tr>\n",
 851 |        "    <tr>\n",
 852 |        "      <th>2010-01-04</th>\n",
 853 |        "      <td>7.643214</td>\n",
 854 |        "      <td>56.180000</td>\n",
 855 |        "      <td>32.07</td>\n",
 856 |        "      <td>132.449997</td>\n",
 857 |        "      <td>28.520000</td>\n",
 858 |        "      <td>30.950001</td>\n",
 859 |        "      <td>Monday</td>\n",
 860 |        "      <td>1</td>\n",
 861 |        "    </tr>\n",
 862 |        "    <tr>\n",
 863 |        "      <th>2010-01-05</th>\n",
 864 |        "      <td>7.656428</td>\n",
 865 |        "      <td>58.020000</td>\n",
 866 |        "      <td>31.99</td>\n",
 867 |        "      <td>130.850006</td>\n",
 868 |        "      <td>28.174999</td>\n",
 869 |        "      <td>30.959999</td>\n",
 870 |        "      <td>Tuesday</td>\n",
 871 |        "      <td>1</td>\n",
 872 |        "    </tr>\n",
 873 |        "    <tr>\n",
 874 |        "      <th>2010-01-06</th>\n",
 875 |        "      <td>7.534643</td>\n",
 876 |        "      <td>59.779999</td>\n",
 877 |        "      <td>31.82</td>\n",
 878 |        "      <td>130.000000</td>\n",
 879 |        "      <td>28.165001</td>\n",
 880 |        "      <td>30.770000</td>\n",
 881 |        "      <td>Wednesday</td>\n",
 882 |        "      <td>1</td>\n",
 883 |        "    </tr>\n",
 884 |        "    <tr>\n",
 885 |        "      <th>2010-01-07</th>\n",
 886 |        "      <td>7.520714</td>\n",
 887 |        "      <td>62.200001</td>\n",
 888 |        "      <td>31.83</td>\n",
 889 |        "      <td>129.550003</td>\n",
 890 |        "      <td>28.094999</td>\n",
 891 |        "      <td>30.450001</td>\n",
 892 |        "      <td>Thursday</td>\n",
 893 |        "      <td>1</td>\n",
 894 |        "    </tr>\n",
 895 |        "  </tbody>\n",
 896 |        "</table>\n",
 897 |        "</div>"
 898 |       ],
 899 |       "text/plain": [
 900 |        "                AAPL         BA    DIS         IBM         KO       MSFT  \\\n",
 901 |        "Date                                                                       \n",
 902 |        "2009-12-31  7.526072  54.130001  32.25  130.899994  28.500000  30.480000   \n",
 903 |        "2010-01-04  7.643214  56.180000  32.07  132.449997  28.520000  30.950001   \n",
 904 |        "2010-01-05  7.656428  58.020000  31.99  130.850006  28.174999  30.959999   \n",
 905 |        "2010-01-06  7.534643  59.779999  31.82  130.000000  28.165001  30.770000   \n",
 906 |        "2010-01-07  7.520714  62.200001  31.83  129.550003  28.094999  30.450001   \n",
 907 |        "\n",
 908 |        "                  Day  Quarter  \n",
 909 |        "Date                            \n",
 910 |        "2009-12-31   Thursday        4  \n",
 911 |        "2010-01-04     Monday        1  \n",
 912 |        "2010-01-05    Tuesday        1  \n",
 913 |        "2010-01-06  Wednesday        1  \n",
 914 |        "2010-01-07   Thursday        1  "
 915 |       ]
 916 |      },
 917 |      "execution_count": 32,
 918 |      "metadata": {},
 919 |      "output_type": "execute_result"
 920 |     }
 921 |    ],
 922 |    "source": [
 923 |     "close.head()"
 924 |    ]
 925 |   },
 926 |   {
 927 |    "cell_type": "markdown",
 928 |    "id": "33a0087e",
 929 |    "metadata": {},
 930 |    "source": [
 931 |     "# Filling NA values with backfill bfill, forwardfill ffill and interpolation\n",
 932 |     "When we resample, reindex or merge time series, we might end up with NA or empty rows. We can handle such situations using\n",
 933 |     "these methods"
 934 |    ]
 935 |   },
 936 |   {
 937 |    "cell_type": "code",
 938 |    "execution_count": 33,
 939 |    "id": "fb9c8419",
 940 |    "metadata": {},
 941 |    "outputs": [
 942 |     {
 943 |      "data": {
 944 |       "text/html": [
 945 |        "<div>\n",
 946 |        "<style scoped>\n",
 947 |        "    .dataframe tbody tr th:only-of-type {\n",
 948 |        "        vertical-align: middle;\n",
 949 |        "    }\n",
 950 |        "\n",
 951 |        "    .dataframe tbody tr th {\n",
 952 |        "        vertical-align: top;\n",
 953 |        "    }\n",
 954 |        "\n",
 955 |        "    .dataframe thead th {\n",
 956 |        "        text-align: right;\n",
 957 |        "    }\n",
 958 |        "</style>\n",
 959 |        "<table border=\"1\" class=\"dataframe\">\n",
 960 |        "  <thead>\n",
 961 |        "    <tr style=\"text-align: right;\">\n",
 962 |        "      <th></th>\n",
 963 |        "      <th>AAPL</th>\n",
 964 |        "      <th>BA</th>\n",
 965 |        "      <th>DIS</th>\n",
 966 |        "      <th>IBM</th>\n",
 967 |        "      <th>KO</th>\n",
 968 |        "      <th>MSFT</th>\n",
 969 |        "      <th>Day</th>\n",
 970 |        "      <th>Quarter</th>\n",
 971 |        "    </tr>\n",
 972 |        "    <tr>\n",
 973 |        "      <th>Date</th>\n",
 974 |        "      <th></th>\n",
 975 |        "      <th></th>\n",
 976 |        "      <th></th>\n",
 977 |        "      <th></th>\n",
 978 |        "      <th></th>\n",
 979 |        "      <th></th>\n",
 980 |        "      <th></th>\n",
 981 |        "      <th></th>\n",
 982 |        "    </tr>\n",
 983 |        "  </thead>\n",
 984 |        "  <tbody>\n",
 985 |        "    <tr>\n",
 986 |        "      <th>2009-12-31</th>\n",
 987 |        "      <td>7.526072</td>\n",
 988 |        "      <td>54.130001</td>\n",
 989 |        "      <td>32.25</td>\n",
 990 |        "      <td>130.899994</td>\n",
 991 |        "      <td>28.500000</td>\n",
 992 |        "      <td>30.480000</td>\n",
 993 |        "      <td>Thursday</td>\n",
 994 |        "      <td>4</td>\n",
 995 |        "    </tr>\n",
 996 |        "    <tr>\n",
 997 |        "      <th>2010-01-04</th>\n",
 998 |        "      <td>7.643214</td>\n",
 999 |        "      <td>56.180000</td>\n",
1000 |        "      <td>32.07</td>\n",
1001 |        "      <td>132.449997</td>\n",
1002 |        "      <td>28.520000</td>\n",
1003 |        "      <td>30.950001</td>\n",
1004 |        "      <td>Monday</td>\n",
1005 |        "      <td>1</td>\n",
1006 |        "    </tr>\n",
1007 |        "    <tr>\n",
1008 |        "      <th>2010-01-05</th>\n",
1009 |        "      <td>7.656428</td>\n",
1010 |        "      <td>58.020000</td>\n",
1011 |        "      <td>31.99</td>\n",
1012 |        "      <td>130.850006</td>\n",
1013 |        "      <td>28.174999</td>\n",
1014 |        "      <td>30.959999</td>\n",
1015 |        "      <td>Tuesday</td>\n",
1016 |        "      <td>1</td>\n",
1017 |        "    </tr>\n",
1018 |        "    <tr>\n",
1019 |        "      <th>2010-01-06</th>\n",
1020 |        "      <td>7.534643</td>\n",
1021 |        "      <td>59.779999</td>\n",
1022 |        "      <td>31.82</td>\n",
1023 |        "      <td>130.000000</td>\n",
1024 |        "      <td>28.165001</td>\n",
1025 |        "      <td>30.770000</td>\n",
1026 |        "      <td>Wednesday</td>\n",
1027 |        "      <td>1</td>\n",
1028 |        "    </tr>\n",
1029 |        "    <tr>\n",
1030 |        "      <th>2010-01-07</th>\n",
1031 |        "      <td>7.520714</td>\n",
1032 |        "      <td>62.200001</td>\n",
1033 |        "      <td>31.83</td>\n",
1034 |        "      <td>129.550003</td>\n",
1035 |        "      <td>28.094999</td>\n",
1036 |        "      <td>30.450001</td>\n",
1037 |        "      <td>Thursday</td>\n",
1038 |        "      <td>1</td>\n",
1039 |        "    </tr>\n",
1040 |        "  </tbody>\n",
1041 |        "</table>\n",
1042 |        "</div>"
1043 |       ],
1044 |       "text/plain": [
1045 |        "                AAPL         BA    DIS         IBM         KO       MSFT  \\\n",
1046 |        "Date                                                                       \n",
1047 |        "2009-12-31  7.526072  54.130001  32.25  130.899994  28.500000  30.480000   \n",
1048 |        "2010-01-04  7.643214  56.180000  32.07  132.449997  28.520000  30.950001   \n",
1049 |        "2010-01-05  7.656428  58.020000  31.99  130.850006  28.174999  30.959999   \n",
1050 |        "2010-01-06  7.534643  59.779999  31.82  130.000000  28.165001  30.770000   \n",
1051 |        "2010-01-07  7.520714  62.200001  31.83  129.550003  28.094999  30.450001   \n",
1052 |        "\n",
1053 |        "                  Day  Quarter  \n",
1054 |        "Date                            \n",
1055 |        "2009-12-31   Thursday        4  \n",
1056 |        "2010-01-04     Monday        1  \n",
1057 |        "2010-01-05    Tuesday        1  \n",
1058 |        "2010-01-06  Wednesday        1  \n",
1059 |        "2010-01-07   Thursday        1  "
1060 |       ]
1061 |      },
1062 |      "execution_count": 33,
1063 |      "metadata": {},
1064 |      "output_type": "execute_result"
1065 |     }
1066 |    ],
1067 |    "source": [
1068 |     "close.head()"
1069 |    ]
1070 |   },
1071 |   {
1072 |    "cell_type": "code",
1073 |    "execution_count": 34,
1074 |    "id": "abda8e90",
1075 |    "metadata": {},
1076 |    "outputs": [
1077 |     {
1078 |      "data": {
1079 |       "text/html": [
1080 |        "<div>\n",
1081 |        "<style scoped>\n",
1082 |        "    .dataframe tbody tr th:only-of-type {\n",
1083 |        "        vertical-align: middle;\n",
1084 |        "    }\n",
1085 |        "\n",
1086 |        "    .dataframe tbody tr th {\n",
1087 |        "        vertical-align: top;\n",
1088 |        "    }\n",
1089 |        "\n",
1090 |        "    .dataframe thead th {\n",
1091 |        "        text-align: right;\n",
1092 |        "    }\n",
1093 |        "</style>\n",
1094 |        "<table border=\"1\" class=\"dataframe\">\n",
1095 |        "  <thead>\n",
1096 |        "    <tr style=\"text-align: right;\">\n",
1097 |        "      <th></th>\n",
1098 |        "      <th>AAPL</th>\n",
1099 |        "      <th>BA</th>\n",
1100 |        "      <th>DIS</th>\n",
1101 |        "      <th>IBM</th>\n",
1102 |        "      <th>KO</th>\n",
1103 |        "      <th>MSFT</th>\n",
1104 |        "      <th>Day</th>\n",
1105 |        "      <th>Quarter</th>\n",
1106 |        "    </tr>\n",
1107 |        "    <tr>\n",
1108 |        "      <th>Date</th>\n",
1109 |        "      <th></th>\n",
1110 |        "      <th></th>\n",
1111 |        "      <th></th>\n",
1112 |        "      <th></th>\n",
1113 |        "      <th></th>\n",
1114 |        "      <th></th>\n",
1115 |        "      <th></th>\n",
1116 |        "      <th></th>\n",
1117 |        "    </tr>\n",
1118 |        "  </thead>\n",
1119 |        "  <tbody>\n",
1120 |        "    <tr>\n",
1121 |        "      <th>2019-01-30</th>\n",
1122 |        "      <td>41.312500</td>\n",
1123 |        "      <td>387.720001</td>\n",
1124 |        "      <td>110.129997</td>\n",
1125 |        "      <td>134.380005</td>\n",
1126 |        "      <td>47.860001</td>\n",
1127 |        "      <td>106.379997</td>\n",
1128 |        "      <td>Wednesday</td>\n",
1129 |        "      <td>1</td>\n",
1130 |        "    </tr>\n",
1131 |        "    <tr>\n",
1132 |        "      <th>2019-01-31</th>\n",
1133 |        "      <td>41.610001</td>\n",
1134 |        "      <td>385.619995</td>\n",
1135 |        "      <td>111.519997</td>\n",
1136 |        "      <td>134.419998</td>\n",
1137 |        "      <td>48.130001</td>\n",
1138 |        "      <td>104.430000</td>\n",
1139 |        "      <td>Thursday</td>\n",
1140 |        "      <td>1</td>\n",
1141 |        "    </tr>\n",
1142 |        "    <tr>\n",
1143 |        "      <th>2019-02-01</th>\n",
1144 |        "      <td>41.630001</td>\n",
1145 |        "      <td>387.429993</td>\n",
1146 |        "      <td>111.300003</td>\n",
1147 |        "      <td>134.100006</td>\n",
1148 |        "      <td>48.700001</td>\n",
1149 |        "      <td>102.779999</td>\n",
1150 |        "      <td>Friday</td>\n",
1151 |        "      <td>1</td>\n",
1152 |        "    </tr>\n",
1153 |        "    <tr>\n",
1154 |        "      <th>2019-02-04</th>\n",
1155 |        "      <td>42.812500</td>\n",
1156 |        "      <td>397.000000</td>\n",
1157 |        "      <td>111.800003</td>\n",
1158 |        "      <td>135.190002</td>\n",
1159 |        "      <td>49.250000</td>\n",
1160 |        "      <td>105.739998</td>\n",
1161 |        "      <td>Monday</td>\n",
1162 |        "      <td>1</td>\n",
1163 |        "    </tr>\n",
1164 |        "    <tr>\n",
1165 |        "      <th>2019-02-05</th>\n",
1166 |        "      <td>43.544998</td>\n",
1167 |        "      <td>410.179993</td>\n",
1168 |        "      <td>112.660004</td>\n",
1169 |        "      <td>135.550003</td>\n",
1170 |        "      <td>49.259998</td>\n",
1171 |        "      <td>107.220001</td>\n",
1172 |        "      <td>Tuesday</td>\n",
1173 |        "      <td>1</td>\n",
1174 |        "    </tr>\n",
1175 |        "  </tbody>\n",
1176 |        "</table>\n",
1177 |        "</div>"
1178 |       ],
1179 |       "text/plain": [
1180 |        "                 AAPL          BA         DIS         IBM         KO  \\\n",
1181 |        "Date                                                                   \n",
1182 |        "2019-01-30  41.312500  387.720001  110.129997  134.380005  47.860001   \n",
1183 |        "2019-01-31  41.610001  385.619995  111.519997  134.419998  48.130001   \n",
1184 |        "2019-02-01  41.630001  387.429993  111.300003  134.100006  48.700001   \n",
1185 |        "2019-02-04  42.812500  397.000000  111.800003  135.190002  49.250000   \n",
1186 |        "2019-02-05  43.544998  410.179993  112.660004  135.550003  49.259998   \n",
1187 |        "\n",
1188 |        "                  MSFT        Day  Quarter  \n",
1189 |        "Date                                        \n",
1190 |        "2019-01-30  106.379997  Wednesday        1  \n",
1191 |        "2019-01-31  104.430000   Thursday        1  \n",
1192 |        "2019-02-01  102.779999     Friday        1  \n",
1193 |        "2019-02-04  105.739998     Monday        1  \n",
1194 |        "2019-02-05  107.220001    Tuesday        1  "
1195 |       ]
1196 |      },
1197 |      "execution_count": 34,
1198 |      "metadata": {},
1199 |      "output_type": "execute_result"
1200 |     }
1201 |    ],
1202 |    "source": [
1203 |     "close.tail()"
1204 |    ]
1205 |   },
1206 |   {
1207 |    "cell_type": "code",
1208 |    "execution_count": 36,
1209 |    "id": "cceeca3f",
1210 |    "metadata": {},
1211 |    "outputs": [
1212 |     {
1213 |      "data": {
1214 |       "text/plain": [
1215 |        "DatetimeIndex(['2009-12-31', '2010-01-01', '2010-01-02', '2010-01-03',\n",
1216 |        "               '2010-01-04', '2010-01-05', '2010-01-06', '2010-01-07',\n",
1217 |        "               '2010-01-08', '2010-01-09',\n",
1218 |        "               ...\n",
1219 |        "               '2019-01-28', '2019-01-29', '2019-01-30', '2019-01-31',\n",
1220 |        "               '2019-02-01', '2019-02-02', '2019-02-03', '2019-02-04',\n",
1221 |        "               '2019-02-05', '2019-02-06'],\n",
1222 |        "              dtype='datetime64[ns]', length=3325, freq='D')"
1223 |       ]
1224 |      },
1225 |      "execution_count": 36,
1226 |      "metadata": {},
1227 |      "output_type": "execute_result"
1228 |     }
1229 |    ],
1230 |    "source": [
1231 |     "# Creating a new Datetime index with all days\n",
1232 |     "all_days = pd.date_range(start = '2009-12-31', end = '2019-02-06', freq = 'D')\n",
1233 |     "all_days"
1234 |    ]
1235 |   },
1236 |   {
1237 |    "cell_type": "code",
1238 |    "execution_count": 37,
1239 |    "id": "c6cc6edb",
1240 |    "metadata": {},
1241 |    "outputs": [],
1242 |    "source": [
1243 |     "# Reindexing the close dataframe to this new all_days datetime index\n",
1244 |     "# NOTE: Missing values can be filled in reindexing step itself using the method parameter and putting ffill\n",
1245 |     "close = close.reindex(all_days)"
1246 |    ]
1247 |   },
1248 |   {
1249 |    "cell_type": "code",
1250 |    "execution_count": 38,
1251 |    "id": "7175d543",
1252 |    "metadata": {},
1253 |    "outputs": [
1254 |     {
1255 |      "data": {
1256 |       "text/html": [
1257 |        "<div>\n",
1258 |        "<style scoped>\n",
1259 |        "    .dataframe tbody tr th:only-of-type {\n",
1260 |        "        vertical-align: middle;\n",
1261 |        "    }\n",
1262 |        "\n",
1263 |        "    .dataframe tbody tr th {\n",
1264 |        "        vertical-align: top;\n",
1265 |        "    }\n",
1266 |        "\n",
1267 |        "    .dataframe thead th {\n",
1268 |        "        text-align: right;\n",
1269 |        "    }\n",
1270 |        "</style>\n",
1271 |        "<table border=\"1\" class=\"dataframe\">\n",
1272 |        "  <thead>\n",
1273 |        "    <tr style=\"text-align: right;\">\n",
1274 |        "      <th></th>\n",
1275 |        "      <th>AAPL</th>\n",
1276 |        "      <th>BA</th>\n",
1277 |        "      <th>DIS</th>\n",
1278 |        "      <th>IBM</th>\n",
