├── Confusion Matrix - Jupyter Notebook.pdf
├── DecisionTree - Jupyter Notebook.pdf
├── K-Means Clustering - Jupyter Notebook.pdf
├── KNN - Jupyter Notebook.pdf
├── Lab_1.ipynb
├── Linear Regression - Jupyter Notebook.pdf
├── Matpltlib - Jupyter Notebook.pdf
├── Multiple Regression - Jupyter Notebook.pdf
├── Numpy -1 - Jupyter Notebook.pdf
├── Numpy -2 - Jupyter Notebook.pdf
├── Numpy-3 - Jupyter Notebook.pdf
├── Numpy-4 - Jupyter Notebook.pdf
├── PANDASONE - Jupyter Notebook.pdf
├── PANDASTWO - Jupyter Notebook.pdf
├── Pandas CSV.pdf
├── Pandas Series & Data Frame.pdf
├── README.md
├── SEABORN - Jupyter Notebook.pdf
├── groupby - Jupyter Notebook.pdf
├── groupby.ipynb
├── groupby1 - Jupyter Notebook.pdf
├── handling missing data - Jupyter Notebook.pdf
├── homeprices.csv
├── income.csv
├── min -max-Normalization - Jupyter Notebook.pdf
├── multiple .homeprices.csv
├── naive_bayes - Jupyter Notebook.pdf
├── salaries.csv
└── training-testing split - Jupyter Notebook.pdf


/Confusion Matrix - Jupyter Notebook.pdf:
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https://raw.githubusercontent.com/poojavjpy/Data-Analytics/0d965cc93ab906ec8d04ed7daac99589569c0c5e/Confusion Matrix - Jupyter Notebook.pdf


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/DecisionTree - Jupyter Notebook.pdf:
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https://raw.githubusercontent.com/poojavjpy/Data-Analytics/0d965cc93ab906ec8d04ed7daac99589569c0c5e/DecisionTree - Jupyter Notebook.pdf


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/K-Means Clustering - Jupyter Notebook.pdf:
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https://raw.githubusercontent.com/poojavjpy/Data-Analytics/0d965cc93ab906ec8d04ed7daac99589569c0c5e/K-Means Clustering - Jupyter Notebook.pdf


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/KNN - Jupyter Notebook.pdf:
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https://raw.githubusercontent.com/poojavjpy/Data-Analytics/0d965cc93ab906ec8d04ed7daac99589569c0c5e/KNN - Jupyter Notebook.pdf


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/Lab_1.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/poojavjpy/Data-Analytics/blob/main/Lab_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "markdown",
 15 |       "metadata": {
 16 |         "id": "QK5LbElIDyEF"
 17 |       },
 18 |       "source": [
 19 |         "### ***Print Command***"
 20 |       ]
 21 |     },
 22 |     {
 23 |       "cell_type": "code",
 24 |       "execution_count": null,
 25 |       "metadata": {
 26 |         "colab": {
 27 |           "base_uri": "https://localhost:8080/"
 28 |         },
 29 |         "id": "XCgsvkPw1lpD",
 30 |         "outputId": "b5d84dfc-ca97-4b04-b15f-c2afbe42ab68"
 31 |       },
 32 |       "outputs": [
 33 |         {
 34 |           "name": "stdout",
 35 |           "output_type": "stream",
 36 |           "text": [
 37 |             "hello, Welcome to First Lab\n"
 38 |           ]
 39 |         }
 40 |       ],
 41 |       "source": [
 42 |         "print(\"hello, Welcome to First Lab\")   #this command is to print."
 43 |       ]
 44 |     },
 45 |     {
 46 |       "cell_type": "markdown",
 47 |       "metadata": {
 48 |         "id": "Vga6Z7eus-Fx"
 49 |       },
 50 |       "source": [
 51 |         "## ***Variables***"
 52 |       ]
 53 |     },
 54 |     {
 55 |       "cell_type": "code",
 56 |       "execution_count": null,
 57 |       "metadata": {
 58 |         "colab": {
 59 |           "base_uri": "https://localhost:8080/"
 60 |         },
 61 |         "id": "teGkrTDc100E",
 62 |         "outputId": "69f65e65-e032-4662-919d-f22b1996bb37"
 63 |       },
 64 |       "outputs": [
 65 |         {
 66 |           "name": "stdout",
 67 |           "output_type": "stream",
 68 |           "text": [
 69 |             "pooja\n",
 70 |             "23.9\n"
 71 |           ]
 72 |         }
 73 |       ],
 74 |       "source": [
 75 |         "name='pooja'\n",
 76 |         "age =23.9\n",
 77 |         "\n",
 78 |         "print(name)\n",
 79 |         "print(age)"
 80 |       ]
 81 |     },
 82 |     {
 83 |       "cell_type": "code",
 84 |       "execution_count": null,
 85 |       "metadata": {
 86 |         "colab": {
 87 |           "base_uri": "https://localhost:8080/"
 88 |         },
 89 |         "id": "elmxitQtg0Xg",
 90 |         "outputId": "b9f153df-716e-4522-80d0-2a2844acd668"
 91 |       },
 92 |       "outputs": [
 93 |         {
 94 |           "name": "stdout",
 95 |           "output_type": "stream",
 96 |           "text": [
 97 |             "priya\n",
 98 |             "27\n"
 99 |           ]
100 |         }
101 |       ],
102 |       "source": [
103 |         "name=\"payal\"\n",
104 |         "age =23\n",
105 |         "name=\"priya\"\n",
106 |         "age =27\n",
107 |         "print(name)\n",
108 |         "print(age)"
109 |       ]
110 |     },
111 |     {
112 |       "cell_type": "code",
113 |       "execution_count": null,
114 |       "metadata": {
115 |         "colab": {
116 |           "base_uri": "https://localhost:8080/"
117 |         },
118 |         "id": "8_OXWc8chAkT",
119 |         "outputId": "8c3bd7bd-49ef-4729-b067-b2e3ebe65098"
120 |       },
121 |       "outputs": [
122 |         {
123 |           "name": "stdout",
124 |           "output_type": "stream",
125 |           "text": [
126 |             "payal sharma\n",
127 |             "19\n",
128 |             "True\n"
129 |           ]
130 |         }
131 |       ],
132 |       "source": [
133 |         "first_name=\"payal\"\n",
134 |         "last_name= \"sharma\"\n",
135 |         "age= 19\n",
136 |         "is_adult=True\n",
137 |         "print(first_name,last_name)\n",
138 |         "#printlast_name)\n",
139 |         "print(age)\n",
140 |         "print(is_adult)"
141 |       ]
142 |     },
143 |     {
144 |       "cell_type": "markdown",
145 |       "metadata": {
146 |         "id": "fuuqgoEPjxqw"
147 |       },
148 |       "source": [
149 |         "## ***Taking Input from user***"
150 |       ]
151 |     },
152 |     {
153 |       "cell_type": "code",
154 |       "execution_count": null,
155 |       "metadata": {
156 |         "colab": {
157 |           "base_uri": "https://localhost:8080/"
158 |         },
159 |         "id": "7CodKmQoh33e",
160 |         "outputId": "2e88a75e-2f8c-4b8a-8376-2603c9807d0b"
161 |       },
162 |       "outputs": [
163 |         {
164 |           "name": "stdout",
165 |           "output_type": "stream",
166 |           "text": [
167 |             "whats your name  ? pooja\n",
168 |             " hello pooja\n"
169 |           ]
170 |         }
171 |       ],
172 |       "source": [
173 |         "name= input(\"whats your name  ? \")\n",
174 |         "print(\" hello\" , name)"
175 |       ]
176 |     },
177 |     {
178 |       "cell_type": "markdown",
179 |       "metadata": {
180 |         "id": "w1I1FSxdjfpR"
181 |       },
182 |       "source": [
183 |         "### ***Arithmetic Operators***"
184 |       ]
185 |     },
186 |     {
187 |       "cell_type": "code",
188 |       "execution_count": null,
189 |       "metadata": {
190 |         "colab": {
191 |           "base_uri": "https://localhost:8080/"
192 |         },
193 |         "id": "edpRvF8ujvjb",
194 |         "outputId": "4c2c6bd8-4a6e-4b08-fa95-ad05a2053bcf"
195 |       },
196 |       "outputs": [
197 |         {
198 |           "name": "stdout",
199 |           "output_type": "stream",
200 |           "text": [
201 |             "15\n",
202 |             "3\n",
203 |             "54\n",
204 |             "1.5\n",
205 |             "1\n",
206 |             "3\n",
207 |             "8\n"
208 |           ]
209 |         }
210 |       ],
211 |       "source": [
212 |         "print(9+6)\n",
213 |         "print(9-6)\n",
214 |         "print(9*6)\n",
215 |         "print(9/6)\n",
216 |         "print(9//6)\n",
217 |         "print(9%6)\n",
218 |         "print(2**3)"
219 |       ]
220 |     },
221 |     {
222 |       "cell_type": "code",
223 |       "execution_count": null,
224 |       "metadata": {
225 |         "colab": {
226 |           "base_uri": "https://localhost:8080/"
227 |         },
228 |         "id": "zzp32ZJQkmi3",
229 |         "outputId": "a267a0e6-136e-4625-eff3-cab1d9ed92e3"
230 |       },
231 |       "outputs": [
232 |         {
233 |           "name": "stdout",
234 |           "output_type": "stream",
235 |           "text": [
236 |             "12\n",
237 |             "14\n",
238 |             "12\n",
239 |             "24\n"
240 |           ]
241 |         }
242 |       ],
243 |       "source": [
244 |         "i=5\n",
245 |         "i=i+7\n",
246 |         "print(i)\n",
247 |         "i +=2\n",
248 |         "print(i)\n",
249 |         "i -=2\n",
250 |         "print(i)\n",
251 |         "i *=2\n",
252 |         "print(i)"
253 |       ]
254 |     },
255 |     {
256 |       "cell_type": "markdown",
257 |       "metadata": {
258 |         "id": "1ncuhbEvlY03"
259 |       },
260 |       "source": [
261 |         "### ***Operator Precedence***"
262 |       ]
263 |     },
264 |     {
265 |       "cell_type": "code",
266 |       "execution_count": null,
267 |       "metadata": {
268 |         "colab": {
269 |           "base_uri": "https://localhost:8080/"
270 |         },
271 |         "id": "Op1xFtkclGGg",
272 |         "outputId": "e2da2291-daf0-4139-a26d-265df140dad0"
273 |       },
274 |       "outputs": [
275 |         {
276 |           "name": "stdout",
277 |           "output_type": "stream",
278 |           "text": [
279 |             "13\n",
280 |             "16\n"
281 |           ]
282 |         }
283 |       ],
284 |       "source": [
285 |         "result = 3+5*2\n",
286 |         "print(result)\n",
287 |         "result = (3+5)*2\n",
288 |         "print(result)"
289 |       ]
290 |     },
291 |     {
292 |       "cell_type": "markdown",
293 |       "metadata": {
