├── 1-IntroToNumpy.ipynb
├── 10-MatplotlibStyles.ipynb
├── 11-VisualizationSeaborn.ipynb
├── 12-BoxPlotExercise.ipynb
├── 13-EDAExercise.ipynb
├── 13-WineQT.csv
├── 14-IntroToFeatureEngineering.ipynb
├── 15-BalancingData.ipynb
├── 16-EncodingData.ipynb
├── 17-FeatureEngineeringEDA.ipynb
├── 17-googleplaystore.csv
├── 17-googleplaystore_user_reviews.csv
├── 2-NumpyMatrices.ipynb
├── 3-NumpyOperations.ipynb
├── 4-IntroToPandas.ipynb
├── 5-IntroToDataFrames.ipynb
├── 6-DataFrameOperations.ipynb
├── 6-employee.csv
├── 6-weather.xlsx
├── 6-weatherna.xlsx
├── 7-DataFramesConcatMerge.ipynb
├── 7-concat_data1.csv
├── 7-concat_data2.csv
├── 7-merge_data1.csv
├── 7-merge_data2.csv
├── 8-DataFramesApply.ipynb
├── 8-apply_function_data.csv
├── 9-IntroToMatplotlib.ipynb
└── athlete_events.csv.zip


/1-IntroToNumpy.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "1fb57f0e-09e4-4d94-8f0e-8309e1a87a03",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "id": "0d49ad03-441c-4029-8f66-f7df7229e30d",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "# !pip install numpy"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 3,
 26 |    "id": "26d84fa4-cbb6-4918-8578-1a53254d5a42",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "my_list = [10,20,30,40]"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 4,
 36 |    "id": "0259ed6a-0ada-4fbd-bd28-24d5a9de56f9",
 37 |    "metadata": {},
 38 |    "outputs": [
 39 |     {
 40 |      "data": {
 41 |       "text/plain": [
 42 |        "list"
 43 |       ]
 44 |      },
 45 |      "execution_count": 4,
 46 |      "metadata": {},
 47 |      "output_type": "execute_result"
 48 |     }
 49 |    ],
 50 |    "source": [
 51 |     "type(my_list)"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 5,
 57 |    "id": "c2203e96-654d-4ef7-99a6-7da8898d821c",
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "data": {
 62 |       "text/plain": [
 63 |        "array([10, 20, 30, 40])"
 64 |       ]
 65 |      },
 66 |      "execution_count": 5,
 67 |      "metadata": {},
 68 |      "output_type": "execute_result"
 69 |     }
 70 |    ],
 71 |    "source": [
 72 |     "np.array(my_list)"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 7,
 78 |    "id": "da2872d8-9302-46da-b3e4-3985b54e3f38",
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "my_numpy_array = np.array([10,20,30,40,50])"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 8,
 88 |    "id": "5ccd584b-4de3-4559-b483-ff1ee1163a6e",
 89 |    "metadata": {},
 90 |    "outputs": [
 91 |     {
 92 |      "data": {
 93 |       "text/plain": [
 94 |        "numpy.ndarray"
 95 |       ]
 96 |      },
 97 |      "execution_count": 8,
 98 |      "metadata": {},
 99 |      "output_type": "execute_result"
100 |     }
101 |    ],
102 |    "source": [
103 |     "type(my_numpy_array)"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 9,
109 |    "id": "bf67ef44-7500-4ebc-bfa5-7ddd7fc50d45",
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "data": {
114 |       "text/plain": [
115 |        "50"
116 |       ]
117 |      },
118 |      "execution_count": 9,
119 |      "metadata": {},
120 |      "output_type": "execute_result"
121 |     }
122 |    ],
123 |    "source": [
124 |     "my_numpy_array.max()"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "code",
129 |    "execution_count": 10,
130 |    "id": "4ab51f52-414d-420c-86cc-1c9ea70927b4",
131 |    "metadata": {},
132 |    "outputs": [],
133 |    "source": [
134 |     "# https://jalammar.github.io/visual-numpy/"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 11,
140 |    "id": "36a5ac42-ed4d-4e90-bc25-573eba461423",
141 |    "metadata": {},
142 |    "outputs": [
143 |     {
144 |      "data": {
145 |       "text/plain": [
146 |        "array([1., 1., 1., 1., 1.])"
147 |       ]
148 |      },
149 |      "execution_count": 11,
150 |      "metadata": {},
151 |      "output_type": "execute_result"
152 |     }
153 |    ],
154 |    "source": [
155 |     "np.ones(5)"
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": 12,
161 |    "id": "1d9da7af-82f9-45aa-9653-03d38878215b",
162 |    "metadata": {},
163 |    "outputs": [
164 |     {
165 |      "data": {
166 |       "text/plain": [
167 |        "array([0., 0., 0., 0., 0.])"
168 |       ]
169 |      },
170 |      "execution_count": 12,
171 |      "metadata": {},
172 |      "output_type": "execute_result"
173 |     }
174 |    ],
175 |    "source": [
176 |     "np.zeros(5)"
177 |    ]
178 |   },
179 |   {
180 |    "cell_type": "code",
181 |    "execution_count": 13,
182 |    "id": "1282564c-1772-4e36-b7f6-93878ecd6338",
183 |    "metadata": {},
184 |    "outputs": [
185 |     {
186 |      "data": {
187 |       "text/plain": [
188 |        "array([0.92592341, 0.22830596, 0.30502652, 0.8765612 , 0.98434607])"
189 |       ]
190 |      },
191 |      "execution_count": 13,
192 |      "metadata": {},
193 |      "output_type": "execute_result"
194 |     }
195 |    ],
196 |    "source": [
197 |     "np.random.random(5)"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": 14,
203 |    "id": "c14068c5-c994-4f88-b2f7-cebafcd29dda",
204 |    "metadata": {},
205 |    "outputs": [],
206 |    "source": [
207 |     "#array arithmetic "
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 15,
213 |    "id": "d49721cc-0826-4afb-9dd4-47b7544e6a35",
214 |    "metadata": {},
215 |    "outputs": [],
216 |    "source": [
217 |     "my_list1 = [1,2]"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": 16,
223 |    "id": "feb571da-8d3c-4bbc-9ec4-2031e861e5e9",
224 |    "metadata": {},
225 |    "outputs": [],
226 |    "source": [
227 |     "my_list2 = [2,3]"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "code",
232 |    "execution_count": 17,
233 |    "id": "3d62e70c-a553-4ec6-9f2d-b34cd839abf4",
234 |    "metadata": {},
235 |    "outputs": [
236 |     {
237 |      "data": {
238 |       "text/plain": [
239 |        "[1, 2, 2, 3]"
240 |       ]
241 |      },
242 |      "execution_count": 17,
243 |      "metadata": {},
244 |      "output_type": "execute_result"
245 |     }
246 |    ],
247 |    "source": [
248 |     "my_list1 + my_list2"
249 |    ]
250 |   },
251 |   {
252 |    "cell_type": "code",
253 |    "execution_count": 18,
254 |    "id": "80c32ce8-2b96-46c6-98a6-0e88b92d0cc5",
255 |    "metadata": {},
256 |    "outputs": [],
257 |    "source": [
258 |     "my_numpy_array1 = np.array(my_list1)"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 19,
264 |    "id": "1adc383b-a188-47ad-9b76-7e250654f463",
265 |    "metadata": {},
266 |    "outputs": [],
267 |    "source": [
268 |     "my_numpy_array2 = np.array(my_list2)"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 20,
274 |    "id": "62e29b38-b57c-45bb-aa11-c7125f5185e2",
275 |    "metadata": {},
276 |    "outputs": [
277 |     {
278 |      "data": {
279 |       "text/plain": [
280 |        "array([3, 5])"
281 |       ]
282 |      },
283 |      "execution_count": 20,
284 |      "metadata": {},
285 |      "output_type": "execute_result"
286 |     }
287 |    ],
288 |    "source": [
289 |     "my_numpy_array1 + my_numpy_array2"
290 |    ]
291 |   },
292 |   {
293 |    "cell_type": "code",
294 |    "execution_count": 21,
295 |    "id": "a5bb5256-50c9-4543-9893-fc511cbdfd24",
296 |    "metadata": {},
297 |    "outputs": [
298 |     {
299 |      "data": {
300 |       "text/plain": [
301 |        "array([-1, -1])"
302 |       ]
303 |      },
304 |      "execution_count": 21,
305 |      "metadata": {},
306 |      "output_type": "execute_result"
307 |     }
308 |    ],
309 |    "source": [
310 |     "my_numpy_array1 - my_numpy_array2"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": 22,
316 |    "id": "58eab283-49f5-4ad2-b70d-6421b7815aa2",
317 |    "metadata": {},
318 |    "outputs": [
319 |     {
320 |      "data": {
321 |       "text/plain": [
322 |        "array([2, 6])"
323 |       ]
324 |      },
325 |      "execution_count": 22,
326 |      "metadata": {},
327 |      "output_type": "execute_result"
328 |     }
329 |    ],
330 |    "source": [
331 |     "my_numpy_array1 * my_numpy_array2"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 23,
337 |    "id": "5d2893cc-c4b2-4526-9496-e2255bc3fd85",
338 |    "metadata": {},
339 |    "outputs": [
340 |     {
341 |      "data": {
342 |       "text/plain": [
343 |        "array([0.5       , 0.66666667])"
344 |       ]
345 |      },
346 |      "execution_count": 23,
347 |      "metadata": {},
348 |      "output_type": "execute_result"
349 |     }
350 |    ],
351 |    "source": [
352 |     "my_numpy_array1 / my_numpy_array2"
353 |    ]
354 |   },
355 |   {
356 |    "cell_type": "code",
357 |    "execution_count": 24,
358 |    "id": "c87b0273-3a58-421b-aec3-4340b2c1b4f6",
359 |    "metadata": {},
360 |    "outputs": [
361 |     {
362 |      "data": {
363 |       "text/plain": [
364 |        "array([ 5, 10])"
365 |       ]
366 |      },
367 |      "execution_count": 24,
368 |      "metadata": {},
369 |      "output_type": "execute_result"
370 |     }
371 |    ],
372 |    "source": [
373 |     "my_numpy_array1 * 5"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": 36,
379 |    "id": "ad8366a9-31c8-4a77-82ac-216d11beab90",
380 |    "metadata": {},
381 |    "outputs": [],
382 |    "source": [
383 |     "other_array = np.array([10,20,30,40,50])"
384 |    ]
385 |   },
386 |   {
387 |    "cell_type": "code",
388 |    "execution_count": 39,
389 |    "id": "c8ee65c3-e766-4a11-bb64-3be6186dfc9d",
390 |    "metadata": {},
391 |    "outputs": [
392 |     {
393 |      "data": {
394 |       "text/plain": [
395 |        "10"
396 |       ]
397 |      },
398 |      "execution_count": 39,
399 |      "metadata": {},
400 |      "output_type": "execute_result"
401 |     }
402 |    ],
403 |    "source": [
404 |     "other_array.min()"
405 |    ]
406 |   },
407 |   {
408 |    "cell_type": "code",
409 |    "execution_count": 38,
410 |    "id": "eeffe892-315f-4dca-b65c-c8138a466ae3",
411 |    "metadata": {},
412 |    "outputs": [
413 |     {
414 |      "data": {
415 |       "text/plain": [
416 |        "150"
417 |       ]
418 |      },
419 |      "execution_count": 38,
420 |      "metadata": {},
421 |      "output_type": "execute_result"
422 |     }
423 |    ],
424 |    "source": [
425 |     "other_array.sum()"
426 |    ]
427 |   },
428 |   {
429 |    "cell_type": "code",
430 |    "execution_count": 26,
431 |    "id": "9c0c51b0-ef15-43ee-afc5-e8835d0d3035",
432 |    "metadata": {},
433 |    "outputs": [],
434 |    "source": [
435 |     "#indexing & arange"
436 |    ]
437 |   },
438 |   {
439 |    "cell_type": "code",
440 |    "execution_count": 26,
441 |    "id": "c5c3fd4d-c92e-43ca-8783-739f27fd2f44",
442 |    "metadata": {},
443 |    "outputs": [],
444 |    "source": [
445 |     "#indexing & arange"
446 |    ]
447 |   },
448 |   {
449 |    "cell_type": "code",
450 |    "execution_count": 27,
451 |    "id": "ef6d052d-c978-4315-9a79-ada784e4c88f",
452 |    "metadata": {},
453 |    "outputs": [
454 |     {
455 |      "data": {
456 |       "text/plain": [
457 |        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
458 |       ]
459 |      },
460 |      "execution_count": 27,
461 |      "metadata": {},
462 |      "output_type": "execute_result"
463 |     }
464 |    ],
465 |    "source": [
466 |     "list(range(0,10))"
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "code",
471 |    "execution_count": 28,
472 |    "id": "626bfbd7-bf3b-4726-aff0-c3a9695ff2a9",
473 |    "metadata": {},
474 |    "outputs": [
475 |     {
476 |      "data": {
477 |       "text/plain": [
478 |        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
479 |       ]
480 |      },
481 |      "execution_count": 28,
482 |      "metadata": {},
483 |      "output_type": "execute_result"
484 |     }
485 |    ],
486 |    "source": [
487 |     "np.arange(0,10)"
488 |    ]
489 |   },
490 |   {
491 |    "cell_type": "code",
492 |    "execution_count": 29,
493 |    "id": "e53ca57a-f857-496e-a5e5-fc96bf150782",
494 |    "metadata": {},
495 |    "outputs": [
496 |     {
497 |      "data": {
498 |       "text/plain": [
499 |        "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
500 |       ]
501 |      },
502 |      "execution_count": 29,
503 |      "metadata": {},
504 |      "output_type": "execute_result"
505 |     }
506 |    ],
507 |    "source": [
508 |     "np.arange(0,20,2)"
509 |    ]
510 |   },
511 |   {
512 |    "cell_type": "code",
513 |    "execution_count": 30,
514 |    "id": "927f9dc2-82f0-4b43-aef7-110d0cd175d1",
515 |    "metadata": {},
516 |    "outputs": [],
517 |    "source": [
518 |     "np_array = np.arange(0,10)"
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": 31,
524 |    "id": "fc8d78cf-aafa-4c6c-a4ba-f0aa61eb8013",
525 |    "metadata": {},
526 |    "outputs": [
527 |     {
528 |      "data": {
529 |       "text/plain": [
530 |        "0"
531 |       ]
532 |      },
533 |      "execution_count": 31,
534 |      "metadata": {},
535 |      "output_type": "execute_result"
536 |     }
537 |    ],
538 |    "source": [
539 |     "np_array[0]"
540 |    ]
541 |   },
542 |   {
543 |    "cell_type": "code",
544 |    "execution_count": 32,
545 |    "id": "82641c03-30ac-4fbe-bb83-97a070874bbd",
546 |    "metadata": {},
547 |    "outputs": [
548 |     {
549 |      "data": {
550 |       "text/plain": [
551 |        "9"
552 |       ]
553 |      },
554 |      "execution_count": 32,
555 |      "metadata": {},
556 |      "output_type": "execute_result"
557 |     }
558 |    ],
559 |    "source": [
560 |     "np_array[-1]"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": 33,
566 |    "id": "4471d6f3-e97c-41f0-878e-96297bc8f954",
567 |    "metadata": {},
568 |    "outputs": [
569 |     {
570 |      "data": {
571 |       "text/plain": [
572 |        "array([1, 2, 3])"
573 |       ]
574 |      },
575 |      "execution_count": 33,
576 |      "metadata": {},
577 |      "output_type": "execute_result"
578 |     }
579 |    ],
580 |    "source": [
581 |     "np_array[1:4:]"
582 |    ]
583 |   },
584 |   {
585 |    "cell_type": "code",
586 |    "execution_count": 34,
587 |    "id": "e825564c-4794-4fa2-a36e-7febfc5c1e0b",
588 |    "metadata": {},
589 |    "outputs": [
590 |     {
591 |      "data": {
592 |       "text/plain": [
593 |        "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
594 |       ]
595 |      },
596 |      "execution_count": 34,
597 |      "metadata": {},
598 |      "output_type": "execute_result"
599 |     }
600 |    ],
601 |    "source": [
602 |     "np_array[::-1]"
603 |    ]
604 |   },
605 |   {
606 |    "cell_type": "code",
607 |    "execution_count": 35,
608 |    "id": "f365c8f6-3d33-42ba-b22b-7de141eb0fcd",
609 |    "metadata": {},
610 |    "outputs": [
611 |     {
612 |      "data": {
613 |       "text/plain": [
614 |        "array([2, 4])"
615 |       ]
616 |      },
617 |      "execution_count": 35,
618 |      "metadata": {},
619 |      "output_type": "execute_result"
620 |     }
621 |    ],
622 |    "source": [
623 |     "np_array[2:6:2]"
624 |    ]
625 |   },
626 |   {
627 |    "cell_type": "code",
628 |    "execution_count": 40,
629 |    "id": "9b8a4c06-e364-4281-b1d8-5972ba54235f",
630 |    "metadata": {},
631 |    "outputs": [],
632 |    "source": [
633 |     "# random"
634 |    ]
635 |   },
636 |   {
637 |    "cell_type": "code",
638 |    "execution_count": 41,
639 |    "id": "c14f32bf-8f4e-48cf-b477-59972e009ffd",
640 |    "metadata": {},
641 |    "outputs": [
642 |     {
643 |      "data": {
644 |       "text/plain": [
645 |        "array([-1.01630674, -1.09829885,  0.70948785,  2.54035352])"
646 |       ]
647 |      },
648 |      "execution_count": 41,
649 |      "metadata": {},
650 |      "output_type": "execute_result"
651 |     }
652 |    ],
653 |    "source": [
654 |     "np.random.randn(4)"
655 |    ]
656 |   },
657 |   {
658 |    "cell_type": "code",
659 |    "execution_count": 42,
660 |    "id": "765bda37-7d9f-4a5e-8a96-cbe295f3c17b",
661 |    "metadata": {},
662 |    "outputs": [
663 |     {
664 |      "data": {
665 |       "text/plain": [
666 |        "array([[-1.92619892,  0.6432985 ,  1.52906877, -0.45387039],\n",
667 |        "       [ 1.09507191, -1.56907651, -0.0185524 ,  0.21799423],\n",
668 |        "       [-0.8186812 , -1.12639982, -1.59773834,  0.36370991],\n",
669 |        "       [ 0.71767334,  0.64516878, -0.37454103,  0.50293794]])"
670 |       ]
671 |      },
672 |      "execution_count": 42,
673 |      "metadata": {},
674 |      "output_type": "execute_result"
675 |     }
676 |    ],
677 |    "source": [
678 |     "np.random.randn(4,4) #we will see matrices in details"
679 |    ]
680 |   },
681 |   {
682 |    "cell_type": "code",
683 |    "execution_count": 43,
684 |    "id": "78e2d329-f546-4370-9d5b-cc7421d78abd",
685 |    "metadata": {},
686 |    "outputs": [
687 |     {
688 |      "data": {
689 |       "text/plain": [
690 |        "array([ 89, 169, 249,  29, 161])"
691 |       ]
692 |      },
693 |      "execution_count": 43,
694 |      "metadata": {},
695 |      "output_type": "execute_result"
696 |     }
697 |    ],
698 |    "source": [
699 |     "np.random.randint(1,300,5)"
700 |    ]
701 |   },
702 |   {
703 |    "cell_type": "code",
704 |    "execution_count": null,
705 |    "id": "ca004933-ae90-4d7b-9cf7-be50d4abfa1a",
706 |    "metadata": {},
707 |    "outputs": [],
708 |    "source": []
709 |   }
710 |  ],
711 |  "metadata": {
712 |   "kernelspec": {
713 |    "display_name": "Python 3 (ipykernel)",
714 |    "language": "python",
715 |    "name": "python3"
716 |   },
717 |   "language_info": {
718 |    "codemirror_mode": {
719 |     "name": "ipython",
720 |     "version": 3
721 |    },
722 |    "file_extension": ".py",
723 |    "mimetype": "text/x-python",
724 |    "name": "python",
725 |    "nbconvert_exporter": "python",
726 |    "pygments_lexer": "ipython3",
727 |    "version": "3.12.7"
728 |   }
729 |  },
730 |  "nbformat": 4,
731 |  "nbformat_minor": 5
732 | }
733 | 


--------------------------------------------------------------------------------
/2-NumpyMatrices.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "72f6ebd3-aa54-4af5-b500-01d0763f6325",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 7,
 16 |    "id": "954bbbad-343b-4292-84c1-bc2bf84646cc",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "my_matrix = [[5,10],[15,20]]"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 8,
 26 |    "id": "c5ce2b9d-1fed-423d-81a5-4b14a3d0c78c",
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/plain": [
 32 |        "[5, 10]"
 33 |       ]
 34 |      },
 35 |      "execution_count": 8,
 36 |      "metadata": {},
 37 |      "output_type": "execute_result"
 38 |     }
 39 |    ],
 40 |    "source": [
 41 |     "my_matrix[0]"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 9,
 47 |    "id": "950601d3-cbad-4ed5-9cda-450e16f0ced3",
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "data": {
 52 |       "text/plain": [
 53 |        "[15, 20]"
 54 |       ]
 55 |      },
 56 |      "execution_count": 9,
 57 |      "metadata": {},
 58 |      "output_type": "execute_result"
 59 |     }
 60 |    ],
 61 |    "source": [
 62 |     "my_matrix[1]"
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "code",
 67 |    "execution_count": 12,
 68 |    "id": "6159f2ac-8a24-4d03-b925-d4019db1c551",
 69 |    "metadata": {},
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "#my_matrix.sum()"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 10,
 78 |    "id": "7bdd8805-da83-44cf-9585-8c5751810b98",
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "numpy_matrix = np.array([[5,10],[15,20]])"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 13,
 88 |    "id": "122650fc-f8bc-49ef-a028-aaa76d4f0365",
 89 |    "metadata": {},
 90 |    "outputs": [
 91 |     {
 92 |      "data": {
 93 |       "text/plain": [
 94 |        "50"
 95 |       ]
 96 |      },
 97 |      "execution_count": 13,
 98 |      "metadata": {},
 99 |      "output_type": "execute_result"
100 |     }
101 |    ],
102 |    "source": [
103 |     "numpy_matrix.sum()"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 20,
109 |    "id": "5cac9c72-f3ee-4fef-9f25-aafce4dffc35",
110 |    "metadata": {},
111 |    "outputs": [],
112 |    "source": [
113 |     "# row x column"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 21,
119 |    "id": "f6609207-a1e5-4b34-8f55-8a82a628d052",
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "array([[1., 1.],\n",
126 |        "       [1., 1.],\n",
127 |        "       [1., 1.],\n",
128 |        "       [1., 1.]])"
129 |       ]
130 |      },
131 |      "execution_count": 21,
132 |      "metadata": {},
133 |      "output_type": "execute_result"
134 |     }
135 |    ],
136 |    "source": [
137 |     "np.ones((4,2))"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 22,
143 |    "id": "aee3b6df-1975-47cd-b591-faa33008fedf",
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "data": {
148 |       "text/plain": [
149 |        "array([[0., 0., 0.],\n",
150 |        "       [0., 0., 0.],\n",
151 |        "       [0., 0., 0.],\n",
152 |        "       [0., 0., 0.],\n",
153 |        "       [0., 0., 0.]])"
154 |       ]
155 |      },
156 |      "execution_count": 22,
157 |      "metadata": {},
158 |      "output_type": "execute_result"
159 |     }
160 |    ],
161 |    "source": [
162 |     "np.zeros((5,3))"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": 23,
168 |    "id": "7363c000-e3b8-4bd5-9707-2c503371062e",
169 |    "metadata": {},
170 |    "outputs": [
171 |     {
172 |      "data": {
173 |       "text/plain": [
174 |        "array([[0.16776112, 0.00703872],\n",
175 |        "       [0.00533717, 0.89356651],\n",
176 |        "       [0.11160896, 0.6911002 ]])"
177 |       ]
178 |      },
179 |      "execution_count": 23,
180 |      "metadata": {},
181 |      "output_type": "execute_result"
182 |     }
183 |    ],
184 |    "source": [
185 |     "np.random.random((3,2))"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 24,
191 |    "id": "cc49bbe3-a8af-42b2-8805-a3500bf8f941",
192 |    "metadata": {},
193 |    "outputs": [],
194 |    "source": [
195 |     "# matrix arithmetic "
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 25,
201 |    "id": "19cc3b60-84d3-4545-8d21-34fe71fe6879",
202 |    "metadata": {},
203 |    "outputs": [],
204 |    "source": [
205 |     "first_array = np.array([[10,20],[30,40]])"
206 |    ]
207 |   },
208 |   {
209 |    "cell_type": "code",
210 |    "execution_count": 26,
211 |    "id": "771b116e-9e59-481d-8864-582578be9e84",
212 |    "metadata": {},
213 |    "outputs": [],
214 |    "source": [
215 |     "second_array = np.array([[5,15],[25,35]])"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": 27,
221 |    "id": "72be3206-0a4c-4809-9481-6a1910087c5c",
222 |    "metadata": {},
223 |    "outputs": [
224 |     {
225 |      "data": {
226 |       "text/plain": [
227 |        "array([[15, 35],\n",
228 |        "       [55, 75]])"
229 |       ]
230 |      },
231 |      "execution_count": 27,
232 |      "metadata": {},
233 |      "output_type": "execute_result"
234 |     }
235 |    ],
236 |    "source": [
237 |     "first_array + second_array"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 28,
243 |    "id": "62b64bd9-a3bd-4cc2-be1f-935476760d7c",
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "data": {
248 |       "text/plain": [
249 |        "array([[20, 40],\n",
250 |        "       [60, 80]])"
251 |       ]
252 |      },
253 |      "execution_count": 28,
254 |      "metadata": {},
255 |      "output_type": "execute_result"
256 |     }
257 |    ],
258 |    "source": [
259 |     "first_array * 2"
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "code",
264 |    "execution_count": 29,
265 |    "id": "5db28430-a4c7-4f2b-a49a-37f6e379dac1",
266 |    "metadata": {},
267 |    "outputs": [
268 |     {
269 |      "data": {
270 |       "text/plain": [
271 |        "array([[ 2.5,  5. ],\n",
272 |        "       [ 7.5, 10. ]])"
273 |       ]
274 |      },
275 |      "execution_count": 29,
276 |      "metadata": {},
277 |      "output_type": "execute_result"
278 |     }
279 |    ],
280 |    "source": [
281 |     "first_array / 4"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 30,
287 |    "id": "1179266b-7e27-432c-a654-1a6561673e5e",
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": [
291 |     "# We can add and multiply matrices using arithmetic operators (+-*/) if the two matrices are the same size."
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": 31,
297 |    "id": "1d87b8cc-6e70-4be7-ae60-86a1767cedea",
298 |    "metadata": {},
299 |    "outputs": [],
300 |    "source": [
301 |     "third_array = np.array([[10],[20]])"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": 32,
307 |    "id": "f687de5e-aef8-4cc0-ad37-828efd0dfb66",
308 |    "metadata": {},
309 |    "outputs": [
310 |     {
311 |      "data": {
312 |       "text/plain": [
313 |        "array([[10],\n",
314 |        "       [20]])"
315 |       ]
316 |      },
317 |      "execution_count": 32,
318 |      "metadata": {},
319 |      "output_type": "execute_result"
320 |     }
321 |    ],
322 |    "source": [
323 |     "third_array"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "code",
328 |    "execution_count": 34,
329 |    "id": "d24223ca-2633-4ac1-b1a8-69445fb2cf2c",
330 |    "metadata": {},
331 |    "outputs": [
332 |     {
333 |      "data": {
334 |       "text/plain": [
335 |        "(2, 1)"
336 |       ]
337 |      },
338 |      "execution_count": 34,
339 |      "metadata": {},
340 |      "output_type": "execute_result"
341 |     }
342 |    ],
343 |    "source": [
344 |     "third_array.shape"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": 35,
350 |    "id": "c5bc1e6f-24f9-44f8-953d-b82f37dac569",
351 |    "metadata": {},
352 |    "outputs": [
353 |     {
354 |      "data": {
355 |       "text/plain": [
356 |        "(2, 2)"
357 |       ]
358 |      },
359 |      "execution_count": 35,
360 |      "metadata": {},
361 |      "output_type": "execute_result"
362 |     }
363 |    ],
364 |    "source": [
365 |     "first_array.shape"
366 |    ]
367 |   },
368 |   {
369 |    "cell_type": "code",
370 |    "execution_count": 39,
371 |    "id": "1f633a46-e0a8-4862-b4e3-7d89ef4b6ccb",
372 |    "metadata": {},
373 |    "outputs": [
374 |     {
375 |      "data": {
376 |       "text/plain": [
377 |        "array([[10, 20],\n",
378 |        "       [30, 40]])"
379 |       ]
380 |      },
381 |      "execution_count": 39,
382 |      "metadata": {},
383 |      "output_type": "execute_result"
384 |     }
385 |    ],
386 |    "source": [
387 |     "first_array"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 36,
393 |    "id": "206689b4-63c1-44e5-ae36-52ae81ab72a7",
394 |    "metadata": {},
395 |    "outputs": [
396 |     {
397 |      "data": {
398 |       "text/plain": [
399 |        "array([[20, 30],\n",
400 |        "       [50, 60]])"
401 |       ]
402 |      },
403 |      "execution_count": 36,
404 |      "metadata": {},
405 |      "output_type": "execute_result"
406 |     }
407 |    ],
408 |    "source": [
409 |     "first_array + third_array"
410 |    ]
411 |   },
412 |   {
413 |    "cell_type": "code",
414 |    "execution_count": 38,
415 |    "id": "99ad1db3-fb96-48a5-9768-4f3a7a3cc007",
416 |    "metadata": {},
417 |    "outputs": [],
418 |    "source": [
419 |     "# We do these arithmetic operations on matrices of different size only if the different dimension is one \n",
420 |     "#in which case NumPy uses its broadcast rules for that operation:"
421 |    ]
422 |   },
423 |   {
424 |    "cell_type": "code",
425 |    "execution_count": 40,
426 |    "id": "2454ca51-f2b0-4d03-96f2-a29c5edbabdd",
427 |    "metadata": {},
428 |    "outputs": [],
429 |    "source": [
430 |     "fourth_array = np.ones((3,4))"
431 |    ]
432 |   },
433 |   {
434 |    "cell_type": "code",
435 |    "execution_count": 41,
436 |    "id": "1d906d74-8433-403c-8124-edd33d66a861",
437 |    "metadata": {},
438 |    "outputs": [
439 |     {
440 |      "data": {
441 |       "text/plain": [
442 |        "array([[1., 1., 1., 1.],\n",
443 |        "       [1., 1., 1., 1.],\n",
444 |        "       [1., 1., 1., 1.]])"
445 |       ]
446 |      },
447 |      "execution_count": 41,
448 |      "metadata": {},
449 |      "output_type": "execute_result"
450 |     }
451 |    ],
452 |    "source": [
453 |     "fourth_array"
454 |    ]
455 |   },
456 |   {
457 |    "cell_type": "code",
458 |    "execution_count": 43,
459 |    "id": "cb738a02-12cb-4a72-9d24-071d93fb4953",
460 |    "metadata": {},
461 |    "outputs": [],
462 |    "source": [
463 |     "#fourth_array + first_array"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 44,
469 |    "id": "5ce99420-058e-42e4-9902-c476cc1b2187",
470 |    "metadata": {},
471 |    "outputs": [],
472 |    "source": [
473 |     "# matrix multiplication"
474 |    ]
475 |   },
476 |   {
477 |    "cell_type": "code",
478 |    "execution_count": 45,
479 |    "id": "69ba681c-383f-4edc-a426-9bd6b33b1ad4",
480 |    "metadata": {},
481 |    "outputs": [],
482 |    "source": [
483 |     "first_matrix = np.array([[10,20,30]])"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "code",
488 |    "execution_count": 46,
489 |    "id": "f3bb87c5-c15e-4442-9039-b3bb9c5d8281",
490 |    "metadata": {},
491 |    "outputs": [
492 |     {
493 |      "data": {
494 |       "text/plain": [
495 |        "array([[10, 20, 30]])"
496 |       ]
497 |      },
498 |      "execution_count": 46,
499 |      "metadata": {},
500 |      "output_type": "execute_result"
501 |     }
502 |    ],
503 |    "source": [
504 |     "first_matrix"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": 48,
510 |    "id": "a72ba3ee-fa2b-4994-963b-f7ee7f7bd98e",
511 |    "metadata": {},
512 |    "outputs": [],
513 |    "source": [
514 |     "second_matrix = np.array([[2,3],[2,3],[2,3]])"
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 49,
520 |    "id": "27bdfcc0-422e-44a1-86b2-02eae12bddf0",
521 |    "metadata": {},
522 |    "outputs": [
523 |     {
524 |      "data": {
525 |       "text/plain": [
526 |        "array([[2, 3],\n",
527 |        "       [2, 3],\n",
528 |        "       [2, 3]])"
529 |       ]
530 |      },
531 |      "execution_count": 49,
532 |      "metadata": {},
533 |      "output_type": "execute_result"
534 |     }
535 |    ],
536 |    "source": [
537 |     "second_matrix"
538 |    ]
539 |   },
540 |   {
541 |    "cell_type": "code",
542 |    "execution_count": 50,
543 |    "id": "a2da66f7-9f6f-41e8-9a9f-9369097e1eae",
544 |    "metadata": {},
545 |    "outputs": [
546 |     {
547 |      "data": {
548 |       "text/plain": [
549 |        "(1, 3)"
550 |       ]
551 |      },
552 |      "execution_count": 50,
553 |      "metadata": {},
554 |      "output_type": "execute_result"
555 |     }
556 |    ],
557 |    "source": [
558 |     "first_matrix.shape"
559 |    ]
560 |   },
561 |   {
562 |    "cell_type": "code",
563 |    "execution_count": 51,
564 |    "id": "e2f0f5b3-44c9-4215-b168-0ecffacbbbdd",
565 |    "metadata": {},
566 |    "outputs": [
567 |     {
568 |      "data": {
569 |       "text/plain": [
570 |        "(3, 2)"
571 |       ]
572 |      },
573 |      "execution_count": 51,
574 |      "metadata": {},
575 |      "output_type": "execute_result"
576 |     }
577 |    ],
578 |    "source": [
579 |     "second_matrix.shape"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": 53,
585 |    "id": "ec0e77f1-6a73-48fc-9319-8f4632210cbc",
586 |    "metadata": {},
587 |    "outputs": [],
588 |    "source": [
589 |     "#first_matrix * second_matrix"
590 |    ]
591 |   },
592 |   {
593 |    "cell_type": "code",
594 |    "execution_count": 55,
595 |    "id": "b4bc56b7-4306-4faf-8e9d-1c10635d707b",
596 |    "metadata": {},
597 |    "outputs": [],
598 |    "source": [
599 |     "result_matrix = first_matrix.dot(second_matrix)"
600 |    ]
601 |   },
602 |   {
603 |    "cell_type": "code",
604 |    "execution_count": 56,
605 |    "id": "2acd6119-f2fb-4e6c-a665-545e4081a259",
606 |    "metadata": {},
607 |    "outputs": [
608 |     {
609 |      "data": {
610 |       "text/plain": [
611 |        "array([[120, 180]])"
612 |       ]
613 |      },
614 |      "execution_count": 56,
615 |      "metadata": {},
616 |      "output_type": "execute_result"
617 |     }
618 |    ],
619 |    "source": [
620 |     "result_matrix"
621 |    ]
622 |   },
623 |   {
624 |    "cell_type": "code",
625 |    "execution_count": 57,
626 |    "id": "669d4ef7-c56c-48bc-b1d4-754e2a8071ac",
627 |    "metadata": {},
628 |    "outputs": [
629 |     {
630 |      "data": {
631 |       "text/plain": [
632 |        "(1, 2)"
633 |       ]
634 |      },
635 |      "execution_count": 57,
636 |      "metadata": {},
637 |      "output_type": "execute_result"
638 |     }
639 |    ],
640 |    "source": [
641 |     "result_matrix.shape"
642 |    ]
643 |   },
644 |   {
645 |    "cell_type": "code",
646 |    "execution_count": null,
647 |    "id": "cd818bc9-1470-40c4-b4d9-c9ba2d07279c",
648 |    "metadata": {},
649 |    "outputs": [],
650 |    "source": []
651 |   }
652 |  ],
653 |  "metadata": {
654 |   "kernelspec": {
655 |    "display_name": "Python 3 (ipykernel)",
656 |    "language": "python",
657 |    "name": "python3"
658 |   },
659 |   "language_info": {
660 |    "codemirror_mode": {
661 |     "name": "ipython",
662 |     "version": 3
663 |    },
664 |    "file_extension": ".py",
665 |    "mimetype": "text/x-python",
666 |    "name": "python",
667 |    "nbconvert_exporter": "python",
668 |    "pygments_lexer": "ipython3",
669 |    "version": "3.12.7"
670 |   }
671 |  },
672 |  "nbformat": 4,
673 |  "nbformat_minor": 5
674 | }
675 | 


