├── README.md
└── ipl_data
    ├── Python IPL workshop.ipynb
    ├── high_score_in.csv
    ├── ipl.jpeg
    ├── most_sixes_all_times.csv
    └── most_wickets_all_times.csv


/README.md:
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1 | # Python-Beginner-Series-IPL
2 | we are starting our python beginner series for scholar who want start career in python 
3 | ![alt text](https://github.com/thepradip/Python-Beginner-Series-IPL/blob/master/ipl_data/ipl.jpeg)
4 | 


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/ipl_data/Python IPL workshop.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {
   6 |     "collapsed": true,
   7 |     "scrolled": true
   8 |    },
   9 |    "source": [
  10 |     "# IPL with  Python "
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "markdown",
  15 |    "metadata": {},
  16 |    "source": [
  17 |     "<img src=\"ipl.jpeg\" height=800, width=800>\n",
  18 |     "    \n",
  19 |     " "
  20 |    ]
  21 |   },
  22 |   {
  23 |    "cell_type": "markdown",
  24 |    "metadata": {},
  25 |    "source": [
  26 |     "# Software Installation\n",
  27 |     "\n",
  28 |     "\n",
  29 |     "## Using pip \n",
  30 |     "\n",
  31 |     "1)Install Python \n",
  32 |     "<br>\n",
  33 |     "https://www.python.org/downloads/\n",
  34 |     "\n",
  35 |     "2.Install python then pip comes default <br>\n",
  36 |     "<code> python get-pip.py</code>\n",
  37 |     "#### Updating pip\n",
  38 |     "##### On linux and MacOS\n",
  39 |     "<code> python install -U pip</code>\n",
  40 |     "#### Window \n",
  41 |     "<code> python -m pip install -U pip</code><br><br>\n",
  42 |     "3.Install plotly express <br>\n",
  43 |     "    <code> pip install plotly-express </code>\n",
  44 |     "    \n",
  45 |     "## Using Anaconda: \n",
  46 |     "### Windows:\n",
  47 |     "    Python and  Anaconda\n",
  48 |     "1) https://www.python.org/downloads/\n",
  49 |     "\n",
  50 |     "2) https://www.anaconda.com/download/\n",
  51 |     "<br> \n",
  52 |     "3) Install plotly express <br> <br>\n",
  53 |     "\n",
  54 |     "<code> conda install -c plotly plotly_express <code>\n",
  55 |     "\n",
  56 |     "\n",
  57 |     "### Mac os:\n",
  58 |     "    Same as Windows\n",
  59 |     "\n",
  60 |     "### Linux\n",
  61 |     "#### python comes with preinstalled in ubuntu 14+\n",
  62 |     "1)Download anaconda\n",
  63 |     "https://www.anaconda.com/download/ <br>\n",
  64 |     "2)<code>chmod +x </code><br>\n",
  65 |     "3)Go to home directory\n",
  66 |     "<code>cd ~ </code>\n",
  67 |     "<code>wget https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh <br>\n",
  68 |     "bash Anaconda3-4.2.0-Linux-x86_64.sh -b -p ~/anaconda  <br>\n",
  69 |     "rm Anaconda3-4.2.0-Linux-x86_64.sh<br>\n",
  70 |     "echo 'export PATH=\"~/anaconda/bin:$PATH\"' >> ~/.bashrc     \n",
  71 |     "source .bashrc\n",
  72 |     "conda update conda</code>\n"
  73 |    ]
  74 |   },
  75 |   {
  76 |    "cell_type": "markdown",
  77 |    "metadata": {},
  78 |    "source": [
  79 |     "## Checking version of software"
  80 |    ]
  81 |   },
  82 |   {
  83 |    "cell_type": "code",
  84 |    "execution_count": 183,
  85 |    "metadata": {},
  86 |    "outputs": [
  87 |     {
  88 |      "name": "stdout",
  89 |      "output_type": "stream",
  90 |      "text": [
  91 |       "Python: 3.7.1 (default, Dec 14 2018, 19:28:38) \n",
  92 |       "[GCC 7.3.0]\n",
  93 |       "numpy: <module 'numpy.version' from '/home/pradip/.local/lib/python3.7/site-packages/numpy/version.py'>\n",
  94 |       "matplotlib: 3.0.2\n",
  95 |       "pandas: 0.23.4\n",
  96 |       "plotly: 3.10.0\n"
  97 |      ]
  98 |     }
  99 |    ],
 100 |    "source": [
 101 |     "# Python version\n",
 102 |     "import sys\n",
 103 |     "print('Python: {}'.format(sys.version))\n",
 104 |     "# numpy\n",
 105 |     "import numpy\n",
 106 |     "print('numpy: {}'.format(numpy.version))\n",
 107 |     "# matplotlib\n",
 108 |     "import matplotlib\n",
 109 |     "print('matplotlib: {}'.format(matplotlib.__version__))\n",
 110 |     "# pandas\n",
 111 |     "import pandas\n",
 112 |     "print('pandas: {}'.format(pandas.__version__))\n",
 113 |     "# Data visualizaion\n",
 114 |     "import plotly\n",
 115 |     "import plotly_express as px\n",
 116 |     "print(\"plotly:\",plotly.__version__)\n"
 117 |    ]
 118 |   },
 119 |   {
 120 |    "cell_type": "code",
 121 |    "execution_count": null,
 122 |    "metadata": {},
 123 |    "outputs": [],
 124 |    "source": []
 125 |   },
 126 |   {
 127 |    "cell_type": "markdown",
 128 |    "metadata": {},
 129 |    "source": [
 130 |     "#### Arthmatic operation:\n"
 131 |    ]
 132 |   },
 133 |   {
 134 |    "cell_type": "code",
 135 |    "execution_count": 184,
 136 |    "metadata": {},
 137 |    "outputs": [
 138 |     {
 139 |      "data": {
 140 |       "text/plain": [
 141 |        "235"
 142 |       ]
 143 |      },
 144 |      "execution_count": 184,
 145 |      "metadata": {},
 146 |      "output_type": "execute_result"
 147 |     }
 148 |    ],
 149 |    "source": [
 150 |     "12+223"
 151 |    ]
 152 |   },
 153 |   {
 154 |    "cell_type": "code",
 155 |    "execution_count": 185,
 156 |    "metadata": {},
 157 |    "outputs": [
 158 |     {
 159 |      "data": {
 160 |       "text/plain": [
 161 |        "11"
 162 |       ]
 163 |      },
 164 |      "execution_count": 185,
 165 |      "metadata": {},
 166 |      "output_type": "execute_result"
 167 |     }
 168 |    ],
 169 |    "source": [
 170 |     "34-23"
 171 |    ]
 172 |   },
 173 |   {
 174 |    "cell_type": "code",
 175 |    "execution_count": 186,
 176 |    "metadata": {},
 177 |    "outputs": [
 178 |     {
 179 |      "data": {
 180 |       "text/plain": [
 181 |        "108"
 182 |       ]
 183 |      },
 184 |      "execution_count": 186,
 185 |      "metadata": {},
 186 |      "output_type": "execute_result"
 187 |     }
 188 |    ],
 189 |    "source": [
 190 |     "54*2"
 191 |    ]
 192 |   },
 193 |   {
 194 |    "cell_type": "code",
 195 |    "execution_count": 187,
 196 |    "metadata": {},
 197 |    "outputs": [
 198 |     {
 199 |      "data": {
 200 |       "text/plain": [
 201 |        "21.0"
 202 |       ]
 203 |      },
 204 |      "execution_count": 187,
 205 |      "metadata": {},
 206 |      "output_type": "execute_result"
 207 |     }
 208 |    ],
 209 |    "source": [
 210 |     "42/2"
 211 |    ]
 212 |   },
 213 |   {
 214 |    "cell_type": "code",
 215 |    "execution_count": 188,
 216 |    "metadata": {},
 217 |    "outputs": [
 218 |     {
 219 |      "data": {
 220 |       "text/plain": [
 221 |        "1728"
 222 |       ]
 223 |      },
 224 |      "execution_count": 188,
 225 |      "metadata": {},
 226 |      "output_type": "execute_result"
 227 |     }
 228 |    ],
 229 |    "source": [
 230 |     "12**3"
 231 |    ]
 232 |   },
 233 |   {
 234 |    "cell_type": "code",
 235 |    "execution_count": 189,
 236 |    "metadata": {},
 237 |    "outputs": [
 238 |     {
 239 |      "data": {
 240 |       "text/plain": [
 241 |        "3"
 242 |       ]
 243 |      },
 244 |      "execution_count": 189,
 245 |      "metadata": {},
 246 |      "output_type": "execute_result"
 247 |     }
 248 |    ],
 249 |    "source": [
 250 |     "45%6"
 251 |    ]
 252 |   },
 253 |   {
 254 |    "cell_type": "code",
 255 |    "execution_count": 190,
 256 |    "metadata": {},
 257 |    "outputs": [
 258 |     {
 259 |      "data": {
 260 |       "text/plain": [
 261 |        "97.0"
 262 |       ]
 263 |      },
 264 |      "execution_count": 190,
 265 |      "metadata": {},
 266 |      "output_type": "execute_result"
 267 |     }
 268 |    ],
 269 |    "source": [
 270 |     "23+43*4/2-12"
 271 |    ]
 272 |   },
 273 |   {
 274 |    "cell_type": "markdown",
 275 |    "metadata": {},
 276 |    "source": [
 277 |     "Assignment "
 278 |    ]
 279 |   },
 280 |   {
 281 |    "cell_type": "code",
 282 |    "execution_count": 191,
 283 |    "metadata": {
 284 |     "scrolled": true
 285 |    },
 286 |    "outputs": [
 287 |     {
 288 |      "data": {
 289 |       "text/plain": [
 290 |        "3"
 291 |       ]
 292 |      },
 293 |      "execution_count": 191,
 294 |      "metadata": {},
 295 |      "output_type": "execute_result"
 296 |     }
 297 |    ],
 298 |    "source": [
 299 |     "c=15\n",
 300 |     "d=4\n",
 301 |     "c%d"
 302 |    ]
 303 |   },
 304 |   {
 305 |    "cell_type": "markdown",
 306 |    "metadata": {},
 307 |    "source": [
 308 |     "## Data type"
 309 |    ]
 310 |   },
 311 |   {
 312 |    "cell_type": "code",
 313 |    "execution_count": 192,
 314 |    "metadata": {
 315 |     "scrolled": true
 316 |    },
 317 |    "outputs": [
 318 |     {
 319 |      "name": "stdout",
 320 |      "output_type": "stream",
 321 |      "text": [
 322 |       "Indian Premier League\n",
 323 |       "24\n"
 324 |      ]
 325 |     }
 326 |    ],
 327 |    "source": [
 328 |     " #str \"abc\"\n",
 329 |     "12 #int\n",
 330 |     "2.4 #float\n",
 331 |     "#Numbers likes foats, integers\n",
 332 |     "#Boolean values like  True, False\n",
 333 |     "title= 'Indian Premier League'\n",
 334 |     "print(title)\n",
 335 |     "title=12\n",
 336 |     "print(title*2)\n"
 337 |    ]
 338 |   },
 339 |   {
 340 |    "cell_type": "code",
 341 |    "execution_count": 193,
 342 |    "metadata": {},
 343 |    "outputs": [
 344 |     {
 345 |      "name": "stdout",
 346 |      "output_type": "stream",
 347 |      "text": [
 348 |       "46\n",
 349 |       "-22\n",
 350 |       "408\n",
 351 |       "4922235242952026704037113243122008064\n",
 352 |       "0.35294117647058826\n",
 353 |       "12\n"
 354 |      ]
 355 |     }
 356 |    ],
 357 |    "source": [
 358 |     "a=12\n",
 359 |     "b=34\n",
 360 |     "print(a+b)\n",
 361 |     "print(a-b)\n",
 362 |     "print(a*b)\n",
 363 |     "print(a**b)\n",
 364 |     "print(a/b)\n",
 365 |     "print(a%b)"
 366 |    ]
 367 |   },
 368 |   {
 369 |    "cell_type": "code",
 370 |    "execution_count": null,
 371 |    "metadata": {},
 372 |    "outputs": [],
 373 |    "source": []
 374 |   },
 375 |   {
 376 |    "cell_type": "code",
 377 |    "execution_count": 194,
 378 |    "metadata": {},
 379 |    "outputs": [
 380 |     {
 381 |      "data": {
 382 |       "text/plain": [
 383 |        "16"
 384 |       ]
 385 |      },
 386 |      "execution_count": 194,
 387 |      "metadata": {},
 388 |      "output_type": "execute_result"
 389 |     }
 390 |    ],
 391 |    "source": [
 392 |     "name= 2**4\n",
 393 |     "name"
 394 |    ]
 395 |   },
 396 |   {
 397 |    "cell_type": "markdown",
 398 |    "metadata": {},
 399 |    "source": [
 400 |     "## String operation"
 401 |    ]
 402 |   },
 403 |   {
 404 |    "cell_type": "markdown",
 405 |    "metadata": {},
 406 |    "source": [
 407 |     "string addition "
 408 |    ]
 409 |   },
 410 |   {
 411 |    "cell_type": "code",
 412 |    "execution_count": 195,
 413 |    "metadata": {},
 414 |    "outputs": [
 415 |     {
 416 |      "data": {
 417 |       "text/plain": [
 418 |        "'We are Understanding IPL with help of python'"
 419 |       ]
 420 |      },
 421 |      "execution_count": 195,
 422 |      "metadata": {},
 423 |      "output_type": "execute_result"
 424 |     }
 425 |    ],
 426 |    "source": [
 427 |     "a=\"We are \"\n",
 428 |     "b=\"Understanding IPL \"\n",
 429 |     "c=\"with help of python\"\n",