294 |         "id": "HfuwcNUbmPE0"
295 |       },
296 |       "source": [
297 |         "### ***Comparision Operator***"
298 |       ]
299 |     },
300 |     {
301 |       "cell_type": "code",
302 |       "execution_count": null,
303 |       "metadata": {
304 |         "colab": {
305 |           "base_uri": "https://localhost:8080/"
306 |         },
307 |         "id": "q24BgFp8mVQY",
308 |         "outputId": "a5ffc4d4-1241-4582-8a01-739adc095db0"
309 |       },
310 |       "outputs": [
311 |         {
312 |           "name": "stdout",
313 |           "output_type": "stream",
314 |           "text": [
315 |             "False\n",
316 |             "True\n",
317 |             "False\n",
318 |             "True\n"
319 |           ]
320 |         }
321 |       ],
322 |       "source": [
323 |         "print(3>8)\n",
324 |         "print(3<=8)\n",
325 |         "print(3==8)\n",
326 |         "print(3!=8)"
327 |       ]
328 |     },
329 |     {
330 |       "cell_type": "markdown",
331 |       "metadata": {
332 |         "id": "zQZDFW39mkqv"
333 |       },
334 |       "source": [
335 |         "### ***Logical Operator***\n",
336 |         "\n",
337 |         "\n"
338 |       ]
339 |     },
340 |     {
341 |       "cell_type": "code",
342 |       "execution_count": null,
343 |       "metadata": {
344 |         "colab": {
345 |           "base_uri": "https://localhost:8080/"
346 |         },
347 |         "id": "PcQcYegHmwSr",
348 |         "outputId": "b1764675-2ef4-4e8a-a843-5e14b7e80e12"
349 |       },
350 |       "outputs": [
351 |         {
352 |           "name": "stdout",
353 |           "output_type": "stream",
354 |           "text": [
355 |             "False\n",
356 |             "True\n",
357 |             "True\n"
358 |           ]
359 |         }
360 |       ],
361 |       "source": [
362 |         "print(3>2 and 1>10)\n",
363 |         "print(3>2 or 1>10)\n",
364 |         "print(not 1>10)"
365 |       ]
366 |     },
367 |     {
368 |       "cell_type": "markdown",
369 |       "metadata": {
370 |         "id": "fT_nawtYutD6"
371 |       },
372 |       "source": [
373 |         "### ***Data Types   : int, float, str, boolean***"
374 |       ]
375 |     },
376 |     {
377 |       "cell_type": "code",
378 |       "execution_count": null,
379 |       "metadata": {
380 |         "colab": {
381 |           "base_uri": "https://localhost:8080/"
382 |         },
383 |         "id": "5Jl_yZO1omwA",
384 |         "outputId": "aa65a64d-995b-4d58-a64c-5cd458e0b0b5"
385 |       },
386 |       "outputs": [
387 |         {
388 |           "name": "stdout",
389 |           "output_type": "stream",
390 |           "text": [
391 |             "32\n"
392 |           ]
393 |         }
394 |       ],
395 |       "source": [
396 |         "age=29\n",
397 |         "new_age = age+3\n",
398 |         "print(new_age)"
399 |       ]
400 |     },
401 |     {
402 |       "cell_type": "code",
403 |       "execution_count": null,
404 |       "metadata": {
405 |         "colab": {
406 |           "base_uri": "https://localhost:8080/"
407 |         },
408 |         "id": "wvH9L7xxo64C",
409 |         "outputId": "70528c2b-aaae-4ddd-881e-ab686a07f056"
410 |       },
411 |       "outputs": [
412 |         {
413 |           "output_type": "stream",
414 |           "name": "stdout",
415 |           "text": [
416 |             "enter your age  33\n",
417 |             "36\n"
418 |           ]
419 |         }
420 |       ],
421 |       "source": [
422 |         "age= input(\"enter your age  \") #Typecasting\n",
423 |         "new_age=  int (age) + 3\n",
424 |         "print(new_age)"
425 |       ]
426 |     },
427 |     {
428 |       "cell_type": "code",
429 |       "execution_count": null,
430 |       "metadata": {
431 |         "colab": {
432 |           "base_uri": "https://localhost:8080/"
433 |         },
434 |         "id": "D9tiRUsSsf2B",
435 |         "outputId": "189bb053-14ea-45db-b63e-1521c671e8f7"
436 |       },
437 |       "outputs": [
438 |         {
439 |           "name": "stdout",
440 |           "output_type": "stream",
441 |           "text": [
442 |             "45\n"
443 |           ]
444 |         }
445 |       ],
446 |       "source": [
447 |         "number=45\n",
448 |         "float(number)\n",
449 |         "print(number)"
450 |       ]
451 |     },
452 |     {
453 |       "cell_type": "code",
454 |       "execution_count": null,
455 |       "metadata": {
456 |         "colab": {
457 |           "base_uri": "https://localhost:8080/"
458 |         },
459 |         "id": "vgzdNN-fsnZN",
460 |         "outputId": "30a3562c-3a33-4ac7-d1a8-46c84f91d41e"
461 |       },
462 |       "outputs": [
463 |         {
464 |           "name": "stdout",
465 |           "output_type": "stream",
466 |           "text": [
467 |             "90.0\n"
468 |           ]
469 |         }
470 |       ],
471 |       "source": [
472 |         "num=number+float(number)\n",
473 |         "print(num)"
474 |       ]
475 |     },
476 |     {
477 |       "cell_type": "markdown",
478 |       "metadata": {
479 |         "id": "twR70yCJm7MP"
480 |       },
481 |       "source": [
482 |         "### ***This is first program***"
483 |       ]
484 |     },
485 |     {
486 |       "cell_type": "code",
487 |       "execution_count": null,
488 |       "metadata": {
489 |         "colab": {
490 |           "base_uri": "https://localhost:8080/"
491 |         },
492 |         "id": "BcyCzaB3t50C",
493 |         "outputId": "9a2f365e-0398-4aaa-e9f8-1bd60849fc1c"
494 |       },
495 |       "outputs": [
496 |         {
497 |           "output_type": "stream",
498 |           "name": "stdout",
499 |           "text": [
500 |             "first number  3\n",
501 |             "second number  5\n",
502 |             "sum is : 35\n"
503 |           ]
504 |         }
505 |       ],
506 |       "source": [
507 |         "first_num=input(\"first number  \")\n",
508 |         "second_num=input(\"second number  \")\n",
509 |         "\n",
510 |         "sum=first_num+second_num\n",
511 |         "#print(sum)\n",
512 |         "print(\"sum is : \" + sum)"
513 |       ]
514 |     },
515 |     {
516 |       "cell_type": "code",
517 |       "execution_count": null,
518 |       "metadata": {
519 |         "colab": {
520 |           "base_uri": "https://localhost:8080/"
521 |         },
522 |         "id": "X7EWwis5ug4N",
523 |         "outputId": "35bb4e05-ed30-4286-92b1-62991858483e"
524 |       },
525 |       "outputs": [
526 |         {
527 |           "output_type": "stream",
528 |           "name": "stdout",
529 |           "text": [
530 |             "first number  3\n",
531 |             "second number  3\n",
532 |             "sum is : 6\n"
533 |           ]
534 |         }
535 |       ],
536 |       "source": [
537 |         "first_num=input(\"first number  \")\n",
538 |         "second_num=input(\"second number  \")\n",
539 |         "\n",
540 |         "sum=int (first_num) + int (second_num)\n",
541 |         "#print(sum)\n",
542 |         "#print(\"sum is : \" + sum)\n",
543 |         "print(\"sum is : \" + str (sum))"
544 |       ]
545 |     },
546 |     {
547 |       "cell_type": "markdown",
548 |       "metadata": {
549 |         "id": "lpoq6RC01FPi"
550 |       },
551 |       "source": [
552 |         "### ***Operation on String***"
553 |       ]
554 |     },
555 |     {
556 |       "cell_type": "code",
557 |       "execution_count": null,
558 |       "metadata": {
559 |         "colab": {
560 |           "base_uri": "https://localhost:8080/"
561 |         },
562 |         "id": "ye4RLhjVuzhg",
563 |         "outputId": "04602d87-3175-45a9-d999-1b2c78508d11"
564 |       },
565 |       "outputs": [
566 |         {
567 |           "output_type": "stream",
568 |           "name": "stdout",
569 |           "text": [
570 |             "Pooja Vajpayee\n"
571 |           ]
572 |         }
573 |       ],
574 |       "source": [
575 |         "name= \"Pooja Vajpayee\"\n",
576 |         "print(name)"
577 |       ]
578 |     },
579 |     {
580 |       "cell_type": "code",
581 |       "execution_count": null,
582 |       "metadata": {
583 |         "colab": {
584 |           "base_uri": "https://localhost:8080/"
585 |         },
586 |         "id": "9xa7Vl4bwNRz",
587 |         "outputId": "648e3f65-d0c3-4069-aa05-514d623cb7b4"
588 |       },
589 |       "outputs": [
590 |         {
591 |           "output_type": "stream",
592 |           "name": "stdout",
593 |           "text": [
594 |             "POOJA VAJPAYEE\n"
595 |           ]
596 |         }
597 |       ],
598 |       "source": [
599 |         "print(name.upper())"
600 |       ]
601 |     },
602 |     {
603 |       "cell_type": "code",
604 |       "execution_count": null,
605 |       "metadata": {
606 |         "colab": {
607 |           "base_uri": "https://localhost:8080/"
608 |         },
609 |         "id": "0epvbOpqwSim",
610 |         "outputId": "80ef6973-56cb-4d64-ba84-15be1fe216bc"
611 |       },
612 |       "outputs": [
613 |         {
614 |           "output_type": "stream",
615 |           "name": "stdout",
616 |           "text": [
617 |             "pooja vajpayee\n"
618 |           ]
619 |         }
620 |       ],
621 |       "source": [
622 |         "print(name.lower())"
623 |       ]
624 |     },
625 |     {
626 |       "cell_type": "code",
627 |       "execution_count": null,
628 |       "metadata": {
629 |         "colab": {
630 |           "base_uri": "https://localhost:8080/"
631 |         },
632 |         "id": "SopEoDvywfvj",
633 |         "outputId": "7a2f3317-edb9-40de-c710-6dc40e72f910"
634 |       },
635 |       "outputs": [
636 |         {
637 |           "output_type": "stream",
638 |           "name": "stdout",
639 |           "text": [
640 |             "4\n"
641 |           ]
642 |         }
643 |       ],
644 |       "source": [
645 |         "print(name.find('a')) #return location starting from 0."