1279 |        "      <th>KO</th>\n",
1280 |        "      <th>MSFT</th>\n",
1281 |        "      <th>Day</th>\n",
1282 |        "      <th>Quarter</th>\n",
1283 |        "    </tr>\n",
1284 |        "  </thead>\n",
1285 |        "  <tbody>\n",
1286 |        "    <tr>\n",
1287 |        "      <th>2009-12-31</th>\n",
1288 |        "      <td>7.526072</td>\n",
1289 |        "      <td>54.130001</td>\n",
1290 |        "      <td>32.250000</td>\n",
1291 |        "      <td>130.899994</td>\n",
1292 |        "      <td>28.500000</td>\n",
1293 |        "      <td>30.480000</td>\n",
1294 |        "      <td>Thursday</td>\n",
1295 |        "      <td>4.0</td>\n",
1296 |        "    </tr>\n",
1297 |        "    <tr>\n",
1298 |        "      <th>2010-01-01</th>\n",
1299 |        "      <td>NaN</td>\n",
1300 |        "      <td>NaN</td>\n",
1301 |        "      <td>NaN</td>\n",
1302 |        "      <td>NaN</td>\n",
1303 |        "      <td>NaN</td>\n",
1304 |        "      <td>NaN</td>\n",
1305 |        "      <td>NaN</td>\n",
1306 |        "      <td>NaN</td>\n",
1307 |        "    </tr>\n",
1308 |        "    <tr>\n",
1309 |        "      <th>2010-01-02</th>\n",
1310 |        "      <td>NaN</td>\n",
1311 |        "      <td>NaN</td>\n",
1312 |        "      <td>NaN</td>\n",
1313 |        "      <td>NaN</td>\n",
1314 |        "      <td>NaN</td>\n",
1315 |        "      <td>NaN</td>\n",
1316 |        "      <td>NaN</td>\n",
1317 |        "      <td>NaN</td>\n",
1318 |        "    </tr>\n",
1319 |        "    <tr>\n",
1320 |        "      <th>2010-01-03</th>\n",
1321 |        "      <td>NaN</td>\n",
1322 |        "      <td>NaN</td>\n",
1323 |        "      <td>NaN</td>\n",
1324 |        "      <td>NaN</td>\n",
1325 |        "      <td>NaN</td>\n",
1326 |        "      <td>NaN</td>\n",
1327 |        "      <td>NaN</td>\n",
1328 |        "      <td>NaN</td>\n",
1329 |        "    </tr>\n",
1330 |        "    <tr>\n",
1331 |        "      <th>2010-01-04</th>\n",
1332 |        "      <td>7.643214</td>\n",
1333 |        "      <td>56.180000</td>\n",
1334 |        "      <td>32.070000</td>\n",
1335 |        "      <td>132.449997</td>\n",
1336 |        "      <td>28.520000</td>\n",
1337 |        "      <td>30.950001</td>\n",
1338 |        "      <td>Monday</td>\n",
1339 |        "      <td>1.0</td>\n",
1340 |        "    </tr>\n",
1341 |        "    <tr>\n",
1342 |        "      <th>2010-01-05</th>\n",
1343 |        "      <td>7.656428</td>\n",
1344 |        "      <td>58.020000</td>\n",
1345 |        "      <td>31.990000</td>\n",
1346 |        "      <td>130.850006</td>\n",
1347 |        "      <td>28.174999</td>\n",
1348 |        "      <td>30.959999</td>\n",
1349 |        "      <td>Tuesday</td>\n",
1350 |        "      <td>1.0</td>\n",
1351 |        "    </tr>\n",
1352 |        "    <tr>\n",
1353 |        "      <th>2010-01-06</th>\n",
1354 |        "      <td>7.534643</td>\n",
1355 |        "      <td>59.779999</td>\n",
1356 |        "      <td>31.820000</td>\n",
1357 |        "      <td>130.000000</td>\n",
1358 |        "      <td>28.165001</td>\n",
1359 |        "      <td>30.770000</td>\n",
1360 |        "      <td>Wednesday</td>\n",
1361 |        "      <td>1.0</td>\n",
1362 |        "    </tr>\n",
1363 |        "    <tr>\n",
1364 |        "      <th>2010-01-07</th>\n",
1365 |        "      <td>7.520714</td>\n",
1366 |        "      <td>62.200001</td>\n",
1367 |        "      <td>31.830000</td>\n",
1368 |        "      <td>129.550003</td>\n",
1369 |        "      <td>28.094999</td>\n",
1370 |        "      <td>30.450001</td>\n",
1371 |        "      <td>Thursday</td>\n",
1372 |        "      <td>1.0</td>\n",
1373 |        "    </tr>\n",
1374 |        "    <tr>\n",
1375 |        "      <th>2010-01-08</th>\n",
1376 |        "      <td>7.570714</td>\n",
1377 |        "      <td>61.599998</td>\n",
1378 |        "      <td>31.879999</td>\n",
1379 |        "      <td>130.850006</td>\n",
1380 |        "      <td>27.575001</td>\n",
1381 |        "      <td>30.660000</td>\n",
1382 |        "      <td>Friday</td>\n",
1383 |        "      <td>1.0</td>\n",
1384 |        "    </tr>\n",
1385 |        "    <tr>\n",
1386 |        "      <th>2010-01-09</th>\n",
1387 |        "      <td>NaN</td>\n",
1388 |        "      <td>NaN</td>\n",
1389 |        "      <td>NaN</td>\n",
1390 |        "      <td>NaN</td>\n",
1391 |        "      <td>NaN</td>\n",
1392 |        "      <td>NaN</td>\n",
1393 |        "      <td>NaN</td>\n",
1394 |        "      <td>NaN</td>\n",
1395 |        "    </tr>\n",
1396 |        "    <tr>\n",
1397 |        "      <th>2010-01-10</th>\n",
1398 |        "      <td>NaN</td>\n",
1399 |        "      <td>NaN</td>\n",
1400 |        "      <td>NaN</td>\n",
1401 |        "      <td>NaN</td>\n",
1402 |        "      <td>NaN</td>\n",
1403 |        "      <td>NaN</td>\n",
1404 |        "      <td>NaN</td>\n",
1405 |        "      <td>NaN</td>\n",
1406 |        "    </tr>\n",
1407 |        "    <tr>\n",
1408 |        "      <th>2010-01-11</th>\n",
1409 |        "      <td>7.503929</td>\n",
1410 |        "      <td>60.869999</td>\n",
1411 |        "      <td>31.360001</td>\n",
1412 |        "      <td>129.479996</td>\n",
1413 |        "      <td>28.135000</td>\n",
1414 |        "      <td>30.270000</td>\n",
1415 |        "      <td>Monday</td>\n",
1416 |        "      <td>1.0</td>\n",
1417 |        "    </tr>\n",
1418 |        "    <tr>\n",
1419 |        "      <th>2010-01-12</th>\n",
1420 |        "      <td>7.418571</td>\n",
1421 |        "      <td>60.430000</td>\n",
1422 |        "      <td>30.820000</td>\n",
1423 |        "      <td>130.509995</td>\n",
1424 |        "      <td>28.440001</td>\n",
1425 |        "      <td>30.070000</td>\n",
1426 |        "      <td>Tuesday</td>\n",
1427 |        "      <td>1.0</td>\n",
1428 |        "    </tr>\n",
1429 |        "    <tr>\n",
1430 |        "      <th>2010-01-13</th>\n",
1431 |        "      <td>7.523214</td>\n",
1432 |        "      <td>61.160000</td>\n",
1433 |        "      <td>31.290001</td>\n",
1434 |        "      <td>130.229996</td>\n",
1435 |        "      <td>28.514999</td>\n",
1436 |        "      <td>30.350000</td>\n",
1437 |        "      <td>Wednesday</td>\n",
1438 |        "      <td>1.0</td>\n",
1439 |        "    </tr>\n",
1440 |        "    <tr>\n",
1441 |        "      <th>2010-01-14</th>\n",
1442 |        "      <td>7.479643</td>\n",
1443 |        "      <td>61.560001</td>\n",
1444 |        "      <td>31.020000</td>\n",
1445 |        "      <td>132.309998</td>\n",
1446 |        "      <td>28.565001</td>\n",
1447 |        "      <td>30.959999</td>\n",
1448 |        "      <td>Thursday</td>\n",
1449 |        "      <td>1.0</td>\n",
1450 |        "    </tr>\n",
1451 |        "    <tr>\n",
1452 |        "      <th>2010-01-15</th>\n",
1453 |        "      <td>7.354643</td>\n",
1454 |        "      <td>60.820000</td>\n",
1455 |        "      <td>30.600000</td>\n",
1456 |        "      <td>131.779999</td>\n",
1457 |        "      <td>28.145000</td>\n",
1458 |        "      <td>30.860001</td>\n",
1459 |        "      <td>Friday</td>\n",
1460 |        "      <td>1.0</td>\n",
1461 |        "    </tr>\n",
1462 |        "    <tr>\n",
1463 |        "      <th>2010-01-16</th>\n",
1464 |        "      <td>NaN</td>\n",
1465 |        "      <td>NaN</td>\n",
1466 |        "      <td>NaN</td>\n",
1467 |        "      <td>NaN</td>\n",
1468 |        "      <td>NaN</td>\n",
1469 |        "      <td>NaN</td>\n",
1470 |        "      <td>NaN</td>\n",
1471 |        "      <td>NaN</td>\n",
1472 |        "    </tr>\n",
1473 |        "    <tr>\n",
1474 |        "      <th>2010-01-17</th>\n",
1475 |        "      <td>NaN</td>\n",
1476 |        "      <td>NaN</td>\n",
1477 |        "      <td>NaN</td>\n",
1478 |        "      <td>NaN</td>\n",
1479 |        "      <td>NaN</td>\n",
1480 |        "      <td>NaN</td>\n",
1481 |        "      <td>NaN</td>\n",
1482 |        "      <td>NaN</td>\n",
1483 |        "    </tr>\n",
1484 |        "    <tr>\n",
1485 |        "      <th>2010-01-18</th>\n",
1486 |        "      <td>NaN</td>\n",
1487 |        "      <td>NaN</td>\n",
1488 |        "      <td>NaN</td>\n",
1489 |        "      <td>NaN</td>\n",
1490 |        "      <td>NaN</td>\n",
1491 |        "      <td>NaN</td>\n",
1492 |        "      <td>NaN</td>\n",
1493 |        "      <td>NaN</td>\n",
1494 |        "    </tr>\n",
1495 |        "    <tr>\n",
1496 |        "      <th>2010-01-19</th>\n",
1497 |        "      <td>7.680000</td>\n",
1498 |        "      <td>60.650002</td>\n",
1499 |        "      <td>31.010000</td>\n",
1500 |        "      <td>134.139999</td>\n",
1501 |        "      <td>28.209999</td>\n",
1502 |        "      <td>31.100000</td>\n",
1503 |        "      <td>Tuesday</td>\n",
1504 |        "      <td>1.0</td>\n",
1505 |        "    </tr>\n",
1506 |        "  </tbody>\n",
1507 |        "</table>\n",
1508 |        "</div>"
1509 |       ],
1510 |       "text/plain": [
1511 |        "                AAPL         BA        DIS         IBM         KO       MSFT  \\\n",
1512 |        "2009-12-31  7.526072  54.130001  32.250000  130.899994  28.500000  30.480000   \n",
1513 |        "2010-01-01       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1514 |        "2010-01-02       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1515 |        "2010-01-03       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1516 |        "2010-01-04  7.643214  56.180000  32.070000  132.449997  28.520000  30.950001   \n",
1517 |        "2010-01-05  7.656428  58.020000  31.990000  130.850006  28.174999  30.959999   \n",
1518 |        "2010-01-06  7.534643  59.779999  31.820000  130.000000  28.165001  30.770000   \n",
1519 |        "2010-01-07  7.520714  62.200001  31.830000  129.550003  28.094999  30.450001   \n",
1520 |        "2010-01-08  7.570714  61.599998  31.879999  130.850006  27.575001  30.660000   \n",
1521 |        "2010-01-09       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1522 |        "2010-01-10       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1523 |        "2010-01-11  7.503929  60.869999  31.360001  129.479996  28.135000  30.270000   \n",
1524 |        "2010-01-12  7.418571  60.430000  30.820000  130.509995  28.440001  30.070000   \n",
1525 |        "2010-01-13  7.523214  61.160000  31.290001  130.229996  28.514999  30.350000   \n",
1526 |        "2010-01-14  7.479643  61.560001  31.020000  132.309998  28.565001  30.959999   \n",
1527 |        "2010-01-15  7.354643  60.820000  30.600000  131.779999  28.145000  30.860001   \n",
1528 |        "2010-01-16       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1529 |        "2010-01-17       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1530 |        "2010-01-18       NaN        NaN        NaN         NaN        NaN        NaN   \n",
1531 |        "2010-01-19  7.680000  60.650002  31.010000  134.139999  28.209999  31.100000   \n",
1532 |        "\n",
1533 |        "                  Day  Quarter  \n",
1534 |        "2009-12-31   Thursday      4.0  \n",
1535 |        "2010-01-01        NaN      NaN  \n",
1536 |        "2010-01-02        NaN      NaN  \n",
1537 |        "2010-01-03        NaN      NaN  \n",
1538 |        "2010-01-04     Monday      1.0  \n",
1539 |        "2010-01-05    Tuesday      1.0  \n",
1540 |        "2010-01-06  Wednesday      1.0  \n",
1541 |        "2010-01-07   Thursday      1.0  \n",
1542 |        "2010-01-08     Friday      1.0  \n",
1543 |        "2010-01-09        NaN      NaN  \n",
1544 |        "2010-01-10        NaN      NaN  \n",
1545 |        "2010-01-11     Monday      1.0  \n",
1546 |        "2010-01-12    Tuesday      1.0  \n",
1547 |        "2010-01-13  Wednesday      1.0  \n",
1548 |        "2010-01-14   Thursday      1.0  \n",
1549 |        "2010-01-15     Friday      1.0  \n",
1550 |        "2010-01-16        NaN      NaN  \n",
1551 |        "2010-01-17        NaN      NaN  \n",
1552 |        "2010-01-18        NaN      NaN  \n",
1553 |        "2010-01-19    Tuesday      1.0  "
1554 |       ]
1555 |      },
1556 |      "execution_count": 38,
1557 |      "metadata": {},
1558 |      "output_type": "execute_result"
1559 |     }
1560 |    ],
1561 |    "source": [
1562 |     "close.head(20)"
1563 |    ]
1564 |   },
1565 |   {
1566 |    "cell_type": "code",
1567 |    "execution_count": 42,
1568 |    "id": "36ecc948",
1569 |    "metadata": {},
1570 |    "outputs": [],
1571 |    "source": [
1572 |     "# Reassinging the weekday name attribute and quarter to fill missing values\n",
1573 |     "# weekday_name does not work. Use day_name()\n",
1574 |     "close.Day = close.index.day_name()\n",
1575 |     "close.Quarter = close.index.quarter"
1576 |    ]
1577 |   },
1578 |   {
1579 |    "cell_type": "code",
1580 |    "execution_count": 43,
1581 |    "id": "7b6272b0",
1582 |    "metadata": {},
1583 |    "outputs": [
1584 |     {
1585 |      "data": {
1586 |       "text/html": [
1587 |        "<div>\n",
1588 |        "<style scoped>\n",
1589 |        "    .dataframe tbody tr th:only-of-type {\n",
1590 |        "        vertical-align: middle;\n",
1591 |        "    }\n",
1592 |        "\n",
1593 |        "    .dataframe tbody tr th {\n",
1594 |        "        vertical-align: top;\n",
1595 |        "    }\n",
1596 |        "\n",
1597 |        "    .dataframe thead th {\n",
1598 |        "        text-align: right;\n",
1599 |        "    }\n",
1600 |        "</style>\n",
1601 |        "<table border=\"1\" class=\"dataframe\">\n",
1602 |        "  <thead>\n",
1603 |        "    <tr style=\"text-align: right;\">\n",
1604 |        "      <th></th>\n",
1605 |        "      <th>AAPL</th>\n",
1606 |        "      <th>BA</th>\n",
1607 |        "      <th>DIS</th>\n",
1608 |        "      <th>IBM</th>\n",
1609 |        "      <th>KO</th>\n",
1610 |        "      <th>MSFT</th>\n",
1611 |        "      <th>Day</th>\n",
1612 |        "      <th>Quarter</th>\n",
1613 |        "    </tr>\n",
1614 |        "  </thead>\n",
1615 |        "  <tbody>\n",
1616 |        "    <tr>\n",
1617 |        "      <th>2009-12-31</th>\n",
1618 |        "      <td>7.526072</td>\n",
1619 |        "      <td>54.130001</td>\n",
1620 |        "      <td>32.25</td>\n",
1621 |        "      <td>130.899994</td>\n",
1622 |        "      <td>28.500000</td>\n",
1623 |        "      <td>30.480000</td>\n",
1624 |        "      <td>Thursday</td>\n",
1625 |        "      <td>4</td>\n",
1626 |        "    </tr>\n",
1627 |        "    <tr>\n",
1628 |        "      <th>2010-01-01</th>\n",
1629 |        "      <td>NaN</td>\n",
1630 |        "      <td>NaN</td>\n",
1631 |        "      <td>NaN</td>\n",
1632 |        "      <td>NaN</td>\n",
1633 |        "      <td>NaN</td>\n",
1634 |        "      <td>NaN</td>\n",
1635 |        "      <td>Friday</td>\n",
1636 |        "      <td>1</td>\n",
1637 |        "    </tr>\n",
1638 |        "    <tr>\n",
1639 |        "      <th>2010-01-02</th>\n",
1640 |        "      <td>NaN</td>\n",
1641 |        "      <td>NaN</td>\n",
1642 |        "      <td>NaN</td>\n",
1643 |        "      <td>NaN</td>\n",
1644 |        "      <td>NaN</td>\n",
1645 |        "      <td>NaN</td>\n",
1646 |        "      <td>Saturday</td>\n",
1647 |        "      <td>1</td>\n",
1648 |        "    </tr>\n",
1649 |        "    <tr>\n",
1650 |        "      <th>2010-01-03</th>\n",
1651 |        "      <td>NaN</td>\n",
1652 |        "      <td>NaN</td>\n",
1653 |        "      <td>NaN</td>\n",
1654 |        "      <td>NaN</td>\n",
1655 |        "      <td>NaN</td>\n",
1656 |        "      <td>NaN</td>\n",
1657 |        "      <td>Sunday</td>\n",
1658 |        "      <td>1</td>\n",
1659 |        "    </tr>\n",
1660 |        "    <tr>\n",
1661 |        "      <th>2010-01-04</th>\n",
1662 |        "      <td>7.643214</td>\n",
1663 |        "      <td>56.180000</td>\n",
1664 |        "      <td>32.07</td>\n",
1665 |        "      <td>132.449997</td>\n",
1666 |        "      <td>28.520000</td>\n",
1667 |        "      <td>30.950001</td>\n",
1668 |        "      <td>Monday</td>\n",
1669 |        "      <td>1</td>\n",
1670 |        "    </tr>\n",
1671 |        "    <tr>\n",
1672 |        "      <th>2010-01-05</th>\n",
1673 |        "      <td>7.656428</td>\n",
1674 |        "      <td>58.020000</td>\n",
1675 |        "      <td>31.99</td>\n",
1676 |        "      <td>130.850006</td>\n",
1677 |        "      <td>28.174999</td>\n",
1678 |        "      <td>30.959999</td>\n",
1679 |        "      <td>Tuesday</td>\n",
1680 |        "      <td>1</td>\n",
1681 |        "    </tr>\n",
1682 |        "    <tr>\n",
1683 |        "      <th>2010-01-06</th>\n",
1684 |        "      <td>7.534643</td>\n",
1685 |        "      <td>59.779999</td>\n",
1686 |        "      <td>31.82</td>\n",
1687 |        "      <td>130.000000</td>\n",
1688 |        "      <td>28.165001</td>\n",
1689 |        "      <td>30.770000</td>\n",
1690 |        "      <td>Wednesday</td>\n",
