├── Data_Analysis_Tools_NumPy_and_Pandas.ipynb
├── Data_Visualization_Crash_Course.ipynb
├── Day 1 - IIT ISM Fundamentals of ML .ipynb
├── IIT_ISM_Intro_to_ML_for_PE.ipynb
├── Petroleum_Data_Analytics_Projects.ipynb
├── Predicting_Oil_Production_Rates_with_ML.ipynb
├── Pt.csv
├── Python_Crash_Course.ipynb
└── README.md


/Data_Analysis_Tools_NumPy_and_Pandas.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "Data Analysis Tools : NumPy and Pandas.ipynb",
   7 |       "provenance": [],
   8 |       "authorship_tag": "ABX9TyPSoXVA/bpIKLB5LLJe+Cv8",
   9 |       "include_colab_link": true
  10 |     },
  11 |     "kernelspec": {
  12 |       "name": "python3",
  13 |       "display_name": "Python 3"
  14 |     },
  15 |     "language_info": {
  16 |       "name": "python"
  17 |     }
  18 |   },
  19 |   "cells": [
  20 |     {
  21 |       "cell_type": "markdown",
  22 |       "metadata": {
  23 |         "id": "view-in-github",
  24 |         "colab_type": "text"
  25 |       },
  26 |       "source": [
  27 |         "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Petroleum-DS-ML-with-Python/blob/main/Data_Analysis_Tools_NumPy_and_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  28 |       ]
  29 |     },
  30 |     {
  31 |       "cell_type": "markdown",
  32 |       "metadata": {
  33 |         "id": "Ced_XJ7Wx8Le"
  34 |       },
  35 |       "source": [
  36 |         "## 1. NumPy\n",
  37 |         "\n",
  38 |         "\n",
  39 |         "NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.\n",
  40 |         "\n",
  41 |         "Numpy is also incredibly fast, as it has bindings to C libraries."
  42 |       ]
  43 |     },
  44 |     {
  45 |       "cell_type": "code",
  46 |       "metadata": {
  47 |         "id": "49dh9gX4Mgtt"
  48 |       },
  49 |       "source": [
  50 |         "#Anaconda prompt\n",
  51 |         "#pip install numpy"
  52 |       ],
  53 |       "execution_count": null,
  54 |       "outputs": []
  55 |     },
  56 |     {
  57 |       "cell_type": "code",
  58 |       "metadata": {
  59 |         "id": "oHVSMaDzwnfK"
  60 |       },
  61 |       "source": [
  62 |         "import numpy as np"
  63 |       ],
  64 |       "execution_count": 84,
  65 |       "outputs": []
  66 |     },
  67 |     {
  68 |       "cell_type": "markdown",
  69 |       "metadata": {
  70 |         "id": "Rdk9B10IyITF"
  71 |       },
  72 |       "source": [
  73 |         "Most important aspects of Numpy: vectors,arrays,matrices, and number generation."
  74 |       ]
  75 |     },
  76 |     {
  77 |       "cell_type": "markdown",
  78 |       "metadata": {
  79 |         "id": "VEBo_SD_yMdl"
  80 |       },
  81 |       "source": [
  82 |         "Disadvantage of using a list."