646 |       ]
647 |     },
648 |     {
649 |       "cell_type": "code",
650 |       "execution_count": null,
651 |       "metadata": {
652 |         "colab": {
653 |           "base_uri": "https://localhost:8080/"
654 |         },
655 |         "id": "VUu1sw3dzWZJ",
656 |         "outputId": "69115bff-51b4-4948-a4ea-d13ca007ace8"
657 |       },
658 |       "outputs": [
659 |         {
660 |           "output_type": "stream",
661 |           "name": "stdout",
662 |           "text": [
663 |             "-1\n"
664 |           ]
665 |         }
666 |       ],
667 |       "source": [
668 |         "print(name.find('pooja'))"
669 |       ]
670 |     },
671 |     {
672 |       "cell_type": "code",
673 |       "execution_count": null,
674 |       "metadata": {
675 |         "colab": {
676 |           "base_uri": "https://localhost:8080/"
677 |         },
678 |         "id": "EoP5KzMkzaUR",
679 |         "outputId": "92bb376b-8174-4528-bd25-c3a6fc846a66"
680 |       },
681 |       "outputs": [
682 |         {
683 |           "output_type": "stream",
684 |           "name": "stdout",
685 |           "text": [
686 |             "12\n"
687 |           ]
688 |         }
689 |       ],
690 |       "source": [
691 |         "print(name.find('e'))"
692 |       ]
693 |     },
694 |     {
695 |       "cell_type": "code",
696 |       "execution_count": null,
697 |       "metadata": {
698 |         "colab": {
699 |           "base_uri": "https://localhost:8080/"
700 |         },
701 |         "id": "obs9DWqpzNKL",
702 |         "outputId": "819c56e0-143c-4e2b-eeec-354469b642a5"
703 |       },
704 |       "outputs": [
705 |         {
706 |           "output_type": "stream",
707 |           "name": "stdout",
708 |           "text": [
709 |             "6\n"
710 |           ]
711 |         }
712 |       ],
713 |       "source": [
714 |         "print(name.find('Vajpayee'))"
715 |       ]
716 |     },
717 |     {
718 |       "cell_type": "code",
719 |       "execution_count": null,
720 |       "metadata": {
721 |         "colab": {
722 |           "base_uri": "https://localhost:8080/"
723 |         },
724 |         "id": "tSg8UQCNxcD5",
725 |         "outputId": "8ce2cfa1-0cab-4c64-8135-d21b591f17b6"
726 |       },
727 |       "outputs": [
728 |         {
729 |           "output_type": "stream",
730 |           "name": "stdout",
731 |           "text": [
732 |             "Pooja Bajpai\n",
733 |             "Pooja Vajpayee\n"
734 |           ]
735 |         }
736 |       ],
737 |       "source": [
738 |         "print(name.replace(\"Pooja Vajpayee\" , \"Pooja Bajpai\"))\n",
739 |         "print(name)"
740 |       ]
741 |     },
742 |     {
743 |       "cell_type": "code",
744 |       "execution_count": null,
745 |       "metadata": {
746 |         "colab": {
747 |           "base_uri": "https://localhost:8080/"
748 |         },
749 |         "id": "BIk_A78Wxs8h",
750 |         "outputId": "1ee6cf0d-01c0-4320-d57e-e56934db0678"
751 |       },
752 |       "outputs": [
753 |         {
754 |           "output_type": "stream",
755 |           "name": "stdout",
756 |           "text": [
757 |             "Dr. Pooja Vajpayee\n"
758 |           ]
759 |         }
760 |       ],
761 |       "source": [
762 |         "print(name.replace(\"Pooja\" , \"Dr. Pooja\"))"
763 |       ]
764 |     },
765 |     {
766 |       "cell_type": "code",
767 |       "execution_count": null,
768 |       "metadata": {
769 |         "colab": {
770 |           "base_uri": "https://localhost:8080/"
771 |         },
772 |         "id": "_EvNWD08yF30",
773 |         "outputId": "4083c054-139f-45d8-8409-2dff51af803b"
774 |       },
775 |       "outputs": [
776 |         {
777 |           "output_type": "stream",
778 |           "name": "stdout",
779 |           "text": [
780 |             "Pooja Bajpayee\n"
781 |           ]
782 |         }
783 |       ],
784 |       "source": [
785 |         "print(name.replace(\"V\" , \"B\"))"
786 |       ]
787 |     },
788 |     {
789 |       "cell_type": "code",
790 |       "execution_count": null,
791 |       "metadata": {
792 |         "colab": {
793 |           "base_uri": "https://localhost:8080/"
794 |         },
795 |         "id": "tX7LZ2aJyeYZ",
796 |         "outputId": "8c01d796-31b3-4575-f191-f49ff965936f"
797 |       },
798 |       "outputs": [
799 |         {
800 |           "output_type": "stream",
801 |           "name": "stdout",
802 |           "text": [
803 |             "False\n"
804 |           ]
805 |         }
806 |       ],
807 |       "source": [
808 |         "print(\"t\" in name)"
809 |       ]
810 |     },
811 |     {
812 |       "cell_type": "code",
813 |       "execution_count": null,
814 |       "metadata": {
815 |         "colab": {
816 |           "base_uri": "https://localhost:8080/"
817 |         },
818 |         "id": "KmsG8l9hykFd",
819 |         "outputId": "abdea27c-5c32-4701-a9f4-7a980aadb8c7"
820 |       },
821 |       "outputs": [
822 |         {
823 |           "output_type": "stream",
824 |           "name": "stdout",
825 |           "text": [
826 |             "True\n"
827 |           ]
828 |         }
829 |       ],
830 |       "source": [
831 |         "print(\"V\" in name)"
832 |       ]
833 |     }
834 |   ],
835 |   "metadata": {
836 |     "colab": {
837 |       "provenance": [],
838 |       "authorship_tag": "ABX9TyPLehWswDaYMOEC/9Mqe3sJ",
839 |       "include_colab_link": true
840 |     },
841 |     "kernelspec": {
842 |       "display_name": "Python 3",
843 |       "name": "python3"
844 |     },
845 |     "language_info": {
846 |       "name": "python"
847 |     }
848 |   },
849 |   "nbformat": 4,
850 |   "nbformat_minor": 0
851 | }


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/README.md:
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1 | # Data-Analytics
2 | Author : POOJA VAJPAYEE
3 | 


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/groupby.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "1b9c8b21",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "# groupby and aggregate function in panda¶"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "code",
  13 |    "execution_count": 1,
  14 |    "id": "b568b77d",
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "import pandas as pd\n"
  19 |    ]
  20 |   },
  21 |   {
  22 |    "cell_type": "code",
  23 |    "execution_count": 2,
  24 |    "id": "1a88630d",
  25 |    "metadata": {},
  26 |    "outputs": [
  27 |     {
  28 |      "data": {
  29 |       "text/html": [
  30 |        "<div>\n",
  31 |        "<style scoped>\n",
  32 |        "    .dataframe tbody tr th:only-of-type {\n",
  33 |        "        vertical-align: middle;\n",
  34 |        "    }\n",
  35 |        "\n",
  36 |        "    .dataframe tbody tr th {\n",
  37 |        "        vertical-align: top;\n",
  38 |        "    }\n",
  39 |        "\n",
  40 |        "    .dataframe thead th {\n",
  41 |        "        text-align: right;\n",
  42 |        "    }\n",
  43 |        "</style>\n",
  44 |        "<table border=\"1\" class=\"dataframe\">\n",
  45 |        "  <thead>\n",
  46 |        "    <tr style=\"text-align: right;\">\n",
  47 |        "      <th></th>\n",
  48 |        "      <th>Flavor</th>\n",
  49 |        "      <th>Base Flavor</th>\n",
  50 |        "      <th>Liked</th>\n",
  51 |        "      <th>Flavor Rating</th>\n",
  52 |        "      <th>Texture Rating</th>\n",
  53 |        "      <th>Total Rating</th>\n",
  54 |        "    </tr>\n",
  55 |        "  </thead>\n",
  56 |        "  <tbody>\n",
  57 |        "    <tr>\n",
  58 |        "      <th>0</th>\n",
  59 |        "      <td>Mint Chocolate Chip</td>\n",
  60 |        "      <td>Vanilla</td>\n",
  61 |        "      <td>Yes</td>\n",
  62 |        "      <td>10.0</td>\n",
  63 |        "      <td>8.0</td>\n",
  64 |        "      <td>18.0</td>\n",
  65 |        "    </tr>\n",
  66 |        "    <tr>\n",
  67 |        "      <th>1</th>\n",
  68 |        "      <td>Chocolate</td>\n",
  69 |        "      <td>Chocolate</td>\n",
  70 |        "      <td>Yes</td>\n",
  71 |        "      <td>8.8</td>\n",
  72 |        "      <td>7.6</td>\n",
  73 |        "      <td>16.6</td>\n",
  74 |        "    </tr>\n",
  75 |        "    <tr>\n",
  76 |        "      <th>2</th>\n",
  77 |        "      <td>Vanilla</td>\n",
  78 |        "      <td>Vanilla</td>\n",
  79 |        "      <td>No</td>\n",
  80 |        "      <td>4.7</td>\n",
  81 |        "      <td>5.0</td>\n",
  82 |        "      <td>9.7</td>\n",
  83 |        "    </tr>\n",
  84 |        "    <tr>\n",
  85 |        "      <th>3</th>\n",
  86 |        "      <td>Cookie Dough</td>\n",
  87 |        "      <td>Vanilla</td>\n",
  88 |        "      <td>Yes</td>\n",
  89 |        "      <td>6.9</td>\n",
  90 |        "      <td>6.5</td>\n",
  91 |        "      <td>13.4</td>\n",