 430 |     "a+b+c"
 431 |    ]
 432 |   },
 433 |   {
 434 |    "cell_type": "markdown",
 435 |    "metadata": {},
 436 |    "source": [
 437 |     "String multiplication"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 196,
 443 |    "metadata": {
 444 |     "scrolled": true
 445 |    },
 446 |    "outputs": [],
 447 |    "source": [
 448 |     "name=' IPL '\n"
 449 |    ]
 450 |   },
 451 |   {
 452 |    "cell_type": "code",
 453 |    "execution_count": 197,
 454 |    "metadata": {
 455 |     "scrolled": true
 456 |    },
 457 |    "outputs": [
 458 |     {
 459 |      "data": {
 460 |       "text/plain": [
 461 |        "' IPL  IPL  IPL  IPL  IPL  IPL '"
 462 |       ]
 463 |      },
 464 |      "execution_count": 197,
 465 |      "metadata": {},
 466 |      "output_type": "execute_result"
 467 |     }
 468 |    ],
 469 |    "source": [
 470 |     "name*6"
 471 |    ]
 472 |   },
 473 |   {
 474 |    "cell_type": "code",
 475 |    "execution_count": 198,
 476 |    "metadata": {
 477 |     "scrolled": true
 478 |    },
 479 |    "outputs": [
 480 |     {
 481 |      "data": {
 482 |       "text/plain": [
 483 |        "True"
 484 |       ]
 485 |      },
 486 |      "execution_count": 198,
 487 |      "metadata": {},
 488 |      "output_type": "execute_result"
 489 |     }
 490 |    ],
 491 |    "source": [
 492 |     "'I' in name"
 493 |    ]
 494 |   },
 495 |   {
 496 |    "cell_type": "code",
 497 |    "execution_count": 199,
 498 |    "metadata": {
 499 |     "scrolled": true
 500 |    },
 501 |    "outputs": [
 502 |     {
 503 |      "data": {
 504 |       "text/plain": [
 505 |        "False"
 506 |       ]
 507 |      },
 508 |      "execution_count": 199,
 509 |      "metadata": {},
 510 |      "output_type": "execute_result"
 511 |     }
 512 |    ],
 513 |    "source": [
 514 |     "'l' in name"
 515 |    ]
 516 |   },
 517 |   {
 518 |    "cell_type": "markdown",
 519 |    "metadata": {},
 520 |    "source": [
 521 |     "## List"
 522 |    ]
 523 |   },
 524 |   {
 525 |    "cell_type": "code",
 526 |    "execution_count": 200,
 527 |    "metadata": {},
 528 |    "outputs": [],
 529 |    "source": [
 530 |     "team_ranking = [1, 2, 3, 4, 5, 6, 7, 8]\n",
 531 |     "team_name = ['MI', 'CSK', 'DC', 'SH', 'KKR','KXIP','RR', 'RCB']\n",
 532 |     "\n",
 533 |     "zipped_list = zip(team_ranking, team_name)\n",
 534 |     "\n",
 535 |     "#print(list(zipped_list))"
 536 |    ]
 537 |   },
 538 |   {
 539 |    "cell_type": "code",
 540 |    "execution_count": 201,
 541 |    "metadata": {},
 542 |    "outputs": [],
 543 |    "source": [
 544 |     "#print(list(zipped_list))"
 545 |    ]
 546 |   },
 547 |   {
 548 |    "cell_type": "code",
 549 |    "execution_count": 202,
 550 |    "metadata": {},
 551 |    "outputs": [
 552 |     {
 553 |      "name": "stdout",
 554 |      "output_type": "stream",
 555 |      "text": [
 556 |       "(1, 2, 3, 4, 5, 6, 7, 8)\n",
 557 |       "('MI', 'CSK', 'DC', 'SH', 'KKR', 'KXIP', 'RR', 'RCB')\n"
 558 |      ]
 559 |     }
 560 |    ],
 561 |    "source": [
 562 |     "#zipped_list\n",
 563 |     "list_a, list_b = zip(*zipped_list)\n",
 564 |     "print(list_a) \n",
 565 |     "print(list_b)"
 566 |    ]
 567 |   },
 568 |   {
 569 |    "cell_type": "code",
 570 |    "execution_count": 203,
 571 |    "metadata": {
 572 |     "scrolled": true
 573 |    },
 574 |    "outputs": [
 575 |     {
 576 |      "data": {
 577 |       "text/plain": [
 578 |        "[[4, 5, 6, 7], [3, 4, 5, 6]]"
 579 |       ]
 580 |      },
 581 |      "execution_count": 203,
 582 |      "metadata": {},
 583 |      "output_type": "execute_result"
 584 |     }
 585 |    ],
 586 |    "source": [
 587 |     "a = 'HAVE A'\n",
 588 |     "b = 'wonderful day'\n",
 589 |     "my_list = ['my', 'list', a, b]\n",
 590 |     "my_list3=[1,2,3]\n",
 591 |     "my_list2 = [[4,5,6,7], [3,4,5,6]]\n",
 592 |     "my_list2"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 204,
 598 |    "metadata": {},
 599 |    "outputs": [
 600 |     {
 601 |      "data": {
 602 |       "text/plain": [
 603 |        "[1, 2, 3]"
 604 |       ]
 605 |      },
 606 |      "execution_count": 204,
 607 |      "metadata": {},
 608 |      "output_type": "execute_result"
 609 |     }
 610 |    ],
 611 |    "source": [
 612 |     "my_list3"
 613 |    ]
 614 |   },
 615 |   {
 616 |    "cell_type": "code",
 617 |    "execution_count": 205,
 618 |    "metadata": {},
 619 |    "outputs": [
 620 |     {
 621 |      "name": "stdout",
 622 |      "output_type": "stream",
 623 |      "text": [
 624 |       "[[4, 5, 6, 7], [3, 4, 5, 6]]\n"
 625 |      ]
 626 |     }
 627 |    ],
 628 |    "source": [
 629 |     "print(my_list2)"
 630 |    ]
 631 |   },
 632 |   {
 633 |    "cell_type": "code",
 634 |    "execution_count": 206,
 635 |    "metadata": {
 636 |     "scrolled": true
 637 |    },
 638 |    "outputs": [
 639 |     {
 640 |      "name": "stdout",
 641 |      "output_type": "stream",
 642 |      "text": [
 643 |       "list\n",
 644 |       "list\n"
 645 |      ]
 646 |     }
 647 |    ],
 648 |    "source": [
 649 |     "print(my_list[1])\n",
 650 |     "print(my_list[-3])\n",
 651 |     "#Slice\n"
 652 |    ]
 653 |   },
 654 |   {
 655 |    "cell_type": "code",
 656 |    "execution_count": 207,
 657 |    "metadata": {
 658 |     "scrolled": true
 659 |    },
 660 |    "outputs": [
 661 |     {
 662 |      "name": "stdout",
 663 |      "output_type": "stream",
 664 |      "text": [
 665 |       "['list', 'HAVE A']\n",
 666 |       "['list', 'HAVE A', 'wonderful day']\n",
 667 |       "['my', 'list', 'HAVE A']\n",
 668 |       "['my', 'list', 'HAVE A', 'wonderful day']\n"
 669 |      ]
 670 |     }
 671 |    ],
 672 |    "source": [
 673 |     "print(my_list[1:3])\n",
 674 |     "print(my_list[1:])\n",
 675 |     "print(my_list[:3])\n",
 676 |     "print(my_list[:])\n",
 677 |     "#Subset Lists of Lists\n"
 678 |    ]
 679 |   },
 680 |   {
 681 |    "cell_type": "code",
 682 |    "execution_count": 208,
 683 |    "metadata": {
 684 |     "scrolled": true
 685 |    },
 686 |    "outputs": [
 687 |     {
 688 |      "name": "stdout",
 689 |      "output_type": "stream",
 690 |      "text": [
 691 |       "3\n",
 692 |       "[3, 4]\n"
 693 |      ]
 694 |     }
 695 |    ],
 696 |    "source": [
 697 |     "print(my_list2[1][0])\n",
 698 |     "print(my_list2[1][:2])"
 699 |    ]
 700 |   },
 701 |   {
 702 |    "cell_type": "code",
 703 |    "execution_count": 209,
 704 |    "metadata": {
 705 |     "scrolled": true
 706 |    },
 707 |    "outputs": [
 708 |     {
 709 |      "name": "stdout",
 710 |      "output_type": "stream",
 711 |      "text": [
 712 |       "['my', 'list', 'HAVE A', 'wonderful day', 'my', 'list', 'HAVE A', 'wonderful day']\n"
 713 |      ]
 714 |     }
 715 |    ],
 716 |    "source": [
 717 |     "new_list=my_list + my_list\n",
 718 |     "print(new_list)"
 719 |    ]
 720 |   },
 721 |   {
 722 |    "cell_type": "code",
 723 |    "execution_count": 210,
 724 |    "metadata": {
 725 |     "scrolled": true
 726 |    },
 727 |    "outputs": [
 728 |     {
 729 |      "data": {
 730 |       "text/plain": [
 731 |        "['my',\n",
 732 |        " 'list',\n",
 733 |        " 'HAVE A',\n",
 734 |        " 'wonderful day',\n",
 735 |        " 'my',\n",
 736 |        " 'list',\n",
 737 |        " 'HAVE A',\n",
 738 |        " 'wonderful day']"
 739 |       ]
 740 |      },
 741 |      "execution_count": 210,
 742 |      "metadata": {},
 743 |      "output_type": "execute_result"
 744 |     }
 745 |    ],
 746 |    "source": [
 747 |     "my_list * 2"
 748 |    ]
 749 |   },
 750 |   {
 751 |    "cell_type": "code",
 752 |    "execution_count": 211,
 753 |    "metadata": {},
 754 |    "outputs": [
 755 |     {
 756 |      "data": {
 757 |       "text/plain": [
 758 |        "[1, 2, 3, 4, 5, 6]"
 759 |       ]
 760 |      },
 761 |      "execution_count": 211,
 762 |      "metadata": {},
 763 |      "output_type": "execute_result"
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "x = [1, 2, 3]\n",
 768 |     "x.extend([4, 5, 6])\n",
 769 |     "x"
 770 |    ]
 771 |   },
 772 |   {
 773 |    "cell_type": "code",
 774 |    "execution_count": 212,
 775 |    "metadata": {
 776 |     "scrolled": true
 777 |    },
 778 |    "outputs": [
 779 |     {
 780 |      "data": {
 781 |       "text/plain": [
 782 |        "4"
 783 |       ]
 784 |      },
 785 |      "execution_count": 212,
 786 |      "metadata": {},
 787 |      "output_type": "execute_result"
 788 |     }
 789 |    ],
 790 |    "source": [
 791 |     "len(my_list)"
 792 |    ]
 793 |   },
 794 |   {
 795 |    "cell_type": "code",
 796 |    "execution_count": 213,
 797 |    "metadata": {
 798 |     "scrolled": true
 799 |    },
 800 |    "outputs": [
 801 |     {
 802 |      "data": {
 803 |       "text/plain": [
 804 |        "2"
 805 |       ]
 806 |      },
 807 |      "execution_count": 213,
 808 |      "metadata": {},
 809 |      "output_type": "execute_result"
 810 |     }
 811 |    ],
 812 |    "source": [
 813 |     "len(my_list2)"
 814 |    ]
 815 |   },
 816 |   {
 817 |    "cell_type": "code",
 818 |    "execution_count": 214,
 819 |    "metadata": {
 820 |     "scrolled": true
 821 |    },
 822 |    "outputs": [
 823 |     {
 824 |      "name": "stdout",
 825 |      "output_type": "stream",
 826 |      "text": [
 827 |       "['my', 'list', 'HAVE A', 'wonderful day']\n",
 828 |       "2\n"
 829 |      ]
 830 |     }
 831 |    ],
 832 |    "source": [
 833 |     "print(my_list)\n",
 834 |     "print(my_list.index(a))"
 835 |    ]
 836 |   },
 837 |   {
 838 |    "cell_type": "code",
 839 |    "execution_count": 215,
 840 |    "metadata": {
 841 |     "scrolled": true
 842 |    },
 843 |    "outputs": [
 844 |     {
 845 |      "name": "stdout",
 846 |      "output_type": "stream",
 847 |      "text": [
 848 |       "['RR', 'DC', 'CSK', 'CSK', 'KKR', 'MI', 'KKR', 'MI', 'SH', 'MI', 'CSK', 'MI']\n",
 849 |       "KKR winners for  2\n"
 850 |      ]
 851 |     }
 852 |    ],
 853 |    "source": [
 854 |     "ipl_winner_list=[\"RR\",\"DC\",\"CSK\",'CSK','KKR','MI','KKR', 'MI', 'SH', 'MI','CSK', 'MI']\n",
 855 |     "print(ipl_winner_list)\n",
 856 |     "print(\"KKR winners for \", ipl_winner_list.count('KKR'))"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "code",
 861 |    "execution_count": 216,
 862 |    "metadata": {
 863 |     "scrolled": true
 864 |    },
 865 |    "outputs": [
 866 |     {
 867 |      "name": "stdout",
 868 |      "output_type": "stream",
 869 |      "text": [
 870 |       "['RR', 'DC', 'CSK', 'CSK', 'KKR', 'MI', 'KKR', 'MI', 'SH', 'MI', 'CSK', 'MI', 'BB']\n"
 871 |      ]
 872 |     }
 873 |    ],
 874 |    "source": [
 875 |     "ipl_winner_list.append('BB')\n",
 876 |     "print(ipl_winner_list)"
 877 |    ]
 878 |   },
 879 |   {
 880 |    "cell_type": "code",
 881 |    "execution_count": 217,
 882 |    "metadata": {
 883 |     "scrolled": true
 884 |    },
 885 |    "outputs": [
 886 |     {
 887 |      "data": {
 888 |       "text/plain": [
 889 |        "['RR', 'DC', 'CSK', 'CSK', 'KKR', 'MI', 'KKR', 'MI', 'SH', 'MI', 'CSK', 'MI']"
 890 |       ]
 891 |      },
 892 |      "metadata": {},
 893 |      "output_type": "display_data"
 894 |     }
 895 |    ],
 896 |    "source": [
 897 |     "ipl_winner_list.remove('BB')\n",
 898 |     "display(ipl_winner_list)"
 899 |    ]
 900 |   },
 901 |   {
 902 |    "cell_type": "code",
 903 |    "execution_count": 218,
 904 |    "metadata": {
