Upload numpy_practice_DataEngg.ipynb
#1
by
Azarthehulk
- opened
- numpy_practice_DataEngg.ipynb +1225 -0
numpy_practice_DataEngg.ipynb
ADDED
@@ -0,0 +1,1225 @@
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1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 1,
|
6 |
+
"id": "5d2d2387",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [],
|
9 |
+
"source": [
|
10 |
+
"import numpy as np"
|
11 |
+
]
|
12 |
+
},
|
13 |
+
{
|
14 |
+
"cell_type": "code",
|
15 |
+
"execution_count": 8,
|
16 |
+
"id": "0c5af0b5",
|
17 |
+
"metadata": {},
|
18 |
+
"outputs": [],
|
19 |
+
"source": [
|
20 |
+
"arr1=np.array((1,2,4,7,8,9,4,5,6,7,9))"
|
21 |
+
]
|
22 |
+
},
|
23 |
+
{
|
24 |
+
"cell_type": "code",
|
25 |
+
"execution_count": 9,
|
26 |
+
"id": "2e0d0565",
|
27 |
+
"metadata": {},
|
28 |
+
"outputs": [
|
29 |
+
{
|
30 |
+
"name": "stdout",
|
31 |
+
"output_type": "stream",
|
32 |
+
"text": [
|
33 |
+
"single dimentional: [1 2 4 7 8 9 4 5 6 7 9]\n"
|
34 |
+
]
|
35 |
+
}
|
36 |
+
],
|
37 |
+
"source": [
|
38 |
+
"print(\"single dimentional:\",arr1)"
|
39 |
+
]
|
40 |
+
},
|
41 |
+
{
|
42 |
+
"cell_type": "code",
|
43 |
+
"execution_count": 6,
|
44 |
+
"id": "51a43691",
|
45 |
+
"metadata": {},
|
46 |
+
"outputs": [
|
47 |
+
{
|
48 |
+
"data": {
|
49 |
+
"text/plain": [
|
50 |
+
"dtype('int64')"
|
51 |
+
]
|
52 |
+
},
|
53 |
+
"execution_count": 6,
|
54 |
+
"metadata": {},
|
55 |
+
"output_type": "execute_result"
|
56 |
+
}
|
57 |
+
],
|
58 |
+
"source": [
|
59 |
+
"arr1.dtype"
|
60 |
+
]
|
61 |
+
},
|
62 |
+
{
|
63 |
+
"cell_type": "code",
|
64 |
+
"execution_count": 11,
|
65 |
+
"id": "f6520b45",
|
66 |
+
"metadata": {},
|
67 |
+
"outputs": [
|
68 |
+
{
|
69 |
+
"name": "stdout",
|
70 |
+
"output_type": "stream",
|
71 |
+
"text": [
|
72 |
+
"2-d arrays:\n",
|
73 |
+
" [[ 10 20 30 40]\n",
|
74 |
+
" [100 200 300 400]]\n"
|
75 |
+
]
|
76 |
+
}
|
77 |
+
],
|
78 |
+
"source": [
|
79 |
+
"arr2=np.array([[10,20,30,40],[100,200,300,400]])\n",
|
80 |
+
"print(\"2-d arrays:\\n\",arr2)"
|
81 |
+
]
|
82 |
+
},
|
83 |
+
{
|
84 |
+
"cell_type": "code",
|
85 |
+
"execution_count": 12,
|
86 |
+
"id": "319161ca",
|
87 |
+
"metadata": {},
|
88 |
+
"outputs": [
|
89 |
+
{
|
90 |
+
"name": "stdout",
|
91 |
+
"output_type": "stream",
|
92 |
+
"text": [
|
93 |
+
"arr1: 1\n",
|
94 |
+
"arr2: 2\n"
|
95 |
+
]
|
96 |
+
}
|
97 |
+
],
|
98 |
+
"source": [
|
99 |
+
"#checking thair dimensions:\n",
|
100 |
+
"print(\"arr1:\",arr1.ndim)\n",
|
101 |
+
"print(\"arr2:\",arr2.ndim)"
|
102 |
+
]
|
103 |
+
},
|
104 |
+
{
|
105 |
+
"cell_type": "code",
|
106 |
+
"execution_count": 15,
|
107 |
+
"id": "c71c8ff9",
|
108 |
+
"metadata": {},
|
109 |
+
"outputs": [
|
110 |
+
{
|
111 |
+
"name": "stdout",
|
112 |
+
"output_type": "stream",
|
113 |
+
"text": [
|
114 |
+
"3-d array:\n",
|
115 |
+
" [[[ 1 2 3 4]\n",
|
116 |
+
" [ 10 20 30 40]\n",
|
117 |
+
" [100 200 300 400]]]\n",
|
118 |
+
"arr3: 3\n"
|
119 |
+
]
|
120 |
+
}
|
121 |
+
],
|
122 |
+
"source": [
|
123 |
+
"arr3=np.array([[[1,2,3,4],[10,20,30,40],[100,200,300,400]]])\n",
|
124 |
+
"print(\"3-d array:\\n\",arr3)\n",
|
125 |
+
"print(\"arr3:\",arr3.ndim)"
|
126 |
+
]
|
127 |
+
},
|
128 |
+
{
|
129 |
+
"cell_type": "code",
|
130 |
+
"execution_count": 16,
|
131 |
+
"id": "a426eb80",
|
132 |
+
"metadata": {},
|
133 |
+
"outputs": [],
|
134 |
+
"source": [
|
135 |
+
"#for casual cheking with data frames :\n",
|
136 |
+
"import pandas as pd"
|
137 |
+
]
|
138 |
+
},
|
139 |
+
{
|
140 |
+
"cell_type": "code",
|
141 |
+
"execution_count": 25,
|
142 |
+
"id": "3c61781d",
|
143 |
+
"metadata": {},
|
144 |
+
"outputs": [
|
145 |
+
{
|
146 |
+
"name": "stdout",
|
147 |
+
"output_type": "stream",
|
148 |
+
"text": [
|
149 |
+
"0 1\n",
|
150 |
+
"1 2\n",
|
151 |
+
"2 4\n",
|
152 |
+
"3 7\n",
|
153 |
+