  92 |        "    </tr>\n",
  93 |        "    <tr>\n",
  94 |        "      <th>4</th>\n",
  95 |        "      <td>Rocky Road</td>\n",
  96 |        "      <td>Chocolate</td>\n",
  97 |        "      <td>Yes</td>\n",
  98 |        "      <td>8.2</td>\n",
  99 |        "      <td>7.0</td>\n",
 100 |        "      <td>15.2</td>\n",
 101 |        "    </tr>\n",
 102 |        "    <tr>\n",
 103 |        "      <th>5</th>\n",
 104 |        "      <td>Pistachio</td>\n",
 105 |        "      <td>Vanilla</td>\n",
 106 |        "      <td>No</td>\n",
 107 |        "      <td>2.3</td>\n",
 108 |        "      <td>3.4</td>\n",
 109 |        "      <td>5.7</td>\n",
 110 |        "    </tr>\n",
 111 |        "    <tr>\n",
 112 |        "      <th>6</th>\n",
 113 |        "      <td>Cake Batter</td>\n",
 114 |        "      <td>Vanilla</td>\n",
 115 |        "      <td>Yes</td>\n",
 116 |        "      <td>6.5</td>\n",
 117 |        "      <td>6.0</td>\n",
 118 |        "      <td>12.5</td>\n",
 119 |        "    </tr>\n",
 120 |        "    <tr>\n",
 121 |        "      <th>7</th>\n",
 122 |        "      <td>Neapolitan</td>\n",
 123 |        "      <td>Vanilla</td>\n",
 124 |        "      <td>No</td>\n",
 125 |        "      <td>3.8</td>\n",
 126 |        "      <td>5.0</td>\n",
 127 |        "      <td>8.8</td>\n",
 128 |        "    </tr>\n",
 129 |        "    <tr>\n",
 130 |        "      <th>8</th>\n",
 131 |        "      <td>Chocolte Fudge Brownie</td>\n",
 132 |        "      <td>Chocolate</td>\n",
 133 |        "      <td>Yes</td>\n",
 134 |        "      <td>8.2</td>\n",
 135 |        "      <td>7.1</td>\n",
 136 |        "      <td>15.3</td>\n",
 137 |        "    </tr>\n",
 138 |        "  </tbody>\n",
 139 |        "</table>\n",
 140 |        "</div>"
 141 |       ],
 142 |       "text/plain": [
 143 |        "                   Flavor Base Flavor Liked  Flavor Rating  Texture Rating  \\\n",
 144 |        "0     Mint Chocolate Chip     Vanilla   Yes           10.0             8.0   \n",
 145 |        "1               Chocolate   Chocolate   Yes            8.8             7.6   \n",
 146 |        "2                 Vanilla     Vanilla    No            4.7             5.0   \n",
 147 |        "3            Cookie Dough     Vanilla   Yes            6.9             6.5   \n",
 148 |        "4              Rocky Road   Chocolate   Yes            8.2             7.0   \n",
 149 |        "5               Pistachio     Vanilla    No            2.3             3.4   \n",
 150 |        "6             Cake Batter     Vanilla   Yes            6.5             6.0   \n",
 151 |        "7              Neapolitan     Vanilla    No            3.8             5.0   \n",
 152 |        "8  Chocolte Fudge Brownie   Chocolate   Yes            8.2             7.1   \n",
 153 |        "\n",
 154 |        "   Total Rating  \n",
 155 |        "0          18.0  \n",
 156 |        "1          16.6  \n",
 157 |        "2           9.7  \n",
 158 |        "3          13.4  \n",
 159 |        "4          15.2  \n",
 160 |        "5           5.7  \n",
 161 |        "6          12.5  \n",
 162 |        "7           8.8  \n",
 163 |        "8          15.3  "
 164 |       ]
 165 |      },
 166 |      "execution_count": 2,
 167 |      "metadata": {},
 168 |      "output_type": "execute_result"
 169 |     }
 170 |    ],
 171 |    "source": [
 172 |     "df = pd.read_csv('C:\\\\Users\\\\Asus\\\\Downloads\\\\Flavors.csv')\n",
 173 |     "df"
 174 |    ]
 175 |   },
 176 |   {
 177 |    "cell_type": "code",
 178 |    "execution_count": 3,
 179 |    "id": "5035ce95",
 180 |    "metadata": {},
 181 |    "outputs": [
 182 |     {
 183 |      "data": {
 184 |       "text/plain": [
 185 |        "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F17EA3DDB0>"
 186 |       ]
 187 |      },
 188 |      "execution_count": 3,
 189 |      "metadata": {},
 190 |      "output_type": "execute_result"
 191 |     }
 192 |    ],
 193 |    "source": [
 194 |     "df.groupby('Base Flavor')"
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": 4,
 200 |    "id": "cadaaeec",
 201 |    "metadata": {},
 202 |    "outputs": [],
 203 |    "source": [
 204 |     "df1=df.groupby('Base Flavor')\n",
 205 |     "\n"
 206 |    ]
 207 |   },
 208 |   {
 209 |    "cell_type": "code",
 210 |    "execution_count": 5,
 211 |    "id": "24310f83",
 212 |    "metadata": {},
 213 |    "outputs": [
 214 |     {
 215 |      "name": "stderr",
 216 |      "output_type": "stream",
 217 |      "text": [
 218 |       "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_23396\\2053335143.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
 219 |       "  df1.mean()\n"
 220 |      ]
 221 |     },
 222 |     {
 223 |      "data": {
 224 |       "text/html": [
 225 |        "<div>\n",
 226 |        "<style scoped>\n",
 227 |        "    .dataframe tbody tr th:only-of-type {\n",
 228 |        "        vertical-align: middle;\n",
 229 |        "    }\n",
 230 |        "\n",
 231 |        "    .dataframe tbody tr th {\n",
 232 |        "        vertical-align: top;\n",
 233 |        "    }\n",
 234 |        "\n",
 235 |        "    .dataframe thead th {\n",
 236 |        "        text-align: right;\n",
 237 |        "    }\n",
 238 |        "</style>\n",
 239 |        "<table border=\"1\" class=\"dataframe\">\n",
 240 |        "  <thead>\n",
 241 |        "    <tr style=\"text-align: right;\">\n",
 242 |        "      <th></th>\n",
 243 |        "      <th>Flavor Rating</th>\n",
 244 |        "      <th>Texture Rating</th>\n",
 245 |        "      <th>Total Rating</th>\n",
 246 |        "    </tr>\n",
 247 |        "    <tr>\n",
 248 |        "      <th>Base Flavor</th>\n",
 249 |        "      <th></th>\n",
 250 |        "      <th></th>\n",
 251 |        "      <th></th>\n",
 252 |        "    </tr>\n",
 253 |        "  </thead>\n",
 254 |        "  <tbody>\n",
 255 |        "    <tr>\n",
 256 |        "      <th>Chocolate</th>\n",
 257 |        "      <td>8.4</td>\n",
 258 |        "      <td>7.233333</td>\n",
 259 |        "      <td>15.70</td>\n",
 260 |        "    </tr>\n",
 261 |        "    <tr>\n",
 262 |        "      <th>Vanilla</th>\n",
 263 |        "      <td>5.7</td>\n",
 264 |        "      <td>5.650000</td>\n",
 265 |        "      <td>11.35</td>\n",
 266 |        "    </tr>\n",
 267 |        "  </tbody>\n",
 268 |        "</table>\n",
 269 |        "</div>"
 270 |       ],
 271 |       "text/plain": [
 272 |        "             Flavor Rating  Texture Rating  Total Rating\n",
 273 |        "Base Flavor                                             \n",
 274 |        "Chocolate              8.4        7.233333         15.70\n",
 275 |        "Vanilla                5.7        5.650000         11.35"
 276 |       ]
 277 |      },
 278 |      "execution_count": 5,
 279 |      "metadata": {},
 280 |      "output_type": "execute_result"
 281 |     }
 282 |    ],
 283 |    "source": [
 284 |     "df1.mean()"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "code",
 289 |    "execution_count": 6,
 290 |    "id": "a0834186",
 291 |    "metadata": {},
 292 |    "outputs": [
 293 |     {
 294 |      "name": "stderr",
 295 |      "output_type": "stream",
 296 |      "text": [
 297 |       "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_23396\\2409314591.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
 298 |       "  df.groupby('Base Flavor').mean()\n"
 299 |      ]
 300 |     },
 301 |     {
 302 |      "data": {
 303 |       "text/html": [
 304 |        "<div>\n",
 305 |        "<style scoped>\n",
 306 |        "    .dataframe tbody tr th:only-of-type {\n",
 307 |        "        vertical-align: middle;\n",
 308 |        "    }\n",
 309 |        "\n",
 310 |        "    .dataframe tbody tr th {\n",
 311 |        "        vertical-align: top;\n",
 312 |        "    }\n",
 313 |        "\n",
 314 |        "    .dataframe thead th {\n",
 315 |        "        text-align: right;\n",
 316 |        "    }\n",
 317 |        "</style>\n",
 318 |        "<table border=\"1\" class=\"dataframe\">\n",
 319 |        "  <thead>\n",
 320 |        "    <tr style=\"text-align: right;\">\n",
 321 |        "      <th></th>\n",
 322 |        "      <th>Flavor Rating</th>\n",
 323 |        "      <th>Texture Rating</th>\n",
 324 |        "      <th>Total Rating</th>\n",
 325 |        "    </tr>\n",
 326 |        "    <tr>\n",
 327 |        "      <th>Base Flavor</th>\n",
 328 |        "      <th></th>\n",
 329 |        "      <th></th>\n",
 330 |        "      <th></th>\n",
 331 |        "    </tr>\n",
 332 |        "  </thead>\n",
 333 |        "  <tbody>\n",
 334 |        "    <tr>\n",
 335 |        "      <th>Chocolate</th>\n",
 336 |        "      <td>8.4</td>\n",
 337 |        "      <td>7.233333</td>\n",
 338 |        "      <td>15.70</td>\n",
 339 |        "    </tr>\n",
