├── jugwater.py
├── waterjug.py
└── md_lateef.ipynb


/jugwater.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | from collections import deque
 3 | a = int(input("Enter Jug A Capacity:"))
 4 | b = int(input("Enter Jug B Capacity:"))
 5 | ai = int(input("Initially Water in Jug A: "))
 6 | bi = int(input("Initially Water in Jug B: "))
 7 | af = int(input("Final State of Jug A: "))
 8 | bf = int(input("Final State of Jug B: "))
 9 | if a <= 0 or b <=0:
10 |     print("Jug capacities must be positive.")
11 |     exit(1)
12 | if ai < 0 or bi < 0 or af < 0 or bf < 0:
13 |     print("Negative values are not allowed.")
14 |     exit()
15 | if ai==af and bi==bf:
16 |     print(f"initial state is already the final state: juga{ai} and jugb={bi}")
17 |     exit()
18 | def bfs_wjug(a, b, ai, bi, af, bf):
19 |     visited = set()
20 |     queue = deque([(ai, bi,[])])
21 | 
22 |     while queue:
23 |         curr_ai, curr_bi, operations = queue.popleft()
24 | 
25 |         if(curr_ai,curr_bi) in visited:
26 |             continue
27 |         visited.add((curr_ai, curr_bi))
28 | 
29 |         if curr_ai == af and curr_bi ==bf:
30 |             for i,op in enumerate(operations):
31 |                 print(f"Step {i + 1}: {op}")
32 |                 print(f"Final State Reached Jug A = {curr_ai},Jug B = {curr_bi}")
33 |                 return
34 |             possible_operations = [
35 |                   (a,curr_bi,"Fill Jug A"),
36 |                   (curr_ai,b,"Fill Jug B"),
37 |                   (0, curr_bi,"Empty Jug A"),
38 |                   (curr_ai, 0, "Empry Jug B"),
39 |                   (curr_ai - min(curr_ai, b - curr_bi),curr_bi + min(curr_ai, b - curr_bi), "Pour from A to B"),
40 |                   (curr_ai + min(curr_bi, a - curr_ai), curr_bi -min(curr_bi, a - curr_ai), "Pour from B to A"),
41 |                 ]
42 | 
43 |             for next_ai, next_bi, op in possible_operatons:
44 |                 if(next_ai, next_bi) not in visited:
45 |                     queue.append((next_ai, next_bi, operations + [op]))
46 | 
47 |         print("No solution found.")
48 |         return
49 | 
50 |     gcd = math.gcd(a,b)
51 | 
52 |     if (af <=a and bf <=b) and (af % gcd == bf % gcd ==0):
53 |                     bfs_wjug(a, b, ai, bi, af, bf)
54 |     else:
55 |         print("the final state is not achievable with the given capacites.")
56 |         exit()
57 | 


--------------------------------------------------------------------------------
/waterjug.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | 
 3 | 
 4 | a = int(input("Enter Jug A Capacity: "))
 5 | b = int(input("Enter Jug B Capacity: "))
 6 | ai = int(input("Initially Water in Jug A: "))
 7 | bi = int(input("Initially Water in Jug B: "))
 8 | af = int(input("Final State of Jug A: "))
 9 | bf = int(input("Final State of Jug B: "))
10 | 
11 | 
12 | if a <= 0 or b <= 0:
13 |     print("Jug capacities must be positive.")
14 |     exit(1)
15 | if ai < 0 or bi < 0 or af < 0 or bf < 0:
16 |     print("Negative values are not allowed.")
17 |     exit(1)
18 | 
19 | 
20 | def wjug(a, b, ai, bi, af, bf):
21 |     print("List Of Operations You Can Do:\n")
22 |     print("1. Fill Jug A Completely")
23 |     print("2. Fill Jug B Completely")
24 |     print("3. Empty Jug A Completely")
25 |     print("4. Empty Jug B Completely")
26 |     print("5. Pour From Jug A till Jug B is full or A becomes empty")
27 |     print("6. Pour From Jug B till Jug A is full or B becomes empty")
28 |     print("7. Pour all from Jug B to Jug A")
29 |     print("8. Pour all from Jug A to Jug B")
30 | 
31 | 
32 |     while ai != af or bi != bf:
33 |         op = int(input("Enter the Operation (1-8): "))
34 | 
35 |         if op == 1:  
36 |             ai = a
37 |         elif op == 2: 
38 |             bi = b
39 |         elif op == 3:  
40 |             ai = 0
41 |         elif op == 4:  
42 |             bi = 0
43 |         elif op == 5:  
44 |             pour_amount = min(ai, b - bi)
45 |             ai -= pour_amount
46 |             bi += pour_amount
47 |         elif op == 6:  
48 |             pour_amount = min(bi, a - ai)
49 |             bi -= pour_amount
50 |             ai += pour_amount
51 |         elif op == 7:  
52 |             pour_amount = min(bi, a - ai)
53 |             ai += pour_amount
54 |             bi -= pour_amount
55 |         elif op == 8:  
56 |             pour_amount = min(ai, b - bi)
57 |             bi += pour_amount
58 |             ai -= pour_amount
59 |         else:
60 |             print("Invalid operation. Please choose a number between 1 and 8.")
61 |             continue
62 | 
63 |         print(f"Jug A: {ai}, Jug B: {bi}")
64 | 
65 |         if ai == af and bi == bf:
66 |             print("Final State Reached: Jug A =", ai, ", Jug B =", bi)
67 |             return
68 |        
69 | 
70 | 
71 |     print("Final State Reached: Jug A =", ai, ", Jug B =", bi)
72 | 
73 | 
74 | gcd = math.gcd(a, b)
75 | 
76 | if (af <= a and bf <= b) and (af % gcd == bf % gcd == 0):
77 |     wjug(a, b, ai, bi, af, bf)
78 | else:
79 |     print("The final state is not achievable with the given capacities.")
80 |     exit(1)
81 | 


--------------------------------------------------------------------------------
/md_lateef.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "b64296ff",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import numpy as np"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 2,
  16 |    "id": "c2b5bcae",
  17 |    "metadata": {},
  18 |    "outputs": [
  19 |     {
  20 |      "name": "stdout",
  21 |      "output_type": "stream",
  22 |      "text": [
  23 |       "Defaulting to user installation because normal site-packages is not writeable\n",
  24 |       "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (1.24.3)\n",
  25 |       "Note: you may need to restart the kernel to use updated packages.\n"
  26 |      ]
  27 |     },
  28 |     {
  29 |      "name": "stderr",
  30 |      "output_type": "stream",
  31 |      "text": [
  32 |       "\n",
  33 |       "[notice] A new release of pip is available: 23.3.1 -> 24.3.1\n",
  34 |       "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
  35 |      ]
  36 |     }
  37 |    ],
  38 |    "source": [
  39 |     "pip install numpy "
  40 |    ]
  41 |   },
  42 |   {
  43 |    "cell_type": "code",
  44 |    "execution_count": 4,
  45 |    "id": "e70d6410",
  46 |    "metadata": {},
  47 |    "outputs": [
  48 |     {
  49 |      "data": {
  50 |       "text/plain": [
  51 |        "'1.24.3'"
  52 |       ]
  53 |      },
  54 |      "execution_count": 4,
  55 |      "metadata": {},
  56 |      "output_type": "execute_result"
  57 |     }
  58 |    ],
  59 |    "source": [
  60 |     "np.__version__"
  61 |    ]
  62 |   },
  63 |   {
  64 |    "cell_type": "code",
  65 |    "execution_count": 14,
  66 |    "id": "c730909c",
  67 |    "metadata": {},
  68 |    "outputs": [
  69 |     {
  70 |      "data": {
  71 |       "text/plain": [
  72 |        "array([1, 2, 3])"
  73 |       ]
  74 |      },
  75 |      "execution_count": 14,
  76 |      "metadata": {},
  77 |      "output_type": "execute_result"
  78 |     }
  79 |    ],
  80 |    "source": [
  81 |     "array1=np.array([1,2,3])\n",
  82 |     "array1"
  83 |    ]
  84 |   },
  85 |   {
  86 |    "cell_type": "code",
  87 |    "execution_count": 18,
  88 |    "id": "c23d099c",
  89 |    "metadata": {},
  90 |    "outputs": [
  91 |     {
  92 |      "data": {
  93 |       "text/plain": [
  94 |        "numpy.ndarray"
  95 |       ]
  96 |      },
  97 |      "execution_count": 18,
  98 |      "metadata": {},
  99 |      "output_type": "execute_result"
 100 |     }
 101 |    ],
 102 |    "source": [
 103 |     "type(array1)"
 104 |    ]
 105 |   },
 106 |   {
 107 |    "cell_type": "code",
 108 |    "execution_count": 22,
 109 |    "id": "6468481c",
 110 |    "metadata": {},
 111 |    "outputs": [
 112 |     {
 113 |      "data": {
 114 |       "text/plain": [
 115 |        "array([[1, 2],\n",
 116 |        "       [3, 4]])"
 117 |       ]
 118 |      },
 119 |      "execution_count": 22,
 120 |      "metadata": {},
 121 |      "output_type": "execute_result"
 122 |     }
 123 |    ],
 124 |    "source": [
 125 |     "array2=np.array([[1,2],[3,4]])\n",
 126 |     "array2"
 127 |    ]
 128 |   },
 129 |   {
 130 |    "cell_type": "code",
 131 |    "execution_count": 21,
 132 |    "id": "edc97436",
 133 |    "metadata": {},
 134 |    "outputs": [
 135 |     {
 136 |      "data": {
 137 |       "text/plain": [
 138 |        "array([[1. , 2. , 3.3],\n",