"4 8\n",
|
154 |
+
"5 9\n",
|
155 |
+
"6 4\n",
|
156 |
+
"7 5\n",
|
157 |
+
"8 6\n",
|
158 |
+
"9 7\n",
|
159 |
+
"10 9\n",
|
160 |
+
"dtype: int64\n"
|
161 |
+
]
|
162 |
+
}
|
163 |
+
],
|
164 |
+
"source": [
|
165 |
+
"d_frame=pd.Series(arr1)\n",
|
166 |
+
"print(d_frame)"
|
167 |
+
]
|
168 |
+
},
|
169 |
+
{
|
170 |
+
"cell_type": "code",
|
171 |
+
"execution_count": 26,
|
172 |
+
"id": "aeed557b",
|
173 |
+
"metadata": {},
|
174 |
+
"outputs": [
|
175 |
+
{
|
176 |
+
"name": "stdout",
|
177 |
+
"output_type": "stream",
|
178 |
+
"text": [
|
179 |
+
" 0 1 2 3\n",
|
180 |
+
"0 10 20 30 40\n",
|
181 |
+
"1 100 200 300 400\n"
|
182 |
+
]
|
183 |
+
}
|
184 |
+
],
|
185 |
+
"source": [
|
186 |
+
"#array to data frame\n",
|
187 |
+
"#here the data frame support only the 2-d arry for printing other wise the print function will show error message:\n",
|
188 |
+
"d_frame=pd.DataFrame(arr2)\n",
|
189 |
+
"print(d_frame)"
|
190 |
+
]
|
191 |
+
},
|
192 |
+
{
|
193 |
+
"cell_type": "code",
|
194 |
+
"execution_count": 27,
|
195 |
+
"id": "88ae926a",
|
196 |
+
"metadata": {},
|
197 |
+
"outputs": [
|
198 |
+
{
|
199 |
+
"data": {
|
200 |
+
"text/plain": [
|
201 |
+
"(2, 4)"
|
202 |
+
]
|
203 |
+
},
|
204 |
+
"execution_count": 27,
|
205 |
+
"metadata": {},
|
206 |
+
"output_type": "execute_result"
|
207 |
+
}
|
208 |
+
],
|
209 |
+
"source": [
|
210 |
+
"d_frame.shape"
|
211 |
+
]
|
212 |
+
},
|
213 |
+
{
|
214 |
+
"cell_type": "code",
|
215 |
+
"execution_count": 28,
|
216 |
+
"id": "5d15a9ed",
|
217 |
+
"metadata": {},
|
218 |
+
"outputs": [],
|
219 |
+
"source": [
|
220 |
+
"#data frame to array\n",
|
221 |
+
"check1=np.array(d_frame)"
|
222 |
+
]
|
223 |
+
},
|
224 |
+
{
|
225 |
+
"cell_type": "code",
|
226 |
+
"execution_count": 29,
|
227 |
+
"id": "c4c87770",
|
228 |
+
"metadata": {},
|
229 |
+
"outputs": [
|
230 |
+
{
|
231 |
+
"name": "stdout",
|
232 |
+
"output_type": "stream",
|
233 |
+
"text": [
|
234 |
+
"[[ 10 20 30 40]\n",
|
235 |
+
" [100 200 300 400]]\n"
|
236 |
+
]
|
237 |
+
}
|
238 |
+
],
|
239 |
+
"source": [
|
240 |
+
"print(check1)"
|
241 |
+
]
|
242 |
+
},
|
243 |
+
{
|
244 |
+
"cell_type": "code",
|
245 |
+
"execution_count": 34,
|
246 |
+
"id": "e84c9006",
|
247 |
+
"metadata": {},
|
248 |
+
"outputs": [
|
249 |
+
{
|
250 |
+
"name": "stdout",
|
251 |
+
"output_type": "stream",
|
252 |
+
"text": [
|
253 |
+
"1-D:\t [1 2 4 7 8 9 4 5 6 7 9] \n",
|
254 |
+
"\n",
|
255 |
+
"2-D:\t [[ 10 20 30 40]\n",
|
256 |
+
" [100 200 300 400]] \n",
|
257 |
+
"\n",
|
258 |
+
"3-D:\t [[[ 1 2 3 4]\n",
|
259 |
+
" [ 10 20 30 40]\n",
|
260 |
+
" [100 200 300 400]]]\n"
|
261 |
+
]
|
262 |
+
}
|
263 |
+
],
|
264 |
+
"source": [
|
265 |
+
"print(\"1-D:\\t\",arr1,\"\\n\")\n",
|
266 |
+
"print(\"2-D:\\t\",arr2,\"\\n\")\n",
|
267 |
+
"print(\"3-D:\\t\",arr3)"
|
268 |
+
]
|
269 |
+
},
|
270 |
+
{
|
271 |
+
"cell_type": "code",
|
272 |
+
"execution_count": 41,
|
273 |
+
"id": "df3e081a",
|
274 |
+
"metadata": {},
|
275 |
+
"outputs": [
|
276 |
+
{
|
277 |
+
"name": "stdout",
|
278 |
+
"output_type": "stream",
|
279 |
+
"text": [
|
280 |
+
"[1 2 4 7 8 9 4 5 6 7 9]\n",
|
281 |
+
"[1 2 4 7 8 9 4]\n"
|
282 |
+
]
|
283 |
+
}
|
284 |
+
],
|
285 |
+
"source": [
|
286 |
+
"#nupy array indexing with step value:\n",
|
287 |
+
"print(arr1)\n",
|
288 |
+
"print(arr1[0:7])"
|
289 |
+
]
|
290 |
+
},
|
291 |
+
{
|
292 |
+
"cell_type": "code",
|
293 |
+
"execution_count": 44,
|
294 |
+
"id": "ab12aa05",
|
295 |
+
"metadata": {},
|
296 |
+
"outputs": [
|
297 |
+
{
|
298 |
+
"name": "stdout",
|
299 |
+
"output_type": "stream",
|
300 |
+
"text": [
|
301 |
+
"first half of the elements: [1 2 4 7 8]\n"
|
302 |
+
]
|
303 |
+
}
|
304 |
+
],
|
305 |
+
"source": [
|
306 |
+
"#half of the elements are printing:\n",
|
307 |
+