 905 |     "scrolled": true
 906 |    },
 907 |    "outputs": [
 908 |     {
 909 |      "data": {
 910 |       "text/plain": [
 911 |        "['MI', 'CSK', 'MI', 'SH', 'MI', 'KKR', 'MI', 'KKR', 'CSK', 'CSK', 'DC', 'RR']"
 912 |       ]
 913 |      },
 914 |      "execution_count": 218,
 915 |      "metadata": {},
 916 |      "output_type": "execute_result"
 917 |     }
 918 |    ],
 919 |    "source": [
 920 |     "a=ipl_winner_list.reverse()\n",
 921 |     "a\n",
 922 |     "ipl_winner_list"
 923 |    ]
 924 |   },
 925 |   {
 926 |    "cell_type": "code",
 927 |    "execution_count": 219,
 928 |    "metadata": {
 929 |     "scrolled": true
 930 |    },
 931 |    "outputs": [
 932 |     {
 933 |      "data": {
 934 |       "text/plain": [
 935 |        "['MI', 'CSK', 'MI', 'SH', 'MI', 'KKR', 'MI', 'KKR', 'CSK', 'CSK', 'DC']"
 936 |       ]
 937 |      },
 938 |      "execution_count": 219,
 939 |      "metadata": {},
 940 |      "output_type": "execute_result"
 941 |     }
 942 |    ],
 943 |    "source": [
 944 |     "ipl_winner_list.pop()\n",
 945 |     "ipl_winner_list"
 946 |    ]
 947 |   },
 948 |   {
 949 |    "cell_type": "code",
 950 |    "execution_count": 220,
 951 |    "metadata": {
 952 |     "scrolled": true
 953 |    },
 954 |    "outputs": [
 955 |     {
 956 |      "data": {
 957 |       "text/plain": [
 958 |        "['CSK', 'CSK', 'CSK', 'DC', 'KKR', 'KKR', 'MI', 'MI', 'MI', 'MI', 'SH']"
 959 |       ]
 960 |      },
 961 |      "execution_count": 220,
 962 |      "metadata": {},
 963 |      "output_type": "execute_result"
 964 |     }
 965 |    ],
 966 |    "source": [
 967 |     "ipl_winner_list.sort()\n",
 968 |     "ipl_winner_list"
 969 |    ]
 970 |   },
 971 |   {
 972 |    "cell_type": "code",
 973 |    "execution_count": 221,
 974 |    "metadata": {
 975 |     "scrolled": true
 976 |    },
 977 |    "outputs": [
 978 |     {
 979 |      "data": {
 980 |       "text/plain": [
 981 |        "'won IPL titl'"
 982 |       ]
 983 |      },
 984 |      "execution_count": 221,
 985 |      "metadata": {},
 986 |      "output_type": "execute_result"
 987 |     }
 988 |    ],
 989 |    "source": [
 990 |     "my_string='MI won IPL title 4 times'\n",
 991 |     "my_string[3:15]\n"
 992 |    ]
 993 |   },
 994 |   {
 995 |    "cell_type": "code",
 996 |    "execution_count": 222,
 997 |    "metadata": {
 998 |     "scrolled": true
 999 |    },
1000 |    "outputs": [
1001 |     {
1002 |      "data": {
1003 |       "text/plain": [
1004 |        "'on IP'"
1005 |       ]
1006 |      },
1007 |      "execution_count": 222,
1008 |      "metadata": {},
1009 |      "output_type": "execute_result"
1010 |     }
1011 |    ],
1012 |    "source": [
1013 |     "my_string[4:9]"
1014 |    ]
1015 |   },
1016 |   {
1017 |    "cell_type": "code",
1018 |    "execution_count": 223,
1019 |    "metadata": {
1020 |     "scrolled": true
1021 |    },
1022 |    "outputs": [
1023 |     {
1024 |      "data": {
1025 |       "text/plain": [
1026 |        "'MI WON IPL TITLE 4 TIMES'"
1027 |       ]
1028 |      },
1029 |      "execution_count": 223,
1030 |      "metadata": {},
1031 |      "output_type": "execute_result"
1032 |     }
1033 |    ],
1034 |    "source": [
1035 |     "up=my_string.upper()\n",
1036 |     "up"
1037 |    ]
1038 |   },
1039 |   {
1040 |    "cell_type": "code",
1041 |    "execution_count": 224,
1042 |    "metadata": {
1043 |     "scrolled": true
1044 |    },
1045 |    "outputs": [
1046 |     {
1047 |      "data": {
1048 |       "text/plain": [
1049 |        "'mi won ipl title 4 times'"
1050 |       ]
1051 |      },
1052 |      "execution_count": 224,
1053 |      "metadata": {},
1054 |      "output_type": "execute_result"
1055 |     }
1056 |    ],
1057 |    "source": [
1058 |     "low=my_string.lower()\n",
1059 |     "low"
1060 |    ]
1061 |   },
1062 |   {
1063 |    "cell_type": "code",
1064 |    "execution_count": 225,
1065 |    "metadata": {
1066 |     "scrolled": true
1067 |    },
1068 |    "outputs": [
1069 |     {
1070 |      "data": {
1071 |       "text/plain": [
1072 |        "1"
1073 |       ]
1074 |      },
1075 |      "execution_count": 225,
1076 |      "metadata": {},
1077 |      "output_type": "execute_result"
1078 |     }
1079 |    ],
1080 |    "source": [
1081 |     "cout=my_string.count('o')\n",
1082 |     "cout"
1083 |    ]
1084 |   },
1085 |   {
1086 |    "cell_type": "code",
1087 |    "execution_count": 226,
1088 |    "metadata": {
1089 |     "scrolled": true
1090 |    },
1091 |    "outputs": [
1092 |     {
1093 |      "data": {
1094 |       "text/plain": [
1095 |        "'CSK won IPL title 2 times'"
1096 |       ]
1097 |      },
1098 |      "execution_count": 226,
1099 |      "metadata": {},
1100 |      "output_type": "execute_result"
1101 |     }
1102 |    ],
1103 |    "source": [
1104 |     "\n",
1105 |     "my_string=my_string.replace('MI', 'CSK')\n",
1106 |     "my_string=my_string.replace('4','2')\n",
1107 |     "my_string"
1108 |    ]
1109 |   },
1110 |   {
1111 |    "cell_type": "markdown",
1112 |    "metadata": {},
1113 |    "source": [
1114 |     "## Dictionary\n"
1115 |    ]
1116 |   },
1117 |   {
1118 |    "cell_type": "code",
1119 |    "execution_count": 227,
1120 |    "metadata": {},
1121 |    "outputs": [],
1122 |    "source": [
1123 |     "# empty dictionary\n",
1124 |     "my_dict = {}\n",
1125 |     "\n",
1126 |     "# dictionary with string key\n",
1127 |     "ipl_sixes = {'russell':62, 'gayle':34,'Pandya':29,'pant':27,'ABD':26,'rahul':25}\n",
1128 |     "\n",
1129 |     "# dictionary with integer key\n",
1130 |     "ipl_bolwer_rank = {1:'Imran Tahir',2:'Kagiso Rabada',3:'Deepak Chahar',4: 'Shreyas Gopal',5:' Jasprit Bumrah',6:' Khaleel Ahmed',7:' Mohammed Shami',8:' Yuzvendra Chahal',9:'Rashid Khan',10:' Harbhajan Singh'}\n",
1131 |     "\n",
1132 |     "# using dict()\n",
1133 |     "ipl_team_rank  = dict([(1,'MI'), (2,'CSK'), (3,'DC'), (4,'SH'), (5,'KKR'),(6,'KXIP'),(7,'RR'),(8,'RCB')])"
1134 |    ]
1135 |   },
1136 |   {
1137 |    "cell_type": "code",
1138 |    "execution_count": 228,
1139 |    "metadata": {},
1140 |    "outputs": [
1141 |     {
1142 |      "data": {
1143 |       "text/plain": [
1144 |        "62"
1145 |       ]
1146 |      },
1147 |      "execution_count": 228,
1148 |      "metadata": {},
1149 |      "output_type": "execute_result"
1150 |     }
1151 |    ],
1152 |    "source": [
1153 |     "ipl_sixes['russell']"
1154 |    ]
1155 |   },
1156 |   {
1157 |    "cell_type": "code",
1158 |    "execution_count": 229,
1159 |    "metadata": {},
1160 |    "outputs": [
1161 |     {
1162 |      "data": {
1163 |       "text/plain": [
1164 |        "'Shreyas Gopal'"
1165 |       ]
1166 |      },
1167 |      "execution_count": 229,
1168 |      "metadata": {},
1169 |      "output_type": "execute_result"
1170 |     }
1171 |    ],
1172 |    "source": [
1173 |     "ipl_bolwer_rank[4]"
1174 |    ]
1175 |   },
1176 |   {
1177 |    "cell_type": "code",
1178 |    "execution_count": 230,
1179 |    "metadata": {},
1180 |    "outputs": [
1181 |     {
1182 |      "data": {
1183 |       "text/plain": [
1184 |        "'MI'"
1185 |       ]
1186 |      },
1187 |      "execution_count": 230,
1188 |      "metadata": {},
1189 |      "output_type": "execute_result"
1190 |     }
1191 |    ],
1192 |    "source": [
1193 |     "ipl_team_rank[1]"
1194 |    ]
1195 |   },
1196 |   {
1197 |    "cell_type": "markdown",
1198 |    "metadata": {},
1199 |    "source": [
1200 |     "## Loops"
1201 |    ]
1202 |   },
1203 |   {
1204 |    "cell_type": "code",
1205 |    "execution_count": 231,
1206 |    "metadata": {
1207 |     "scrolled": true
1208 |    },
1209 |    "outputs": [
1210 |     {
1211 |      "name": "stdout",
1212 |      "output_type": "stream",
1213 |      "text": [
1214 |       "0\n",
1215 |       "1\n",
1216 |       "2\n",
1217 |       "3\n",
1218 |       "4\n",
1219 |       "5\n",
1220 |       "6\n",
1221 |       "7\n",
1222 |       "8\n",
1223 |       "9\n"
1224 |      ]
1225 |     }
1226 |    ],
1227 |    "source": [
1228 |     "for i in range(0,10):\n",
1229 |     "    print(i)"
1230 |    ]
1231 |   },
1232 |   {
1233 |    "cell_type": "code",
1234 |    "execution_count": 232,
1235 |    "metadata": {
1236 |     "scrolled": true
1237 |    },
1238 |    "outputs": [
1239 |     {
1240 |      "name": "stdout",
1241 |      "output_type": "stream",
1242 |      "text": [
1243 |       "1\n",
1244 |       "3\n",
1245 |       "5\n",
1246 |       "7\n",
1247 |       "9\n"
1248 |      ]
1249 |     }
1250 |    ],
1251 |    "source": [
1252 |     "for i in range(1,10,2):\n",
1253 |     "    print(i)"
1254 |    ]
1255 |   },
1256 |   {
1257 |    "cell_type": "code",
1258 |    "execution_count": 233,
1259 |    "metadata": {
1260 |     "scrolled": true
1261 |    },
1262 |    "outputs": [
1263 |     {
1264 |      "name": "stdout",
1265 |      "output_type": "stream",
1266 |      "text": [
1267 |       "0\n",
1268 |       "1\n",
1269 |       "4\n",
1270 |       "9\n",
1271 |       "16\n"
1272 |      ]
1273 |     }
1274 |    ],
1275 |    "source": [
1276 |     "for x in range(5):\n",
1277 |     "    print(x**2)"
1278 |    ]
1279 |   },
1280 |   {
1281 |    "cell_type": "code",
1282 |    "execution_count": 234,
1283 |    "metadata": {
1284 |     "scrolled": true
1285 |    },
1286 |    "outputs": [
1287 |     {
1288 |      "name": "stdout",
1289 |      "output_type": "stream",
1290 |      "text": [
1291 |       "I love to watching play Virat Kohli\n",
1292 |       "I love to watching play KL Rahul\n",
1293 |       "I love to watching play  Jasprit Bumrah\n"
1294 |      ]
1295 |     }
1296 |    ],
1297 |    "source": [
1298 |     "names=['Virat Kohli','KL Rahul',' Jasprit Bumrah']\n",
1299 |     "for i in names:\n",
1300 |     "    print(\"I love to watching play \"+i)"
1301 |    ]
1302 |   },
1303 |   {
1304 |    "cell_type": "code",
1305 |    "execution_count": 235,
1306 |    "metadata": {
1307 |     "scrolled": true
1308 |    },
1309 |    "outputs": [],
1310 |    "source": [
1311 |     "count = 0\n",
1312 |     "while count >5  :\n",
1313 |     "    print(count)\n",
1314 |     "    count += 1"
1315 |    ]
1316 |   },
1317 |   {
1318 |    "cell_type": "code",
1319 |    "execution_count": 236,
1320 |    "metadata": {
1321 |     "scrolled": true
1322 |    },
1323 |    "outputs": [
1324 |     {
1325 |      "name": "stdout",
1326 |      "output_type": "stream",
1327 |      "text": [
1328 |       "0\n",
1329 |       "1\n",
1330 |       "2\n",
1331 |       "3\n",
1332 |       "4\n",
1333 |       "exit value 5\n"
1334 |      ]
1335 |     }
1336 |    ],
1337 |    "source": [
1338 |     "count=0\n",
1339 |     "while(count<5):\n",
1340 |     "    print(count)\n",
1341 |     "    count +=1\n",
1342 |     "else:\n",
1343 |     "    print(\"exit value %d\" %(count))"
1344 |    ]
1345 |   },
1346 |   {
1347 |    "cell_type": "code",
1348 |    "execution_count": 237,
1349 |    "metadata": {
1350 |     "scrolled": true
1351 |    },
1352 |    "outputs": [
1353 |     {
1354 |      "name": "stdout",
1355 |      "output_type": "stream",
1356 |      "text": [
1357 |       "this is else part 1\n",
1358 |       "this is else part 2\n",
1359 |       "this is else part 3\n",
1360 |       "this is else part 4\n",
1361 |       "5\n",
1362 |       "this is else part 6\n",
1363 |       "this is else part 7\n",
1364 |       "this is else part 8\n",
1365 |       "this is else part 9\n"
1366 |      ]
1367 |     }
1368 |    ],
1369 |    "source": [
1370 |     "for i in range(1, 10):\n",