 139 |        "       [4. , 5. , 6.4]])"
 140 |       ]
 141 |      },
 142 |      "execution_count": 21,
 143 |      "metadata": {},
 144 |      "output_type": "execute_result"
 145 |     }
 146 |    ],
 147 |    "source": [
 148 |     "arr2=np.array([ \n",
 149 |     "                [1,2,3.3],\n",
 150 |     "                [4,5,6.4]\n",
 151 |     "])\n",
 152 |     "arr2"
 153 |    ]
 154 |   },
 155 |   {
 156 |    "cell_type": "code",
 157 |    "execution_count": 27,
 158 |    "id": "53c222cf",
 159 |    "metadata": {},
 160 |    "outputs": [
 161 |     {
 162 |      "data": {
 163 |       "text/plain": [
 164 |        "array([[[1, 2],\n",
 165 |        "        [3, 4]],\n",
 166 |        "\n",
 167 |        "       [[5, 6],\n",
 168 |        "        [7, 8]]])"
 169 |       ]
 170 |      },
 171 |      "execution_count": 27,
 172 |      "metadata": {},
 173 |      "output_type": "execute_result"
 174 |     }
 175 |    ],
 176 |    "source": [
 177 |     "arr=np.array([\n",
 178 |     "              [\n",
 179 |     "                  [1,2],\n",
 180 |     "                  [3,4]\n",
 181 |     "              ],\n",
 182 |     "              [\n",
 183 |     "                  [5,6],\n",
 184 |     "                  [7,8]\n",
 185 |     "              ]\n",
 186 |     "])\n",
 187 |     "arr"
 188 |    ]
 189 |   },
 190 |   {
 191 |    "cell_type": "code",
 192 |    "execution_count": 19,
 193 |    "id": "f2fa6d7c",
 194 |    "metadata": {},
 195 |    "outputs": [
 196 |     {
 197 |      "ename": "NameError",
 198 |      "evalue": "name 'np' is not defined",
 199 |      "output_type": "error",
 200 |      "traceback": [
 201 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
 202 |       "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
 203 |       "Cell \u001b[1;32mIn[19], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m array3\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\n\u001b[0;32m      2\u001b[0m                 [\n\u001b[0;32m      3\u001b[0m                     [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],\n\u001b[0;32m      4\u001b[0m                     [\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m],\n\u001b[0;32m      5\u001b[0m                     [\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m9\u001b[39m]\n\u001b[0;32m      6\u001b[0m                 ],\n\u001b[0;32m      7\u001b[0m                  [\n\u001b[0;32m      8\u001b[0m                     [\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m21\u001b[39m,\u001b[38;5;241m10\u001b[39m],\n\u001b[0;32m      9\u001b[0m                     [\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m45\u001b[39m,\u001b[38;5;241m11\u001b[39m],\n\u001b[0;32m     10\u001b[0m                     [\u001b[38;5;241m47\u001b[39m,\u001b[38;5;241m91\u001b[39m,\u001b[38;5;241m51\u001b[39m]\n\u001b[0;32m     11\u001b[0m                  ]\n\u001b[0;32m     12\u001b[0m \n\u001b[0;32m     13\u001b[0m ])\n\u001b[0;32m     14\u001b[0m array3\n",
 204 |       "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
 205 |      ]
 206 |     }
 207 |    ],
 208 |    "source": [
 209 |     "array3=np.array([\n",
 210 |     "                [\n",
 211 |     "                    [1,2,3],\n",
 212 |     "                    [4,5,6],\n",
 213 |     "                    [7,8,9]\n",
 214 |     "                ],\n",
 215 |     "                 [\n",
 216 |     "                    [7,21,10],\n",
 217 |     "                    [20,45,11],\n",
 218 |     "                    [47,91,51]\n",
 219 |     "                 ]\n",
 220 |     "\n",
 221 |     "])\n",
 222 |     "array3"
 223 |    ]
 224 |   },
 225 |   {
 226 |    "cell_type": "code",
 227 |    "execution_count": null,
 228 |    "id": "ba61af3e",
 229 |    "metadata": {},
 230 |    "outputs": [],
 231 |    "source": [
 232 |     "arr=np.array((12,13,14))\n",
 233 |     "array"
 234 |    ]
 235 |   },
 236 |   {
 237 |    "cell_type": "code",
 238 |    "execution_count": 30,
 239 |    "id": "4a6f7675",
 240 |    "metadata": {},
 241 |    "outputs": [
 242 |     {
 243 |      "name": "stdout",
 244 |      "output_type": "stream",
 245 |      "text": [
 246 |       "3\n"
 247 |      ]
 248 |     },
 249 |     {
 250 |      "data": {
 251 |       "text/plain": [
 252 |        "3"
 253 |       ]
 254 |      },
 255 |      "execution_count": 30,
 256 |      "metadata": {},
 257 |      "output_type": "execute_result"
 258 |     }
 259 |    ],
 260 |    "source": [
 261 |     "print(array3.ndim)\n",
 262 |     "3"
 263 |    ]
 264 |   },
 265 |   {
 266 |    "cell_type": "code",
 267 |    "execution_count": 31,
 268 |    "id": "f004d379",
 269 |    "metadata": {},
 270 |    "outputs": [
 271 |     {
 272 |      "data": {
 273 |       "text/plain": [
 274 |        "array([[[[1, 2, 3, 5]]]])"
 275 |       ]
 276 |      },
 277 |      "execution_count": 31,
 278 |      "metadata": {},
 279 |      "output_type": "execute_result"
 280 |     }
 281 |    ],
 282 |    "source": [
 283 |     "array5=np.array([1,2,3,5],ndmin=4)\n",
 284 |     "array5"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "code",
 289 |    "execution_count": 33,
 290 |    "id": "015b69b0",
 291 |    "metadata": {},
 292 |    "outputs": [
 293 |     {
 294 |      "data": {
 295 |       "text/plain": [
 296 |        "(3,)"
 297 |       ]
 298 |      },
 299 |      "execution_count": 33,
 300 |      "metadata": {},
 301 |      "output_type": "execute_result"
 302 |     }
 303 |    ],
 304 |    "source": [
 305 |     "array1.shape"
 306 |    ]
 307 |   },
 308 |   {
 309 |    "cell_type": "code",
 310 |    "execution_count": 35,
 311 |    "id": "34e01ca5",
 312 |    "metadata": {},
 313 |    "outputs": [
 314 |     {
 315 |      "data": {
 316 |       "text/plain": [
 317 |        "(2, 3)"
 318 |       ]
 319 |      },
 320 |      "execution_count": 35,
 321 |      "metadata": {},
 322 |      "output_type": "execute_result"
 323 |     }
 324 |    ],
 325 |    "source": [
 326 |     "arr2.shape"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": 39,
 332 |    "id": "720e18c6",
 333 |    "metadata": {},
 334 |    "outputs": [
 335 |     {
 336 |      "data": {
 337 |       "text/plain": [
 338 |        "(2, 3, 3)"
 339 |       ]
 340 |      },
 341 |      "execution_count": 39,
 342 |      "metadata": {},
 343 |      "output_type": "execute_result"
 344 |     }
 345 |    ],
 346 |    "source": [
 347 |     "array3.shape"
 348 |    ]
 349 |   },
 350 |   {
 351 |    "cell_type": "code",
 352 |    "execution_count": 40,
 353 |    "id": "219ed834",
 354 |    "metadata": {},
 355 |    "outputs": [
 356 |     {
 357 |      "data": {
 358 |       "text/plain": [
 359 |        "(1, 1, 1, 4)"
 360 |       ]
 361 |      },
 362 |      "execution_count": 40,
 363 |      "metadata": {},
 364 |      "output_type": "execute_result"
 365 |     }
 366 |    ],
 367 |    "source": [
 368 |     "array5.shape"
 369 |    ]
 370 |   },
 371 |   {
 372 |    "cell_type": "code",
 373 |    "execution_count": 45,
 374 |    "id": "d43b496e",
 375 |    "metadata": {},
 376 |    "outputs": [
 377 |     {
 378 |      "name": "stdout",
 379 |      "output_type": "stream",
 380 |      "text": [
 381 |       "1.0\n",
 382 |       "2.0\n",
 383 |       "3.3\n",
 384 |       "4.0\n",
 385 |       "5.0\n",
 386 |       "6.4\n"
 387 |      ]
 388 |     }
 389 |    ],
 390 |    "source": [
 391 |     "for x in range(0, 2):\n",
 392 |     "    for y in range(0, 3):\n",
 393 |     "        print(arr2[x][y])"
 394 |    ]
 395 |   },
 396 |   {
 397 |    "cell_type": "code",
 398 |    "execution_count": 46,
 399 |    "id": "a9ebe058",
 400 |    "metadata": {},
 401 |    "outputs": [
 402 |     {
 403 |      "data": {
 404 |       "text/plain": [
 405 |        "dtype('int32')"
 406 |       ]
 407 |      },
 408 |      "execution_count": 46,
 409 |      "metadata": {},
 410 |      "output_type": "execute_result"
 411 |     }
 412 |    ],
 413 |    "source": [
 414 |     "array1.dtype"
 415 |    ]
 416 |   },
 417 |   {
 418 |    "cell_type": "code",
 419 |    "execution_count": 47,
 420 |    "id": "9a6dfde9",
 421 |    "metadata": {},
 422 |    "outputs": [
 423 |     {
 424 |      "data": {
 425 |       "text/plain": [
 426 |        "dtype('float64')"
 427 |       ]
 428 |      },
 429 |      "execution_count": 47,
 430 |      "metadata": {},
 431 |      "output_type": "execute_result"
 432 |     }
 433 |    ],
 434 |    "source": [
 435 |     "arr2.dtype"
 436 |    ]
 437 |   },
 438 |   {
 439 |    "cell_type": "code",