--------------------------------------------------------------------------------
/3-NumpyOperations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "437a0fe2-0f98-4934-bb29-38e5d7165254",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "id": "e1deac09-18e9-4935-90b0-c91c7735bd9d",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "new_array = np.random.randint(1,100,20)"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 3,
 26 |    "id": "472dbcc3-af2f-4dff-8769-0e3c145c4727",
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/plain": [
 32 |        "array([37, 64, 64, 69, 81, 36, 23, 62, 10, 95, 40, 83, 59, 76, 28, 19,  7,\n",
 33 |        "       81, 61, 47])"
 34 |       ]
 35 |      },
 36 |      "execution_count": 3,
 37 |      "metadata": {},
 38 |      "output_type": "execute_result"
 39 |     }
 40 |    ],
 41 |    "source": [
 42 |     "new_array"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 4,
 48 |    "id": "24dab151-b634-4ded-b23f-c38328eba437",
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/plain": [
 54 |        "array([ True,  True,  True,  True,  True,  True, False,  True, False,\n",
 55 |        "        True,  True,  True,  True,  True,  True, False, False,  True,\n",
 56 |        "        True,  True])"
 57 |       ]
 58 |      },
 59 |      "execution_count": 4,
 60 |      "metadata": {},
 61 |      "output_type": "execute_result"
 62 |     }
 63 |    ],
 64 |    "source": [
 65 |     "new_array > 25"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 5,
 71 |    "id": "312ad7b2-b85c-44b7-aadd-e921dbadc105",
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "data": {
 76 |       "text/plain": [
 77 |        "array([37, 64, 64, 69, 81, 36, 62, 95, 40, 83, 59, 76, 28, 81, 61, 47])"
 78 |       ]
 79 |      },
 80 |      "execution_count": 5,
 81 |      "metadata": {},
 82 |      "output_type": "execute_result"
 83 |     }
 84 |    ],
 85 |    "source": [
 86 |     "new_array[new_array > 25]"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 6,
 92 |    "id": "15e8535a-5ec9-4591-a815-dff5e5984a39",
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "#transpose & reshape"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": 7,
102 |    "id": "3ab27706-fdc1-4872-b0b2-4f06d60667fb",
103 |    "metadata": {},
104 |    "outputs": [],
105 |    "source": [
106 |     "matrix_array = np.array([[10,20],[20,30],[30,40]])"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": 8,
112 |    "id": "086bf92a-1e69-408f-8254-79247608801c",
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "text/plain": [
118 |        "array([[10, 20],\n",
119 |        "       [20, 30],\n",
120 |        "       [30, 40]])"
121 |       ]
122 |      },
123 |      "execution_count": 8,
124 |      "metadata": {},
125 |      "output_type": "execute_result"
126 |     }
127 |    ],
128 |    "source": [
129 |     "matrix_array"
130 |    ]
131 |   },
132 |   {
133 |    "cell_type": "code",
134 |    "execution_count": 9,
135 |    "id": "43d1afcd-7433-4136-ae52-32465301311b",
136 |    "metadata": {},
137 |    "outputs": [
138 |     {
139 |      "data": {
140 |       "text/plain": [
141 |        "array([[10, 20, 30],\n",
142 |        "       [20, 30, 40]])"
143 |       ]
144 |      },
145 |      "execution_count": 9,
146 |      "metadata": {},
147 |      "output_type": "execute_result"
148 |     }
149 |    ],
150 |    "source": [
151 |     "matrix_array.transpose()"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": 10,
157 |    "id": "02538ca8-983f-4c62-b789-5f5402203d08",
158 |    "metadata": {},
159 |    "outputs": [
160 |     {
161 |      "data": {
162 |       "text/plain": [
163 |        "array([[10, 20, 30],\n",
164 |        "       [20, 30, 40]])"
165 |       ]
166 |      },
167 |      "execution_count": 10,
168 |      "metadata": {},
169 |      "output_type": "execute_result"
170 |     }
171 |    ],
172 |    "source": [
173 |     "matrix_array.T"
174 |    ]
175 |   },
176 |   {
177 |    "cell_type": "code",
178 |    "execution_count": 11,
179 |    "id": "f5765d0e-743f-44eb-b561-e9f7786eaa28",
180 |    "metadata": {},
181 |    "outputs": [],
182 |    "source": [
183 |     "random_array = np.random.random((6,1))"
184 |    ]
185 |   },
186 |   {
187 |    "cell_type": "code",
188 |    "execution_count": 12,
189 |    "id": "8a3a534f-a4ae-412f-9ac0-f1373e8c495e",
190 |    "metadata": {},
191 |    "outputs": [
192 |     {
193 |      "data": {
194 |       "text/plain": [
195 |        "array([[0.24590505],\n",
196 |        "       [0.82413243],\n",
197 |        "       [0.57636962],\n",
198 |        "       [0.03514044],\n",
199 |        "       [0.87443472],\n",
200 |        "       [0.97933209]])"
201 |       ]
202 |      },
203 |      "execution_count": 12,
204 |      "metadata": {},
205 |      "output_type": "execute_result"
206 |     }
207 |    ],
208 |    "source": [
209 |     "random_array"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "code",
214 |    "execution_count": 13,
215 |    "id": "d77eb2ad-00ea-4ed8-864b-acc8cebdd873",
216 |    "metadata": {},
217 |    "outputs": [
218 |     {
219 |      "data": {
220 |       "text/plain": [
221 |        "array([[0.24590505, 0.82413243, 0.57636962],\n",
222 |        "       [0.03514044, 0.87443472, 0.97933209]])"
223 |       ]
224 |      },
225 |      "execution_count": 13,
226 |      "metadata": {},
227 |      "output_type": "execute_result"
228 |     }
229 |    ],
230 |    "source": [
231 |     "random_array.reshape(2,3)"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": 14,
237 |    "id": "4632a7c3-3dee-48c6-9bcc-c040d2a5ca06",
238 |    "metadata": {},
239 |    "outputs": [
240 |     {
241 |      "data": {
242 |       "text/plain": [
243 |        "array([[0.24590505, 0.82413243],\n",
244 |        "       [0.57636962, 0.03514044],\n",
245 |        "       [0.87443472, 0.97933209]])"
246 |       ]
247 |      },
248 |      "execution_count": 14,
249 |      "metadata": {},
250 |      "output_type": "execute_result"
251 |     }
252 |    ],
253 |    "source": [
254 |     "random_array.reshape(3,2)"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": 15,
260 |    "id": "804bec25-7d21-4316-b09b-8ea10facd0b5",
261 |    "metadata": {},
262 |    "outputs": [],
263 |    "source": [
264 |     "# real life cases"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 16,
270 |    "id": "fe86536a-d9a8-4232-b4f1-c2e114ee7473",
271 |    "metadata": {},
272 |    "outputs": [],
273 |    "source": [
274 |     "data = np.array([10, 12, 13, 15, 18, 25, 100, 105])"
275 |    ]
276 |   },
277 |   {
278 |    "cell_type": "code",
279 |    "execution_count": 17,
280 |    "id": "11db04e7-7468-4b6d-b334-39aa2b25b16e",
281 |    "metadata": {},
282 |    "outputs": [],
283 |    "source": [
284 |     "# Compute Z-scores\n",
285 |     "mean = np.mean(data)"
286 |    ]
287 |   },
288 |   {
289 |    "cell_type": "code",
290 |    "execution_count": 33,
291 |    "id": "dc22ef31-9fbf-4d09-894a-a65ac72173e1",
292 |    "metadata": {},
293 |    "outputs": [
294 |     {
295 |      "data": {
296 |       "text/plain": [
297 |        "37.25"
298 |       ]
299 |      },
300 |      "execution_count": 33,
301 |      "metadata": {},
302 |      "output_type": "execute_result"
303 |     }
304 |    ],
305 |    "source": [
306 |     "mean"
307 |    ]
308 |   },
309 |   {
310 |    "cell_type": "code",
311 |    "execution_count": 18,
312 |    "id": "5d881ccf-38ef-4ae1-8668-782f97e84718",
313 |    "metadata": {},
314 |    "outputs": [],
315 |    "source": [
316 |     "std = np.std(data)"
317 |    ]
318 |   },
319 |   {
320 |    "cell_type": "code",
321 |    "execution_count": 34,
322 |    "id": "574be2c0-da02-4c85-8bcf-65894540611a",
323 |    "metadata": {},
324 |    "outputs": [
325 |     {
326 |      "data": {
327 |       "text/plain": [
328 |        "37.9333296719389"
329 |       ]
330 |      },
331 |      "execution_count": 34,
332 |      "metadata": {},
333 |      "output_type": "execute_result"
334 |     }
335 |    ],
336 |    "source": [
337 |     "std"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "code",
342 |    "execution_count": 19,
343 |    "id": "ecdec468-346f-4383-8ccb-526aa4a6a0cf",
344 |    "metadata": {},
345 |    "outputs": [],
346 |    "source": [
347 |     "z_scores = (data - mean) / std"
348 |    ]
349 |   },
350 |   {
351 |    "cell_type": "code",
352 |    "execution_count": 20,
353 |    "id": "8a3f41f4-4353-4f42-97ba-9551524caa26",
354 |    "metadata": {},
355 |    "outputs": [
356 |     {
357 |      "data": {
358 |       "text/plain": [
359 |        "array([-0.71836562, -0.66564154, -0.6392795 , -0.58655542, -0.50746929,\n",
360 |        "       -0.322935  ,  1.65421809,  1.78602829])"
361 |       ]
362 |      },
363 |      "execution_count": 20,
364 |      "metadata": {},
365 |      "output_type": "execute_result"
366 |     }
367 |    ],
368 |    "source": [
369 |     "z_scores"
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "code",
374 |    "execution_count": 27,
375 |    "id": "104fa3fb-1f3a-4c86-8295-58ed55437a19",
376 |    "metadata": {},
377 |    "outputs": [
378 |     {
379 |      "data": {
380 |       "text/plain": [
381 |        "array([False, False, False, False, False, False,  True,  True])"
382 |       ]
383 |      },
384 |      "execution_count": 27,
385 |      "metadata": {},
386 |      "output_type": "execute_result"
387 |     }
388 |    ],
389 |    "source": [
390 |     "np.abs(z_scores) > 1"
391 |    ]
392 |   },
393 |   {
394 |    "cell_type": "code",
395 |    "execution_count": 31,
396 |    "id": "b313bd09-9756-477d-b31f-f0789a0ffa02",
397 |    "metadata": {},
398 |    "outputs": [],
399 |    "source": [
400 |     "outliers = data[np.abs(z_scores) > 1]"
401 |    ]
402 |   },
403 |   {
404 |    "cell_type": "code",
405 |    "execution_count": 32,
406 |    "id": "9382f411-a1af-4832-8773-6d40cf825c1d",
407 |    "metadata": {},
408 |    "outputs": [
409 |     {
410 |      "data": {
411 |       "text/plain": [
412 |        "array([100, 105])"
413 |       ]
414 |      },
415 |      "execution_count": 32,
416 |      "metadata": {},
417 |      "output_type": "execute_result"
418 |     }
419 |    ],
420 |    "source": [
421 |     "outliers"
422 |    ]
423 |   },
424 |   {
425 |    "cell_type": "code",
426 |    "execution_count": 35,
427 |    "id": "d29b964f-e793-476f-8769-d3682fb1f722",
428 |    "metadata": {},
429 |    "outputs": [],
430 |    "source": [
431 |     "# math equations"
432 |    ]
433 |   },
434 |   {
435 |    "cell_type": "code",
436 |    "execution_count": 36,
437 |    "id": "85dde08e-5413-4f15-90b1-2288aed891f0",
438 |    "metadata": {},
439 |    "outputs": [],
440 |    "source": [
441 |     "# Solve 2x + 3y = 8 and 5x + 7y = 19"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 37,
447 |    "id": "9273c982-8e1f-431b-9483-d180845a611b",
448 |    "metadata": {},
449 |    "outputs": [],
450 |    "source": [
451 |     "# Coefficients matrix\n",
452 |     "A = np.array([[2, 3], [5, 7]])"
453 |    ]
454 |   },
455 |   {
456 |    "cell_type": "code",
457 |    "execution_count": 38,
458 |    "id": "9ab5dfce-3da3-47c1-b2c8-33534b8ca273",
459 |    "metadata": {},
460 |    "outputs": [],
461 |    "source": [
462 |     "# Constants matrix\n",
463 |     "b = np.array([8, 19])"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 39,
469 |    "id": "cd363c3a-f124-48dd-ba10-01884419b0c1",
470 |    "metadata": {},
471 |    "outputs": [],
472 |    "source": [
473 |     "# Solve for x and y\n",
474 |     "solution = np.linalg.solve(A, b)"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 40,
480 |    "id": "5df130f7-c9cb-4386-8c0c-0bc49a298cfc",
481 |    "metadata": {},
482 |    "outputs": [
483 |     {
484 |      "data": {
485 |       "text/plain": [
486 |        "array([1., 2.])"
487 |       ]
488 |      },
489 |      "execution_count": 40,
490 |      "metadata": {},
491 |      "output_type": "execute_result"
492 |     }
493 |    ],
494 |    "source": [
495 |     "solution"
496 |    ]
497 |   },
498 |   {
499 |    "cell_type": "code",
500 |    "execution_count": 41,
501 |    "id": "3009a2b7-24b3-453d-b826-474fc5325fbe",
502 |    "metadata": {},
503 |    "outputs": [],
504 |    "source": [
505 |     "# data analysis"
506 |    ]
507 |   },
508 |   {
509 |    "cell_type": "code",
510 |    "execution_count": 42,
511 |    "id": "3256f315-c092-4513-a216-1b9de7e0d277",
512 |    "metadata": {},
513 |    "outputs": [],
514 |    "source": [
515 |     "# Stock prices over 5 days\n",
516 |     "prices = np.array([100, 102, 105, 107, 110])"
517 |    ]
518 |   },
519 |   {
520 |    "cell_type": "code",
521 |    "execution_count": 48,
522 |    "id": "c20fbdc5-ab57-40ba-a956-73c54102c3c1",
523 |    "metadata": {},
524 |    "outputs": [
525 |     {
526 |      "data": {
527 |       "text/plain": [
528 |        "array([2, 3, 2, 3])"
529 |       ]
530 |      },
531 |      "execution_count": 48,
532 |      "metadata": {},
533 |      "output_type": "execute_result"
534 |     }
535 |    ],
536 |    "source": [
537 |     "np.diff(prices)"
538 |    ]
539 |   },
540 |   {
541 |    "cell_type": "code",
542 |    "execution_count": 51,
543 |    "id": "d4c4ab0f-cc36-45a4-ae5c-ca7995b18d2a",
544 |    "metadata": {},
545 |    "outputs": [
546 |     {
547 |      "data": {
548 |       "text/plain": [
549 |        "array([100, 102, 105, 107])"
550 |       ]
551 |      },
552 |      "execution_count": 51,
553 |      "metadata": {},
554 |      "output_type": "execute_result"
555 |     }
556 |    ],
557 |    "source": [
558 |     "prices[:len(prices)-1:]"
559 |    ]
560 |   },
561 |   {
562 |    "cell_type": "code",
563 |    "execution_count": 52,
564 |    "id": "19d00e9e-d786-4796-be84-de6cf5dd99c2",
565 |    "metadata": {},
566 |    "outputs": [
567 |     {
568 |      "data": {
569 |       "text/plain": [
570 |        "array([100, 102, 105, 107])"
571 |       ]
572 |      },
573 |      "execution_count": 52,
574 |      "metadata": {},
575 |      "output_type": "execute_result"
576 |     }
577 |    ],
578 |    "source": [
579 |     "prices[:-1:]"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": 53,
585 |    "id": "1b28d184-4bc4-4a56-81d4-a85d0e0a8930",
586 |    "metadata": {},
587 |    "outputs": [
588 |     {
589 |      "data": {
590 |       "text/plain": [
591 |        "array([0.02      , 0.02941176, 0.01904762, 0.02803738])"
592 |       ]
593 |      },
594 |      "execution_count": 53,
595 |      "metadata": {},
596 |      "output_type": "execute_result"
597 |     }
598 |    ],
599 |    "source": [
600 |     "np.diff(prices) / prices[:-1]"
601 |    ]
602 |   },
603 |   {
604 |    "cell_type": "code",
605 |    "execution_count": 54,
606 |    "id": "07d1d026-8960-4338-b46d-8c1d43eaed81",
607 |    "metadata": {},
608 |    "outputs": [],
609 |    "source": [
610 |     "returns = np.diff(prices) / prices[:-1] * 100"
611 |    ]
612 |   },
613 |   {
614 |    "cell_type": "code",
615 |    "execution_count": 55,
616 |    "id": "d4cf197f-0f40-46cb-a992-3a82b4a91d58",
617 |    "metadata": {},
618 |    "outputs": [
619 |     {
620 |      "data": {
621 |       "text/plain": [
622 |        "array([2.        , 2.94117647, 1.9047619 , 2.80373832])"
623 |       ]
624 |      },
625 |      "execution_count": 55,
626 |      "metadata": {},
627 |      "output_type": "execute_result"
628 |     }
629 |    ],
630 |    "source": [
631 |     "returns"
632 |    ]
633 |   },
634 |   {
635 |    "cell_type": "code",
636 |    "execution_count": null,
637 |    "id": "19720d75-517e-4bb1-8fb5-15538cade503",
638 |    "metadata": {},
639 |    "outputs": [],
640 |    "source": []
641 |   }
642 |  ],
643 |  "metadata": {
644 |   "kernelspec": {
645 |    "display_name": "Python 3 (ipykernel)",
646 |    "language": "python",
647 |    "name": "python3"
648 |   },
649 |   "language_info": {
650 |    "codemirror_mode": {
651 |     "name": "ipython",
652 |     "version": 3
653 |    },
654 |    "file_extension": ".py",
655 |    "mimetype": "text/x-python",
656 |    "name": "python",
657 |    "nbconvert_exporter": "python",
658 |    "pygments_lexer": "ipython3",
659 |    "version": "3.12.7"
660 |   }
661 |  },
662 |  "nbformat": 4,
663 |  "nbformat_minor": 5
664 | }
665 | 


--------------------------------------------------------------------------------
/4-IntroToPandas.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "23d1ec8c-a21d-42a4-8d58-5965122143f9",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "id": "af91d4f5-f4ea-4a8b-9d25-daeda74a11fb",
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "grades = {\"Atil\" : 50, \"James\" : 60, \"Lars\" : 30}"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 3,
 27 |    "id": "2ff32a67-6dc8-4072-9886-246df48cc766",
 28 |    "metadata": {},
 29 |    "outputs": [
 30 |     {
 31 |      "data": {
 32 |       "text/plain": [
 33 |        "Atil     50\n",
 34 |        "James    60\n",
 35 |        "Lars     30\n",
 36 |        "dtype: int64"
 37 |       ]
 38 |      },
 39 |      "execution_count": 3,
 40 |      "metadata": {},
 41 |      "output_type": "execute_result"
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "pd.Series(grades)"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 4,
 51 |    "id": "a1692e7c-c1a7-43d4-9244-fcd1c3100900",
 52 |    "metadata": {},
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "names = [\"Atil\", \"James\", \"Lars\"]\n",
 56 |     "grades = [50,60,30]"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 5,
 62 |    "id": "7e321ef4-9c4a-42b7-94ab-f6aaf4dabf45",
 63 |    "metadata": {},
 64 |    "outputs": [
 65 |     {
 66 |      "data": {
 67 |       "text/plain": [
 68 |        "0     Atil\n",
 69 |        "1    James\n",
 70 |        "2     Lars\n",
 71 |        "dtype: object"
 72 |       ]
 73 |      },
 74 |      "execution_count": 5,
 75 |      "metadata": {},
 76 |      "output_type": "execute_result"
 77 |     }
 78 |    ],
 79 |    "source": [
 80 |     "pd.Series(names)"
 81 |    ]
 82 |   },
 83 |   {
 84 |    "cell_type": "code",
 85 |    "execution_count": 6,
 86 |    "id": "2b205ae0-9a96-4159-86ae-792c5055dd58",
 87 |    "metadata": {},
 88 |    "outputs": [
 89 |     {
 90 |      "data": {
 91 |       "text/plain": [
 92 |        "0    50\n",
 93 |        "1    60\n",
 94 |        "2    30\n",
 95 |        "dtype: int64"
 96 |       ]
 97 |      },
 98 |      "execution_count": 6,
 99 |      "metadata": {},
100 |      "output_type": "execute_result"
101 |     }
102 |    ],
103 |    "source": [
104 |     "pd.Series(grades)"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "code",
109 |    "execution_count": 8,
110 |    "id": "7fed230e-a1dc-4107-bd5d-ad5fac6f9222",
111 |    "metadata": {},
112 |    "outputs": [
113 |     {
114 |      "data": {
115 |       "text/plain": [
116 |        "Atil     50\n",
117 |        "James    60\n",
118 |        "Lars     30\n",
119 |        "dtype: int64"
120 |       ]
121 |      },
122 |      "execution_count": 8,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "pd.Series(grades, names)"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 9,
134 |    "id": "df5075ea-cb92-40f2-8b4f-26fdf0a5e59b",
135 |    "metadata": {},
136 |    "outputs": [
137 |     {
138 |      "data": {
139 |       "text/plain": [
140 |        "Atil     50\n",
141 |        "James    60\n",
142 |        "Lars     30\n",
143 |        "dtype: int64"
144 |       ]
145 |      },
146 |      "execution_count": 9,
147 |      "metadata": {},
148 |      "output_type": "execute_result"
149 |     }
150 |    ],
151 |    "source": [
152 |     "pd.Series(data=grades, index=names)"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 12,
158 |    "id": "ac4c4496-e0dc-48d2-b43d-1272de5c4c55",
159 |    "metadata": {},
160 |    "outputs": [],
161 |    "source": [
162 |     "# with numpy"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": 13,
168 |    "id": "3815d62c-e2a1-4967-b16a-24253772f37e",
169 |    "metadata": {},
170 |    "outputs": [],
171 |    "source": [
172 |     "numpy_array = np.array([50,40,30,20])"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": 14,
178 |    "id": "75a32580-c465-4b56-89bc-bc14e67c4e33",
179 |    "metadata": {},
180 |    "outputs": [
181 |     {
182 |      "data": {
183 |       "text/plain": [
184 |        "0    50\n",
185 |        "1    40\n",
186 |        "2    30\n",
187 |        "3    20\n",
188 |        "dtype: int64"
189 |       ]
190 |      },
191 |      "execution_count": 14,
192 |      "metadata": {},
193 |      "output_type": "execute_result"
194 |     }
195 |    ],
196 |    "source": [
197 |     "pd.Series(numpy_array)"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": 15,
203 |    "id": "637e26d3-384e-4dc9-92e6-4789faf19737",
204 |    "metadata": {},
205 |    "outputs": [],
206 |    "source": [
207 |     "#arithmetic"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 16,
213 |    "id": "fb84603e-bbbe-4616-b4f6-6c3c910201ac",
214 |    "metadata": {},
215 |    "outputs": [],
216 |    "source": [
217 |     "contest_result = pd.Series([10,5,100],[\"Atil\",\"James\",\"Lars\"])"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": 17,
223 |    "id": "8e0a72d5-05b4-40ca-8b8c-b7bf071dbbcf",
224 |    "metadata": {},
225 |    "outputs": [],
226 |    "source": [
227 |     "contest_result2 = pd.Series([20,50,10],[\"Atil\",\"James\",\"Lars\"])"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "code",
232 |    "execution_count": 18,
233 |    "id": "b23585b5-3bcf-4660-8d20-62df036e057b",
234 |    "metadata": {},
235 |    "outputs": [
236 |     {
237 |      "data": {
238 |       "text/plain": [
239 |        "10"
240 |       ]
241 |      },
242 |      "execution_count": 18,
243 |      "metadata": {},
244 |      "output_type": "execute_result"
245 |     }
246 |    ],
247 |    "source": [
248 |     "contest_result[\"Atil\"]"
249 |    ]
250 |   },
251 |   {
252 |    "cell_type": "code",
253 |    "execution_count": 20,
254 |    "id": "a71c3814-c119-4d44-b4b8-f1dce7772844",
255 |    "metadata": {},
256 |    "outputs": [
257 |     {
258 |      "data": {
259 |       "text/plain": [
260 |        "50"
261 |       ]
262 |      },
263 |      "execution_count": 20,
264 |      "metadata": {},
265 |      "output_type": "execute_result"
266 |     }
267 |    ],
268 |    "source": [
269 |     "contest_result2[\"James\"]"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 21,
275 |    "id": "1c0fcf04-3499-482f-80f3-73c208ec0b7b",
276 |    "metadata": {},
277 |    "outputs": [],
278 |    "source": [
279 |     "final_result = contest_result + contest_result2"
280 |    ]
281 |   },
282 |   {
283 |    "cell_type": "code",
284 |    "execution_count": 22,
285 |    "id": "58435907-ea8e-4006-ac86-dde519e69995",
286 |    "metadata": {},
287 |    "outputs": [
288 |     {
289 |      "data": {
290 |       "text/plain": [
291 |        "Atil      30\n",
292 |        "James     55\n",
293 |        "Lars     110\n",
294 |        "dtype: int64"
295 |       ]
296 |      },
297 |      "execution_count": 22,
298 |      "metadata": {},
299 |      "output_type": "execute_result"
300 |     }
301 |    ],
302 |    "source": [
303 |     "final_result"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 28,
309 |    "id": "51b27973-0dd6-4538-9d57-76b968ec1d42",
310 |    "metadata": {},
311 |    "outputs": [
312 |     {
313 |      "data": {
314 |       "text/plain": [
315 |        "Atil      200\n",
316 |        "James     250\n",
317 |        "Lars     1000\n",
318 |        "dtype: int64"
319 |       ]
320 |      },
321 |      "execution_count": 28,
322 |      "metadata": {},
323 |      "output_type": "execute_result"
324 |     }
325 |    ],
326 |    "source": [
327 |     "contest_result * contest_result2"
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": 29,
333 |    "id": "196dd510-5972-4326-ba45-4ff5c52ded15",
334 |    "metadata": {},
335 |    "outputs": [
336 |     {
337 |      "data": {
338 |       "text/plain": [
339 |        "Atil    -10\n",
340 |        "James   -45\n",
341 |        "Lars     90\n",
342 |        "dtype: int64"
343 |       ]
344 |      },
345 |      "execution_count": 29,
346 |      "metadata": {},
347 |      "output_type": "execute_result"
348 |     }
349 |    ],
350 |    "source": [
351 |     "contest_result - contest_result2"
352 |    ]
353 |   },
354 |   {
355 |    "cell_type": "code",
356 |    "execution_count": 30,
357 |    "id": "250259b1-01e7-4939-b220-2b1a20bc8358",
358 |    "metadata": {},
359 |    "outputs": [
360 |     {
361 |      "data": {
362 |       "text/plain": [
363 |        "Atil      0.5\n",
364 |        "James     0.1\n",
365 |        "Lars     10.0\n",
366 |        "dtype: float64"
367 |       ]
368 |      },
369 |      "execution_count": 30,
370 |      "metadata": {},
371 |      "output_type": "execute_result"
372 |     }
373 |    ],
374 |    "source": [
375 |     "contest_result / contest_result2"
376 |    ]
377 |   },
378 |   {
379 |    "cell_type": "code",
380 |    "execution_count": 31,
381 |    "id": "1f08ec1e-e86b-44c4-8cf4-3d825fa2f798",
382 |    "metadata": {},
383 |    "outputs": [],
384 |    "source": [
385 |     "# if indices are different"
386 |    ]
387 |   },
388 |   {
389 |    "cell_type": "code",
390 |    "execution_count": 32,
391 |    "id": "ae2dc7ac-49e0-4ee6-b332-45cc26c9a63c",
392 |    "metadata": {},
393 |    "outputs": [],
394 |    "source": [
395 |     "different_series = pd.Series([20,30,40,50],[\"a\",\"b\",\"c\",\"d\"])"
396 |    ]
397 |   },
398 |   {
399 |    "cell_type": "code",
400 |    "execution_count": 33,
401 |    "id": "0f28aed8-e468-4d77-bf28-27c5c25a47f4",
402 |    "metadata": {},
403 |    "outputs": [],
404 |    "source": [
405 |     "different_series_2 = pd.Series([10,5,3,1],[\"a\",\"c\",\"f\",\"g\"])"
406 |    ]
407 |   },
408 |   {
409 |    "cell_type": "code",
410 |    "execution_count": 34,
411 |    "id": "0fbe6585-8637-4cc5-acc8-e670ce193cb9",
412 |    "metadata": {},
413 |    "outputs": [
414 |     {
415 |      "data": {
416 |       "text/plain": [
417 |        "a    30.0\n",
418 |        "b     NaN\n",
419 |        "c    45.0\n",
420 |        "d     NaN\n",
421 |        "f     NaN\n",
422 |        "g     NaN\n",
423 |        "dtype: float64"
424 |       ]
425 |      },
426 |      "execution_count": 34,
427 |      "metadata": {},
428 |      "output_type": "execute_result"
429 |     }
430 |    ],
431 |    "source": [
432 |     "different_series + different_series_2"
433 |    ]
434 |   },
435 |   {
436 |    "cell_type": "code",
437 |    "execution_count": null,
438 |    "id": "d1e61ce6-7028-447b-9687-a3616f3aebdd",
439 |    "metadata": {},
440 |    "outputs": [],
441 |    "source": []
442 |   },
443 |   {
444 |    "cell_type": "code",
445 |    "execution_count": null,
446 |    "id": "1e6a8522-9530-4366-aa1c-7891c72c187f",
447 |    "metadata": {},
448 |    "outputs": [],
449 |    "source": []
450 |   }
451 |  ],
452 |  "metadata": {
453 |   "kernelspec": {
454 |    "display_name": "Python 3 (ipykernel)",
455 |    "language": "python",
456 |    "name": "python3"
457 |   },
458 |   "language_info": {
459 |    "codemirror_mode": {
460 |     "name": "ipython",
461 |     "version": 3
462 |    },
463 |    "file_extension": ".py",
464 |    "mimetype": "text/x-python",
465 |    "name": "python",
466 |    "nbconvert_exporter": "python",
467 |    "pygments_lexer": "ipython3",
468 |    "version": "3.12.7"
469 |   }
470 |  },
471 |  "nbformat": 4,
472 |  "nbformat_minor": 5
473 | }
474 | 