1371 |     "    if(i%5==0):\n",
1372 |     "        print(i)\n",
1373 |     "    else:\n",
1374 |     "        print(\"this is else part\",i)"
1375 |    ]
1376 |   },
1377 |   {
1378 |    "cell_type": "code",
1379 |    "execution_count": 238,
1380 |    "metadata": {
1381 |     "scrolled": true
1382 |    },
1383 |    "outputs": [
1384 |     {
1385 |      "name": "stdout",
1386 |      "output_type": "stream",
1387 |      "text": [
1388 |       " is even 10\n",
1389 |       " is odd 11\n",
1390 |       " is even 12\n",
1391 |       " is odd 13\n",
1392 |       " is even 14\n",
1393 |       " is odd 15\n",
1394 |       " is even 16\n",
1395 |       " is odd 17\n",
1396 |       " is even 18\n",
1397 |       " is odd 19\n"
1398 |      ]
1399 |     }
1400 |    ],
1401 |    "source": [
1402 |     "for i in range(10,20):\n",
1403 |     "    if(i%2==0):\n",
1404 |     "        print(\" is even\",i)\n",
1405 |     "    else:\n",
1406 |     "        print(\" is odd\",i)"
1407 |    ]
1408 |   },
1409 |   {
1410 |    "cell_type": "code",
1411 |    "execution_count": 239,
1412 |    "metadata": {
1413 |     "scrolled": true
1414 |    },
1415 |    "outputs": [
1416 |     {
1417 |      "name": "stdout",
1418 |      "output_type": "stream",
1419 |      "text": [
1420 |       "1 * 1 = 1\n",
1421 |       "1 * 2 = 1\n",
1422 |       "2 * 1 = 2\n",
1423 |       "2 * 2 = 4\n"
1424 |      ]
1425 |     }
1426 |    ],
1427 |    "source": [
1428 |     "for x in range(1, 3):\n",
1429 |     "    for y in range(1, 3):\n",
1430 |     "        print('%d * %d = %d' % (x, y, x**y))"
1431 |    ]
1432 |   },
1433 |   {
1434 |    "cell_type": "markdown",
1435 |    "metadata": {},
1436 |    "source": [
1437 |     "### Pass Continue break"
1438 |    ]
1439 |   },
1440 |   {
1441 |    "cell_type": "code",
1442 |    "execution_count": 240,
1443 |    "metadata": {},
1444 |    "outputs": [
1445 |     {
1446 |      "name": "stdout",
1447 |      "output_type": "stream",
1448 |      "text": [
1449 |       "c\n",
1450 |       "The end\n"
1451 |      ]
1452 |     }
1453 |    ],
1454 |    "source": [
1455 |     "for val in \"I am coder\":\n",
1456 |     "    if val == \"c\":\n",
1457 |     "        #break\n",
1458 |     "        #continue\n",
1459 |     "        pass\n",
1460 |     "        print(val)\n",
1461 |     "print(\"The end\")"
1462 |    ]
1463 |   },
1464 |   {
1465 |    "cell_type": "markdown",
1466 |    "metadata": {},
1467 |    "source": [
1468 |     "## Function\n"
1469 |    ]
1470 |   },
1471 |   {
1472 |    "cell_type": "code",
1473 |    "execution_count": 241,
1474 |    "metadata": {
1475 |     "scrolled": true
1476 |    },
1477 |    "outputs": [],
1478 |    "source": [
1479 |     "def add(a,b):\n",
1480 |     "  return a + b"
1481 |    ]
1482 |   },
1483 |   {
1484 |    "cell_type": "code",
1485 |    "execution_count": 242,
1486 |    "metadata": {
1487 |     "scrolled": true
1488 |    },
1489 |    "outputs": [
1490 |     {
1491 |      "data": {
1492 |       "text/plain": [
1493 |        "13"
1494 |       ]
1495 |      },
1496 |      "execution_count": 242,
1497 |      "metadata": {},
1498 |      "output_type": "execute_result"
1499 |     }
1500 |    ],
1501 |    "source": [
1502 |     "add(7,6)"
1503 |    ]
1504 |   },
1505 |   {
1506 |    "cell_type": "code",
1507 |    "execution_count": 243,
1508 |    "metadata": {
1509 |     "scrolled": true
1510 |    },
1511 |    "outputs": [
1512 |     {
1513 |      "data": {
1514 |       "text/plain": [
1515 |        "8"
1516 |       ]
1517 |      },
1518 |      "execution_count": 243,
1519 |      "metadata": {},
1520 |      "output_type": "execute_result"
1521 |     }
1522 |    ],
1523 |    "source": [
1524 |     "def cube(a):\n",
1525 |     "    a=a*a*a\n",
1526 |     "    return a\n",
1527 |     "cube(2)"
1528 |    ]
1529 |   },
1530 |   {
1531 |    "cell_type": "markdown",
1532 |    "metadata": {},
1533 |    "source": [
1534 |     "user input and calculate nth fibonacci number"
1535 |    ]
1536 |   },
1537 |   {
1538 |    "cell_type": "code",
1539 |    "execution_count": 244,
1540 |    "metadata": {},
1541 |    "outputs": [
1542 |     {
1543 |      "name": "stdout",
1544 |      "output_type": "stream",
1545 |      "text": [
1546 |       "enter number : 66\n"
1547 |      ]
1548 |     }
1549 |    ],
1550 |    "source": [
1551 |     "inp=input(\"enter number : \")"
1552 |    ]
1553 |   },
1554 |   {
1555 |    "cell_type": "code",
1556 |    "execution_count": 245,
1557 |    "metadata": {},
1558 |    "outputs": [
1559 |     {
1560 |      "data": {
1561 |       "text/plain": [
1562 |        "66"
1563 |       ]
1564 |      },
1565 |      "execution_count": 245,
1566 |      "metadata": {},
1567 |      "output_type": "execute_result"
1568 |     }
1569 |    ],
1570 |    "source": [
1571 |     "a=int(inp)  \n",
1572 |     "a"
1573 |    ]
1574 |   },
1575 |   {
1576 |    "cell_type": "code",
1577 |    "execution_count": 246,
1578 |    "metadata": {},
1579 |    "outputs": [
1580 |     {
1581 |      "name": "stdout",
1582 |      "output_type": "stream",
1583 |      "text": [
1584 |       "the 3 term of fibonacci is : 1\n",
1585 |       "the 4 term of fibonacci is : 2\n",
1586 |       "the 5 term of fibonacci is : 3\n",
1587 |       "the 6 term of fibonacci is : 5\n",
1588 |       "the 7 term of fibonacci is : 8\n",
1589 |       "the 8 term of fibonacci is : 13\n",
1590 |       "the 9 term of fibonacci is : 21\n",
1591 |       "the 10 term of fibonacci is : 34\n",
1592 |       "the 11 term of fibonacci is : 55\n",
1593 |       "the 12 term of fibonacci is : 89\n",
1594 |       "the 13 term of fibonacci is : 144\n",
1595 |       "the 14 term of fibonacci is : 233\n",
1596 |       "the 15 term of fibonacci is : 377\n",
1597 |       "the 16 term of fibonacci is : 610\n",
1598 |       "the 17 term of fibonacci is : 987\n",
1599 |       "the 18 term of fibonacci is : 1597\n",
1600 |       "the 19 term of fibonacci is : 2584\n",
1601 |       "the 20 term of fibonacci is : 4181\n",
1602 |       "the 21 term of fibonacci is : 6765\n",
1603 |       "the 22 term of fibonacci is : 10946\n",
1604 |       "the 23 term of fibonacci is : 17711\n",
1605 |       "the 24 term of fibonacci is : 28657\n",
1606 |       "the 25 term of fibonacci is : 46368\n",
1607 |       "the 26 term of fibonacci is : 75025\n",
1608 |       "the 27 term of fibonacci is : 121393\n",
1609 |       "the 28 term of fibonacci is : 196418\n",
1610 |       "the 29 term of fibonacci is : 317811\n",
1611 |       "the 30 term of fibonacci is : 514229\n",
1612 |       "the 31 term of fibonacci is : 832040\n",
1613 |       "the 32 term of fibonacci is : 1346269\n",
1614 |       "the 33 term of fibonacci is : 2178309\n",
1615 |       "the 34 term of fibonacci is : 3524578\n",
1616 |       "the 35 term of fibonacci is : 5702887\n",
1617 |       "the 36 term of fibonacci is : 9227465\n",
1618 |       "the 37 term of fibonacci is : 14930352\n",
1619 |       "the 38 term of fibonacci is : 24157817\n",
1620 |       "the 39 term of fibonacci is : 39088169\n",
1621 |       "the 40 term of fibonacci is : 63245986\n",
1622 |       "the 41 term of fibonacci is : 102334155\n",
1623 |       "the 42 term of fibonacci is : 165580141\n",
1624 |       "the 43 term of fibonacci is : 267914296\n",
1625 |       "the 44 term of fibonacci is : 433494437\n",
1626 |       "the 45 term of fibonacci is : 701408733\n",
1627 |       "the 46 term of fibonacci is : 1134903170\n",
1628 |       "the 47 term of fibonacci is : 1836311903\n",
1629 |       "the 48 term of fibonacci is : 2971215073\n",
1630 |       "the 49 term of fibonacci is : 4807526976\n",
1631 |       "the 50 term of fibonacci is : 7778742049\n",
1632 |       "the 51 term of fibonacci is : 12586269025\n",
1633 |       "the 52 term of fibonacci is : 20365011074\n",
1634 |       "the 53 term of fibonacci is : 32951280099\n",
1635 |       "the 54 term of fibonacci is : 53316291173\n",
1636 |       "the 55 term of fibonacci is : 86267571272\n",
1637 |       "the 56 term of fibonacci is : 139583862445\n",
1638 |       "the 57 term of fibonacci is : 225851433717\n",
1639 |       "the 58 term of fibonacci is : 365435296162\n",
1640 |       "the 59 term of fibonacci is : 591286729879\n",
1641 |       "the 60 term of fibonacci is : 956722026041\n",
1642 |       "the 61 term of fibonacci is : 1548008755920\n",
1643 |       "the 62 term of fibonacci is : 2504730781961\n",
1644 |       "the 63 term of fibonacci is : 4052739537881\n",
1645 |       "the 64 term of fibonacci is : 6557470319842\n",
1646 |       "the 65 term of fibonacci is : 10610209857723\n",
1647 |       "the 66 term of fibonacci is : 17167680177565\n",
1648 |       "the 67 term of fibonacci is : 27777890035288\n",
1649 |       "the 68 term of fibonacci is : 44945570212853\n"
1650 |      ]
1651 |     }
1652 |    ],
1653 |    "source": [
1654 |     "first=0;second=1\n",
1655 |     "n=0;\n",
1656 |     "for i in range(a):\n",
1657 |     "    n=first+second;\n",
1658 |     "    first=second;\n",
1659 |     "    second=n;\n",
1660 |     "    print(\"the \"+str(i+3)+\" term of fibonacci is : \"+str(n)) \n",
1661 |     "    #print(\"The\",i+3,\"term of fibonacci is :\",n) \n",
1662 |     "\n",
1663 |     "    \n",
1664 |     "    "
1665 |    ]
1666 |   },
1667 |   {
1668 |    "cell_type": "markdown",
1669 |    "metadata": {
1670 |     "collapsed": true,
1671 |     "scrolled": true
1672 |    },
1673 |    "source": [
1674 |     "##  File I/O"
1675 |    ]
1676 |   },
1677 |   {
1678 |    "cell_type": "code",
1679 |    "execution_count": 247,
1680 |    "metadata": {
1681 |     "scrolled": true
1682 |    },
1683 |    "outputs": [
1684 |     {
1685 |      "name": "stdout",
1686 |      "output_type": "stream",
1687 |      "text": [
1688 |       "How are you ?\n",
1689 |       "\n"
1690 |      ]
1691 |     }
1692 |    ],
1693 |    "source": [
1694 |     "dat=input(\"How are you ?\\n\")"
1695 |    ]
1696 |   },
1697 |   {
1698 |    "cell_type": "code",
1699 |    "execution_count": 248,
1700 |    "metadata": {},
1701 |    "outputs": [],
1702 |    "source": [
1703 |     "#open file\n",
1704 |     "fo = open(\"ipl_team.txt\", \"w\")\n",
1705 |     "fo.write( \"The Indian Premier League (IPL) is a professional Twenty20 cricket league in India contested during March or April and May of every year by eight teams representing eight different cities in India.[3] The league was founded by the Board of Control for Cricket in India (BCCI) in 2008. IPL has an exclusive window in ICC Future Tours Programme.[4] The IPL is the most-attended cricket league in the world and in 2014 ranked\")\n",
1706 |     "# Close opend file\n",
1707 |     "fo.close()"
1708 |    ]
1709 |   },
1710 |   {
1711 |    "cell_type": "code",
1712 |    "execution_count": 249,
1713 |    "metadata": {},
1714 |    "outputs": [
1715 |     {
1716 |      "data": {
1717 |       "text/plain": [
1718 |        "'/home/pradip'"
1719 |       ]
1720 |      },
1721 |      "execution_count": 249,
1722 |      "metadata": {},
1723 |      "output_type": "execute_result"
1724 |     }
1725 |    ],
1726 |    "source": [
1727 |     "pwd"
1728 |    ]
1729 |   },
1730 |   {
1731 |    "cell_type": "code",
1732 |    "execution_count": 250,
1733 |    "metadata": {},
1734 |    "outputs": [
1735 |     {
1736 |      "name": "stdout",
1737 |      "output_type": "stream",
1738 |      "text": [