1691 |        "      <td>1</td>\n",
1692 |        "    </tr>\n",
1693 |        "    <tr>\n",
1694 |        "      <th>2010-01-07</th>\n",
1695 |        "      <td>7.520714</td>\n",
1696 |        "      <td>62.200001</td>\n",
1697 |        "      <td>31.83</td>\n",
1698 |        "      <td>129.550003</td>\n",
1699 |        "      <td>28.094999</td>\n",
1700 |        "      <td>30.450001</td>\n",
1701 |        "      <td>Thursday</td>\n",
1702 |        "      <td>1</td>\n",
1703 |        "    </tr>\n",
1704 |        "  </tbody>\n",
1705 |        "</table>\n",
1706 |        "</div>"
1707 |       ],
1708 |       "text/plain": [
1709 |        "                AAPL         BA    DIS         IBM         KO       MSFT  \\\n",
1710 |        "2009-12-31  7.526072  54.130001  32.25  130.899994  28.500000  30.480000   \n",
1711 |        "2010-01-01       NaN        NaN    NaN         NaN        NaN        NaN   \n",
1712 |        "2010-01-02       NaN        NaN    NaN         NaN        NaN        NaN   \n",
1713 |        "2010-01-03       NaN        NaN    NaN         NaN        NaN        NaN   \n",
1714 |        "2010-01-04  7.643214  56.180000  32.07  132.449997  28.520000  30.950001   \n",
1715 |        "2010-01-05  7.656428  58.020000  31.99  130.850006  28.174999  30.959999   \n",
1716 |        "2010-01-06  7.534643  59.779999  31.82  130.000000  28.165001  30.770000   \n",
1717 |        "2010-01-07  7.520714  62.200001  31.83  129.550003  28.094999  30.450001   \n",
1718 |        "\n",
1719 |        "                  Day  Quarter  \n",
1720 |        "2009-12-31   Thursday        4  \n",
1721 |        "2010-01-01     Friday        1  \n",
1722 |        "2010-01-02   Saturday        1  \n",
1723 |        "2010-01-03     Sunday        1  \n",
1724 |        "2010-01-04     Monday        1  \n",
1725 |        "2010-01-05    Tuesday        1  \n",
1726 |        "2010-01-06  Wednesday        1  \n",
1727 |        "2010-01-07   Thursday        1  "
1728 |       ]
1729 |      },
1730 |      "execution_count": 43,
1731 |      "metadata": {},
1732 |      "output_type": "execute_result"
1733 |     }
1734 |    ],
1735 |    "source": [
1736 |     "close.head(8)"
1737 |    ]
1738 |   },
1739 |   {
1740 |    "cell_type": "code",
1741 |    "execution_count": 44,
1742 |    "id": "ed0590a3",
1743 |    "metadata": {},
1744 |    "outputs": [
1745 |     {
1746 |      "data": {
1747 |       "text/html": [
1748 |        "<div>\n",
1749 |        "<style scoped>\n",
1750 |        "    .dataframe tbody tr th:only-of-type {\n",
1751 |        "        vertical-align: middle;\n",
1752 |        "    }\n",
1753 |        "\n",
1754 |        "    .dataframe tbody tr th {\n",
1755 |        "        vertical-align: top;\n",
1756 |        "    }\n",
1757 |        "\n",
1758 |        "    .dataframe thead th {\n",
1759 |        "        text-align: right;\n",
1760 |        "    }\n",
1761 |        "</style>\n",
1762 |        "<table border=\"1\" class=\"dataframe\">\n",
1763 |        "  <thead>\n",
1764 |        "    <tr style=\"text-align: right;\">\n",
1765 |        "      <th></th>\n",
1766 |        "      <th>AAPL</th>\n",
1767 |        "      <th>BA</th>\n",
1768 |        "      <th>DIS</th>\n",
1769 |        "      <th>IBM</th>\n",
1770 |        "      <th>KO</th>\n",
1771 |        "      <th>MSFT</th>\n",
1772 |        "      <th>Day</th>\n",
1773 |        "      <th>Quarter</th>\n",
1774 |        "    </tr>\n",
1775 |        "  </thead>\n",
1776 |        "  <tbody>\n",
1777 |        "    <tr>\n",
1778 |        "      <th>2009-12-31</th>\n",
1779 |        "      <td>7.526072</td>\n",
1780 |        "      <td>54.130001</td>\n",
1781 |        "      <td>32.250000</td>\n",
1782 |        "      <td>130.899994</td>\n",
1783 |        "      <td>28.500000</td>\n",
1784 |        "      <td>30.480000</td>\n",
1785 |        "      <td>Thursday</td>\n",
1786 |        "      <td>4</td>\n",
1787 |        "    </tr>\n",
1788 |        "    <tr>\n",
1789 |        "      <th>2010-01-01</th>\n",
1790 |        "      <td>7.643214</td>\n",
1791 |        "      <td>56.180000</td>\n",
1792 |        "      <td>32.070000</td>\n",
1793 |        "      <td>132.449997</td>\n",
1794 |        "      <td>28.520000</td>\n",
1795 |        "      <td>30.950001</td>\n",
1796 |        "      <td>Friday</td>\n",
1797 |        "      <td>1</td>\n",
1798 |        "    </tr>\n",
1799 |        "    <tr>\n",
1800 |        "      <th>2010-01-02</th>\n",
1801 |        "      <td>7.643214</td>\n",
1802 |        "      <td>56.180000</td>\n",
1803 |        "      <td>32.070000</td>\n",
1804 |        "      <td>132.449997</td>\n",
1805 |        "      <td>28.520000</td>\n",
1806 |        "      <td>30.950001</td>\n",
1807 |        "      <td>Saturday</td>\n",
1808 |        "      <td>1</td>\n",
1809 |        "    </tr>\n",
1810 |        "    <tr>\n",
1811 |        "      <th>2010-01-03</th>\n",
1812 |        "      <td>7.643214</td>\n",
1813 |        "      <td>56.180000</td>\n",
1814 |        "      <td>32.070000</td>\n",
1815 |        "      <td>132.449997</td>\n",
1816 |        "      <td>28.520000</td>\n",
1817 |        "      <td>30.950001</td>\n",
1818 |        "      <td>Sunday</td>\n",
1819 |        "      <td>1</td>\n",
1820 |        "    </tr>\n",
1821 |        "    <tr>\n",
1822 |        "      <th>2010-01-04</th>\n",
1823 |        "      <td>7.643214</td>\n",
1824 |        "      <td>56.180000</td>\n",
1825 |        "      <td>32.070000</td>\n",
1826 |        "      <td>132.449997</td>\n",
1827 |        "      <td>28.520000</td>\n",
1828 |        "      <td>30.950001</td>\n",
1829 |        "      <td>Monday</td>\n",
1830 |        "      <td>1</td>\n",
1831 |        "    </tr>\n",
1832 |        "    <tr>\n",
1833 |        "      <th>...</th>\n",
1834 |        "      <td>...</td>\n",
1835 |        "      <td>...</td>\n",
1836 |        "      <td>...</td>\n",
1837 |        "      <td>...</td>\n",
1838 |        "      <td>...</td>\n",
1839 |        "      <td>...</td>\n",
1840 |        "      <td>...</td>\n",
1841 |        "      <td>...</td>\n",
1842 |        "    </tr>\n",
1843 |        "    <tr>\n",
1844 |        "      <th>2019-02-02</th>\n",
1845 |        "      <td>42.812500</td>\n",
1846 |        "      <td>397.000000</td>\n",
1847 |        "      <td>111.800003</td>\n",
1848 |        "      <td>135.190002</td>\n",
1849 |        "      <td>49.250000</td>\n",
1850 |        "      <td>105.739998</td>\n",
1851 |        "      <td>Saturday</td>\n",
1852 |        "      <td>1</td>\n",
1853 |        "    </tr>\n",
1854 |        "    <tr>\n",
1855 |        "      <th>2019-02-03</th>\n",
1856 |        "      <td>42.812500</td>\n",
1857 |        "      <td>397.000000</td>\n",
1858 |        "      <td>111.800003</td>\n",
1859 |        "      <td>135.190002</td>\n",
1860 |        "      <td>49.250000</td>\n",
1861 |        "      <td>105.739998</td>\n",
1862 |        "      <td>Sunday</td>\n",
1863 |        "      <td>1</td>\n",
1864 |        "    </tr>\n",
1865 |        "    <tr>\n",
1866 |        "      <th>2019-02-04</th>\n",
1867 |        "      <td>42.812500</td>\n",
1868 |        "      <td>397.000000</td>\n",
1869 |        "      <td>111.800003</td>\n",
1870 |        "      <td>135.190002</td>\n",
1871 |        "      <td>49.250000</td>\n",
1872 |        "      <td>105.739998</td>\n",
1873 |        "      <td>Monday</td>\n",
1874 |        "      <td>1</td>\n",
1875 |        "    </tr>\n",
1876 |        "    <tr>\n",
1877 |        "      <th>2019-02-05</th>\n",
1878 |        "      <td>43.544998</td>\n",
1879 |        "      <td>410.179993</td>\n",
1880 |        "      <td>112.660004</td>\n",
1881 |        "      <td>135.550003</td>\n",
1882 |        "      <td>49.259998</td>\n",
1883 |        "      <td>107.220001</td>\n",
1884 |        "      <td>Tuesday</td>\n",
1885 |        "      <td>1</td>\n",
1886 |        "    </tr>\n",
1887 |        "    <tr>\n",
1888 |        "      <th>2019-02-06</th>\n",
1889 |        "      <td>NaN</td>\n",
1890 |        "      <td>NaN</td>\n",
1891 |        "      <td>NaN</td>\n",
1892 |        "      <td>NaN</td>\n",
1893 |        "      <td>NaN</td>\n",
1894 |        "      <td>NaN</td>\n",
1895 |        "      <td>Wednesday</td>\n",
1896 |        "      <td>1</td>\n",
1897 |        "    </tr>\n",
1898 |        "  </tbody>\n",
1899 |        "</table>\n",
1900 |        "<p>3325 rows × 8 columns</p>\n",
1901 |        "</div>"
1902 |       ],
1903 |       "text/plain": [
1904 |        "                 AAPL          BA         DIS         IBM         KO  \\\n",
1905 |        "2009-12-31   7.526072   54.130001   32.250000  130.899994  28.500000   \n",
1906 |        "2010-01-01   7.643214   56.180000   32.070000  132.449997  28.520000   \n",
1907 |        "2010-01-02   7.643214   56.180000   32.070000  132.449997  28.520000   \n",
1908 |        "2010-01-03   7.643214   56.180000   32.070000  132.449997  28.520000   \n",
1909 |        "2010-01-04   7.643214   56.180000   32.070000  132.449997  28.520000   \n",
1910 |        "...               ...         ...         ...         ...        ...   \n",
1911 |        "2019-02-02  42.812500  397.000000  111.800003  135.190002  49.250000   \n",
1912 |        "2019-02-03  42.812500  397.000000  111.800003  135.190002  49.250000   \n",
1913 |        "2019-02-04  42.812500  397.000000  111.800003  135.190002  49.250000   \n",
1914 |        "2019-02-05  43.544998  410.179993  112.660004  135.550003  49.259998   \n",
1915 |        "2019-02-06        NaN         NaN         NaN         NaN        NaN   \n",
1916 |        "\n",
1917 |        "                  MSFT        Day  Quarter  \n",
1918 |        "2009-12-31   30.480000   Thursday        4  \n",
1919 |        "2010-01-01   30.950001     Friday        1  \n",
1920 |        "2010-01-02   30.950001   Saturday        1  \n",
1921 |        "2010-01-03   30.950001     Sunday        1  \n",
1922 |        "2010-01-04   30.950001     Monday        1  \n",
1923 |        "...                ...        ...      ...  \n",
1924 |        "2019-02-02  105.739998   Saturday        1  \n",
1925 |        "2019-02-03  105.739998     Sunday        1  \n",
1926 |        "2019-02-04  105.739998     Monday        1  \n",
1927 |        "2019-02-05  107.220001    Tuesday        1  \n",
1928 |        "2019-02-06         NaN  Wednesday        1  \n",
1929 |        "\n",
1930 |        "[3325 rows x 8 columns]"
1931 |       ]
1932 |      },
1933 |      "execution_count": 44,
1934 |      "metadata": {},
1935 |      "output_type": "execute_result"
1936 |     }
1937 |    ],
1938 |    "source": [
1939 |     "# Filling the missing stock prices for days using fillna() and backfill parameter\n",
1940 |     "# bfill parameter takes the Monday value and fills it to preceeding missing values on Friday, Saturday and Sunday\n",
1941 |     "close.fillna(method = 'bfill')"
1942 |    ]
1943 |   },
1944 |   {
1945 |    "cell_type": "code",
1946 |    "execution_count": 45,
1947 |    "id": "288c86bc",
1948 |    "metadata": {},
1949 |    "outputs": [
1950 |     {
1951 |      "data": {
1952 |       "text/html": [
1953 |        "<div>\n",
1954 |        "<style scoped>\n",
1955 |        "    .dataframe tbody tr th:only-of-type {\n",
1956 |        "        vertical-align: middle;\n",
1957 |        "    }\n",
1958 |        "\n",
1959 |        "    .dataframe tbody tr th {\n",
1960 |        "        vertical-align: top;\n",
1961 |        "    }\n",
1962 |        "\n",
1963 |        "    .dataframe thead th {\n",
1964 |        "        text-align: right;\n",
1965 |        "    }\n",
1966 |        "</style>\n",
1967 |        "<table border=\"1\" class=\"dataframe\">\n",
1968 |        "  <thead>\n",
1969 |        "    <tr style=\"text-align: right;\">\n",
1970 |        "      <th></th>\n",
1971 |        "      <th>AAPL</th>\n",
1972 |        "      <th>BA</th>\n",
1973 |        "      <th>DIS</th>\n",
1974 |        "      <th>IBM</th>\n",
1975 |        "      <th>KO</th>\n",
1976 |        "      <th>MSFT</th>\n",
1977 |        "      <th>Day</th>\n",
1978 |        "      <th>Quarter</th>\n",
1979 |        "    </tr>\n",
1980 |        "  </thead>\n",
1981 |        "  <tbody>\n",
1982 |        "    <tr>\n",
1983 |        "      <th>2009-12-31</th>\n",
1984 |        "      <td>7.526072</td>\n",
1985 |        "      <td>54.130001</td>\n",
1986 |        "      <td>32.250000</td>\n",
1987 |        "      <td>130.899994</td>\n",
1988 |        "      <td>28.500000</td>\n",
1989 |        "      <td>30.480000</td>\n",
1990 |        "      <td>Thursday</td>\n",
1991 |        "      <td>4</td>\n",
1992 |        "    </tr>\n",
1993 |        "    <tr>\n",
1994 |        "      <th>2010-01-01</th>\n",
1995 |        "      <td>7.526072</td>\n",
1996 |        "      <td>54.130001</td>\n",
1997 |        "      <td>32.250000</td>\n",
1998 |        "      <td>130.899994</td>\n",
1999 |        "      <td>28.500000</td>\n",
2000 |        "      <td>30.480000</td>\n",
2001 |        "      <td>Friday</td>\n",
2002 |        "      <td>1</td>\n",
2003 |        "    </tr>\n",
2004 |        "    <tr>\n",
2005 |        "      <th>2010-01-02</th>\n",
2006 |        "      <td>7.526072</td>\n",
2007 |        "      <td>54.130001</td>\n",
2008 |        "      <td>32.250000</td>\n",
2009 |        "      <td>130.899994</td>\n",
2010 |        "      <td>28.500000</td>\n",
2011 |        "      <td>30.480000</td>\n",
2012 |        "      <td>Saturday</td>\n",
2013 |        "      <td>1</td>\n",
2014 |        "    </tr>\n",
2015 |        "    <tr>\n",
2016 |        "      <th>2010-01-03</th>\n",
2017 |        "      <td>7.526072</td>\n",
2018 |        "      <td>54.130001</td>\n",
2019 |        "      <td>32.250000</td>\n",
2020 |        "      <td>130.899994</td>\n",
2021 |        "      <td>28.500000</td>\n",
2022 |        "      <td>30.480000</td>\n",
2023 |        "      <td>Sunday</td>\n",
2024 |        "      <td>1</td>\n",
2025 |        "    </tr>\n",
2026 |        "    <tr>\n",
2027 |        "      <th>2010-01-04</th>\n",
2028 |        "      <td>7.643214</td>\n",
2029 |        "      <td>56.180000</td>\n",
2030 |        "      <td>32.070000</td>\n",
2031 |        "      <td>132.449997</td>\n",
2032 |        "      <td>28.520000</td>\n",
2033 |        "      <td>30.950001</td>\n",
2034 |        "      <td>Monday</td>\n",
2035 |        "      <td>1</td>\n",
2036 |        "    </tr>\n",
2037 |        "    <tr>\n",
2038 |        "      <th>...</th>\n",
2039 |        "      <td>...</td>\n",
2040 |        "      <td>...</td>\n",
2041 |        "      <td>...</td>\n",
2042 |        "      <td>...</td>\n",
2043 |        "      <td>...</td>\n",
2044 |        "      <td>...</td>\n",
2045 |        "      <td>...</td>\n",
2046 |        "      <td>...</td>\n",
2047 |        "    </tr>\n",
2048 |        "    <tr>\n",
2049 |        "      <th>2019-02-02</th>\n",
2050 |        "      <td>41.630001</td>\n",
2051 |        "      <td>387.429993</td>\n",
2052 |        "      <td>111.300003</td>\n",
2053 |        "      <td>134.100006</td>\n",
2054 |        "      <td>48.700001</td>\n",
2055 |        "      <td>102.779999</td>\n",
2056 |        "      <td>Saturday</td>\n",
2057 |        "      <td>1</td>\n",
2058 |        "    </tr>\n",
2059 |        "    <tr>\n",
2060 |        "      <th>2019-02-03</th>\n",
2061 |        "      <td>41.630001</td>\n",
2062 |        "      <td>387.429993</td>\n",
2063 |        "      <td>111.300003</td>\n",
2064 |        "      <td>134.100006</td>\n",
2065 |        "      <td>48.700001</td>\n",
2066 |        "      <td>102.779999</td>\n",
2067 |        "      <td>Sunday</td>\n",
2068 |        "      <td>1</td>\n",
2069 |        "    </tr>\n",
2070 |        "    <tr>\n",
2071 |        "      <th>2019-02-04</th>\n",
2072 |        "      <td>42.812500</td>\n",
2073 |        "      <td>397.000000</td>\n",
2074 |        "      <td>111.800003</td>\n",
2075 |        "      <td>135.190002</td>\n",
2076 |        "      <td>49.250000</td>\n",
2077 |        "      <td>105.739998</td>\n",
2078 |        "      <td>Monday</td>\n",
2079 |        "      <td>1</td>\n",
2080 |        "    </tr>\n",
2081 |        "    <tr>\n",
2082 |        "      <th>2019-02-05</th>\n",
2083 |        "      <td>43.544998</td>\n",
2084 |        "      <td>410.179993</td>\n",
2085 |        "      <td>112.660004</td>\n",
2086 |        "      <td>135.550003</td>\n",
2087 |        "      <td>49.259998</td>\n",
2088 |        "      <td>107.220001</td>\n",
2089 |        "      <td>Tuesday</td>\n",
2090 |        "      <td>1</td>\n",
2091 |        "    </tr>\n",
2092 |        "    <tr>\n",
2093 |        "      <th>2019-02-06</th>\n",
2094 |        "      <td>43.544998</td>\n",
2095 |        "      <td>410.179993</td>\n",
2096 |        "      <td>112.660004</td>\n",
2097 |        "      <td>135.550003</td>\n",
2098 |        "      <td>49.259998</td>\n",
2099 |        "      <td>107.220001</td>\n",