--------------------------------------------------------------------------------
/5-IntroToDataFrames.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "fb4d27a5-0b89-48a5-8709-58c043eed003",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import pandas as pd"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 2,
  16 |    "id": "e4bf5df2-7b4b-43a6-8b7d-3a8d22338b68",
  17 |    "metadata": {},
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "import numpy as np"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 4,
  26 |    "id": "ad99a045-88b7-48a1-a5f4-05250b69bcda",
  27 |    "metadata": {},
  28 |    "outputs": [],
  29 |    "source": [
  30 |     "data = np.random.randn(4,3)"
  31 |    ]
  32 |   },
  33 |   {
  34 |    "cell_type": "code",
  35 |    "execution_count": 5,
  36 |    "id": "2cebcf7a-eb73-45ca-a930-66844ebb4c85",
  37 |    "metadata": {},
  38 |    "outputs": [
  39 |     {
  40 |      "data": {
  41 |       "text/plain": [
  42 |        "array([[ 0.3004821 ,  0.16147545,  0.18858324],\n",
  43 |        "       [-0.33678892,  0.0432555 ,  0.91340869],\n",
  44 |        "       [ 0.27501465,  1.64987553, -0.30196493],\n",
  45 |        "       [ 0.47401973, -1.33659515,  1.79410449]])"
  46 |       ]
  47 |      },
  48 |      "execution_count": 5,
  49 |      "metadata": {},
  50 |      "output_type": "execute_result"
  51 |     }
  52 |    ],
  53 |    "source": [
  54 |     "data"
  55 |    ]
  56 |   },
  57 |   {
  58 |    "cell_type": "code",
  59 |    "execution_count": 6,
  60 |    "id": "3a982c95-4ab3-452a-b5f0-1079c631a196",
  61 |    "metadata": {},
  62 |    "outputs": [],
  63 |    "source": [
  64 |     "data_frame = pd.DataFrame(data)"
  65 |    ]
  66 |   },
  67 |   {
  68 |    "cell_type": "code",
  69 |    "execution_count": 7,
  70 |    "id": "c626d8ef-e786-456b-8a9e-2df6ad5ddd5a",
  71 |    "metadata": {},
  72 |    "outputs": [
  73 |     {
  74 |      "data": {
  75 |       "text/html": [
  76 |        "<div>\n",
  77 |        "<style scoped>\n",
  78 |        "    .dataframe tbody tr th:only-of-type {\n",
  79 |        "        vertical-align: middle;\n",
  80 |        "    }\n",
  81 |        "\n",
  82 |        "    .dataframe tbody tr th {\n",
  83 |        "        vertical-align: top;\n",
  84 |        "    }\n",
  85 |        "\n",
  86 |        "    .dataframe thead th {\n",
  87 |        "        text-align: right;\n",
  88 |        "    }\n",
  89 |        "</style>\n",
  90 |        "<table border=\"1\" class=\"dataframe\">\n",
  91 |        "  <thead>\n",
  92 |        "    <tr style=\"text-align: right;\">\n",
  93 |        "      <th></th>\n",
  94 |        "      <th>0</th>\n",
  95 |        "      <th>1</th>\n",
  96 |        "      <th>2</th>\n",
  97 |        "    </tr>\n",
  98 |        "  </thead>\n",
  99 |        "  <tbody>\n",
 100 |        "    <tr>\n",
 101 |        "      <th>0</th>\n",
 102 |        "      <td>0.300482</td>\n",
 103 |        "      <td>0.161475</td>\n",
 104 |        "      <td>0.188583</td>\n",
 105 |        "    </tr>\n",
 106 |        "    <tr>\n",
 107 |        "      <th>1</th>\n",
 108 |        "      <td>-0.336789</td>\n",
 109 |        "      <td>0.043255</td>\n",
 110 |        "      <td>0.913409</td>\n",
 111 |        "    </tr>\n",
 112 |        "    <tr>\n",
 113 |        "      <th>2</th>\n",
 114 |        "      <td>0.275015</td>\n",
 115 |        "      <td>1.649876</td>\n",
 116 |        "      <td>-0.301965</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>3</th>\n",
 120 |        "      <td>0.474020</td>\n",
 121 |        "      <td>-1.336595</td>\n",
 122 |        "      <td>1.794104</td>\n",
 123 |        "    </tr>\n",
 124 |        "  </tbody>\n",
 125 |        "</table>\n",
 126 |        "</div>"
 127 |       ],
 128 |       "text/plain": [
 129 |        "          0         1         2\n",
 130 |        "0  0.300482  0.161475  0.188583\n",
 131 |        "1 -0.336789  0.043255  0.913409\n",
 132 |        "2  0.275015  1.649876 -0.301965\n",
 133 |        "3  0.474020 -1.336595  1.794104"
 134 |       ]
 135 |      },
 136 |      "execution_count": 7,
 137 |      "metadata": {},
 138 |      "output_type": "execute_result"
 139 |     }
 140 |    ],
 141 |    "source": [
 142 |     "data_frame"
 143 |    ]
 144 |   },
 145 |   {
 146 |    "cell_type": "code",
 147 |    "execution_count": 8,
 148 |    "id": "7a6d22b0-2f1e-4f3e-81a5-3ad01abc8f11",
 149 |    "metadata": {},
 150 |    "outputs": [
 151 |     {
 152 |      "data": {
 153 |       "text/plain": [
 154 |        "pandas.core.frame.DataFrame"
 155 |       ]
 156 |      },
 157 |      "execution_count": 8,
 158 |      "metadata": {},
 159 |      "output_type": "execute_result"
 160 |     }
 161 |    ],
 162 |    "source": [
 163 |     "type(data_frame)"
 164 |    ]
 165 |   },
 166 |   {
 167 |    "cell_type": "code",
 168 |    "execution_count": 9,
 169 |    "id": "40edb828-6209-44f5-836f-26aeafee9b99",
 170 |    "metadata": {},
 171 |    "outputs": [
 172 |     {
 173 |      "data": {
 174 |       "text/plain": [
 175 |        "pandas.core.series.Series"
 176 |       ]
 177 |      },
 178 |      "execution_count": 9,
 179 |      "metadata": {},
 180 |      "output_type": "execute_result"
 181 |     }
 182 |    ],
 183 |    "source": [
 184 |     "type(data_frame[0])"
 185 |    ]
 186 |   },
 187 |   {
 188 |    "cell_type": "code",
 189 |    "execution_count": 10,
 190 |    "id": "ae7d6353-4dcf-4dad-938a-daf83726e537",
 191 |    "metadata": {},
 192 |    "outputs": [],
 193 |    "source": [
 194 |     "new_df = pd.DataFrame(data,index=[\"Atil\",\"Zeynep\",\"Atlas\",\"Mehmet\"],columns = [\"Salary\",\"Age\",\"Seniority\"])"
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": 11,
 200 |    "id": "469beed2-06c2-4dff-b3cc-76f7129d5ab3",
 201 |    "metadata": {},
 202 |    "outputs": [
 203 |     {
 204 |      "data": {
 205 |       "text/html": [
 206 |        "<div>\n",
 207 |        "<style scoped>\n",
 208 |        "    .dataframe tbody tr th:only-of-type {\n",
 209 |        "        vertical-align: middle;\n",
 210 |        "    }\n",
 211 |        "\n",
 212 |        "    .dataframe tbody tr th {\n",
 213 |        "        vertical-align: top;\n",
 214 |        "    }\n",
 215 |        "\n",
 216 |        "    .dataframe thead th {\n",
 217 |        "        text-align: right;\n",
 218 |        "    }\n",
 219 |        "</style>\n",
 220 |        "<table border=\"1\" class=\"dataframe\">\n",
 221 |        "  <thead>\n",
 222 |        "    <tr style=\"text-align: right;\">\n",
 223 |        "      <th></th>\n",
 224 |        "      <th>Salary</th>\n",
 225 |        "      <th>Age</th>\n",
 226 |        "      <th>Seniority</th>\n",
 227 |        "    </tr>\n",
 228 |        "  </thead>\n",
 229 |        "  <tbody>\n",
 230 |        "    <tr>\n",
 231 |        "      <th>Atil</th>\n",
 232 |        "      <td>0.300482</td>\n",
 233 |        "      <td>0.161475</td>\n",
 234 |        "      <td>0.188583</td>\n",
 235 |        "    </tr>\n",
 236 |        "    <tr>\n",
 237 |        "      <th>Zeynep</th>\n",
 238 |        "      <td>-0.336789</td>\n",
 239 |        "      <td>0.043255</td>\n",
 240 |        "      <td>0.913409</td>\n",
 241 |        "    </tr>\n",
 242 |        "    <tr>\n",
 243 |        "      <th>Atlas</th>\n",
 244 |        "      <td>0.275015</td>\n",
 245 |        "      <td>1.649876</td>\n",
 246 |        "      <td>-0.301965</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>Mehmet</th>\n",
 250 |        "      <td>0.474020</td>\n",
 251 |        "      <td>-1.336595</td>\n",
 252 |        "      <td>1.794104</td>\n",
 253 |        "    </tr>\n",
 254 |        "  </tbody>\n",
 255 |        "</table>\n",
 256 |        "</div>"
 257 |       ],
 258 |       "text/plain": [
 259 |        "          Salary       Age  Seniority\n",
 260 |        "Atil    0.300482  0.161475   0.188583\n",
 261 |        "Zeynep -0.336789  0.043255   0.913409\n",
 262 |        "Atlas   0.275015  1.649876  -0.301965\n",
 263 |        "Mehmet  0.474020 -1.336595   1.794104"
 264 |       ]
 265 |      },
 266 |      "execution_count": 11,
 267 |      "metadata": {},
 268 |      "output_type": "execute_result"
 269 |     }
 270 |    ],
 271 |    "source": [
 272 |     "new_df"
 273 |    ]
 274 |   },
 275 |   {
 276 |    "cell_type": "code",
 277 |    "execution_count": 12,
 278 |    "id": "2651f0ac-813f-46ce-b689-4f3512a67f7c",
 279 |    "metadata": {},
 280 |    "outputs": [
 281 |     {
 282 |      "data": {
 283 |       "text/plain": [
 284 |        "Atil      0.161475\n",
 285 |        "Zeynep    0.043255\n",
 286 |        "Atlas     1.649876\n",
 287 |        "Mehmet   -1.336595\n",
 288 |        "Name: Age, dtype: float64"
 289 |       ]
 290 |      },
 291 |      "execution_count": 12,
 292 |      "metadata": {},
 293 |      "output_type": "execute_result"
 294 |     }
 295 |    ],
 296 |    "source": [
 297 |     "new_df[\"Age\"]"
 298 |    ]
 299 |   },
 300 |   {
 301 |    "cell_type": "code",
 302 |    "execution_count": 13,
 303 |    "id": "ac07ca3d-7d94-4e49-a4d6-0e51f4db0eba",
 304 |    "metadata": {},
 305 |    "outputs": [
 306 |     {
 307 |      "data": {
 308 |       "text/plain": [
 309 |        "Atil      0.300482\n",
 310 |        "Zeynep   -0.336789\n",
 311 |        "Atlas     0.275015\n",
 312 |        "Mehmet    0.474020\n",
 313 |        "Name: Salary, dtype: float64"
 314 |       ]
 315 |      },
 316 |      "execution_count": 13,
 317 |      "metadata": {},
 318 |      "output_type": "execute_result"
 319 |     }
 320 |    ],
 321 |    "source": [
 322 |     "new_df[\"Salary\"]"
 323 |    ]
 324 |   },
 325 |   {
 326 |    "cell_type": "code",
 327 |    "execution_count": 14,
 328 |    "id": "12b66c00-3245-434d-ae2c-58ea8ef5b6ba",
 329 |    "metadata": {},
 330 |    "outputs": [
 331 |     {
 332 |      "data": {
 333 |       "text/html": [
 334 |        "<div>\n",
 335 |        "<style scoped>\n",
 336 |        "    .dataframe tbody tr th:only-of-type {\n",
 337 |        "        vertical-align: middle;\n",
 338 |        "    }\n",
 339 |        "\n",
 340 |        "    .dataframe tbody tr th {\n",
 341 |        "        vertical-align: top;\n",
 342 |        "    }\n",
 343 |        "\n",
 344 |        "    .dataframe thead th {\n",
 345 |        "        text-align: right;\n",
 346 |        "    }\n",
 347 |        "</style>\n",
 348 |        "<table border=\"1\" class=\"dataframe\">\n",
 349 |        "  <thead>\n",
 350 |        "    <tr style=\"text-align: right;\">\n",
 351 |        "      <th></th>\n",
 352 |        "      <th>Salary</th>\n",
 353 |        "      <th>Age</th>\n",
 354 |        "    </tr>\n",
 355 |        "  </thead>\n",
 356 |        "  <tbody>\n",
 357 |        "    <tr>\n",
 358 |        "      <th>Atil</th>\n",
 359 |        "      <td>0.300482</td>\n",
 360 |        "      <td>0.161475</td>\n",
 361 |        "    </tr>\n",
 362 |        "    <tr>\n",
 363 |        "      <th>Zeynep</th>\n",
 364 |        "      <td>-0.336789</td>\n",
 365 |        "      <td>0.043255</td>\n",
 366 |        "    </tr>\n",
 367 |        "    <tr>\n",
 368 |        "      <th>Atlas</th>\n",
 369 |        "      <td>0.275015</td>\n",
 370 |        "      <td>1.649876</td>\n",
 371 |        "    </tr>\n",
 372 |        "    <tr>\n",
 373 |        "      <th>Mehmet</th>\n",
 374 |        "      <td>0.474020</td>\n",
 375 |        "      <td>-1.336595</td>\n",
 376 |        "    </tr>\n",
 377 |        "  </tbody>\n",
 378 |        "</table>\n",
 379 |        "</div>"
 380 |       ],
 381 |       "text/plain": [
 382 |        "          Salary       Age\n",
 383 |        "Atil    0.300482  0.161475\n",
 384 |        "Zeynep -0.336789  0.043255\n",
 385 |        "Atlas   0.275015  1.649876\n",
 386 |        "Mehmet  0.474020 -1.336595"
 387 |       ]
 388 |      },
 389 |      "execution_count": 14,
 390 |      "metadata": {},
 391 |      "output_type": "execute_result"
 392 |     }
 393 |    ],
 394 |    "source": [
 395 |     "new_df[[\"Salary\",\"Age\"]]"
 396 |    ]
 397 |   },
 398 |   {
 399 |    "cell_type": "code",
 400 |    "execution_count": 16,
 401 |    "id": "8150b626-c434-4f51-9972-3a77779f7ceb",
 402 |    "metadata": {},
 403 |    "outputs": [
 404 |     {
 405 |      "data": {
 406 |       "text/plain": [
 407 |        "Salary       0.300482\n",
 408 |        "Age          0.161475\n",
 409 |        "Seniority    0.188583\n",
 410 |        "Name: Atil, dtype: float64"
 411 |       ]
 412 |      },
 413 |      "execution_count": 16,
 414 |      "metadata": {},
 415 |      "output_type": "execute_result"
 416 |     }
 417 |    ],
 418 |    "source": [
 419 |     "new_df.loc[\"Atil\"]"
 420 |    ]
 421 |   },
 422 |   {
 423 |    "cell_type": "code",
 424 |    "execution_count": 17,
 425 |    "id": "770fac31-a010-4609-b833-c73674a58ad7",
 426 |    "metadata": {},
 427 |    "outputs": [
 428 |     {
 429 |      "data": {
 430 |       "text/plain": [
 431 |        "Salary       0.300482\n",
 432 |        "Age          0.161475\n",
 433 |        "Seniority    0.188583\n",
 434 |        "Name: Atil, dtype: float64"
 435 |       ]
 436 |      },
 437 |      "execution_count": 17,
 438 |      "metadata": {},
 439 |      "output_type": "execute_result"
 440 |     }
 441 |    ],
 442 |    "source": [
 443 |     "new_df.iloc[0]"
 444 |    ]
 445 |   },
 446 |   {
 447 |    "cell_type": "code",
 448 |    "execution_count": 18,
 449 |    "id": "aff2262a-0c9a-46ef-ae3a-2981843fb2c3",
 450 |    "metadata": {},
 451 |    "outputs": [
 452 |     {
 453 |      "data": {
 454 |       "text/plain": [
 455 |        "Salary      -0.336789\n",
 456 |        "Age          0.043255\n",
 457 |        "Seniority    0.913409\n",
 458 |        "Name: Zeynep, dtype: float64"
 459 |       ]
 460 |      },
 461 |      "execution_count": 18,
 462 |      "metadata": {},
 463 |      "output_type": "execute_result"
 464 |     }
 465 |    ],
 466 |    "source": [
 467 |     "new_df.iloc[1]"
 468 |    ]
 469 |   },
 470 |   {
 471 |    "cell_type": "code",
 472 |    "execution_count": 19,
 473 |    "id": "d96971a1-59f7-48bf-a765-0ca2f3cd37e0",
 474 |    "metadata": {},
 475 |    "outputs": [
 476 |     {
 477 |      "data": {
 478 |       "text/html": [
 479 |        "<div>\n",
 480 |        "<style scoped>\n",
 481 |        "    .dataframe tbody tr th:only-of-type {\n",
 482 |        "        vertical-align: middle;\n",
 483 |        "    }\n",
 484 |        "\n",
 485 |        "    .dataframe tbody tr th {\n",
 486 |        "        vertical-align: top;\n",
 487 |        "    }\n",
 488 |        "\n",
 489 |        "    .dataframe thead th {\n",
 490 |        "        text-align: right;\n",
 491 |        "    }\n",
 492 |        "</style>\n",
 493 |        "<table border=\"1\" class=\"dataframe\">\n",
 494 |        "  <thead>\n",
 495 |        "    <tr style=\"text-align: right;\">\n",
 496 |        "      <th></th>\n",
 497 |        "      <th>Salary</th>\n",
 498 |        "      <th>Age</th>\n",
 499 |        "      <th>Seniority</th>\n",
 500 |        "    </tr>\n",
 501 |        "  </thead>\n",
 502 |        "  <tbody>\n",
 503 |        "    <tr>\n",
 504 |        "      <th>Atil</th>\n",
 505 |        "      <td>0.300482</td>\n",
 506 |        "      <td>0.161475</td>\n",
 507 |        "      <td>0.188583</td>\n",
 508 |        "    </tr>\n",
 509 |        "    <tr>\n",
 510 |        "      <th>Zeynep</th>\n",
 511 |        "      <td>-0.336789</td>\n",
 512 |        "      <td>0.043255</td>\n",
 513 |        "      <td>0.913409</td>\n",
 514 |        "    </tr>\n",
 515 |        "    <tr>\n",
 516 |        "      <th>Atlas</th>\n",
 517 |        "      <td>0.275015</td>\n",
 518 |        "      <td>1.649876</td>\n",
 519 |        "      <td>-0.301965</td>\n",
 520 |        "    </tr>\n",
 521 |        "    <tr>\n",
 522 |        "      <th>Mehmet</th>\n",
 523 |        "      <td>0.474020</td>\n",
 524 |        "      <td>-1.336595</td>\n",
 525 |        "      <td>1.794104</td>\n",
 526 |        "    </tr>\n",
 527 |        "  </tbody>\n",
 528 |        "</table>\n",
 529 |        "</div>"
 530 |       ],
 531 |       "text/plain": [
 532 |        "          Salary       Age  Seniority\n",
 533 |        "Atil    0.300482  0.161475   0.188583\n",
 534 |        "Zeynep -0.336789  0.043255   0.913409\n",
 535 |        "Atlas   0.275015  1.649876  -0.301965\n",
 536 |        "Mehmet  0.474020 -1.336595   1.794104"
 537 |       ]
 538 |      },
 539 |      "execution_count": 19,
 540 |      "metadata": {},
 541 |      "output_type": "execute_result"
 542 |     }
 543 |    ],
 544 |    "source": [
 545 |     "new_df"
 546 |    ]
 547 |   },
 548 |   {
 549 |    "cell_type": "code",
 550 |    "execution_count": 20,
 551 |    "id": "49ef5767-4cc4-4d1b-b484-aea1c7e3a850",
 552 |    "metadata": {},
 553 |    "outputs": [],
 554 |    "source": [
 555 |     "new_df[\"Extra\"] = 10"
 556 |    ]
 557 |   },
 558 |   {
 559 |    "cell_type": "code",
 560 |    "execution_count": 21,
 561 |    "id": "6b11f54d-5269-43dd-be52-88aa5700183f",
 562 |    "metadata": {},
 563 |    "outputs": [
 564 |     {
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 575 |        "    }\n",
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 580 |        "</style>\n",
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 585 |        "      <th>Salary</th>\n",
 586 |        "      <th>Age</th>\n",
 587 |        "      <th>Seniority</th>\n",
 588 |        "      <th>Extra</th>\n",
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 590 |        "  </thead>\n",
 591 |        "  <tbody>\n",
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 593 |        "      <th>Atil</th>\n",
 594 |        "      <td>0.300482</td>\n",
 595 |        "      <td>0.161475</td>\n",
 596 |        "      <td>0.188583</td>\n",
 597 |        "      <td>10</td>\n",
 598 |        "    </tr>\n",
 599 |        "    <tr>\n",
 600 |        "      <th>Zeynep</th>\n",
 601 |        "      <td>-0.336789</td>\n",
 602 |        "      <td>0.043255</td>\n",
 603 |        "      <td>0.913409</td>\n",
 604 |        "      <td>10</td>\n",
 605 |        "    </tr>\n",
 606 |        "    <tr>\n",
 607 |        "      <th>Atlas</th>\n",
 608 |        "      <td>0.275015</td>\n",
 609 |        "      <td>1.649876</td>\n",
 610 |        "      <td>-0.301965</td>\n",
 611 |        "      <td>10</td>\n",
 612 |        "    </tr>\n",
 613 |        "    <tr>\n",
 614 |        "      <th>Mehmet</th>\n",
 615 |        "      <td>0.474020</td>\n",
 616 |        "      <td>-1.336595</td>\n",
 617 |        "      <td>1.794104</td>\n",
 618 |        "      <td>10</td>\n",
 619 |        "    </tr>\n",
 620 |        "  </tbody>\n",
 621 |        "</table>\n",
 622 |        "</div>"
 623 |       ],
 624 |       "text/plain": [
 625 |        "          Salary       Age  Seniority  Extra\n",
 626 |        "Atil    0.300482  0.161475   0.188583     10\n",
 627 |        "Zeynep -0.336789  0.043255   0.913409     10\n",
 628 |        "Atlas   0.275015  1.649876  -0.301965     10\n",
 629 |        "Mehmet  0.474020 -1.336595   1.794104     10"
 630 |       ]
 631 |      },
 632 |      "execution_count": 21,
 633 |      "metadata": {},
 634 |      "output_type": "execute_result"
 635 |     }
 636 |    ],
 637 |    "source": [
 638 |     "new_df"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "code",
 643 |    "execution_count": 22,
 644 |    "id": "c8a88b85-17b0-484d-8b35-7da0fdb8bcc4",
 645 |    "metadata": {},
 646 |    "outputs": [
 647 |     {
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 650 |        "<div>\n",
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 652 |        "    .dataframe tbody tr th:only-of-type {\n",
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 655 |        "\n",
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 663 |        "</style>\n",
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 668 |        "      <th>Salary</th>\n",
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 677 |        "      <td>0.161475</td>\n",
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 685 |        "    </tr>\n",
 686 |        "    <tr>\n",
 687 |        "      <th>Atlas</th>\n",
 688 |        "      <td>0.275015</td>\n",
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 690 |        "      <td>-0.301965</td>\n",
 691 |        "    </tr>\n",
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 693 |        "      <th>Mehmet</th>\n",
 694 |        "      <td>0.474020</td>\n",
 695 |        "      <td>-1.336595</td>\n",
 696 |        "      <td>1.794104</td>\n",
 697 |        "    </tr>\n",
 698 |        "  </tbody>\n",
 699 |        "</table>\n",
 700 |        "</div>"
 701 |       ],
 702 |       "text/plain": [
 703 |        "          Salary       Age  Seniority\n",
 704 |        "Atil    0.300482  0.161475   0.188583\n",
 705 |        "Zeynep -0.336789  0.043255   0.913409\n",
 706 |        "Atlas   0.275015  1.649876  -0.301965\n",
 707 |        "Mehmet  0.474020 -1.336595   1.794104"
 708 |       ]
 709 |      },
 710 |      "execution_count": 22,
 711 |      "metadata": {},
 712 |      "output_type": "execute_result"
 713 |     }
 714 |    ],
 715 |    "source": [
 716 |     "new_df.drop(\"Extra\", axis = 1)"
 717 |    ]
 718 |   },
 719 |   {
 720 |    "cell_type": "code",
 721 |    "execution_count": 23,
 722 |    "id": "ef32c4df-d97e-458f-8a3d-ecad21971ba7",
 723 |    "metadata": {},
 724 |    "outputs": [
 725 |     {
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 748 |        "      <th>Seniority</th>\n",
 749 |        "      <th>Extra</th>\n",
 750 |        "    </tr>\n",
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 775 |        "      <th>Mehmet</th>\n",
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 777 |        "      <td>-1.336595</td>\n",
 778 |        "      <td>1.794104</td>\n",
 779 |        "      <td>10</td>\n",
 780 |        "    </tr>\n",
 781 |        "  </tbody>\n",
 782 |        "</table>\n",
 783 |        "</div>"
 784 |       ],
 785 |       "text/plain": [
 786 |        "          Salary       Age  Seniority  Extra\n",
 787 |        "Atil    0.300482  0.161475   0.188583     10\n",
 788 |        "Zeynep -0.336789  0.043255   0.913409     10\n",
 789 |        "Atlas   0.275015  1.649876  -0.301965     10\n",
 790 |        "Mehmet  0.474020 -1.336595   1.794104     10"
 791 |       ]
 792 |      },
 793 |      "execution_count": 23,
 794 |      "metadata": {},
 795 |      "output_type": "execute_result"
 796 |     }
 797 |    ],
 798 |    "source": [
 799 |     "new_df"
 800 |    ]
 801 |   },
 802 |   {
 803 |    "cell_type": "code",
 804 |    "execution_count": 24,
 805 |    "id": "6a3d8308-03c4-4f07-80e0-2571bdbe2125",
 806 |    "metadata": {},
 807 |    "outputs": [],
 808 |    "source": [
 809 |     "new_df.drop(\"Extra\", axis=1, inplace=True)"
 810 |    ]
 811 |   },
 812 |   {
 813 |    "cell_type": "code",
 814 |    "execution_count": 25,
 815 |    "id": "8231f9ec-9fab-4c94-8105-83d3b29f2dc7",
 816 |    "metadata": {},
 817 |    "outputs": [
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 836 |        "  <thead>\n",
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 841 |        "      <th>Seniority</th>\n",
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 843 |        "  </thead>\n",
 844 |        "  <tbody>\n",
 845 |        "    <tr>\n",
 846 |        "      <th>Atil</th>\n",
 847 |        "      <td>0.300482</td>\n",
 848 |        "      <td>0.161475</td>\n",
 849 |        "      <td>0.188583</td>\n",
 850 |        "    </tr>\n",
 851 |        "    <tr>\n",
 852 |        "      <th>Zeynep</th>\n",
 853 |        "      <td>-0.336789</td>\n",
 854 |        "      <td>0.043255</td>\n",
 855 |        "      <td>0.913409</td>\n",
 856 |        "    </tr>\n",
 857 |        "    <tr>\n",
 858 |        "      <th>Atlas</th>\n",
 859 |        "      <td>0.275015</td>\n",
 860 |        "      <td>1.649876</td>\n",
 861 |        "      <td>-0.301965</td>\n",
 862 |        "    </tr>\n",
 863 |        "    <tr>\n",
 864 |        "      <th>Mehmet</th>\n",
 865 |        "      <td>0.474020</td>\n",
 866 |        "      <td>-1.336595</td>\n",
 867 |        "      <td>1.794104</td>\n",
 868 |        "    </tr>\n",
 869 |        "  </tbody>\n",
 870 |        "</table>\n",
 871 |        "</div>"
 872 |       ],
 873 |       "text/plain": [
 874 |        "          Salary       Age  Seniority\n",
 875 |        "Atil    0.300482  0.161475   0.188583\n",
 876 |        "Zeynep -0.336789  0.043255   0.913409\n",
 877 |        "Atlas   0.275015  1.649876  -0.301965\n",
 878 |        "Mehmet  0.474020 -1.336595   1.794104"
 879 |       ]
 880 |      },
 881 |      "execution_count": 25,
 882 |      "metadata": {},
 883 |      "output_type": "execute_result"
 884 |     }
 885 |    ],
 886 |    "source": [
 887 |     "new_df"
 888 |    ]
 889 |   },
 890 |   {
 891 |    "cell_type": "code",
 892 |    "execution_count": 26,
 893 |    "id": "908e5ffd-55ed-48b3-ba93-ab7b85d56962",
 894 |    "metadata": {},
 895 |    "outputs": [
 896 |     {
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 899 |        "<div>\n",
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 901 |        "    .dataframe tbody tr th:only-of-type {\n",
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 907 |        "    }\n",
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 911 |        "    }\n",
 912 |        "</style>\n",
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 914 |        "  <thead>\n",
 915 |        "    <tr style=\"text-align: right;\">\n",
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 918 |        "      <th>Age</th>\n",
 919 |        "      <th>Seniority</th>\n",
 920 |        "    </tr>\n",
 921 |        "  </thead>\n",
 922 |        "  <tbody>\n",
 923 |        "    <tr>\n",
 924 |        "      <th>Atil</th>\n",
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 926 |        "      <td>0.161475</td>\n",
 927 |        "      <td>0.188583</td>\n",
 928 |        "    </tr>\n",
 929 |        "    <tr>\n",
 930 |        "      <th>Zeynep</th>\n",
 931 |        "      <td>-0.336789</td>\n",
 932 |        "      <td>0.043255</td>\n",
 933 |        "      <td>0.913409</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <th>Atlas</th>\n",
 937 |        "      <td>0.275015</td>\n",
 938 |        "      <td>1.649876</td>\n",
 939 |        "      <td>-0.301965</td>\n",
 940 |        "    </tr>\n",
 941 |        "  </tbody>\n",
 942 |        "</table>\n",
 943 |        "</div>"
 944 |       ],
 945 |       "text/plain": [
 946 |        "          Salary       Age  Seniority\n",
 947 |        "Atil    0.300482  0.161475   0.188583\n",
 948 |        "Zeynep -0.336789  0.043255   0.913409\n",
 949 |        "Atlas   0.275015  1.649876  -0.301965"
 950 |       ]
 951 |      },
 952 |      "execution_count": 26,
 953 |      "metadata": {},
 954 |      "output_type": "execute_result"
 955 |     }
 956 |    ],
 957 |    "source": [
 958 |     "new_df.drop(\"Mehmet\", axis=0)"
 959 |    ]
 960 |   },
 961 |   {
 962 |    "cell_type": "code",
 963 |    "execution_count": 27,
 964 |    "id": "2535f6b2-d9e7-49ef-838b-71fa8ce63367",
 965 |    "metadata": {},
 966 |    "outputs": [
 967 |     {
 968 |      "data": {
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 970 |        "<div>\n",
 971 |        "<style scoped>\n",
 972 |        "    .dataframe tbody tr th:only-of-type {\n",
 973 |        "        vertical-align: middle;\n",
 974 |        "    }\n",
 975 |        "\n",
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 978 |        "    }\n",
 979 |        "\n",
 980 |        "    .dataframe thead th {\n",
 981 |        "        text-align: right;\n",
 982 |        "    }\n",
 983 |        "</style>\n",
 984 |        "<table border=\"1\" class=\"dataframe\">\n",
 985 |        "  <thead>\n",
 986 |        "    <tr style=\"text-align: right;\">\n",
 987 |        "      <th></th>\n",
 988 |        "      <th>Salary</th>\n",
 989 |        "      <th>Age</th>\n",
 990 |        "      <th>Seniority</th>\n",
 991 |        "    </tr>\n",
 992 |        "  </thead>\n",
 993 |        "  <tbody>\n",
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 995 |        "      <th>Atil</th>\n",
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 997 |        "      <td>0.161475</td>\n",
 998 |        "      <td>0.188583</td>\n",
 999 |        "    </tr>\n",
1000 |        "    <tr>\n",
1001 |        "      <th>Zeynep</th>\n",
1002 |        "      <td>-0.336789</td>\n",
1003 |        "      <td>0.043255</td>\n",
1004 |        "      <td>0.913409</td>\n",
1005 |        "    </tr>\n",
1006 |        "    <tr>\n",
1007 |        "      <th>Atlas</th>\n",
1008 |        "      <td>0.275015</td>\n",
1009 |        "      <td>1.649876</td>\n",
1010 |        "      <td>-0.301965</td>\n",
1011 |        "    </tr>\n",
1012 |        "    <tr>\n",
1013 |        "      <th>Mehmet</th>\n",
1014 |        "      <td>0.474020</td>\n",
1015 |        "      <td>-1.336595</td>\n",
1016 |        "      <td>1.794104</td>\n",
1017 |        "    </tr>\n",
1018 |        "  </tbody>\n",
1019 |        "</table>\n",
1020 |        "</div>"
1021 |       ],
1022 |       "text/plain": [
1023 |        "          Salary       Age  Seniority\n",
1024 |        "Atil    0.300482  0.161475   0.188583\n",
1025 |        "Zeynep -0.336789  0.043255   0.913409\n",
1026 |        "Atlas   0.275015  1.649876  -0.301965\n",
1027 |        "Mehmet  0.474020 -1.336595   1.794104"
1028 |       ]
1029 |      },
1030 |      "execution_count": 27,
1031 |      "metadata": {},
1032 |      "output_type": "execute_result"
1033 |     }
1034 |    ],
1035 |    "source": [
1036 |     "new_df"
1037 |    ]
1038 |   },
1039 |   {
1040 |    "cell_type": "code",
1041 |    "execution_count": 28,
1042 |    "id": "70b2482d-fd9e-4f75-8621-fd4d8e06fb59",
1043 |    "metadata": {},
1044 |    "outputs": [
1045 |     {
1046 |      "data": {
1047 |       "text/plain": [
1048 |        "Salary       0.275015\n",
1049 |        "Age          1.649876\n",
1050 |        "Seniority   -0.301965\n",
1051 |        "Name: Atlas, dtype: float64"
1052 |       ]
1053 |      },
1054 |      "execution_count": 28,
1055 |      "metadata": {},
1056 |      "output_type": "execute_result"
1057 |     }
1058 |    ],
1059 |    "source": [
1060 |     "new_df.loc[\"Atlas\"]"
1061 |    ]
1062 |   },
1063 |   {
1064 |    "cell_type": "code",
1065 |    "execution_count": 29,
1066 |    "id": "7846d57c-c7a7-4199-a1f3-c48938896365",
1067 |    "metadata": {},
1068 |    "outputs": [
1069 |     {
1070 |      "data": {
1071 |       "text/plain": [
1072 |        "0.2750146502922042"
1073 |       ]
1074 |      },
1075 |      "execution_count": 29,
1076 |      "metadata": {},
1077 |      "output_type": "execute_result"
1078 |     }
1079 |    ],
1080 |    "source": [
1081 |     "new_df.loc[\"Atlas\"][\"Salary\"]"
1082 |    ]
1083 |   },
1084 |   {
1085 |    "cell_type": "code",
1086 |    "execution_count": 30,
1087 |    "id": "f379c5be-ca84-43a9-af87-874e7ba2b5a2",
1088 |    "metadata": {},
1089 |    "outputs": [
1090 |     {
1091 |      "data": {
1092 |       "text/plain": [
1093 |        "0.2750146502922042"
1094 |       ]
1095 |      },
1096 |      "execution_count": 30,
1097 |      "metadata": {},
1098 |      "output_type": "execute_result"
1099 |     }
1100 |    ],
1101 |    "source": [
1102 |     "new_df.loc[\"Atlas\",\"Salary\"]"
1103 |    ]
1104 |   },
1105 |   {
1106 |    "cell_type": "code",
1107 |    "execution_count": 31,
1108 |    "id": "e93d2e83-471c-4ad9-af11-cc407827b735",
1109 |    "metadata": {},
1110 |    "outputs": [
1111 |     {
1112 |      "data": {
1113 |       "text/html": [
1114 |        "<div>\n",
1115 |        "<style scoped>\n",
1116 |        "    .dataframe tbody tr th:only-of-type {\n",
1117 |        "        vertical-align: middle;\n",
1118 |        "    }\n",
1119 |        "\n",
1120 |        "    .dataframe tbody tr th {\n",
1121 |        "        vertical-align: top;\n",
1122 |        "    }\n",
1123 |        "\n",
1124 |        "    .dataframe thead th {\n",
1125 |        "        text-align: right;\n",
1126 |        "    }\n",
1127 |        "</style>\n",
1128 |        "<table border=\"1\" class=\"dataframe\">\n",
1129 |        "  <thead>\n",
1130 |        "    <tr style=\"text-align: right;\">\n",
1131 |        "      <th></th>\n",
1132 |        "      <th>Salary</th>\n",
1133 |        "      <th>Age</th>\n",
1134 |        "      <th>Seniority</th>\n",
1135 |        "    </tr>\n",
1136 |        "  </thead>\n",
1137 |        "  <tbody>\n",
1138 |        "    <tr>\n",
1139 |        "      <th>Atil</th>\n",
1140 |        "      <td>0.300482</td>\n",
1141 |        "      <td>0.161475</td>\n",
1142 |        "      <td>0.188583</td>\n",
1143 |        "    </tr>\n",
1144 |        "    <tr>\n",
1145 |        "      <th>Zeynep</th>\n",
1146 |        "      <td>-0.336789</td>\n",
1147 |        "      <td>0.043255</td>\n",
1148 |        "      <td>0.913409</td>\n",
1149 |        "    </tr>\n",
1150 |        "    <tr>\n",
1151 |        "      <th>Atlas</th>\n",
1152 |        "      <td>0.275015</td>\n",
1153 |        "      <td>1.649876</td>\n",
1154 |        "      <td>-0.301965</td>\n",
1155 |        "    </tr>\n",
1156 |        "    <tr>\n",
1157 |        "      <th>Mehmet</th>\n",
1158 |        "      <td>0.474020</td>\n",
1159 |        "      <td>-1.336595</td>\n",
1160 |        "      <td>1.794104</td>\n",
1161 |        "    </tr>\n",
1162 |        "  </tbody>\n",
1163 |        "</table>\n",
1164 |        "</div>"
1165 |       ],
1166 |       "text/plain": [
1167 |        "          Salary       Age  Seniority\n",
1168 |        "Atil    0.300482  0.161475   0.188583\n",
1169 |        "Zeynep -0.336789  0.043255   0.913409\n",
1170 |        "Atlas   0.275015  1.649876  -0.301965\n",
1171 |        "Mehmet  0.474020 -1.336595   1.794104"
1172 |       ]
1173 |      },
1174 |      "execution_count": 31,
1175 |      "metadata": {},
1176 |      "output_type": "execute_result"
1177 |     }
1178 |    ],
1179 |    "source": [
1180 |     "new_df"
1181 |    ]
1182 |   },
1183 |   {
1184 |    "cell_type": "code",
1185 |    "execution_count": 32,
1186 |    "id": "0c271a08-8024-432b-a5ff-2ccf2efb4b5e",
1187 |    "metadata": {},
1188 |    "outputs": [],
1189 |    "source": [
1190 |     "boolean_frame = new_df > 0"
1191 |    ]
1192 |   },
1193 |   {
1194 |    "cell_type": "code",
1195 |    "execution_count": 33,
1196 |    "id": "f8765d12-35ae-49f9-9dba-2d1fdb6e932f",
1197 |    "metadata": {},
1198 |    "outputs": [
1199 |     {
1200 |      "data": {
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1204 |        "    .dataframe tbody tr th:only-of-type {\n",
1205 |        "        vertical-align: middle;\n",
1206 |        "    }\n",
1207 |        "\n",
1208 |        "    .dataframe tbody tr th {\n",
1209 |        "        vertical-align: top;\n",
1210 |        "    }\n",
1211 |        "\n",
1212 |        "    .dataframe thead th {\n",
1213 |        "        text-align: right;\n",
1214 |        "    }\n",
1215 |        "</style>\n",
1216 |        "<table border=\"1\" class=\"dataframe\">\n",
1217 |        "  <thead>\n",
1218 |        "    <tr style=\"text-align: right;\">\n",
1219 |        "      <th></th>\n",
1220 |        "      <th>Salary</th>\n",
1221 |        "      <th>Age</th>\n",
1222 |        "      <th>Seniority</th>\n",