1739 |       "The Indian Premier League (IPL) is a professional Twenty20 cricket league in India contested during March or April and May of every year by eight teams representing eight different cities in India.[3] The league was founded by the Board of Control for Cricket in India (BCCI) in 2008. IPL has an exclusive window in ICC Future Tours Programme.[4] The IPL is the most-attended cricket league in the world and in 2014 ranked\n"
1740 |      ]
1741 |     }
1742 |    ],
1743 |    "source": [
1744 |     "fo = open(\"ipl_team.txt\", \"r\")\n",
1745 |     "st1=fo.readline()\n",
1746 |     "print( st1)\n"
1747 |    ]
1748 |   },
1749 |   {
1750 |    "cell_type": "code",
1751 |    "execution_count": null,
1752 |    "metadata": {},
1753 |    "outputs": [],
1754 |    "source": []
1755 |   },
1756 |   {
1757 |    "cell_type": "code",
1758 |    "execution_count": null,
1759 |    "metadata": {},
1760 |    "outputs": [],
1761 |    "source": []
1762 |   },
1763 |   {
1764 |    "cell_type": "code",
1765 |    "execution_count": 251,
1766 |    "metadata": {},
1767 |    "outputs": [],
1768 |    "source": [
1769 |     "sixes_data = pd.read_csv(\"Desktop/ipl_data/most_sixes_all_times.csv\")\n",
1770 |     "wickets_data = pd.read_csv(\"Desktop/ipl_data/most_wickets_all_times.csv\")\n"
1771 |    ]
1772 |   },
1773 |   {
1774 |    "cell_type": "code",
1775 |    "execution_count": 252,
1776 |    "metadata": {},
1777 |    "outputs": [
1778 |     {
1779 |      "name": "stdout",
1780 |      "output_type": "stream",
1781 |      "text": [
1782 |       "Sixes:\n"
1783 |      ]
1784 |     },
1785 |     {
1786 |      "data": {
1787 |       "text/html": [
1788 |        "<div>\n",
1789 |        "<style scoped>\n",
1790 |        "    .dataframe tbody tr th:only-of-type {\n",
1791 |        "        vertical-align: middle;\n",
1792 |        "    }\n",
1793 |        "\n",
1794 |        "    .dataframe tbody tr th {\n",
1795 |        "        vertical-align: top;\n",
1796 |        "    }\n",
1797 |        "\n",
1798 |        "    .dataframe thead th {\n",
1799 |        "        text-align: right;\n",
1800 |        "    }\n",
1801 |        "</style>\n",
1802 |        "<table border=\"1\" class=\"dataframe\">\n",
1803 |        "  <thead>\n",
1804 |        "    <tr style=\"text-align: right;\">\n",
1805 |        "      <th></th>\n",
1806 |        "      <th>id</th>\n",
1807 |        "      <th>Player</th>\n",
1808 |        "      <th>Sixes</th>\n",
1809 |        "      <th>Country</th>\n",
1810 |        "    </tr>\n",
1811 |        "  </thead>\n",
1812 |        "  <tbody>\n",
1813 |        "    <tr>\n",
1814 |        "      <th>0</th>\n",
1815 |        "      <td>1</td>\n",
1816 |        "      <td>ChrisGayle</td>\n",
1817 |        "      <td>326</td>\n",
1818 |        "      <td>WI</td>\n",
1819 |        "    </tr>\n",
1820 |        "    <tr>\n",
1821 |        "      <th>1</th>\n",
1822 |        "      <td>2</td>\n",
1823 |        "      <td>AB de Villiers</td>\n",
1824 |        "      <td>212</td>\n",
1825 |        "      <td>SA</td>\n",
1826 |        "    </tr>\n",
1827 |        "    <tr>\n",
1828 |        "      <th>2</th>\n",
1829 |        "      <td>3</td>\n",
1830 |        "      <td>MS Dhoni</td>\n",
1831 |        "      <td>209</td>\n",
1832 |        "      <td>Ind</td>\n",
1833 |        "    </tr>\n",
1834 |        "    <tr>\n",
1835 |        "      <th>3</th>\n",
1836 |        "      <td>4</td>\n",
1837 |        "      <td>Rohit Sharma</td>\n",
1838 |        "      <td>194</td>\n",
1839 |        "      <td>Ind</td>\n",
1840 |        "    </tr>\n",
1841 |        "    <tr>\n",
1842 |        "      <th>4</th>\n",
1843 |        "      <td>5</td>\n",
1844 |        "      <td>Suresh Raina</td>\n",
1845 |        "      <td>194</td>\n",
1846 |        "      <td>Ind</td>\n",
1847 |        "    </tr>\n",
1848 |        "  </tbody>\n",
1849 |        "</table>\n",
1850 |        "</div>"
1851 |       ],
1852 |       "text/plain": [
1853 |        "   id           Player  Sixes Country\n",
1854 |        "0   1       ChrisGayle    326      WI\n",
1855 |        "1   2   AB de Villiers    212      SA\n",
1856 |        "2   3         MS Dhoni    209     Ind\n",
1857 |        "3   4     Rohit Sharma    194     Ind\n",
1858 |        "4   5     Suresh Raina    194     Ind"
1859 |       ]
1860 |      },
1861 |      "metadata": {},
1862 |      "output_type": "display_data"
1863 |     },
1864 |     {
1865 |      "name": "stdout",
1866 |      "output_type": "stream",
1867 |      "text": [
1868 |       "\n",
1869 |       "\n"
1870 |      ]
1871 |     }
1872 |    ],
1873 |    "source": [
1874 |     "print(\"Sixes:\")\n",
1875 |     "display(sixes_data.head())\n",
1876 |     "print(\"\\n\")\n"
1877 |    ]
1878 |   },
1879 |   {
1880 |    "cell_type": "code",
1881 |    "execution_count": 253,
1882 |    "metadata": {},
1883 |    "outputs": [
1884 |     {
1885 |      "data": {
1886 |       "application/vnd.plotly.v1+json": {
1887 |        "config": {
1888 |         "linkText": "Export to plot.ly",
1889 |         "plotlyServerURL": "https://plot.ly",
1890 |         "showLink": false
1891 |        },
1892 |        "data": [
1893 |         {
1894 |          "alignmentgroup": "True",
1895 |          "hoverlabel": {
1896 |           "namelength": 0
1897 |          },
1898 |          "hovertemplate": "Player=%{x}<br>Sixes=%{y}",
1899 |          "legendgroup": "",
1900 |          "marker": {
1901 |           "color": "#636efa"
1902 |          },
1903 |          "name": "",
1904 |          "offsetgroup": "",
1905 |          "orientation": "v",
1906 |          "showlegend": false,
1907 |          "textposition": "auto",
1908 |          "type": "bar",
1909 |          "uid": "716929d5-2ec6-4153-9ca5-19d9e727af57",
1910 |          "x": [
1911 |           "ChrisGayle",
1912 |           " AB de Villiers",
1913 |           " MS Dhoni",
1914 |           " Rohit Sharma",
1915 |           " Suresh Raina",
1916 |           " Virat Kohli",
1917 |           " David Warner",
1918 |           " Shane Watson",
1919 |           " Kieron Pollard",
1920 |           " Yusuf Pathan",
1921 |           " Robin Uthappa",
1922 |           " Yuvraj Singh",
1923 |           " Brendon McCullum",
1924 |           " Andre Russell",
1925 |           " Ambati Rayudu",
1926 |           " Dwayne Smith",
1927 |           " Virender Sehwag",
1928 |           " Dinesh Karthik",
1929 |           " Shikhar Dhawan",
1930 |           " Rishabh Pant",
1931 |           " Adam Gilchrist",
1932 |           " Glenn Maxwell",
1933 |           " Murali Vijay",
1934 |           " Sanju Samson",
1935 |           " David Miller",
1936 |           " KL Rahul",
1937 |           " JP Duminy",
1938 |           " Naman Ojha",
1939 |           " Shaun Marsh",
1940 |           " Manish Pandey"
1941 |          ],
1942 |          "xaxis": "x",
1943 |          "y": [
1944 |           326,
1945 |           212,
1946 |           209,
1947 |           194,
1948 |           194,
1949 |           191,
1950 |           181,
1951 |           177,
1952 |           176,
1953 |           158,
1954 |           156,
1955 |           149,
1956 |           130,
1957 |           120,
1958 |           120,
1959 |           117,
1960 |           106,
1961 |           101,
1962 |           96,
1963 |           94,
1964 |           92,
1965 |           91,
1966 |           91,
1967 |           89,
1968 |           87,
1969 |           81,
1970 |           79,
1971 |           79,
1972 |           78,
1973 |           75
1974 |          ],
1975 |          "yaxis": "y"
1976 |         }
1977 |        ],
1978 |        "layout": {
1979 |         "barmode": "relative",
1980 |         "grid": {
1981 |          "xaxes": [
1982 |           "x"
1983 |          ],
1984 |          "xgap": 0.1,
1985 |          "xside": "bottom",
1986 |          "yaxes": [
1987 |           "y"
1988 |          ],
1989 |          "ygap": 0.1,
1990 |          "yside": "left"
1991 |         },
1992 |         "height": 600,
1993 |         "legend": {
1994 |          "tracegroupgap": 0
1995 |         },
1996 |         "template": {
1997 |          "data": {
1998 |           "bar": [
1999 |            {
2000 |             "marker": {
2001 |              "line": {
2002 |               "color": "#E5ECF6",
2003 |               "width": 0.5
2004 |              }
2005 |             },
2006 |             "type": "bar"
2007 |            }
2008 |           ],
2009 |           "barpolar": [
2010 |            {
2011 |             "marker": {
2012 |              "line": {
2013 |               "color": "#E5ECF6",
2014 |               "width": 0.5
2015 |              }
2016 |             },
2017 |             "type": "barpolar"
2018 |            }
2019 |           ],
2020 |           "carpet": [
2021 |            {
2022 |             "aaxis": {
2023 |              "endlinecolor": "#2a3f5f",
2024 |              "gridcolor": "white",
2025 |              "linecolor": "white",
2026 |              "minorgridcolor": "white",
2027 |              "startlinecolor": "#2a3f5f"
2028 |             },
2029 |             "baxis": {
2030 |              "endlinecolor": "#2a3f5f",
2031 |              "gridcolor": "white",
2032 |              "linecolor": "white",
2033 |              "minorgridcolor": "white",
2034 |              "startlinecolor": "#2a3f5f"
2035 |             },
2036 |             "type": "carpet"
2037 |            }
2038 |           ],
2039 |           "choropleth": [
2040 |            {
2041 |             "colorbar": {
2042 |              "outlinewidth": 0,
2043 |              "ticks": ""
2044 |             },
2045 |             "type": "choropleth"
2046 |            }
2047 |           ],
2048 |           "contour": [
2049 |            {
2050 |             "colorbar": {
2051 |              "outlinewidth": 0,
2052 |              "ticks": ""
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2054 |             "colorscale": [
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2056 |               0,
2057 |               "#0508b8"
2058 |              ],
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2060 |               0.0893854748603352,
2061 |               "#1910d8"
2062 |              ],
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2064 |               0.1787709497206704,
2065 |               "#3c19f0"
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2073 |               "#981cfd"
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2089 |               "#fc67fd"
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2091 |              [
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2093 |               "#fe88fc"
2094 |              ],
2095 |              [
2096 |               0.8938547486033519,
2097 |               "#fea5fd"
2098 |              ],
2099 |              [
2100 |               0.9832402234636871,
2101 |               "#febefe"
2102 |              ],
2103 |              [
2104 |               1,
2105 |               "#fec3fe"
2106 |              ]
2107 |             ],
2108 |             "type": "contour"
2109 |            }
2110 |           ],
2111 |           "contourcarpet": [
2112 |            {
2113 |             "colorbar": {
2114 |              "outlinewidth": 0,
2115 |              "ticks": ""
2116 |             },
2117 |             "type": "contourcarpet"
2118 |            }
2119 |           ],
2120 |           "heatmap": [
2121 |            {
2122 |             "colorbar": {
2123 |              "outlinewidth": 0,
2124 |              "ticks": ""
2125 |             },
2126 |             "colorscale": [
2127 |              [
2128 |               0,