2100 |        "      <td>Wednesday</td>\n",
2101 |        "      <td>1</td>\n",
2102 |        "    </tr>\n",
2103 |        "  </tbody>\n",
2104 |        "</table>\n",
2105 |        "<p>3325 rows × 8 columns</p>\n",
2106 |        "</div>"
2107 |       ],
2108 |       "text/plain": [
2109 |        "                 AAPL          BA         DIS         IBM         KO  \\\n",
2110 |        "2009-12-31   7.526072   54.130001   32.250000  130.899994  28.500000   \n",
2111 |        "2010-01-01   7.526072   54.130001   32.250000  130.899994  28.500000   \n",
2112 |        "2010-01-02   7.526072   54.130001   32.250000  130.899994  28.500000   \n",
2113 |        "2010-01-03   7.526072   54.130001   32.250000  130.899994  28.500000   \n",
2114 |        "2010-01-04   7.643214   56.180000   32.070000  132.449997  28.520000   \n",
2115 |        "...               ...         ...         ...         ...        ...   \n",
2116 |        "2019-02-02  41.630001  387.429993  111.300003  134.100006  48.700001   \n",
2117 |        "2019-02-03  41.630001  387.429993  111.300003  134.100006  48.700001   \n",
2118 |        "2019-02-04  42.812500  397.000000  111.800003  135.190002  49.250000   \n",
2119 |        "2019-02-05  43.544998  410.179993  112.660004  135.550003  49.259998   \n",
2120 |        "2019-02-06  43.544998  410.179993  112.660004  135.550003  49.259998   \n",
2121 |        "\n",
2122 |        "                  MSFT        Day  Quarter  \n",
2123 |        "2009-12-31   30.480000   Thursday        4  \n",
2124 |        "2010-01-01   30.480000     Friday        1  \n",
2125 |        "2010-01-02   30.480000   Saturday        1  \n",
2126 |        "2010-01-03   30.480000     Sunday        1  \n",
2127 |        "2010-01-04   30.950001     Monday        1  \n",
2128 |        "...                ...        ...      ...  \n",
2129 |        "2019-02-02  102.779999   Saturday        1  \n",
2130 |        "2019-02-03  102.779999     Sunday        1  \n",
2131 |        "2019-02-04  105.739998     Monday        1  \n",
2132 |        "2019-02-05  107.220001    Tuesday        1  \n",
2133 |        "2019-02-06  107.220001  Wednesday        1  \n",
2134 |        "\n",
2135 |        "[3325 rows x 8 columns]"
2136 |       ]
2137 |      },
2138 |      "execution_count": 45,
2139 |      "metadata": {},
2140 |      "output_type": "execute_result"
2141 |     }
2142 |    ],
2143 |    "source": [
2144 |     "# Forward fill method conversely takes the last available values and fills it forward to missing values until next value\n",
2145 |     "close.fillna(method = 'ffill')"
2146 |    ]
2147 |   },
2148 |   {
2149 |    "cell_type": "code",
2150 |    "execution_count": 46,
2151 |    "id": "c79bc100",
2152 |    "metadata": {},
2153 |    "outputs": [],
2154 |    "source": [
2155 |     "# Incase of stock prices forward fill method is used ffill"
2156 |    ]
2157 |   },
2158 |   {
2159 |    "cell_type": "code",
2160 |    "execution_count": 47,
2161 |    "id": "5d8aa920",
2162 |    "metadata": {},
2163 |    "outputs": [],
2164 |    "source": [
2165 |     "# Overwriting the close dataframe using inplace parameter\n",
2166 |     "close.fillna(method = 'ffill', inplace = True)"
2167 |    ]
2168 |   },
2169 |   {
2170 |    "cell_type": "code",
2171 |    "execution_count": 48,
2172 |    "id": "877a8942",
2173 |    "metadata": {},
2174 |    "outputs": [
2175 |     {
2176 |      "data": {
2177 |       "text/html": [
2178 |        "<div>\n",
2179 |        "<style scoped>\n",
2180 |        "    .dataframe tbody tr th:only-of-type {\n",
2181 |        "        vertical-align: middle;\n",
2182 |        "    }\n",
2183 |        "\n",
2184 |        "    .dataframe tbody tr th {\n",
2185 |        "        vertical-align: top;\n",
2186 |        "    }\n",
2187 |        "\n",
2188 |        "    .dataframe thead th {\n",
2189 |        "        text-align: right;\n",
2190 |        "    }\n",
2191 |        "</style>\n",
2192 |        "<table border=\"1\" class=\"dataframe\">\n",
2193 |        "  <thead>\n",
2194 |        "    <tr style=\"text-align: right;\">\n",
2195 |        "      <th></th>\n",
2196 |        "      <th>AAPL</th>\n",
2197 |        "      <th>BA</th>\n",
2198 |        "      <th>DIS</th>\n",
2199 |        "      <th>IBM</th>\n",
2200 |        "      <th>KO</th>\n",
2201 |        "      <th>MSFT</th>\n",
2202 |        "      <th>Day</th>\n",
2203 |        "      <th>Quarter</th>\n",
2204 |        "    </tr>\n",
2205 |        "  </thead>\n",
2206 |        "  <tbody>\n",
2207 |        "    <tr>\n",
2208 |        "      <th>2009-12-31</th>\n",
2209 |        "      <td>7.526072</td>\n",
2210 |        "      <td>54.130001</td>\n",
2211 |        "      <td>32.250000</td>\n",
2212 |        "      <td>130.899994</td>\n",
2213 |        "      <td>28.500000</td>\n",
2214 |        "      <td>30.480000</td>\n",
2215 |        "      <td>Thursday</td>\n",
2216 |        "      <td>4</td>\n",
2217 |        "    </tr>\n",
2218 |        "    <tr>\n",
2219 |        "      <th>2010-01-01</th>\n",
2220 |        "      <td>7.526072</td>\n",
2221 |        "      <td>54.130001</td>\n",
2222 |        "      <td>32.250000</td>\n",
2223 |        "      <td>130.899994</td>\n",
2224 |        "      <td>28.500000</td>\n",
2225 |        "      <td>30.480000</td>\n",
2226 |        "      <td>Friday</td>\n",
2227 |        "      <td>1</td>\n",
2228 |        "    </tr>\n",
2229 |        "    <tr>\n",
2230 |        "      <th>2010-01-02</th>\n",
2231 |        "      <td>7.526072</td>\n",
2232 |        "      <td>54.130001</td>\n",
2233 |        "      <td>32.250000</td>\n",
2234 |        "      <td>130.899994</td>\n",
2235 |        "      <td>28.500000</td>\n",
2236 |        "      <td>30.480000</td>\n",
2237 |        "      <td>Saturday</td>\n",
2238 |        "      <td>1</td>\n",
2239 |        "    </tr>\n",
2240 |        "    <tr>\n",
2241 |        "      <th>2010-01-03</th>\n",
2242 |        "      <td>7.526072</td>\n",
2243 |        "      <td>54.130001</td>\n",
2244 |        "      <td>32.250000</td>\n",
2245 |        "      <td>130.899994</td>\n",
2246 |        "      <td>28.500000</td>\n",
2247 |        "      <td>30.480000</td>\n",
2248 |        "      <td>Sunday</td>\n",
2249 |        "      <td>1</td>\n",
2250 |        "    </tr>\n",
2251 |        "    <tr>\n",
2252 |        "      <th>2010-01-04</th>\n",
2253 |        "      <td>7.643214</td>\n",
2254 |        "      <td>56.180000</td>\n",
2255 |        "      <td>32.070000</td>\n",
2256 |        "      <td>132.449997</td>\n",
2257 |        "      <td>28.520000</td>\n",
2258 |        "      <td>30.950001</td>\n",
2259 |        "      <td>Monday</td>\n",
2260 |        "      <td>1</td>\n",
2261 |        "    </tr>\n",
2262 |        "    <tr>\n",
2263 |        "      <th>2010-01-05</th>\n",
2264 |        "      <td>7.656428</td>\n",
2265 |        "      <td>58.020000</td>\n",
2266 |        "      <td>31.990000</td>\n",
2267 |        "      <td>130.850006</td>\n",
2268 |        "      <td>28.174999</td>\n",
2269 |        "      <td>30.959999</td>\n",
2270 |        "      <td>Tuesday</td>\n",
2271 |        "      <td>1</td>\n",
2272 |        "    </tr>\n",
2273 |        "    <tr>\n",
2274 |        "      <th>2010-01-06</th>\n",
2275 |        "      <td>7.534643</td>\n",
2276 |        "      <td>59.779999</td>\n",
2277 |        "      <td>31.820000</td>\n",
2278 |        "      <td>130.000000</td>\n",
2279 |        "      <td>28.165001</td>\n",
2280 |        "      <td>30.770000</td>\n",
2281 |        "      <td>Wednesday</td>\n",
2282 |        "      <td>1</td>\n",
2283 |        "    </tr>\n",
2284 |        "    <tr>\n",
2285 |        "      <th>2010-01-07</th>\n",
2286 |        "      <td>7.520714</td>\n",
2287 |        "      <td>62.200001</td>\n",
2288 |        "      <td>31.830000</td>\n",
2289 |        "      <td>129.550003</td>\n",
2290 |        "      <td>28.094999</td>\n",
2291 |        "      <td>30.450001</td>\n",
2292 |        "      <td>Thursday</td>\n",
2293 |        "      <td>1</td>\n",
2294 |        "    </tr>\n",
2295 |        "    <tr>\n",
2296 |        "      <th>2010-01-08</th>\n",
2297 |        "      <td>7.570714</td>\n",
2298 |        "      <td>61.599998</td>\n",
2299 |        "      <td>31.879999</td>\n",
2300 |        "      <td>130.850006</td>\n",
2301 |        "      <td>27.575001</td>\n",
2302 |        "      <td>30.660000</td>\n",
2303 |        "      <td>Friday</td>\n",
2304 |        "      <td>1</td>\n",
2305 |        "    </tr>\n",
2306 |        "    <tr>\n",
2307 |        "      <th>2010-01-09</th>\n",
2308 |        "      <td>7.570714</td>\n",
2309 |        "      <td>61.599998</td>\n",
2310 |        "      <td>31.879999</td>\n",
2311 |        "      <td>130.850006</td>\n",
2312 |        "      <td>27.575001</td>\n",
2313 |        "      <td>30.660000</td>\n",
2314 |        "      <td>Saturday</td>\n",
2315 |        "      <td>1</td>\n",
2316 |        "    </tr>\n",
2317 |        "  </tbody>\n",
2318 |        "</table>\n",
2319 |        "</div>"
2320 |       ],
2321 |       "text/plain": [
2322 |        "                AAPL         BA        DIS         IBM         KO       MSFT  \\\n",
2323 |        "2009-12-31  7.526072  54.130001  32.250000  130.899994  28.500000  30.480000   \n",
2324 |        "2010-01-01  7.526072  54.130001  32.250000  130.899994  28.500000  30.480000   \n",
2325 |        "2010-01-02  7.526072  54.130001  32.250000  130.899994  28.500000  30.480000   \n",
2326 |        "2010-01-03  7.526072  54.130001  32.250000  130.899994  28.500000  30.480000   \n",
2327 |        "2010-01-04  7.643214  56.180000  32.070000  132.449997  28.520000  30.950001   \n",
2328 |        "2010-01-05  7.656428  58.020000  31.990000  130.850006  28.174999  30.959999   \n",
2329 |        "2010-01-06  7.534643  59.779999  31.820000  130.000000  28.165001  30.770000   \n",
2330 |        "2010-01-07  7.520714  62.200001  31.830000  129.550003  28.094999  30.450001   \n",
2331 |        "2010-01-08  7.570714  61.599998  31.879999  130.850006  27.575001  30.660000   \n",
2332 |        "2010-01-09  7.570714  61.599998  31.879999  130.850006  27.575001  30.660000   \n",
2333 |        "\n",
2334 |        "                  Day  Quarter  \n",
2335 |        "2009-12-31   Thursday        4  \n",
2336 |        "2010-01-01     Friday        1  \n",
2337 |        "2010-01-02   Saturday        1  \n",
2338 |        "2010-01-03     Sunday        1  \n",
2339 |        "2010-01-04     Monday        1  \n",
2340 |        "2010-01-05    Tuesday        1  \n",
2341 |        "2010-01-06  Wednesday        1  \n",
2342 |        "2010-01-07   Thursday        1  \n",
2343 |        "2010-01-08     Friday        1  \n",
2344 |        "2010-01-09   Saturday        1  "
2345 |       ]
2346 |      },
2347 |      "execution_count": 48,
2348 |      "metadata": {},
2349 |      "output_type": "execute_result"
2350 |     }
2351 |    ],
2352 |    "source": [
2353 |     "close.head(10)"
2354 |    ]
2355 |   },
2356 |   {
2357 |    "cell_type": "code",
2358 |    "execution_count": 49,
2359 |    "id": "915a063b",
2360 |    "metadata": {},
2361 |    "outputs": [],
2362 |    "source": [
2363 |     "# Importing temperature dataframe to manipulate\n",
2364 |     "temp = pd.read_csv('temp.csv', parse_dates = ['datetime'], index_col = 'datetime')"
2365 |    ]
2366 |   },
2367 |   {
2368 |    "cell_type": "code",
2369 |    "execution_count": 50,
2370 |    "id": "069f34b7",
2371 |    "metadata": {},
2372 |    "outputs": [
2373 |     {
2374 |      "data": {
2375 |       "text/html": [
2376 |        "<div>\n",
2377 |        "<style scoped>\n",
2378 |        "    .dataframe tbody tr th:only-of-type {\n",
2379 |        "        vertical-align: middle;\n",
2380 |        "    }\n",
2381 |        "\n",
2382 |        "    .dataframe tbody tr th {\n",
2383 |        "        vertical-align: top;\n",
2384 |        "    }\n",
2385 |        "\n",
2386 |        "    .dataframe thead th {\n",
2387 |        "        text-align: right;\n",
2388 |        "    }\n",
2389 |        "</style>\n",
2390 |        "<table border=\"1\" class=\"dataframe\">\n",
2391 |        "  <thead>\n",
2392 |        "    <tr style=\"text-align: right;\">\n",
2393 |        "      <th></th>\n",
2394 |        "      <th>LA</th>\n",
2395 |        "      <th>NY</th>\n",
2396 |        "    </tr>\n",
2397 |        "    <tr>\n",
2398 |        "      <th>datetime</th>\n",
2399 |        "      <th></th>\n",
2400 |        "      <th></th>\n",
2401 |        "    </tr>\n",
2402 |        "  </thead>\n",
2403 |        "  <tbody>\n",
2404 |        "    <tr>\n",
2405 |        "      <th>2013-01-01 00:00:00</th>\n",
2406 |        "      <td>11.7</td>\n",
2407 |        "      <td>-1.1</td>\n",
2408 |        "    </tr>\n",
2409 |        "    <tr>\n",
2410 |        "      <th>2013-01-01 01:00:00</th>\n",
2411 |        "      <td>10.7</td>\n",
2412 |        "      <td>-1.7</td>\n",
2413 |        "    </tr>\n",
2414 |        "    <tr>\n",
2415 |        "      <th>2013-01-01 02:00:00</th>\n",
2416 |        "      <td>9.9</td>\n",
2417 |        "      <td>-2.0</td>\n",
2418 |        "    </tr>\n",
2419 |        "    <tr>\n",
2420 |        "      <th>2013-01-01 03:00:00</th>\n",
2421 |        "      <td>9.3</td>\n",
2422 |        "      <td>-2.1</td>\n",
2423 |        "    </tr>\n",
2424 |        "    <tr>\n",
2425 |        "      <th>2013-01-01 04:00:00</th>\n",
2426 |        "      <td>8.8</td>\n",
2427 |        "      <td>-2.3</td>\n",
2428 |        "    </tr>\n",
2429 |        "    <tr>\n",
2430 |        "      <th>2013-01-01 05:00:00</th>\n",
2431 |        "      <td>8.7</td>\n",
2432 |        "      <td>-2.5</td>\n",
2433 |        "    </tr>\n",
2434 |        "    <tr>\n",
2435 |        "      <th>2013-01-01 06:00:00</th>\n",
2436 |        "      <td>6.9</td>\n",
2437 |        "      <td>-3.2</td>\n",
2438 |        "    </tr>\n",
2439 |        "    <tr>\n",
2440 |        "      <th>2013-01-01 07:00:00</th>\n",
2441 |        "      <td>7.8</td>\n",
2442 |        "      <td>-3.4</td>\n",
2443 |        "    </tr>\n",
2444 |        "    <tr>\n",
2445 |        "      <th>2013-01-01 08:00:00</th>\n",
2446 |        "      <td>6.7</td>\n",
2447 |        "      <td>-3.0</td>\n",
2448 |        "    </tr>\n",
2449 |        "    <tr>\n",
2450 |        "      <th>2013-01-01 09:00:00</th>\n",
2451 |        "      <td>6.6</td>\n",
2452 |        "      <td>-1.8</td>\n",
2453 |        "    </tr>\n",
2454 |        "  </tbody>\n",
2455 |        "</table>\n",
2456 |        "</div>"
2457 |       ],
2458 |       "text/plain": [
2459 |        "                       LA   NY\n",
2460 |        "datetime                      \n",
2461 |        "2013-01-01 00:00:00  11.7 -1.1\n",
2462 |        "2013-01-01 01:00:00  10.7 -1.7\n",
2463 |        "2013-01-01 02:00:00   9.9 -2.0\n",
2464 |        "2013-01-01 03:00:00   9.3 -2.1\n",
2465 |        "2013-01-01 04:00:00   8.8 -2.3\n",
2466 |        "2013-01-01 05:00:00   8.7 -2.5\n",
2467 |        "2013-01-01 06:00:00   6.9 -3.2\n",
2468 |        "2013-01-01 07:00:00   7.8 -3.4\n",
2469 |        "2013-01-01 08:00:00   6.7 -3.0\n",
2470 |        "2013-01-01 09:00:00   6.6 -1.8"
2471 |       ]
2472 |      },
2473 |      "execution_count": 50,
2474 |      "metadata": {},
2475 |      "output_type": "execute_result"
2476 |     }
2477 |    ],
2478 |    "source": [
2479 |     "temp.head(10)"
2480 |    ]
2481 |   },
2482 |   {
2483 |    "cell_type": "code",
2484 |    "execution_count": 51,
2485 |    "id": "48c69d94",
2486 |    "metadata": {},
2487 |    "outputs": [
2488 |     {
2489 |      "data": {
2490 |       "text/html": [
2491 |        "<div>\n",
2492 |        "<style scoped>\n",
2493 |        "    .dataframe tbody tr th:only-of-type {\n",
2494 |        "        vertical-align: middle;\n",
2495 |        "    }\n",
2496 |        "\n",
2497 |        "    .dataframe tbody tr th {\n",
2498 |        "        vertical-align: top;\n",
2499 |        "    }\n",
2500 |        "\n",
2501 |        "    .dataframe thead th {\n",
2502 |        "        text-align: right;\n",
2503 |        "    }\n",
2504 |        "</style>\n",
2505 |        "<table border=\"1\" class=\"dataframe\">\n",
2506 |        "  <thead>\n",
2507 |        "    <tr style=\"text-align: right;\">\n",
2508 |        "      <th></th>\n",
2509 |        "      <th>LA</th>\n",
2510 |        "      <th>NY</th>\n",
2511 |        "    </tr>\n",
2512 |        "    <tr>\n",
2513 |        "      <th>datetime</th>\n",
2514 |        "      <th></th>\n",
2515 |        "      <th></th>\n",
2516 |        "    </tr>\n",
2517 |        "  </thead>\n",
2518 |        "  <tbody>\n",
2519 |        "    <tr>\n",
2520 |        "      <th>2013-01-01 00:00:00</th>\n",
2521 |        "      <td>11.7</td>\n",
2522 |        "      <td>-1.1</td>\n",
2523 |        "    </tr>\n",
2524 |        "    <tr>\n",
2525 |        "      <th>2013-01-01 00:30:00</th>\n",
2526 |        "      <td>NaN</td>\n",