"print(\"first half of the elements:\",arr1[:len(arr1)//2])"
|
308 |
+
]
|
309 |
+
},
|
310 |
+
{
|
311 |
+
"cell_type": "code",
|
312 |
+
"execution_count": 45,
|
313 |
+
"id": "55929e0a",
|
314 |
+
"metadata": {},
|
315 |
+
"outputs": [
|
316 |
+
{
|
317 |
+
"name": "stdout",
|
318 |
+
"output_type": "stream",
|
319 |
+
"text": [
|
320 |
+
"1\n",
|
321 |
+
"2\n",
|
322 |
+
"4\n",
|
323 |
+
"7\n",
|
324 |
+
"8\n",
|
325 |
+
"9\n",
|
326 |
+
"4\n",
|
327 |
+
"5\n",
|
328 |
+
"6\n",
|
329 |
+
"7\n",
|
330 |
+
"9\n"
|
331 |
+
]
|
332 |
+
}
|
333 |
+
],
|
334 |
+
"source": [
|
335 |
+
"for i in arr1:\n",
|
336 |
+
" print(i)"
|
337 |
+
]
|
338 |
+
},
|
339 |
+
{
|
340 |
+
"cell_type": "code",
|
341 |
+
"execution_count": 49,
|
342 |
+
"id": "0e15e750",
|
343 |
+
"metadata": {},
|
344 |
+
"outputs": [
|
345 |
+
{
|
346 |
+
"name": "stdout",
|
347 |
+
"output_type": "stream",
|
348 |
+
"text": [
|
349 |
+
"1\n",
|
350 |
+
"2\n",
|
351 |
+
"3\n",
|
352 |
+
"4\n",
|
353 |
+
"10\n",
|
354 |
+
"20\n",
|
355 |
+
"30\n",
|
356 |
+
"40\n",
|
357 |
+
"100\n",
|
358 |
+
"200\n",
|
359 |
+
"300\n",
|
360 |
+
"400\n"
|
361 |
+
]
|
362 |
+
}
|
363 |
+
],
|
364 |
+
"source": [
|
365 |
+
"#iterating the 3-D array in one loop:\n",
|
366 |
+
"for i in np.nditer(arr3[:,::1]):\n",
|
367 |
+
" print(i)"
|
368 |
+
]
|
369 |
+
},
|
370 |
+
{
|
371 |
+
"cell_type": "code",
|
372 |
+
"execution_count": 67,
|
373 |
+
"id": "141305fd",
|
374 |
+
"metadata": {},
|
375 |
+
"outputs": [
|
376 |
+
{
|
377 |
+
"data": {
|
378 |
+
"text/plain": [
|
379 |
+
"(2, 4)"
|
380 |
+
]
|
381 |
+
},
|
382 |
+
"execution_count": 67,
|
383 |
+
"metadata": {},
|
384 |
+
"output_type": "execute_result"
|
385 |
+
}
|
386 |
+
],
|
387 |
+
"source": [
|
388 |
+
"arr2.shape"
|
389 |
+
]
|
390 |
+
},
|
391 |
+
{
|
392 |
+
"cell_type": "code",
|
393 |
+
"execution_count": 68,
|
394 |
+
"id": "a8cf04d8",
|
395 |
+
"metadata": {},
|
396 |
+
"outputs": [
|
397 |
+
{
|
398 |
+
"data": {
|
399 |
+
"text/plain": [
|
400 |
+
"(1, 3, 4)"
|
401 |
+
]
|
402 |
+
},
|
403 |
+
"execution_count": 68,
|
404 |
+
"metadata": {},
|
405 |
+
"output_type": "execute_result"
|
406 |
+
}
|
407 |
+
],
|
408 |
+
"source": [
|
409 |
+
"#doubt:ask to sir:\n",
|
410 |
+
"arr3.shape"
|
411 |
+
]
|
412 |
+
},
|
413 |
+
{
|
414 |
+
"cell_type": "code",
|
415 |
+
"execution_count": 69,
|
416 |
+
"id": "661a05b5",
|
417 |
+
"metadata": {},
|
418 |
+
"outputs": [
|
419 |
+
{
|
420 |
+
"data": {
|
421 |
+
"text/plain": [
|
422 |
+
"3"
|
423 |
+
]
|
424 |
+
},
|
425 |
+
"execution_count": 69,
|
426 |
+
"metadata": {},
|
427 |
+
"output_type": "execute_result"
|
428 |
+
}
|
429 |
+
],
|
430 |
+
"source": [
|
431 |
+
"np.ndim(arr3)"
|
432 |
+
]
|
433 |
+
},
|
434 |
+
{
|
435 |
+
"cell_type": "code",
|
436 |
+
"execution_count": 70,
|
437 |
+
"id": "2e53a428",
|
438 |
+
"metadata": {},
|
439 |
+
"outputs": [
|
440 |
+
{
|
441 |
+
"data": {
|
442 |
+
"text/plain": [
|
443 |
+
"(11,)"
|
444 |
+
]
|
445 |
+
},
|
446 |
+
"execution_count": 70,
|
447 |
+
"metadata": {},
|
448 |
+
"output_type": "execute_result"
|
449 |
+
}
|
450 |
+
],
|
451 |
+
"source": [
|
452 |
+
"arr1.shape"
|
453 |
+
]
|
454 |
+
},
|
455 |
+
{
|
456 |
+
"cell_type": "code",
|
457 |
+
"execution_count": 73,
|
458 |
+
"id": "f52621d0",
|
459 |
+
"metadata": {},
|
460 |
+
"outputs": [
|
461 |
+
{
|
462 |
+
"name": "stdout",
|
463 |
+
"output_type": "stream",
|
464 |
+
"text": [
|
465 |
+
"10\n"
|
466 |
+
]
|
467 |
+
}
|
468 |
+
],
|
469 |
+
"source": [
|
470 |
+
"#accessign the nd arry elements:\n",
|
471 |
+
"#element - 1st row and first column\n",
|
472 |
+
"print(arr2[0,0])"
|
473 |
+
]
|
474 |
+
},
|
475 |
+
{
|
476 |
+
"cell_type": "code",
|
477 |
+
"execution_count": 76,
|
478 |
+
"id": "78f2bb26",
|
479 |
+
"metadata": {},
|
480 |
+
"outputs": [
|
481 |