 440 |    "execution_count": 49,
 441 |    "id": "eb078713",
 442 |    "metadata": {},
 443 |    "outputs": [
 444 |     {
 445 |      "data": {
 446 |       "text/plain": [
 447 |        "dtype('int32')"
 448 |       ]
 449 |      },
 450 |      "execution_count": 49,
 451 |      "metadata": {},
 452 |      "output_type": "execute_result"
 453 |     }
 454 |    ],
 455 |    "source": [
 456 |     "array3.dtype"
 457 |    ]
 458 |   },
 459 |   {
 460 |    "cell_type": "code",
 461 |    "execution_count": 55,
 462 |    "id": "4591808d",
 463 |    "metadata": {},
 464 |    "outputs": [
 465 |     {
 466 |      "data": {
 467 |       "text/plain": [
 468 |        "dtype('S1')"
 469 |       ]
 470 |      },
 471 |      "execution_count": 55,
 472 |      "metadata": {},
 473 |      "output_type": "execute_result"
 474 |     }
 475 |    ],
 476 |    "source": [
 477 |     "array5.dtype"
 478 |    ]
 479 |   },
 480 |   {
 481 |    "cell_type": "code",
 482 |    "execution_count": 54,
 483 |    "id": "d3a0bd0e",
 484 |    "metadata": {},
 485 |    "outputs": [
 486 |     {
 487 |      "data": {
 488 |       "text/plain": [
 489 |        "array([b'1', b'2', b'3', b'4', b'5'], dtype='|S1')"
 490 |       ]
 491 |      },
 492 |      "execution_count": 54,
 493 |      "metadata": {},
 494 |      "output_type": "execute_result"
 495 |     }
 496 |    ],
 497 |    "source": [
 498 |     "array5=np.array([1,2,3,4,5],dtype='S')\n",
 499 |     "array5"
 500 |    ]
 501 |   },
 502 |   {
 503 |    "cell_type": "code",
 504 |    "execution_count": 56,
 505 |    "id": "a344d27f",
 506 |    "metadata": {},
 507 |    "outputs": [
 508 |     {
 509 |      "data": {
 510 |       "text/plain": [
 511 |        "array([[1, 2, 3],\n",
 512 |        "       [4, 5, 6]], dtype=int32)"
 513 |       ]
 514 |      },
 515 |      "execution_count": 56,
 516 |      "metadata": {},
 517 |      "output_type": "execute_result"
 518 |     }
 519 |    ],
 520 |    "source": [
 521 |     "arr2conv=arr2.astype('i')\n",
 522 |     "arr2conv"
 523 |    ]
 524 |   },
 525 |   {
 526 |    "cell_type": "code",
 527 |    "execution_count": 57,
 528 |    "id": "3c9ae405",
 529 |    "metadata": {},
 530 |    "outputs": [
 531 |     {
 532 |      "data": {
 533 |       "text/plain": [
 534 |        "array([[1., 2., 3.],\n",
 535 |        "       [4., 5., 6.]], dtype=float32)"
 536 |       ]
 537 |      },
 538 |      "execution_count": 57,
 539 |      "metadata": {},
 540 |      "output_type": "execute_result"
 541 |     }
 542 |    ],
 543 |    "source": [
 544 |     "arr2float=arr2conv.astype('f')\n",
 545 |     "arr2float"
 546 |    ]
 547 |   },
 548 |   {
 549 |    "cell_type": "code",
 550 |    "execution_count": 58,
 551 |    "id": "5aa558b6",
 552 |    "metadata": {},
 553 |    "outputs": [
 554 |     {
 555 |      "data": {
 556 |       "text/plain": [
 557 |        "array([[1, 1],\n",
 558 |        "       [1, 1]])"
 559 |       ]
 560 |      },
 561 |      "execution_count": 58,
 562 |      "metadata": {},
 563 |      "output_type": "execute_result"
 564 |     }
 565 |    ],
 566 |    "source": [
 567 |     "numone=np.ones((2,2),dtype=int)\n",
 568 |     "numone"
 569 |    ]
 570 |   },
 571 |   {
 572 |    "cell_type": "code",
 573 |    "execution_count": 59,
 574 |    "id": "d757dc09",
 575 |    "metadata": {},
 576 |    "outputs": [
 577 |     {
 578 |      "data": {
 579 |       "text/plain": [
 580 |        "array([1., 1., 1.])"
 581 |       ]
 582 |      },
 583 |      "execution_count": 59,
 584 |      "metadata": {},
 585 |      "output_type": "execute_result"
 586 |     }
 587 |    ],
 588 |    "source": [
 589 |     "num=np.ones(3)\n",
 590 |     "num"
 591 |    ]
 592 |   },
 593 |   {
 594 |    "cell_type": "code",
 595 |    "execution_count": 64,
 596 |    "id": "cda04f31",
 597 |    "metadata": {},
 598 |    "outputs": [
 599 |     {
 600 |      "data": {
 601 |       "text/plain": [
 602 |        "array([[[1., 1., 1.],\n",
 603 |        "        [1., 1., 1.]],\n",
 604 |        "\n",
 605 |        "       [[1., 1., 1.],\n",
 606 |        "        [1., 1., 1.]]])"
 607 |       ]
 608 |      },
 609 |      "execution_count": 64,
 610 |      "metadata": {},
 611 |      "output_type": "execute_result"
 612 |     }
 613 |    ],
 614 |    "source": [
 615 |     "num=np.ones((2,2,3))\n",
 616 |     "num"
 617 |    ]
 618 |   },
 619 |   {
 620 |    "cell_type": "code",
 621 |    "execution_count": 66,
 622 |    "id": "77675c95",
 623 |    "metadata": {},
 624 |    "outputs": [
 625 |     {
 626 |      "data": {
 627 |       "text/plain": [
 628 |        "array([[0, 0],\n",
 629 |        "       [0, 0],\n",
 630 |        "       [0, 0]])"
 631 |       ]
 632 |      },
 633 |      "execution_count": 66,
 634 |      "metadata": {},
 635 |      "output_type": "execute_result"
 636 |     }
 637 |    ],
 638 |    "source": [
 639 |     "numszero=np.zeros((3,2),dtype=int)\n",
 640 |     "numszero"
 641 |    ]
 642 |   },
 643 |   {
 644 |    "cell_type": "code",
 645 |    "execution_count": 67,
 646 |    "id": "790e4ae8",
 647 |    "metadata": {},
 648 |    "outputs": [
 649 |     {
 650 |      "data": {
 651 |       "text/plain": [
 652 |        "array([0, 3, 6, 9])"
 653 |       ]
 654 |      },
 655 |      "execution_count": 67,
 656 |      "metadata": {},
 657 |      "output_type": "execute_result"
 658 |     }
 659 |    ],
 660 |    "source": [
 661 |     "range_array=np.arange(0,10,3)\n",
 662 |     "range_array"
 663 |    ]
 664 |   },
 665 |   {
 666 |    "cell_type": "code",
 667 |    "execution_count": 70,
 668 |    "id": "28f20acd",
 669 |    "metadata": {},
 670 |    "outputs": [
 671 |     {
 672 |      "data": {
 673 |       "text/plain": [
 674 |        "array([0, 0, 0, 0, 0])"
 675 |       ]
 676 |      },
 677 |      "execution_count": 70,
 678 |      "metadata": {},
 679 |      "output_type": "execute_result"
 680 |     }
 681 |    ],
 682 |    "source": [
 683 |     "a=np.random.randint(low=1,size=5)\n",
 684 |     "a"
 685 |    ]
 686 |   },
 687 |   {
 688 |    "cell_type": "code",
 689 |    "execution_count": 71,
 690 |    "id": "26686419",
 691 |    "metadata": {},
 692 |    "outputs": [
 693 |     {
 694 |      "data": {
 695 |       "text/plain": [
 696 |        "array([[3, 1, 1],\n",
 697 |        "       [3, 1, 2]])"
 698 |       ]
 699 |      },
 700 |      "execution_count": 71,
 701 |      "metadata": {},
 702 |      "output_type": "execute_result"
 703 |     }
 704 |    ],
 705 |    "source": [
 706 |     "b=np.random.randint(low=1,high=6,size=(2,3))\n",
 707 |     "b"
 708 |    ]
 709 |   },
 710 |   {
 711 |    "cell_type": "code",
 712 |    "execution_count": 74,
 713 |    "id": "b84b808d",
 714 |    "metadata": {},
 715 |    "outputs": [
 716 |     {
 717 |      "data": {
 718 |       "text/plain": [
 719 |        "array([b'1', b'2', b'3', b'1', b'2', b'3', b'4', b'5'], dtype='|S11')"
 720 |       ]
 721 |      },
 722 |      "execution_count": 74,
 723 |      "metadata": {},
 724 |      "output_type": "execute_result"
 725 |     }
 726 |    ],
 727 |    "source": [
 728 |     "np.concatenate([array1,array5])"
 729 |    ]
 730 |   },
 731 |   {
 732 |    "cell_type": "code",
 733 |    "execution_count": 78,
 734 |    "id": "3d576c44",
 735 |    "metadata": {},
 736 |    "outputs": [
 737 |     {
 738 |      "data": {
 739 |       "text/plain": [
 740 |        "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9,  7, 21, 10, 20, 45, 11, 47, 91,\n",
 741 |        "       51])"
 742 |       ]
 743 |      },
 744 |      "execution_count": 78,
 745 |      "metadata": {},
 746 |      "output_type": "execute_result"
 747 |     }
 748 |    ],
 749 |    "source": [
 750 |     "flatarr=array3.flatten()\n",
 751 |     "flatarr"
 752 |    ]
 753 |   },
 754 |   {
 755 |    "cell_type": "code",
 756 |    "execution_count": 80,
 757 |    "id": "5a7b0918",
 758 |    "metadata": {},
 759 |    "outputs": [
 760 |     {
 761 |      "data": {
 762 |       "text/plain": [
 763 |        "array([[1, 2],\n",
 764 |        "       [3, 4]])"
 765 |       ]
 766 |      },
 767 |      "execution_count": 80,
 768 |      "metadata": {},
 769 |      "output_type": "execute_result"
 770 |     }
 771 |    ],
 772 |    "source": [
 773 |     "np.reshape(flatarr[:4],(2,2))"
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "code",
 778 |    "execution_count": 82,
 779 |    "id": "e733d482",
 780 |    "metadata": {},
 781 |    "outputs": [
 782 |     {
 783 |      "data": {
 784 |       "text/plain": [
 785 |        "array([[1., 0.],\n",
 786 |        "       [0., 1.]])"