2527 |        "      <td>NaN</td>\n",
2528 |        "    </tr>\n",
2529 |        "    <tr>\n",
2530 |        "      <th>2013-01-01 01:00:00</th>\n",
2531 |        "      <td>10.7</td>\n",
2532 |        "      <td>-1.7</td>\n",
2533 |        "    </tr>\n",
2534 |        "    <tr>\n",
2535 |        "      <th>2013-01-01 01:30:00</th>\n",
2536 |        "      <td>NaN</td>\n",
2537 |        "      <td>NaN</td>\n",
2538 |        "    </tr>\n",
2539 |        "    <tr>\n",
2540 |        "      <th>2013-01-01 02:00:00</th>\n",
2541 |        "      <td>9.9</td>\n",
2542 |        "      <td>-2.0</td>\n",
2543 |        "    </tr>\n",
2544 |        "    <tr>\n",
2545 |        "      <th>2013-01-01 02:30:00</th>\n",
2546 |        "      <td>NaN</td>\n",
2547 |        "      <td>NaN</td>\n",
2548 |        "    </tr>\n",
2549 |        "    <tr>\n",
2550 |        "      <th>2013-01-01 03:00:00</th>\n",
2551 |        "      <td>9.3</td>\n",
2552 |        "      <td>-2.1</td>\n",
2553 |        "    </tr>\n",
2554 |        "    <tr>\n",
2555 |        "      <th>2013-01-01 03:30:00</th>\n",
2556 |        "      <td>NaN</td>\n",
2557 |        "      <td>NaN</td>\n",
2558 |        "    </tr>\n",
2559 |        "    <tr>\n",
2560 |        "      <th>2013-01-01 04:00:00</th>\n",
2561 |        "      <td>8.8</td>\n",
2562 |        "      <td>-2.3</td>\n",
2563 |        "    </tr>\n",
2564 |        "    <tr>\n",
2565 |        "      <th>2013-01-01 04:30:00</th>\n",
2566 |        "      <td>NaN</td>\n",
2567 |        "      <td>NaN</td>\n",
2568 |        "    </tr>\n",
2569 |        "  </tbody>\n",
2570 |        "</table>\n",
2571 |        "</div>"
2572 |       ],
2573 |       "text/plain": [
2574 |        "                       LA   NY\n",
2575 |        "datetime                      \n",
2576 |        "2013-01-01 00:00:00  11.7 -1.1\n",
2577 |        "2013-01-01 00:30:00   NaN  NaN\n",
2578 |        "2013-01-01 01:00:00  10.7 -1.7\n",
2579 |        "2013-01-01 01:30:00   NaN  NaN\n",
2580 |        "2013-01-01 02:00:00   9.9 -2.0\n",
2581 |        "2013-01-01 02:30:00   NaN  NaN\n",
2582 |        "2013-01-01 03:00:00   9.3 -2.1\n",
2583 |        "2013-01-01 03:30:00   NaN  NaN\n",
2584 |        "2013-01-01 04:00:00   8.8 -2.3\n",
2585 |        "2013-01-01 04:30:00   NaN  NaN"
2586 |       ]
2587 |      },
2588 |      "execution_count": 51,
2589 |      "metadata": {},
2590 |      "output_type": "execute_result"
2591 |     }
2592 |    ],
2593 |    "source": [
2594 |     "# Resampling the data from hourly to 30 min frequency creates missing values, which we want to fill by mean of two \n",
2595 |     "# adjacent values\n",
2596 |     "temp = temp.resample('30 Min').mean()\n",
2597 |     "temp.head(10)"
2598 |    ]
2599 |   },
2600 |   {
2601 |    "cell_type": "code",
2602 |    "execution_count": 53,
2603 |    "id": "68877e6c",
2604 |    "metadata": {},
2605 |    "outputs": [
2606 |     {
2607 |      "data": {
2608 |       "text/html": [
2609 |        "<div>\n",
2610 |        "<style scoped>\n",
2611 |        "    .dataframe tbody tr th:only-of-type {\n",
2612 |        "        vertical-align: middle;\n",
2613 |        "    }\n",
2614 |        "\n",
2615 |        "    .dataframe tbody tr th {\n",
2616 |        "        vertical-align: top;\n",
2617 |        "    }\n",
2618 |        "\n",
2619 |        "    .dataframe thead th {\n",
2620 |        "        text-align: right;\n",
2621 |        "    }\n",
2622 |        "</style>\n",
2623 |        "<table border=\"1\" class=\"dataframe\">\n",
2624 |        "  <thead>\n",
2625 |        "    <tr style=\"text-align: right;\">\n",
2626 |        "      <th></th>\n",
2627 |        "      <th>LA</th>\n",
2628 |        "      <th>NY</th>\n",
2629 |        "    </tr>\n",
2630 |        "    <tr>\n",
2631 |        "      <th>datetime</th>\n",
2632 |        "      <th></th>\n",
2633 |        "      <th></th>\n",
2634 |        "    </tr>\n",
2635 |        "  </thead>\n",
2636 |        "  <tbody>\n",
2637 |        "    <tr>\n",
2638 |        "      <th>2013-01-01 00:00:00</th>\n",
2639 |        "      <td>11.70</td>\n",
2640 |        "      <td>-1.10</td>\n",
2641 |        "    </tr>\n",
2642 |        "    <tr>\n",
2643 |        "      <th>2013-01-01 00:30:00</th>\n",
2644 |        "      <td>11.20</td>\n",
2645 |        "      <td>-1.40</td>\n",
2646 |        "    </tr>\n",
2647 |        "    <tr>\n",
2648 |        "      <th>2013-01-01 01:00:00</th>\n",
2649 |        "      <td>10.70</td>\n",
2650 |        "      <td>-1.70</td>\n",
2651 |        "    </tr>\n",
2652 |        "    <tr>\n",
2653 |        "      <th>2013-01-01 01:30:00</th>\n",
2654 |        "      <td>10.30</td>\n",
2655 |        "      <td>-1.85</td>\n",
2656 |        "    </tr>\n",
2657 |        "    <tr>\n",
2658 |        "      <th>2013-01-01 02:00:00</th>\n",
2659 |        "      <td>9.90</td>\n",
2660 |        "      <td>-2.00</td>\n",
2661 |        "    </tr>\n",
2662 |        "    <tr>\n",
2663 |        "      <th>...</th>\n",
2664 |        "      <td>...</td>\n",
2665 |        "      <td>...</td>\n",
2666 |        "    </tr>\n",
2667 |        "    <tr>\n",
2668 |        "      <th>2016-12-31 21:00:00</th>\n",
2669 |        "      <td>12.80</td>\n",
2670 |        "      <td>5.80</td>\n",
2671 |        "    </tr>\n",
2672 |        "    <tr>\n",
2673 |        "      <th>2016-12-31 21:30:00</th>\n",
2674 |        "      <td>12.55</td>\n",
2675 |        "      <td>5.75</td>\n",
2676 |        "    </tr>\n",
2677 |        "    <tr>\n",
2678 |        "      <th>2016-12-31 22:00:00</th>\n",
2679 |        "      <td>12.30</td>\n",
2680 |        "      <td>5.70</td>\n",
2681 |        "    </tr>\n",
2682 |        "    <tr>\n",
2683 |        "      <th>2016-12-31 22:30:00</th>\n",
2684 |        "      <td>12.10</td>\n",
2685 |        "      <td>5.60</td>\n",
2686 |        "    </tr>\n",
2687 |        "    <tr>\n",
2688 |        "      <th>2016-12-31 23:00:00</th>\n",
2689 |        "      <td>11.90</td>\n",
2690 |        "      <td>5.50</td>\n",
2691 |        "    </tr>\n",
2692 |        "  </tbody>\n",
2693 |        "</table>\n",
2694 |        "<p>70127 rows × 2 columns</p>\n",
2695 |        "</div>"
2696 |       ],
2697 |       "text/plain": [
2698 |        "                        LA    NY\n",
2699 |        "datetime                        \n",
2700 |        "2013-01-01 00:00:00  11.70 -1.10\n",
2701 |        "2013-01-01 00:30:00  11.20 -1.40\n",
2702 |        "2013-01-01 01:00:00  10.70 -1.70\n",
2703 |        "2013-01-01 01:30:00  10.30 -1.85\n",
2704 |        "2013-01-01 02:00:00   9.90 -2.00\n",
2705 |        "...                    ...   ...\n",
2706 |        "2016-12-31 21:00:00  12.80  5.80\n",
2707 |        "2016-12-31 21:30:00  12.55  5.75\n",
2708 |        "2016-12-31 22:00:00  12.30  5.70\n",
2709 |        "2016-12-31 22:30:00  12.10  5.60\n",
2710 |        "2016-12-31 23:00:00  11.90  5.50\n",
2711 |        "\n",
2712 |        "[70127 rows x 2 columns]"
2713 |       ]
2714 |      },
2715 |      "execution_count": 53,
2716 |      "metadata": {},
2717 |      "output_type": "execute_result"
2718 |     }
2719 |    ],
2720 |    "source": [
2721 |     "# Using before and after values to interpolate\n",
2722 |     "temp.interpolate()"
2723 |    ]
2724 |   },
2725 |   {
2726 |    "cell_type": "markdown",
2727 |    "id": "e5644432",
2728 |    "metadata": {},
2729 |    "source": [
2730 |     "# Timezones and Converting\n",
2731 |     "Generally UTC timezone is taken as standard to avoid confusion"
2732 |    ]
2733 |   },
2734 |   {
2735 |    "cell_type": "code",
2736 |    "execution_count": 54,
2737 |    "id": "94f6e348",
2738 |    "metadata": {},
2739 |    "outputs": [],
2740 |    "source": [
2741 |     "import pandas as pd"
2742 |    ]
2743 |   },
2744 |   {
2745 |    "cell_type": "code",
2746 |    "execution_count": 55,
2747 |    "id": "a0b3e630",
2748 |    "metadata": {},
2749 |    "outputs": [],
2750 |    "source": [
2751 |     "# Importing data from csv file and saving it to a new dataframe\n",
2752 |     "ge = pd.read_csv('GE_prices.csv', parse_dates = ['date'], index_col = 'date')"
2753 |    ]
2754 |   },
2755 |   {
2756 |    "cell_type": "code",
2757 |    "execution_count": 56,
2758 |    "id": "e1c2e7be",
2759 |    "metadata": {},
2760 |    "outputs": [
2761 |     {
2762 |      "data": {
2763 |       "text/html": [
2764 |        "<div>\n",
2765 |        "<style scoped>\n",
2766 |        "    .dataframe tbody tr th:only-of-type {\n",
2767 |        "        vertical-align: middle;\n",
2768 |        "    }\n",
2769 |        "\n",
2770 |        "    .dataframe tbody tr th {\n",
2771 |        "        vertical-align: top;\n",
2772 |        "    }\n",
2773 |        "\n",
2774 |        "    .dataframe thead th {\n",
2775 |        "        text-align: right;\n",
2776 |        "    }\n",
2777 |        "</style>\n",
2778 |        "<table border=\"1\" class=\"dataframe\">\n",
2779 |        "  <thead>\n",
2780 |        "    <tr style=\"text-align: right;\">\n",
2781 |        "      <th></th>\n",
2782 |        "      <th>1. open</th>\n",
2783 |        "      <th>2. high</th>\n",
2784 |        "      <th>3. low</th>\n",
2785 |        "      <th>4. close</th>\n",
2786 |        "      <th>5. volume</th>\n",
2787 |        "    </tr>\n",
2788 |        "    <tr>\n",
2789 |        "      <th>date</th>\n",
2790 |        "      <th></th>\n",
2791 |        "      <th></th>\n",
2792 |        "      <th></th>\n",
2793 |        "      <th></th>\n",
2794 |        "      <th></th>\n",
2795 |        "    </tr>\n",
2796 |        "  </thead>\n",
2797 |        "  <tbody>\n",
2798 |        "    <tr>\n",
2799 |        "      <th>2019-07-29 10:00:00</th>\n",
2800 |        "      <td>10.5000</td>\n",
2801 |        "      <td>10.5800</td>\n",
2802 |        "      <td>10.4300</td>\n",
2803 |        "      <td>10.4400</td>\n",
2804 |        "      <td>4840329.0</td>\n",
2805 |        "    </tr>\n",
2806 |        "    <tr>\n",
2807 |        "      <th>2019-07-29 10:30:00</th>\n",
2808 |        "      <td>10.4404</td>\n",
2809 |        "      <td>10.4847</td>\n",
2810 |        "      <td>10.3997</td>\n",
2811 |        "      <td>10.4104</td>\n",
2812 |        "      <td>3860280.0</td>\n",
2813 |        "    </tr>\n",
2814 |        "    <tr>\n",
2815 |        "      <th>2019-07-29 11:00:00</th>\n",
2816 |        "      <td>10.4200</td>\n",
2817 |        "      <td>10.5000</td>\n",
2818 |        "      <td>10.4100</td>\n",
2819 |        "      <td>10.5000</td>\n",
2820 |        "      <td>1912153.0</td>\n",
2821 |        "    </tr>\n",
2822 |        "    <tr>\n",
2823 |        "      <th>2019-07-29 11:30:00</th>\n",
2824 |        "      <td>10.4931</td>\n",
2825 |        "      <td>10.5300</td>\n",
2826 |        "      <td>10.4800</td>\n",
2827 |        "      <td>10.4850</td>\n",
2828 |        "      <td>1916453.0</td>\n",
2829 |        "    </tr>\n",
2830 |        "    <tr>\n",
2831 |        "      <th>2019-07-29 12:00:00</th>\n",
2832 |        "      <td>10.4900</td>\n",
2833 |        "      <td>10.5000</td>\n",
2834 |        "      <td>10.4400</td>\n",
2835 |        "      <td>10.4500</td>\n",
2836 |        "      <td>2467638.0</td>\n",
2837 |        "    </tr>\n",
2838 |        "  </tbody>\n",
2839 |        "</table>\n",
2840 |        "</div>"
2841 |       ],
2842 |       "text/plain": [
2843 |        "                     1. open  2. high   3. low  4. close  5. volume\n",
2844 |        "date                                                               \n",
2845 |        "2019-07-29 10:00:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
2846 |        "2019-07-29 10:30:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
2847 |        "2019-07-29 11:00:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
2848 |        "2019-07-29 11:30:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
2849 |        "2019-07-29 12:00:00  10.4900  10.5000  10.4400   10.4500  2467638.0"
2850 |       ]
2851 |      },
2852 |      "execution_count": 56,
2853 |      "metadata": {},
2854 |      "output_type": "execute_result"
2855 |     }
2856 |    ],
2857 |    "source": [
2858 |     "ge.head()"
2859 |    ]
2860 |   },
2861 |   {
2862 |    "cell_type": "code",
2863 |    "execution_count": 57,
2864 |    "id": "3f1dd1ab",
2865 |    "metadata": {},
2866 |    "outputs": [
2867 |     {
2868 |      "name": "stdout",
2869 |      "output_type": "stream",
2870 |      "text": [
2871 |       "<class 'pandas.core.frame.DataFrame'>\n",
2872 |       "DatetimeIndex: 389 entries, 2019-07-29 10:00:00 to 2019-09-09 16:00:00\n",
2873 |       "Data columns (total 5 columns):\n",
2874 |       " #   Column     Non-Null Count  Dtype  \n",
2875 |       "---  ------     --------------  -----  \n",
2876 |       " 0   1. open    389 non-null    float64\n",
2877 |       " 1   2. high    389 non-null    float64\n",
2878 |       " 2   3. low     389 non-null    float64\n",
2879 |       " 3   4. close   389 non-null    float64\n",
2880 |       " 4   5. volume  389 non-null    float64\n",
2881 |       "dtypes: float64(5)\n",
2882 |       "memory usage: 18.2 KB\n"
2883 |      ]
2884 |     }
2885 |    ],
2886 |    "source": [
2887 |     "ge.info()"
2888 |    ]
2889 |   },
2890 |   {
2891 |    "cell_type": "code",
2892 |    "execution_count": 58,
2893 |    "id": "1b48170b",
2894 |    "metadata": {},
2895 |    "outputs": [],
2896 |    "source": [
2897 |     "# These timestamps are based on Eastern Time which is 4 hours behind UTC"
2898 |    ]
2899 |   },
2900 |   {
2901 |    "cell_type": "code",
2902 |    "execution_count": 59,
2903 |    "id": "5d85c24e",
2904 |    "metadata": {},
2905 |    "outputs": [
2906 |     {
2907 |      "data": {
2908 |       "text/plain": [
2909 |        "DatetimeIndex(['2019-07-29 10:00:00', '2019-07-29 10:30:00',\n",
2910 |        "               '2019-07-29 11:00:00', '2019-07-29 11:30:00',\n",
2911 |        "               '2019-07-29 12:00:00', '2019-07-29 12:30:00',\n",
2912 |        "               '2019-07-29 13:00:00', '2019-07-29 13:30:00',\n",
2913 |        "               '2019-07-29 14:00:00', '2019-07-29 14:30:00',\n",
2914 |        "               ...\n",
2915 |        "               '2019-09-09 11:30:00', '2019-09-09 12:00:00',\n",
2916 |        "               '2019-09-09 12:30:00', '2019-09-09 13:00:00',\n",
2917 |        "               '2019-09-09 13:30:00', '2019-09-09 14:00:00',\n",
2918 |        "               '2019-09-09 14:30:00', '2019-09-09 15:00:00',\n",
2919 |        "               '2019-09-09 15:30:00', '2019-09-09 16:00:00'],\n",
2920 |        "              dtype='datetime64[ns]', name='date', length=389, freq=None)"
2921 |       ]
2922 |      },
2923 |      "execution_count": 59,
2924 |      "metadata": {},
2925 |      "output_type": "execute_result"
2926 |     }
2927 |    ],
2928 |    "source": [
2929 |     "ge.index"
2930 |    ]
2931 |   },
2932 |   {
2933 |    "cell_type": "code",
2934 |    "execution_count": 60,
2935 |    "id": "3a0154e7",
2936 |    "metadata": {},
2937 |    "outputs": [
2938 |     {
2939 |      "name": "stdout",
2940 |      "output_type": "stream",
2941 |      "text": [
2942 |       "None\n"
2943 |      ]
2944 |     }
2945 |    ],
2946 |    "source": [
2947 |     "# Getting information about timezones\n",
2948 |     "print(ge.index.tz)"
2949 |    ]
2950 |   },
2951 |   {
2952 |    "cell_type": "code",
2953 |    "execution_count": 61,
2954 |    "id": "694373fb",
2955 |    "metadata": {},
2956 |    "outputs": [
2957 |     {
2958 |      "data": {
2959 |       "text/html": [
2960 |        "<div>\n",
2961 |        "<style scoped>\n",
2962 |        "    .dataframe tbody tr th:only-of-type {\n",
2963 |        "        vertical-align: middle;\n",
2964 |        "    }\n",
2965 |        "\n",
2966 |        "    .dataframe tbody tr th {\n",
2967 |        "        vertical-align: top;\n",
2968 |        "    }\n",
2969 |        "\n",
2970 |        "    .dataframe thead th {\n",
2971 |        "        text-align: right;\n",
2972 |        "    }\n",
2973 |        "</style>\n",
2974 |        "<table border=\"1\" class=\"dataframe\">\n",
2975 |        "  <thead>\n",
2976 |        "    <tr style=\"text-align: right;\">\n",
2977 |        "      <th></th>\n",
2978 |        "      <th>1. open</th>\n",
2979 |        "      <th>2. high</th>\n",
2980 |        "      <th>3. low</th>\n",
2981 |        "      <th>4. close</th>\n",
2982 |        "      <th>5. volume</th>\n",
2983 |        "    </tr>\n",
2984 |        "    <tr>\n",
2985 |        "      <th>date</th>\n",
2986 |        "      <th></th>\n",
2987 |        "      <th></th>\n",
2988 |        "      <th></th>\n",
2989 |        "      <th></th>\n",
2990 |        "      <th></th>\n",
2991 |        "    </tr>\n",
2992 |        "  </thead>\n",
2993 |        "  <tbody>\n",
2994 |        "    <tr>\n",
2995 |        "      <th>2019-07-29 10:00:00+00:00</th>\n",
2996 |        "      <td>10.5000</td>\n",