1223 |        "    </tr>\n",
1224 |        "  </thead>\n",
1225 |        "  <tbody>\n",
1226 |        "    <tr>\n",
1227 |        "      <th>Atil</th>\n",
1228 |        "      <td>True</td>\n",
1229 |        "      <td>True</td>\n",
1230 |        "      <td>True</td>\n",
1231 |        "    </tr>\n",
1232 |        "    <tr>\n",
1233 |        "      <th>Zeynep</th>\n",
1234 |        "      <td>False</td>\n",
1235 |        "      <td>True</td>\n",
1236 |        "      <td>True</td>\n",
1237 |        "    </tr>\n",
1238 |        "    <tr>\n",
1239 |        "      <th>Atlas</th>\n",
1240 |        "      <td>True</td>\n",
1241 |        "      <td>True</td>\n",
1242 |        "      <td>False</td>\n",
1243 |        "    </tr>\n",
1244 |        "    <tr>\n",
1245 |        "      <th>Mehmet</th>\n",
1246 |        "      <td>True</td>\n",
1247 |        "      <td>False</td>\n",
1248 |        "      <td>True</td>\n",
1249 |        "    </tr>\n",
1250 |        "  </tbody>\n",
1251 |        "</table>\n",
1252 |        "</div>"
1253 |       ],
1254 |       "text/plain": [
1255 |        "        Salary    Age  Seniority\n",
1256 |        "Atil      True   True       True\n",
1257 |        "Zeynep   False   True       True\n",
1258 |        "Atlas     True   True      False\n",
1259 |        "Mehmet    True  False       True"
1260 |       ]
1261 |      },
1262 |      "execution_count": 33,
1263 |      "metadata": {},
1264 |      "output_type": "execute_result"
1265 |     }
1266 |    ],
1267 |    "source": [
1268 |     "boolean_frame"
1269 |    ]
1270 |   },
1271 |   {
1272 |    "cell_type": "code",
1273 |    "execution_count": 34,
1274 |    "id": "c2587736-dbae-489d-8e4d-ed8adc820e6f",
1275 |    "metadata": {},
1276 |    "outputs": [
1277 |     {
1278 |      "data": {
1279 |       "text/html": [
1280 |        "<div>\n",
1281 |        "<style scoped>\n",
1282 |        "    .dataframe tbody tr th:only-of-type {\n",
1283 |        "        vertical-align: middle;\n",
1284 |        "    }\n",
1285 |        "\n",
1286 |        "    .dataframe tbody tr th {\n",
1287 |        "        vertical-align: top;\n",
1288 |        "    }\n",
1289 |        "\n",
1290 |        "    .dataframe thead th {\n",
1291 |        "        text-align: right;\n",
1292 |        "    }\n",
1293 |        "</style>\n",
1294 |        "<table border=\"1\" class=\"dataframe\">\n",
1295 |        "  <thead>\n",
1296 |        "    <tr style=\"text-align: right;\">\n",
1297 |        "      <th></th>\n",
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1299 |        "      <th>Age</th>\n",
1300 |        "      <th>Seniority</th>\n",
1301 |        "    </tr>\n",
1302 |        "  </thead>\n",
1303 |        "  <tbody>\n",
1304 |        "    <tr>\n",
1305 |        "      <th>Atil</th>\n",
1306 |        "      <td>0.300482</td>\n",
1307 |        "      <td>0.161475</td>\n",
1308 |        "      <td>0.188583</td>\n",
1309 |        "    </tr>\n",
1310 |        "    <tr>\n",
1311 |        "      <th>Zeynep</th>\n",
1312 |        "      <td>NaN</td>\n",
1313 |        "      <td>0.043255</td>\n",
1314 |        "      <td>0.913409</td>\n",
1315 |        "    </tr>\n",
1316 |        "    <tr>\n",
1317 |        "      <th>Atlas</th>\n",
1318 |        "      <td>0.275015</td>\n",
1319 |        "      <td>1.649876</td>\n",
1320 |        "      <td>NaN</td>\n",
1321 |        "    </tr>\n",
1322 |        "    <tr>\n",
1323 |        "      <th>Mehmet</th>\n",
1324 |        "      <td>0.474020</td>\n",
1325 |        "      <td>NaN</td>\n",
1326 |        "      <td>1.794104</td>\n",
1327 |        "    </tr>\n",
1328 |        "  </tbody>\n",
1329 |        "</table>\n",
1330 |        "</div>"
1331 |       ],
1332 |       "text/plain": [
1333 |        "          Salary       Age  Seniority\n",
1334 |        "Atil    0.300482  0.161475   0.188583\n",
1335 |        "Zeynep       NaN  0.043255   0.913409\n",
1336 |        "Atlas   0.275015  1.649876        NaN\n",
1337 |        "Mehmet  0.474020       NaN   1.794104"
1338 |       ]
1339 |      },
1340 |      "execution_count": 34,
1341 |      "metadata": {},
1342 |      "output_type": "execute_result"
1343 |     }
1344 |    ],
1345 |    "source": [
1346 |     "new_df[boolean_frame]"
1347 |    ]
1348 |   },
1349 |   {
1350 |    "cell_type": "code",
1351 |    "execution_count": 35,
1352 |    "id": "33882312-56e3-46af-9570-243d9823c235",
1353 |    "metadata": {},
1354 |    "outputs": [
1355 |     {
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1357 |       "text/html": [
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1360 |        "    .dataframe tbody tr th:only-of-type {\n",
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1362 |        "    }\n",
1363 |        "\n",
1364 |        "    .dataframe tbody tr th {\n",
1365 |        "        vertical-align: top;\n",
1366 |        "    }\n",
1367 |        "\n",
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1369 |        "        text-align: right;\n",
1370 |        "    }\n",
1371 |        "</style>\n",
1372 |        "<table border=\"1\" class=\"dataframe\">\n",
1373 |        "  <thead>\n",
1374 |        "    <tr style=\"text-align: right;\">\n",
1375 |        "      <th></th>\n",
1376 |        "      <th>Salary</th>\n",
1377 |        "      <th>Age</th>\n",
1378 |        "      <th>Seniority</th>\n",
1379 |        "    </tr>\n",
1380 |        "  </thead>\n",
1381 |        "  <tbody>\n",
1382 |        "    <tr>\n",
1383 |        "      <th>Atil</th>\n",
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1387 |        "    </tr>\n",
1388 |        "    <tr>\n",
1389 |        "      <th>Zeynep</th>\n",
1390 |        "      <td>NaN</td>\n",
1391 |        "      <td>0.043255</td>\n",
1392 |        "      <td>0.913409</td>\n",
1393 |        "    </tr>\n",
1394 |        "    <tr>\n",
1395 |        "      <th>Atlas</th>\n",
1396 |        "      <td>0.275015</td>\n",
1397 |        "      <td>1.649876</td>\n",
1398 |        "      <td>NaN</td>\n",
1399 |        "    </tr>\n",
1400 |        "    <tr>\n",
1401 |        "      <th>Mehmet</th>\n",
1402 |        "      <td>0.474020</td>\n",
1403 |        "      <td>NaN</td>\n",
1404 |        "      <td>1.794104</td>\n",
1405 |        "    </tr>\n",
1406 |        "  </tbody>\n",
1407 |        "</table>\n",
1408 |        "</div>"
1409 |       ],
1410 |       "text/plain": [
1411 |        "          Salary       Age  Seniority\n",
1412 |        "Atil    0.300482  0.161475   0.188583\n",
1413 |        "Zeynep       NaN  0.043255   0.913409\n",
1414 |        "Atlas   0.275015  1.649876        NaN\n",
1415 |        "Mehmet  0.474020       NaN   1.794104"
1416 |       ]
1417 |      },
1418 |      "execution_count": 35,
1419 |      "metadata": {},
1420 |      "output_type": "execute_result"
1421 |     }
1422 |    ],
1423 |    "source": [
1424 |     "new_df[new_df > 0]"
1425 |    ]
1426 |   },
1427 |   {
1428 |    "cell_type": "code",
1429 |    "execution_count": 36,
1430 |    "id": "a593066a-4f14-44cf-b523-d8ee16249eed",
1431 |    "metadata": {},
1432 |    "outputs": [
1433 |     {
1434 |      "data": {
1435 |       "text/html": [
1436 |        "<div>\n",
1437 |        "<style scoped>\n",
1438 |        "    .dataframe tbody tr th:only-of-type {\n",
1439 |        "        vertical-align: middle;\n",
1440 |        "    }\n",
1441 |        "\n",
1442 |        "    .dataframe tbody tr th {\n",
1443 |        "        vertical-align: top;\n",
1444 |        "    }\n",
1445 |        "\n",
1446 |        "    .dataframe thead th {\n",
1447 |        "        text-align: right;\n",
1448 |        "    }\n",
1449 |        "</style>\n",
1450 |        "<table border=\"1\" class=\"dataframe\">\n",
1451 |        "  <thead>\n",
1452 |        "    <tr style=\"text-align: right;\">\n",
1453 |        "      <th></th>\n",
1454 |        "      <th>Salary</th>\n",
1455 |        "      <th>Age</th>\n",
1456 |        "      <th>Seniority</th>\n",
1457 |        "    </tr>\n",
1458 |        "  </thead>\n",
1459 |        "  <tbody>\n",
1460 |        "    <tr>\n",
1461 |        "      <th>Atil</th>\n",
1462 |        "      <td>0.300482</td>\n",
1463 |        "      <td>0.161475</td>\n",
1464 |        "      <td>0.188583</td>\n",
1465 |        "    </tr>\n",
1466 |        "    <tr>\n",
1467 |        "      <th>Mehmet</th>\n",
1468 |        "      <td>0.474020</td>\n",
1469 |        "      <td>-1.336595</td>\n",
1470 |        "      <td>1.794104</td>\n",
1471 |        "    </tr>\n",
1472 |        "  </tbody>\n",
1473 |        "</table>\n",
1474 |        "</div>"
1475 |       ],
1476 |       "text/plain": [
1477 |        "          Salary       Age  Seniority\n",
1478 |        "Atil    0.300482  0.161475   0.188583\n",
1479 |        "Mehmet  0.474020 -1.336595   1.794104"
1480 |       ]
1481 |      },
1482 |      "execution_count": 36,
1483 |      "metadata": {},
1484 |      "output_type": "execute_result"
1485 |     }
1486 |    ],
1487 |    "source": [
1488 |     "new_df[new_df[\"Salary\"] > 0.3]"
1489 |    ]
1490 |   },
1491 |   {
1492 |    "cell_type": "code",
1493 |    "execution_count": 37,
1494 |    "id": "f3e7d662-dfb0-4926-9ae3-cd02b9f04770",
1495 |    "metadata": {},
1496 |    "outputs": [
1497 |     {
1498 |      "data": {
1499 |       "text/html": [
1500 |        "<div>\n",
1501 |        "<style scoped>\n",
1502 |        "    .dataframe tbody tr th:only-of-type {\n",
1503 |        "        vertical-align: middle;\n",
1504 |        "    }\n",
1505 |        "\n",
1506 |        "    .dataframe tbody tr th {\n",
1507 |        "        vertical-align: top;\n",
1508 |        "    }\n",
1509 |        "\n",
1510 |        "    .dataframe thead th {\n",
1511 |        "        text-align: right;\n",
1512 |        "    }\n",
1513 |        "</style>\n",
1514 |        "<table border=\"1\" class=\"dataframe\">\n",
1515 |        "  <thead>\n",
1516 |        "    <tr style=\"text-align: right;\">\n",
1517 |        "      <th></th>\n",
1518 |        "      <th>Salary</th>\n",
1519 |        "      <th>Age</th>\n",
1520 |        "      <th>Seniority</th>\n",
1521 |        "    </tr>\n",
1522 |        "  </thead>\n",
1523 |        "  <tbody>\n",
1524 |        "    <tr>\n",
1525 |        "      <th>Atlas</th>\n",
1526 |        "      <td>0.275015</td>\n",
1527 |        "      <td>1.649876</td>\n",
1528 |        "      <td>-0.301965</td>\n",
1529 |        "    </tr>\n",
1530 |        "  </tbody>\n",
1531 |        "</table>\n",
1532 |        "</div>"
1533 |       ],
1534 |       "text/plain": [
1535 |        "         Salary       Age  Seniority\n",
1536 |        "Atlas  0.275015  1.649876  -0.301965"
1537 |       ]
1538 |      },
1539 |      "execution_count": 37,
1540 |      "metadata": {},
1541 |      "output_type": "execute_result"
1542 |     }
1543 |    ],
1544 |    "source": [
1545 |     "new_df[new_df[\"Age\"] > 0.5]"
1546 |    ]
1547 |   },
1548 |   {
1549 |    "cell_type": "code",
1550 |    "execution_count": 38,
1551 |    "id": "9097f182-6c2b-4905-8e23-90efa65a671b",
1552 |    "metadata": {},
1553 |    "outputs": [
1554 |     {
1555 |      "data": {
1556 |       "text/html": [
1557 |        "<div>\n",
1558 |        "<style scoped>\n",
1559 |        "    .dataframe tbody tr th:only-of-type {\n",
1560 |        "        vertical-align: middle;\n",
1561 |        "    }\n",
1562 |        "\n",
1563 |        "    .dataframe tbody tr th {\n",
1564 |        "        vertical-align: top;\n",
1565 |        "    }\n",
1566 |        "\n",
1567 |        "    .dataframe thead th {\n",
1568 |        "        text-align: right;\n",
1569 |        "    }\n",
1570 |        "</style>\n",
1571 |        "<table border=\"1\" class=\"dataframe\">\n",
1572 |        "  <thead>\n",
1573 |        "    <tr style=\"text-align: right;\">\n",
1574 |        "      <th></th>\n",
1575 |        "      <th>Salary</th>\n",
1576 |        "      <th>Age</th>\n",
1577 |        "      <th>Seniority</th>\n",
1578 |        "    </tr>\n",
1579 |        "  </thead>\n",
1580 |        "  <tbody>\n",
1581 |        "    <tr>\n",
1582 |        "      <th>Atil</th>\n",
1583 |        "      <td>0.300482</td>\n",
1584 |        "      <td>0.161475</td>\n",
1585 |        "      <td>0.188583</td>\n",
1586 |        "    </tr>\n",
1587 |        "    <tr>\n",
1588 |        "      <th>Zeynep</th>\n",
1589 |        "      <td>-0.336789</td>\n",
1590 |        "      <td>0.043255</td>\n",
1591 |        "      <td>0.913409</td>\n",
1592 |        "    </tr>\n",
1593 |        "    <tr>\n",
1594 |        "      <th>Atlas</th>\n",
1595 |        "      <td>0.275015</td>\n",
1596 |        "      <td>1.649876</td>\n",
1597 |        "      <td>-0.301965</td>\n",
1598 |        "    </tr>\n",
1599 |        "    <tr>\n",
1600 |        "      <th>Mehmet</th>\n",
1601 |        "      <td>0.474020</td>\n",
1602 |        "      <td>-1.336595</td>\n",
1603 |        "      <td>1.794104</td>\n",
1604 |        "    </tr>\n",
1605 |        "  </tbody>\n",
1606 |        "</table>\n",
1607 |        "</div>"
1608 |       ],
1609 |       "text/plain": [
1610 |        "          Salary       Age  Seniority\n",
1611 |        "Atil    0.300482  0.161475   0.188583\n",
1612 |        "Zeynep -0.336789  0.043255   0.913409\n",
1613 |        "Atlas   0.275015  1.649876  -0.301965\n",
1614 |        "Mehmet  0.474020 -1.336595   1.794104"
1615 |       ]
1616 |      },
1617 |      "execution_count": 38,
1618 |      "metadata": {},
1619 |      "output_type": "execute_result"
1620 |     }
1621 |    ],
1622 |    "source": [
1623 |     "new_df"
1624 |    ]
1625 |   },
1626 |   {
1627 |    "cell_type": "code",
1628 |    "execution_count": 39,
1629 |    "id": "2b3bbb0e-8717-44a8-b46b-21e993d496a2",
1630 |    "metadata": {},
1631 |    "outputs": [
1632 |     {
1633 |      "data": {
1634 |       "text/html": [
1635 |        "<div>\n",
1636 |        "<style scoped>\n",
1637 |        "    .dataframe tbody tr th:only-of-type {\n",
1638 |        "        vertical-align: middle;\n",
1639 |        "    }\n",
1640 |        "\n",
1641 |        "    .dataframe tbody tr th {\n",
1642 |        "        vertical-align: top;\n",
1643 |        "    }\n",
1644 |        "\n",
1645 |        "    .dataframe thead th {\n",
1646 |        "        text-align: right;\n",
1647 |        "    }\n",
1648 |        "</style>\n",
1649 |        "<table border=\"1\" class=\"dataframe\">\n",
1650 |        "  <thead>\n",
1651 |        "    <tr style=\"text-align: right;\">\n",
1652 |        "      <th></th>\n",
1653 |        "      <th>index</th>\n",
1654 |        "      <th>Salary</th>\n",
1655 |        "      <th>Age</th>\n",
1656 |        "      <th>Seniority</th>\n",
1657 |        "    </tr>\n",
1658 |        "  </thead>\n",
1659 |        "  <tbody>\n",
1660 |        "    <tr>\n",
1661 |        "      <th>0</th>\n",
1662 |        "      <td>Atil</td>\n",
1663 |        "      <td>0.300482</td>\n",
1664 |        "      <td>0.161475</td>\n",
1665 |        "      <td>0.188583</td>\n",
1666 |        "    </tr>\n",
1667 |        "    <tr>\n",
1668 |        "      <th>1</th>\n",
1669 |        "      <td>Zeynep</td>\n",
1670 |        "      <td>-0.336789</td>\n",
1671 |        "      <td>0.043255</td>\n",
1672 |        "      <td>0.913409</td>\n",
1673 |        "    </tr>\n",
1674 |        "    <tr>\n",
1675 |        "      <th>2</th>\n",
1676 |        "      <td>Atlas</td>\n",
1677 |        "      <td>0.275015</td>\n",
1678 |        "      <td>1.649876</td>\n",
1679 |        "      <td>-0.301965</td>\n",
1680 |        "    </tr>\n",
1681 |        "    <tr>\n",
1682 |        "      <th>3</th>\n",
1683 |        "      <td>Mehmet</td>\n",
1684 |        "      <td>0.474020</td>\n",
1685 |        "      <td>-1.336595</td>\n",
1686 |        "      <td>1.794104</td>\n",
1687 |        "    </tr>\n",
1688 |        "  </tbody>\n",
1689 |        "</table>\n",
1690 |        "</div>"
1691 |       ],
1692 |       "text/plain": [
1693 |        "    index    Salary       Age  Seniority\n",
1694 |        "0    Atil  0.300482  0.161475   0.188583\n",
1695 |        "1  Zeynep -0.336789  0.043255   0.913409\n",
1696 |        "2   Atlas  0.275015  1.649876  -0.301965\n",
1697 |        "3  Mehmet  0.474020 -1.336595   1.794104"
1698 |       ]
1699 |      },
1700 |      "execution_count": 39,
1701 |      "metadata": {},
1702 |      "output_type": "execute_result"
1703 |     }
1704 |    ],
1705 |    "source": [
1706 |     "new_df.reset_index()"
1707 |    ]
1708 |   },
1709 |   {
1710 |    "cell_type": "code",
1711 |    "execution_count": 40,
1712 |    "id": "8b5f12e7-0126-43bc-9709-64b62f5eea30",
1713 |    "metadata": {},
1714 |    "outputs": [],
1715 |    "source": [
1716 |     "new_indices = [\"Ati\", \"Zey\", \"Atl\", \"Meh\"]"
1717 |    ]
1718 |   },
1719 |   {
1720 |    "cell_type": "code",
1721 |    "execution_count": 41,
1722 |    "id": "0ade90f3-bfeb-41ca-8ca6-c96f45bf7f4e",
1723 |    "metadata": {},
1724 |    "outputs": [],
1725 |    "source": [
1726 |     "new_df[\"NewIndex\"] = new_indices"
1727 |    ]
1728 |   },
1729 |   {
1730 |    "cell_type": "code",
1731 |    "execution_count": 42,
1732 |    "id": "2ff6fc16-2eb2-4a1a-8a67-203f46701015",
1733 |    "metadata": {},
1734 |    "outputs": [
1735 |     {
1736 |      "data": {
1737 |       "text/html": [
1738 |        "<div>\n",
1739 |        "<style scoped>\n",
1740 |        "    .dataframe tbody tr th:only-of-type {\n",
1741 |        "        vertical-align: middle;\n",
1742 |        "    }\n",
1743 |        "\n",
1744 |        "    .dataframe tbody tr th {\n",
1745 |        "        vertical-align: top;\n",
1746 |        "    }\n",
1747 |        "\n",
1748 |        "    .dataframe thead th {\n",
1749 |        "        text-align: right;\n",
1750 |        "    }\n",
1751 |        "</style>\n",
1752 |        "<table border=\"1\" class=\"dataframe\">\n",
1753 |        "  <thead>\n",
1754 |        "    <tr style=\"text-align: right;\">\n",
1755 |        "      <th></th>\n",
1756 |        "      <th>Salary</th>\n",
1757 |        "      <th>Age</th>\n",
1758 |        "      <th>Seniority</th>\n",
1759 |        "      <th>NewIndex</th>\n",
1760 |        "    </tr>\n",
1761 |        "  </thead>\n",
1762 |        "  <tbody>\n",
1763 |        "    <tr>\n",
1764 |        "      <th>Atil</th>\n",
1765 |        "      <td>0.300482</td>\n",
1766 |        "      <td>0.161475</td>\n",
1767 |        "      <td>0.188583</td>\n",
1768 |        "      <td>Ati</td>\n",
1769 |        "    </tr>\n",
1770 |        "    <tr>\n",
1771 |        "      <th>Zeynep</th>\n",
1772 |        "      <td>-0.336789</td>\n",
1773 |        "      <td>0.043255</td>\n",
1774 |        "      <td>0.913409</td>\n",
1775 |        "      <td>Zey</td>\n",
1776 |        "    </tr>\n",
1777 |        "    <tr>\n",
1778 |        "      <th>Atlas</th>\n",
1779 |        "      <td>0.275015</td>\n",
1780 |        "      <td>1.649876</td>\n",
1781 |        "      <td>-0.301965</td>\n",
1782 |        "      <td>Atl</td>\n",
1783 |        "    </tr>\n",
1784 |        "    <tr>\n",
1785 |        "      <th>Mehmet</th>\n",
1786 |        "      <td>0.474020</td>\n",
1787 |        "      <td>-1.336595</td>\n",
1788 |        "      <td>1.794104</td>\n",
1789 |        "      <td>Meh</td>\n",
1790 |        "    </tr>\n",
1791 |        "  </tbody>\n",
1792 |        "</table>\n",
1793 |        "</div>"
1794 |       ],
1795 |       "text/plain": [
1796 |        "          Salary       Age  Seniority NewIndex\n",
1797 |        "Atil    0.300482  0.161475   0.188583      Ati\n",
1798 |        "Zeynep -0.336789  0.043255   0.913409      Zey\n",
1799 |        "Atlas   0.275015  1.649876  -0.301965      Atl\n",
1800 |        "Mehmet  0.474020 -1.336595   1.794104      Meh"
1801 |       ]
1802 |      },
1803 |      "execution_count": 42,
1804 |      "metadata": {},
1805 |      "output_type": "execute_result"
1806 |     }
1807 |    ],
1808 |    "source": [
1809 |     "new_df"
1810 |    ]
1811 |   },
1812 |   {
1813 |    "cell_type": "code",
1814 |    "execution_count": 43,
1815 |    "id": "96a3578d-2f19-4bf5-ae93-2f8941783bc0",
1816 |    "metadata": {},
1817 |    "outputs": [],
1818 |    "source": [
1819 |     "new_df.set_index(\"NewIndex\", inplace=True)"
1820 |    ]
1821 |   },
1822 |   {
1823 |    "cell_type": "code",
1824 |    "execution_count": 44,
1825 |    "id": "09bc1e88-a21e-4081-854f-19f8fae6a6c2",
1826 |    "metadata": {},
1827 |    "outputs": [
1828 |     {
1829 |      "data": {
1830 |       "text/html": [
1831 |        "<div>\n",
1832 |        "<style scoped>\n",
1833 |        "    .dataframe tbody tr th:only-of-type {\n",
1834 |        "        vertical-align: middle;\n",
1835 |        "    }\n",
1836 |        "\n",
1837 |        "    .dataframe tbody tr th {\n",
1838 |        "        vertical-align: top;\n",
1839 |        "    }\n",
1840 |        "\n",
1841 |        "    .dataframe thead th {\n",
1842 |        "        text-align: right;\n",
1843 |        "    }\n",
1844 |        "</style>\n",
1845 |        "<table border=\"1\" class=\"dataframe\">\n",
1846 |        "  <thead>\n",
1847 |        "    <tr style=\"text-align: right;\">\n",
1848 |        "      <th></th>\n",
1849 |        "      <th>Salary</th>\n",
1850 |        "      <th>Age</th>\n",
1851 |        "      <th>Seniority</th>\n",
1852 |        "    </tr>\n",
1853 |        "    <tr>\n",
1854 |        "      <th>NewIndex</th>\n",
1855 |        "      <th></th>\n",
1856 |        "      <th></th>\n",
1857 |        "      <th></th>\n",
1858 |        "    </tr>\n",
1859 |        "  </thead>\n",
1860 |        "  <tbody>\n",
1861 |        "    <tr>\n",
1862 |        "      <th>Ati</th>\n",
1863 |        "      <td>0.300482</td>\n",
1864 |        "      <td>0.161475</td>\n",
1865 |        "      <td>0.188583</td>\n",
1866 |        "    </tr>\n",
1867 |        "    <tr>\n",
1868 |        "      <th>Zey</th>\n",
1869 |        "      <td>-0.336789</td>\n",
1870 |        "      <td>0.043255</td>\n",
1871 |        "      <td>0.913409</td>\n",
1872 |        "    </tr>\n",
1873 |        "    <tr>\n",
1874 |        "      <th>Atl</th>\n",
1875 |        "      <td>0.275015</td>\n",
1876 |        "      <td>1.649876</td>\n",
1877 |        "      <td>-0.301965</td>\n",
1878 |        "    </tr>\n",
1879 |        "    <tr>\n",
1880 |        "      <th>Meh</th>\n",
1881 |        "      <td>0.474020</td>\n",
1882 |        "      <td>-1.336595</td>\n",
1883 |        "      <td>1.794104</td>\n",
1884 |        "    </tr>\n",
1885 |        "  </tbody>\n",
1886 |        "</table>\n",
1887 |        "</div>"
1888 |       ],
1889 |       "text/plain": [
1890 |        "            Salary       Age  Seniority\n",
1891 |        "NewIndex                               \n",
1892 |        "Ati       0.300482  0.161475   0.188583\n",
1893 |        "Zey      -0.336789  0.043255   0.913409\n",
1894 |        "Atl       0.275015  1.649876  -0.301965\n",
1895 |        "Meh       0.474020 -1.336595   1.794104"
1896 |       ]
1897 |      },
1898 |      "execution_count": 44,
1899 |      "metadata": {},
1900 |      "output_type": "execute_result"
1901 |     }
1902 |    ],
1903 |    "source": [
1904 |     "new_df"
1905 |    ]
1906 |   },
1907 |   {
1908 |    "cell_type": "code",
1909 |    "execution_count": 45,
1910 |    "id": "9aca0264-b96d-4a03-9d64-2da407d8f9ef",
1911 |    "metadata": {},
1912 |    "outputs": [
1913 |     {
1914 |      "data": {
1915 |       "text/plain": [
1916 |        "Salary       0.300482\n",
1917 |        "Age          0.161475\n",
1918 |        "Seniority    0.188583\n",
1919 |        "Name: Ati, dtype: float64"
1920 |       ]
1921 |      },
1922 |      "execution_count": 45,
1923 |      "metadata": {},
1924 |      "output_type": "execute_result"
1925 |     }
1926 |    ],
1927 |    "source": [
1928 |     "new_df.loc[\"Ati\"]"
1929 |    ]
1930 |   },
1931 |   {
1932 |    "cell_type": "code",
1933 |    "execution_count": 46,
1934 |    "id": "384455e4-9156-43f8-8e72-a210ec07d396",
1935 |    "metadata": {},
1936 |    "outputs": [],
1937 |    "source": [
1938 |     "# multi index"
1939 |    ]
1940 |   },
1941 |   {
1942 |    "cell_type": "code",
1943 |    "execution_count": 47,
1944 |    "id": "23e87726-c40a-4263-8f20-7836628f1fc8",
1945 |    "metadata": {},
1946 |    "outputs": [],
1947 |    "source": [
1948 |     "first_index = [\"Simpson\",\"Simpson\",\"Simpson\",\"South Park\", \"South Park\", \"South Park\"]"
1949 |    ]
1950 |   },
1951 |   {
1952 |    "cell_type": "code",
1953 |    "execution_count": 48,
1954 |    "id": "ec9fa9ee-d22d-4913-bce2-0888a616d32d",
1955 |    "metadata": {},
1956 |    "outputs": [],
1957 |    "source": [
1958 |     "inner_index = [\"Homer\",\"Bart\",\"Marge\",\"Cartman\",\"Kenny\",\"Kyle\"]"
1959 |    ]
1960 |   },
1961 |   {
1962 |    "cell_type": "code",
1963 |    "execution_count": 49,
1964 |    "id": "f781774e-8bc9-4a88-a953-2ee69e25c719",
1965 |    "metadata": {},
1966 |    "outputs": [],
1967 |    "source": [
1968 |     "zipped_index = list(zip(first_index, inner_index))"
1969 |    ]
1970 |   },
1971 |   {
1972 |    "cell_type": "code",
1973 |    "execution_count": 50,
1974 |    "id": "c01bede1-f541-4816-b50b-971c8a12ae0a",
1975 |    "metadata": {},
1976 |    "outputs": [
1977 |     {
1978 |      "data": {
1979 |       "text/plain": [
1980 |        "[('Simpson', 'Homer'),\n",
1981 |        " ('Simpson', 'Bart'),\n",
1982 |        " ('Simpson', 'Marge'),\n",
1983 |        " ('South Park', 'Cartman'),\n",
1984 |        " ('South Park', 'Kenny'),\n",
1985 |        " ('South Park', 'Kyle')]"
1986 |       ]
1987 |      },
1988 |      "execution_count": 50,
1989 |      "metadata": {},
1990 |      "output_type": "execute_result"
1991 |     }
1992 |    ],
1993 |    "source": [
1994 |     "zipped_index"
1995 |    ]
1996 |   },
1997 |   {
1998 |    "cell_type": "code",
1999 |    "execution_count": 51,
2000 |    "id": "ac824a0b-2faf-467e-8fcb-6ed8e522635b",
2001 |    "metadata": {},
2002 |    "outputs": [],
2003 |    "source": [
2004 |     "zipped_index = pd.MultiIndex.from_tuples(zipped_index)"
2005 |    ]
2006 |   },
2007 |   {
2008 |    "cell_type": "code",
2009 |    "execution_count": 52,
2010 |    "id": "a84c1a99-7e64-4ac2-9db9-49be3321e8d8",
2011 |    "metadata": {},
2012 |    "outputs": [
2013 |     {
2014 |      "data": {
2015 |       "text/plain": [
2016 |        "MultiIndex([(   'Simpson',   'Homer'),\n",
2017 |        "            (   'Simpson',    'Bart'),\n",
2018 |        "            (   'Simpson',   'Marge'),\n",
2019 |        "            ('South Park', 'Cartman'),\n",
2020 |        "            ('South Park',   'Kenny'),\n",
2021 |        "            ('South Park',    'Kyle')],\n",
2022 |        "           )"
2023 |       ]
2024 |      },
2025 |      "execution_count": 52,
2026 |      "metadata": {},
2027 |      "output_type": "execute_result"
2028 |     }
2029 |    ],
2030 |    "source": [
2031 |     "zipped_index"
2032 |    ]
2033 |   },
2034 |   {
2035 |    "cell_type": "code",
2036 |    "execution_count": 56,
2037 |    "id": "d1427639-c668-4150-9ab0-edb50616a1d4",
2038 |    "metadata": {},
2039 |    "outputs": [],
2040 |    "source": [
2041 |     "sample_values = np.ones((6,2))"
2042 |    ]
2043 |   },
2044 |   {
2045 |    "cell_type": "code",
2046 |    "execution_count": 57,
2047 |    "id": "5e4c3616-87cf-4e08-9179-e2c22c3ae662",
2048 |    "metadata": {},
2049 |    "outputs": [
2050 |     {
2051 |      "data": {
2052 |       "text/plain": [
2053 |        "array([[1., 1.],\n",
2054 |        "       [1., 1.],\n",
2055 |        "       [1., 1.],\n",
2056 |        "       [1., 1.],\n",
2057 |        "       [1., 1.],\n",
2058 |        "       [1., 1.]])"
2059 |       ]
2060 |      },
2061 |      "execution_count": 57,
2062 |      "metadata": {},
2063 |      "output_type": "execute_result"
2064 |     }
2065 |    ],
2066 |    "source": [
2067 |     "sample_values"
2068 |    ]
2069 |   },
2070 |   {
2071 |    "cell_type": "code",
2072 |    "execution_count": 58,
2073 |    "id": "02a010e2-fa34-45b0-93b4-e1adddd6cc7c",
2074 |    "metadata": {},
2075 |    "outputs": [],
2076 |    "source": [
2077 |     "big_df = pd.DataFrame(sample_values, index=zipped_index, columns=[\"Age\", \"Salary\"])"
2078 |    ]
2079 |   },
2080 |   {
2081 |    "cell_type": "code",
2082 |    "execution_count": 59,
2083 |    "id": "60c50439-55a0-4dab-9d6d-654e87136103",
2084 |    "metadata": {},
2085 |    "outputs": [
2086 |     {
2087 |      "data": {
2088 |       "text/html": [
2089 |        "<div>\n",
2090 |        "<style scoped>\n",
2091 |        "    .dataframe tbody tr th:only-of-type {\n",
2092 |        "        vertical-align: middle;\n",
2093 |        "    }\n",
2094 |        "\n",
2095 |        "    .dataframe tbody tr th {\n",
2096 |        "        vertical-align: top;\n",
2097 |        "    }\n",
2098 |        "\n",
2099 |        "    .dataframe thead th {\n",
2100 |        "        text-align: right;\n",
2101 |        "    }\n",
2102 |        "</style>\n",
2103 |        "<table border=\"1\" class=\"dataframe\">\n",
2104 |        "  <thead>\n",
2105 |        "    <tr style=\"text-align: right;\">\n",
2106 |        "      <th></th>\n",
2107 |        "      <th></th>\n",
2108 |        "      <th>Age</th>\n",
2109 |        "      <th>Salary</th>\n",
2110 |        "    </tr>\n",
2111 |        "  </thead>\n",
2112 |        "  <tbody>\n",
2113 |        "    <tr>\n",
2114 |        "      <th rowspan=\"3\" valign=\"top\">Simpson</th>\n",
2115 |        "      <th>Homer</th>\n",
2116 |        "      <td>1.0</td>\n",
2117 |        "      <td>1.0</td>\n",
2118 |        "    </tr>\n",
2119 |        "    <tr>\n",
2120 |        "      <th>Bart</th>\n",
2121 |        "      <td>1.0</td>\n",
2122 |        "      <td>1.0</td>\n",
2123 |        "    </tr>\n",
2124 |        "    <tr>\n",
2125 |        "      <th>Marge</th>\n",
2126 |        "      <td>1.0</td>\n",
2127 |        "      <td>1.0</td>\n",
2128 |        "    </tr>\n",
2129 |        "    <tr>\n",
2130 |        "      <th rowspan=\"3\" valign=\"top\">South Park</th>\n",
2131 |        "      <th>Cartman</th>\n",
2132 |        "      <td>1.0</td>\n",
2133 |        "      <td>1.0</td>\n",
2134 |        "    </tr>\n",
2135 |        "    <tr>\n",
2136 |        "      <th>Kenny</th>\n",
2137 |        "      <td>1.0</td>\n",
2138 |        "      <td>1.0</td>\n",
2139 |        "    </tr>\n",
2140 |        "    <tr>\n",
2141 |        "      <th>Kyle</th>\n",
2142 |        "      <td>1.0</td>\n",
2143 |        "      <td>1.0</td>\n",
2144 |        "    </tr>\n",
2145 |        "  </tbody>\n",
2146 |        "</table>\n",
2147 |        "</div>"
2148 |       ],
2149 |       "text/plain": [
2150 |        "                    Age  Salary\n",
2151 |        "Simpson    Homer    1.0     1.0\n",
2152 |        "           Bart     1.0     1.0\n",
2153 |        "           Marge    1.0     1.0\n",
2154 |        "South Park Cartman  1.0     1.0\n",
2155 |        "           Kenny    1.0     1.0\n",
2156 |        "           Kyle     1.0     1.0"
2157 |       ]
2158 |      },
2159 |      "execution_count": 59,
2160 |      "metadata": {},
2161 |      "output_type": "execute_result"
2162 |     }
2163 |    ],
2164 |    "source": [
2165 |     "big_df"
2166 |    ]
2167 |   },
2168 |   {
2169 |    "cell_type": "code",
2170 |    "execution_count": 60,
2171 |    "id": "2e77bb05-1fb1-4fef-9616-1ec198d36b87",
2172 |    "metadata": {},
2173 |    "outputs": [
2174 |     {
2175 |      "data": {
2176 |       "text/plain": [
2177 |        "Simpson     Homer      1.0\n",
2178 |        "            Bart       1.0\n",
2179 |        "            Marge      1.0\n",
2180 |        "South Park  Cartman    1.0\n",
2181 |        "            Kenny      1.0\n",
2182 |        "            Kyle       1.0\n",
2183 |        "Name: Age, dtype: float64"
2184 |       ]
2185 |      },
2186 |      "execution_count": 60,
2187 |      "metadata": {},
2188 |      "output_type": "execute_result"
2189 |     }
2190 |    ],
2191 |    "source": [
2192 |     "big_df[\"Age\"]"
2193 |    ]
2194 |   },
2195 |   {
2196 |    "cell_type": "code",
2197 |    "execution_count": 61,
2198 |    "id": "fa95107f-89fb-4818-b95f-29a213129d53",
2199 |    "metadata": {},
2200 |    "outputs": [
2201 |     {
2202 |      "data": {
2203 |       "text/html": [
2204 |        "<div>\n",
2205 |        "<style scoped>\n",
2206 |        "    .dataframe tbody tr th:only-of-type {\n",
2207 |        "        vertical-align: middle;\n",
2208 |        "    }\n",
2209 |        "\n",
2210 |        "    .dataframe tbody tr th {\n",
2211 |        "        vertical-align: top;\n",
2212 |        "    }\n",
2213 |        "\n",
2214 |        "    .dataframe thead th {\n",
2215 |        "        text-align: right;\n",
2216 |        "    }\n",
2217 |        "</style>\n",
2218 |        "<table border=\"1\" class=\"dataframe\">\n",
2219 |        "  <thead>\n",
2220 |        "    <tr style=\"text-align: right;\">\n",
2221 |        "      <th></th>\n",
2222 |        "      <th>Age</th>\n",
2223 |        "      <th>Salary</th>\n",
2224 |        "    </tr>\n",
2225 |        "  </thead>\n",
2226 |        "  <tbody>\n",
2227 |        "    <tr>\n",
2228 |        "      <th>Homer</th>\n",
2229 |        "      <td>1.0</td>\n",
2230 |        "      <td>1.0</td>\n",
2231 |        "    </tr>\n",
2232 |        "    <tr>\n",
2233 |        "      <th>Bart</th>\n",
2234 |        "      <td>1.0</td>\n",
2235 |        "      <td>1.0</td>\n",
2236 |        "    </tr>\n",
2237 |        "    <tr>\n",
2238 |        "      <th>Marge</th>\n",
2239 |        "      <td>1.0</td>\n",
2240 |        "      <td>1.0</td>\n",
2241 |        "    </tr>\n",
2242 |        "  </tbody>\n",
2243 |        "</table>\n",
2244 |        "</div>"
2245 |       ],
2246 |       "text/plain": [
2247 |        "       Age  Salary\n",
2248 |        "Homer  1.0     1.0\n",
2249 |        "Bart   1.0     1.0\n",
2250 |        "Marge  1.0     1.0"
2251 |       ]
2252 |      },
2253 |      "execution_count": 61,
2254 |      "metadata": {},
2255 |      "output_type": "execute_result"
2256 |     }
2257 |    ],
2258 |    "source": [
2259 |     "big_df.loc[\"Simpson\"]"
2260 |    ]
2261 |   },
2262 |   {
2263 |    "cell_type": "code",
2264 |    "execution_count": 62,
2265 |    "id": "5790bcf7-d6be-47bf-8cb3-1c249e70fce6",
2266 |    "metadata": {},
2267 |    "outputs": [
2268 |     {
2269 |      "data": {
2270 |       "text/plain": [
2271 |        "Age       1.0\n",
2272 |        "Salary    1.0\n",
2273 |        "Name: Homer, dtype: float64"
2274 |       ]
2275 |      },
2276 |      "execution_count": 62,
2277 |      "metadata": {},
2278 |      "output_type": "execute_result"
2279 |     }
2280 |    ],
2281 |    "source": [
2282 |     "big_df.loc[\"Simpson\"].loc[\"Homer\"]"
2283 |    ]
2284 |   },
2285 |   {
2286 |    "cell_type": "code",
2287 |    "execution_count": null,
2288 |    "id": "258bdd5c-a051-4fae-bcde-e4a0d7ebc18c",
2289 |    "metadata": {},
2290 |    "outputs": [],
2291 |    "source": []
2292 |   }
2293 |  ],
2294 |  "metadata": {
2295 |   "kernelspec": {
2296 |    "display_name": "Python 3 (ipykernel)",
2297 |    "language": "python",
2298 |    "name": "python3"
2299 |   },
2300 |   "language_info": {
2301 |    "codemirror_mode": {
2302 |     "name": "ipython",
2303 |     "version": 3
2304 |    },
2305 |    "file_extension": ".py",
2306 |    "mimetype": "text/x-python",
2307 |    "name": "python",
2308 |    "nbconvert_exporter": "python",
2309 |    "pygments_lexer": "ipython3",
2310 |    "version": "3.12.7"
2311 |   }
2312 |  },
2313 |  "nbformat": 4,
2314 |  "nbformat_minor": 5
2315 | }
2316 | 