 787 |       ]
 788 |      },
 789 |      "execution_count": 82,
 790 |      "metadata": {},
 791 |      "output_type": "execute_result"
 792 |     }
 793 |    ],
 794 |    "source": [
 795 |     "np.eye(2)"
 796 |    ]
 797 |   },
 798 |   {
 799 |    "cell_type": "code",
 800 |    "execution_count": 84,
 801 |    "id": "b0e12cec",
 802 |    "metadata": {},
 803 |    "outputs": [
 804 |     {
 805 |      "data": {
 806 |       "text/plain": [
 807 |        "array([[1., 0., 0.],\n",
 808 |        "       [0., 1., 0.],\n",
 809 |        "       [0., 0., 1.]])"
 810 |       ]
 811 |      },
 812 |      "execution_count": 84,
 813 |      "metadata": {},
 814 |      "output_type": "execute_result"
 815 |     }
 816 |    ],
 817 |    "source": [
 818 |     "np.eye(3)"
 819 |    ]
 820 |   },
 821 |   {
 822 |    "cell_type": "code",
 823 |    "execution_count": 86,
 824 |    "id": "c3615387",
 825 |    "metadata": {},
 826 |    "outputs": [
 827 |     {
 828 |      "data": {
 829 |       "text/plain": [
 830 |        "array([[1, 0, 0, 0],\n",
 831 |        "       [0, 1, 0, 0],\n",
 832 |        "       [0, 0, 1, 0],\n",
 833 |        "       [0, 0, 0, 1]])"
 834 |       ]
 835 |      },
 836 |      "execution_count": 86,
 837 |      "metadata": {},
 838 |      "output_type": "execute_result"
 839 |     }
 840 |    ],
 841 |    "source": [
 842 |     "np.eye(4,dtype=int)"
 843 |    ]
 844 |   },
 845 |   {
 846 |    "cell_type": "code",
 847 |    "execution_count": 88,
 848 |    "id": "b5823987",
 849 |    "metadata": {},
 850 |    "outputs": [
 851 |     {
 852 |      "data": {
 853 |       "text/html": [
 854 |        "<div>\n",
 855 |        "<style scoped>\n",
 856 |        "    .dataframe tbody tr th:only-of-type {\n",
 857 |        "        vertical-align: middle;\n",
 858 |        "    }\n",
 859 |        "\n",
 860 |        "    .dataframe tbody tr th {\n",
 861 |        "        vertical-align: top;\n",
 862 |        "    }\n",
 863 |        "\n",
 864 |        "    .dataframe thead th {\n",
 865 |        "        text-align: right;\n",
 866 |        "    }\n",
 867 |        "</style>\n",
 868 |        "<table border=\"1\" class=\"dataframe\">\n",
 869 |        "  <thead>\n",
 870 |        "    <tr style=\"text-align: right;\">\n",
 871 |        "      <th></th>\n",
 872 |        "      <th>0</th>\n",
 873 |        "      <th>1</th>\n",
 874 |        "    </tr>\n",
 875 |        "  </thead>\n",
 876 |        "  <tbody>\n",
 877 |        "    <tr>\n",
 878 |        "      <th>0</th>\n",
 879 |        "      <td>1</td>\n",
 880 |        "      <td>2</td>\n",
 881 |        "    </tr>\n",
 882 |        "    <tr>\n",
 883 |        "      <th>1</th>\n",
 884 |        "      <td>3</td>\n",
 885 |        "      <td>4</td>\n",
 886 |        "    </tr>\n",
 887 |        "  </tbody>\n",
 888 |        "</table>\n",
 889 |        "</div>"
 890 |       ],
 891 |       "text/plain": [
 892 |        "   0  1\n",
 893 |        "0  1  2\n",
 894 |        "1  3  4"
 895 |       ]
 896 |      },
 897 |      "execution_count": 88,
 898 |      "metadata": {},
 899 |      "output_type": "execute_result"
 900 |     }
 901 |    ],
 902 |    "source": [
 903 |     "import pandas as pd\n",
 904 |     "df=pd.DataFrame(array2)\n",
 905 |     "df"
 906 |    ]
 907 |   },
 908 |   {
 909 |    "cell_type": "code",
 910 |    "execution_count": 1,
 911 |    "id": "e24bbccd",
 912 |    "metadata": {},
 913 |    "outputs": [],
 914 |    "source": [
 915 |     "import pandas as pd"
 916 |    ]
 917 |   },
 918 |   {
 919 |    "cell_type": "code",
 920 |    "execution_count": 2,
 921 |    "id": "54265fca",
 922 |    "metadata": {},
 923 |    "outputs": [
 924 |     {
 925 |      "data": {
 926 |       "text/plain": [
 927 |        "'1.5.3'"
 928 |       ]
 929 |      },
 930 |      "execution_count": 2,
 931 |      "metadata": {},
 932 |      "output_type": "execute_result"
 933 |     }
 934 |    ],
 935 |    "source": [
 936 |     "pd.__version__"
 937 |    ]
 938 |   },
 939 |   {
 940 |    "cell_type": "code",
 941 |    "execution_count": 3,
 942 |    "id": "961aa7f0",
 943 |    "metadata": {},
 944 |    "outputs": [
 945 |     {
 946 |      "data": {
 947 |       "text/plain": [
 948 |        "0       BMW\n",
 949 |        "1    TOYOTA\n",
 950 |        "2     HONDA\n",
 951 |        "dtype: object"
 952 |       ]
 953 |      },
 954 |      "execution_count": 3,
 955 |      "metadata": {},
 956 |      "output_type": "execute_result"
 957 |     }
 958 |    ],
 959 |    "source": [
 960 |     "cars=pd.Series([\"BMW\",\"TOYOTA\",\"HONDA\"])\n",
 961 |     "cars"
 962 |    ]
 963 |   },
 964 |   {
 965 |    "cell_type": "code",
 966 |    "execution_count": 4,
 967 |    "id": "f328336e",
 968 |    "metadata": {},
 969 |    "outputs": [
 970 |     {
 971 |      "data": {
 972 |       "text/plain": [
 973 |        "first        BMW\n",
 974 |        "second    TOYOTA\n",
 975 |        "third      HONDA\n",
 976 |        "dtype: object"
 977 |       ]
 978 |      },
 979 |      "execution_count": 4,
 980 |      "metadata": {},
 981 |      "output_type": "execute_result"
 982 |     }
 983 |    ],
 984 |    "source": [
 985 |     "sindex=pd.Series([\"BMW\",\"TOYOTA\",\"HONDA\"],\n",
 986 |     "                 index=[\"first\",\"second\",\"third\"])\n",
 987 |     "sindex"
 988 |    ]
 989 |   },
 990 |   {
 991 |    "cell_type": "code",
 992 |    "execution_count": 5,
 993 |    "id": "5522e096",
 994 |    "metadata": {},
 995 |    "outputs": [
 996 |     {
 997 |      "data": {
 998 |       "text/plain": [
 999 |        "'TOYOTA'"
1000 |       ]
1001 |      },
1002 |      "execution_count": 5,
1003 |      "metadata": {},
1004 |      "output_type": "execute_result"
1005 |     }
1006 |    ],
1007 |    "source": [
1008 |     "sindex[\"second\"]"
1009 |    ]
1010 |   },
1011 |   {
1012 |    "cell_type": "code",
1013 |    "execution_count": 6,
1014 |    "id": "8fe1c64a",
1015 |    "metadata": {},
1016 |    "outputs": [
1017 |     {
1018 |      "data": {
1019 |       "text/plain": [
1020 |        "0      Red\n",
1021 |        "1     Blue\n",
1022 |        "2    White\n",
1023 |        "dtype: object"
1024 |       ]
1025 |      },
1026 |      "execution_count": 6,
1027 |      "metadata": {},
1028 |      "output_type": "execute_result"
1029 |     }
1030 |    ],
1031 |    "source": [
1032 |     "colors=pd.Series([\"Red\",\"Blue\",\"White\"])\n",
1033 |     "colors"
1034 |    ]
1035 |   },
1036 |   {
1037 |    "cell_type": "code",
1038 |    "execution_count": 7,
1039 |    "id": "7247da0d",
1040 |    "metadata": {},
1041 |    "outputs": [
1042 |     {
1043 |      "data": {
1044 |       "text/html": [
1045 |        "<div>\n",
1046 |        "<style scoped>\n",
1047 |        "    .dataframe tbody tr th:only-of-type {\n",
1048 |        "        vertical-align: middle;\n",
1049 |        "    }\n",
1050 |        "\n",
1051 |        "    .dataframe tbody tr th {\n",
1052 |        "        vertical-align: top;\n",
1053 |        "    }\n",
1054 |        "\n",
1055 |        "    .dataframe thead th {\n",
1056 |        "        text-align: right;\n",
1057 |        "    }\n",
1058 |        "</style>\n",
1059 |        "<table border=\"1\" class=\"dataframe\">\n",
1060 |        "  <thead>\n",
1061 |        "    <tr style=\"text-align: right;\">\n",
1062 |        "      <th></th>\n",
1063 |        "      <th>car make</th>\n",
1064 |        "      <th>color</th>\n",
1065 |        "    </tr>\n",
1066 |        "  </thead>\n",
1067 |        "  <tbody>\n",
1068 |        "    <tr>\n",
1069 |        "      <th>0</th>\n",
1070 |        "      <td>BMW</td>\n",
1071 |        "      <td>Red</td>\n",
1072 |        "    </tr>\n",
1073 |        "    <tr>\n",
1074 |        "      <th>1</th>\n",
1075 |        "      <td>TOYOTA</td>\n",
1076 |        "      <td>Blue</td>\n",
1077 |        "    </tr>\n",
1078 |        "    <tr>\n",
1079 |        "      <th>2</th>\n",
1080 |        "      <td>HONDA</td>\n",
1081 |        "      <td>White</td>\n",
1082 |        "    </tr>\n",
1083 |        "  </tbody>\n",
1084 |        "</table>\n",
1085 |        "</div>"
1086 |       ],
1087 |       "text/plain": [
1088 |        "  car make  color\n",
1089 |        "0      BMW    Red\n",
1090 |        "1   TOYOTA   Blue\n",
1091 |        "2    HONDA  White"
1092 |       ]
1093 |      },
1094 |      "execution_count": 7,
1095 |      "metadata": {},