  83 |       ]
  84 |     },
  85 |     {
  86 |       "cell_type": "code",
  87 |       "metadata": {
  88 |         "id": "6ZPPlMgVyDw_"
  89 |       },
  90 |       "source": [
  91 |         "phi = [0.4 , 0.2, 0.35, 0.27, 0.44]\n",
  92 |         "\n",
  93 |         "# phi**2\n",
  94 |         "for i in range(5):\n",
  95 |         "  phi[i] = phi[i]**2"
  96 |       ],
  97 |       "execution_count": 89,
  98 |       "outputs": []
  99 |     },
 100 |     {
 101 |       "cell_type": "code",
 102 |       "metadata": {
 103 |         "colab": {
 104 |           "base_uri": "https://localhost:8080/"
 105 |         },
 106 |         "id": "-K62Bm_eNXyJ",
 107 |         "outputId": "67b35891-0290-4256-f4a0-8385ff1c3a61"
 108 |       },
 109 |       "source": [
 110 |         "phi"
 111 |       ],
 112 |       "execution_count": 86,
 113 |       "outputs": [
 114 |         {
 115 |           "output_type": "execute_result",
 116 |           "data": {
 117 |             "text/plain": [
 118 |               "[0.4, 0.2, 0.35, 0.27, 0.44]"
 119 |             ]
 120 |           },
 121 |           "metadata": {
 122 |             "tags": []
 123 |           },
 124 |           "execution_count": 86
 125 |         }
 126 |       ]
 127 |     },
 128 |     {
 129 |       "cell_type": "code",
 130 |       "metadata": {
 131 |         "colab": {
 132 |           "base_uri": "https://localhost:8080/"
 133 |         },
 134 |         "id": "IYYVRGClyVii",
 135 |         "outputId": "72d127f0-2990-4bfa-f1d2-48e4c364c9d1"
 136 |       },
 137 |       "source": [
 138 |         "2*phi"
 139 |       ],
 140 |       "execution_count": 3,
 141 |       "outputs": [
 142 |         {
 143 |           "output_type": "execute_result",
 144 |           "data": {
 145 |             "text/plain": [
 146 |               "[0.4, 0.2, 0.35, 0.27, 0.44, 0.4, 0.2, 0.35, 0.27, 0.44]"
 147 |             ]
 148 |           },
 149 |           "metadata": {
 150 |             "tags": []
 151 |           },
 152 |           "execution_count": 3
 153 |         }
 154 |       ]
 155 |     },
 156 |     {
 157 |       "cell_type": "markdown",
 158 |       "metadata": {
 159 |         "id": "AKhO0dKzyZXp"
 160 |       },
 161 |       "source": [
 162 |         "Not Doing what we want it to do. "
 163 |       ]
 164 |     },
 165 |     {
 166 |       "cell_type": "code",
 167 |       "metadata": {
 168 |         "colab": {
 169 |           "base_uri": "https://localhost:8080/"
 170 |         },
 171 |         "id": "f_zO7Q-qyYYy",
 172 |         "outputId": "dd63feb3-f08f-4cb3-c12d-417b317183c2"
 173 |       },
 174 |       "source": [
 175 |         "#Let's convert the list to a numpy array and do it again. \n",
 176 |         "phi_array = np.array(phi)\n",
 177 |         "\n",
 178 |         "phi_array**0.5"
 179 |       ],
 180 |       "execution_count": 90,
 181 |       "outputs": [
 182 |         {
 183 |           "output_type": "execute_result",
 184 |           "data": {
 185 |             "text/plain": [
 186 |               "array([0.4 , 0.2 , 0.35, 0.27, 0.44])"
 187 |             ]
 188 |           },
 189 |           "metadata": {
 190 |             "tags": []
 191 |           },
 192 |           "execution_count": 90
 193 |         }
 194 |       ]
 195 |     },
 196 |     {
 197 |       "cell_type": "code",
 198 |       "metadata": {
 199 |         "colab": {
 200 |           "base_uri": "https://localhost:8080/"
 201 |         },
 202 |         "id": "Pdo7YEccylEx",
 203 |         "outputId": "d91b2c71-2327-4d53-e3fc-c9dc6cb75516"
 204 |       },
 205 |       "source": [
 206 |         "#A 2D Matrix\n",
 207 |         "my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
 208 |         "my_matrix"
 209 |       ],
 210 |       "execution_count": 5,
 211 |       "outputs": [
 212 |         {
 213 |           "output_type": "execute_result",
 214 |           "data": {
 215 |             "text/plain": [
 216 |               "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
 217 |             ]
 218 |           },
 219 |           "metadata": {
 220 |             "tags": []
 221 |           },
 222 |           "execution_count": 5
 223 |         }
 224 |       ]
 225 |     },
 226 |     {
 227 |       "cell_type": "code",
 228 |       "metadata": {
 229 |         "colab": {
 230 |           "base_uri": "https://localhost:8080/"
 231 |         },
 232 |         "id": "xTLGcEbzyqfP",
 233 |         "outputId": "2ab15168-81d3-47ee-96e6-5456040d4848"
 234 |       },
 235 |       "source": [
 236 |         "\n",
 237 |         "A2 = np.array(my_matrix)\n",
 238 |         "\n",
 239 |         "A2**2"
 240 |       ],
 241 |       "execution_count": 91,
 242 |       "outputs": [
 243 |         {
 244 |           "output_type": "execute_result",
 245 |           "data": {
 246 |             "text/plain": [
 247 |               "array([[ 1,  4,  9],\n",
 248 |               "       [16, 25, 36],\n",
 249 |               "       [49, 64, 81]])"
 250 |             ]
 251 |           },
 252 |           "metadata": {
 253 |             "tags": []
 254 |           },
 255 |           "execution_count": 91
 256 |         }
 257 |       ]
 258 |     },
 259 |     {
 260 |       "cell_type": "markdown",
 261 |       "metadata": {
 262 |         "id": "i0ki6ZcDywsP"
 263 |       },
 264 |       "source": [
 265 |         "# Built-in methods of NumPy"
 266 |       ]
 267 |     },
 268 |     {
 269 |       "cell_type": "markdown",
 270 |       "metadata": {
 271 |         "id": "9F4PLRpmy1Mm"
 272 |       },
 273 |       "source": [
 274 |         "1. np.arange : Generating a Vector of Range of Numbers. \n",
 275 |         "\n",
 276 |         "Eg. Saturation."