+
{
|
482 |
+
"name": "stdout",
|
483 |
+
"output_type": "stream",
|
484 |
+
"text": [
|
485 |
+
"300\n"
|
486 |
+
]
|
487 |
+
}
|
488 |
+
],
|
489 |
+
"source": [
|
490 |
+
"#3-d arrray:\n",
|
491 |
+
"print(arr3[0,2,2])"
|
492 |
+
]
|
493 |
+
},
|
494 |
+
{
|
495 |
+
"cell_type": "code",
|
496 |
+
"execution_count": 81,
|
497 |
+
"id": "f1dcee88",
|
498 |
+
"metadata": {},
|
499 |
+
"outputs": [
|
500 |
+
{
|
501 |
+
"name": "stdout",
|
502 |
+
"output_type": "stream",
|
503 |
+
"text": [
|
504 |
+
"[ 10 20 400 183 142]\n"
|
505 |
+
]
|
506 |
+
}
|
507 |
+
],
|
508 |
+
"source": [
|
509 |
+
"#for the absolute we willl create another array with -ve value\n",
|
510 |
+
"arr4=np.array([-10,-20,-400,-183,142])\n",
|
511 |
+
"print(abs(arr4))"
|
512 |
+
]
|
513 |
+
},
|
514 |
+
{
|
515 |
+
"cell_type": "code",
|
516 |
+
"execution_count": 82,
|
517 |
+
"id": "27348c65",
|
518 |
+
"metadata": {},
|
519 |
+
"outputs": [],
|
520 |
+
"source": [
|
521 |
+
"#reshaping\n",
|
522 |
+
"#add or remove from the existing array"
|
523 |
+
]
|
524 |
+
},
|
525 |
+
{
|
526 |
+
"cell_type": "code",
|
527 |
+
"execution_count": 84,
|
528 |
+
"id": "6248d02a",
|
529 |
+
"metadata": {},
|
530 |
+
"outputs": [
|
531 |
+
{
|
532 |
+
"data": {
|
533 |
+
"text/plain": [
|
534 |
+
"[array([1, 2, 4, 7, 8, 9]), array([4, 5, 6, 7, 9])]"
|
535 |
+
]
|
536 |
+
},
|
537 |
+
"execution_count": 84,
|
538 |
+
"metadata": {},
|
539 |
+
"output_type": "execute_result"
|
540 |
+
}
|
541 |
+
],
|
542 |
+
"source": [
|
543 |
+
"np.array_split(arr1,2)"
|
544 |
+
]
|
545 |
+
},
|
546 |
+
{
|
547 |
+
"cell_type": "code",
|
548 |
+
"execution_count": 87,
|
549 |
+
"id": "072c9d81",
|
550 |
+
"metadata": {},
|
551 |
+
"outputs": [
|
552 |
+
{
|
553 |
+
"data": {
|
554 |
+
"text/plain": [
|
555 |
+
"[array([[10, 20, 30, 40]]), array([[100, 200, 300, 400]])]"
|
556 |
+
]
|
557 |
+
},
|
558 |
+
"execution_count": 87,
|
559 |
+
"metadata": {},
|
560 |
+
"output_type": "execute_result"
|
561 |
+
}
|
562 |
+
],
|
563 |
+
"source": [
|
564 |
+
"np.array_split(arr2,2)"
|
565 |
+
]
|
566 |
+
},
|
567 |
+
{
|
568 |
+
"cell_type": "code",
|
569 |
+
"execution_count": 88,
|
570 |
+
"id": "4883ec53",
|
571 |
+
"metadata": {},
|
572 |
+
"outputs": [
|
573 |
+
{
|
574 |
+
"data": {
|
575 |
+
"text/plain": [
|
576 |
+
"[array([[[ 1, 2, 3, 4],\n",
|
577 |
+
" [ 10, 20, 30, 40],\n",
|
578 |
+
" [100, 200, 300, 400]]]),\n",
|
579 |
+
" array([], shape=(0, 3, 4), dtype=int64),\n",
|
580 |
+
" array([], shape=(0, 3, 4), dtype=int64)]"
|
581 |
+
]
|
582 |
+
},
|
583 |
+
"execution_count": 88,
|
584 |
+
"metadata": {},
|
585 |
+
"output_type": "execute_result"
|
586 |
+
}
|
587 |
+
],
|
588 |
+
"source": [
|
589 |
+
"np.array_split(arr3,3)"
|
590 |
+
]
|
591 |
+
},
|
592 |
+
{
|
593 |
+
"cell_type": "code",
|
594 |
+
"execution_count": 90,
|
595 |
+
"id": "02a8971e",
|
596 |
+
"metadata": {},
|
597 |
+
"outputs": [
|
598 |
+
{
|
599 |
+
"data": {
|
600 |
+
"text/plain": [
|
601 |
+
"[array([1, 2, 4]), array([7, 8]), array([9, 4]), array([5, 6]), array([7, 9])]"
|
602 |
+
]
|
603 |
+
},
|
604 |
+
"execution_count": 90,
|
605 |
+
"metadata": {},
|
606 |
+
"output_type": "execute_result"
|
607 |
+
}
|
608 |
+
],
|
609 |
+
"source": [
|
610 |
+
"np.array_split(arr1,5)"
|
611 |
+
]
|
612 |
+
},
|
613 |
+
{
|
614 |
+
"cell_type": "code",
|
615 |
+
"execution_count": 103,
|
616 |
+
"id": "e5a999ce",
|
617 |
+
"metadata": {},
|
618 |
+
"outputs": [
|
619 |
+
{
|
620 |
+
"name": "stdout",
|
621 |
+
"output_type": "stream",
|
622 |
+
"text": [
|
623 |
+
"5\n"
|
624 |
+
]
|
625 |
+
}
|
626 |
+
],
|
627 |
+
"source": [
|
628 |
+
"print(len(arr4))"
|
629 |
+
]
|
630 |
+
},
|
631 |
+
{
|
632 |
+
"cell_type": "code",
|
633 |
+
"execution_count": 106,
|
634 |
+
"id": "7b58a5c0",
|
635 |