2997 |        "      <td>10.5800</td>\n",
2998 |        "      <td>10.4300</td>\n",
2999 |        "      <td>10.4400</td>\n",
3000 |        "      <td>4840329.0</td>\n",
3001 |        "    </tr>\n",
3002 |        "    <tr>\n",
3003 |        "      <th>2019-07-29 10:30:00+00:00</th>\n",
3004 |        "      <td>10.4404</td>\n",
3005 |        "      <td>10.4847</td>\n",
3006 |        "      <td>10.3997</td>\n",
3007 |        "      <td>10.4104</td>\n",
3008 |        "      <td>3860280.0</td>\n",
3009 |        "    </tr>\n",
3010 |        "    <tr>\n",
3011 |        "      <th>2019-07-29 11:00:00+00:00</th>\n",
3012 |        "      <td>10.4200</td>\n",
3013 |        "      <td>10.5000</td>\n",
3014 |        "      <td>10.4100</td>\n",
3015 |        "      <td>10.5000</td>\n",
3016 |        "      <td>1912153.0</td>\n",
3017 |        "    </tr>\n",
3018 |        "    <tr>\n",
3019 |        "      <th>2019-07-29 11:30:00+00:00</th>\n",
3020 |        "      <td>10.4931</td>\n",
3021 |        "      <td>10.5300</td>\n",
3022 |        "      <td>10.4800</td>\n",
3023 |        "      <td>10.4850</td>\n",
3024 |        "      <td>1916453.0</td>\n",
3025 |        "    </tr>\n",
3026 |        "    <tr>\n",
3027 |        "      <th>2019-07-29 12:00:00+00:00</th>\n",
3028 |        "      <td>10.4900</td>\n",
3029 |        "      <td>10.5000</td>\n",
3030 |        "      <td>10.4400</td>\n",
3031 |        "      <td>10.4500</td>\n",
3032 |        "      <td>2467638.0</td>\n",
3033 |        "    </tr>\n",
3034 |        "    <tr>\n",
3035 |        "      <th>...</th>\n",
3036 |        "      <td>...</td>\n",
3037 |        "      <td>...</td>\n",
3038 |        "      <td>...</td>\n",
3039 |        "      <td>...</td>\n",
3040 |        "      <td>...</td>\n",
3041 |        "    </tr>\n",
3042 |        "    <tr>\n",
3043 |        "      <th>2019-09-09 14:00:00+00:00</th>\n",
3044 |        "      <td>8.9900</td>\n",
3045 |        "      <td>8.9950</td>\n",
3046 |        "      <td>8.9700</td>\n",
3047 |        "      <td>8.9800</td>\n",
3048 |        "      <td>1740722.0</td>\n",
3049 |        "    </tr>\n",
3050 |        "    <tr>\n",
3051 |        "      <th>2019-09-09 14:30:00+00:00</th>\n",
3052 |        "      <td>8.9735</td>\n",
3053 |        "      <td>9.0000</td>\n",
3054 |        "      <td>8.9700</td>\n",
3055 |        "      <td>8.9835</td>\n",
3056 |        "      <td>1412823.0</td>\n",
3057 |        "    </tr>\n",
3058 |        "    <tr>\n",
3059 |        "      <th>2019-09-09 15:00:00+00:00</th>\n",
3060 |        "      <td>8.9900</td>\n",
3061 |        "      <td>9.0100</td>\n",
3062 |        "      <td>8.9350</td>\n",
3063 |        "      <td>8.9500</td>\n",
3064 |        "      <td>4177838.0</td>\n",
3065 |        "    </tr>\n",
3066 |        "    <tr>\n",
3067 |        "      <th>2019-09-09 15:30:00+00:00</th>\n",
3068 |        "      <td>8.9453</td>\n",
3069 |        "      <td>8.9700</td>\n",
3070 |        "      <td>8.9300</td>\n",
3071 |        "      <td>8.9603</td>\n",
3072 |        "      <td>2254279.0</td>\n",
3073 |        "    </tr>\n",
3074 |        "    <tr>\n",
3075 |        "      <th>2019-09-09 16:00:00+00:00</th>\n",
3076 |        "      <td>8.9600</td>\n",
3077 |        "      <td>8.9900</td>\n",
3078 |        "      <td>8.9200</td>\n",
3079 |        "      <td>8.9700</td>\n",
3080 |        "      <td>6887530.0</td>\n",
3081 |        "    </tr>\n",
3082 |        "  </tbody>\n",
3083 |        "</table>\n",
3084 |        "<p>389 rows × 5 columns</p>\n",
3085 |        "</div>"
3086 |       ],
3087 |       "text/plain": [
3088 |        "                           1. open  2. high   3. low  4. close  5. volume\n",
3089 |        "date                                                                     \n",
3090 |        "2019-07-29 10:00:00+00:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
3091 |        "2019-07-29 10:30:00+00:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
3092 |        "2019-07-29 11:00:00+00:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
3093 |        "2019-07-29 11:30:00+00:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
3094 |        "2019-07-29 12:00:00+00:00  10.4900  10.5000  10.4400   10.4500  2467638.0\n",
3095 |        "...                            ...      ...      ...       ...        ...\n",
3096 |        "2019-09-09 14:00:00+00:00   8.9900   8.9950   8.9700    8.9800  1740722.0\n",
3097 |        "2019-09-09 14:30:00+00:00   8.9735   9.0000   8.9700    8.9835  1412823.0\n",
3098 |        "2019-09-09 15:00:00+00:00   8.9900   9.0100   8.9350    8.9500  4177838.0\n",
3099 |        "2019-09-09 15:30:00+00:00   8.9453   8.9700   8.9300    8.9603  2254279.0\n",
3100 |        "2019-09-09 16:00:00+00:00   8.9600   8.9900   8.9200    8.9700  6887530.0\n",
3101 |        "\n",
3102 |        "[389 rows x 5 columns]"
3103 |       ]
3104 |      },
3105 |      "execution_count": 61,
3106 |      "metadata": {},
3107 |      "output_type": "execute_result"
3108 |     }
3109 |    ],
3110 |    "source": [
3111 |     "# Localising our datetime index to UTC\n",
3112 |     "ge.tz_localize('UTC')"
3113 |    ]
3114 |   },
3115 |   {
3116 |    "cell_type": "code",
3117 |    "execution_count": 65,
3118 |    "id": "fd102fc0",
3119 |    "metadata": {},
3120 |    "outputs": [],
3121 |    "source": [
3122 |     "# Localising our datetime index to Eastern Time\n",
3123 |     "ge = ge.tz_localize('America/New_York')"
3124 |    ]
3125 |   },
3126 |   {
3127 |    "cell_type": "code",
3128 |    "execution_count": 66,
3129 |    "id": "49ee2288",
3130 |    "metadata": {},
3131 |    "outputs": [
3132 |     {
3133 |      "data": {
3134 |       "text/html": [
3135 |        "<div>\n",
3136 |        "<style scoped>\n",
3137 |        "    .dataframe tbody tr th:only-of-type {\n",
3138 |        "        vertical-align: middle;\n",
3139 |        "    }\n",
3140 |        "\n",
3141 |        "    .dataframe tbody tr th {\n",
3142 |        "        vertical-align: top;\n",
3143 |        "    }\n",
3144 |        "\n",
3145 |        "    .dataframe thead th {\n",
3146 |        "        text-align: right;\n",
3147 |        "    }\n",
3148 |        "</style>\n",
3149 |        "<table border=\"1\" class=\"dataframe\">\n",
3150 |        "  <thead>\n",
3151 |        "    <tr style=\"text-align: right;\">\n",
3152 |        "      <th></th>\n",
3153 |        "      <th>1. open</th>\n",
3154 |        "      <th>2. high</th>\n",
3155 |        "      <th>3. low</th>\n",
3156 |        "      <th>4. close</th>\n",
3157 |        "      <th>5. volume</th>\n",
3158 |        "    </tr>\n",
3159 |        "    <tr>\n",
3160 |        "      <th>date</th>\n",
3161 |        "      <th></th>\n",
3162 |        "      <th></th>\n",
3163 |        "      <th></th>\n",
3164 |        "      <th></th>\n",
3165 |        "      <th></th>\n",
3166 |        "    </tr>\n",
3167 |        "  </thead>\n",
3168 |        "  <tbody>\n",
3169 |        "    <tr>\n",
3170 |        "      <th>2019-07-29 10:00:00-04:00</th>\n",
3171 |        "      <td>10.5000</td>\n",
3172 |        "      <td>10.5800</td>\n",
3173 |        "      <td>10.4300</td>\n",
3174 |        "      <td>10.4400</td>\n",
3175 |        "      <td>4840329.0</td>\n",
3176 |        "    </tr>\n",
3177 |        "    <tr>\n",
3178 |        "      <th>2019-07-29 10:30:00-04:00</th>\n",
3179 |        "      <td>10.4404</td>\n",
3180 |        "      <td>10.4847</td>\n",
3181 |        "      <td>10.3997</td>\n",
3182 |        "      <td>10.4104</td>\n",
3183 |        "      <td>3860280.0</td>\n",
3184 |        "    </tr>\n",
3185 |        "    <tr>\n",
3186 |        "      <th>2019-07-29 11:00:00-04:00</th>\n",
3187 |        "      <td>10.4200</td>\n",
3188 |        "      <td>10.5000</td>\n",
3189 |        "      <td>10.4100</td>\n",
3190 |        "      <td>10.5000</td>\n",
3191 |        "      <td>1912153.0</td>\n",
3192 |        "    </tr>\n",
3193 |        "    <tr>\n",
3194 |        "      <th>2019-07-29 11:30:00-04:00</th>\n",
3195 |        "      <td>10.4931</td>\n",
3196 |        "      <td>10.5300</td>\n",
3197 |        "      <td>10.4800</td>\n",
3198 |        "      <td>10.4850</td>\n",
3199 |        "      <td>1916453.0</td>\n",
3200 |        "    </tr>\n",
3201 |        "    <tr>\n",
3202 |        "      <th>2019-07-29 12:00:00-04:00</th>\n",
3203 |        "      <td>10.4900</td>\n",
3204 |        "      <td>10.5000</td>\n",
3205 |        "      <td>10.4400</td>\n",
3206 |        "      <td>10.4500</td>\n",
3207 |        "      <td>2467638.0</td>\n",
3208 |        "    </tr>\n",
3209 |        "  </tbody>\n",
3210 |        "</table>\n",
3211 |        "</div>"
3212 |       ],
3213 |       "text/plain": [
3214 |        "                           1. open  2. high   3. low  4. close  5. volume\n",
3215 |        "date                                                                     \n",
3216 |        "2019-07-29 10:00:00-04:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
3217 |        "2019-07-29 10:30:00-04:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
3218 |        "2019-07-29 11:00:00-04:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
3219 |        "2019-07-29 11:30:00-04:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
3220 |        "2019-07-29 12:00:00-04:00  10.4900  10.5000  10.4400   10.4500  2467638.0"
3221 |       ]
3222 |      },
3223 |      "execution_count": 66,
3224 |      "metadata": {},
3225 |      "output_type": "execute_result"
3226 |     }
3227 |    ],
3228 |    "source": [
3229 |     "ge.head()"
3230 |    ]
3231 |   },
3232 |   {
3233 |    "cell_type": "code",
3234 |    "execution_count": 67,
3235 |    "id": "cf9df850",
3236 |    "metadata": {},
3237 |    "outputs": [
3238 |     {
3239 |      "data": {
3240 |       "text/html": [
3241 |        "<div>\n",
3242 |        "<style scoped>\n",
3243 |        "    .dataframe tbody tr th:only-of-type {\n",
3244 |        "        vertical-align: middle;\n",
3245 |        "    }\n",
3246 |        "\n",
3247 |        "    .dataframe tbody tr th {\n",
3248 |        "        vertical-align: top;\n",
3249 |        "    }\n",
3250 |        "\n",
3251 |        "    .dataframe thead th {\n",
3252 |        "        text-align: right;\n",
3253 |        "    }\n",
3254 |        "</style>\n",
3255 |        "<table border=\"1\" class=\"dataframe\">\n",
3256 |        "  <thead>\n",
3257 |        "    <tr style=\"text-align: right;\">\n",
3258 |        "      <th></th>\n",
3259 |        "      <th>1. open</th>\n",
3260 |        "      <th>2. high</th>\n",
3261 |        "      <th>3. low</th>\n",
3262 |        "      <th>4. close</th>\n",
3263 |        "      <th>5. volume</th>\n",
3264 |        "    </tr>\n",
3265 |        "    <tr>\n",
3266 |        "      <th>date</th>\n",
3267 |        "      <th></th>\n",
3268 |        "      <th></th>\n",
3269 |        "      <th></th>\n",
3270 |        "      <th></th>\n",
3271 |        "      <th></th>\n",
3272 |        "    </tr>\n",
3273 |        "  </thead>\n",
3274 |        "  <tbody>\n",
3275 |        "    <tr>\n",
3276 |        "      <th>2019-07-29 07:00:00-07:00</th>\n",
3277 |        "      <td>10.5000</td>\n",
3278 |        "      <td>10.5800</td>\n",
3279 |        "      <td>10.4300</td>\n",
3280 |        "      <td>10.4400</td>\n",
3281 |        "      <td>4840329.0</td>\n",
3282 |        "    </tr>\n",
3283 |        "    <tr>\n",
3284 |        "      <th>2019-07-29 07:30:00-07:00</th>\n",
3285 |        "      <td>10.4404</td>\n",
3286 |        "      <td>10.4847</td>\n",
3287 |        "      <td>10.3997</td>\n",
3288 |        "      <td>10.4104</td>\n",
3289 |        "      <td>3860280.0</td>\n",
3290 |        "    </tr>\n",
3291 |        "    <tr>\n",
3292 |        "      <th>2019-07-29 08:00:00-07:00</th>\n",
3293 |        "      <td>10.4200</td>\n",
3294 |        "      <td>10.5000</td>\n",
3295 |        "      <td>10.4100</td>\n",
3296 |        "      <td>10.5000</td>\n",
3297 |        "      <td>1912153.0</td>\n",
3298 |        "    </tr>\n",
3299 |        "    <tr>\n",
3300 |        "      <th>2019-07-29 08:30:00-07:00</th>\n",
3301 |        "      <td>10.4931</td>\n",
3302 |        "      <td>10.5300</td>\n",
3303 |        "      <td>10.4800</td>\n",
3304 |        "      <td>10.4850</td>\n",
3305 |        "      <td>1916453.0</td>\n",
3306 |        "    </tr>\n",
3307 |        "    <tr>\n",
3308 |        "      <th>2019-07-29 09:00:00-07:00</th>\n",
3309 |        "      <td>10.4900</td>\n",
3310 |        "      <td>10.5000</td>\n",
3311 |        "      <td>10.4400</td>\n",
3312 |        "      <td>10.4500</td>\n",
3313 |        "      <td>2467638.0</td>\n",
3314 |        "    </tr>\n",
3315 |        "    <tr>\n",
3316 |        "      <th>...</th>\n",
3317 |        "      <td>...</td>\n",
3318 |        "      <td>...</td>\n",
3319 |        "      <td>...</td>\n",
3320 |        "      <td>...</td>\n",
3321 |        "      <td>...</td>\n",
3322 |        "    </tr>\n",
3323 |        "    <tr>\n",
3324 |        "      <th>2019-09-09 11:00:00-07:00</th>\n",
3325 |        "      <td>8.9900</td>\n",
3326 |        "      <td>8.9950</td>\n",
3327 |        "      <td>8.9700</td>\n",
3328 |        "      <td>8.9800</td>\n",
3329 |        "      <td>1740722.0</td>\n",
3330 |        "    </tr>\n",
3331 |        "    <tr>\n",
3332 |        "      <th>2019-09-09 11:30:00-07:00</th>\n",
3333 |        "      <td>8.9735</td>\n",
3334 |        "      <td>9.0000</td>\n",
3335 |        "      <td>8.9700</td>\n",
3336 |        "      <td>8.9835</td>\n",
3337 |        "      <td>1412823.0</td>\n",
3338 |        "    </tr>\n",
3339 |        "    <tr>\n",
3340 |        "      <th>2019-09-09 12:00:00-07:00</th>\n",
3341 |        "      <td>8.9900</td>\n",
3342 |        "      <td>9.0100</td>\n",
3343 |        "      <td>8.9350</td>\n",
3344 |        "      <td>8.9500</td>\n",
3345 |        "      <td>4177838.0</td>\n",
3346 |        "    </tr>\n",
3347 |        "    <tr>\n",
3348 |        "      <th>2019-09-09 12:30:00-07:00</th>\n",
3349 |        "      <td>8.9453</td>\n",
3350 |        "      <td>8.9700</td>\n",
3351 |        "      <td>8.9300</td>\n",
3352 |        "      <td>8.9603</td>\n",
3353 |        "      <td>2254279.0</td>\n",
3354 |        "    </tr>\n",
3355 |        "    <tr>\n",
3356 |        "      <th>2019-09-09 13:00:00-07:00</th>\n",
3357 |        "      <td>8.9600</td>\n",
3358 |        "      <td>8.9900</td>\n",
3359 |        "      <td>8.9200</td>\n",
3360 |        "      <td>8.9700</td>\n",
3361 |        "      <td>6887530.0</td>\n",
3362 |        "    </tr>\n",
3363 |        "  </tbody>\n",
3364 |        "</table>\n",
3365 |        "<p>389 rows × 5 columns</p>\n",
3366 |        "</div>"
3367 |       ],
3368 |       "text/plain": [
3369 |        "                           1. open  2. high   3. low  4. close  5. volume\n",
3370 |        "date                                                                     \n",
3371 |        "2019-07-29 07:00:00-07:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
3372 |        "2019-07-29 07:30:00-07:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
3373 |        "2019-07-29 08:00:00-07:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
3374 |        "2019-07-29 08:30:00-07:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
3375 |        "2019-07-29 09:00:00-07:00  10.4900  10.5000  10.4400   10.4500  2467638.0\n",
3376 |        "...                            ...      ...      ...       ...        ...\n",
3377 |        "2019-09-09 11:00:00-07:00   8.9900   8.9950   8.9700    8.9800  1740722.0\n",
3378 |        "2019-09-09 11:30:00-07:00   8.9735   9.0000   8.9700    8.9835  1412823.0\n",
3379 |        "2019-09-09 12:00:00-07:00   8.9900   9.0100   8.9350    8.9500  4177838.0\n",
3380 |        "2019-09-09 12:30:00-07:00   8.9453   8.9700   8.9300    8.9603  2254279.0\n",
3381 |        "2019-09-09 13:00:00-07:00   8.9600   8.9900   8.9200    8.9700  6887530.0\n",
3382 |        "\n",
3383 |        "[389 rows x 5 columns]"
3384 |       ]
3385 |      },
3386 |      "execution_count": 67,
3387 |      "metadata": {},
3388 |      "output_type": "execute_result"
3389 |     }
3390 |    ],
3391 |    "source": [
3392 |     "# Converting from one time zone to another\n",
3393 |     "ge.tz_convert('America/Los_Angeles')"
3394 |    ]
3395 |   },
3396 |   {
3397 |    "cell_type": "code",
3398 |    "execution_count": 68,
3399 |    "id": "d279b290",
3400 |    "metadata": {},
3401 |    "outputs": [],
3402 |    "source": [
3403 |     "ge_la = ge.tz_convert('America/Los_Angeles')"
3404 |    ]
3405 |   },
3406 |   {
3407 |    "cell_type": "code",
3408 |    "execution_count": 69,
3409 |    "id": "4dd295aa",
3410 |    "metadata": {},
3411 |    "outputs": [
3412 |     {
3413 |      "data": {
3414 |       "text/html": [
3415 |        "<div>\n",
3416 |        "<style scoped>\n",
3417 |        "    .dataframe tbody tr th:only-of-type {\n",