 277 |       ]
 278 |     },
 279 |     {
 280 |       "cell_type": "code",
 281 |       "metadata": {
 282 |         "colab": {
 283 |           "base_uri": "https://localhost:8080/"
 284 |         },
 285 |         "id": "1JlZ01KcyusC",
 286 |         "outputId": "a416bec5-3db5-4051-f0c8-0118e6410261"
 287 |       },
 288 |       "source": [
 289 |         "Sw = np.arange(start=0 , stop = 1, step = 0.005)\n",
 290 |         "\n",
 291 |         "Sw"
 292 |       ],
 293 |       "execution_count": 92,
 294 |       "outputs": [
 295 |         {
 296 |           "output_type": "execute_result",
 297 |           "data": {
 298 |             "text/plain": [
 299 |               "array([0.   , 0.005, 0.01 , 0.015, 0.02 , 0.025, 0.03 , 0.035, 0.04 ,\n",
 300 |               "       0.045, 0.05 , 0.055, 0.06 , 0.065, 0.07 , 0.075, 0.08 , 0.085,\n",
 301 |               "       0.09 , 0.095, 0.1  , 0.105, 0.11 , 0.115, 0.12 , 0.125, 0.13 ,\n",
 302 |               "       0.135, 0.14 , 0.145, 0.15 , 0.155, 0.16 , 0.165, 0.17 , 0.175,\n",
 303 |               "       0.18 , 0.185, 0.19 , 0.195, 0.2  , 0.205, 0.21 , 0.215, 0.22 ,\n",
 304 |               "       0.225, 0.23 , 0.235, 0.24 , 0.245, 0.25 , 0.255, 0.26 , 0.265,\n",
 305 |               "       0.27 , 0.275, 0.28 , 0.285, 0.29 , 0.295, 0.3  , 0.305, 0.31 ,\n",
 306 |               "       0.315, 0.32 , 0.325, 0.33 , 0.335, 0.34 , 0.345, 0.35 , 0.355,\n",
 307 |               "       0.36 , 0.365, 0.37 , 0.375, 0.38 , 0.385, 0.39 , 0.395, 0.4  ,\n",
 308 |               "       0.405, 0.41 , 0.415, 0.42 , 0.425, 0.43 , 0.435, 0.44 , 0.445,\n",
 309 |               "       0.45 , 0.455, 0.46 , 0.465, 0.47 , 0.475, 0.48 , 0.485, 0.49 ,\n",
 310 |               "       0.495, 0.5  , 0.505, 0.51 , 0.515, 0.52 , 0.525, 0.53 , 0.535,\n",
 311 |               "       0.54 , 0.545, 0.55 , 0.555, 0.56 , 0.565, 0.57 , 0.575, 0.58 ,\n",
 312 |               "       0.585, 0.59 , 0.595, 0.6  , 0.605, 0.61 , 0.615, 0.62 , 0.625,\n",
 313 |               "       0.63 , 0.635, 0.64 , 0.645, 0.65 , 0.655, 0.66 , 0.665, 0.67 ,\n",
 314 |               "       0.675, 0.68 , 0.685, 0.69 , 0.695, 0.7  , 0.705, 0.71 , 0.715,\n",
 315 |               "       0.72 , 0.725, 0.73 , 0.735, 0.74 , 0.745, 0.75 , 0.755, 0.76 ,\n",
 316 |               "       0.765, 0.77 , 0.775, 0.78 , 0.785, 0.79 , 0.795, 0.8  , 0.805,\n",
 317 |               "       0.81 , 0.815, 0.82 , 0.825, 0.83 , 0.835, 0.84 , 0.845, 0.85 ,\n",
 318 |               "       0.855, 0.86 , 0.865, 0.87 , 0.875, 0.88 , 0.885, 0.89 , 0.895,\n",
 319 |               "       0.9  , 0.905, 0.91 , 0.915, 0.92 , 0.925, 0.93 , 0.935, 0.94 ,\n",
 320 |               "       0.945, 0.95 , 0.955, 0.96 , 0.965, 0.97 , 0.975, 0.98 , 0.985,\n",
 321 |               "       0.99 , 0.995])"
 322 |             ]
 323 |           },
 324 |           "metadata": {
 325 |             "tags": []
 326 |           },
 327 |           "execution_count": 92
 328 |         }
 329 |       ]
 330 |     },
 331 |     {
 332 |       "cell_type": "markdown",
 333 |       "metadata": {
 334 |         "id": "4qoEy36RzL7e"
 335 |       },
 336 |       "source": [
 337 |         "2. Generate a Matrix/Array of 0s or 1s."
 338 |       ]
 339 |     },
 340 |     {
 341 |       "cell_type": "code",
 342 |       "metadata": {
 343 |         "colab": {
 344 |           "base_uri": "https://localhost:8080/"
 345 |         },
 346 |         "id": "mBMCdeLPzHhF",
 347 |         "outputId": "c9bc88f3-477b-496e-9208-25425ec60118"