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2791 |        "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
2792 |        "                    ).then(function(){\n",
2793 |        "                            \n",
2794 |        "var gd = document.getElementById('a2bafb9a-5ce7-49e5-9ac5-b939a6462fd0');\n",
2795 |        "var x = new MutationObserver(function (mutations, observer) {{\n",
2796 |        "        var display = window.getComputedStyle(gd).display;\n",
2797 |        "        if (!display || display === 'none') {{\n",
2798 |        "            console.log([gd, 'removed!']);\n",
2799 |        "            Plotly.purge(gd);\n",
2800 |        "            observer.disconnect();\n",
2801 |        "        }}\n",
2802 |        "}});\n",
2803 |        "\n",
2804 |        "// Listen for the removal of the full notebook cells\n",
2805 |        "var notebookContainer = gd.closest('#notebook-container');\n",
2806 |        "if (notebookContainer) {{\n",
2807 |        "    x.observe(notebookContainer, {childList: true});\n",
2808 |        "}}\n",
2809 |        "\n",
2810 |        "// Listen for the clearing of the current output cell\n",
2811 |        "var outputEl = gd.closest('.output');\n",
2812 |        "if (outputEl) {{\n",
2813 |        "    x.observe(outputEl, {childList: true});\n",
2814 |        "}}\n",
2815 |        "\n",
2816 |        "                        })\n",
2817 |        "                };\n",
2818 |        "                });\n",
2819 |        "            </script>\n",
2820 |        "        </div>"
2821 |       ]
2822 |      },
2823 |      "metadata": {},
2824 |      "output_type": "display_data"
2825 |     }
2826 |    ],
2827 |    "source": [
2828 |     "px.bar(sixes_data, y = \"Sixes\",x = \"Player\", title = \"Sixes by IPL Batsmen\")"
2829 |    ]
2830 |   },
2831 |   {
2832 |    "cell_type": "code",
2833 |    "execution_count": 254,
2834 |    "metadata": {},
2835 |    "outputs": [
2836 |     {
2837 |      "name": "stdout",
2838 |      "output_type": "stream",
2839 |      "text": [
2840 |       "Wickets:\n"
2841 |      ]
2842 |     },
2843 |     {
2844 |      "data": {
2845 |       "text/html": [
2846 |        "<div>\n",
2847 |        "<style scoped>\n",
2848 |        "    .dataframe tbody tr th:only-of-type {\n",
2849 |        "        vertical-align: middle;\n",
2850 |        "    }\n",
2851 |        "\n",
2852 |        "    .dataframe tbody tr th {\n",
2853 |        "        vertical-align: top;\n",
2854 |        "    }\n",
2855 |        "\n",
2856 |        "    .dataframe thead th {\n",
2857 |        "        text-align: right;\n",
2858 |        "    }\n",
2859 |        "</style>\n",
2860 |        "<table border=\"1\" class=\"dataframe\">\n",
2861 |        "  <thead>\n",
2862 |        "    <tr style=\"text-align: right;\">\n",
2863 |        "      <th></th>\n",
2864 |        "      <th>id</th>\n",
2865 |        "      <th>Player</th>\n",
2866 |        "      <th>Mat</th>\n",
2867 |        "      <th>Wkts</th>\n",
2868 |        "      <th>Country</th>\n",
2869 |        "    </tr>\n",
2870 |        "  </thead>\n",
2871 |        "  <tbody>\n",
2872 |        "    <tr>\n",
2873 |        "      <th>0</th>\n",
2874 |        "      <td>1</td>\n",
2875 |        "      <td>LasithMalinga</td>\n",
2876 |        "      <td>122</td>\n",
2877 |        "      <td>170</td>\n",
2878 |        "      <td>SL</td>\n",
2879 |        "    </tr>\n",
2880 |        "    <tr>\n",
2881 |        "      <th>1</th>\n",
2882 |        "      <td>2</td>\n",
2883 |        "      <td>Amit Mishra</td>\n",
2884 |        "      <td>147</td>\n",
2885 |        "      <td>157</td>\n",
2886 |        "      <td>Ind</td>\n",
2887 |        "    </tr>\n",
2888 |        "    <tr>\n",
2889 |        "      <th>2</th>\n",
2890 |        "      <td>3</td>\n",
2891 |        "      <td>Harbhajan Singh</td>\n",
2892 |        "      <td>160</td>\n",
2893 |        "      <td>150</td>\n",
2894 |        "      <td>Ind</td>\n",
2895 |        "    </tr>\n",
2896 |        "    <tr>\n",
2897 |        "      <th>3</th>\n",
2898 |        "      <td>4</td>\n",
2899 |        "      <td>Piyush Chawla</td>\n",
2900 |        "      <td>157</td>\n",
2901 |        "      <td>150</td>\n",
2902 |        "      <td>Ind</td>\n",
2903 |        "    </tr>\n",
2904 |        "    <tr>\n",
2905 |        "      <th>4</th>\n",
2906 |        "      <td>5</td>\n",
2907 |        "      <td>Dwayne Bravo</td>\n",
2908 |        "      <td>134</td>\n",
2909 |        "      <td>147</td>\n",
2910 |        "      <td>WI</td>\n",
2911 |        "    </tr>\n",
2912 |        "  </tbody>\n",
2913 |        "</table>\n",
2914 |        "</div>"
2915 |       ],
2916 |       "text/plain": [
2917 |        "   id            Player  Mat  Wkts Country\n",
2918 |        "0   1     LasithMalinga  122   170      SL\n",
2919 |        "1   2       Amit Mishra  147   157     Ind\n",
2920 |        "2   3   Harbhajan Singh  160   150     Ind\n",
2921 |        "3   4     Piyush Chawla  157   150     Ind\n",
2922 |        "4   5      Dwayne Bravo  134   147      WI"
2923 |       ]
2924 |      },
2925 |      "metadata": {},
2926 |      "output_type": "display_data"
2927 |     }
2928 |    ],
2929 |    "source": [
2930 |     "print(\"Wickets:\")\n",
2931 |     "display(wickets_data.head())"
2932 |    ]
2933 |   },
2934 |   {
2935 |    "cell_type": "code",
2936 |    "execution_count": 255,
2937 |    "metadata": {},
2938 |    "outputs": [
2939 |     {
2940 |      "data": {
2941 |       "application/vnd.plotly.v1+json": {
2942 |        "config": {
2943 |         "linkText": "Export to plot.ly",
2944 |         "plotlyServerURL": "https://plot.ly",
2945 |         "showLink": false
2946 |        },
2947 |        "data": [
2948 |         {
2949 |          "alignmentgroup": "True",
2950 |          "hoverlabel": {
2951 |           "namelength": 0
2952 |          },
2953 |          "hovertemplate": "Country=SL<br>Wkts=%{x}<br>Player=%{y}",
2954 |          "legendgroup": "Country=SL",
2955 |          "marker": {
2956 |           "color": "#636efa"
2957 |          },
2958 |          "name": "Country=SL",
2959 |          "offsetgroup": "Country=SL",
2960 |          "orientation": "h",
2961 |          "showlegend": true,
2962 |          "textposition": "auto",
2963 |          "type": "bar",
2964 |          "uid": "564eeeb3-5d89-494f-a772-28d4c9300cd3",
2965 |          "x": [
2966 |           170
2967 |          ],
2968 |          "xaxis": "x",
2969 |          "y": [
2970 |           "LasithMalinga"
2971 |          ],
2972 |          "yaxis": "y"
2973 |         },
2974 |         {
2975 |          "alignmentgroup": "True",
2976 |          "hoverlabel": {
2977 |           "namelength": 0
2978 |          },
2979 |          "hovertemplate": "Country=Ind<br>Wkts=%{x}<br>Player=%{y}",
2980 |          "legendgroup": "Country=Ind",
2981 |          "marker": {
2982 |           "color": "#EF553B"
2983 |          },
2984 |          "name": "Country=Ind",
2985 |          "offsetgroup": "Country=Ind",
2986 |          "orientation": "h",
2987 |          "showlegend": true,
2988 |          "textposition": "auto",
2989 |          "type": "bar",
2990 |          "uid": "c81b81f9-a6ff-4a74-8b55-3e93c132b053",
2991 |          "x": [
2992 |           157,
2993 |           150,
2994 |           150,
2995 |           133,
2996 |           125,
2997 |           119,
2998 |           108,
2999 |           106,
3000 |           105,
3001 |           102,
3002 |           100,
3003 |           95,
3004 |           91,
3005 |           90,
3006 |           90,
3007 |           89,
3008 |           86,
3009 |           82,
3010 |           80,
3011 |           77,
3012 |           76,
3013 |           74
3014 |          ],
3015 |          "xaxis": "x",
3016 |          "y": [
3017 |           " Amit Mishra",
3018 |           " Harbhajan Singh",
3019 |           " Piyush Chawla",
3020 |           " Bhuvneshwar Kumar",
3021 |           " Ravichandran Ashwin",
3022 |           " Umesh Yadav",
3023 |           " Ravindra Jadeja",
3024 |           " Ashish Nehra",
3025 |           " Vinay Kumar",
3026 |           " Zaheer Khan",
3027 |           " Yuzvendra Chahal",
3028 |           " Sandeep Sharma",
3029 |           " Mohit Sharma",
3030 |           " Praveen Kumar",
3031 |           " RP Singh",
3032 |           " Pragyan Ojha",
3033 |           " Dhawal Kulkarni",
3034 |           " Jasprit Bumrah",
3035 |           " Irfan Pathan",
3036 |           " Jaydev Unadkat",
3037 |           " Lakshmipathy Balaji",
3038 |           " Munaf Patel"
3039 |          ],
3040 |          "yaxis": "y"
3041 |         },
3042 |         {
3043 |          "alignmentgroup": "True",
3044 |          "hoverlabel": {
3045 |           "namelength": 0
3046 |          },
3047 |          "hovertemplate": "Country=WI<br>Wkts=%{x}<br>Player=%{y}",
3048 |          "legendgroup": "Country=WI",
3049 |          "marker": {
3050 |           "color": "#00cc96"
3051 |          },
3052 |          "name": "Country=WI",
3053 |          "offsetgroup": "Country=WI",
3054 |          "orientation": "h",
3055 |          "showlegend": true,
3056 |          "textposition": "auto",
3057 |          "type": "bar",
3058 |          "uid": "befa7a6f-79d3-4b24-8b36-df7b462dd952",
3059 |          "x": [
3060 |           147,
3061 |           122
3062 |          ],
3063 |          "xaxis": "x",
3064 |          "y": [
3065 |           " Dwayne Bravo",
3066 |           " Sunil Narine"
3067 |          ],
3068 |          "yaxis": "y"
3069 |         },
3070 |         {
3071 |          "alignmentgroup": "True",
3072 |          "hoverlabel": {
3073 |           "namelength": 0
3074 |          },
3075 |          "hovertemplate": "Country=SA<br>Wkts=%{x}<br>Player=%{y}",
3076 |          "legendgroup": "Country=SA",
3077 |          "marker": {
3078 |           "color": "#ab63fa"
3079 |          },
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3866 |           "shapedefaults": {
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3870 |            },
3871 |            "opacity": 0.4
3872 |           },
3873 |           "ternary": {
3874 |            "aaxis": {
3875 |             "gridcolor": "white",
3876 |             "linecolor": "white",
3877 |             "ticks": ""
3878 |            },
3879 |            "baxis": {
3880 |             "gridcolor": "white",
3881 |             "linecolor": "white",
3882 |             "ticks": ""
3883 |            },
3884 |            "bgcolor": "#E5ECF6",
3885 |            "caxis": {
3886 |             "gridcolor": "white",
3887 |             "linecolor": "white",
3888 |             "ticks": ""
3889 |            }
3890 |           },
3891 |           "title": {
3892 |            "x": 0.05
3893 |           },
3894 |           "xaxis": {
3895 |            "automargin": true,
3896 |            "gridcolor": "white",
3897 |            "linecolor": "white",
3898 |            "ticks": "",
3899 |            "zerolinecolor": "white",
3900 |            "zerolinewidth": 2
3901 |           },
3902 |           "yaxis": {
3903 |            "automargin": true,
3904 |            "gridcolor": "white",
3905 |            "linecolor": "white",
3906 |            "ticks": "",
3907 |            "zerolinecolor": "white",
3908 |            "zerolinewidth": 2
3909 |           }
3910 |          }
3911 |         },
3912 |         "title": {
3913 |          "text": "Sixes by IPL Batsmen"
3914 |         },
3915 |         "xaxis": {
3916 |          "title": {