1096 |      "output_type": "execute_result"
1097 |     }
1098 |    ],
1099 |    "source": [
1100 |     "car_data=pd.DataFrame({\"car make\":cars,\n",
1101 |     "                       \"color\":colors})\n",
1102 |     "car_data"
1103 |    ]
1104 |   },
1105 |   {
1106 |    "cell_type": "code",
1107 |    "execution_count": 8,
1108 |    "id": "68bf9452",
1109 |    "metadata": {},
1110 |    "outputs": [
1111 |     {
1112 |      "data": {
1113 |       "text/html": [
1114 |        "<div>\n",
1115 |        "<style scoped>\n",
1116 |        "    .dataframe tbody tr th:only-of-type {\n",
1117 |        "        vertical-align: middle;\n",
1118 |        "    }\n",
1119 |        "\n",
1120 |        "    .dataframe tbody tr th {\n",
1121 |        "        vertical-align: top;\n",
1122 |        "    }\n",
1123 |        "\n",
1124 |        "    .dataframe thead th {\n",
1125 |        "        text-align: right;\n",
1126 |        "    }\n",
1127 |        "</style>\n",
1128 |        "<table border=\"1\" class=\"dataframe\">\n",
1129 |        "  <thead>\n",
1130 |        "    <tr style=\"text-align: right;\">\n",
1131 |        "      <th></th>\n",
1132 |        "      <th>Make</th>\n",
1133 |        "      <th>Colour</th>\n",
1134 |        "      <th>Odometer (KM)</th>\n",
1135 |        "      <th>Doors</th>\n",
1136 |        "      <th>Price</th>\n",
1137 |        "    </tr>\n",
1138 |        "  </thead>\n",
1139 |        "  <tbody>\n",
1140 |        "    <tr>\n",
1141 |        "      <th>0</th>\n",
1142 |        "      <td>Toyota</td>\n",
1143 |        "      <td>White</td>\n",
1144 |        "      <td>150043</td>\n",
1145 |        "      <td>4</td>\n",
1146 |        "      <td>$4,000.00</td>\n",
1147 |        "    </tr>\n",
1148 |        "    <tr>\n",
1149 |        "      <th>1</th>\n",
1150 |        "      <td>Honda</td>\n",
1151 |        "      <td>Red</td>\n",
1152 |        "      <td>87899</td>\n",
1153 |        "      <td>4</td>\n",
1154 |        "      <td>$5,000.00</td>\n",
1155 |        "    </tr>\n",
1156 |        "    <tr>\n",
1157 |        "      <th>2</th>\n",
1158 |        "      <td>Toyota</td>\n",
1159 |        "      <td>Blue</td>\n",
1160 |        "      <td>32549</td>\n",
1161 |        "      <td>3</td>\n",
1162 |        "      <td>$7,000.00</td>\n",
1163 |        "    </tr>\n",
1164 |        "    <tr>\n",
1165 |        "      <th>3</th>\n",
1166 |        "      <td>BMW</td>\n",
1167 |        "      <td>Black</td>\n",
1168 |        "      <td>11179</td>\n",
1169 |        "      <td>5</td>\n",
1170 |        "      <td>$22,000.00</td>\n",
1171 |        "    </tr>\n",
1172 |        "    <tr>\n",
1173 |        "      <th>4</th>\n",
1174 |        "      <td>Nissan</td>\n",
1175 |        "      <td>White</td>\n",
1176 |        "      <td>213095</td>\n",
1177 |        "      <td>4</td>\n",
1178 |        "      <td>$3,500.00</td>\n",
1179 |        "    </tr>\n",
1180 |        "    <tr>\n",
1181 |        "      <th>5</th>\n",
1182 |        "      <td>Toyota</td>\n",
1183 |        "      <td>Green</td>\n",
1184 |        "      <td>99213</td>\n",
1185 |        "      <td>4</td>\n",
1186 |        "      <td>$4,500.00</td>\n",
1187 |        "    </tr>\n",
1188 |        "    <tr>\n",
1189 |        "      <th>6</th>\n",
1190 |        "      <td>Honda</td>\n",
1191 |        "      <td>Blue</td>\n",
1192 |        "      <td>45698</td>\n",
1193 |        "      <td>4</td>\n",
1194 |        "      <td>$7,500.00</td>\n",
1195 |        "    </tr>\n",
1196 |        "    <tr>\n",
1197 |        "      <th>7</th>\n",
1198 |        "      <td>Honda</td>\n",
1199 |        "      <td>Blue</td>\n",
1200 |        "      <td>54738</td>\n",
1201 |        "      <td>4</td>\n",
1202 |        "      <td>$7,000.00</td>\n",
1203 |        "    </tr>\n",
1204 |        "    <tr>\n",
1205 |        "      <th>8</th>\n",
1206 |        "      <td>Toyota</td>\n",
1207 |        "      <td>White</td>\n",
1208 |        "      <td>60000</td>\n",
1209 |        "      <td>4</td>\n",
1210 |        "      <td>$6,250.00</td>\n",
1211 |        "    </tr>\n",
1212 |        "    <tr>\n",
1213 |        "      <th>9</th>\n",
1214 |        "      <td>Nissan</td>\n",
1215 |        "      <td>White</td>\n",
1216 |        "      <td>31600</td>\n",
1217 |        "      <td>4</td>\n",
1218 |        "      <td>$9,700.00</td>\n",
1219 |        "    </tr>\n",
1220 |        "  </tbody>\n",
1221 |        "</table>\n",
1222 |        "</div>"
1223 |       ],
1224 |       "text/plain": [
1225 |        "     Make Colour  Odometer (KM)  Doors       Price\n",
1226 |        "0  Toyota  White         150043      4   $4,000.00\n",
1227 |        "1   Honda    Red          87899      4   $5,000.00\n",
1228 |        "2  Toyota   Blue          32549      3   $7,000.00\n",
1229 |        "3     BMW  Black          11179      5  $22,000.00\n",
1230 |        "4  Nissan  White         213095      4   $3,500.00\n",
1231 |        "5  Toyota  Green          99213      4   $4,500.00\n",
1232 |        "6   Honda   Blue          45698      4   $7,500.00\n",
1233 |        "7   Honda   Blue          54738      4   $7,000.00\n",
1234 |        "8  Toyota  White          60000      4   $6,250.00\n",
1235 |        "9  Nissan  White          31600      4   $9,700.00"
1236 |       ]
1237 |      },
1238 |      "execution_count": 8,
1239 |      "metadata": {},
1240 |      "output_type": "execute_result"
1241 |     }
1242 |    ],
1243 |    "source": [
1244 |     "car_sales=pd.read_csv(\"car-sales.csv\")\n",
1245 |     "car_sales"
1246 |    ]
1247 |   },
1248 |   {
1249 |    "cell_type": "code",
1250 |    "execution_count": 9,
1251 |    "id": "50054e0d",
1252 |    "metadata": {},
1253 |    "outputs": [
1254 |     {
1255 |      "data": {
1256 |       "text/html": [
1257 |        "<div>\n",
1258 |        "<style scoped>\n",
1259 |        "    .dataframe tbody tr th:only-of-type {\n",
1260 |        "        vertical-align: middle;\n",
1261 |        "    }\n",
1262 |        "\n",
1263 |        "    .dataframe tbody tr th {\n",
1264 |        "        vertical-align: top;\n",
1265 |        "    }\n",
1266 |        "\n",
1267 |        "    .dataframe thead th {\n",
1268 |        "        text-align: right;\n",
1269 |        "    }\n",
1270 |        "</style>\n",
1271 |        "<table border=\"1\" class=\"dataframe\">\n",
1272 |        "  <thead>\n",
1273 |        "    <tr style=\"text-align: right;\">\n",
1274 |        "      <th></th>\n",
1275 |        "      <th>Make</th>\n",
1276 |        "      <th>Colour</th>\n",
1277 |        "      <th>Odometer (KM)</th>\n",
1278 |        "      <th>Doors</th>\n",
1279 |        "      <th>Price</th>\n",
1280 |        "    </tr>\n",
1281 |        "  </thead>\n",
1282 |        "  <tbody>\n",
1283 |        "    <tr>\n",
1284 |        "      <th>0</th>\n",
1285 |        "      <td>Toyota</td>\n",
1286 |        "      <td>White</td>\n",
1287 |        "      <td>150043</td>\n",
1288 |        "      <td>4</td>\n",
1289 |        "      <td>$4,000.00</td>\n",
1290 |        "    </tr>\n",
1291 |        "    <tr>\n",
1292 |        "      <th>1</th>\n",
1293 |        "      <td>Honda</td>\n",
1294 |        "      <td>Red</td>\n",
1295 |        "      <td>87899</td>\n",
1296 |        "      <td>4</td>\n",
1297 |        "      <td>$5,000.00</td>\n",
1298 |        "    </tr>\n",
1299 |        "    <tr>\n",
1300 |        "      <th>2</th>\n",
1301 |        "      <td>Toyota</td>\n",
1302 |        "      <td>Blue</td>\n",
1303 |        "      <td>32549</td>\n",
1304 |        "      <td>3</td>\n",
1305 |        "      <td>$7,000.00</td>\n",
1306 |        "    </tr>\n",
1307 |        "    <tr>\n",
1308 |        "      <th>3</th>\n",
1309 |        "      <td>BMW</td>\n",
1310 |        "      <td>Black</td>\n",
1311 |        "      <td>11179</td>\n",
1312 |        "      <td>5</td>\n",
1313 |        "      <td>$22,000.00</td>\n",
1314 |        "    </tr>\n",
1315 |        "    <tr>\n",
1316 |        "      <th>4</th>\n",
1317 |        "      <td>Nissan</td>\n",
1318 |        "      <td>White</td>\n",
1319 |        "      <td>213095</td>\n",
1320 |        "      <td>4</td>\n",
1321 |        "      <td>$3,500.00</td>\n",
1322 |        "    </tr>\n",
1323 |        "    <tr>\n",
1324 |        "      <th>5</th>\n",
1325 |        "      <td>Toyota</td>\n",
1326 |        "      <td>Green</td>\n",
1327 |        "      <td>99213</td>\n",
1328 |        "      <td>4</td>\n",