+
"metadata": {},
|
636 |
+
"outputs": [
|
637 |
+
{
|
638 |
+
"name": "stdout",
|
639 |
+
"output_type": "stream",
|
640 |
+
"text": [
|
641 |
+
"[[[ 1 2]\n",
|
642 |
+
" [ 3 4]\n",
|
643 |
+
" [ 5 6]]\n",
|
644 |
+
"\n",
|
645 |
+
" [[ 7 8]\n",
|
646 |
+
" [ 9 10]\n",
|
647 |
+
" [11 12]]]\n"
|
648 |
+
]
|
649 |
+
}
|
650 |
+
],
|
651 |
+
"source": [
|
652 |
+
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
|
653 |
+
"\n",
|
654 |
+
"newarr = arr.reshape(2, 3, 2)\n",
|
655 |
+
"print(newarr)"
|
656 |
+
]
|
657 |
+
},
|
658 |
+
{
|
659 |
+
"cell_type": "code",
|
660 |
+
"execution_count": 107,
|
661 |
+
"id": "4bb019ce",
|
662 |
+
"metadata": {},
|
663 |
+
"outputs": [
|
664 |
+
{
|
665 |
+
"name": "stdout",
|
666 |
+
"output_type": "stream",
|
667 |
+
"text": [
|
668 |
+
"[1 2 4 7 8 9 4 5 6 7 9]\n"
|
669 |
+
]
|
670 |
+
}
|
671 |
+
],
|
672 |
+
"source": [
|
673 |
+
"#splitting the array:\n",
|
674 |
+
"print(arr1)"
|
675 |
+
]
|
676 |
+
},
|
677 |
+
{
|
678 |
+
"cell_type": "code",
|
679 |
+
"execution_count": 109,
|
680 |
+
"id": "6fbdf5b6",
|
681 |
+
"metadata": {},
|
682 |
+
"outputs": [],
|
683 |
+
"source": [
|
684 |
+
"newarr2=np.array_split(arr1,3)"
|
685 |
+
]
|
686 |
+
},
|
687 |
+
{
|
688 |
+
"cell_type": "code",
|
689 |
+
"execution_count": 111,
|
690 |
+
"id": "a254994b",
|
691 |
+
"metadata": {},
|
692 |
+
"outputs": [
|
693 |
+
{
|
694 |
+
"ename": "TypeError",
|
695 |
+
"evalue": "only integer scalar arrays can be converted to a scalar index",
|
696 |
+
"output_type": "error",
|
697 |
+
"traceback": [
|
698 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
699 |
+
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
700 |
+
"Input \u001b[0;32mIn [111]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m newarr2:\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mnewarr2\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m)\n",
|
701 |
+
"\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
|
702 |
+
]
|
703 |
+
}
|
704 |
+
],
|
705 |
+
"source": [
|
706 |
+
"for i in newarr2:\n",
|
707 |
+
" print(newarr2[i])"
|
708 |
+
]
|
709 |
+
},
|
710 |
+
{
|
711 |
+
"cell_type": "code",
|
712 |
+
"execution_count": 117,
|
713 |
+
"id": "45ede19a",
|
714 |
+
"metadata": {},
|
715 |
+
"outputs": [
|
716 |
+
{
|
717 |
+
"name": "stdout",
|
718 |
+
"output_type": "stream",
|
719 |
+
"text": [
|
720 |
+
"[1 2 4 7]\n",
|
721 |
+
"[8 9 4 5]\n",
|
722 |
+
"[6 7 9]\n"
|
723 |
+
]
|
724 |
+
}
|
725 |
+
],
|
726 |
+
"source": [
|
727 |
+
"print(newarr2[0])\n",
|
728 |
+
"print(newarr2[1])\n",
|
729 |
+
"print(newarr2[2])"
|
730 |
+
]
|
731 |
+
},
|
732 |
+
{
|
733 |
+
"cell_type": "code",
|
734 |
+
"execution_count": 118,
|
735 |
+
"id": "b6dc5efb",
|
736 |
+
"metadata": {},
|
737 |
+
"outputs": [],
|
738 |
+
"source": [
|
739 |
+
"##searching arrays"
|
740 |
+
]
|
741 |
+
},
|
742 |
+
{
|
743 |
+
"cell_type": "code",
|
744 |
+
"execution_count": 126,
|
745 |
+
"id": "db8ca2e0",
|
746 |
+
"metadata": {},
|
747 |
+
"outputs": [
|
748 |
+
{
|
749 |
+
"name": "stdout",
|
750 |
+
"output_type": "stream",
|
751 |
+
"text": [
|
752 |
+
"[1 2 4 7 8 9 4 5 6 7 9]\n",
|
753 |
+
"(array([], dtype=int64),)\n"
|
754 |
+
]
|
755 |
+
}
|
756 |
+
],
|
757 |
+
"source": [
|
758 |
+
"arr1"
|
759 |
+
]
|
760 |
+
},
|
761 |
+
{
|
762 |
+
"cell_type": "code",
|
763 |
+
"execution_count": 129,
|
764 |
+
"id": "b0d63d87",
|
765 |
+
"metadata": {},
|
766 |
+
"outputs": [
|
767 |
+
{
|
768 |
+
"name": "stdout",
|
769 |
+
"output_type": "stream",
|
770 |
+
"text": [
|
771 |
+
"(array([0, 2, 3]),)\n"
|
772 |
+
]
|
773 |
+
}
|
774 |
+
],
|
775 |
+
"source": [
|
776 |
+
"arr = np.array([1, 2, 1, 1, 5, 6, 7, 8])\n",
|
777 |
+
"\n",
|
778 |
+
"x = np.where(arr == 1)\n",
|
779 |
+
"\n",
|
780 |
+
"print(x)"
|
781 |
+
]
|