--------------------------------------------------------------------------------
/6-employee.csv:
--------------------------------------------------------------------------------
 1 | Department,Employee,Salary,Experience,City
 2 | Marketing,Emp_1,53483,1,New York
 3 | Sales,Emp_2,78555,8,Austin
 4 | Finance,Emp_3,47159,3,Austin
 5 | Sales,Emp_4,110077,3,San Francisco
 6 | Sales,Emp_5,65920,1,New York
 7 | IT,Emp_6,97121,11,Chicago
 8 | Finance,Emp_7,99479,5,Chicago
 9 | Finance,Emp_8,119475,10,New York
10 | Finance,Emp_9,49457,7,Chicago
11 | Sales,Emp_10,96557,10,Chicago
12 | Marketing,Emp_11,107189,9,New York
13 | Finance,Emp_12,108953,12,Austin
14 | Sales,Emp_13,82995,7,New York
15 | IT,Emp_14,70757,9,Austin
16 | Marketing,Emp_15,39692,8,Chicago
17 | IT,Emp_16,75758,12,Chicago
18 | Marketing,Emp_17,102409,2,Chicago
19 | Sales,Emp_18,101211,1,San Francisco
20 | HR,Emp_19,95697,7,Austin
21 | Marketing,Emp_20,67065,7,San Francisco
22 | IT,Emp_21,62606,14,San Francisco
23 | Sales,Emp_22,41534,8,New York
24 | Marketing,Emp_23,70397,5,San Francisco
25 | HR,Emp_24,31016,3,New York
26 | HR,Emp_25,119789,12,New York
27 | Finance,Emp_26,85591,8,San Francisco
28 | Finance,Emp_27,119812,6,Austin
29 | IT,Emp_28,53247,11,Austin
30 | Marketing,Emp_29,54300,3,Austin
31 | Marketing,Emp_30,104065,1,Austin
32 | Finance,Emp_31,112798,3,Austin
33 | Marketing,Emp_32,39268,5,San Francisco
34 | Marketing,Emp_33,116807,14,San Francisco
35 | HR,Emp_34,42185,3,Chicago
36 | Finance,Emp_35,93704,1,Austin
37 | Sales,Emp_36,116779,5,San Francisco
38 | Finance,Emp_37,69099,10,Chicago
39 | Sales,Emp_38,38571,7,Austin
40 | HR,Emp_39,68044,14,New York
41 | IT,Emp_40,81214,7,Chicago
42 | Marketing,Emp_41,91228,11,San Francisco
43 | HR,Emp_42,78984,9,New York
44 | Marketing,Emp_43,70774,10,New York
45 | IT,Emp_44,32568,10,New York
46 | IT,Emp_45,92592,12,Chicago
47 | HR,Emp_46,97563,13,San Francisco
48 | IT,Emp_47,32695,3,New York
49 | Sales,Emp_48,78190,7,Austin
50 | IT,Emp_49,35258,1,New York
51 | Marketing,Emp_50,117538,4,New York
52 | 