3418 |        "        vertical-align: middle;\n",
3419 |        "    }\n",
3420 |        "\n",
3421 |        "    .dataframe tbody tr th {\n",
3422 |        "        vertical-align: top;\n",
3423 |        "    }\n",
3424 |        "\n",
3425 |        "    .dataframe thead th {\n",
3426 |        "        text-align: right;\n",
3427 |        "    }\n",
3428 |        "</style>\n",
3429 |        "<table border=\"1\" class=\"dataframe\">\n",
3430 |        "  <thead>\n",
3431 |        "    <tr style=\"text-align: right;\">\n",
3432 |        "      <th></th>\n",
3433 |        "      <th>1. open</th>\n",
3434 |        "      <th>2. high</th>\n",
3435 |        "      <th>3. low</th>\n",
3436 |        "      <th>4. close</th>\n",
3437 |        "      <th>5. volume</th>\n",
3438 |        "    </tr>\n",
3439 |        "    <tr>\n",
3440 |        "      <th>date</th>\n",
3441 |        "      <th></th>\n",
3442 |        "      <th></th>\n",
3443 |        "      <th></th>\n",
3444 |        "      <th></th>\n",
3445 |        "      <th></th>\n",
3446 |        "    </tr>\n",
3447 |        "  </thead>\n",
3448 |        "  <tbody>\n",
3449 |        "    <tr>\n",
3450 |        "      <th>2019-07-29 07:00:00-07:00</th>\n",
3451 |        "      <td>10.5000</td>\n",
3452 |        "      <td>10.5800</td>\n",
3453 |        "      <td>10.4300</td>\n",
3454 |        "      <td>10.4400</td>\n",
3455 |        "      <td>4840329.0</td>\n",
3456 |        "    </tr>\n",
3457 |        "    <tr>\n",
3458 |        "      <th>2019-07-29 07:30:00-07:00</th>\n",
3459 |        "      <td>10.4404</td>\n",
3460 |        "      <td>10.4847</td>\n",
3461 |        "      <td>10.3997</td>\n",
3462 |        "      <td>10.4104</td>\n",
3463 |        "      <td>3860280.0</td>\n",
3464 |        "    </tr>\n",
3465 |        "    <tr>\n",
3466 |        "      <th>2019-07-29 08:00:00-07:00</th>\n",
3467 |        "      <td>10.4200</td>\n",
3468 |        "      <td>10.5000</td>\n",
3469 |        "      <td>10.4100</td>\n",
3470 |        "      <td>10.5000</td>\n",
3471 |        "      <td>1912153.0</td>\n",
3472 |        "    </tr>\n",
3473 |        "    <tr>\n",
3474 |        "      <th>2019-07-29 08:30:00-07:00</th>\n",
3475 |        "      <td>10.4931</td>\n",
3476 |        "      <td>10.5300</td>\n",
3477 |        "      <td>10.4800</td>\n",
3478 |        "      <td>10.4850</td>\n",
3479 |        "      <td>1916453.0</td>\n",
3480 |        "    </tr>\n",
3481 |        "    <tr>\n",
3482 |        "      <th>2019-07-29 09:00:00-07:00</th>\n",
3483 |        "      <td>10.4900</td>\n",
3484 |        "      <td>10.5000</td>\n",
3485 |        "      <td>10.4400</td>\n",
3486 |        "      <td>10.4500</td>\n",
3487 |        "      <td>2467638.0</td>\n",
3488 |        "    </tr>\n",
3489 |        "  </tbody>\n",
3490 |        "</table>\n",
3491 |        "</div>"
3492 |       ],
3493 |       "text/plain": [
3494 |        "                           1. open  2. high   3. low  4. close  5. volume\n",
3495 |        "date                                                                     \n",
3496 |        "2019-07-29 07:00:00-07:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
3497 |        "2019-07-29 07:30:00-07:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
3498 |        "2019-07-29 08:00:00-07:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
3499 |        "2019-07-29 08:30:00-07:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
3500 |        "2019-07-29 09:00:00-07:00  10.4900  10.5000  10.4400   10.4500  2467638.0"
3501 |       ]
3502 |      },
3503 |      "execution_count": 69,
3504 |      "metadata": {},
3505 |      "output_type": "execute_result"
3506 |     }
3507 |    ],
3508 |    "source": [
3509 |     "ge_la.head()"
3510 |    ]
3511 |   },
3512 |   {
3513 |    "cell_type": "code",
3514 |    "execution_count": 70,
3515 |    "id": "13baa049",
3516 |    "metadata": {},
3517 |    "outputs": [
3518 |     {
3519 |      "data": {
3520 |       "text/html": [
3521 |        "<div>\n",
3522 |        "<style scoped>\n",
3523 |        "    .dataframe tbody tr th:only-of-type {\n",
3524 |        "        vertical-align: middle;\n",
3525 |        "    }\n",
3526 |        "\n",
3527 |        "    .dataframe tbody tr th {\n",
3528 |        "        vertical-align: top;\n",
3529 |        "    }\n",
3530 |        "\n",
3531 |        "    .dataframe thead th {\n",
3532 |        "        text-align: right;\n",
3533 |        "    }\n",
3534 |        "</style>\n",
3535 |        "<table border=\"1\" class=\"dataframe\">\n",
3536 |        "  <thead>\n",
3537 |        "    <tr style=\"text-align: right;\">\n",
3538 |        "      <th></th>\n",
3539 |        "      <th>1. open</th>\n",
3540 |        "      <th>2. high</th>\n",
3541 |        "      <th>3. low</th>\n",
3542 |        "      <th>4. close</th>\n",
3543 |        "      <th>5. volume</th>\n",
3544 |        "    </tr>\n",
3545 |        "    <tr>\n",
3546 |        "      <th>date</th>\n",
3547 |        "      <th></th>\n",
3548 |        "      <th></th>\n",
3549 |        "      <th></th>\n",
3550 |        "      <th></th>\n",
3551 |        "      <th></th>\n",
3552 |        "    </tr>\n",
3553 |        "  </thead>\n",
3554 |        "  <tbody>\n",
3555 |        "    <tr>\n",
3556 |        "      <th>2019-07-29 10:00:00-04:00</th>\n",
3557 |        "      <td>10.5000</td>\n",
3558 |        "      <td>10.5800</td>\n",
3559 |        "      <td>10.4300</td>\n",
3560 |        "      <td>10.4400</td>\n",
3561 |        "      <td>4840329.0</td>\n",
3562 |        "    </tr>\n",
3563 |        "    <tr>\n",
3564 |        "      <th>2019-07-29 10:30:00-04:00</th>\n",
3565 |        "      <td>10.4404</td>\n",
3566 |        "      <td>10.4847</td>\n",
3567 |        "      <td>10.3997</td>\n",
3568 |        "      <td>10.4104</td>\n",
3569 |        "      <td>3860280.0</td>\n",
3570 |        "    </tr>\n",
3571 |        "    <tr>\n",
3572 |        "      <th>2019-07-29 11:00:00-04:00</th>\n",
3573 |        "      <td>10.4200</td>\n",
3574 |        "      <td>10.5000</td>\n",
3575 |        "      <td>10.4100</td>\n",
3576 |        "      <td>10.5000</td>\n",
3577 |        "      <td>1912153.0</td>\n",
3578 |        "    </tr>\n",
3579 |        "    <tr>\n",
3580 |        "      <th>2019-07-29 11:30:00-04:00</th>\n",
3581 |        "      <td>10.4931</td>\n",
3582 |        "      <td>10.5300</td>\n",
3583 |        "      <td>10.4800</td>\n",
3584 |        "      <td>10.4850</td>\n",
3585 |        "      <td>1916453.0</td>\n",
3586 |        "    </tr>\n",
3587 |        "    <tr>\n",
3588 |        "      <th>2019-07-29 12:00:00-04:00</th>\n",
3589 |        "      <td>10.4900</td>\n",
3590 |        "      <td>10.5000</td>\n",
3591 |        "      <td>10.4400</td>\n",
3592 |        "      <td>10.4500</td>\n",
3593 |        "      <td>2467638.0</td>\n",
3594 |        "    </tr>\n",
3595 |        "  </tbody>\n",
3596 |        "</table>\n",
3597 |        "</div>"
3598 |       ],
3599 |       "text/plain": [
3600 |        "                           1. open  2. high   3. low  4. close  5. volume\n",
3601 |        "date                                                                     \n",
3602 |        "2019-07-29 10:00:00-04:00  10.5000  10.5800  10.4300   10.4400  4840329.0\n",
3603 |        "2019-07-29 10:30:00-04:00  10.4404  10.4847  10.3997   10.4104  3860280.0\n",
3604 |        "2019-07-29 11:00:00-04:00  10.4200  10.5000  10.4100   10.5000  1912153.0\n",
3605 |        "2019-07-29 11:30:00-04:00  10.4931  10.5300  10.4800   10.4850  1916453.0\n",
3606 |        "2019-07-29 12:00:00-04:00  10.4900  10.5000  10.4400   10.4500  2467638.0"
3607 |       ]
3608 |      },
3609 |      "execution_count": 70,
3610 |      "metadata": {},
3611 |      "output_type": "execute_result"
3612 |     }
3613 |    ],
3614 |    "source": [
3615 |     "ge.head()"
3616 |    ]
3617 |   },
3618 |   {
3619 |    "cell_type": "code",
3620 |    "execution_count": 71,
3621 |    "id": "5a29b8bd",
3622 |    "metadata": {},
3623 |    "outputs": [],
3624 |    "source": [
3625 |     "# Concatenating our ge_la and ge dataframes horizontally\n",
3626 |     "comb = pd.concat([ge, ge_la], axis = 1)"
3627 |    ]
3628 |   },
3629 |   {
3630 |    "cell_type": "code",
3631 |    "execution_count": 72,
3632 |    "id": "8eb65c71",
3633 |    "metadata": {},
3634 |    "outputs": [
3635 |     {
3636 |      "data": {
3637 |       "text/html": [
3638 |        "<div>\n",
3639 |        "<style scoped>\n",
3640 |        "    .dataframe tbody tr th:only-of-type {\n",
3641 |        "        vertical-align: middle;\n",
3642 |        "    }\n",
3643 |        "\n",
3644 |        "    .dataframe tbody tr th {\n",
3645 |        "        vertical-align: top;\n",
3646 |        "    }\n",
3647 |        "\n",
3648 |        "    .dataframe thead th {\n",
3649 |        "        text-align: right;\n",
3650 |        "    }\n",
3651 |        "</style>\n",
3652 |        "<table border=\"1\" class=\"dataframe\">\n",
3653 |        "  <thead>\n",
3654 |        "    <tr style=\"text-align: right;\">\n",
3655 |        "      <th></th>\n",
3656 |        "      <th>1. open</th>\n",
3657 |        "      <th>2. high</th>\n",
3658 |        "      <th>3. low</th>\n",
3659 |        "      <th>4. close</th>\n",
3660 |        "      <th>5. volume</th>\n",
3661 |        "      <th>1. open</th>\n",
3662 |        "      <th>2. high</th>\n",
3663 |        "      <th>3. low</th>\n",
3664 |        "      <th>4. close</th>\n",
3665 |        "      <th>5. volume</th>\n",
3666 |        "    </tr>\n",
3667 |        "    <tr>\n",
3668 |        "      <th>date</th>\n",
3669 |        "      <th></th>\n",
3670 |        "      <th></th>\n",
3671 |        "      <th></th>\n",
3672 |        "      <th></th>\n",
3673 |        "      <th></th>\n",
3674 |        "      <th></th>\n",
3675 |        "      <th></th>\n",
3676 |        "      <th></th>\n",
3677 |        "      <th></th>\n",
3678 |        "      <th></th>\n",
3679 |        "    </tr>\n",
3680 |        "  </thead>\n",
3681 |        "  <tbody>\n",
3682 |        "    <tr>\n",
3683 |        "      <th>2019-07-29 14:00:00+00:00</th>\n",
3684 |        "      <td>10.5000</td>\n",
3685 |        "      <td>10.5800</td>\n",
3686 |        "      <td>10.4300</td>\n",
3687 |        "      <td>10.4400</td>\n",
3688 |        "      <td>4840329.0</td>\n",
3689 |        "      <td>10.5000</td>\n",
3690 |        "      <td>10.5800</td>\n",
3691 |        "      <td>10.4300</td>\n",
3692 |        "      <td>10.4400</td>\n",
3693 |        "      <td>4840329.0</td>\n",
3694 |        "    </tr>\n",
3695 |        "    <tr>\n",
3696 |        "      <th>2019-07-29 14:30:00+00:00</th>\n",
3697 |        "      <td>10.4404</td>\n",
3698 |        "      <td>10.4847</td>\n",
3699 |        "      <td>10.3997</td>\n",
3700 |        "      <td>10.4104</td>\n",
3701 |        "      <td>3860280.0</td>\n",
3702 |        "      <td>10.4404</td>\n",
3703 |        "      <td>10.4847</td>\n",
3704 |        "      <td>10.3997</td>\n",
3705 |        "      <td>10.4104</td>\n",
3706 |        "      <td>3860280.0</td>\n",
3707 |        "    </tr>\n",
3708 |        "    <tr>\n",
3709 |        "      <th>2019-07-29 15:00:00+00:00</th>\n",
3710 |        "      <td>10.4200</td>\n",
3711 |        "      <td>10.5000</td>\n",
3712 |        "      <td>10.4100</td>\n",
3713 |        "      <td>10.5000</td>\n",
3714 |        "      <td>1912153.0</td>\n",
3715 |        "      <td>10.4200</td>\n",
3716 |        "      <td>10.5000</td>\n",
3717 |        "      <td>10.4100</td>\n",
3718 |        "      <td>10.5000</td>\n",
3719 |        "      <td>1912153.0</td>\n",
3720 |        "    </tr>\n",
3721 |        "    <tr>\n",
3722 |        "      <th>2019-07-29 15:30:00+00:00</th>\n",
3723 |        "      <td>10.4931</td>\n",
3724 |        "      <td>10.5300</td>\n",
3725 |        "      <td>10.4800</td>\n",
3726 |        "      <td>10.4850</td>\n",
3727 |        "      <td>1916453.0</td>\n",
3728 |        "      <td>10.4931</td>\n",
3729 |        "      <td>10.5300</td>\n",
3730 |        "      <td>10.4800</td>\n",
3731 |        "      <td>10.4850</td>\n",
3732 |        "      <td>1916453.0</td>\n",
3733 |        "    </tr>\n",
3734 |        "    <tr>\n",
3735 |        "      <th>2019-07-29 16:00:00+00:00</th>\n",
3736 |        "      <td>10.4900</td>\n",
3737 |        "      <td>10.5000</td>\n",
3738 |        "      <td>10.4400</td>\n",
3739 |        "      <td>10.4500</td>\n",
3740 |        "      <td>2467638.0</td>\n",
3741 |        "      <td>10.4900</td>\n",
3742 |        "      <td>10.5000</td>\n",
3743 |        "      <td>10.4400</td>\n",
3744 |        "      <td>10.4500</td>\n",
3745 |        "      <td>2467638.0</td>\n",
3746 |        "    </tr>\n",
3747 |        "  </tbody>\n",
3748 |        "</table>\n",
3749 |        "</div>"
3750 |       ],
3751 |       "text/plain": [
3752 |        "                           1. open  2. high   3. low  4. close  5. volume  \\\n",
3753 |        "date                                                                        \n",
3754 |        "2019-07-29 14:00:00+00:00  10.5000  10.5800  10.4300   10.4400  4840329.0   \n",
3755 |        "2019-07-29 14:30:00+00:00  10.4404  10.4847  10.3997   10.4104  3860280.0   \n",
3756 |        "2019-07-29 15:00:00+00:00  10.4200  10.5000  10.4100   10.5000  1912153.0   \n",
3757 |        "2019-07-29 15:30:00+00:00  10.4931  10.5300  10.4800   10.4850  1916453.0   \n",
3758 |        "2019-07-29 16:00:00+00:00  10.4900  10.5000  10.4400   10.4500  2467638.0   \n",
3759 |        "\n",
3760 |        "                           1. open  2. high   3. low  4. close  5. volume  \n",
3761 |        "date                                                                       \n",
3762 |        "2019-07-29 14:00:00+00:00  10.5000  10.5800  10.4300   10.4400  4840329.0  \n",
3763 |        "2019-07-29 14:30:00+00:00  10.4404  10.4847  10.3997   10.4104  3860280.0  \n",
3764 |        "2019-07-29 15:00:00+00:00  10.4200  10.5000  10.4100   10.5000  1912153.0  \n",
3765 |        "2019-07-29 15:30:00+00:00  10.4931  10.5300  10.4800   10.4850  1916453.0  \n",
3766 |        "2019-07-29 16:00:00+00:00  10.4900  10.5000  10.4400   10.4500  2467638.0  "
3767 |       ]
3768 |      },
3769 |      "execution_count": 72,
3770 |      "metadata": {},
3771 |      "output_type": "execute_result"
3772 |     }
3773 |    ],
3774 |    "source": [
3775 |     "comb.head()"
3776 |    ]
3777 |   },
3778 |   {
3779 |    "cell_type": "code",
3780 |    "execution_count": 73,
3781 |    "id": "92af0b87",
3782 |    "metadata": {},
3783 |    "outputs": [
3784 |     {
3785 |      "data": {
3786 |       "text/plain": [
3787 |        "DatetimeIndex(['2019-07-29 14:00:00+00:00', '2019-07-29 14:30:00+00:00',\n",
3788 |        "               '2019-07-29 15:00:00+00:00', '2019-07-29 15:30:00+00:00',\n",
3789 |        "               '2019-07-29 16:00:00+00:00', '2019-07-29 16:30:00+00:00',\n",
3790 |        "               '2019-07-29 17:00:00+00:00', '2019-07-29 17:30:00+00:00',\n",
3791 |        "               '2019-07-29 18:00:00+00:00', '2019-07-29 18:30:00+00:00',\n",
3792 |        "               ...\n",
3793 |        "               '2019-09-09 15:30:00+00:00', '2019-09-09 16:00:00+00:00',\n",
3794 |        "               '2019-09-09 16:30:00+00:00', '2019-09-09 17:00:00+00:00',\n",
3795 |        "               '2019-09-09 17:30:00+00:00', '2019-09-09 18:00:00+00:00',\n",
3796 |        "               '2019-09-09 18:30:00+00:00', '2019-09-09 19:00:00+00:00',\n",
3797 |        "               '2019-09-09 19:30:00+00:00', '2019-09-09 20:00:00+00:00'],\n",
3798 |        "              dtype='datetime64[ns, UTC]', name='date', length=389, freq=None)"
3799 |       ]
3800 |      },
3801 |      "execution_count": 73,
3802 |      "metadata": {},
3803 |      "output_type": "execute_result"
3804 |     }
3805 |    ],
3806 |    "source": [
3807 |     "comb.index"
3808 |    ]
3809 |   },
3810 |   {
3811 |    "cell_type": "code",
3812 |    "execution_count": 74,
3813 |    "id": "f1fec104",
3814 |    "metadata": {},
3815 |    "outputs": [],
3816 |    "source": [
3817 |     "# Creating new columns in our dataframe for LA Time and NY Timezones\n",
3818 |     "comb['NY_time'] = comb.index.tz_convert('America/New_York')\n",
3819 |     "comb['LA_time'] = comb.index.tz_convert('America/Los_Angeles')"
3820 |    ]
3821 |   },
3822 |   {
3823 |    "cell_type": "code",
3824 |    "execution_count": 75,
3825 |    "id": "fd01065d",
3826 |    "metadata": {},
3827 |    "outputs": [
3828 |     {
3829 |      "data": {
3830 |       "text/html": [
3831 |        "<div>\n",
3832 |        "<style scoped>\n",
3833 |        "    .dataframe tbody tr th:only-of-type {\n",
3834 |        "        vertical-align: middle;\n",
3835 |        "    }\n",
3836 |        "\n",
3837 |        "    .dataframe tbody tr th {\n",
3838 |        "        vertical-align: top;\n",