1329 |        "      <td>$4,500.00</td>\n",
1330 |        "    </tr>\n",
1331 |        "    <tr>\n",
1332 |        "      <th>6</th>\n",
1333 |        "      <td>Honda</td>\n",
1334 |        "      <td>Blue</td>\n",
1335 |        "      <td>45698</td>\n",
1336 |        "      <td>4</td>\n",
1337 |        "      <td>$7,500.00</td>\n",
1338 |        "    </tr>\n",
1339 |        "    <tr>\n",
1340 |        "      <th>7</th>\n",
1341 |        "      <td>Honda</td>\n",
1342 |        "      <td>Blue</td>\n",
1343 |        "      <td>54738</td>\n",
1344 |        "      <td>4</td>\n",
1345 |        "      <td>$7,000.00</td>\n",
1346 |        "    </tr>\n",
1347 |        "    <tr>\n",
1348 |        "      <th>8</th>\n",
1349 |        "      <td>Toyota</td>\n",
1350 |        "      <td>White</td>\n",
1351 |        "      <td>60000</td>\n",
1352 |        "      <td>4</td>\n",
1353 |        "      <td>$6,250.00</td>\n",
1354 |        "    </tr>\n",
1355 |        "    <tr>\n",
1356 |        "      <th>9</th>\n",
1357 |        "      <td>Nissan</td>\n",
1358 |        "      <td>White</td>\n",
1359 |        "      <td>31600</td>\n",
1360 |        "      <td>4</td>\n",
1361 |        "      <td>$9,700.00</td>\n",
1362 |        "    </tr>\n",
1363 |        "  </tbody>\n",
1364 |        "</table>\n",
1365 |        "</div>"
1366 |       ],
1367 |       "text/plain": [
1368 |        "     Make Colour  Odometer (KM)  Doors       Price\n",
1369 |        "0  Toyota  White         150043      4   $4,000.00\n",
1370 |        "1   Honda    Red          87899      4   $5,000.00\n",
1371 |        "2  Toyota   Blue          32549      3   $7,000.00\n",
1372 |        "3     BMW  Black          11179      5  $22,000.00\n",
1373 |        "4  Nissan  White         213095      4   $3,500.00\n",
1374 |        "5  Toyota  Green          99213      4   $4,500.00\n",
1375 |        "6   Honda   Blue          45698      4   $7,500.00\n",
1376 |        "7   Honda   Blue          54738      4   $7,000.00\n",
1377 |        "8  Toyota  White          60000      4   $6,250.00\n",
1378 |        "9  Nissan  White          31600      4   $9,700.00"
1379 |       ]
1380 |      },
1381 |      "execution_count": 9,
1382 |      "metadata": {},
1383 |      "output_type": "execute_result"
1384 |     }
1385 |    ],
1386 |    "source": [
1387 |     "export_cars=pd.read_csv(\"export_cars.csv\")\n",
1388 |     "export_cars"
1389 |    ]
1390 |   },
1391 |   {
1392 |    "cell_type": "code",
1393 |    "execution_count": 10,
1394 |    "id": "37e6d990",
1395 |    "metadata": {},
1396 |    "outputs": [
1397 |     {
1398 |      "data": {
1399 |       "text/html": [
1400 |        "<div>\n",
1401 |        "<style scoped>\n",
1402 |        "    .dataframe tbody tr th:only-of-type {\n",
1403 |        "        vertical-align: middle;\n",
1404 |        "    }\n",
1405 |        "\n",
1406 |        "    .dataframe tbody tr th {\n",
1407 |        "        vertical-align: top;\n",
1408 |        "    }\n",
1409 |        "\n",
1410 |        "    .dataframe thead th {\n",
1411 |        "        text-align: right;\n",
1412 |        "    }\n",
1413 |        "</style>\n",
1414 |        "<table border=\"1\" class=\"dataframe\">\n",
1415 |        "  <thead>\n",
1416 |        "    <tr style=\"text-align: right;\">\n",
1417 |        "      <th></th>\n",
1418 |        "      <th>Odometer (KM)</th>\n",
1419 |        "      <th>Doors</th>\n",
1420 |        "    </tr>\n",
1421 |        "  </thead>\n",
1422 |        "  <tbody>\n",
1423 |        "    <tr>\n",
1424 |        "      <th>count</th>\n",
1425 |        "      <td>10.000000</td>\n",
1426 |        "      <td>10.000000</td>\n",
1427 |        "    </tr>\n",
1428 |        "    <tr>\n",
1429 |        "      <th>mean</th>\n",
1430 |        "      <td>78601.400000</td>\n",
1431 |        "      <td>4.000000</td>\n",
1432 |        "    </tr>\n",
1433 |        "    <tr>\n",
1434 |        "      <th>std</th>\n",
1435 |        "      <td>61983.471735</td>\n",
1436 |        "      <td>0.471405</td>\n",
1437 |        "    </tr>\n",
1438 |        "    <tr>\n",
1439 |        "      <th>min</th>\n",
1440 |        "      <td>11179.000000</td>\n",
1441 |        "      <td>3.000000</td>\n",
1442 |        "    </tr>\n",
1443 |        "    <tr>\n",
1444 |        "      <th>25%</th>\n",
1445 |        "      <td>35836.250000</td>\n",
1446 |        "      <td>4.000000</td>\n",
1447 |        "    </tr>\n",
1448 |        "    <tr>\n",
1449 |        "      <th>50%</th>\n",
1450 |        "      <td>57369.000000</td>\n",
1451 |        "      <td>4.000000</td>\n",
1452 |        "    </tr>\n",
1453 |        "    <tr>\n",
1454 |        "      <th>75%</th>\n",
1455 |        "      <td>96384.500000</td>\n",
1456 |        "      <td>4.000000</td>\n",
1457 |        "    </tr>\n",
1458 |        "    <tr>\n",
1459 |        "      <th>max</th>\n",
1460 |        "      <td>213095.000000</td>\n",
1461 |        "      <td>5.000000</td>\n",
1462 |        "    </tr>\n",
1463 |        "  </tbody>\n",
1464 |        "</table>\n",
1465 |        "</div>"
1466 |       ],
1467 |       "text/plain": [
1468 |        "       Odometer (KM)      Doors\n",
1469 |        "count      10.000000  10.000000\n",
1470 |        "mean    78601.400000   4.000000\n",
1471 |        "std     61983.471735   0.471405\n",
1472 |        "min     11179.000000   3.000000\n",
1473 |        "25%     35836.250000   4.000000\n",
1474 |        "50%     57369.000000   4.000000\n",
1475 |        "75%     96384.500000   4.000000\n",
1476 |        "max    213095.000000   5.000000"
1477 |       ]
1478 |      },
1479 |      "execution_count": 10,
1480 |      "metadata": {},
1481 |      "output_type": "execute_result"
1482 |     }
1483 |    ],
1484 |    "source": [
1485 |     "export_cars.describe()"
1486 |    ]
1487 |   },
1488 |   {
1489 |    "cell_type": "code",
1490 |    "execution_count": 12,
1491 |    "id": "910850ef",
1492 |    "metadata": {},
1493 |    "outputs": [
1494 |     {
1495 |      "data": {
1496 |       "text/plain": [
1497 |        "Make             object\n",
1498 |        "Colour           object\n",
1499 |        "Odometer (KM)     int64\n",
1500 |        "Doors             int64\n",
1501 |        "Price            object\n",
1502 |        "dtype: object"
1503 |       ]
1504 |      },
1505 |      "execution_count": 12,
1506 |      "metadata": {},
1507 |      "output_type": "execute_result"
1508 |     }
1509 |    ],
1510 |    "source": [
1511 |     "export_cars.dtypes"
1512 |    ]
1513 |   },
1514 |   {
1515 |    "cell_type": "code",
1516 |    "execution_count": 14,
1517 |    "id": "21cba821",
1518 |    "metadata": {},
1519 |    "outputs": [
1520 |     {
1521 |      "data": {
1522 |       "text/plain": [
1523 |        "(10, 5)"
1524 |       ]
1525 |      },
1526 |      "execution_count": 14,
1527 |      "metadata": {},
1528 |      "output_type": "execute_result"
1529 |     }
1530 |    ],
1531 |    "source": [
1532 |     "export_cars.shape"
1533 |    ]
1534 |   },
1535 |   {
1536 |    "cell_type": "code",
1537 |    "execution_count": 15,
1538 |    "id": "98368512",
1539 |    "metadata": {},
1540 |    "outputs": [
1541 |     {
1542 |      "name": "stdout",
1543 |      "output_type": "stream",
1544 |      "text": [
1545 |       "<class 'pandas.core.frame.DataFrame'>\n",
1546 |       "RangeIndex: 10 entries, 0 to 9\n",
1547 |       "Data columns (total 5 columns):\n",
1548 |       " #   Column         Non-Null Count  Dtype \n",
1549 |       "---  ------         --------------  ----- \n",
1550 |       " 0   Make           10 non-null     object\n",
1551 |       " 1   Colour         10 non-null     object\n",
1552 |       " 2   Odometer (KM)  10 non-null     int64 \n",
1553 |       " 3   Doors          10 non-null     int64 \n",
1554 |       " 4   Price          10 non-null     object\n",
1555 |       "dtypes: int64(2), object(3)\n",
1556 |       "memory usage: 532.0+ bytes\n"
1557 |      ]
1558 |     }
1559 |    ],
1560 |    "source": [
1561 |     "export_cars.info()"
1562 |    ]
1563 |   },
1564 |   {
1565 |    "cell_type": "code",
1566 |    "execution_count": 16,
1567 |    "id": "d3a9fc81",
1568 |    "metadata": {},
1569 |    "outputs": [
1570 |     {
1571 |      "data": {
1572 |       "text/plain": [
1573 |        "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
1574 |       ]
1575 |      },
1576 |      "execution_count": 16,
1577 |      "metadata": {},