782 |
+
},
|
783 |
+
{
|
784 |
+
"cell_type": "code",
|
785 |
+
"execution_count": 134,
|
786 |
+
"id": "32dd0731",
|
787 |
+
"metadata": {},
|
788 |
+
"outputs": [
|
789 |
+
{
|
790 |
+
"name": "stdout",
|
791 |
+
"output_type": "stream",
|
792 |
+
"text": [
|
793 |
+
"(array([4, 7]),)\n"
|
794 |
+
]
|
795 |
+
}
|
796 |
+
],
|
797 |
+
"source": [
|
798 |
+
"arr=np.array((1,2,3,4,5,2,3,5,6,7,8))\n",
|
799 |
+
"x=np.where(arr==5)\n",
|
800 |
+
"print(x)"
|
801 |
+
]
|
802 |
+
},
|
803 |
+
{
|
804 |
+
"cell_type": "code",
|
805 |
+
"execution_count": 136,
|
806 |
+
"id": "709be631",
|
807 |
+
"metadata": {},
|
808 |
+
"outputs": [
|
809 |
+
{
|
810 |
+
"name": "stdout",
|
811 |
+
"output_type": "stream",
|
812 |
+
"text": [
|
813 |
+
"8\n"
|
814 |
+
]
|
815 |
+
}
|
816 |
+
],
|
817 |
+
"source": [
|
818 |
+
"x = np.searchsorted(arr, 6)\n",
|
819 |
+
"print(x)"
|
820 |
+
]
|
821 |
+
},
|
822 |
+
{
|
823 |
+
"cell_type": "code",
|
824 |
+
"execution_count": 138,
|
825 |
+
"id": "eaa35f66",
|
826 |
+
"metadata": {},
|
827 |
+
"outputs": [
|
828 |
+
{
|
829 |
+
"name": "stdout",
|
830 |
+
"output_type": "stream",
|
831 |
+
"text": [
|
832 |
+
"[0 2 2 3 5 6 8 8]\n"
|
833 |
+
]
|
834 |
+
}
|
835 |
+
],
|
836 |
+
"source": [
|
837 |
+
"#sorting:\n",
|
838 |
+
"arr=np.array((2,5,2,8,0,3,6,8))\n",
|
839 |
+
"print(np.sort(arr))"
|
840 |
+
]
|
841 |
+
},
|
842 |
+
{
|
843 |
+
"cell_type": "code",
|
844 |
+
"execution_count": 139,
|
845 |
+
"id": "66b658a1",
|
846 |
+
"metadata": {},
|
847 |
+
"outputs": [
|
848 |
+
{
|
849 |
+
"name": "stdout",
|
850 |
+
"output_type": "stream",
|
851 |
+
"text": [
|
852 |
+
"['azar' 'parmesh' 'ravi' 'sanath']\n"
|
853 |
+
]
|
854 |
+
}
|
855 |
+
],
|
856 |
+
"source": [
|
857 |
+
"arr=np.array((\"sanath\",\"ravi\",\"parmesh\",\"azar\"))\n",
|
858 |
+
"print(np.sort(arr))"
|
859 |
+
]
|
860 |
+
},
|
861 |
+
{
|
862 |
+
"cell_type": "code",
|
863 |
+
"execution_count": 140,
|
864 |
+
"id": "2aef5294",
|
865 |
+
"metadata": {},
|
866 |
+
"outputs": [
|
867 |
+
{
|
868 |
+
"name": "stdout",
|
869 |
+
"output_type": "stream",
|
870 |
+
"text": [
|
871 |
+
"[[10 20 29]\n",
|
872 |
+
" [ 0 20 83]]\n"
|
873 |
+
]
|
874 |
+
}
|
875 |
+
],
|
876 |
+
"source": [
|
877 |
+
"#we can also sort the 2-d array:\n",
|
878 |
+
"arr=np.array([[20,10,29],[0,20,83]])\n",
|
879 |
+
"print(np.sort(arr))"
|
880 |
+
]
|
881 |
+
},
|
882 |
+
{
|
883 |
+
"cell_type": "code",
|
884 |
+
"execution_count": 143,
|
885 |
+
"id": "60eeeed6",
|
886 |
+
"metadata": {},
|
887 |
+
"outputs": [
|
888 |
+
{
|
889 |
+
"name": "stdout",
|
890 |
+
"output_type": "stream",
|
891 |
+
"text": [
|
892 |
+
"[1 3 4]\n"
|
893 |
+
]
|
894 |
+
}
|
895 |
+
],
|
896 |
+
"source": [
|
897 |
+
"arr=np.array([1,2,3,4])\n",
|
898 |
+
"x=[True,False,True,True]\n",
|
899 |
+
"print(arr[x])"
|
900 |
+
]
|
901 |
+
},
|
902 |
+
{
|
903 |
+
"cell_type": "code",
|
904 |
+
"execution_count": 144,
|
905 |
+
"id": "35d8dba5",
|
906 |
+
"metadata": {},
|
907 |
+
"outputs": [],
|
908 |
+
"source": [
|
909 |
+
"#nupy rondom class working:\n",
|
910 |
+
"#we can predict theese numbers:\n",
|
911 |
+
"#machine will generate this:\n",
|
912 |
+
"from numpy import random"
|
913 |
+
]
|
914 |
+
},
|
915 |
+
{
|
916 |
+
"cell_type": "code",
|
917 |
+
"execution_count": 153,
|
918 |
+
"id": "859d5dbb",
|
919 |
+
"metadata": {},
|
920 |
+
"outputs": [
|
921 |
+
{
|
922 |
+
"data": {
|
923 |
+
"text/plain": [
|
924 |
+
"77"
|
925 |
+
]
|
926 |
+
},
|
927 |
+
"execution_count": 153,
|
928 |
+
"metadata": {},
|
929 |
+
"output_type": "execute_result"
|
930 |
+
}
|
931 |
+
],
|
932 |
+
"source": [
|
933 |
+
"x=random.randint(100)\n",
|
934 |
+
"x"
|
935 |
+
]
|
936 |
+
},
|
937 |
+
{
|
938 |
+
"cell_type": "code",
|
939 |
+
"execution_count": 148,
|
940 |
+