3839 |        "    }\n",
3840 |        "\n",
3841 |        "    .dataframe thead th {\n",
3842 |        "        text-align: right;\n",
3843 |        "    }\n",
3844 |        "</style>\n",
3845 |        "<table border=\"1\" class=\"dataframe\">\n",
3846 |        "  <thead>\n",
3847 |        "    <tr style=\"text-align: right;\">\n",
3848 |        "      <th></th>\n",
3849 |        "      <th>1. open</th>\n",
3850 |        "      <th>2. high</th>\n",
3851 |        "      <th>3. low</th>\n",
3852 |        "      <th>4. close</th>\n",
3853 |        "      <th>5. volume</th>\n",
3854 |        "      <th>1. open</th>\n",
3855 |        "      <th>2. high</th>\n",
3856 |        "      <th>3. low</th>\n",
3857 |        "      <th>4. close</th>\n",
3858 |        "      <th>5. volume</th>\n",
3859 |        "      <th>NY_time</th>\n",
3860 |        "      <th>LA_time</th>\n",
3861 |        "    </tr>\n",
3862 |        "    <tr>\n",
3863 |        "      <th>date</th>\n",
3864 |        "      <th></th>\n",
3865 |        "      <th></th>\n",
3866 |        "      <th></th>\n",
3867 |        "      <th></th>\n",
3868 |        "      <th></th>\n",
3869 |        "      <th></th>\n",
3870 |        "      <th></th>\n",
3871 |        "      <th></th>\n",
3872 |        "      <th></th>\n",
3873 |        "      <th></th>\n",
3874 |        "      <th></th>\n",
3875 |        "      <th></th>\n",
3876 |        "    </tr>\n",
3877 |        "  </thead>\n",
3878 |        "  <tbody>\n",
3879 |        "    <tr>\n",
3880 |        "      <th>2019-07-29 14:00:00+00:00</th>\n",
3881 |        "      <td>10.5000</td>\n",
3882 |        "      <td>10.5800</td>\n",
3883 |        "      <td>10.4300</td>\n",
3884 |        "      <td>10.4400</td>\n",
3885 |        "      <td>4840329.0</td>\n",
3886 |        "      <td>10.5000</td>\n",
3887 |        "      <td>10.5800</td>\n",
3888 |        "      <td>10.4300</td>\n",
3889 |        "      <td>10.4400</td>\n",
3890 |        "      <td>4840329.0</td>\n",
3891 |        "      <td>2019-07-29 10:00:00-04:00</td>\n",
3892 |        "      <td>2019-07-29 07:00:00-07:00</td>\n",
3893 |        "    </tr>\n",
3894 |        "    <tr>\n",
3895 |        "      <th>2019-07-29 14:30:00+00:00</th>\n",
3896 |        "      <td>10.4404</td>\n",
3897 |        "      <td>10.4847</td>\n",
3898 |        "      <td>10.3997</td>\n",
3899 |        "      <td>10.4104</td>\n",
3900 |        "      <td>3860280.0</td>\n",
3901 |        "      <td>10.4404</td>\n",
3902 |        "      <td>10.4847</td>\n",
3903 |        "      <td>10.3997</td>\n",
3904 |        "      <td>10.4104</td>\n",
3905 |        "      <td>3860280.0</td>\n",
3906 |        "      <td>2019-07-29 10:30:00-04:00</td>\n",
3907 |        "      <td>2019-07-29 07:30:00-07:00</td>\n",
3908 |        "    </tr>\n",
3909 |        "    <tr>\n",
3910 |        "      <th>2019-07-29 15:00:00+00:00</th>\n",
3911 |        "      <td>10.4200</td>\n",
3912 |        "      <td>10.5000</td>\n",
3913 |        "      <td>10.4100</td>\n",
3914 |        "      <td>10.5000</td>\n",
3915 |        "      <td>1912153.0</td>\n",
3916 |        "      <td>10.4200</td>\n",
3917 |        "      <td>10.5000</td>\n",
3918 |        "      <td>10.4100</td>\n",
3919 |        "      <td>10.5000</td>\n",
3920 |        "      <td>1912153.0</td>\n",
3921 |        "      <td>2019-07-29 11:00:00-04:00</td>\n",
3922 |        "      <td>2019-07-29 08:00:00-07:00</td>\n",
3923 |        "    </tr>\n",
3924 |        "    <tr>\n",
3925 |        "      <th>2019-07-29 15:30:00+00:00</th>\n",
3926 |        "      <td>10.4931</td>\n",
3927 |        "      <td>10.5300</td>\n",
3928 |        "      <td>10.4800</td>\n",
3929 |        "      <td>10.4850</td>\n",
3930 |        "      <td>1916453.0</td>\n",
3931 |        "      <td>10.4931</td>\n",
3932 |        "      <td>10.5300</td>\n",
3933 |        "      <td>10.4800</td>\n",
3934 |        "      <td>10.4850</td>\n",
3935 |        "      <td>1916453.0</td>\n",
3936 |        "      <td>2019-07-29 11:30:00-04:00</td>\n",
3937 |        "      <td>2019-07-29 08:30:00-07:00</td>\n",
3938 |        "    </tr>\n",
3939 |        "    <tr>\n",
3940 |        "      <th>2019-07-29 16:00:00+00:00</th>\n",
3941 |        "      <td>10.4900</td>\n",
3942 |        "      <td>10.5000</td>\n",
3943 |        "      <td>10.4400</td>\n",
3944 |        "      <td>10.4500</td>\n",
3945 |        "      <td>2467638.0</td>\n",
3946 |        "      <td>10.4900</td>\n",
3947 |        "      <td>10.5000</td>\n",
3948 |        "      <td>10.4400</td>\n",
3949 |        "      <td>10.4500</td>\n",
3950 |        "      <td>2467638.0</td>\n",
3951 |        "      <td>2019-07-29 12:00:00-04:00</td>\n",
3952 |        "      <td>2019-07-29 09:00:00-07:00</td>\n",
3953 |        "    </tr>\n",
3954 |        "  </tbody>\n",
3955 |        "</table>\n",
3956 |        "</div>"
3957 |       ],
3958 |       "text/plain": [
3959 |        "                           1. open  2. high   3. low  4. close  5. volume  \\\n",
3960 |        "date                                                                        \n",
3961 |        "2019-07-29 14:00:00+00:00  10.5000  10.5800  10.4300   10.4400  4840329.0   \n",
3962 |        "2019-07-29 14:30:00+00:00  10.4404  10.4847  10.3997   10.4104  3860280.0   \n",
3963 |        "2019-07-29 15:00:00+00:00  10.4200  10.5000  10.4100   10.5000  1912153.0   \n",
3964 |        "2019-07-29 15:30:00+00:00  10.4931  10.5300  10.4800   10.4850  1916453.0   \n",
3965 |        "2019-07-29 16:00:00+00:00  10.4900  10.5000  10.4400   10.4500  2467638.0   \n",
3966 |        "\n",
3967 |        "                           1. open  2. high   3. low  4. close  5. volume  \\\n",
3968 |        "date                                                                        \n",
3969 |        "2019-07-29 14:00:00+00:00  10.5000  10.5800  10.4300   10.4400  4840329.0   \n",
3970 |        "2019-07-29 14:30:00+00:00  10.4404  10.4847  10.3997   10.4104  3860280.0   \n",
3971 |        "2019-07-29 15:00:00+00:00  10.4200  10.5000  10.4100   10.5000  1912153.0   \n",
3972 |        "2019-07-29 15:30:00+00:00  10.4931  10.5300  10.4800   10.4850  1916453.0   \n",
3973 |        "2019-07-29 16:00:00+00:00  10.4900  10.5000  10.4400   10.4500  2467638.0   \n",
3974 |        "\n",
3975 |        "                                            NY_time                   LA_time  \n",
3976 |        "date                                                                           \n",
3977 |        "2019-07-29 14:00:00+00:00 2019-07-29 10:00:00-04:00 2019-07-29 07:00:00-07:00  \n",
3978 |        "2019-07-29 14:30:00+00:00 2019-07-29 10:30:00-04:00 2019-07-29 07:30:00-07:00  \n",
3979 |        "2019-07-29 15:00:00+00:00 2019-07-29 11:00:00-04:00 2019-07-29 08:00:00-07:00  \n",
3980 |        "2019-07-29 15:30:00+00:00 2019-07-29 11:30:00-04:00 2019-07-29 08:30:00-07:00  \n",
3981 |        "2019-07-29 16:00:00+00:00 2019-07-29 12:00:00-04:00 2019-07-29 09:00:00-07:00  "
3982 |       ]
3983 |      },
3984 |      "execution_count": 75,
3985 |      "metadata": {},
3986 |      "output_type": "execute_result"
3987 |     }
3988 |    ],
3989 |    "source": [
3990 |     "comb.head()"
3991 |    ]
3992 |   },
3993 |   {
3994 |    "cell_type": "code",
3995 |    "execution_count": 76,
3996 |    "id": "b1fd5c7a",
3997 |    "metadata": {},
3998 |    "outputs": [],
3999 |    "source": [
4000 |     "# importing timezone module\n",
4001 |     "import pytz"
4002 |    ]
4003 |   },
4004 |   {
4005 |    "cell_type": "code",
4006 |    "execution_count": 77,
4007 |    "id": "c6c36cb1",
4008 |    "metadata": {},
4009 |    "outputs": [
4010 |     {
4011 |      "data": {
4012 |       "text/plain": [
4013 |        "593"
4014 |       ]
4015 |      },
4016 |      "execution_count": 77,
4017 |      "metadata": {},
4018 |      "output_type": "execute_result"
4019 |     }
4020 |    ],
4021 |    "source": [
4022 |     "# Checking total timezones available\n",
4023 |     "len(pytz.all_timezones)"
4024 |    ]
4025 |   },
4026 |   {
4027 |    "cell_type": "code",
4028 |    "execution_count": 78,
4029 |    "id": "ffcc9ba6",
4030 |    "metadata": {},
4031 |    "outputs": [
4032 |     {
4033 |      "data": {
4034 |       "text/plain": [
4035 |        "['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Juba', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Argentina/Buenos_Aires', 'America/Argentina/Catamarca', 'America/Argentina/Cordoba', 'America/Argentina/Jujuy', 'America/Argentina/La_Rioja', 'America/Argentina/Mendoza', 'America/Argentina/Rio_Gallegos', 'America/Argentina/Salta', 'America/Argentina/San_Juan', 'America/Argentina/San_Luis', 'America/Argentina/Tucuman', 'America/Argentina/Ushuaia', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Bahia', 'America/Bahia_Banderas', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Costa_Rica', 'America/Creston', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Fort_Nelson', 'America/Fortaleza', 'America/Glace_Bay', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indiana/Indianapolis', 'America/Indiana/Knox', 'America/Indiana/Marengo', 'America/Indiana/Petersburg', 'America/Indiana/Tell_City', 'America/Indiana/Vevay', 'America/Indiana/Vincennes', 'America/Indiana/Winamac', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Juneau', 'America/Kentucky/Louisville', 'America/Kentucky/Monticello', 'America/Kralendijk', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Lower_Princes', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Matamoros', 'America/Mazatlan', 'America/Menominee', 'America/Merida', 'America/Metlakatla', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/North_Dakota/Beulah', 'America/North_Dakota/Center', 'America/North_Dakota/New_Salem', 'America/Nuuk', 'America/Ojinaga', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Punta_Arenas', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Santarem', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Sitka', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Macquarie', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/Syowa', 'Antarctica/Troll', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Atyrau', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Barnaul', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Chita', 'Asia/Choibalsan', 'Asia/Colombo', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Famagusta', 'Asia/Gaza', 'Asia/Hebron', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kathmandu', 'Asia/Khandyga', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novokuznetsk', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qostanay', 'Asia/Qyzylorda', 'Asia/Riyadh', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Srednekolymsk', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Tomsk', 'Asia/Ulaanbaatar', 'Asia/Urumqi', 'Asia/Ust-Nera', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yangon', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faroe', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/Perth', 'Australia/Sydney', 'Canada/Atlantic', 'Canada/Central', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Astrakhan', 'Europe/Athens', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Busingen', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Kirov', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Saratov', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Ulyanovsk', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GMT', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Bougainville', 'Pacific/Chatham', 'Pacific/Chuuk', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Pohnpei', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Wake', 'Pacific/Wallis', 'US/Alaska', 'US/Arizona', 'US/Central', 'US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
4036 |       ]
4037 |      },
4038 |      "execution_count": 78,
4039 |      "metadata": {},
4040 |      "output_type": "execute_result"
4041 |     }
4042 |    ],
4043 |    "source": [
4044 |     "# Checking common time zones\n",
4045 |     "pytz.common_timezones"
4046 |    ]
4047 |   },
4048 |   {
4049 |    "cell_type": "code",
4050 |    "execution_count": null,
4051 |    "id": "7532f92f",
4052 |    "metadata": {},
4053 |    "outputs": [],
4054 |    "source": []
4055 |   }
4056 |  ],
4057 |  "metadata": {
4058 |   "kernelspec": {
4059 |    "display_name": "Python 3 (ipykernel)",
4060 |    "language": "python",
4061 |    "name": "python3"
4062 |   },
4063 |   "language_info": {
4064 |    "codemirror_mode": {
4065 |     "name": "ipython",
4066 |     "version": 3
4067 |    },
4068 |    "file_extension": ".py",
4069 |    "mimetype": "text/x-python",
4070 |    "name": "python",
4071 |    "nbconvert_exporter": "python",
4072 |    "pygments_lexer": "ipython3",
4073 |    "version": "3.8.5"
4074 |   }
4075 |  },
4076 |  "nbformat": 4,
4077 |  "nbformat_minor": 5
4078 | }
4079 | 


--------------------------------------------------------------------------------
/Time Series operations with Pandas/README.md:
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 1 | In this folder, following operations were performed on time-series data
 2 | 
 3 | __1: Time series basic Operations__
 4 | 
 5 |   o	Learning to use pandas library
 6 |   
 7 |   o	Importing time series data from csv files using pd.read_csv()
 8 |   
 9 |   o	Using the df.head() and df.info() methods to get a snapshot of dataframe
10 |   
11 |   o	Parsing datetime and making it as index column parse_dates, index_col
12 |   
13 |   o	Finding an object at a particular location in the dataframe iloc[,]
14 |   
15 |   o	Checking datatype type()
16 |   
17 |   o	Converting strings to datetime objects pd.to_datetime()
18 |   
19 |   o	Setting datetime column as index using set_index()
20 |   
21 |   
22 |   
23 | __2: Financial Data Analysis with Pandas__
24 | 
25 |   o	Importing price data from yahoo finance and saving it in a pandas dataframe, using yfinance library
26 |   
27 |   o	Choosing stock ticker symbols, and downloading close price, adjusted close price, open price, and volume data for each ticker and for a particular time period using yf.download()
28 |   
29 |   o	Exporting the stock prices dataframe to a csv file using df.to_csv()
30 |   
31 |   o	Using header to redefine multi-index positions, resetting date as index column, and parsing it as datetime index
32 |   
33 |   o	Flattening (or converting to a tuple) the multi-index columns using df.columns.to_flat_index()
34 |   
35 |   o	Converting tuple index to multi-index using pd.MultiIndex.from_tuples()
36 |   
37 |   o	Swapping column levels from inner to outer using df.swaplevel(), and then sorting index using df.sort_index()
38 |   
39 |   o	Finding basic descriptive statistics for the dataframe using df.describe()
40 |   
41 |   o	Creating a new dataframe from columns in existing dataframe by copying contents using new_df = df.loc[:, ''].copy()
42 |   
43 |   o	Visualising closing stock prices using plot style seaborn from matplotlib library
44 |   
45 |   o	Normalising a time series to 100
46 |   
47 |   o	Using shift() method to calculate absolute and relative change in stock price over previous period (last week to this week, yesterday to today etc.)
48 |   
49 |   o	Creating and adding new columns to a dataframe
50 |   
51 |   o	Calculating Absolute and Relative changes in time series using diff() and pct_change() methods
52 |   
53 |   o	Comparing contents of two columns using equals()
54 |   
55 |   o	Resampling data into monthly, weekly, quarterly or annual periods using resample()
56 |   
57 |   o	Measuring stock performance with MEAN Return and STD of Returns
58 |   
59 |   o	Visualising returns using a histogram plot
60 | 
61 | __3: Advanced Financial Data Analysis__
62 | 
63 | __4: Datetime Index Methods, Timezones and NA values__
64 | 
65 | o	A closer look at Datetime index
66 | 
67 | o	Finding and inserting name of the day, quarter number, week number columns in a dataframe
68 | 
69 | o	Filling NA values with backfill (bfill), forwardfill (ffill) and interpolation. When we resample, reindex or merge time series, we might end up with NA or empty rows. We can handle such situations using these methods
70 | 
71 | o	Reindexing Datetime index
72 | 
73 | o	Interpolating missing entries using adjacent values
74 | 
75 | o	Localising datetime index to various time zones
76 | 
77 | o	Converting from one time zone to another
78 | 
79 | o	Concatenating data from various time zones
80 | 
81 | o	Importing and analysing time zone module pytz
82 | 


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/Trading_Strategies/Machine_Learning/README.md:
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/Trading_Strategies/Mean-Reversion/README.md:
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/Trading_Strategies/Moving_Average_Crossover/README.md:
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