1578 |      "output_type": "execute_result"
1579 |     }
1580 |    ],
1581 |    "source": [
1582 |     "export_cars.columns"
1583 |    ]
1584 |   },
1585 |   {
1586 |    "cell_type": "code",
1587 |    "execution_count": 17,
1588 |    "id": "92cd5466",
1589 |    "metadata": {},
1590 |    "outputs": [
1591 |     {
1592 |      "data": {
1593 |       "text/plain": [
1594 |        "RangeIndex(start=0, stop=10, step=1)"
1595 |       ]
1596 |      },
1597 |      "execution_count": 17,
1598 |      "metadata": {},
1599 |      "output_type": "execute_result"
1600 |     }
1601 |    ],
1602 |    "source": [
1603 |     "export_cars.index"
1604 |    ]
1605 |   },
1606 |   {
1607 |    "cell_type": "code",
1608 |    "execution_count": 18,
1609 |    "id": "b68d7976",
1610 |    "metadata": {},
1611 |    "outputs": [
1612 |     {
1613 |      "data": {
1614 |       "text/plain": [
1615 |        "<bound method NDFrame.describe of      Make Colour  Odometer (KM)  Doors       Price\n",
1616 |        "0  Toyota  White         150043      4   $4,000.00\n",
1617 |        "1   Honda    Red          87899      4   $5,000.00\n",
1618 |        "2  Toyota   Blue          32549      3   $7,000.00\n",
1619 |        "3     BMW  Black          11179      5  $22,000.00\n",
1620 |        "4  Nissan  White         213095      4   $3,500.00\n",
1621 |        "5  Toyota  Green          99213      4   $4,500.00\n",
1622 |        "6   Honda   Blue          45698      4   $7,500.00\n",
1623 |        "7   Honda   Blue          54738      4   $7,000.00\n",
1624 |        "8  Toyota  White          60000      4   $6,250.00\n",
1625 |        "9  Nissan  White          31600      4   $9,700.00>"
1626 |       ]
1627 |      },
1628 |      "execution_count": 18,
1629 |      "metadata": {},
1630 |      "output_type": "execute_result"
1631 |     }
1632 |    ],
1633 |    "source": [
1634 |     "export_cars.describe"
1635 |    ]
1636 |   },
1637 |   {
1638 |    "cell_type": "code",
1639 |    "execution_count": 20,
1640 |    "id": "639a50de",
1641 |    "metadata": {},
1642 |    "outputs": [
1643 |     {
1644 |      "data": {
1645 |       "text/plain": [
1646 |        "Odometer (KM)    78601.4\n",
1647 |        "Doors                4.0\n",
1648 |        "dtype: float64"
1649 |       ]
1650 |      },
1651 |      "execution_count": 20,
1652 |      "metadata": {},
1653 |      "output_type": "execute_result"
1654 |     }
1655 |    ],
1656 |    "source": [
1657 |     "car_sales[[\"Odometer (KM)\",\"Doors\"]].mean()"
1658 |    ]
1659 |   },
1660 |   {
1661 |    "cell_type": "code",
1662 |    "execution_count": 21,
1663 |    "id": "4d284ac2",
1664 |    "metadata": {},
1665 |    "outputs": [
1666 |     {
1667 |      "data": {
1668 |       "text/plain": [
1669 |        "Make             ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...\n",
1670 |        "Colour               WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite\n",
1671 |        "Odometer (KM)                                               786014\n",
1672 |        "Doors                                                           40\n",
1673 |        "Price            $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...\n",
1674 |        "dtype: object"
1675 |       ]
1676 |      },
1677 |      "execution_count": 21,
1678 |      "metadata": {},
1679 |      "output_type": "execute_result"
1680 |     }
1681 |    ],
1682 |    "source": [
1683 |     "car_sales.sum()"
1684 |    ]
1685 |   },
1686 |   {
1687 |    "cell_type": "code",
1688 |    "execution_count": 22,
1689 |    "id": "666aefab",
1690 |    "metadata": {},
1691 |    "outputs": [
1692 |     {
1693 |      "data": {
1694 |       "text/plain": [
1695 |        "40"
1696 |       ]
1697 |      },
1698 |      "execution_count": 22,
1699 |      "metadata": {},
1700 |      "output_type": "execute_result"
1701 |     }
1702 |    ],
1703 |    "source": [
1704 |     "car_sales[\"Doors\"].sum()"
1705 |    ]
1706 |   },
1707 |   {
1708 |    "cell_type": "code",
1709 |    "execution_count": 23,
1710 |    "id": "7fcf4d0f",
1711 |    "metadata": {},
1712 |    "outputs": [
1713 |     {
1714 |      "data": {
1715 |       "text/plain": [
1716 |        "10"
1717 |       ]
1718 |      },
1719 |      "execution_count": 23,
1720 |      "metadata": {},
1721 |      "output_type": "execute_result"
1722 |     }
1723 |    ],
1724 |    "source": [
1725 |     "len(car_sales)"
1726 |    ]
1727 |   },
1728 |   {
1729 |    "cell_type": "code",
1730 |    "execution_count": 24,
1731 |    "id": "3baa4297",
1732 |    "metadata": {},
1733 |    "outputs": [
1734 |     {
1735 |      "data": {
1736 |       "text/html": [
1737 |        "<div>\n",
1738 |        "<style scoped>\n",
1739 |        "    .dataframe tbody tr th:only-of-type {\n",
1740 |        "        vertical-align: middle;\n",
1741 |        "    }\n",
1742 |        "\n",
1743 |        "    .dataframe tbody tr th {\n",
1744 |        "        vertical-align: top;\n",
1745 |        "    }\n",
1746 |        "\n",
1747 |        "    .dataframe thead th {\n",
1748 |        "        text-align: right;\n",
1749 |        "    }\n",
1750 |        "</style>\n",
1751 |        "<table border=\"1\" class=\"dataframe\">\n",
1752 |        "  <thead>\n",
1753 |        "    <tr style=\"text-align: right;\">\n",
1754 |        "      <th></th>\n",
1755 |        "      <th>Make</th>\n",
1756 |        "      <th>Colour</th>\n",
1757 |        "      <th>Odometer (KM)</th>\n",
1758 |        "      <th>Doors</th>\n",
1759 |        "      <th>Price</th>\n",
1760 |        "    </tr>\n",
1761 |        "  </thead>\n",
1762 |        "  <tbody>\n",
1763 |        "    <tr>\n",
1764 |        "      <th>0</th>\n",
1765 |        "      <td>Toyota</td>\n",
1766 |        "      <td>White</td>\n",
1767 |        "      <td>150043</td>\n",
1768 |        "      <td>4</td>\n",
1769 |        "      <td>$4,000.00</td>\n",
1770 |        "    </tr>\n",
1771 |        "    <tr>\n",
1772 |        "      <th>1</th>\n",
1773 |        "      <td>Honda</td>\n",
1774 |        "      <td>Red</td>\n",
1775 |        "      <td>87899</td>\n",
1776 |        "      <td>4</td>\n",
1777 |        "      <td>$5,000.00</td>\n",
1778 |        "    </tr>\n",
1779 |        "    <tr>\n",
1780 |        "      <th>2</th>\n",
1781 |        "      <td>Toyota</td>\n",
1782 |        "      <td>Blue</td>\n",
1783 |        "      <td>32549</td>\n",
1784 |        "      <td>3</td>\n",
1785 |        "      <td>$7,000.00</td>\n",
1786 |        "    </tr>\n",
1787 |        "    <tr>\n",
1788 |        "      <th>3</th>\n",
1789 |        "      <td>BMW</td>\n",
1790 |        "      <td>Black</td>\n",
1791 |        "      <td>11179</td>\n",
1792 |        "      <td>5</td>\n",
1793 |        "      <td>$22,000.00</td>\n",
1794 |        "    </tr>\n",
1795 |        "    <tr>\n",
1796 |        "      <th>4</th>\n",
1797 |        "      <td>Nissan</td>\n",
1798 |        "      <td>White</td>\n",
1799 |        "      <td>213095</td>\n",
1800 |        "      <td>4</td>\n",
1801 |        "      <td>$3,500.00</td>\n",
1802 |        "    </tr>\n",
1803 |        "  </tbody>\n",
1804 |        "</table>\n",
1805 |        "</div>"
1806 |       ],
1807 |       "text/plain": [
1808 |        "     Make Colour  Odometer (KM)  Doors       Price\n",
1809 |        "0  Toyota  White         150043      4   $4,000.00\n",
1810 |        "1   Honda    Red          87899      4   $5,000.00\n",
1811 |        "2  Toyota   Blue          32549      3   $7,000.00\n",
1812 |        "3     BMW  Black          11179      5  $22,000.00\n",
1813 |        "4  Nissan  White         213095      4   $3,500.00"
1814 |       ]
1815 |      },
1816 |      "execution_count": 24,
1817 |      "metadata": {},
1818 |      "output_type": "execute_result"
1819 |     }
1820 |    ],
1821 |    "source": [
1822 |     "car_sales.head()"
1823 |    ]
1824 |   },
1825 |   {
1826 |    "cell_type": "code",
1827 |    "execution_count": 27,
1828 |    "id": "1c6e1a98",
1829 |    "metadata": {},
1830 |    "outputs": [
1831 |     {
1832 |      "data": {
1833 |       "text/html": [
1834 |        "<div>\n",
1835 |        "<style scoped>\n",
1836 |        "    .dataframe tbody tr th:only-of-type {\n",
1837 |        "        vertical-align: middle;\n",
1838 |        "    }\n",
1839 |        "\n",
1840 |        "    .dataframe tbody tr th {\n",
1841 |        "        vertical-align: top;\n",
1842 |        "    }\n",