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/6-weather.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/atilsamancioglu/PythonForDataScienceNotebooks/HEAD/6-weather.xlsx


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/6-weatherna.xlsx:
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https://raw.githubusercontent.com/atilsamancioglu/PythonForDataScienceNotebooks/HEAD/6-weatherna.xlsx


--------------------------------------------------------------------------------
/7-DataFramesConcatMerge.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "7a53bee4-7e67-4878-868e-6fea4031cedb",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import pandas as pd"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 2,
  16 |    "id": "694e35f2-64ec-458d-ad1a-2ff0faf2eea3",
  17 |    "metadata": {},
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "df1 = pd.read_csv(\"7-concat_data1.csv\")"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 3,
  26 |    "id": "1c5e1b97-c618-4728-a648-2b659dd82971",
  27 |    "metadata": {},
  28 |    "outputs": [
  29 |     {
  30 |      "data": {
  31 |       "text/html": [
  32 |        "<div>\n",
  33 |        "<style scoped>\n",
  34 |        "    .dataframe tbody tr th:only-of-type {\n",
  35 |        "        vertical-align: middle;\n",
  36 |        "    }\n",
  37 |        "\n",
  38 |        "    .dataframe tbody tr th {\n",
  39 |        "        vertical-align: top;\n",
  40 |        "    }\n",
  41 |        "\n",
  42 |        "    .dataframe thead th {\n",
  43 |        "        text-align: right;\n",
  44 |        "    }\n",
  45 |        "</style>\n",
  46 |        "<table border=\"1\" class=\"dataframe\">\n",
  47 |        "  <thead>\n",
  48 |        "    <tr style=\"text-align: right;\">\n",
  49 |        "      <th></th>\n",
  50 |        "      <th>Employee_ID</th>\n",
  51 |        "      <th>Name</th>\n",
  52 |        "      <th>Department</th>\n",
  53 |        "    </tr>\n",
  54 |        "  </thead>\n",
  55 |        "  <tbody>\n",
  56 |        "    <tr>\n",
  57 |        "      <th>0</th>\n",
  58 |        "      <td>1</td>\n",
  59 |        "      <td>Emp_1</td>\n",
  60 |        "      <td>Finance</td>\n",
  61 |        "    </tr>\n",
  62 |        "    <tr>\n",
  63 |        "      <th>1</th>\n",
  64 |        "      <td>2</td>\n",
  65 |        "      <td>Emp_2</td>\n",
  66 |        "      <td>Marketing</td>\n",
  67 |        "    </tr>\n",
  68 |        "    <tr>\n",
  69 |        "      <th>2</th>\n",
  70 |        "      <td>3</td>\n",
  71 |        "      <td>Emp_3</td>\n",
  72 |        "      <td>HR</td>\n",
  73 |        "    </tr>\n",
  74 |        "    <tr>\n",
  75 |        "      <th>3</th>\n",
  76 |        "      <td>4</td>\n",
  77 |        "      <td>Emp_4</td>\n",
  78 |        "      <td>Finance</td>\n",
  79 |        "    </tr>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>4</th>\n",
  82 |        "      <td>5</td>\n",
  83 |        "      <td>Emp_5</td>\n",
  84 |        "      <td>Finance</td>\n",
  85 |        "    </tr>\n",
  86 |        "    <tr>\n",
  87 |        "      <th>5</th>\n",
  88 |        "      <td>6</td>\n",
  89 |        "      <td>Emp_6</td>\n",
  90 |        "      <td>Marketing</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>6</th>\n",
  94 |        "      <td>7</td>\n",
  95 |        "      <td>Emp_7</td>\n",
  96 |        "      <td>HR</td>\n",
  97 |        "    </tr>\n",
  98 |        "    <tr>\n",
  99 |        "      <th>7</th>\n",
 100 |        "      <td>8</td>\n",
 101 |        "      <td>Emp_8</td>\n",
 102 |        "      <td>HR</td>\n",
 103 |        "    </tr>\n",
 104 |        "    <tr>\n",
 105 |        "      <th>8</th>\n",
 106 |        "      <td>9</td>\n",
 107 |        "      <td>Emp_9</td>\n",
 108 |        "      <td>Finance</td>\n",
 109 |        "    </tr>\n",
 110 |        "    <tr>\n",
 111 |        "      <th>9</th>\n",
 112 |        "      <td>10</td>\n",
 113 |        "      <td>Emp_10</td>\n",
 114 |        "      <td>IT</td>\n",
 115 |        "    </tr>\n",
 116 |        "  </tbody>\n",
 117 |        "</table>\n",
 118 |        "</div>"
 119 |       ],
 120 |       "text/plain": [
 121 |        "   Employee_ID    Name Department\n",
 122 |        "0            1   Emp_1    Finance\n",
 123 |        "1            2   Emp_2  Marketing\n",
 124 |        "2            3   Emp_3         HR\n",
 125 |        "3            4   Emp_4    Finance\n",
 126 |        "4            5   Emp_5    Finance\n",
 127 |        "5            6   Emp_6  Marketing\n",
 128 |        "6            7   Emp_7         HR\n",
 129 |        "7            8   Emp_8         HR\n",
 130 |        "8            9   Emp_9    Finance\n",
 131 |        "9           10  Emp_10         IT"
 132 |       ]
 133 |      },
 134 |      "execution_count": 3,
 135 |      "metadata": {},
 136 |      "output_type": "execute_result"
 137 |     }
 138 |    ],
 139 |    "source": [
 140 |     "df1"
 141 |    ]
 142 |   },
 143 |   {
 144 |    "cell_type": "code",
 145 |    "execution_count": 4,
 146 |    "id": "44e6b9f3-e5bc-4a2f-9b79-dc212e17d6cf",
 147 |    "metadata": {},
 148 |    "outputs": [],
 149 |    "source": [
 150 |     "df2 = pd.read_csv(\"7-concat_data2.csv\")"
 151 |    ]
 152 |   },
 153 |   {
 154 |    "cell_type": "code",
 155 |    "execution_count": 5,
 156 |    "id": "b111ec8a-0921-4205-9499-ff321605e058",
 157 |    "metadata": {},
 158 |    "outputs": [
 159 |     {
 160 |      "data": {
 161 |       "text/html": [
 162 |        "<div>\n",
 163 |        "<style scoped>\n",
 164 |        "    .dataframe tbody tr th:only-of-type {\n",
 165 |        "        vertical-align: middle;\n",
 166 |        "    }\n",
 167 |        "\n",
 168 |        "    .dataframe tbody tr th {\n",
 169 |        "        vertical-align: top;\n",
 170 |        "    }\n",
 171 |        "\n",
 172 |        "    .dataframe thead th {\n",
 173 |        "        text-align: right;\n",
 174 |        "    }\n",
 175 |        "</style>\n",
 176 |        "<table border=\"1\" class=\"dataframe\">\n",
 177 |        "  <thead>\n",
 178 |        "    <tr style=\"text-align: right;\">\n",
 179 |        "      <th></th>\n",
 180 |        "      <th>Employee_ID</th>\n",
 181 |        "      <th>Name</th>\n",
 182 |        "      <th>Department</th>\n",
 183 |        "    </tr>\n",
 184 |        "  </thead>\n",
 185 |        "  <tbody>\n",
 186 |        "    <tr>\n",
 187 |        "      <th>0</th>\n",
 188 |        "      <td>11</td>\n",
 189 |        "      <td>Emp_11</td>\n",
 190 |        "      <td>Finance</td>\n",
 191 |        "    </tr>\n",
 192 |        "    <tr>\n",
 193 |        "      <th>1</th>\n",
 194 |        "      <td>12</td>\n",
 195 |        "      <td>Emp_12</td>\n",
 196 |        "      <td>Finance</td>\n",
 197 |        "    </tr>\n",
 198 |        "    <tr>\n",
 199 |        "      <th>2</th>\n",
 200 |        "      <td>13</td>\n",
 201 |        "      <td>Emp_13</td>\n",
 202 |        "      <td>Finance</td>\n",
 203 |        "    </tr>\n",
 204 |        "    <tr>\n",
 205 |        "      <th>3</th>\n",
 206 |        "      <td>14</td>\n",
 207 |        "      <td>Emp_14</td>\n",
 208 |        "      <td>Finance</td>\n",
 209 |        "    </tr>\n",
 210 |        "    <tr>\n",
 211 |        "      <th>4</th>\n",
 212 |        "      <td>15</td>\n",
 213 |        "      <td>Emp_15</td>\n",
 214 |        "      <td>Marketing</td>\n",
 215 |        "    </tr>\n",
 216 |        "    <tr>\n",
 217 |        "      <th>5</th>\n",
 218 |        "      <td>16</td>\n",
 219 |        "      <td>Emp_16</td>\n",
 220 |        "      <td>HR</td>\n",
 221 |        "    </tr>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>6</th>\n",
 224 |        "      <td>17</td>\n",
 225 |        "      <td>Emp_17</td>\n",
 226 |        "      <td>Marketing</td>\n",
 227 |        "    </tr>\n",
 228 |        "    <tr>\n",
 229 |        "      <th>7</th>\n",
 230 |        "      <td>18</td>\n",
 231 |        "      <td>Emp_18</td>\n",
 232 |        "      <td>Marketing</td>\n",
 233 |        "    </tr>\n",
 234 |        "    <tr>\n",
 235 |        "      <th>8</th>\n",
 236 |        "      <td>19</td>\n",
 237 |        "      <td>Emp_19</td>\n",
 238 |        "      <td>Marketing</td>\n",
 239 |        "    </tr>\n",
 240 |        "    <tr>\n",
 241 |        "      <th>9</th>\n",
 242 |        "      <td>20</td>\n",
 243 |        "      <td>Emp_20</td>\n",
 244 |        "      <td>Finance</td>\n",
 245 |        "    </tr>\n",
 246 |        "  </tbody>\n",
 247 |        "</table>\n",
 248 |        "</div>"
 249 |       ],
 250 |       "text/plain": [
 251 |        "   Employee_ID    Name Department\n",
 252 |        "0           11  Emp_11    Finance\n",
 253 |        "1           12  Emp_12    Finance\n",
 254 |        "2           13  Emp_13    Finance\n",
 255 |        "3           14  Emp_14    Finance\n",
 256 |        "4           15  Emp_15  Marketing\n",
 257 |        "5           16  Emp_16         HR\n",
 258 |        "6           17  Emp_17  Marketing\n",
 259 |        "7           18  Emp_18  Marketing\n",
 260 |        "8           19  Emp_19  Marketing\n",
 261 |        "9           20  Emp_20    Finance"
 262 |       ]
 263 |      },
 264 |      "execution_count": 5,
 265 |      "metadata": {},
 266 |      "output_type": "execute_result"
 267 |     }
 268 |    ],
 269 |    "source": [
 270 |     "df2"
 271 |    ]
 272 |   },
 273 |   {
 274 |    "cell_type": "code",
 275 |    "execution_count": 6,
 276 |    "id": "c9cada82-1121-4bab-bcde-9c5915fc8643",
 277 |    "metadata": {},
 278 |    "outputs": [],
 279 |    "source": [
 280 |     "#concat"
 281 |    ]
 282 |   },
 283 |   {
 284 |    "cell_type": "code",
 285 |    "execution_count": 7,
 286 |    "id": "89f54b38-a58e-467f-a7c0-529beae35bbb",
 287 |    "metadata": {},
 288 |    "outputs": [],
 289 |    "source": [
 290 |     "df_concat = pd.concat([df1, df2], ignore_index=True)"
 291 |    ]
 292 |   },
 293 |   {
 294 |    "cell_type": "code",
 295 |    "execution_count": 8,
 296 |    "id": "5ea30f98-78b3-4b39-ab09-948b2c9730b9",
 297 |    "metadata": {},
 298 |    "outputs": [
 299 |     {
 300 |      "data": {
 301 |       "text/html": [
 302 |        "<div>\n",
 303 |        "<style scoped>\n",
 304 |        "    .dataframe tbody tr th:only-of-type {\n",
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 306 |        "    }\n",
 307 |        "\n",
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 309 |        "        vertical-align: top;\n",
 310 |        "    }\n",
 311 |        "\n",
 312 |        "    .dataframe thead th {\n",
 313 |        "        text-align: right;\n",
 314 |        "    }\n",
 315 |        "</style>\n",
 316 |        "<table border=\"1\" class=\"dataframe\">\n",
 317 |        "  <thead>\n",
 318 |        "    <tr style=\"text-align: right;\">\n",
 319 |        "      <th></th>\n",
 320 |        "      <th>Employee_ID</th>\n",
 321 |        "      <th>Name</th>\n",
 322 |        "      <th>Department</th>\n",
 323 |        "    </tr>\n",
 324 |        "  </thead>\n",
 325 |        "  <tbody>\n",
 326 |        "    <tr>\n",
 327 |        "      <th>0</th>\n",
 328 |        "      <td>1</td>\n",
 329 |        "      <td>Emp_1</td>\n",
 330 |        "      <td>Finance</td>\n",
 331 |        "    </tr>\n",
 332 |        "    <tr>\n",
 333 |        "      <th>1</th>\n",
 334 |        "      <td>2</td>\n",
 335 |        "      <td>Emp_2</td>\n",
 336 |        "      <td>Marketing</td>\n",
 337 |        "    </tr>\n",
 338 |        "    <tr>\n",
 339 |        "      <th>2</th>\n",
 340 |        "      <td>3</td>\n",
 341 |        "      <td>Emp_3</td>\n",
 342 |        "      <td>HR</td>\n",
 343 |        "    </tr>\n",
 344 |        "    <tr>\n",
 345 |        "      <th>3</th>\n",
 346 |        "      <td>4</td>\n",
 347 |        "      <td>Emp_4</td>\n",
 348 |        "      <td>Finance</td>\n",
 349 |        "    </tr>\n",
 350 |        "    <tr>\n",
 351 |        "      <th>4</th>\n",
 352 |        "      <td>5</td>\n",
 353 |        "      <td>Emp_5</td>\n",
 354 |        "      <td>Finance</td>\n",
 355 |        "    </tr>\n",
 356 |        "    <tr>\n",
 357 |        "      <th>5</th>\n",
 358 |        "      <td>6</td>\n",
 359 |        "      <td>Emp_6</td>\n",
 360 |        "      <td>Marketing</td>\n",
 361 |        "    </tr>\n",
 362 |        "    <tr>\n",
 363 |        "      <th>6</th>\n",
 364 |        "      <td>7</td>\n",
 365 |        "      <td>Emp_7</td>\n",
 366 |        "      <td>HR</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>7</th>\n",
 370 |        "      <td>8</td>\n",
 371 |        "      <td>Emp_8</td>\n",
 372 |        "      <td>HR</td>\n",
 373 |        "    </tr>\n",
 374 |        "    <tr>\n",
 375 |        "      <th>8</th>\n",
 376 |        "      <td>9</td>\n",
 377 |        "      <td>Emp_9</td>\n",
 378 |        "      <td>Finance</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>9</th>\n",
 382 |        "      <td>10</td>\n",
 383 |        "      <td>Emp_10</td>\n",
 384 |        "      <td>IT</td>\n",
 385 |        "    </tr>\n",
 386 |        "    <tr>\n",
 387 |        "      <th>10</th>\n",
 388 |        "      <td>11</td>\n",
 389 |        "      <td>Emp_11</td>\n",
 390 |        "      <td>Finance</td>\n",
 391 |        "    </tr>\n",
 392 |        "    <tr>\n",
 393 |        "      <th>11</th>\n",
 394 |        "      <td>12</td>\n",
 395 |        "      <td>Emp_12</td>\n",
 396 |        "      <td>Finance</td>\n",
 397 |        "    </tr>\n",
 398 |        "    <tr>\n",
 399 |        "      <th>12</th>\n",
 400 |        "      <td>13</td>\n",
 401 |        "      <td>Emp_13</td>\n",
 402 |        "      <td>Finance</td>\n",
 403 |        "    </tr>\n",
 404 |        "    <tr>\n",
 405 |        "      <th>13</th>\n",
 406 |        "      <td>14</td>\n",
 407 |        "      <td>Emp_14</td>\n",
 408 |        "      <td>Finance</td>\n",
 409 |        "    </tr>\n",
 410 |        "    <tr>\n",
 411 |        "      <th>14</th>\n",
 412 |        "      <td>15</td>\n",
 413 |        "      <td>Emp_15</td>\n",
 414 |        "      <td>Marketing</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>15</th>\n",
 418 |        "      <td>16</td>\n",
 419 |        "      <td>Emp_16</td>\n",
 420 |        "      <td>HR</td>\n",
 421 |        "    </tr>\n",
 422 |        "    <tr>\n",
 423 |        "      <th>16</th>\n",
 424 |        "      <td>17</td>\n",
 425 |        "      <td>Emp_17</td>\n",
 426 |        "      <td>Marketing</td>\n",
 427 |        "    </tr>\n",
 428 |        "    <tr>\n",
 429 |        "      <th>17</th>\n",
 430 |        "      <td>18</td>\n",
 431 |        "      <td>Emp_18</td>\n",
 432 |        "      <td>Marketing</td>\n",
 433 |        "    </tr>\n",
 434 |        "    <tr>\n",
 435 |        "      <th>18</th>\n",
 436 |        "      <td>19</td>\n",
 437 |        "      <td>Emp_19</td>\n",
 438 |        "      <td>Marketing</td>\n",
 439 |        "    </tr>\n",
 440 |        "    <tr>\n",
 441 |        "      <th>19</th>\n",
 442 |        "      <td>20</td>\n",
 443 |        "      <td>Emp_20</td>\n",
 444 |        "      <td>Finance</td>\n",
 445 |        "    </tr>\n",
 446 |        "  </tbody>\n",
 447 |        "</table>\n",
 448 |        "</div>"
 449 |       ],
 450 |       "text/plain": [
 451 |        "    Employee_ID    Name Department\n",
 452 |        "0             1   Emp_1    Finance\n",
 453 |        "1             2   Emp_2  Marketing\n",
 454 |        "2             3   Emp_3         HR\n",
 455 |        "3             4   Emp_4    Finance\n",
 456 |        "4             5   Emp_5    Finance\n",
 457 |        "5             6   Emp_6  Marketing\n",
 458 |        "6             7   Emp_7         HR\n",
 459 |        "7             8   Emp_8         HR\n",
 460 |        "8             9   Emp_9    Finance\n",
 461 |        "9            10  Emp_10         IT\n",
 462 |        "10           11  Emp_11    Finance\n",
 463 |        "11           12  Emp_12    Finance\n",
 464 |        "12           13  Emp_13    Finance\n",
 465 |        "13           14  Emp_14    Finance\n",
 466 |        "14           15  Emp_15  Marketing\n",
 467 |        "15           16  Emp_16         HR\n",
 468 |        "16           17  Emp_17  Marketing\n",
 469 |        "17           18  Emp_18  Marketing\n",
 470 |        "18           19  Emp_19  Marketing\n",
 471 |        "19           20  Emp_20    Finance"
 472 |       ]
 473 |      },
 474 |      "execution_count": 8,
 475 |      "metadata": {},
 476 |      "output_type": "execute_result"
 477 |     }
 478 |    ],
 479 |    "source": [
 480 |     "df_concat"
 481 |    ]
 482 |   },
 483 |   {
 484 |    "cell_type": "code",
 485 |    "execution_count": 9,
 486 |    "id": "e338deec-cdb0-4cb0-a158-6c9c895d5d81",
 487 |    "metadata": {},
 488 |    "outputs": [],
 489 |    "source": [
 490 |     "df_merge1 = pd.read_csv(\"7-merge_data1.csv\")"
 491 |    ]
 492 |   },
 493 |   {
 494 |    "cell_type": "code",
 495 |    "execution_count": 10,
 496 |    "id": "4e2ccfc5-d9b0-41d4-8ab4-d3fef3391d17",
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "data": {
 501 |       "text/html": [
 502 |        "<div>\n",
 503 |        "<style scoped>\n",
 504 |        "    .dataframe tbody tr th:only-of-type {\n",
 505 |        "        vertical-align: middle;\n",
 506 |        "    }\n",
 507 |        "\n",
 508 |        "    .dataframe tbody tr th {\n",
 509 |        "        vertical-align: top;\n",
 510 |        "    }\n",
 511 |        "\n",
 512 |        "    .dataframe thead th {\n",
 513 |        "        text-align: right;\n",
 514 |        "    }\n",
 515 |        "</style>\n",
 516 |        "<table border=\"1\" class=\"dataframe\">\n",
 517 |        "  <thead>\n",
 518 |        "    <tr style=\"text-align: right;\">\n",
 519 |        "      <th></th>\n",
 520 |        "      <th>Employee_ID</th>\n",
 521 |        "      <th>Name</th>\n",
 522 |        "      <th>Department</th>\n",
 523 |        "    </tr>\n",
 524 |        "  </thead>\n",
 525 |        "  <tbody>\n",
 526 |        "    <tr>\n",
 527 |        "      <th>0</th>\n",
 528 |        "      <td>1</td>\n",
 529 |        "      <td>Emp_1</td>\n",
 530 |        "      <td>IT</td>\n",
 531 |        "    </tr>\n",
 532 |        "    <tr>\n",
 533 |        "      <th>1</th>\n",
 534 |        "      <td>2</td>\n",
 535 |        "      <td>Emp_2</td>\n",
 536 |        "      <td>HR</td>\n",
 537 |        "    </tr>\n",
 538 |        "    <tr>\n",
 539 |        "      <th>2</th>\n",
 540 |        "      <td>3</td>\n",
 541 |        "      <td>Emp_3</td>\n",
 542 |        "      <td>IT</td>\n",
 543 |        "    </tr>\n",
 544 |        "    <tr>\n",
 545 |        "      <th>3</th>\n",
 546 |        "      <td>4</td>\n",
 547 |        "      <td>Emp_4</td>\n",
 548 |        "      <td>Marketing</td>\n",
 549 |        "    </tr>\n",
 550 |        "    <tr>\n",
 551 |        "      <th>4</th>\n",
 552 |        "      <td>5</td>\n",
 553 |        "      <td>Emp_5</td>\n",
 554 |        "      <td>Marketing</td>\n",
 555 |        "    </tr>\n",
 556 |        "    <tr>\n",
 557 |        "      <th>5</th>\n",
 558 |        "      <td>6</td>\n",
 559 |        "      <td>Emp_6</td>\n",
 560 |        "      <td>IT</td>\n",
 561 |        "    </tr>\n",
 562 |        "    <tr>\n",
 563 |        "      <th>6</th>\n",
 564 |        "      <td>7</td>\n",
 565 |        "      <td>Emp_7</td>\n",
 566 |        "      <td>IT</td>\n",
 567 |        "    </tr>\n",
 568 |        "    <tr>\n",
 569 |        "      <th>7</th>\n",
 570 |        "      <td>8</td>\n",
 571 |        "      <td>Emp_8</td>\n",
 572 |        "      <td>IT</td>\n",
 573 |        "    </tr>\n",
 574 |        "    <tr>\n",
 575 |        "      <th>8</th>\n",
 576 |        "      <td>9</td>\n",
 577 |        "      <td>Emp_9</td>\n",
 578 |        "      <td>Marketing</td>\n",
 579 |        "    </tr>\n",
 580 |        "    <tr>\n",
 581 |        "      <th>9</th>\n",
 582 |        "      <td>10</td>\n",
 583 |        "      <td>Emp_10</td>\n",
 584 |        "      <td>Marketing</td>\n",
 585 |        "    </tr>\n",
 586 |        "  </tbody>\n",
 587 |        "</table>\n",
 588 |        "</div>"
 589 |       ],
 590 |       "text/plain": [
 591 |        "   Employee_ID    Name Department\n",
 592 |        "0            1   Emp_1         IT\n",
 593 |        "1            2   Emp_2         HR\n",
 594 |        "2            3   Emp_3         IT\n",
 595 |        "3            4   Emp_4  Marketing\n",
 596 |        "4            5   Emp_5  Marketing\n",
 597 |        "5            6   Emp_6         IT\n",
 598 |        "6            7   Emp_7         IT\n",
 599 |        "7            8   Emp_8         IT\n",
 600 |        "8            9   Emp_9  Marketing\n",
 601 |        "9           10  Emp_10  Marketing"
 602 |       ]
 603 |      },
 604 |      "execution_count": 10,
 605 |      "metadata": {},
 606 |      "output_type": "execute_result"
 607 |     }
 608 |    ],
 609 |    "source": [
 610 |     "df_merge1"
 611 |    ]
 612 |   },
 613 |   {
 614 |    "cell_type": "code",
 615 |    "execution_count": 11,
 616 |    "id": "b47a333e-e41c-4a8d-8205-1078a3122f5e",
 617 |    "metadata": {},
 618 |    "outputs": [],
 619 |    "source": [
 620 |     "df_merge2 = pd.read_csv(\"7-merge_data2.csv\")"
 621 |    ]
 622 |   },
 623 |   {
 624 |    "cell_type": "code",
 625 |    "execution_count": 12,
 626 |    "id": "5ed9e197-b2ac-4edd-9778-7ffbd25aa965",
 627 |    "metadata": {},
 628 |    "outputs": [
 629 |     {
 630 |      "data": {
 631 |       "text/html": [
 632 |        "<div>\n",
 633 |        "<style scoped>\n",
 634 |        "    .dataframe tbody tr th:only-of-type {\n",
 635 |        "        vertical-align: middle;\n",
 636 |        "    }\n",
 637 |        "\n",
 638 |        "    .dataframe tbody tr th {\n",
 639 |        "        vertical-align: top;\n",
 640 |        "    }\n",
 641 |        "\n",
 642 |        "    .dataframe thead th {\n",
 643 |        "        text-align: right;\n",
 644 |        "    }\n",
 645 |        "</style>\n",
 646 |        "<table border=\"1\" class=\"dataframe\">\n",
 647 |        "  <thead>\n",
 648 |        "    <tr style=\"text-align: right;\">\n",
 649 |        "      <th></th>\n",
 650 |        "      <th>Employee_ID</th>\n",
 651 |        "      <th>Salary</th>\n",
 652 |        "      <th>Experience</th>\n",
 653 |        "    </tr>\n",
 654 |        "  </thead>\n",
 655 |        "  <tbody>\n",
 656 |        "    <tr>\n",
 657 |        "      <th>0</th>\n",
 658 |        "      <td>5</td>\n",
 659 |        "      <td>55658</td>\n",
 660 |        "      <td>3</td>\n",
 661 |        "    </tr>\n",
 662 |        "    <tr>\n",
 663 |        "      <th>1</th>\n",
 664 |        "      <td>1</td>\n",
 665 |        "      <td>114478</td>\n",
 666 |        "      <td>5</td>\n",
 667 |        "    </tr>\n",
 668 |        "    <tr>\n",
 669 |        "      <th>2</th>\n",
 670 |        "      <td>12</td>\n",
 671 |        "      <td>48431</td>\n",
 672 |        "      <td>19</td>\n",
 673 |        "    </tr>\n",
 674 |        "    <tr>\n",
 675 |        "      <th>3</th>\n",
 676 |        "      <td>10</td>\n",
 677 |        "      <td>32747</td>\n",
 678 |        "      <td>7</td>\n",
 679 |        "    </tr>\n",
 680 |        "    <tr>\n",
 681 |        "      <th>4</th>\n",
 682 |        "      <td>6</td>\n",
 683 |        "      <td>89150</td>\n",
 684 |        "      <td>9</td>\n",
 685 |        "    </tr>\n",
 686 |        "    <tr>\n",
 687 |        "      <th>5</th>\n",
 688 |        "      <td>13</td>\n",
 689 |        "      <td>95725</td>\n",
 690 |        "      <td>7</td>\n",
 691 |        "    </tr>\n",
 692 |        "    <tr>\n",
 693 |        "      <th>6</th>\n",
 694 |        "      <td>9</td>\n",
 695 |        "      <td>65773</td>\n",
 696 |        "      <td>4</td>\n",
 697 |        "    </tr>\n",
 698 |        "    <tr>\n",
 699 |        "      <th>7</th>\n",
 700 |        "      <td>11</td>\n",
 701 |        "      <td>86886</td>\n",
 702 |        "      <td>18</td>\n",
 703 |        "    </tr>\n",
 704 |        "  </tbody>\n",
 705 |        "</table>\n",
 706 |        "</div>"
 707 |       ],
 708 |       "text/plain": [
 709 |        "   Employee_ID  Salary  Experience\n",
 710 |        "0            5   55658           3\n",
 711 |        "1            1  114478           5\n",
 712 |        "2           12   48431          19\n",
 713 |        "3           10   32747           7\n",
 714 |        "4            6   89150           9\n",
 715 |        "5           13   95725           7\n",
 716 |        "6            9   65773           4\n",
 717 |        "7           11   86886          18"
 718 |       ]
 719 |      },
 720 |      "execution_count": 12,
 721 |      "metadata": {},
 722 |      "output_type": "execute_result"
 723 |     }
 724 |    ],
 725 |    "source": [
 726 |     "df_merge2"
 727 |    ]
 728 |   },
 729 |   {
 730 |    "cell_type": "code",
 731 |    "execution_count": 13,
 732 |    "id": "9370d214-2a69-4512-ba49-c5a507413518",
 733 |    "metadata": {},
 734 |    "outputs": [],
 735 |    "source": [
 736 |     "# merge - inner join"
 737 |    ]
 738 |   },
 739 |   {
 740 |    "cell_type": "code",
 741 |    "execution_count": 14,
 742 |    "id": "12aed07c-0858-4f7f-aed3-99e4ec8cdb4a",
 743 |    "metadata": {},
 744 |    "outputs": [],
 745 |    "source": [
 746 |     "df_merged = pd.merge(df_merge1, df_merge2, on=\"Employee_ID\", how=\"inner\")"
 747 |    ]
 748 |   },
 749 |   {
 750 |    "cell_type": "code",
 751 |    "execution_count": 15,
 752 |    "id": "1382740d-2bbd-4fcd-af80-4702e723dd5c",
 753 |    "metadata": {},
 754 |    "outputs": [
 755 |     {
 756 |      "data": {
 757 |       "text/html": [
 758 |        "<div>\n",
 759 |        "<style scoped>\n",
 760 |        "    .dataframe tbody tr th:only-of-type {\n",
 761 |        "        vertical-align: middle;\n",
 762 |        "    }\n",
 763 |        "\n",
 764 |        "    .dataframe tbody tr th {\n",
 765 |        "        vertical-align: top;\n",
 766 |        "    }\n",
 767 |        "\n",
 768 |        "    .dataframe thead th {\n",
 769 |        "        text-align: right;\n",
 770 |        "    }\n",
 771 |        "</style>\n",
 772 |        "<table border=\"1\" class=\"dataframe\">\n",
 773 |        "  <thead>\n",
 774 |        "    <tr style=\"text-align: right;\">\n",
 775 |        "      <th></th>\n",
 776 |        "      <th>Employee_ID</th>\n",
 777 |        "      <th>Name</th>\n",
 778 |        "      <th>Department</th>\n",
 779 |        "      <th>Salary</th>\n",
 780 |        "      <th>Experience</th>\n",
 781 |        "    </tr>\n",
 782 |        "  </thead>\n",
 783 |        "  <tbody>\n",
 784 |        "    <tr>\n",
 785 |        "      <th>0</th>\n",
 786 |        "      <td>1</td>\n",
 787 |        "      <td>Emp_1</td>\n",
 788 |        "      <td>IT</td>\n",
 789 |        "      <td>114478</td>\n",
 790 |        "      <td>5</td>\n",
 791 |        "    </tr>\n",
 792 |        "    <tr>\n",
 793 |        "      <th>1</th>\n",
 794 |        "      <td>5</td>\n",
 795 |        "      <td>Emp_5</td>\n",
 796 |        "      <td>Marketing</td>\n",
 797 |        "      <td>55658</td>\n",
 798 |        "      <td>3</td>\n",
 799 |        "    </tr>\n",
 800 |        "    <tr>\n",
 801 |        "      <th>2</th>\n",
 802 |        "      <td>6</td>\n",
 803 |        "      <td>Emp_6</td>\n",
 804 |        "      <td>IT</td>\n",
 805 |        "      <td>89150</td>\n",
 806 |        "      <td>9</td>\n",
 807 |        "    </tr>\n",
 808 |        "    <tr>\n",
 809 |        "      <th>3</th>\n",
 810 |        "      <td>9</td>\n",
 811 |        "      <td>Emp_9</td>\n",
 812 |        "      <td>Marketing</td>\n",
 813 |        "      <td>65773</td>\n",
 814 |        "      <td>4</td>\n",
 815 |        "    </tr>\n",
 816 |        "    <tr>\n",
 817 |        "      <th>4</th>\n",
 818 |        "      <td>10</td>\n",
 819 |        "      <td>Emp_10</td>\n",
 820 |        "      <td>Marketing</td>\n",
 821 |        "      <td>32747</td>\n",
 822 |        "      <td>7</td>\n",
 823 |        "    </tr>\n",
 824 |        "  </tbody>\n",
 825 |        "</table>\n",
 826 |        "</div>"
 827 |       ],
 828 |       "text/plain": [
 829 |        "   Employee_ID    Name Department  Salary  Experience\n",
 830 |        "0            1   Emp_1         IT  114478           5\n",
 831 |        "1            5   Emp_5  Marketing   55658           3\n",
 832 |        "2            6   Emp_6         IT   89150           9\n",
 833 |        "3            9   Emp_9  Marketing   65773           4\n",
 834 |        "4           10  Emp_10  Marketing   32747           7"
 835 |       ]
 836 |      },
 837 |      "execution_count": 15,
 838 |      "metadata": {},
 839 |      "output_type": "execute_result"
 840 |     }
 841 |    ],
 842 |    "source": [
 843 |     "df_merged"
 844 |    ]
 845 |   },
 846 |   {
 847 |    "cell_type": "code",
 848 |    "execution_count": 16,
 849 |    "id": "629eb38e-9f76-4725-8137-26df29ad7f90",
 850 |    "metadata": {},
 851 |    "outputs": [],
 852 |    "source": [
 853 |     "# merge - outer join"
 854 |    ]
 855 |   },
 856 |   {
 857 |    "cell_type": "code",
 858 |    "execution_count": 17,
 859 |    "id": "699d9488-a992-444c-8bf8-3bb7fe1d1795",
 860 |    "metadata": {},
 861 |    "outputs": [],
 862 |    "source": [
 863 |     "df_merged_outer = pd.merge(df_merge1, df_merge2, on=\"Employee_ID\", how=\"outer\")"
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "code",
 868 |    "execution_count": 18,
 869 |    "id": "717a2ab0-52d7-45dc-9d5c-6fe129194490",
 870 |    "metadata": {},
 871 |    "outputs": [
 872 |     {
 873 |      "data": {
 874 |       "text/html": [
 875 |        "<div>\n",
 876 |        "<style scoped>\n",
 877 |        "    .dataframe tbody tr th:only-of-type {\n",
 878 |        "        vertical-align: middle;\n",
 879 |        "    }\n",
 880 |        "\n",
 881 |        "    .dataframe tbody tr th {\n",
 882 |        "        vertical-align: top;\n",
 883 |        "    }\n",
 884 |        "\n",
 885 |        "    .dataframe thead th {\n",
 886 |        "        text-align: right;\n",
 887 |        "    }\n",
 888 |        "</style>\n",
 889 |        "<table border=\"1\" class=\"dataframe\">\n",
 890 |        "  <thead>\n",
 891 |        "    <tr style=\"text-align: right;\">\n",
 892 |        "      <th></th>\n",
 893 |        "      <th>Employee_ID</th>\n",
 894 |        "      <th>Name</th>\n",
 895 |        "      <th>Department</th>\n",
 896 |        "      <th>Salary</th>\n",
 897 |        "      <th>Experience</th>\n",
 898 |        "    </tr>\n",
 899 |        "  </thead>\n",
 900 |        "  <tbody>\n",
 901 |        "    <tr>\n",
 902 |        "      <th>0</th>\n",
 903 |        "      <td>1</td>\n",
 904 |        "      <td>Emp_1</td>\n",
 905 |        "      <td>IT</td>\n",
 906 |        "      <td>114478.0</td>\n",
 907 |        "      <td>5.0</td>\n",
 908 |        "    </tr>\n",
 909 |        "    <tr>\n",
 910 |        "      <th>1</th>\n",
 911 |        "      <td>2</td>\n",
 912 |        "      <td>Emp_2</td>\n",
 913 |        "      <td>HR</td>\n",
 914 |        "      <td>NaN</td>\n",
 915 |        "      <td>NaN</td>\n",
 916 |        "    </tr>\n",
 917 |        "    <tr>\n",
 918 |        "      <th>2</th>\n",
 919 |        "      <td>3</td>\n",
 920 |        "      <td>Emp_3</td>\n",
 921 |        "      <td>IT</td>\n",
 922 |        "      <td>NaN</td>\n",
 923 |        "      <td>NaN</td>\n",
 924 |        "    </tr>\n",
 925 |        "    <tr>\n",
 926 |        "      <th>3</th>\n",
 927 |        "      <td>4</td>\n",
 928 |        "      <td>Emp_4</td>\n",
 929 |        "      <td>Marketing</td>\n",
 930 |        "      <td>NaN</td>\n",
 931 |        "      <td>NaN</td>\n",
 932 |        "    </tr>\n",
 933 |        "    <tr>\n",
 934 |        "      <th>4</th>\n",
 935 |        "      <td>5</td>\n",
 936 |        "      <td>Emp_5</td>\n",
 937 |        "      <td>Marketing</td>\n",
 938 |        "      <td>55658.0</td>\n",
 939 |        "      <td>3.0</td>\n",
 940 |        "    </tr>\n",
 941 |        "    <tr>\n",
 942 |        "      <th>5</th>\n",
 943 |        "      <td>6</td>\n",
 944 |        "      <td>Emp_6</td>\n",
 945 |        "      <td>IT</td>\n",
 946 |        "      <td>89150.0</td>\n",
 947 |        "      <td>9.0</td>\n",
 948 |        "    </tr>\n",
 949 |        "    <tr>\n",
 950 |        "      <th>6</th>\n",
 951 |        "      <td>7</td>\n",
 952 |        "      <td>Emp_7</td>\n",
 953 |        "      <td>IT</td>\n",
 954 |        "      <td>NaN</td>\n",
 955 |        "      <td>NaN</td>\n",
 956 |        "    </tr>\n",
 957 |        "    <tr>\n",
 958 |        "      <th>7</th>\n",
 959 |        "      <td>8</td>\n",
 960 |        "      <td>Emp_8</td>\n",
 961 |        "      <td>IT</td>\n",
 962 |        "      <td>NaN</td>\n",
 963 |        "      <td>NaN</td>\n",
 964 |        "    </tr>\n",
 965 |        "    <tr>\n",
 966 |        "      <th>8</th>\n",
 967 |        "      <td>9</td>\n",
 968 |        "      <td>Emp_9</td>\n",
 969 |        "      <td>Marketing</td>\n",
 970 |        "      <td>65773.0</td>\n",
 971 |        "      <td>4.0</td>\n",
 972 |        "    </tr>\n",
 973 |        "    <tr>\n",
 974 |        "      <th>9</th>\n",
 975 |        "      <td>10</td>\n",
 976 |        "      <td>Emp_10</td>\n",
 977 |        "      <td>Marketing</td>\n",
 978 |        "      <td>32747.0</td>\n",
 979 |        "      <td>7.0</td>\n",
 980 |        "    </tr>\n",
 981 |        "    <tr>\n",
 982 |        "      <th>10</th>\n",
 983 |        "      <td>11</td>\n",
 984 |        "      <td>NaN</td>\n",
 985 |        "      <td>NaN</td>\n",
 986 |        "      <td>86886.0</td>\n",
 987 |        "      <td>18.0</td>\n",
 988 |        "    </tr>\n",
 989 |        "    <tr>\n",
 990 |        "      <th>11</th>\n",
 991 |        "      <td>12</td>\n",
 992 |        "      <td>NaN</td>\n",
 993 |        "      <td>NaN</td>\n",
 994 |        "      <td>48431.0</td>\n",
 995 |        "      <td>19.0</td>\n",
 996 |        "    </tr>\n",
 997 |        "    <tr>\n",
 998 |        "      <th>12</th>\n",
 999 |        "      <td>13</td>\n",
1000 |        "      <td>NaN</td>\n",
1001 |        "      <td>NaN</td>\n",
1002 |        "      <td>95725.0</td>\n",
1003 |        "      <td>7.0</td>\n",
1004 |        "    </tr>\n",
1005 |        "  </tbody>\n",
1006 |        "</table>\n",
1007 |        "</div>"
1008 |       ],
1009 |       "text/plain": [
1010 |        "    Employee_ID    Name Department    Salary  Experience\n",
1011 |        "0             1   Emp_1         IT  114478.0         5.0\n",
1012 |        "1             2   Emp_2         HR       NaN         NaN\n",
1013 |        "2             3   Emp_3         IT       NaN         NaN\n",
1014 |        "3             4   Emp_4  Marketing       NaN         NaN\n",
1015 |        "4             5   Emp_5  Marketing   55658.0         3.0\n",
1016 |        "5             6   Emp_6         IT   89150.0         9.0\n",
1017 |        "6             7   Emp_7         IT       NaN         NaN\n",
1018 |        "7             8   Emp_8         IT       NaN         NaN\n",
1019 |        "8             9   Emp_9  Marketing   65773.0         4.0\n",
1020 |        "9            10  Emp_10  Marketing   32747.0         7.0\n",
1021 |        "10           11     NaN        NaN   86886.0        18.0\n",
1022 |        "11           12     NaN        NaN   48431.0        19.0\n",
1023 |        "12           13     NaN        NaN   95725.0         7.0"
1024 |       ]
1025 |      },
1026 |      "execution_count": 18,
1027 |      "metadata": {},
1028 |      "output_type": "execute_result"
1029 |     }
1030 |    ],
1031 |    "source": [
1032 |     "df_merged_outer"
1033 |    ]
1034 |   },
1035 |   {
1036 |    "cell_type": "code",
1037 |    "execution_count": 19,
1038 |    "id": "cb99724f-2980-438c-bb08-fce6e2546d11",
1039 |    "metadata": {},
1040 |    "outputs": [],
1041 |    "source": [
1042 |     "# merge - left join"
1043 |    ]
1044 |   },
1045 |   {
1046 |    "cell_type": "code",
1047 |    "execution_count": 20,
1048 |    "id": "ddd101ad-a66e-45ab-924d-275ca3b4c45c",
1049 |    "metadata": {},
1050 |    "outputs": [],
1051 |    "source": [
1052 |     "df_merged_left = pd.merge(df_merge1, df_merge2, on=\"Employee_ID\", how=\"left\")"
1053 |    ]
1054 |   },
1055 |   {
1056 |    "cell_type": "code",
1057 |    "execution_count": 21,
1058 |    "id": "b1bd3612-1cd4-4b93-b7c9-b6aadf99e431",
1059 |    "metadata": {},
1060 |    "outputs": [
1061 |     {
1062 |      "data": {
1063 |       "text/html": [
1064 |        "<div>\n",
1065 |        "<style scoped>\n",
1066 |        "    .dataframe tbody tr th:only-of-type {\n",
1067 |        "        vertical-align: middle;\n",
1068 |        "    }\n",
1069 |        "\n",
1070 |        "    .dataframe tbody tr th {\n",
1071 |        "        vertical-align: top;\n",
1072 |        "    }\n",
1073 |        "\n",
1074 |        "    .dataframe thead th {\n",
1075 |        "        text-align: right;\n",
1076 |        "    }\n",
1077 |        "</style>\n",
1078 |        "<table border=\"1\" class=\"dataframe\">\n",
1079 |        "  <thead>\n",
1080 |        "    <tr style=\"text-align: right;\">\n",
1081 |        "      <th></th>\n",
1082 |        "      <th>Employee_ID</th>\n",
1083 |        "      <th>Name</th>\n",
1084 |        "      <th>Department</th>\n",
1085 |        "      <th>Salary</th>\n",
1086 |        "      <th>Experience</th>\n",
1087 |        "    </tr>\n",
1088 |        "  </thead>\n",
1089 |        "  <tbody>\n",
1090 |        "    <tr>\n",
1091 |        "      <th>0</th>\n",
1092 |        "      <td>1</td>\n",
1093 |        "      <td>Emp_1</td>\n",
1094 |        "      <td>IT</td>\n",
1095 |        "      <td>114478.0</td>\n",
1096 |        "      <td>5.0</td>\n",
1097 |        "    </tr>\n",
1098 |        "    <tr>\n",
1099 |        "      <th>1</th>\n",
1100 |        "      <td>2</td>\n",
1101 |        "      <td>Emp_2</td>\n",
1102 |        "      <td>HR</td>\n",
1103 |        "      <td>NaN</td>\n",
1104 |        "      <td>NaN</td>\n",
1105 |        "    </tr>\n",
1106 |        "    <tr>\n",
1107 |        "      <th>2</th>\n",
1108 |        "      <td>3</td>\n",
1109 |        "      <td>Emp_3</td>\n",
1110 |        "      <td>IT</td>\n",
1111 |        "      <td>NaN</td>\n",
1112 |        "      <td>NaN</td>\n",
1113 |        "    </tr>\n",
1114 |        "    <tr>\n",
1115 |        "      <th>3</th>\n",
1116 |        "      <td>4</td>\n",
1117 |        "      <td>Emp_4</td>\n",
1118 |        "      <td>Marketing</td>\n",
1119 |        "      <td>NaN</td>\n",
1120 |        "      <td>NaN</td>\n",
1121 |        "    </tr>\n",
1122 |        "    <tr>\n",
1123 |        "      <th>4</th>\n",
1124 |        "      <td>5</td>\n",
1125 |        "      <td>Emp_5</td>\n",
1126 |        "      <td>Marketing</td>\n",
1127 |        "      <td>55658.0</td>\n",
1128 |        "      <td>3.0</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <th>5</th>\n",
1132 |        "      <td>6</td>\n",
1133 |        "      <td>Emp_6</td>\n",
1134 |        "      <td>IT</td>\n",
1135 |        "      <td>89150.0</td>\n",
1136 |        "      <td>9.0</td>\n",
1137 |        "    </tr>\n",
1138 |        "    <tr>\n",
1139 |        "      <th>6</th>\n",
1140 |        "      <td>7</td>\n",
1141 |        "      <td>Emp_7</td>\n",
1142 |        "      <td>IT</td>\n",
1143 |        "      <td>NaN</td>\n",
1144 |        "      <td>NaN</td>\n",
1145 |        "    </tr>\n",
1146 |        "    <tr>\n",
1147 |        "      <th>7</th>\n",
1148 |        "      <td>8</td>\n",
1149 |        "      <td>Emp_8</td>\n",
1150 |        "      <td>IT</td>\n",
1151 |        "      <td>NaN</td>\n",
1152 |        "      <td>NaN</td>\n",
1153 |        "    </tr>\n",
1154 |        "    <tr>\n",
1155 |        "      <th>8</th>\n",
1156 |        "      <td>9</td>\n",
1157 |        "      <td>Emp_9</td>\n",
1158 |        "      <td>Marketing</td>\n",
1159 |        "      <td>65773.0</td>\n",
1160 |        "      <td>4.0</td>\n",
1161 |        "    </tr>\n",
1162 |        "    <tr>\n",
1163 |        "      <th>9</th>\n",
1164 |        "      <td>10</td>\n",
1165 |        "      <td>Emp_10</td>\n",
1166 |        "      <td>Marketing</td>\n",
1167 |        "      <td>32747.0</td>\n",
1168 |        "      <td>7.0</td>\n",
1169 |        "    </tr>\n",
1170 |        "  </tbody>\n",
1171 |        "</table>\n",
1172 |        "</div>"
1173 |       ],
1174 |       "text/plain": [
1175 |        "   Employee_ID    Name Department    Salary  Experience\n",
1176 |        "0            1   Emp_1         IT  114478.0         5.0\n",
1177 |        "1            2   Emp_2         HR       NaN         NaN\n",
1178 |        "2            3   Emp_3         IT       NaN         NaN\n",
1179 |        "3            4   Emp_4  Marketing       NaN         NaN\n",
1180 |        "4            5   Emp_5  Marketing   55658.0         3.0\n",
1181 |        "5            6   Emp_6         IT   89150.0         9.0\n",
1182 |        "6            7   Emp_7         IT       NaN         NaN\n",
1183 |        "7            8   Emp_8         IT       NaN         NaN\n",
1184 |        "8            9   Emp_9  Marketing   65773.0         4.0\n",
1185 |        "9           10  Emp_10  Marketing   32747.0         7.0"
1186 |       ]
1187 |      },
1188 |      "execution_count": 21,
1189 |      "metadata": {},
1190 |      "output_type": "execute_result"
1191 |     }
1192 |    ],
1193 |    "source": [
1194 |     "df_merged_left"
1195 |    ]
1196 |   },
1197 |   {
1198 |    "cell_type": "code",
1199 |    "execution_count": 22,
1200 |    "id": "0ada5b8d-e67b-4689-bd75-5fd2f54c6179",
1201 |    "metadata": {},
1202 |    "outputs": [],
1203 |    "source": [
1204 |     "# right join"
1205 |    ]
1206 |   },
1207 |   {
1208 |    "cell_type": "code",
1209 |    "execution_count": 23,
1210 |    "id": "f858445a-fe5c-4afe-9618-911058b26aed",
1211 |    "metadata": {},
1212 |    "outputs": [],
1213 |    "source": [
1214 |     "df_merged_right = pd.merge(df_merge1, df_merge2, on=\"Employee_ID\", how=\"right\")"
1215 |    ]
1216 |   },
1217 |   {
1218 |    "cell_type": "code",
1219 |    "execution_count": 24,
1220 |    "id": "7e2b7630-e10e-421c-b2eb-d38547e2b5ac",
1221 |    "metadata": {},
1222 |    "outputs": [
1223 |     {
1224 |      "data": {
1225 |       "text/html": [
1226 |        "<div>\n",
1227 |        "<style scoped>\n",
1228 |        "    .dataframe tbody tr th:only-of-type {\n",
1229 |        "        vertical-align: middle;\n",
1230 |        "    }\n",
1231 |        "\n",
1232 |        "    .dataframe tbody tr th {\n",
1233 |        "        vertical-align: top;\n",
1234 |        "    }\n",
1235 |        "\n",
1236 |        "    .dataframe thead th {\n",
1237 |        "        text-align: right;\n",
1238 |        "    }\n",
1239 |        "</style>\n",
1240 |        "<table border=\"1\" class=\"dataframe\">\n",
1241 |        "  <thead>\n",
1242 |        "    <tr style=\"text-align: right;\">\n",
1243 |        "      <th></th>\n",
1244 |        "      <th>Employee_ID</th>\n",
1245 |        "      <th>Name</th>\n",
1246 |        "      <th>Department</th>\n",
1247 |        "      <th>Salary</th>\n",
1248 |        "      <th>Experience</th>\n",
1249 |        "    </tr>\n",
1250 |        "  </thead>\n",
1251 |        "  <tbody>\n",
1252 |        "    <tr>\n",
1253 |        "      <th>0</th>\n",
1254 |        "      <td>5</td>\n",
1255 |        "      <td>Emp_5</td>\n",
1256 |        "      <td>Marketing</td>\n",
1257 |        "      <td>55658</td>\n",
1258 |        "      <td>3</td>\n",
1259 |        "    </tr>\n",
1260 |        "    <tr>\n",
1261 |        "      <th>1</th>\n",
1262 |        "      <td>1</td>\n",
1263 |        "      <td>Emp_1</td>\n",
1264 |        "      <td>IT</td>\n",
1265 |        "      <td>114478</td>\n",
1266 |        "      <td>5</td>\n",
1267 |        "    </tr>\n",
1268 |        "    <tr>\n",
1269 |        "      <th>2</th>\n",
1270 |        "      <td>12</td>\n",
1271 |        "      <td>NaN</td>\n",
1272 |        "      <td>NaN</td>\n",
1273 |        "      <td>48431</td>\n",
1274 |        "      <td>19</td>\n",
1275 |        "    </tr>\n",
1276 |        "    <tr>\n",
1277 |        "      <th>3</th>\n",
1278 |        "      <td>10</td>\n",
1279 |        "      <td>Emp_10</td>\n",
1280 |        "      <td>Marketing</td>\n",
1281 |        "      <td>32747</td>\n",
1282 |        "      <td>7</td>\n",
1283 |        "    </tr>\n",
1284 |        "    <tr>\n",
1285 |        "      <th>4</th>\n",
1286 |        "      <td>6</td>\n",
1287 |        "      <td>Emp_6</td>\n",
1288 |        "      <td>IT</td>\n",
1289 |        "      <td>89150</td>\n",
1290 |        "      <td>9</td>\n",
1291 |        "    </tr>\n",
1292 |        "    <tr>\n",
1293 |        "      <th>5</th>\n",
1294 |        "      <td>13</td>\n",
1295 |        "      <td>NaN</td>\n",
1296 |        "      <td>NaN</td>\n",
1297 |        "      <td>95725</td>\n",
1298 |        "      <td>7</td>\n",
1299 |        "    </tr>\n",
1300 |        "    <tr>\n",
1301 |        "      <th>6</th>\n",
1302 |        "      <td>9</td>\n",
1303 |        "      <td>Emp_9</td>\n",
1304 |        "      <td>Marketing</td>\n",
1305 |        "      <td>65773</td>\n",
1306 |        "      <td>4</td>\n",
1307 |        "    </tr>\n",
1308 |        "    <tr>\n",
1309 |        "      <th>7</th>\n",
1310 |        "      <td>11</td>\n",
1311 |        "      <td>NaN</td>\n",
1312 |        "      <td>NaN</td>\n",
1313 |        "      <td>86886</td>\n",
1314 |        "      <td>18</td>\n",
1315 |        "    </tr>\n",
1316 |        "  </tbody>\n",
1317 |        "</table>\n",
1318 |        "</div>"
1319 |       ],
1320 |       "text/plain": [
1321 |        "   Employee_ID    Name Department  Salary  Experience\n",
1322 |        "0            5   Emp_5  Marketing   55658           3\n",
1323 |        "1            1   Emp_1         IT  114478           5\n",
1324 |        "2           12     NaN        NaN   48431          19\n",
1325 |        "3           10  Emp_10  Marketing   32747           7\n",
1326 |        "4            6   Emp_6         IT   89150           9\n",
1327 |        "5           13     NaN        NaN   95725           7\n",
1328 |        "6            9   Emp_9  Marketing   65773           4\n",
1329 |        "7           11     NaN        NaN   86886          18"
1330 |       ]
1331 |      },
1332 |      "execution_count": 24,
1333 |      "metadata": {},
1334 |      "output_type": "execute_result"
1335 |     }
1336 |    ],
1337 |    "source": [
1338 |     "df_merged_right"
1339 |    ]
1340 |   },
1341 |   {
1342 |    "cell_type": "code",
1343 |    "execution_count": null,
1344 |    "id": "ae49787d-43b9-4e55-845a-b72d3624051a",
1345 |    "metadata": {},
1346 |    "outputs": [],
1347 |    "source": []
1348 |   }
1349 |  ],
1350 |  "metadata": {
1351 |   "kernelspec": {
1352 |    "display_name": "Python 3 (ipykernel)",
1353 |    "language": "python",
1354 |    "name": "python3"
1355 |   },
1356 |   "language_info": {
1357 |    "codemirror_mode": {
1358 |     "name": "ipython",
1359 |     "version": 3
1360 |    },
1361 |    "file_extension": ".py",
1362 |    "mimetype": "text/x-python",
1363 |    "name": "python",
1364 |    "nbconvert_exporter": "python",
1365 |    "pygments_lexer": "ipython3",
1366 |    "version": "3.12.7"
1367 |   }
1368 |  },
1369 |  "nbformat": 4,
1370 |  "nbformat_minor": 5
1371 | }
1372 | 