 340 |        "    <tr>\n",
 341 |        "      <th>Vanilla</th>\n",
 342 |        "      <td>5.7</td>\n",
 343 |        "      <td>5.650000</td>\n",
 344 |        "      <td>11.35</td>\n",
 345 |        "    </tr>\n",
 346 |        "  </tbody>\n",
 347 |        "</table>\n",
 348 |        "</div>"
 349 |       ],
 350 |       "text/plain": [
 351 |        "             Flavor Rating  Texture Rating  Total Rating\n",
 352 |        "Base Flavor                                             \n",
 353 |        "Chocolate              8.4        7.233333         15.70\n",
 354 |        "Vanilla                5.7        5.650000         11.35"
 355 |       ]
 356 |      },
 357 |      "execution_count": 6,
 358 |      "metadata": {},
 359 |      "output_type": "execute_result"
 360 |     }
 361 |    ],
 362 |    "source": [
 363 |     "df.groupby('Base Flavor').mean()"
 364 |    ]
 365 |   },
 366 |   {
 367 |    "cell_type": "code",
 368 |    "execution_count": 7,
 369 |    "id": "547d6a56",
 370 |    "metadata": {},
 371 |    "outputs": [
 372 |     {
 373 |      "data": {
 374 |       "text/html": [
 375 |        "<div>\n",
 376 |        "<style scoped>\n",
 377 |        "    .dataframe tbody tr th:only-of-type {\n",
 378 |        "        vertical-align: middle;\n",
 379 |        "    }\n",
 380 |        "\n",
 381 |        "    .dataframe tbody tr th {\n",
 382 |        "        vertical-align: top;\n",
 383 |        "    }\n",
 384 |        "\n",
 385 |        "    .dataframe thead th {\n",
 386 |        "        text-align: right;\n",
 387 |        "    }\n",
 388 |        "</style>\n",
 389 |        "<table border=\"1\" class=\"dataframe\">\n",
 390 |        "  <thead>\n",
 391 |        "    <tr style=\"text-align: right;\">\n",
 392 |        "      <th></th>\n",
 393 |        "      <th>Flavor</th>\n",
 394 |        "      <th>Liked</th>\n",
 395 |        "      <th>Flavor Rating</th>\n",
 396 |        "      <th>Texture Rating</th>\n",
 397 |        "      <th>Total Rating</th>\n",
 398 |        "    </tr>\n",
 399 |        "    <tr>\n",
 400 |        "      <th>Base Flavor</th>\n",
 401 |        "      <th></th>\n",
 402 |        "      <th></th>\n",
 403 |        "      <th></th>\n",
 404 |        "      <th></th>\n",
 405 |        "      <th></th>\n",
 406 |        "    </tr>\n",
 407 |        "  </thead>\n",
 408 |        "  <tbody>\n",
 409 |        "    <tr>\n",
 410 |        "      <th>Chocolate</th>\n",
 411 |        "      <td>3</td>\n",
 412 |        "      <td>3</td>\n",
 413 |        "      <td>3</td>\n",
 414 |        "      <td>3</td>\n",
 415 |        "      <td>3</td>\n",
 416 |        "    </tr>\n",
 417 |        "    <tr>\n",
 418 |        "      <th>Vanilla</th>\n",
 419 |        "      <td>6</td>\n",
 420 |        "      <td>6</td>\n",
 421 |        "      <td>6</td>\n",
 422 |        "      <td>6</td>\n",
 423 |        "      <td>6</td>\n",
 424 |        "    </tr>\n",
 425 |        "  </tbody>\n",
 426 |        "</table>\n",
 427 |        "</div>"
 428 |       ],
 429 |       "text/plain": [
 430 |        "             Flavor  Liked  Flavor Rating  Texture Rating  Total Rating\n",
 431 |        "Base Flavor                                                            \n",
 432 |        "Chocolate         3      3              3               3             3\n",
 433 |        "Vanilla           6      6              6               6             6"
 434 |       ]
 435 |      },
 436 |      "execution_count": 7,
 437 |      "metadata": {},
 438 |      "output_type": "execute_result"
 439 |     }
 440 |    ],
 441 |    "source": [
 442 |     "df.groupby('Base Flavor').count()"
 443 |    ]
 444 |   },
 445 |   {
 446 |    "cell_type": "code",
 447 |    "execution_count": 8,
 448 |    "id": "275f0291",
 449 |    "metadata": {},
 450 |    "outputs": [
 451 |     {
 452 |      "data": {
 453 |       "text/html": [
 454 |        "<div>\n",
 455 |        "<style scoped>\n",
 456 |        "    .dataframe tbody tr th:only-of-type {\n",
 457 |        "        vertical-align: middle;\n",
 458 |        "    }\n",
 459 |        "\n",
 460 |        "    .dataframe tbody tr th {\n",
 461 |        "        vertical-align: top;\n",
 462 |        "    }\n",
 463 |        "\n",
 464 |        "    .dataframe thead th {\n",
 465 |        "        text-align: right;\n",
 466 |        "    }\n",
 467 |        "</style>\n",
 468 |        "<table border=\"1\" class=\"dataframe\">\n",
 469 |        "  <thead>\n",
 470 |        "    <tr style=\"text-align: right;\">\n",
 471 |        "      <th></th>\n",
 472 |        "      <th>Flavor</th>\n",
 473 |        "      <th>Liked</th>\n",
 474 |        "      <th>Flavor Rating</th>\n",
 475 |        "      <th>Texture Rating</th>\n",
 476 |        "      <th>Total Rating</th>\n",
 477 |        "    </tr>\n",
 478 |        "    <tr>\n",
 479 |        "      <th>Base Flavor</th>\n",
 480 |        "      <th></th>\n",
 481 |        "      <th></th>\n",
 482 |        "      <th></th>\n",
 483 |        "      <th></th>\n",
 484 |        "      <th></th>\n",
 485 |        "    </tr>\n",
 486 |        "  </thead>\n",
 487 |        "  <tbody>\n",
 488 |        "    <tr>\n",
 489 |        "      <th>Chocolate</th>\n",
 490 |        "      <td>Chocolate</td>\n",
 491 |        "      <td>Yes</td>\n",
 492 |        "      <td>8.2</td>\n",
 493 |        "      <td>7.0</td>\n",
 494 |        "      <td>15.2</td>\n",
 495 |        "    </tr>\n",
 496 |        "    <tr>\n",
 497 |        "      <th>Vanilla</th>\n",
 498 |        "      <td>Cake Batter</td>\n",
 499 |        "      <td>No</td>\n",
 500 |        "      <td>2.3</td>\n",
 501 |        "      <td>3.4</td>\n",
 502 |        "      <td>5.7</td>\n",
 503 |        "    </tr>\n",
 504 |        "  </tbody>\n",
 505 |        "</table>\n",
 506 |        "</div>"
 507 |       ],
 508 |       "text/plain": [
 509 |        "                  Flavor Liked  Flavor Rating  Texture Rating  Total Rating\n",
 510 |        "Base Flavor                                                                \n",
 511 |        "Chocolate      Chocolate   Yes            8.2             7.0          15.2\n",
 512 |        "Vanilla      Cake Batter    No            2.3             3.4           5.7"
 513 |       ]
 514 |      },
 515 |      "execution_count": 8,
 516 |      "metadata": {},
 517 |      "output_type": "execute_result"
 518 |     }
 519 |    ],
 520 |    "source": [
 521 |     "df.groupby('Base Flavor').min()"
 522 |    ]
 523 |   },
 524 |   {
 525 |    "cell_type": "code",
 526 |    "execution_count": 9,
 527 |    "id": "45ed0109",
 528 |    "metadata": {},
 529 |    "outputs": [
 530 |     {
 531 |      "data": {
 532 |       "text/html": [
 533 |        "<div>\n",
 534 |        "<style scoped>\n",
 535 |        "    .dataframe tbody tr th:only-of-type {\n",
 536 |        "        vertical-align: middle;\n",
 537 |        "    }\n",
 538 |        "\n",
 539 |        "    .dataframe tbody tr th {\n",
 540 |        "        vertical-align: top;\n",
 541 |        "    }\n",
 542 |        "\n",
 543 |        "    .dataframe thead th {\n",
 544 |        "        text-align: right;\n",
 545 |        "    }\n",
 546 |        "</style>\n",
 547 |        "<table border=\"1\" class=\"dataframe\">\n",
 548 |        "  <thead>\n",
 549 |        "    <tr style=\"text-align: right;\">\n",
 550 |        "      <th></th>\n",
 551 |        "      <th>Flavor</th>\n",
 552 |        "      <th>Liked</th>\n",
 553 |        "      <th>Flavor Rating</th>\n",
 554 |        "      <th>Texture Rating</th>\n",
 555 |        "      <th>Total Rating</th>\n",
 556 |        "    </tr>\n",
 557 |        "    <tr>\n",
 558 |        "      <th>Base Flavor</th>\n",
 559 |        "      <th></th>\n",
 560 |        "      <th></th>\n",
 561 |        "      <th></th>\n",
 562 |        "      <th></th>\n",
 563 |        "      <th></th>\n",
 564 |        "    </tr>\n",
 565 |        "  </thead>\n",
 566 |        "  <tbody>\n",
 567 |        "    <tr>\n",
 568 |        "      <th>Chocolate</th>\n",
 569 |        "      <td>Rocky Road</td>\n",
 570 |        "      <td>Yes</td>\n",
 571 |        "      <td>8.8</td>\n",
 572 |        "      <td>7.6</td>\n",
 573 |        "      <td>16.6</td>\n",
 574 |        "    </tr>\n",
 575 |        "    <tr>\n",
 576 |        "      <th>Vanilla</th>\n",
 577 |        "      <td>Vanilla</td>\n",
 578 |        "      <td>Yes</td>\n",
 579 |        "      <td>10.0</td>\n",
 580 |        "      <td>8.0</td>\n",
 581 |        "      <td>18.0</td>\n",
 582 |        "    </tr>\n",
 583 |        "  </tbody>\n",
 584 |        "</table>\n",
 585 |        "</div>"
 586 |       ],
 587 |       "text/plain": [
 588 |        "                 Flavor Liked  Flavor Rating  Texture Rating  Total Rating\n",