 348 |       },
 349 |       "source": [
 350 |         "np.zeros((10,10))"
 351 |       ],
 352 |       "execution_count": 93,
 353 |       "outputs": [
 354 |         {
 355 |           "output_type": "execute_result",
 356 |           "data": {
 357 |             "text/plain": [
 358 |               "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 359 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 360 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 361 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 362 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 363 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 364 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 365 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 366 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
 367 |               "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
 368 |             ]
 369 |           },
 370 |           "metadata": {
 371 |             "tags": []
 372 |           },
 373 |           "execution_count": 93
 374 |         }
 375 |       ]
 376 |     },
 377 |     {
 378 |       "cell_type": "code",
 379 |       "metadata": {
 380 |         "id": "mLQeRkRdzSO-"
 381 |       },
 382 |       "source": [
 383 |         "P = np.ones((10,10))\n",
 384 |         "Pi = 5000 #psi\n",
 385 |         "P = P*Pi"
 386 |       ],
 387 |       "execution_count": 95,
 388 |       "outputs": []
 389 |     },
 390 |     {
 391 |       "cell_type": "code",
 392 |       "metadata": {
 393 |         "colab": {
 394 |           "base_uri": "https://localhost:8080/"
 395 |         },
 396 |         "id": "7hI5zAYePn_R",
 397 |         "outputId": "372379d4-697c-42f4-ab8d-f840c607d784"
 398 |       },
 399 |       "source": [
 400 |         "P"
 401 |       ],
 402 |       "execution_count": 96,
 403 |       "outputs": [
 404 |         {
 405 |           "output_type": "execute_result",
 406 |           "data": {
 407 |             "text/plain": [
 408 |               "array([[5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 409 |               "        5000.],\n",
 410 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 411 |               "        5000.],\n",
 412 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 413 |               "        5000.],\n",
 414 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 415 |               "        5000.],\n",
 416 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 417 |               "        5000.],\n",
 418 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 419 |               "        5000.],\n",
 420 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 421 |               "        5000.],\n",
 422 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 423 |               "        5000.],\n",
 424 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 425 |               "        5000.],\n",
 426 |               "       [5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000.,\n",
 427 |               "        5000.]])"
 428 |             ]
 429 |           },
 430 |           "metadata": {
 431 |             "tags": []
 432 |           },
 433 |           "execution_count": 96
 434 |         }
 435 |       ]
 436 |     },
 437 |     {
 438 |       "cell_type": "markdown",
 439 |       "metadata": {
 440 |         "id": "i_8mXSf0zZli"
 441 |       },
 442 |       "source": [
 443 |         "3. np.linspace : Create a Linearly Spaced vector."