"id": "7cfa7040",
|
941 |
+
"metadata": {},
|
942 |
+
"outputs": [
|
943 |
+
{
|
944 |
+
"name": "stdout",
|
945 |
+
"output_type": "stream",
|
946 |
+
"text": [
|
947 |
+
"88\n"
|
948 |
+
]
|
949 |
+
}
|
950 |
+
],
|
951 |
+
"source": [
|
952 |
+
"print(x)"
|
953 |
+
]
|
954 |
+
},
|
955 |
+
{
|
956 |
+
"cell_type": "code",
|
957 |
+
"execution_count": 154,
|
958 |
+
"id": "a1748723",
|
959 |
+
"metadata": {},
|
960 |
+
"outputs": [
|
961 |
+
{
|
962 |
+
"data": {
|
963 |
+
"text/plain": [
|
964 |
+
"array([0.88562907, 0.22932804, 0.90857795, 0.2838597 , 0.76885629,\n",
|
965 |
+
" 0.87201837, 0.5121362 , 0.89441225, 0.67149354, 0.03825343])"
|
966 |
+
]
|
967 |
+
},
|
968 |
+
"execution_count": 154,
|
969 |
+
"metadata": {},
|
970 |
+
"output_type": "execute_result"
|
971 |
+
}
|
972 |
+
],
|
973 |
+
"source": [
|
974 |
+
"x=random.rand(10)\n",
|
975 |
+
"x"
|
976 |
+
]
|
977 |
+
},
|
978 |
+
{
|
979 |
+
"cell_type": "code",
|
980 |
+
"execution_count": 170,
|
981 |
+
"id": "62ef85d8",
|
982 |
+
"metadata": {},
|
983 |
+
"outputs": [
|
984 |
+
{
|
985 |
+
"data": {
|
986 |
+
"text/plain": [
|
987 |
+
"array([ 6, 16, 29, 50, 6, 0, 99, 11, 88, 33])"
|
988 |
+
]
|
989 |
+
},
|
990 |
+
"execution_count": 170,
|
991 |
+
"metadata": {},
|
992 |
+
"output_type": "execute_result"
|
993 |
+
}
|
994 |
+
],
|
995 |
+
"source": [
|
996 |
+
"#generating 10 randoom numbers in given range:\n",
|
997 |
+
"#and this method is also called as the creating the 2-d array:\n",
|
998 |
+
"x=random.randint(100,size=10)\n",
|
999 |
+
"x"
|
1000 |
+
]
|
1001 |
+
},
|
1002 |
+
{
|
1003 |
+
"cell_type": "code",
|
1004 |
+
"execution_count": 158,
|
1005 |
+
"id": "34d5e7ce",
|
1006 |
+
"metadata": {},
|
1007 |
+
"outputs": [
|
1008 |
+
{
|
1009 |
+
"data": {
|
1010 |
+
"text/plain": [
|
1011 |
+
"array([[40, 55, 86, 42, 85, 35, 18, 13, 11, 35],\n",
|
1012 |
+
" [66, 31, 61, 65, 23, 49, 65, 30, 90, 3],\n",
|
1013 |
+
" [90, 53, 95, 32, 45, 85, 31, 10, 52, 69]])"
|
1014 |
+
]
|
1015 |
+
},
|
1016 |
+
"execution_count": 158,
|
1017 |
+
"metadata": {},
|
1018 |
+
"output_type": "execute_result"
|
1019 |
+
}
|
1020 |
+
],
|
1021 |
+
"source": [
|
1022 |
+
"#creating the 3-d array from the rndom :\n",
|
1023 |
+
"x=random.randint(100,size=(3,10))\n",
|
1024 |
+
"x"
|
1025 |
+
]
|
1026 |
+
},
|
1027 |
+
{
|
1028 |
+
"cell_type": "code",
|
1029 |
+
"execution_count": 159,
|
1030 |
+
"id": "8ae2661e",
|
1031 |
+
"metadata": {},
|
1032 |
+
"outputs": [
|
1033 |
+
{
|
1034 |
+
"data": {
|
1035 |
+
"text/plain": [
|
1036 |
+
"(3, 10)"
|
1037 |
+
]
|
1038 |
+
},
|
1039 |
+
"execution_count": 159,
|
1040 |
+
"metadata": {},
|
1041 |
+
"output_type": "execute_result"
|
1042 |
+
}
|
1043 |
+
],
|
1044 |
+
"source": [
|
1045 |
+
"x.shape"
|
1046 |
+
]
|
1047 |
+
},
|
1048 |
+
{
|
1049 |
+
"cell_type": "code",
|
1050 |
+
"execution_count": 171,
|
1051 |
+
"id": "37d6319d",
|
1052 |
+
"metadata": {},
|
1053 |
+
"outputs": [
|
1054 |
+
{
|
1055 |
+
"data": {
|
1056 |
+
"text/plain": [
|
1057 |
+
"29"
|
1058 |
+
]
|
1059 |
+
},
|
1060 |
+
"execution_count": 171,
|
1061 |
+
"metadata": {},
|
1062 |
+
"output_type": "execute_result"
|
1063 |
+
}
|
1064 |
+
],
|
1065 |
+
"source": [
|
1066 |
+
"#upto now we have created random array:\n",
|
1067 |
+
"# but now we can get the random element from the generatd arry:\n",
|
1068 |
+
"#only for the 1-d array\n",
|
1069 |
+
"y=random.choice(x)\n",
|
1070 |
+
"y"
|
1071 |
+
]
|
1072 |
+
},
|
1073 |
+
{
|
1074 |
+
"cell_type": "code",
|
1075 |
+
"execution_count": 174,
|
1076 |
+
"id": "7decc1e0",
|
1077 |
+
"metadata": {},
|
1078 |
+
"outputs": [
|
1079 |
+
{
|
1080 |
+
"data": {
|
1081 |
+
"text/plain": [
|
1082 |
+
"array([[5, 7, 9, 7, 3],\n",
|
1083 |
+
" [9, 9, 5, 7, 7],\n",
|
1084 |
+
" [5, 5, 5, 9, 5]])"
|
1085 |
+
]