 589 |        "Base Flavor                                                               \n",
 590 |        "Chocolate    Rocky Road   Yes            8.8             7.6          16.6\n",
 591 |        "Vanilla         Vanilla   Yes           10.0             8.0          18.0"
 592 |       ]
 593 |      },
 594 |      "execution_count": 9,
 595 |      "metadata": {},
 596 |      "output_type": "execute_result"
 597 |     }
 598 |    ],
 599 |    "source": [
 600 |     "df.groupby('Base Flavor').max()"
 601 |    ]
 602 |   },
 603 |   {
 604 |    "cell_type": "code",
 605 |    "execution_count": 10,
 606 |    "id": "7238b0c5",
 607 |    "metadata": {},
 608 |    "outputs": [
 609 |     {
 610 |      "name": "stderr",
 611 |      "output_type": "stream",
 612 |      "text": [
 613 |       "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_23396\\1221781544.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
 614 |       "  df.groupby('Base Flavor').sum()\n"
 615 |      ]
 616 |     },
 617 |     {
 618 |      "data": {
 619 |       "text/html": [
 620 |        "<div>\n",
 621 |        "<style scoped>\n",
 622 |        "    .dataframe tbody tr th:only-of-type {\n",
 623 |        "        vertical-align: middle;\n",
 624 |        "    }\n",
 625 |        "\n",
 626 |        "    .dataframe tbody tr th {\n",
 627 |        "        vertical-align: top;\n",
 628 |        "    }\n",
 629 |        "\n",
 630 |        "    .dataframe thead th {\n",
 631 |        "        text-align: right;\n",
 632 |        "    }\n",
 633 |        "</style>\n",
 634 |        "<table border=\"1\" class=\"dataframe\">\n",
 635 |        "  <thead>\n",
 636 |        "    <tr style=\"text-align: right;\">\n",
 637 |        "      <th></th>\n",
 638 |        "      <th>Flavor Rating</th>\n",
 639 |        "      <th>Texture Rating</th>\n",
 640 |        "      <th>Total Rating</th>\n",
 641 |        "    </tr>\n",
 642 |        "    <tr>\n",
 643 |        "      <th>Base Flavor</th>\n",
 644 |        "      <th></th>\n",
 645 |        "      <th></th>\n",
 646 |        "      <th></th>\n",
 647 |        "    </tr>\n",
 648 |        "  </thead>\n",
 649 |        "  <tbody>\n",
 650 |        "    <tr>\n",
 651 |        "      <th>Chocolate</th>\n",
 652 |        "      <td>25.2</td>\n",
 653 |        "      <td>21.7</td>\n",
 654 |        "      <td>47.1</td>\n",
 655 |        "    </tr>\n",
 656 |        "    <tr>\n",
 657 |        "      <th>Vanilla</th>\n",
 658 |        "      <td>34.2</td>\n",
 659 |        "      <td>33.9</td>\n",
 660 |        "      <td>68.1</td>\n",
 661 |        "    </tr>\n",
 662 |        "  </tbody>\n",
 663 |        "</table>\n",
 664 |        "</div>"
 665 |       ],
 666 |       "text/plain": [
 667 |        "             Flavor Rating  Texture Rating  Total Rating\n",
 668 |        "Base Flavor                                             \n",
 669 |        "Chocolate             25.2            21.7          47.1\n",
 670 |        "Vanilla               34.2            33.9          68.1"
 671 |       ]
 672 |      },
 673 |      "execution_count": 10,
 674 |      "metadata": {},
 675 |      "output_type": "execute_result"
 676 |     }
 677 |    ],
 678 |    "source": [
 679 |     "df.groupby('Base Flavor').sum()"
 680 |    ]
 681 |   },
 682 |   {
 683 |    "cell_type": "code",
 684 |    "execution_count": 11,
 685 |    "id": "ff7756a2",
 686 |    "metadata": {},
 687 |    "outputs": [
 688 |     {
 689 |      "data": {
 690 |       "text/html": [
 691 |        "<div>\n",
 692 |        "<style scoped>\n",
 693 |        "    .dataframe tbody tr th:only-of-type {\n",
 694 |        "        vertical-align: middle;\n",
 695 |        "    }\n",
 696 |        "\n",
 697 |        "    .dataframe tbody tr th {\n",
 698 |        "        vertical-align: top;\n",
 699 |        "    }\n",
 700 |        "\n",
 701 |        "    .dataframe thead tr th {\n",
 702 |        "        text-align: left;\n",
 703 |        "    }\n",
 704 |        "\n",
 705 |        "    .dataframe thead tr:last-of-type th {\n",
 706 |        "        text-align: right;\n",
 707 |        "    }\n",
 708 |        "</style>\n",
 709 |        "<table border=\"1\" class=\"dataframe\">\n",
 710 |        "  <thead>\n",
 711 |        "    <tr>\n",
 712 |        "      <th></th>\n",
 713 |        "      <th colspan=\"4\" halign=\"left\">Flavor Rating</th>\n",
 714 |        "      <th colspan=\"4\" halign=\"left\">Texture Rating</th>\n",
 715 |        "    </tr>\n",
 716 |        "    <tr>\n",
 717 |        "      <th></th>\n",
 718 |        "      <th>mean</th>\n",
 719 |        "      <th>max</th>\n",
 720 |        "      <th>count</th>\n",
 721 |        "      <th>sum</th>\n",
 722 |        "      <th>mean</th>\n",
 723 |        "      <th>max</th>\n",
 724 |        "      <th>count</th>\n",
 725 |        "      <th>sum</th>\n",
 726 |        "    </tr>\n",
 727 |        "    <tr>\n",
 728 |        "      <th>Base Flavor</th>\n",
 729 |        "      <th></th>\n",
 730 |        "      <th></th>\n",
 731 |        "      <th></th>\n",
 732 |        "      <th></th>\n",
 733 |        "      <th></th>\n",
 734 |        "      <th></th>\n",
 735 |        "      <th></th>\n",
 736 |        "      <th></th>\n",
 737 |        "    </tr>\n",
 738 |        "  </thead>\n",
 739 |        "  <tbody>\n",
 740 |        "    <tr>\n",
 741 |        "      <th>Chocolate</th>\n",
 742 |        "      <td>8.4</td>\n",
 743 |        "      <td>8.8</td>\n",
 744 |        "      <td>3</td>\n",
 745 |        "      <td>25.2</td>\n",
 746 |        "      <td>7.233333</td>\n",
 747 |        "      <td>7.6</td>\n",
 748 |        "      <td>3</td>\n",
 749 |        "      <td>21.7</td>\n",
 750 |        "    </tr>\n",
 751 |        "    <tr>\n",
 752 |        "      <th>Vanilla</th>\n",
 753 |        "      <td>5.7</td>\n",
 754 |        "      <td>10.0</td>\n",
 755 |        "      <td>6</td>\n",
 756 |        "      <td>34.2</td>\n",
 757 |        "      <td>5.650000</td>\n",
 758 |        "      <td>8.0</td>\n",
 759 |        "      <td>6</td>\n",
 760 |        "      <td>33.9</td>\n",
 761 |        "    </tr>\n",
 762 |        "  </tbody>\n",
 763 |        "</table>\n",
 764 |        "</div>"
 765 |       ],
 766 |       "text/plain": [
 767 |        "            Flavor Rating                   Texture Rating                 \n",
 768 |        "                     mean   max count   sum           mean  max count   sum\n",
 769 |        "Base Flavor                                                                \n",
 770 |        "Chocolate             8.4   8.8     3  25.2       7.233333  7.6     3  21.7\n",
 771 |        "Vanilla               5.7  10.0     6  34.2       5.650000  8.0     6  33.9"
 772 |       ]
 773 |      },
 774 |      "execution_count": 11,
 775 |      "metadata": {},
 776 |      "output_type": "execute_result"
 777 |     }
 778 |    ],
 779 |    "source": [
 780 |     "df.groupby('Base Flavor').agg({'Flavor Rating': ['mean','max','count','sum'], 'Texture Rating':['mean','max','count','sum'] })"
 781 |    ]
 782 |   },
 783 |   {
 784 |    "cell_type": "code",
 785 |    "execution_count": 12,
 786 |    "id": "442da699",
 787 |    "metadata": {},
 788 |    "outputs": [
 789 |     {
 790 |      "name": "stderr",
 791 |      "output_type": "stream",
 792 |      "text": [
 793 |       "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_23396\\2854014148.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
 794 |       "  df.groupby(['Base Flavor','Liked']).mean()\n"
 795 |      ]
 796 |     },
 797 |     {
 798 |      "data": {
 799 |       "text/html": [
 800 |        "<div>\n",
 801 |        "<style scoped>\n",
 802 |        "    .dataframe tbody tr th:only-of-type {\n",
 803 |        "        vertical-align: middle;\n",
 804 |        "    }\n",
 805 |        "\n",
 806 |        "    .dataframe tbody tr th {\n",
 807 |        "        vertical-align: top;\n",
 808 |        "    }\n",
 809 |        "\n",
 810 |        "    .dataframe thead th {\n",
 811 |        "        text-align: right;\n",
 812 |        "    }\n",
 813 |        "</style>\n",
 814 |        "<table border=\"1\" class=\"dataframe\">\n",
 815 |        "  <thead>\n",
 816 |        "    <tr style=\"text-align: right;\">\n",
 817 |        "      <th></th>\n",
 818 |        "      <th></th>\n",
 819 |        "      <th>Flavor Rating</th>\n",
 820 |        "      <th>Texture Rating</th>\n",
 821 |        "      <th>Total Rating</th>\n",
 822 |        "    </tr>\n",
 823 |        "    <tr>\n",
 824 |        "      <th>Base Flavor</th>\n",
 825 |        "      <th>Liked</th>\n",
 826 |        "      <th></th>\n",
 827 |        "      <th></th>\n",
 828 |        "      <th></th>\n",
 829 |        "    </tr>\n",
 830 |        "  </thead>\n",
 831 |        "  <tbody>\n",