--------------------------------------------------------------------------------
/7-concat_data1.csv:
--------------------------------------------------------------------------------
 1 | Employee_ID,Name,Department
 2 | 1,Emp_1,Finance
 3 | 2,Emp_2,Marketing
 4 | 3,Emp_3,HR
 5 | 4,Emp_4,Finance
 6 | 5,Emp_5,Finance
 7 | 6,Emp_6,Marketing
 8 | 7,Emp_7,HR
 9 | 8,Emp_8,HR
10 | 9,Emp_9,Finance
11 | 10,Emp_10,IT
12 | 


--------------------------------------------------------------------------------
/7-concat_data2.csv:
--------------------------------------------------------------------------------
 1 | Employee_ID,Name,Department
 2 | 11,Emp_11,Finance
 3 | 12,Emp_12,Finance
 4 | 13,Emp_13,Finance
 5 | 14,Emp_14,Finance
 6 | 15,Emp_15,Marketing
 7 | 16,Emp_16,HR
 8 | 17,Emp_17,Marketing
 9 | 18,Emp_18,Marketing
10 | 19,Emp_19,Marketing
11 | 20,Emp_20,Finance
12 | 


--------------------------------------------------------------------------------
/7-merge_data1.csv:
--------------------------------------------------------------------------------
 1 | Employee_ID,Name,Department
 2 | 1,Emp_1,IT
 3 | 2,Emp_2,HR
 4 | 3,Emp_3,IT
 5 | 4,Emp_4,Marketing
 6 | 5,Emp_5,Marketing
 7 | 6,Emp_6,IT
 8 | 7,Emp_7,IT
 9 | 8,Emp_8,IT
10 | 9,Emp_9,Marketing
11 | 10,Emp_10,Marketing
12 | 


--------------------------------------------------------------------------------
/7-merge_data2.csv:
--------------------------------------------------------------------------------
1 | Employee_ID,Salary,Experience
2 | 5,55658,3
3 | 1,114478,5
4 | 12,48431,19
5 | 10,32747,7
6 | 6,89150,9
7 | 13,95725,7
8 | 9,65773,4
9 | 11,86886,18