3917 |           "text": "Wkts"
3918 |          }
3919 |         },
3920 |         "yaxis": {
3921 |          "title": {
3922 |           "text": "Player"
3923 |          }
3924 |         }
3925 |        }
3926 |       },
3927 |       "text/html": [
3928 |        "<div>\n",
3929 |        "        \n",
3930 |        "        \n",
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3942 |        "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
3943 |        "                    ).then(function(){\n",
3944 |        "                            \n",
3945 |        "var gd = document.getElementById('39aee3ee-2430-4d1a-9bae-f7a7b2830e52');\n",
3946 |        "var x = new MutationObserver(function (mutations, observer) {{\n",
3947 |        "        var display = window.getComputedStyle(gd).display;\n",
3948 |        "        if (!display || display === 'none') {{\n",
3949 |        "            console.log([gd, 'removed!']);\n",
3950 |        "            Plotly.purge(gd);\n",
3951 |        "            observer.disconnect();\n",
3952 |        "        }}\n",
3953 |        "}});\n",
3954 |        "\n",
3955 |        "// Listen for the removal of the full notebook cells\n",
3956 |        "var notebookContainer = gd.closest('#notebook-container');\n",
3957 |        "if (notebookContainer) {{\n",
3958 |        "    x.observe(notebookContainer, {childList: true});\n",
3959 |        "}}\n",
3960 |        "\n",
3961 |        "// Listen for the clearing of the current output cell\n",
3962 |        "var outputEl = gd.closest('.output');\n",
3963 |        "if (outputEl) {{\n",
3964 |        "    x.observe(outputEl, {childList: true});\n",
3965 |        "}}\n",
3966 |        "\n",
3967 |        "                        })\n",
3968 |        "                };\n",
3969 |        "                });\n",
3970 |        "            </script>\n",
3971 |        "        </div>"
3972 |       ]
3973 |      },
3974 |      "metadata": {},
3975 |      "output_type": "display_data"
3976 |     }
3977 |    ],
3978 |    "source": [
3979 |     "px.bar(wickets_data, x = \"Wkts\",y = \"Player\", color = \"Country\", title = \"Sixes by IPL Batsmen\", orientation = \"h\")"
3980 |    ]
3981 |   },
3982 |   {
3983 |    "cell_type": "code",
3984 |    "execution_count": 256,
3985 |    "metadata": {},
3986 |    "outputs": [
3987 |     {
3988 |      "data": {
3989 |       "text/html": [
3990 |        "<div>\n",
3991 |        "<style scoped>\n",
3992 |        "    .dataframe tbody tr th:only-of-type {\n",
3993 |        "        vertical-align: middle;\n",
3994 |        "    }\n",
3995 |        "\n",
3996 |        "    .dataframe tbody tr th {\n",
3997 |        "        vertical-align: top;\n",
3998 |        "    }\n",
3999 |        "\n",
4000 |        "    .dataframe thead th {\n",
4001 |        "        text-align: right;\n",
4002 |        "    }\n",
4003 |        "</style>\n",
4004 |        "<table border=\"1\" class=\"dataframe\">\n",
4005 |        "  <thead>\n",
4006 |        "    <tr style=\"text-align: right;\">\n",
4007 |        "      <th></th>\n",
4008 |        "      <th>POS</th>\n",
4009 |        "      <th>PLAYER</th>\n",
4010 |        "      <th>HS</th>\n",
4011 |        "      <th>BF</th>\n",
4012 |        "      <th>4s</th>\n",
4013 |        "      <th>6s</th>\n",
4014 |        "      <th>NO</th>\n",
4015 |        "    </tr>\n",
4016 |        "  </thead>\n",
4017 |        "  <tbody>\n",
4018 |        "    <tr>\n",
4019 |        "      <th>0</th>\n",
4020 |        "      <td>1</td>\n",
4021 |        "      <td>ChrisGayle</td>\n",
4022 |        "      <td>175</td>\n",
4023 |        "      <td>66</td>\n",
4024 |        "      <td>13</td>\n",
4025 |        "      <td>17</td>\n",
4026 |        "      <td>Y</td>\n",
4027 |        "    </tr>\n",
4028 |        "    <tr>\n",
4029 |        "      <th>1</th>\n",
4030 |        "      <td>2</td>\n",
4031 |        "      <td>Brendon McCullum</td>\n",
4032 |        "      <td>158</td>\n",
4033 |        "      <td>73</td>\n",
4034 |        "      <td>10</td>\n",
4035 |        "      <td>13</td>\n",
4036 |        "      <td>Y</td>\n",
4037 |        "    </tr>\n",
4038 |        "    <tr>\n",
4039 |        "      <th>2</th>\n",
4040 |        "      <td>3</td>\n",
4041 |        "      <td>AB de Villiers</td>\n",
4042 |        "      <td>133</td>\n",
4043 |        "      <td>59</td>\n",
4044 |        "      <td>19</td>\n",
4045 |        "      <td>4</td>\n",
4046 |        "      <td>Y</td>\n",
4047 |        "    </tr>\n",
4048 |        "    <tr>\n",
4049 |        "      <th>3</th>\n",
4050 |        "      <td>4</td>\n",
4051 |        "      <td>AB de Villiers</td>\n",
4052 |        "      <td>129</td>\n",
4053 |        "      <td>52</td>\n",
4054 |        "      <td>10</td>\n",
4055 |        "      <td>12</td>\n",
4056 |        "      <td>Y</td>\n",
4057 |        "    </tr>\n",
4058 |        "  </tbody>\n",
4059 |        "</table>\n",
4060 |        "</div>"
4061 |       ],
4062 |       "text/plain": [
4063 |        "   POS             PLAYER   HS  BF  4s  6s NO\n",
4064 |        "0    1         ChrisGayle  175  66  13  17  Y\n",
4065 |        "1    2   Brendon McCullum  158  73  10  13  Y\n",
4066 |        "2    3     AB de Villiers  133  59  19   4  Y\n",
4067 |        "3    4     AB de Villiers  129  52  10  12  Y"
4068 |       ]
4069 |      },
4070 |      "execution_count": 256,
4071 |      "metadata": {},
4072 |      "output_type": "execute_result"
4073 |     }
4074 |    ],
4075 |    "source": [
4076 |     "\n",
4077 |     "high_score_in=pd.read_csv(\"Desktop/ipl_data/high_score_in.csv\")\n",
4078 |     "high_score_in.head(4)"
4079 |    ]
4080 |   },
4081 |   {
4082 |    "cell_type": "code",
4083 |    "execution_count": 257,
4084 |    "metadata": {},
4085 |    "outputs": [
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5155 |        "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
5156 |        "                    ).then(function(){\n",
5157 |        "                            \n",
5158 |        "var gd = document.getElementById('941f94f4-5fad-4131-a49c-32628745906b');\n",
5159 |        "var x = new MutationObserver(function (mutations, observer) {{\n",
5160 |        "        var display = window.getComputedStyle(gd).display;\n",
5161 |        "        if (!display || display === 'none') {{\n",
5162 |        "            console.log([gd, 'removed!']);\n",
5163 |        "            Plotly.purge(gd);\n",
5164 |        "            observer.disconnect();\n",
5165 |        "        }}\n",
5166 |        "}});\n",
5167 |        "\n",
5168 |        "// Listen for the removal of the full notebook cells\n",
5169 |        "var notebookContainer = gd.closest('#notebook-container');\n",
5170 |        "if (notebookContainer) {{\n",
5171 |        "    x.observe(notebookContainer, {childList: true});\n",
5172 |        "}}\n",
5173 |        "\n",
5174 |        "// Listen for the clearing of the current output cell\n",
5175 |        "var outputEl = gd.closest('.output');\n",
5176 |        "if (outputEl) {{\n",
5177 |        "    x.observe(outputEl, {childList: true});\n",
5178 |        "}}\n",
5179 |        "\n",
5180 |        "                        })\n",
5181 |        "                };\n",
5182 |        "                });\n",
5183 |        "            </script>\n",
5184 |        "        </div>"
5185 |       ]
5186 |      },
5187 |      "metadata": {},
5188 |      "output_type": "display_data"
5189 |     }
5190 |    ],
5191 |    "source": [
5192 |     "px.scatter(high_score_in,x=\"PLAYER\",y=\"HS\",color='PLAYER', size=\"HS\",size_max=100)\n"
5193 |    ]
5194 |   },
5195 |   {
5196 |    "cell_type": "code",
5197 |    "execution_count": 258,
5198 |    "metadata": {},
5199 |    "outputs": [
5200 |     {
5201 |      "data": {
5202 |       "application/vnd.plotly.v1+json": {
5203 |        "config": {
5204 |         "linkText": "Export to plot.ly",
5205 |         "plotlyServerURL": "https://plot.ly",
5206 |         "showLink": false
5207 |        },
5208 |        "data": [
5209 |         {
5210 |          "hoverlabel": {
5211 |           "namelength": 0
5212 |          },
5213 |          "hovertemplate": "Country=SL<br>Player=%{x}<br>Wkts=%{marker.size}",
5214 |          "legendgroup": "Country=SL",
5215 |          "marker": {
5216 |           "color": "#636efa",
5217 |           "size": [
5218 |            170
5219 |           ],
5220 |           "sizemode": "area",
5221 |           "sizeref": 0.00425,
5222 |           "symbol": "circle"
5223 |          },
5224 |          "mode": "markers",
5225 |          "name": "Country=SL",
5226 |          "showlegend": true,
5227 |          "type": "scatter",
5228 |          "uid": "1d9b030a-3e94-44f4-8ee9-21d176888298",
5229 |          "x": [
5230 |           "LasithMalinga"
5231 |          ],
5232 |          "xaxis": "x",
5233 |          "y": [
5234 |           170
5235 |          ],
5236 |          "yaxis": "y"
5237 |         },
5238 |         {
5239 |          "hoverlabel": {
5240 |           "namelength": 0
5241 |          },
5242 |          "hovertemplate": "Country=Ind<br>Player=%{x}<br>Wkts=%{marker.size}",
5243 |          "legendgroup": "Country=Ind",
5244 |          "marker": {
5245 |           "color": "#EF553B",
5246 |           "size": [
5247 |            157,
5248 |            150,
5249 |            150,
5250 |            133,
5251 |            125,
5252 |            119,
5253 |            108
5254 |           ],
5255 |           "sizemode": "area",
5256 |           "sizeref": 0.00425,
5257 |           "symbol": "circle"
5258 |          },
5259 |          "mode": "markers",
5260 |          "name": "Country=Ind",
5261 |          "showlegend": true,
5262 |          "type": "scatter",
5263 |          "uid": "771e0850-cf03-4b7e-81e1-c9f12940112b",
5264 |          "x": [
5265 |           " Amit Mishra",
5266 |           " Harbhajan Singh",
5267 |           " Piyush Chawla",
5268 |           " Bhuvneshwar Kumar",
5269 |           " Ravichandran Ashwin",
5270 |           " Umesh Yadav",
5271 |           " Ravindra Jadeja"
5272 |          ],
5273 |          "xaxis": "x",
5274 |          "y": [
5275 |           157,
5276 |           150,
5277 |           150,
5278 |           133,
5279 |           125,
5280 |           119,
5281 |           108
5282 |          ],
5283 |          "yaxis": "y"
5284 |         },
5285 |         {
5286 |          "hoverlabel": {
5287 |           "namelength": 0
5288 |          },
5289 |          "hovertemplate": "Country=WI<br>Player=%{x}<br>Wkts=%{marker.size}",
5290 |          "legendgroup": "Country=WI",
5291 |          "marker": {
5292 |           "color": "#00cc96",
5293 |           "size": [
5294 |            147,
5295 |            122
5296 |           ],
5297 |           "sizemode": "area",
5298 |           "sizeref": 0.00425,
5299 |           "symbol": "circle"
5300 |          },
5301 |          "mode": "markers",
5302 |          "name": "Country=WI",
5303 |          "showlegend": true,
5304 |          "type": "scatter",
5305 |          "uid": "67454994-35bb-4ece-8786-674f28ef188d",
5306 |          "x": [
5307 |           " Dwayne Bravo",
5308 |           " Sunil Narine"
5309 |          ],
5310 |          "xaxis": "x",
5311 |          "y": [
5312 |           147,
5313 |           122
5314 |          ],
5315 |          "yaxis": "y"
5316 |         }
5317 |        ],
5318 |        "layout": {
5319 |         "grid": {
5320 |          "xaxes": [
5321 |           "x"
5322 |          ],