|
1086 |
+
},
|
1087 |
+
"execution_count": 174,
|
1088 |
+
"metadata": {},
|
1089 |
+
"output_type": "execute_result"
|
1090 |
+
}
|
1091 |
+
],
|
1092 |
+
"source": [
|
1093 |
+
"#now we can get the choice of number from the 2-d array by using the size:\n",
|
1094 |
+
"arr2 = random.choice([3, 5, 7, 9], size=(3, 5))\n",
|
1095 |
+
"arr2"
|
1096 |
+
]
|
1097 |
+
},
|
1098 |
+
{
|
1099 |
+
"cell_type": "code",
|
1100 |
+
"execution_count": 179,
|
1101 |
+
"id": "415e52b4",
|
1102 |
+
"metadata": {},
|
1103 |
+
"outputs": [
|
1104 |
+
{
|
1105 |
+
"name": "stdout",
|
1106 |
+
"output_type": "stream",
|
1107 |
+
"text": [
|
1108 |
+
"[1 3 2 4]\n"
|
1109 |
+
]
|
1110 |
+
}
|
1111 |
+
],
|
1112 |
+
"source": [
|
1113 |
+
"#rndom permutatiions\n",
|
1114 |
+
"arr=np.array([1,2,3,4])\n",
|
1115 |
+
"random.shuffle(arr)\n",
|
1116 |
+
"print(arr)"
|
1117 |
+
]
|
1118 |
+
},
|
1119 |
+
{
|
1120 |
+
"cell_type": "code",
|
1121 |
+
"execution_count": 181,
|
1122 |
+
"id": "40dbec86",
|
1123 |
+
"metadata": {},
|
1124 |
+
"outputs": [
|
1125 |
+
{
|
1126 |
+
"name": "stdout",
|
1127 |
+
"output_type": "stream",
|
1128 |
+
"text": [
|
1129 |
+
"[4 1 2 3]\n"
|
1130 |
+
]
|
1131 |
+
}
|
1132 |
+
],
|
1133 |
+
"source": [
|
1134 |
+
"print(random.permutation(arr))"
|
1135 |
+
]
|
1136 |
+
},
|
1137 |
+
{
|
1138 |
+
"cell_type": "code",
|
1139 |
+
"execution_count": 189,
|
1140 |
+
"id": "bb115669",
|
1141 |
+
"metadata": {},
|
1142 |
+
"outputs": [
|
1143 |
+
{
|
1144 |
+
"name": "stdout",
|
1145 |
+
"output_type": "stream",
|
1146 |
+
"text": [
|
1147 |
+
"[5, 5, 5, 96, 86]\n"
|
1148 |
+
]
|
1149 |
+
}
|
1150 |
+
],
|
1151 |
+
"source": [
|
1152 |
+
"#zip method:\n",
|
1153 |
+
"x1=[1,2,3,93,82]\n",
|
1154 |
+
"x2=[4,3,2,3,4,]\n",
|
1155 |
+
"z=[]\n",
|
1156 |
+
"for i,j in zip(x1,x2):\n",
|
1157 |
+
" z.append(i+j)\n",
|
1158 |
+
"print(z)"
|
1159 |
+
]
|
1160 |
+
},
|
1161 |
+
{
|
1162 |
+
"cell_type": "code",
|
1163 |
+
"execution_count": 199,
|
1164 |
+
"id": "2dfc2e91",
|
1165 |
+
"metadata": {},
|
1166 |
+
"outputs": [
|
1167 |
+
{
|
1168 |
+
"name": "stdout",
|
1169 |
+
"output_type": "stream",
|
1170 |
+
"text": [
|
1171 |
+
"[ 5 5 5 96 86]\n",
|
1172 |
+
"[-3 -1 1 90 78]\n",
|
1173 |
+
"[ 4 6 6 279 328]\n",
|
1174 |
+
"[ 0.25 0.66666667 1.5 31. 20.5 ]\n",
|
1175 |
+
"[ 1 8 9 804357 45212176]\n",
|
1176 |
+
"[1 2 1 0 2]\n",
|
1177 |
+
"[1 2 1 0 2]\n",
|
1178 |
+
"(array([ 0, 0, 1, 31, 20]), array([1, 2, 1, 0, 2]))\n"
|
1179 |
+
]
|
1180 |
+
}
|
1181 |
+
],
|
1182 |
+
"source": [
|
1183 |
+
"#arithmatic operations:\n",
|
1184 |
+
"print(np.add(x1,x2))\n",
|
1185 |
+
"print(np.subtract(x1,x2))\n",
|
1186 |
+
"print(np.multiply(x1,x2))\n",
|
1187 |
+
"print(np.divide(x1,x2))\n",
|
1188 |
+
"print(np.power(x1,x2))\n",
|
1189 |
+
"print(np.mod(x1,x2))\n",
|
1190 |
+
"print(np.remainder(x1,x2))\n",
|
1191 |
+
"print(np.divmod(x1,x2))\n",
|
1192 |
+
"print(np.(x1,x2))"
|
1193 |
+
]
|
1194 |
+
},
|
1195 |
+
{
|
1196 |
+
"cell_type": "code",
|
1197 |
+
"execution_count": null,
|
1198 |
+
"id": "6b93fdd9",
|
1199 |
+
"metadata": {},
|
1200 |
+
"outputs": [],
|
1201 |
+
"source": []
|
1202 |
+
}
|
1203 |
+
],
|
1204 |
+
"metadata": {
|
1205 |
+
"kernelspec": {
|
1206 |
+
"display_name": "Python 3 (ipykernel)",
|
1207 |
+
"language": "python",
|
1208 |
+
"name": "python3"
|
1209 |
+
},
|
1210 |
+
"language_info": {
|
1211 |
+
"codemirror_mode": {
|
1212 |
+
"name": "ipython",
|
1213 |
+
"version": 3
|
1214 |
+
},
|
1215 |
+
"file_extension": ".py",
|
1216 |
+
"mimetype": "text/x-python",
|
1217 |
+
"name": "python",
|
1218 |
+
"nbconvert_exporter": "python",
|
1219 |
+
"pygments_lexer": "ipython3",
|
1220 |
+
"version": "3.9.12"
|
1221 |
+
}
|
1222 |
+
},
|
1223 |
+
"nbformat": 4,
|
1224 |
+
"nbformat_minor": 5
|
1225 |
+
}
|