 832 |        "    <tr>\n",
 833 |        "      <th>Chocolate</th>\n",
 834 |        "      <th>Yes</th>\n",
 835 |        "      <td>8.4</td>\n",
 836 |        "      <td>7.233333</td>\n",
 837 |        "      <td>15.700000</td>\n",
 838 |        "    </tr>\n",
 839 |        "    <tr>\n",
 840 |        "      <th rowspan=\"2\" valign=\"top\">Vanilla</th>\n",
 841 |        "      <th>No</th>\n",
 842 |        "      <td>3.6</td>\n",
 843 |        "      <td>4.466667</td>\n",
 844 |        "      <td>8.066667</td>\n",
 845 |        "    </tr>\n",
 846 |        "    <tr>\n",
 847 |        "      <th>Yes</th>\n",
 848 |        "      <td>7.8</td>\n",
 849 |        "      <td>6.833333</td>\n",
 850 |        "      <td>14.633333</td>\n",
 851 |        "    </tr>\n",
 852 |        "  </tbody>\n",
 853 |        "</table>\n",
 854 |        "</div>"
 855 |       ],
 856 |       "text/plain": [
 857 |        "                   Flavor Rating  Texture Rating  Total Rating\n",
 858 |        "Base Flavor Liked                                             \n",
 859 |        "Chocolate   Yes              8.4        7.233333     15.700000\n",
 860 |        "Vanilla     No               3.6        4.466667      8.066667\n",
 861 |        "            Yes              7.8        6.833333     14.633333"
 862 |       ]
 863 |      },
 864 |      "execution_count": 12,
 865 |      "metadata": {},
 866 |      "output_type": "execute_result"
 867 |     }
 868 |    ],
 869 |    "source": [
 870 |     "df.groupby(['Base Flavor','Liked']).mean()"
 871 |    ]
 872 |   },
 873 |   {
 874 |    "cell_type": "code",
 875 |    "execution_count": 13,
 876 |    "id": "a33c6967",
 877 |    "metadata": {},
 878 |    "outputs": [
 879 |     {
 880 |      "data": {
 881 |       "text/html": [
 882 |        "<div>\n",
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 884 |        "    .dataframe tbody tr th:only-of-type {\n",
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 887 |        "\n",
 888 |        "    .dataframe tbody tr th {\n",
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 890 |        "    }\n",
 891 |        "\n",
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 894 |        "    }\n",
 895 |        "\n",
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 897 |        "        text-align: right;\n",
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 899 |        "</style>\n",
 900 |        "<table border=\"1\" class=\"dataframe\">\n",
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 903 |        "      <th></th>\n",
 904 |        "      <th></th>\n",
 905 |        "      <th colspan=\"5\" halign=\"left\">Flavor Rating</th>\n",
 906 |        "    </tr>\n",
 907 |        "    <tr>\n",
 908 |        "      <th></th>\n",
 909 |        "      <th></th>\n",
 910 |        "      <th>mean</th>\n",
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 919 |        "      <th></th>\n",
 920 |        "      <th></th>\n",
 921 |        "      <th></th>\n",
 922 |        "      <th></th>\n",
 923 |        "      <th></th>\n",
 924 |        "    </tr>\n",
 925 |        "  </thead>\n",
 926 |        "  <tbody>\n",
 927 |        "    <tr>\n",
 928 |        "      <th>Chocolate</th>\n",
 929 |        "      <th>Yes</th>\n",
 930 |        "      <td>8.4</td>\n",
 931 |        "      <td>8.8</td>\n",
 932 |        "      <td>8.2</td>\n",
 933 |        "      <td>3</td>\n",
 934 |        "      <td>25.2</td>\n",
 935 |        "    </tr>\n",
 936 |        "    <tr>\n",
 937 |        "      <th rowspan=\"2\" valign=\"top\">Vanilla</th>\n",
 938 |        "      <th>No</th>\n",
 939 |        "      <td>3.6</td>\n",
 940 |        "      <td>4.7</td>\n",
 941 |        "      <td>2.3</td>\n",
 942 |        "      <td>3</td>\n",
 943 |        "      <td>10.8</td>\n",
 944 |        "    </tr>\n",
 945 |        "    <tr>\n",
 946 |        "      <th>Yes</th>\n",
 947 |        "      <td>7.8</td>\n",
 948 |        "      <td>10.0</td>\n",
 949 |        "      <td>6.5</td>\n",
 950 |        "      <td>3</td>\n",
 951 |        "      <td>23.4</td>\n",
 952 |        "    </tr>\n",
 953 |        "  </tbody>\n",
 954 |        "</table>\n",
 955 |        "</div>"
 956 |       ],
 957 |       "text/plain": [
 958 |        "                  Flavor Rating                       \n",
 959 |        "                           mean   max  min count   sum\n",
 960 |        "Base Flavor Liked                                     \n",
 961 |        "Chocolate   Yes             8.4   8.8  8.2     3  25.2\n",
 962 |        "Vanilla     No              3.6   4.7  2.3     3  10.8\n",
 963 |        "            Yes             7.8  10.0  6.5     3  23.4"
 964 |       ]
 965 |      },
 966 |      "execution_count": 13,
 967 |      "metadata": {},
 968 |      "output_type": "execute_result"
 969 |     }
 970 |    ],
 971 |    "source": [
 972 |     "df.groupby(['Base Flavor','Liked']).agg({'Flavor Rating': ['mean','max','min','count','sum']})"
 973 |    ]
 974 |   },
 975 |   {
 976 |    "cell_type": "code",
 977 |    "execution_count": 14,
 978 |    "id": "9a7cdd4f",
 979 |    "metadata": {},
 980 |    "outputs": [
 981 |     {
 982 |      "data": {
 983 |       "text/html": [
 984 |        "<div>\n",
 985 |        "<style scoped>\n",
 986 |        "    .dataframe tbody tr th:only-of-type {\n",
 987 |        "        vertical-align: middle;\n",
 988 |        "    }\n",
 989 |        "\n",
 990 |        "    .dataframe tbody tr th {\n",
 991 |        "        vertical-align: top;\n",
 992 |        "    }\n",
 993 |        "\n",
 994 |        "    .dataframe thead tr th {\n",
 995 |        "        text-align: left;\n",
 996 |        "    }\n",
 997 |        "\n",
 998 |        "    .dataframe thead tr:last-of-type th {\n",
 999 |        "        text-align: right;\n",
1000 |        "    }\n",
1001 |        "</style>\n",
1002 |        "<table border=\"1\" class=\"dataframe\">\n",
1003 |        "  <thead>\n",
1004 |        "    <tr>\n",
1005 |        "      <th></th>\n",
1006 |        "      <th colspan=\"8\" halign=\"left\">Flavor Rating</th>\n",
1007 |        "      <th colspan=\"5\" halign=\"left\">Texture Rating</th>\n",
1008 |        "      <th colspan=\"8\" halign=\"left\">Total Rating</th>\n",
1009 |        "    </tr>\n",
1010 |        "    <tr>\n",
1011 |        "      <th></th>\n",
1012 |        "      <th>count</th>\n",
1013 |        "      <th>mean</th>\n",
1014 |        "      <th>std</th>\n",
1015 |        "      <th>min</th>\n",
1016 |        "      <th>25%</th>\n",
1017 |        "      <th>50%</th>\n",
1018 |        "      <th>75%</th>\n",
1019 |        "      <th>max</th>\n",
1020 |        "      <th>count</th>\n",
1021 |        "      <th>mean</th>\n",
1022 |        "      <th>...</th>\n",
1023 |        "      <th>75%</th>\n",
1024 |        "      <th>max</th>\n",
1025 |        "      <th>count</th>\n",
1026 |        "      <th>mean</th>\n",
1027 |        "      <th>std</th>\n",
1028 |        "      <th>min</th>\n",
1029 |        "      <th>25%</th>\n",
1030 |        "      <th>50%</th>\n",
1031 |        "      <th>75%</th>\n",
1032 |        "      <th>max</th>\n",
1033 |        "    </tr>\n",
1034 |        "    <tr>\n",
1035 |        "      <th>Base Flavor</th>\n",
1036 |        "      <th></th>\n",
1037 |        "      <th></th>\n",
1038 |        "      <th></th>\n",
1039 |        "      <th></th>\n",
1040 |        "      <th></th>\n",
1041 |        "      <th></th>\n",
1042 |        "      <th></th>\n",
1043 |        "      <th></th>\n",
1044 |        "      <th></th>\n",
1045 |        "      <th></th>\n",
1046 |        "      <th></th>\n",
1047 |        "      <th></th>\n",
1048 |        "      <th></th>\n",
1049 |        "      <th></th>\n",
1050 |        "      <th></th>\n",
1051 |        "      <th></th>\n",
1052 |        "      <th></th>\n",
1053 |        "      <th></th>\n",
1054 |        "      <th></th>\n",
1055 |        "      <th></th>\n",
1056 |        "      <th></th>\n",
1057 |        "    </tr>\n",
1058 |        "  </thead>\n",
1059 |        "  <tbody>\n",
1060 |        "    <tr>\n",
1061 |        "      <th>Chocolate</th>\n",
1062 |        "      <td>3.0</td>\n",
1063 |        "      <td>8.4</td>\n",
1064 |        "      <td>0.346410</td>\n",
1065 |        "      <td>8.2</td>\n",
1066 |        "      <td>8.200</td>\n",
1067 |        "      <td>8.2</td>\n",
1068 |        "      <td>8.5</td>\n",
1069 |        "      <td>8.8</td>\n",
1070 |        "      <td>3.0</td>\n",
1071 |        "      <td>7.233333</td>\n",
1072 |        "      <td>...</td>\n",
1073 |        "      <td>7.350</td>\n",
1074 |        "      <td>7.6</td>\n",
1075 |        "      <td>3.0</td>\n",
1076 |        "      <td>15.70</td>\n",
1077 |        "      <td>0.781025</td>\n",
1078 |        "      <td>15.2</td>\n",
1079 |        "      <td>15.250</td>\n",