1843 |        "\n",
1844 |        "    .dataframe thead th {\n",
1845 |        "        text-align: right;\n",
1846 |        "    }\n",
1847 |        "</style>\n",
1848 |        "<table border=\"1\" class=\"dataframe\">\n",
1849 |        "  <thead>\n",
1850 |        "    <tr style=\"text-align: right;\">\n",
1851 |        "      <th></th>\n",
1852 |        "      <th>Make</th>\n",
1853 |        "      <th>Colour</th>\n",
1854 |        "      <th>Odometer (KM)</th>\n",
1855 |        "      <th>Doors</th>\n",
1856 |        "      <th>Price</th>\n",
1857 |        "    </tr>\n",
1858 |        "  </thead>\n",
1859 |        "  <tbody>\n",
1860 |        "    <tr>\n",
1861 |        "      <th>8</th>\n",
1862 |        "      <td>Toyota</td>\n",
1863 |        "      <td>White</td>\n",
1864 |        "      <td>60000</td>\n",
1865 |        "      <td>4</td>\n",
1866 |        "      <td>$6,250.00</td>\n",
1867 |        "    </tr>\n",
1868 |        "    <tr>\n",
1869 |        "      <th>9</th>\n",
1870 |        "      <td>Nissan</td>\n",
1871 |        "      <td>White</td>\n",
1872 |        "      <td>31600</td>\n",
1873 |        "      <td>4</td>\n",
1874 |        "      <td>$9,700.00</td>\n",
1875 |        "    </tr>\n",
1876 |        "  </tbody>\n",
1877 |        "</table>\n",
1878 |        "</div>"
1879 |       ],
1880 |       "text/plain": [
1881 |        "     Make Colour  Odometer (KM)  Doors      Price\n",
1882 |        "8  Toyota  White          60000      4  $6,250.00\n",
1883 |        "9  Nissan  White          31600      4  $9,700.00"
1884 |       ]
1885 |      },
1886 |      "execution_count": 27,
1887 |      "metadata": {},
1888 |      "output_type": "execute_result"
1889 |     }
1890 |    ],
1891 |    "source": [
1892 |     "car_sales.tail(2)"
1893 |    ]
1894 |   },
1895 |   {
1896 |    "cell_type": "code",
1897 |    "execution_count": 29,
1898 |    "id": "22bb3986",
1899 |    "metadata": {},
1900 |    "outputs": [
1901 |     {
1902 |      "data": {
1903 |       "text/plain": [
1904 |        "Make                Toyota\n",
1905 |        "Colour                Blue\n",
1906 |        "Odometer (KM)        32549\n",
1907 |        "Doors                    3\n",
1908 |        "Price            $7,000.00\n",
1909 |        "Name: 2, dtype: object"
1910 |       ]
1911 |      },
1912 |      "execution_count": 29,
1913 |      "metadata": {},
1914 |      "output_type": "execute_result"
1915 |     }
1916 |    ],
1917 |    "source": [
1918 |     "car_sales.loc[9]"
1919 |    ]
1920 |   },
1921 |   {
1922 |    "cell_type": "code",
1923 |    "execution_count": 30,
1924 |    "id": "74811be6",
1925 |    "metadata": {},
1926 |    "outputs": [
1927 |     {
1928 |      "data": {
1929 |       "text/plain": [
1930 |        "Make                Toyota\n",
1931 |        "Colour                Blue\n",
1932 |        "Odometer (KM)        32549\n",
1933 |        "Doors                    3\n",
1934 |        "Price            $7,000.00\n",
1935 |        "Name: 2, dtype: object"
1936 |       ]
1937 |      },
1938 |      "execution_count": 30,
1939 |      "metadata": {},
1940 |      "output_type": "execute_result"
1941 |     }
1942 |    ],
1943 |    "source": [
1944 |     "car_sales.iloc[2]"
1945 |    ]
1946 |   },
1947 |   {
1948 |    "cell_type": "code",
1949 |    "execution_count": 33,
1950 |    "id": "436faa43",
1951 |    "metadata": {},
1952 |    "outputs": [
1953 |     {
1954 |      "data": {
1955 |       "text/plain": [
1956 |        "0       BMW\n",
1957 |        "1     HONDA\n",
1958 |        "3      AUDI\n",
1959 |        "5    TOYOTA\n",
1960 |        "dtype: object"
1961 |       ]
1962 |      },
1963 |      "execution_count": 33,
1964 |      "metadata": {},
1965 |      "output_type": "execute_result"
1966 |     }
1967 |    ],
1968 |    "source": [
1969 |     "cars=pd.Series([\"BMW\",\"HONDA\",\"AUDI\",\"TOYOTA\"],index=[0,1,3,5])\n",
1970 |     "cars"
1971 |    ]
1972 |   },
1973 |   {
1974 |    "cell_type": "code",
1975 |    "execution_count": 34,
1976 |    "id": "4992d065",
1977 |    "metadata": {},
1978 |    "outputs": [
1979 |     {
1980 |      "ename": "KeyError",
1981 |      "evalue": "2",
1982 |      "output_type": "error",
1983 |      "traceback": [
1984 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
1985 |       "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
1986 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
1987 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
1988 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
1989 |       "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:2263\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
1990 |       "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:2273\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
1991 |       "\u001b[1;31mKeyError\u001b[0m: 2",
1992 |       "\nThe above exception was the direct cause of the following exception:\n",
1993 |       "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
1994 |       "Cell \u001b[1;32mIn[34], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m cars\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m2\u001b[39m]\n",
1995 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
1996 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1312\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1310\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m   1311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_label(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
1997 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1260\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m   1259\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mxs(label, axis\u001b[38;5;241m=\u001b[39maxis)\n",
1998 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4056\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   4054\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m   4055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4056\u001b[0m     loc \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   4058\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m   4059\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
1999 |       "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
2000 |       "\u001b[1;31mKeyError\u001b[0m: 2"
2001 |      ]
2002 |     }
2003 |    ],
2004 |    "source": [
2005 |     "cars.loc[2]"
2006 |    ]
2007 |   },
2008 |   {
2009 |    "cell_type": "code",
2010 |    "execution_count": 35,
2011 |    "id": "08e3a447",
2012 |    "metadata": {},
2013 |    "outputs": [],
2014 |    "source": [
2015 |     "import matplotlib.pyplot as plt"
2016 |    ]
2017 |   },
2018 |   {
2019 |    "cell_type": "code",
2020 |    "execution_count": 36,
2021 |    "id": "9d8780d0",
2022 |    "metadata": {},
2023 |    "outputs": [
2024 |     {
2025 |      "data": {
2026 |       "text/plain": [
2027 |        "<Axes: xlabel='Odometer (KM)', ylabel='Doors'>"
2028 |       ]
2029 |      },
2030 |      "execution_count": 36,
2031 |      "metadata": {},
2032 |      "output_type": "execute_result"
2033 |     },
2034 |     {
2035 |      "data": {
2036 |       "image/png": 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",
2037 |       "text/plain": [
2038 |        "<Figure size 640x480 with 1 Axes>"
2039 |       ]
2040 |      },
2041 |      "metadata": {},
2042 |      "output_type": "display_data"
2043 |     }
2044 |    ],
2045 |    "source": [
2046 |     "car_sales.plot(kind=\"scatter\",x=\"Odometer (KM)\",y=\"Doors\")"
2047 |    ]
2048 |   },
2049 |   {
2050 |    "cell_type": "code",
2051 |    "execution_count": null,
2052 |    "id": "ea7a288d",
2053 |    "metadata": {},
2054 |    "outputs": [],
2055 |    "source": []
2056 |   }
2057 |  ],
2058 |  "metadata": {
2059 |   "kernelspec": {
2060 |    "display_name": "Python 3 (ipykernel)",
2061 |    "language": "python",
2062 |    "name": "python3"
2063 |   },
2064 |   "language_info": {
2065 |    "codemirror_mode": {
2066 |     "name": "ipython",
2067 |     "version": 3
2068 |    },
2069 |    "file_extension": ".py",
2070 |    "mimetype": "text/x-python",
2071 |    "name": "python",
2072 |    "nbconvert_exporter": "python",
2073 |    "pygments_lexer": "ipython3",
2074 |    "version": "3.11.4"
2075 |   }
2076 |  },
2077 |  "nbformat": 4,
2078 |  "nbformat_minor": 5
2079 | }
2080 | 


--------------------------------------------------------------------------------