5323 |          "xgap": 0.1,
5324 |          "xside": "bottom",
5325 |          "yaxes": [
5326 |           "y"
5327 |          ],
5328 |          "ygap": 0.1,
5329 |          "yside": "left"
5330 |         },
5331 |         "height": 600,
5332 |         "legend": {
5333 |          "itemsizing": "constant",
5334 |          "tracegroupgap": 0
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5336 |         "margin": {
5337 |          "t": 60
5338 |         },
5339 |         "template": {
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5343 |             "marker": {
5344 |              "line": {
5345 |               "color": "#E5ECF6",
5346 |               "width": 0.5
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5348 |             },
5349 |             "type": "bar"
5350 |            }
5351 |           ],
5352 |           "barpolar": [
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5360 |             "type": "barpolar"
5361 |            }
5362 |           ],
5363 |           "carpet": [
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5367 |              "gridcolor": "white",
5368 |              "linecolor": "white",
5369 |              "minorgridcolor": "white",
5370 |              "startlinecolor": "#2a3f5f"
5371 |             },
5372 |             "baxis": {
5373 |              "endlinecolor": "#2a3f5f",
5374 |              "gridcolor": "white",
5375 |              "linecolor": "white",
5376 |              "minorgridcolor": "white",
5377 |              "startlinecolor": "#2a3f5f"
5378 |             },
5379 |             "type": "carpet"
5380 |            }
5381 |           ],
5382 |           "choropleth": [
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5386 |              "ticks": ""
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5388 |             "type": "choropleth"
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5445 |              ],
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5447 |               1,
5448 |               "#fec3fe"
5449 |              ]
5450 |             ],
5451 |             "type": "contour"
5452 |            }
5453 |           ],
5454 |           "contourcarpet": [
5455 |            {
5456 |             "colorbar": {
5457 |              "outlinewidth": 0,
5458 |              "ticks": ""
5459 |             },
5460 |             "type": "contourcarpet"
5461 |            }
5462 |           ],
5463 |           "heatmap": [
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5465 |             "colorbar": {
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5467 |              "ticks": ""
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5469 |             "colorscale": [
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5520 |               "#fec3fe"
5521 |              ]
5522 |             ],
5523 |             "type": "heatmap"
5524 |            }
5525 |           ],
5526 |           "heatmapgl": [
5527 |            {
5528 |             "colorbar": {
5529 |              "outlinewidth": 0,
5530 |              "ticks": ""
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5532 |             "type": "heatmapgl"
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5534 |           ],
5535 |           "histogram": [
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5543 |             "type": "histogram"
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5546 |           "histogram2d": [
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5678 |             "type": "mesh3d"
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5680 |           ],
5681 |           "parcoords": [
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5683 |             "line": {
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5692 |           "scatter": [
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5702 |           ],
5703 |           "scatter3d": [
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5714 |           "scattercarpet": [
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5722 |             "type": "scattercarpet"
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5725 |           "scattergeo": [
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6109 |         "yaxis": {
6110 |          "title": {
6111 |           "text": "Wkts"
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6115 |       },
6116 |       "text/html": [
6117 |        "<div>\n",
6118 |        "        \n",
6119 |        "        \n",
6120 |        "            <div id=\"6fc4b401-c71d-48ec-8d2e-27058a7655ff\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>\n",
6121 |        "            <script type=\"text/javascript\">\n",
6122 |        "                require([\"plotly\"], function(Plotly) {\n",
6123 |        "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
6124 |        "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
6125 |        "                    \n",
6126 |        "                if (document.getElementById(\"6fc4b401-c71d-48ec-8d2e-27058a7655ff\")) {\n",
6127 |        "                    Plotly.newPlot(\n",
6128 |        "                        '6fc4b401-c71d-48ec-8d2e-27058a7655ff',\n",
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\"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"fillcolor\": \"#506784\", \"line\": {\"width\": 0}, \"opacity\": 0.4}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"title\": {\"text\": \"Player\"}}, \"yaxis\": {\"title\": {\"text\": \"Wkts\"}}},\n",
6131 |        "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
6132 |        "                    ).then(function(){\n",
6133 |        "                            \n",
6134 |        "var gd = document.getElementById('6fc4b401-c71d-48ec-8d2e-27058a7655ff');\n",
6135 |        "var x = new MutationObserver(function (mutations, observer) {{\n",
6136 |        "        var display = window.getComputedStyle(gd).display;\n",
6137 |        "        if (!display || display === 'none') {{\n",
6138 |        "            console.log([gd, 'removed!']);\n",
6139 |        "            Plotly.purge(gd);\n",
6140 |        "            observer.disconnect();\n",
6141 |        "        }}\n",
6142 |        "}});\n",
6143 |        "\n",
6144 |        "// Listen for the removal of the full notebook cells\n",
6145 |        "var notebookContainer = gd.closest('#notebook-container');\n",
6146 |        "if (notebookContainer) {{\n",
6147 |        "    x.observe(notebookContainer, {childList: true});\n",
6148 |        "}}\n",
6149 |        "\n",
6150 |        "// Listen for the clearing of the current output cell\n",
6151 |        "var outputEl = gd.closest('.output');\n",
6152 |        "if (outputEl) {{\n",
6153 |        "    x.observe(outputEl, {childList: true});\n",
6154 |        "}}\n",
6155 |        "\n",
6156 |        "                        })\n",
6157 |        "                };\n",
6158 |        "                });\n",
6159 |        "            </script>\n",
6160 |        "        </div>"
6161 |       ]
6162 |      },
6163 |      "metadata": {},
6164 |      "output_type": "display_data"
6165 |     }
6166 |    ],
6167 |    "source": [
6168 |     "px.scatter(wickets_data[:10],x='Player',y='Wkts',color='Country', size='Wkts',size_max=200)"
6169 |    ]
6170 |   },
6171 |   {
6172 |    "cell_type": "code",
6173 |    "execution_count": null,
6174 |    "metadata": {},
6175 |    "outputs": [],
6176 |    "source": []
6177 |   },
6178 |   {
6179 |    "cell_type": "markdown",
6180 |    "metadata": {
6181 |     "collapsed": true
6182 |    },
6183 |    "source": [
6184 |     "# congrats yourself !!! clap !!! "
6185 |    ]
6186 |   }
6187 |  ],
6188 |  "metadata": {
6189 |   "anaconda-cloud": {},
6190 |   "kernelspec": {
6191 |    "display_name": "Python (bellora)",
6192 |    "language": "python",
6193 |    "name": "bellora"
6194 |   },
6195 |   "language_info": {
6196 |    "codemirror_mode": {
6197 |     "name": "ipython",
6198 |     "version": 3
6199 |    },
6200 |    "file_extension": ".py",
6201 |    "mimetype": "text/x-python",
6202 |    "name": "python",
6203 |    "nbconvert_exporter": "python",
6204 |    "pygments_lexer": "ipython3",
6205 |    "version": "3.7.1"
6206 |   }
6207 |  },
6208 |  "nbformat": 4,
6209 |  "nbformat_minor": 1
6210 | }
6211 | 


--------------------------------------------------------------------------------
/ipl_data/high_score_in.csv:
--------------------------------------------------------------------------------
 1 | POS,PLAYER,HS,BF,4s,6s,NO
 2 | 1,ChrisGayle,175,66,13,17,Y
 3 | 2, Brendon McCullum,158,73,10,13,Y
 4 | 3, AB de Villiers,133,59,19,4,Y
 5 | 4, AB de Villiers,129,52,10,12,Y
 6 | 5, Chris Gayle,128,62,7,13,Y
 7 | 6, Rishabh Pant,128,63,15,7,Y
 8 | 7, Murali Vijay,127,56,8,11,N
 9 | 8, David Warner,126,59,10,8,N
10 | 9, Virender Sehwag,122,58,12,8,N
11 | 


--------------------------------------------------------------------------------
/ipl_data/ipl.jpeg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/thepradip/Python-Beginner-Series-IPL/1db163aa8ea564d75a9f02619de52fda2f136b47/ipl_data/ipl.jpeg


--------------------------------------------------------------------------------
/ipl_data/most_sixes_all_times.csv:
--------------------------------------------------------------------------------
 1 | id,Player,Sixes,Country
 2 | 1,ChrisGayle,326,WI
 3 | 2, AB de Villiers,212,SA
 4 | 3, MS Dhoni,209,Ind
 5 | 4, Rohit Sharma,194,Ind
 6 | 5, Suresh Raina,194,Ind
 7 | 6, Virat Kohli,191,Ind
 8 | 7, David Warner,181,Aus
 9 | 8, Shane Watson,177,Aus
10 | 9, Kieron Pollard,176,WI
11 | 10, Yusuf Pathan,158,Ind
12 | 11, Robin Uthappa,156,Ind
13 | 12, Yuvraj Singh,149,Ind
14 | 13, Brendon McCullum,130,NZ
15 | 14, Andre Russell,120,WI
16 | 15, Ambati Rayudu,120,Ind
17 | 16, Dwayne Smith,117,WI
18 | 17, Virender Sehwag,106,Ind
19 | 18, Dinesh Karthik,101,Ind
20 | 19, Shikhar Dhawan,96,Ind
21 | 20, Rishabh Pant,94,Ind
22 | 21, Adam Gilchrist,92,Aus
23 | 22, Glenn Maxwell,91,Aus
24 | 23, Murali Vijay,91,Ind
25 | 24, Sanju Samson,89,Ind
26 | 25, David Miller,87,SA
27 | 26, KL Rahul,81,Ind
28 | 27, JP Duminy,79,SA
29 | 28, Naman Ojha,79,Ind
30 | 29, Shaun Marsh,78,Aus
31 | 30, Manish Pandey,75,Ind
32 | 


--------------------------------------------------------------------------------
/ipl_data/most_wickets_all_times.csv:
--------------------------------------------------------------------------------
 1 | id,Player,Mat,Wkts,Country
 2 | 1,LasithMalinga,122,170,SL
 3 | 2, Amit Mishra,147,157,Ind
 4 | 3, Harbhajan Singh,160,150,Ind
 5 | 4, Piyush Chawla,157,150,Ind
 6 | 5, Dwayne Bravo,134,147,WI
 7 | 6, Bhuvneshwar Kumar,117,133,Ind
 8 | 7, Ravichandran Ashwin,139,125,Ind
 9 | 8, Sunil Narine,110,122,WI
10 | 9, Umesh Yadav,119,119,Ind
11 | 10, Ravindra Jadeja,170,108,Ind
12 | 11, Ashish Nehra,88,106,Ind
13 | 12, Vinay Kumar,105,105,Ind
14 | 13, Zaheer Khan,100,102,Ind
15 | 14, Yuzvendra Chahal,84,100,Ind
16 | 15, Dale Steyn,92,96,SA
17 | 16, Sandeep Sharma,79,95,Ind
18 | 17, Shane Watson,134,92,Aus
19 | 18, Mohit Sharma,85,91,Ind
20 | 19, Praveen Kumar,119,90,Ind
21 | 20, RP Singh,82,90,Ind
22 | 21, Pragyan Ojha,92,89,Ind
23 | 22, Dhawal Kulkarni,90,86,Ind
24 | 23, Albie Morkel,91,85,SA
25 | 24, Jasprit Bumrah,77,82,Ind
26 | 25, Irfan Pathan,103,80,Ind
27 | 26, Imran Tahir,55,79,SA
28 | 27, Morne Morkel,70,77,SA
29 | 28, Jaydev Unadkat,73,77,Ind
30 | 29, Lakshmipathy Balaji,73,76,Ind
31 | 30, Munaf Patel,63,74,Ind
32 | 


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