1080 |        "      <td>15.3</td>\n",
1081 |        "      <td>15.950</td>\n",
1082 |        "      <td>16.6</td>\n",
1083 |        "    </tr>\n",
1084 |        "    <tr>\n",
1085 |        "      <th>Vanilla</th>\n",
1086 |        "      <td>6.0</td>\n",
1087 |        "      <td>5.7</td>\n",
1088 |        "      <td>2.710719</td>\n",
1089 |        "      <td>2.3</td>\n",
1090 |        "      <td>4.025</td>\n",
1091 |        "      <td>5.6</td>\n",
1092 |        "      <td>6.8</td>\n",
1093 |        "      <td>10.0</td>\n",
1094 |        "      <td>6.0</td>\n",
1095 |        "      <td>5.650000</td>\n",
1096 |        "      <td>...</td>\n",
1097 |        "      <td>6.375</td>\n",
1098 |        "      <td>8.0</td>\n",
1099 |        "      <td>6.0</td>\n",
1100 |        "      <td>11.35</td>\n",
1101 |        "      <td>4.263684</td>\n",
1102 |        "      <td>5.7</td>\n",
1103 |        "      <td>9.025</td>\n",
1104 |        "      <td>11.1</td>\n",
1105 |        "      <td>13.175</td>\n",
1106 |        "      <td>18.0</td>\n",
1107 |        "    </tr>\n",
1108 |        "  </tbody>\n",
1109 |        "</table>\n",
1110 |        "<p>2 rows × 24 columns</p>\n",
1111 |        "</div>"
1112 |       ],
1113 |       "text/plain": [
1114 |        "            Flavor Rating                                             \\\n",
1115 |        "                    count mean       std  min    25%  50%  75%   max   \n",
1116 |        "Base Flavor                                                            \n",
1117 |        "Chocolate             3.0  8.4  0.346410  8.2  8.200  8.2  8.5   8.8   \n",
1118 |        "Vanilla               6.0  5.7  2.710719  2.3  4.025  5.6  6.8  10.0   \n",
1119 |        "\n",
1120 |        "            Texture Rating            ...             Total Rating         \\\n",
1121 |        "                     count      mean  ...    75%  max        count   mean   \n",
1122 |        "Base Flavor                           ...                                   \n",
1123 |        "Chocolate              3.0  7.233333  ...  7.350  7.6          3.0  15.70   \n",
1124 |        "Vanilla                6.0  5.650000  ...  6.375  8.0          6.0  11.35   \n",
1125 |        "\n",
1126 |        "                                                         \n",
1127 |        "                  std   min     25%   50%     75%   max  \n",
1128 |        "Base Flavor                                              \n",
1129 |        "Chocolate    0.781025  15.2  15.250  15.3  15.950  16.6  \n",
1130 |        "Vanilla      4.263684   5.7   9.025  11.1  13.175  18.0  \n",
1131 |        "\n",
1132 |        "[2 rows x 24 columns]"
1133 |       ]
1134 |      },
1135 |      "execution_count": 14,
1136 |      "metadata": {},
1137 |      "output_type": "execute_result"
1138 |     }
1139 |    ],
1140 |    "source": [
1141 |     "df.groupby('Base Flavor').describe()"
1142 |    ]
1143 |   },
1144 |   {
1145 |    "cell_type": "code",
1146 |    "execution_count": 15,
1147 |    "id": "09a5fbc5",
1148 |    "metadata": {},
1149 |    "outputs": [
1150 |     {
1151 |      "name": "stderr",
1152 |      "output_type": "stream",
1153 |      "text": [
1154 |       "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_23396\\2855992873.py:1: FutureWarning: ['Flavor', 'Base Flavor', 'Liked'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
1155 |       "  df.aggregate(['mean'])\n"
1156 |      ]
1157 |     },
1158 |     {
1159 |      "data": {
1160 |       "text/html": [
1161 |        "<div>\n",
1162 |        "<style scoped>\n",
1163 |        "    .dataframe tbody tr th:only-of-type {\n",
1164 |        "        vertical-align: middle;\n",
1165 |        "    }\n",
1166 |        "\n",
1167 |        "    .dataframe tbody tr th {\n",
1168 |        "        vertical-align: top;\n",
1169 |        "    }\n",
1170 |        "\n",
1171 |        "    .dataframe thead th {\n",
1172 |        "        text-align: right;\n",
1173 |        "    }\n",
1174 |        "</style>\n",
1175 |        "<table border=\"1\" class=\"dataframe\">\n",
1176 |        "  <thead>\n",
1177 |        "    <tr style=\"text-align: right;\">\n",
1178 |        "      <th></th>\n",
1179 |        "      <th>Flavor Rating</th>\n",
1180 |        "      <th>Texture Rating</th>\n",
1181 |        "      <th>Total Rating</th>\n",
1182 |        "    </tr>\n",
1183 |        "  </thead>\n",
1184 |        "  <tbody>\n",
1185 |        "    <tr>\n",
1186 |        "      <th>mean</th>\n",
1187 |        "      <td>6.6</td>\n",
1188 |        "      <td>6.177778</td>\n",
1189 |        "      <td>12.8</td>\n",
1190 |        "    </tr>\n",
1191 |        "  </tbody>\n",
1192 |        "</table>\n",
1193 |        "</div>"
1194 |       ],
1195 |       "text/plain": [
1196 |        "      Flavor Rating  Texture Rating  Total Rating\n",
1197 |        "mean            6.6        6.177778          12.8"
1198 |       ]
1199 |      },
1200 |      "execution_count": 15,
1201 |      "metadata": {},
1202 |      "output_type": "execute_result"
1203 |     }
1204 |    ],
1205 |    "source": [
1206 |     "df.aggregate(['mean'])"
1207 |    ]
1208 |   },
1209 |   {
1210 |    "cell_type": "code",
1211 |    "execution_count": null,
1212 |    "id": "9a86a6ca",
1213 |    "metadata": {},
1214 |    "outputs": [],
1215 |    "source": []
1216 |   },
1217 |   {
1218 |    "cell_type": "code",
1219 |    "execution_count": null,
1220 |    "id": "dfcc0745",
1221 |    "metadata": {},
1222 |    "outputs": [],
1223 |    "source": []
1224 |   },
1225 |   {
1226 |    "cell_type": "code",
1227 |    "execution_count": null,
1228 |    "id": "2a7b1762",
1229 |    "metadata": {},
1230 |    "outputs": [],
1231 |    "source": []
1232 |   }
1233 |  ],
1234 |  "metadata": {
1235 |   "kernelspec": {
1236 |    "display_name": "Python 3 (ipykernel)",
1237 |    "language": "python",
1238 |    "name": "python3"
1239 |   },
1240 |   "language_info": {
1241 |    "codemirror_mode": {
1242 |     "name": "ipython",
1243 |     "version": 3
1244 |    },
1245 |    "file_extension": ".py",
1246 |    "mimetype": "text/x-python",
1247 |    "name": "python",
1248 |    "nbconvert_exporter": "python",
1249 |    "pygments_lexer": "ipython3",
1250 |    "version": "3.10.9"
1251 |   }
1252 |  },
1253 |  "nbformat": 4,
1254 |  "nbformat_minor": 5
1255 | }
1256 | 


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/handling missing data - Jupyter Notebook.pdf:
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/homeprices.csv:
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1 | area,price
2 | 2600,550000
3 | 3000,565000
4 | 3200,610000
5 | 3600,680000
6 | 4000,725000
7 | 


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/income.csv:
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 1 | Name,Age,Income(Rs.)
 2 | barjraj,27,70000
 3 | ramdin verma,29,90000
 4 | sharat chandran,29,61000
 5 | birender mandal,28,60000
 6 | amit,42,150000
 7 | kushal,39,155000
 8 | kasid,41,160000
 9 | shiv prakash,38,162000
10 | vikram singh,36,156000
11 | sanjay,35,130000
12 | abhi,37,137000
13 | ram dutt gupta,26,45000
14 | khadak singh,27,48000
15 | gurmit singh,28,51000
16 | chanderpal,29,49500
17 | aman,32,53000
18 | khursid,40,65000
19 | rajeev,41,63000
20 | durgesh,43,64000
21 | nahar singh,39,80000
22 | ram kumar,41,82000
23 | sunder paal,39,58000
24 | maansingh aswal,,
25 | 


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/min -max-Normalization - Jupyter Notebook.pdf:
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/multiple .homeprices.csv:
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1 | area,bedrooms,age,price
2 | 2600,3,20,550000
3 | 3000,4,15,565000
4 | 3200,,18,610000
5 | 3600,3,30,595000
6 | 4000,5,8,760000
7 | 4100,6,8,810000
8 | 


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/naive_bayes - Jupyter Notebook.pdf:
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/salaries.csv:
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 1 | company,job,degree,salary_more_then_100k
 2 | google,sales executive,bachelors,0
 3 | google,sales executive,masters,0
 4 | google,business manager,bachelors,1
 5 | google,business manager,masters,1
 6 | google,computer programmer,bachelors,0
 7 | google,computer programmer,masters,1
 8 | abc pharma,sales executive,masters,0
 9 | abc pharma,computer programmer,bachelors,0
10 | abc pharma,business manager,bachelors,0
11 | abc pharma,business manager,masters,1
12 | facebook,sales executive,bachelors,1
13 | facebook,sales executive,masters,1
14 | facebook,business manager,bachelors,1
15 | facebook,business manager,masters,1
16 | facebook,computer programmer,bachelors,1
17 | facebook,computer programmer,masters,1


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/training-testing split - Jupyter Notebook.pdf:
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