--------------------------------------------------------------------------------
/8-DataFramesApply.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "dcbdddc6-2ff9-420b-b7ae-68817c638a9f",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import pandas as pd"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 2,
  16 |    "id": "208c614e-a2ac-4cc8-a210-0676280be17e",
  17 |    "metadata": {},
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "df = pd.read_csv(\"8-apply_function_data.csv\")"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 3,
  26 |    "id": "44fe998a-47b1-4a38-bcd2-e6929f28c1c4",
  27 |    "metadata": {},
  28 |    "outputs": [
  29 |     {
  30 |      "data": {
  31 |       "text/html": [
  32 |        "<div>\n",
  33 |        "<style scoped>\n",
  34 |        "    .dataframe tbody tr th:only-of-type {\n",
  35 |        "        vertical-align: middle;\n",
  36 |        "    }\n",
  37 |        "\n",
  38 |        "    .dataframe tbody tr th {\n",
  39 |        "        vertical-align: top;\n",
  40 |        "    }\n",
  41 |        "\n",
  42 |        "    .dataframe thead th {\n",
  43 |        "        text-align: right;\n",
  44 |        "    }\n",
  45 |        "</style>\n",
  46 |        "<table border=\"1\" class=\"dataframe\">\n",
  47 |        "  <thead>\n",
  48 |        "    <tr style=\"text-align: right;\">\n",
  49 |        "      <th></th>\n",
  50 |        "      <th>Employee_ID</th>\n",
  51 |        "      <th>Name</th>\n",
  52 |        "      <th>Department</th>\n",
  53 |        "      <th>Salary</th>\n",
  54 |        "      <th>Experience</th>\n",
  55 |        "      <th>Performance_Score</th>\n",
  56 |        "    </tr>\n",
  57 |        "  </thead>\n",
  58 |        "  <tbody>\n",
  59 |        "    <tr>\n",
  60 |        "      <th>0</th>\n",
  61 |        "      <td>1</td>\n",
  62 |        "      <td>Emp_1</td>\n",
  63 |        "      <td>Marketing</td>\n",
  64 |        "      <td>82251</td>\n",
  65 |        "      <td>6</td>\n",
  66 |        "      <td>5</td>\n",
  67 |        "    </tr>\n",
  68 |        "    <tr>\n",
  69 |        "      <th>1</th>\n",
  70 |        "      <td>2</td>\n",
  71 |        "      <td>Emp_2</td>\n",
  72 |        "      <td>Sales</td>\n",
  73 |        "      <td>52662</td>\n",
  74 |        "      <td>24</td>\n",
  75 |        "      <td>3</td>\n",
  76 |        "    </tr>\n",
  77 |        "    <tr>\n",
  78 |        "      <th>2</th>\n",
  79 |        "      <td>3</td>\n",
  80 |        "      <td>Emp_3</td>\n",
  81 |        "      <td>Finance</td>\n",
  82 |        "      <td>38392</td>\n",
  83 |        "      <td>5</td>\n",
  84 |        "      <td>3</td>\n",
  85 |        "    </tr>\n",
  86 |        "    <tr>\n",
  87 |        "      <th>3</th>\n",
  88 |        "      <td>4</td>\n",
  89 |        "      <td>Emp_4</td>\n",
  90 |        "      <td>Sales</td>\n",
  91 |        "      <td>60535</td>\n",
  92 |        "      <td>20</td>\n",
  93 |        "      <td>3</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>4</th>\n",
  97 |        "      <td>5</td>\n",
  98 |        "      <td>Emp_5</td>\n",
  99 |        "      <td>Sales</td>\n",
 100 |        "      <td>108603</td>\n",
 101 |        "      <td>2</td>\n",
 102 |        "      <td>2</td>\n",
 103 |        "    </tr>\n",
 104 |        "    <tr>\n",
 105 |        "      <th>...</th>\n",
 106 |        "      <td>...</td>\n",
 107 |        "      <td>...</td>\n",
 108 |        "      <td>...</td>\n",
 109 |        "      <td>...</td>\n",
 110 |        "      <td>...</td>\n",
 111 |        "      <td>...</td>\n",
 112 |        "    </tr>\n",
 113 |        "    <tr>\n",
 114 |        "      <th>95</th>\n",
 115 |        "      <td>96</td>\n",
 116 |        "      <td>Emp_96</td>\n",
 117 |        "      <td>Finance</td>\n",
 118 |        "      <td>93734</td>\n",
 119 |        "      <td>19</td>\n",
 120 |        "      <td>4</td>\n",
 121 |        "    </tr>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>96</th>\n",
 124 |        "      <td>97</td>\n",
 125 |        "      <td>Emp_97</td>\n",
 126 |        "      <td>Sales</td>\n",
 127 |        "      <td>100467</td>\n",
 128 |        "      <td>22</td>\n",
 129 |        "      <td>2</td>\n",
 130 |        "    </tr>\n",
 131 |        "    <tr>\n",
 132 |        "      <th>97</th>\n",
 133 |        "      <td>98</td>\n",
 134 |        "      <td>Emp_98</td>\n",
 135 |        "      <td>IT</td>\n",
 136 |        "      <td>82662</td>\n",
 137 |        "      <td>23</td>\n",
 138 |        "      <td>3</td>\n",
 139 |        "    </tr>\n",
 140 |        "    <tr>\n",
 141 |        "      <th>98</th>\n",
 142 |        "      <td>99</td>\n",
 143 |        "      <td>Emp_99</td>\n",
 144 |        "      <td>IT</td>\n",
 145 |        "      <td>42688</td>\n",
 146 |        "      <td>22</td>\n",
 147 |        "      <td>1</td>\n",
 148 |        "    </tr>\n",
 149 |        "    <tr>\n",
 150 |        "      <th>99</th>\n",
 151 |        "      <td>100</td>\n",
 152 |        "      <td>Emp_100</td>\n",
 153 |        "      <td>HR</td>\n",
 154 |        "      <td>55342</td>\n",
 155 |        "      <td>14</td>\n",
 156 |        "      <td>3</td>\n",
 157 |        "    </tr>\n",
 158 |        "  </tbody>\n",
 159 |        "</table>\n",
 160 |        "<p>100 rows × 6 columns</p>\n",
 161 |        "</div>"
 162 |       ],
 163 |       "text/plain": [
 164 |        "    Employee_ID     Name Department  Salary  Experience  Performance_Score\n",
 165 |        "0             1    Emp_1  Marketing   82251           6                  5\n",
 166 |        "1             2    Emp_2      Sales   52662          24                  3\n",
 167 |        "2             3    Emp_3    Finance   38392           5                  3\n",
 168 |        "3             4    Emp_4      Sales   60535          20                  3\n",
 169 |        "4             5    Emp_5      Sales  108603           2                  2\n",
 170 |        "..          ...      ...        ...     ...         ...                ...\n",
 171 |        "95           96   Emp_96    Finance   93734          19                  4\n",
 172 |        "96           97   Emp_97      Sales  100467          22                  2\n",
 173 |        "97           98   Emp_98         IT   82662          23                  3\n",
 174 |        "98           99   Emp_99         IT   42688          22                  1\n",
 175 |        "99          100  Emp_100         HR   55342          14                  3\n",
 176 |        "\n",
 177 |        "[100 rows x 6 columns]"
 178 |       ]
 179 |      },
 180 |      "execution_count": 3,
 181 |      "metadata": {},
 182 |      "output_type": "execute_result"
 183 |     }
 184 |    ],
 185 |    "source": [
 186 |     "df"
 187 |    ]
 188 |   },
 189 |   {
 190 |    "cell_type": "code",
 191 |    "execution_count": 4,
 192 |    "id": "20241537-af23-4981-9688-8cdb7f36810b",
 193 |    "metadata": {},
 194 |    "outputs": [
 195 |     {
 196 |      "data": {
 197 |       "text/html": [
 198 |        "<div>\n",
 199 |        "<style scoped>\n",
 200 |        "    .dataframe tbody tr th:only-of-type {\n",
 201 |        "        vertical-align: middle;\n",
 202 |        "    }\n",
 203 |        "\n",
 204 |        "    .dataframe tbody tr th {\n",
 205 |        "        vertical-align: top;\n",
 206 |        "    }\n",
 207 |        "\n",
 208 |        "    .dataframe thead th {\n",
 209 |        "        text-align: right;\n",
 210 |        "    }\n",
 211 |        "</style>\n",
 212 |        "<table border=\"1\" class=\"dataframe\">\n",
 213 |        "  <thead>\n",
 214 |        "    <tr style=\"text-align: right;\">\n",
 215 |        "      <th></th>\n",
 216 |        "      <th>Employee_ID</th>\n",
 217 |        "      <th>Salary</th>\n",
 218 |        "      <th>Experience</th>\n",
 219 |        "      <th>Performance_Score</th>\n",
 220 |        "    </tr>\n",
 221 |        "  </thead>\n",
 222 |        "  <tbody>\n",
 223 |        "    <tr>\n",
 224 |        "      <th>count</th>\n",
 225 |        "      <td>100.000000</td>\n",
 226 |        "      <td>100.000000</td>\n",
 227 |        "      <td>100.000000</td>\n",
 228 |        "      <td>100.00000</td>\n",
 229 |        "    </tr>\n",
 230 |        "    <tr>\n",
 231 |        "      <th>mean</th>\n",
 232 |        "      <td>50.500000</td>\n",
 233 |        "      <td>77508.090000</td>\n",
 234 |        "      <td>12.090000</td>\n",
 235 |        "      <td>3.03000</td>\n",
 236 |        "    </tr>\n",
 237 |        "    <tr>\n",
 238 |        "      <th>std</th>\n",
 239 |        "      <td>29.011492</td>\n",
 240 |        "      <td>26083.327596</td>\n",
 241 |        "      <td>7.543939</td>\n",
 242 |        "      <td>1.41746</td>\n",
 243 |        "    </tr>\n",
 244 |        "    <tr>\n",
 245 |        "      <th>min</th>\n",
 246 |        "      <td>1.000000</td>\n",
 247 |        "      <td>30206.000000</td>\n",
 248 |        "      <td>1.000000</td>\n",
 249 |        "      <td>1.00000</td>\n",
 250 |        "    </tr>\n",
 251 |        "    <tr>\n",
 252 |        "      <th>25%</th>\n",
 253 |        "      <td>25.750000</td>\n",
 254 |        "      <td>54347.500000</td>\n",
 255 |        "      <td>5.000000</td>\n",
 256 |        "      <td>2.00000</td>\n",
 257 |        "    </tr>\n",
 258 |        "    <tr>\n",
 259 |        "      <th>50%</th>\n",
 260 |        "      <td>50.500000</td>\n",
 261 |        "      <td>80932.000000</td>\n",
 262 |        "      <td>12.000000</td>\n",
 263 |        "      <td>3.00000</td>\n",
 264 |        "    </tr>\n",
 265 |        "    <tr>\n",
 266 |        "      <th>75%</th>\n",
 267 |        "      <td>75.250000</td>\n",
 268 |        "      <td>97620.500000</td>\n",
 269 |        "      <td>19.000000</td>\n",
 270 |        "      <td>4.00000</td>\n",
 271 |        "    </tr>\n",
 272 |        "    <tr>\n",
 273 |        "      <th>max</th>\n",
 274 |        "      <td>100.000000</td>\n",
 275 |        "      <td>119474.000000</td>\n",
 276 |        "      <td>24.000000</td>\n",
 277 |        "      <td>5.00000</td>\n",
 278 |        "    </tr>\n",
 279 |        "  </tbody>\n",
 280 |        "</table>\n",
 281 |        "</div>"
 282 |       ],
 283 |       "text/plain": [
 284 |        "       Employee_ID         Salary  Experience  Performance_Score\n",
 285 |        "count   100.000000     100.000000  100.000000          100.00000\n",
 286 |        "mean     50.500000   77508.090000   12.090000            3.03000\n",
 287 |        "std      29.011492   26083.327596    7.543939            1.41746\n",
 288 |        "min       1.000000   30206.000000    1.000000            1.00000\n",
 289 |        "25%      25.750000   54347.500000    5.000000            2.00000\n",
 290 |        "50%      50.500000   80932.000000   12.000000            3.00000\n",
 291 |        "75%      75.250000   97620.500000   19.000000            4.00000\n",
 292 |        "max     100.000000  119474.000000   24.000000            5.00000"
 293 |       ]
 294 |      },
 295 |      "execution_count": 4,
 296 |      "metadata": {},
 297 |      "output_type": "execute_result"
 298 |     }
 299 |    ],
 300 |    "source": [
 301 |     "df.describe()"
 302 |    ]
 303 |   },
 304 |   {
 305 |    "cell_type": "code",
 306 |    "execution_count": 5,
 307 |    "id": "4b9883a1-c25c-40d1-b49f-4757021a8c14",
 308 |    "metadata": {},
 309 |    "outputs": [
 310 |     {
 311 |      "name": "stdout",
 312 |      "output_type": "stream",
 313 |      "text": [
 314 |       "<class 'pandas.core.frame.DataFrame'>\n",
 315 |       "RangeIndex: 100 entries, 0 to 99\n",
 316 |       "Data columns (total 6 columns):\n",
 317 |       " #   Column             Non-Null Count  Dtype \n",
 318 |       "---  ------             --------------  ----- \n",
 319 |       " 0   Employee_ID        100 non-null    int64 \n",
 320 |       " 1   Name               100 non-null    object\n",
 321 |       " 2   Department         100 non-null    object\n",
 322 |       " 3   Salary             100 non-null    int64 \n",
 323 |       " 4   Experience         100 non-null    int64 \n",
 324 |       " 5   Performance_Score  100 non-null    int64 \n",
 325 |       "dtypes: int64(4), object(2)\n",
 326 |       "memory usage: 4.8+ KB\n"
 327 |      ]
 328 |     }
 329 |    ],
 330 |    "source": [
 331 |     "df.info()"
 332 |    ]
 333 |   },
 334 |   {
 335 |    "cell_type": "code",
 336 |    "execution_count": 6,
 337 |    "id": "7add64b9-11d7-410c-893b-e1d06f42533d",
 338 |    "metadata": {},
 339 |    "outputs": [
 340 |     {
 341 |      "data": {
 342 |       "text/plain": [
 343 |        "Index(['Employee_ID', 'Name', 'Department', 'Salary', 'Experience',\n",
 344 |        "       'Performance_Score'],\n",
 345 |        "      dtype='object')"
 346 |       ]
 347 |      },
 348 |      "execution_count": 6,
 349 |      "metadata": {},
 350 |      "output_type": "execute_result"
 351 |     }
 352 |    ],
 353 |    "source": [
 354 |     "df.columns"
 355 |    ]
 356 |   },
 357 |   {
 358 |    "cell_type": "code",
 359 |    "execution_count": 7,
 360 |    "id": "e9878123-fa2a-4970-a847-95b10b0d00ba",
 361 |    "metadata": {},
 362 |    "outputs": [
 363 |     {
 364 |      "data": {
 365 |       "text/html": [
 366 |        "<div>\n",
 367 |        "<style scoped>\n",
 368 |        "    .dataframe tbody tr th:only-of-type {\n",
 369 |        "        vertical-align: middle;\n",
 370 |        "    }\n",
 371 |        "\n",
 372 |        "    .dataframe tbody tr th {\n",
 373 |        "        vertical-align: top;\n",
 374 |        "    }\n",
 375 |        "\n",
 376 |        "    .dataframe thead th {\n",
 377 |        "        text-align: right;\n",
 378 |        "    }\n",
 379 |        "</style>\n",
 380 |        "<table border=\"1\" class=\"dataframe\">\n",
 381 |        "  <thead>\n",
 382 |        "    <tr style=\"text-align: right;\">\n",
 383 |        "      <th></th>\n",
 384 |        "      <th>Employee_ID</th>\n",
 385 |        "      <th>Name</th>\n",
 386 |        "      <th>Department</th>\n",
 387 |        "      <th>Salary</th>\n",
 388 |        "      <th>Experience</th>\n",
 389 |        "      <th>Performance_Score</th>\n",
 390 |        "    </tr>\n",
 391 |        "  </thead>\n",
 392 |        "  <tbody>\n",
 393 |        "    <tr>\n",
 394 |        "      <th>1</th>\n",
 395 |        "      <td>2</td>\n",
 396 |        "      <td>Emp_2</td>\n",
 397 |        "      <td>Sales</td>\n",
 398 |        "      <td>52662</td>\n",
 399 |        "      <td>24</td>\n",
 400 |        "      <td>3</td>\n",
 401 |        "    </tr>\n",
 402 |        "    <tr>\n",
 403 |        "      <th>2</th>\n",
 404 |        "      <td>3</td>\n",
 405 |        "      <td>Emp_3</td>\n",
 406 |        "      <td>Finance</td>\n",
 407 |        "      <td>38392</td>\n",
 408 |        "      <td>5</td>\n",
 409 |        "      <td>3</td>\n",
 410 |        "    </tr>\n",
 411 |        "    <tr>\n",
 412 |        "      <th>3</th>\n",
 413 |        "      <td>4</td>\n",
 414 |        "      <td>Emp_4</td>\n",
 415 |        "      <td>Sales</td>\n",
 416 |        "      <td>60535</td>\n",
 417 |        "      <td>20</td>\n",
 418 |        "      <td>3</td>\n",
 419 |        "    </tr>\n",
 420 |        "    <tr>\n",
 421 |        "      <th>4</th>\n",
 422 |        "      <td>5</td>\n",
 423 |        "      <td>Emp_5</td>\n",
 424 |        "      <td>Sales</td>\n",
 425 |        "      <td>108603</td>\n",
 426 |        "      <td>2</td>\n",
 427 |        "      <td>2</td>\n",
 428 |        "    </tr>\n",
 429 |        "    <tr>\n",
 430 |        "      <th>5</th>\n",
 431 |        "      <td>6</td>\n",
 432 |        "      <td>Emp_6</td>\n",
 433 |        "      <td>IT</td>\n",
 434 |        "      <td>82256</td>\n",
 435 |        "      <td>6</td>\n",
 436 |        "      <td>5</td>\n",
 437 |        "    </tr>\n",
 438 |        "    <tr>\n",
 439 |        "      <th>6</th>\n",
 440 |        "      <td>7</td>\n",
 441 |        "      <td>Emp_7</td>\n",
 442 |        "      <td>Finance</td>\n",
 443 |        "      <td>119135</td>\n",
 444 |        "      <td>22</td>\n",
 445 |        "      <td>1</td>\n",
 446 |        "    </tr>\n",
 447 |        "    <tr>\n",
 448 |        "      <th>7</th>\n",
 449 |        "      <td>8</td>\n",
 450 |        "      <td>Emp_8</td>\n",
 451 |        "      <td>Finance</td>\n",
 452 |        "      <td>65222</td>\n",
 453 |        "      <td>11</td>\n",
 454 |        "      <td>4</td>\n",
 455 |        "    </tr>\n",
 456 |        "    <tr>\n",
 457 |        "      <th>8</th>\n",
 458 |        "      <td>9</td>\n",
 459 |        "      <td>Emp_9</td>\n",
 460 |        "      <td>Finance</td>\n",
 461 |        "      <td>107373</td>\n",
 462 |        "      <td>16</td>\n",
 463 |        "      <td>1</td>\n",
 464 |        "    </tr>\n",
 465 |        "    <tr>\n",
 466 |        "      <th>9</th>\n",
 467 |        "      <td>10</td>\n",
 468 |        "      <td>Emp_10</td>\n",
 469 |        "      <td>Sales</td>\n",
 470 |        "      <td>109575</td>\n",
 471 |        "      <td>16</td>\n",
 472 |        "      <td>5</td>\n",
 473 |        "    </tr>\n",
 474 |        "    <tr>\n",
 475 |        "      <th>10</th>\n",
 476 |        "      <td>11</td>\n",
 477 |        "      <td>Emp_11</td>\n",
 478 |        "      <td>Marketing</td>\n",
 479 |        "      <td>114651</td>\n",
 480 |        "      <td>1</td>\n",
 481 |        "      <td>4</td>\n",
 482 |        "    </tr>\n",
 483 |        "    <tr>\n",
 484 |        "      <th>11</th>\n",
 485 |        "      <td>12</td>\n",
 486 |        "      <td>Emp_12</td>\n",
 487 |        "      <td>Finance</td>\n",
 488 |        "      <td>93335</td>\n",
 489 |        "      <td>9</td>\n",
 490 |        "      <td>5</td>\n",
 491 |        "    </tr>\n",
 492 |        "    <tr>\n",
 493 |        "      <th>12</th>\n",
 494 |        "      <td>13</td>\n",
 495 |        "      <td>Emp_13</td>\n",
 496 |        "      <td>Sales</td>\n",
 497 |        "      <td>40965</td>\n",
 498 |        "      <td>6</td>\n",
 499 |        "      <td>3</td>\n",
 500 |        "    </tr>\n",
 501 |        "    <tr>\n",
 502 |        "      <th>13</th>\n",
 503 |        "      <td>14</td>\n",
 504 |        "      <td>Emp_14</td>\n",
 505 |        "      <td>IT</td>\n",
 506 |        "      <td>54538</td>\n",
 507 |        "      <td>16</td>\n",
 508 |        "      <td>4</td>\n",
 509 |        "    </tr>\n",
 510 |        "    <tr>\n",
 511 |        "      <th>14</th>\n",
 512 |        "      <td>15</td>\n",
 513 |        "      <td>Emp_15</td>\n",
 514 |        "      <td>Marketing</td>\n",
 515 |        "      <td>100592</td>\n",
 516 |        "      <td>3</td>\n",
 517 |        "      <td>3</td>\n",
 518 |        "    </tr>\n",
 519 |        "    <tr>\n",
 520 |        "      <th>15</th>\n",
 521 |        "      <td>16</td>\n",
 522 |        "      <td>Emp_16</td>\n",
 523 |        "      <td>IT</td>\n",
 524 |        "      <td>38110</td>\n",
 525 |        "      <td>20</td>\n",
 526 |        "      <td>1</td>\n",
 527 |        "    </tr>\n",
 528 |        "    <tr>\n",
 529 |        "      <th>16</th>\n",
 530 |        "      <td>17</td>\n",
 531 |        "      <td>Emp_17</td>\n",
 532 |        "      <td>Marketing</td>\n",
 533 |        "      <td>109309</td>\n",
 534 |        "      <td>4</td>\n",
 535 |        "      <td>1</td>\n",
 536 |        "    </tr>\n",
 537 |        "    <tr>\n",
 538 |        "      <th>17</th>\n",
 539 |        "      <td>18</td>\n",
 540 |        "      <td>Emp_18</td>\n",
 541 |        "      <td>Sales</td>\n",
 542 |        "      <td>57266</td>\n",
 543 |        "      <td>19</td>\n",
 544 |        "      <td>4</td>\n",
 545 |        "    </tr>\n",
 546 |        "    <tr>\n",
 547 |        "      <th>18</th>\n",
 548 |        "      <td>19</td>\n",
 549 |        "      <td>Emp_19</td>\n",
 550 |        "      <td>HR</td>\n",
 551 |        "      <td>82992</td>\n",
 552 |        "      <td>3</td>\n",
 553 |        "      <td>4</td>\n",
 554 |        "    </tr>\n",
 555 |        "    <tr>\n",
 556 |        "      <th>19</th>\n",
 557 |        "      <td>20</td>\n",
 558 |        "      <td>Emp_20</td>\n",
 559 |        "      <td>Marketing</td>\n",
 560 |        "      <td>112948</td>\n",
 561 |        "      <td>19</td>\n",
 562 |        "      <td>5</td>\n",
 563 |        "    </tr>\n",
 564 |        "  </tbody>\n",
 565 |        "</table>\n",
 566 |        "</div>"
 567 |       ],
 568 |       "text/plain": [
 569 |        "    Employee_ID    Name Department  Salary  Experience  Performance_Score\n",
 570 |        "1             2   Emp_2      Sales   52662          24                  3\n",
 571 |        "2             3   Emp_3    Finance   38392           5                  3\n",
 572 |        "3             4   Emp_4      Sales   60535          20                  3\n",
 573 |        "4             5   Emp_5      Sales  108603           2                  2\n",
 574 |        "5             6   Emp_6         IT   82256           6                  5\n",
 575 |        "6             7   Emp_7    Finance  119135          22                  1\n",
 576 |        "7             8   Emp_8    Finance   65222          11                  4\n",
 577 |        "8             9   Emp_9    Finance  107373          16                  1\n",
 578 |        "9            10  Emp_10      Sales  109575          16                  5\n",
 579 |        "10           11  Emp_11  Marketing  114651           1                  4\n",
 580 |        "11           12  Emp_12    Finance   93335           9                  5\n",
 581 |        "12           13  Emp_13      Sales   40965           6                  3\n",
 582 |        "13           14  Emp_14         IT   54538          16                  4\n",
 583 |        "14           15  Emp_15  Marketing  100592           3                  3\n",
 584 |        "15           16  Emp_16         IT   38110          20                  1\n",
 585 |        "16           17  Emp_17  Marketing  109309           4                  1\n",
 586 |        "17           18  Emp_18      Sales   57266          19                  4\n",
 587 |        "18           19  Emp_19         HR   82992           3                  4\n",
 588 |        "19           20  Emp_20  Marketing  112948          19                  5"
 589 |       ]
 590 |      },
 591 |      "execution_count": 7,
 592 |      "metadata": {},
 593 |      "output_type": "execute_result"
 594 |     }
 595 |    ],
 596 |    "source": [
 597 |     "df[1:20]"
 598 |    ]
 599 |   },
 600 |   {
 601 |    "cell_type": "code",
 602 |    "execution_count": 8,
 603 |    "id": "f080814d-8459-4c39-a38c-3ca84b623de5",
 604 |    "metadata": {},
 605 |    "outputs": [],
 606 |    "source": [
 607 |     "# Example 1: Apply a function to classify employees based on salary"
 608 |    ]
 609 |   },
 610 |   {
 611 |    "cell_type": "code",
 612 |    "execution_count": 9,
 613 |    "id": "991a2aab-14a5-4165-8b25-b65daff95024",
 614 |    "metadata": {},
 615 |    "outputs": [],
 616 |    "source": [
 617 |     "def salary_category(salary):\n",
 618 |     "    if salary < 50000:\n",
 619 |     "        return \"Low\"\n",
 620 |     "    elif 50000 <= salary < 80000:\n",
 621 |     "        return \"Medium\"\n",
 622 |     "    else:\n",
 623 |     "        return \"High\""
 624 |    ]
 625 |   },
 626 |   {
 627 |    "cell_type": "code",
 628 |    "execution_count": 10,
 629 |    "id": "b300fcbe-b54d-4e07-8b15-823f8c74b326",
 630 |    "metadata": {},
 631 |    "outputs": [],
 632 |    "source": [
 633 |     "df[\"Salary_Category\"] = df[\"Salary\"].apply(salary_category)"
 634 |    ]
 635 |   },
 636 |   {
 637 |    "cell_type": "code",
 638 |    "execution_count": 11,
 639 |    "id": "c20d58df-1a29-4197-aea6-9bf0e50ebb1a",
 640 |    "metadata": {},
 641 |    "outputs": [
 642 |     {
 643 |      "data": {
 644 |       "text/html": [
 645 |        "<div>\n",
 646 |        "<style scoped>\n",
 647 |        "    .dataframe tbody tr th:only-of-type {\n",
 648 |        "        vertical-align: middle;\n",
 649 |        "    }\n",
 650 |        "\n",
 651 |        "    .dataframe tbody tr th {\n",
 652 |        "        vertical-align: top;\n",
 653 |        "    }\n",
 654 |        "\n",
 655 |        "    .dataframe thead th {\n",
 656 |        "        text-align: right;\n",
 657 |        "    }\n",
 658 |        "</style>\n",
 659 |        "<table border=\"1\" class=\"dataframe\">\n",
 660 |        "  <thead>\n",
 661 |        "    <tr style=\"text-align: right;\">\n",
 662 |        "      <th></th>\n",
 663 |        "      <th>Employee_ID</th>\n",
 664 |        "      <th>Name</th>\n",
 665 |        "      <th>Department</th>\n",
 666 |        "      <th>Salary</th>\n",
 667 |        "      <th>Experience</th>\n",
 668 |        "      <th>Performance_Score</th>\n",
 669 |        "      <th>Salary_Category</th>\n",
 670 |        "    </tr>\n",
 671 |        "  </thead>\n",
 672 |        "  <tbody>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>0</th>\n",
 675 |        "      <td>1</td>\n",
 676 |        "      <td>Emp_1</td>\n",
 677 |        "      <td>Marketing</td>\n",
 678 |        "      <td>82251</td>\n",
 679 |        "      <td>6</td>\n",
 680 |        "      <td>5</td>\n",
 681 |        "      <td>High</td>\n",
 682 |        "    </tr>\n",
 683 |        "    <tr>\n",
 684 |        "      <th>1</th>\n",
 685 |        "      <td>2</td>\n",
 686 |        "      <td>Emp_2</td>\n",
 687 |        "      <td>Sales</td>\n",
 688 |        "      <td>52662</td>\n",
 689 |        "      <td>24</td>\n",
 690 |        "      <td>3</td>\n",
 691 |        "      <td>Medium</td>\n",
 692 |        "    </tr>\n",
 693 |        "    <tr>\n",
 694 |        "      <th>2</th>\n",
 695 |        "      <td>3</td>\n",
 696 |        "      <td>Emp_3</td>\n",
 697 |        "      <td>Finance</td>\n",
 698 |        "      <td>38392</td>\n",
 699 |        "      <td>5</td>\n",
 700 |        "      <td>3</td>\n",
 701 |        "      <td>Low</td>\n",
 702 |        "    </tr>\n",
 703 |        "    <tr>\n",
 704 |        "      <th>3</th>\n",
 705 |        "      <td>4</td>\n",
 706 |        "      <td>Emp_4</td>\n",
 707 |        "      <td>Sales</td>\n",
 708 |        "      <td>60535</td>\n",
 709 |        "      <td>20</td>\n",
 710 |        "      <td>3</td>\n",
 711 |        "      <td>Medium</td>\n",
 712 |        "    </tr>\n",
 713 |        "    <tr>\n",
 714 |        "      <th>4</th>\n",
 715 |        "      <td>5</td>\n",
 716 |        "      <td>Emp_5</td>\n",
 717 |        "      <td>Sales</td>\n",
 718 |        "      <td>108603</td>\n",
 719 |        "      <td>2</td>\n",
 720 |        "      <td>2</td>\n",
 721 |        "      <td>High</td>\n",
 722 |        "    </tr>\n",
 723 |        "    <tr>\n",
 724 |        "      <th>...</th>\n",
 725 |        "      <td>...</td>\n",
 726 |        "      <td>...</td>\n",
 727 |        "      <td>...</td>\n",
 728 |        "      <td>...</td>\n",
 729 |        "      <td>...</td>\n",
 730 |        "      <td>...</td>\n",
 731 |        "      <td>...</td>\n",
 732 |        "    </tr>\n",
 733 |        "    <tr>\n",
 734 |        "      <th>95</th>\n",
 735 |        "      <td>96</td>\n",
 736 |        "      <td>Emp_96</td>\n",
 737 |        "      <td>Finance</td>\n",
 738 |        "      <td>93734</td>\n",
 739 |        "      <td>19</td>\n",
 740 |        "      <td>4</td>\n",
 741 |        "      <td>High</td>\n",
 742 |        "    </tr>\n",
 743 |        "    <tr>\n",
 744 |        "      <th>96</th>\n",
 745 |        "      <td>97</td>\n",
 746 |        "      <td>Emp_97</td>\n",
 747 |        "      <td>Sales</td>\n",
 748 |        "      <td>100467</td>\n",
 749 |        "      <td>22</td>\n",
 750 |        "      <td>2</td>\n",
 751 |        "      <td>High</td>\n",
 752 |        "    </tr>\n",
 753 |        "    <tr>\n",
 754 |        "      <th>97</th>\n",
 755 |        "      <td>98</td>\n",
 756 |        "      <td>Emp_98</td>\n",
 757 |        "      <td>IT</td>\n",
 758 |        "      <td>82662</td>\n",
 759 |        "      <td>23</td>\n",
 760 |        "      <td>3</td>\n",
 761 |        "      <td>High</td>\n",
 762 |        "    </tr>\n",
 763 |        "    <tr>\n",
 764 |        "      <th>98</th>\n",
 765 |        "      <td>99</td>\n",
 766 |        "      <td>Emp_99</td>\n",
 767 |        "      <td>IT</td>\n",
 768 |        "      <td>42688</td>\n",
 769 |        "      <td>22</td>\n",
 770 |        "      <td>1</td>\n",
 771 |        "      <td>Low</td>\n",
 772 |        "    </tr>\n",
 773 |        "    <tr>\n",
 774 |        "      <th>99</th>\n",
 775 |        "      <td>100</td>\n",
 776 |        "      <td>Emp_100</td>\n",
 777 |        "      <td>HR</td>\n",
 778 |        "      <td>55342</td>\n",
 779 |        "      <td>14</td>\n",
 780 |        "      <td>3</td>\n",
 781 |        "      <td>Medium</td>\n",
 782 |        "    </tr>\n",
 783 |        "  </tbody>\n",
 784 |        "</table>\n",
 785 |        "<p>100 rows × 7 columns</p>\n",
 786 |        "</div>"
 787 |       ],
 788 |       "text/plain": [
 789 |        "    Employee_ID     Name Department  Salary  Experience  Performance_Score  \\\n",
 790 |        "0             1    Emp_1  Marketing   82251           6                  5   \n",
 791 |        "1             2    Emp_2      Sales   52662          24                  3   \n",
 792 |        "2             3    Emp_3    Finance   38392           5                  3   \n",
 793 |        "3             4    Emp_4      Sales   60535          20                  3   \n",
 794 |        "4             5    Emp_5      Sales  108603           2                  2   \n",
 795 |        "..          ...      ...        ...     ...         ...                ...   \n",
 796 |        "95           96   Emp_96    Finance   93734          19                  4   \n",
 797 |        "96           97   Emp_97      Sales  100467          22                  2   \n",
 798 |        "97           98   Emp_98         IT   82662          23                  3   \n",
 799 |        "98           99   Emp_99         IT   42688          22                  1   \n",
 800 |        "99          100  Emp_100         HR   55342          14                  3   \n",
 801 |        "\n",
 802 |        "   Salary_Category  \n",
 803 |        "0             High  \n",
 804 |        "1           Medium  \n",
 805 |        "2              Low  \n",
 806 |        "3           Medium  \n",
 807 |        "4             High  \n",
 808 |        "..             ...  \n",
 809 |        "95            High  \n",
 810 |        "96            High  \n",
 811 |        "97            High  \n",
 812 |        "98             Low  \n",
 813 |        "99          Medium  \n",
 814 |        "\n",
 815 |        "[100 rows x 7 columns]"
 816 |       ]
 817 |      },
 818 |      "execution_count": 11,
 819 |      "metadata": {},
 820 |      "output_type": "execute_result"
 821 |     }
 822 |    ],
 823 |    "source": [
 824 |     "df"
 825 |    ]
 826 |   },
 827 |   {
 828 |    "cell_type": "code",
 829 |    "execution_count": 12,
 830 |    "id": "e0ded209-e186-4a29-9d3d-d0f9852904af",
 831 |    "metadata": {},
 832 |    "outputs": [],
 833 |    "source": [
 834 |     "# Example 2: Apply a function to calculate adjusted performance score"
 835 |    ]
 836 |   },
 837 |   {
 838 |    "cell_type": "code",
 839 |    "execution_count": 13,
 840 |    "id": "8266a813-daae-4477-a2d7-b8731396e322",
 841 |    "metadata": {},
 842 |    "outputs": [],
 843 |    "source": [
 844 |     "def adjust_performance(row):\n",
 845 |     "    if row[\"Experience\"] > 10:\n",
 846 |     "        return row[\"Performance_Score\"] + 1\n",
 847 |     "    return row[\"Performance_Score\"]"
 848 |    ]
 849 |   },
 850 |   {
 851 |    "cell_type": "code",
 852 |    "execution_count": 14,
 853 |    "id": "a2d66ebf-da27-41b3-9f58-e726dca2ee09",
 854 |    "metadata": {},
 855 |    "outputs": [],
 856 |    "source": [
 857 |     "df[\"Adjusted_Performance\"] = df.apply(adjust_performance, axis=1)"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": 15,
 863 |    "id": "41dcdbb4-9c52-4285-bf0f-baab0bfb681b",
 864 |    "metadata": {},
 865 |    "outputs": [
 866 |     {
 867 |      "data": {
 868 |       "text/html": [
 869 |        "<div>\n",
 870 |        "<style scoped>\n",
 871 |        "    .dataframe tbody tr th:only-of-type {\n",
 872 |        "        vertical-align: middle;\n",
 873 |        "    }\n",
 874 |        "\n",
 875 |        "    .dataframe tbody tr th {\n",
 876 |        "        vertical-align: top;\n",
 877 |        "    }\n",
 878 |        "\n",
 879 |        "    .dataframe thead th {\n",
 880 |        "        text-align: right;\n",
 881 |        "    }\n",
 882 |        "</style>\n",
 883 |        "<table border=\"1\" class=\"dataframe\">\n",
 884 |        "  <thead>\n",
 885 |        "    <tr style=\"text-align: right;\">\n",
 886 |        "      <th></th>\n",
 887 |        "      <th>Employee_ID</th>\n",
 888 |        "      <th>Name</th>\n",
 889 |        "      <th>Department</th>\n",
 890 |        "      <th>Salary</th>\n",
 891 |        "      <th>Experience</th>\n",
 892 |        "      <th>Performance_Score</th>\n",
 893 |        "      <th>Salary_Category</th>\n",
 894 |        "      <th>Adjusted_Performance</th>\n",
 895 |        "    </tr>\n",
 896 |        "  </thead>\n",
 897 |        "  <tbody>\n",
 898 |        "    <tr>\n",
 899 |        "      <th>0</th>\n",
 900 |        "      <td>1</td>\n",
 901 |        "      <td>Emp_1</td>\n",
 902 |        "      <td>Marketing</td>\n",
 903 |        "      <td>82251</td>\n",
 904 |        "      <td>6</td>\n",
 905 |        "      <td>5</td>\n",
 906 |        "      <td>High</td>\n",
 907 |        "      <td>5</td>\n",
 908 |        "    </tr>\n",
 909 |        "    <tr>\n",
 910 |        "      <th>1</th>\n",
 911 |        "      <td>2</td>\n",
 912 |        "      <td>Emp_2</td>\n",
 913 |        "      <td>Sales</td>\n",
 914 |        "      <td>52662</td>\n",
 915 |        "      <td>24</td>\n",
 916 |        "      <td>3</td>\n",
 917 |        "      <td>Medium</td>\n",
 918 |        "      <td>4</td>\n",
 919 |        "    </tr>\n",
 920 |        "    <tr>\n",
 921 |        "      <th>2</th>\n",
 922 |        "      <td>3</td>\n",
 923 |        "      <td>Emp_3</td>\n",
 924 |        "      <td>Finance</td>\n",
 925 |        "      <td>38392</td>\n",
 926 |        "      <td>5</td>\n",
 927 |        "      <td>3</td>\n",
 928 |        "      <td>Low</td>\n",
 929 |        "      <td>3</td>\n",
 930 |        "    </tr>\n",
 931 |        "    <tr>\n",
 932 |        "      <th>3</th>\n",
 933 |        "      <td>4</td>\n",
 934 |        "      <td>Emp_4</td>\n",
 935 |        "      <td>Sales</td>\n",
 936 |        "      <td>60535</td>\n",
 937 |        "      <td>20</td>\n",
 938 |        "      <td>3</td>\n",
 939 |        "      <td>Medium</td>\n",
 940 |        "      <td>4</td>\n",
 941 |        "    </tr>\n",
 942 |        "    <tr>\n",
 943 |        "      <th>4</th>\n",
 944 |        "      <td>5</td>\n",
 945 |        "      <td>Emp_5</td>\n",
 946 |        "      <td>Sales</td>\n",
 947 |        "      <td>108603</td>\n",
 948 |        "      <td>2</td>\n",
 949 |        "      <td>2</td>\n",
 950 |        "      <td>High</td>\n",
 951 |        "      <td>2</td>\n",
 952 |        "    </tr>\n",
 953 |        "    <tr>\n",
 954 |        "      <th>...</th>\n",
 955 |        "      <td>...</td>\n",
 956 |        "      <td>...</td>\n",
 957 |        "      <td>...</td>\n",
 958 |        "      <td>...</td>\n",
 959 |        "      <td>...</td>\n",
 960 |        "      <td>...</td>\n",
 961 |        "      <td>...</td>\n",
 962 |        "      <td>...</td>\n",
 963 |        "    </tr>\n",
 964 |        "    <tr>\n",
 965 |        "      <th>95</th>\n",
 966 |        "      <td>96</td>\n",
 967 |        "      <td>Emp_96</td>\n",
 968 |        "      <td>Finance</td>\n",
 969 |        "      <td>93734</td>\n",
 970 |        "      <td>19</td>\n",
 971 |        "      <td>4</td>\n",
 972 |        "      <td>High</td>\n",
 973 |        "      <td>5</td>\n",
 974 |        "    </tr>\n",
 975 |        "    <tr>\n",
 976 |        "      <th>96</th>\n",
 977 |        "      <td>97</td>\n",
 978 |        "      <td>Emp_97</td>\n",
 979 |        "      <td>Sales</td>\n",
 980 |        "      <td>100467</td>\n",
 981 |        "      <td>22</td>\n",
 982 |        "      <td>2</td>\n",
 983 |        "      <td>High</td>\n",
 984 |        "      <td>3</td>\n",
 985 |        "    </tr>\n",
 986 |        "    <tr>\n",
 987 |        "      <th>97</th>\n",
 988 |        "      <td>98</td>\n",
 989 |        "      <td>Emp_98</td>\n",
 990 |        "      <td>IT</td>\n",
 991 |        "      <td>82662</td>\n",
 992 |        "      <td>23</td>\n",
 993 |        "      <td>3</td>\n",
 994 |        "      <td>High</td>\n",
 995 |        "      <td>4</td>\n",
 996 |        "    </tr>\n",
 997 |        "    <tr>\n",
 998 |        "      <th>98</th>\n",
 999 |        "      <td>99</td>\n",
1000 |        "      <td>Emp_99</td>\n",
1001 |        "      <td>IT</td>\n",
1002 |        "      <td>42688</td>\n",
1003 |        "      <td>22</td>\n",
1004 |        "      <td>1</td>\n",
1005 |        "      <td>Low</td>\n",
1006 |        "      <td>2</td>\n",
1007 |        "    </tr>\n",
1008 |        "    <tr>\n",
1009 |        "      <th>99</th>\n",
1010 |        "      <td>100</td>\n",
1011 |        "      <td>Emp_100</td>\n",
1012 |        "      <td>HR</td>\n",
1013 |        "      <td>55342</td>\n",
1014 |        "      <td>14</td>\n",
1015 |        "      <td>3</td>\n",
1016 |        "      <td>Medium</td>\n",
1017 |        "      <td>4</td>\n",
1018 |        "    </tr>\n",
1019 |        "  </tbody>\n",
1020 |        "</table>\n",
1021 |        "<p>100 rows × 8 columns</p>\n",
1022 |        "</div>"
1023 |       ],
1024 |       "text/plain": [
1025 |        "    Employee_ID     Name Department  Salary  Experience  Performance_Score  \\\n",
1026 |        "0             1    Emp_1  Marketing   82251           6                  5   \n",
1027 |        "1             2    Emp_2      Sales   52662          24                  3   \n",
1028 |        "2             3    Emp_3    Finance   38392           5                  3   \n",
1029 |        "3             4    Emp_4      Sales   60535          20                  3   \n",
1030 |        "4             5    Emp_5      Sales  108603           2                  2   \n",
1031 |        "..          ...      ...        ...     ...         ...                ...   \n",
1032 |        "95           96   Emp_96    Finance   93734          19                  4   \n",
1033 |        "96           97   Emp_97      Sales  100467          22                  2   \n",
1034 |        "97           98   Emp_98         IT   82662          23                  3   \n",
1035 |        "98           99   Emp_99         IT   42688          22                  1   \n",
1036 |        "99          100  Emp_100         HR   55342          14                  3   \n",
1037 |        "\n",
1038 |        "   Salary_Category  Adjusted_Performance  \n",
1039 |        "0             High                     5  \n",
1040 |        "1           Medium                     4  \n",
1041 |        "2              Low                     3  \n",
1042 |        "3           Medium                     4  \n",
1043 |        "4             High                     2  \n",
1044 |        "..             ...                   ...  \n",
1045 |        "95            High                     5  \n",
1046 |        "96            High                     3  \n",
1047 |        "97            High                     4  \n",
1048 |        "98             Low                     2  \n",
1049 |        "99          Medium                     4  \n",
1050 |        "\n",
1051 |        "[100 rows x 8 columns]"
1052 |       ]
1053 |      },
1054 |      "execution_count": 15,
1055 |      "metadata": {},
1056 |      "output_type": "execute_result"
1057 |     }
1058 |    ],
1059 |    "source": [
1060 |     "df"
1061 |    ]
1062 |   },
1063 |   {
1064 |    "cell_type": "code",
1065 |    "execution_count": 16,
1066 |    "id": "3c55d2b4-a0da-4c21-a6ce-b88c57f8a177",
1067 |    "metadata": {},
1068 |    "outputs": [],
1069 |    "source": [
1070 |     "# Example 3: Apply a lambda function to format employee names"
1071 |    ]
1072 |   },
1073 |   {
1074 |    "cell_type": "code",
1075 |    "execution_count": 17,
1076 |    "id": "227442d6-82ef-4822-80a8-2d6388af7b32",
1077 |    "metadata": {},
1078 |    "outputs": [],
1079 |    "source": [
1080 |     "df[\"Formatted_Name\"] = df[\"Name\"].apply(lambda x: x.replace(\"_\", \" \"))"
1081 |    ]
1082 |   },
1083 |   {
1084 |    "cell_type": "code",
1085 |    "execution_count": 18,
1086 |    "id": "c919d0ea-0358-422d-942d-50ed9265ea14",
1087 |    "metadata": {},
1088 |    "outputs": [
1089 |     {
1090 |      "data": {
1091 |       "text/html": [
1092 |        "<div>\n",
1093 |        "<style scoped>\n",
1094 |        "    .dataframe tbody tr th:only-of-type {\n",
1095 |        "        vertical-align: middle;\n",
1096 |        "    }\n",
1097 |        "\n",
1098 |        "    .dataframe tbody tr th {\n",
1099 |        "        vertical-align: top;\n",
1100 |        "    }\n",
1101 |        "\n",
1102 |        "    .dataframe thead th {\n",
1103 |        "        text-align: right;\n",
1104 |        "    }\n",
1105 |        "</style>\n",
1106 |        "<table border=\"1\" class=\"dataframe\">\n",
1107 |        "  <thead>\n",
1108 |        "    <tr style=\"text-align: right;\">\n",
1109 |        "      <th></th>\n",
1110 |        "      <th>Employee_ID</th>\n",
1111 |        "      <th>Name</th>\n",
1112 |        "      <th>Department</th>\n",
1113 |        "      <th>Salary</th>\n",
1114 |        "      <th>Experience</th>\n",
1115 |        "      <th>Performance_Score</th>\n",
1116 |        "      <th>Salary_Category</th>\n",
1117 |        "      <th>Adjusted_Performance</th>\n",
1118 |        "      <th>Formatted_Name</th>\n",
1119 |        "    </tr>\n",
1120 |        "  </thead>\n",
1121 |        "  <tbody>\n",
1122 |        "    <tr>\n",
1123 |        "      <th>0</th>\n",
1124 |        "      <td>1</td>\n",
1125 |        "      <td>Emp_1</td>\n",
1126 |        "      <td>Marketing</td>\n",
1127 |        "      <td>82251</td>\n",
1128 |        "      <td>6</td>\n",
1129 |        "      <td>5</td>\n",
1130 |        "      <td>High</td>\n",
1131 |        "      <td>5</td>\n",
1132 |        "      <td>Emp 1</td>\n",
1133 |        "    </tr>\n",
1134 |        "    <tr>\n",
1135 |        "      <th>1</th>\n",
1136 |        "      <td>2</td>\n",
1137 |        "      <td>Emp_2</td>\n",
1138 |        "      <td>Sales</td>\n",
1139 |        "      <td>52662</td>\n",
1140 |        "      <td>24</td>\n",
1141 |        "      <td>3</td>\n",
1142 |        "      <td>Medium</td>\n",
1143 |        "      <td>4</td>\n",
1144 |        "      <td>Emp 2</td>\n",
1145 |        "    </tr>\n",
1146 |        "    <tr>\n",
1147 |        "      <th>2</th>\n",
1148 |        "      <td>3</td>\n",
1149 |        "      <td>Emp_3</td>\n",
1150 |        "      <td>Finance</td>\n",
1151 |        "      <td>38392</td>\n",
1152 |        "      <td>5</td>\n",
1153 |        "      <td>3</td>\n",
1154 |        "      <td>Low</td>\n",
1155 |        "      <td>3</td>\n",
1156 |        "      <td>Emp 3</td>\n",
1157 |        "    </tr>\n",
1158 |        "    <tr>\n",
1159 |        "      <th>3</th>\n",
1160 |        "      <td>4</td>\n",
1161 |        "      <td>Emp_4</td>\n",
1162 |        "      <td>Sales</td>\n",
1163 |        "      <td>60535</td>\n",
1164 |        "      <td>20</td>\n",
1165 |        "      <td>3</td>\n",
1166 |        "      <td>Medium</td>\n",
1167 |        "      <td>4</td>\n",
1168 |        "      <td>Emp 4</td>\n",
1169 |        "    </tr>\n",
1170 |        "    <tr>\n",
1171 |        "      <th>4</th>\n",
1172 |        "      <td>5</td>\n",
1173 |        "      <td>Emp_5</td>\n",
1174 |        "      <td>Sales</td>\n",
1175 |        "      <td>108603</td>\n",
1176 |        "      <td>2</td>\n",
1177 |        "      <td>2</td>\n",
1178 |        "      <td>High</td>\n",
1179 |        "      <td>2</td>\n",
1180 |        "      <td>Emp 5</td>\n",
1181 |        "    </tr>\n",
1182 |        "  </tbody>\n",
1183 |        "</table>\n",
1184 |        "</div>"
1185 |       ],
1186 |       "text/plain": [
1187 |        "   Employee_ID   Name Department  Salary  Experience  Performance_Score  \\\n",
1188 |        "0            1  Emp_1  Marketing   82251           6                  5   \n",
1189 |        "1            2  Emp_2      Sales   52662          24                  3   \n",
1190 |        "2            3  Emp_3    Finance   38392           5                  3   \n",
1191 |        "3            4  Emp_4      Sales   60535          20                  3   \n",
1192 |        "4            5  Emp_5      Sales  108603           2                  2   \n",
1193 |        "\n",
1194 |        "  Salary_Category  Adjusted_Performance Formatted_Name  \n",
1195 |        "0            High                     5          Emp 1  \n",
1196 |        "1          Medium                     4          Emp 2  \n",
1197 |        "2             Low                     3          Emp 3  \n",
1198 |        "3          Medium                     4          Emp 4  \n",
1199 |        "4            High                     2          Emp 5  "
1200 |       ]
1201 |      },
1202 |      "execution_count": 18,
1203 |      "metadata": {},
1204 |      "output_type": "execute_result"
1205 |     }
1206 |    ],
1207 |    "source": [
1208 |     "df.head()"
1209 |    ]
1210 |   },
1211 |   {
1212 |    "cell_type": "code",
1213 |    "execution_count": null,
1214 |    "id": "13c2ab2e-5971-45ae-864b-d27674e476e0",
1215 |    "metadata": {},
1216 |    "outputs": [],
1217 |    "source": []
1218 |   }
1219 |  ],
1220 |  "metadata": {
1221 |   "kernelspec": {
1222 |    "display_name": "Python 3 (ipykernel)",
1223 |    "language": "python",
1224 |    "name": "python3"
1225 |   },
1226 |   "language_info": {
1227 |    "codemirror_mode": {
1228 |     "name": "ipython",
1229 |     "version": 3
1230 |    },
1231 |    "file_extension": ".py",
1232 |    "mimetype": "text/x-python",
1233 |    "name": "python",
1234 |    "nbconvert_exporter": "python",
1235 |    "pygments_lexer": "ipython3",
1236 |    "version": "3.12.7"
1237 |   }
1238 |  },
1239 |  "nbformat": 4,
1240 |  "nbformat_minor": 5
1241 | }
1242 | 


--------------------------------------------------------------------------------
/8-apply_function_data.csv:
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  1 | Employee_ID,Name,Department,Salary,Experience,Performance_Score
  2 | 1,Emp_1,Marketing,82251,6,5
  3 | 2,Emp_2,Sales,52662,24,3
  4 | 3,Emp_3,Finance,38392,5,3
  5 | 4,Emp_4,Sales,60535,20,3
  6 | 5,Emp_5,Sales,108603,2,2
  7 | 6,Emp_6,IT,82256,6,5
  8 | 7,Emp_7,Finance,119135,22,1
  9 | 8,Emp_8,Finance,65222,11,4
 10 | 9,Emp_9,Finance,107373,16,1
 11 | 10,Emp_10,Sales,109575,16,5
 12 | 11,Emp_11,Marketing,114651,1,4
 13 | 12,Emp_12,Finance,93335,9,5
 14 | 13,Emp_13,Sales,40965,6,3
 15 | 14,Emp_14,IT,54538,16,4
 16 | 15,Emp_15,Marketing,100592,3,3
 17 | 16,Emp_16,IT,38110,20,1
 18 | 17,Emp_17,Marketing,109309,4,1
 19 | 18,Emp_18,Sales,57266,19,4
 20 | 19,Emp_19,HR,82992,3,4
 21 | 20,Emp_20,Marketing,112948,19,5
 22 | 21,Emp_21,IT,36910,20,5
 23 | 22,Emp_22,Sales,30206,7,3
 24 | 23,Emp_23,Marketing,117054,20,4
 25 | 24,Emp_24,HR,117897,9,1
 26 | 25,Emp_25,HR,53419,1,5
 27 | 26,Emp_26,Finance,80636,8,5
 28 | 27,Emp_27,Finance,80015,7,1
 29 | 28,Emp_28,IT,84268,18,5
 30 | 29,Emp_29,Marketing,117939,8,3
 31 | 30,Emp_30,Marketing,48141,1,4
 32 | 31,Emp_31,Finance,110356,11,1
 33 | 32,Emp_32,Marketing,101910,18,4
 34 | 33,Emp_33,Marketing,86044,23,5
 35 | 34,Emp_34,HR,97214,10,5
 36 | 35,Emp_35,Finance,63827,3,1
 37 | 36,Emp_36,Sales,85820,7,3
 38 | 37,Emp_37,Finance,92623,16,2
 39 | 38,Emp_38,Sales,111734,16,1
 40 | 39,Emp_39,HR,105450,20,2
 41 | 40,Emp_40,IT,52299,17,2
 42 | 41,Emp_41,Marketing,73585,2,3
 43 | 42,Emp_42,HR,94044,1,2
 44 | 43,Emp_43,Marketing,72557,16,2
 45 | 44,Emp_44,IT,79080,12,3
 46 | 45,Emp_45,IT,32693,5,2
 47 | 46,Emp_46,HR,99163,5,2
 48 | 47,Emp_47,IT,55939,23,2
 49 | 48,Emp_48,Sales,78925,9,1
 50 | 49,Emp_49,IT,72941,9,1
 51 | 50,Emp_50,Marketing,51834,3,1
 52 | 51,Emp_51,Marketing,48047,19,3
 53 | 52,Emp_52,Marketing,56105,16,5
 54 | 53,Emp_53,Marketing,105766,16,2
 55 | 54,Emp_54,Sales,45707,3,2
 56 | 55,Emp_55,Finance,51976,20,3
 57 | 56,Emp_56,HR,74262,24,2
 58 | 57,Emp_57,Marketing,53776,22,1
 59 | 58,Emp_58,IT,60080,24,5
 60 | 59,Emp_59,Marketing,96842,1,4
 61 | 60,Emp_60,IT,91373,24,2
 62 | 61,Emp_61,IT,36776,20,1
 63 | 62,Emp_62,Marketing,85016,11,4
 64 | 63,Emp_63,Sales,39474,17,5
 65 | 64,Emp_64,IT,88053,8,4
 66 | 65,Emp_65,IT,51959,4,1
 67 | 66,Emp_66,Marketing,35530,6,4
 68 | 67,Emp_67,IT,33748,8,3
 69 | 68,Emp_68,IT,43545,20,4
 70 | 69,Emp_69,Marketing,96199,3,2
 71 | 70,Emp_70,Marketing,64766,16,2
 72 | 71,Emp_71,HR,103530,3,3
 73 | 72,Emp_72,Sales,91087,18,1
 74 | 73,Emp_73,Sales,98840,14,2
 75 | 74,Emp_74,IT,84384,18,5
 76 | 75,Emp_75,Sales,81005,2,2
 77 | 76,Emp_76,IT,76576,22,2
 78 | 77,Emp_77,HR,69353,3,1
 79 | 78,Emp_78,Marketing,92003,16,4
 80 | 79,Emp_79,Marketing,113211,9,2
 81 | 80,Emp_80,Marketing,82733,4,3
 82 | 81,Emp_81,Sales,95318,1,4
 83 | 82,Emp_82,HR,119474,4,5
 84 | 83,Emp_83,Sales,53664,1,1
 85 | 84,Emp_84,Sales,97172,14,5
 86 | 85,Emp_85,HR,115616,21,4
 87 | 86,Emp_86,HR,56736,16,4
 88 | 87,Emp_87,HR,30854,20,4
 89 | 88,Emp_88,HR,68623,24,5
 90 | 89,Emp_89,Marketing,37392,8,4
 91 | 90,Emp_90,Finance,85680,7,5
 92 | 91,Emp_91,Finance,76717,3,4
 93 | 92,Emp_92,HR,117092,17,3
 94 | 93,Emp_93,Finance,80859,1,4
 95 | 94,Emp_94,Finance,56309,16,5
 96 | 95,Emp_95,HR,117455,12,2
 97 | 96,Emp_96,Finance,93734,19,4
 98 | 97,Emp_97,Sales,100467,22,2
 99 | 98,Emp_98,IT,82662,23,3
100 | 99,Emp_99,IT,42688,22,1
101 | 100,Emp_100,HR,55342,14,3
102 | 


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/athlete_events.csv.zip:
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https://raw.githubusercontent.com/atilsamancioglu/PythonForDataScienceNotebooks/HEAD/athlete_events.csv.zip


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