 444 |       ]
 445 |     },
 446 |     {
 447 |       "cell_type": "code",
 448 |       "metadata": {
 449 |         "colab": {
 450 |           "base_uri": "https://localhost:8080/"
 451 |         },
 452 |         "id": "Y-yT0QOuzUyQ",
 453 |         "outputId": "b16af65a-a086-4459-9b85-1df071f61f17"
 454 |       },
 455 |       "source": [
 456 |         "np.linspace(start = 0 , stop = 1 , num = 100)"
 457 |       ],
 458 |       "execution_count": 98,
 459 |       "outputs": [
 460 |         {
 461 |           "output_type": "execute_result",
 462 |           "data": {
 463 |             "text/plain": [
 464 |               "array([0.        , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n",
 465 |               "       0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n",
 466 |               "       0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n",
 467 |               "       0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n",
 468 |               "       0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n",
 469 |               "       0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n",
 470 |               "       0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n",
 471 |               "       0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n",
 472 |               "       0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n",
 473 |               "       0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n",
 474 |               "       0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n",
 475 |               "       0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n",
 476 |               "       0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n",
 477 |               "       0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n",
 478 |               "       0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n",
 479 |               "       0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n",
 480 |               "       0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n",
 481 |               "       0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n",
 482 |               "       0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n",
 483 |               "       0.95959596, 0.96969697, 0.97979798, 0.98989899, 1.        ])"
 484 |             ]
 485 |           },
 486 |           "metadata": {
 487 |             "tags": []
 488 |           },
 489 |           "execution_count": 98
 490 |         }
 491 |       ]
 492 |     },
 493 |     {
 494 |       "cell_type": "markdown",
 495 |       "metadata": {
 496 |         "id": "agNl_qr1zo-3"
 497 |       },
 498 |       "source": [
 499 |         "4. np.eye : Create an Identity Matrix."
 500 |       ]
 501 |     },
 502 |     {
 503 |       "cell_type": "code",
 504 |       "metadata": {
 505 |         "colab": {
 506 |           "base_uri": "https://localhost:8080/"
 507 |         },
 508 |         "id": "SY2d7-jPzkN3",
 509 |         "outputId": "9261f919-0db2-45ad-9b8c-85946565f676"
 510 |       },
 511 |       "source": [
 512 |         "np.eye(5) # 4x4 Identity Matrix"
 513 |       ],
 514 |       "execution_count": 99,
 515 |       "outputs": [
 516 |         {
 517 |           "output_type": "execute_result",
 518 |           "data": {
 519 |             "text/plain": [
 520 |               "array([[1., 0., 0., 0., 0.],\n",
 521 |               "       [0., 1., 0., 0., 0.],\n",
 522 |               "       [0., 0., 1., 0., 0.],\n",
 523 |               "       [0., 0., 0., 1., 0.],\n",
 524 |               "       [0., 0., 0., 0., 1.]])"
 525 |             ]
 526 |           },
 527 |           "metadata": {
 528 |             "tags": []
 529 |           },
 530 |           "execution_count": 99
 531 |         }
 532 |       ]
 533 |     },
 534 |     {
 535 |       "cell_type": "markdown",
 536 |       "metadata": {
 537 |         "id": "9X2fIOtIz0w7"
 538 |       },
 539 |       "source": [
 540 |         "## NumPy's Random Number Generation Capabilities."
 541 |       ]
 542 |     },
 543 |     {
 544 |       "cell_type": "markdown",
 545 |       "metadata": {
 546 |         "id": "lZgwj5PZ0kkG"
 547 |       },
 548 |       "source": [
 549 |         "Generate 3 Random Numbers from a Uniform Distribution in [0,1) interval"
 550 |       ]
 551 |     },
 552 |     {
 553 |       "cell_type": "code",
 554 |       "metadata": {
 555 |         "colab": {
 556 |           "base_uri": "https://localhost:8080/"
 557 |         },
 558 |         "id": "QM3Dz3hWzwxi",
 559 |         "outputId": "c51e4e8d-d63e-4cd6-b688-d506a11bd1b8"
 560 |       },
 561 |       "source": [
 562 |         "np.random.rand(3)"
 563 |       ],
 564 |       "execution_count": 101,
 565 |       "outputs": [
 566 |         {
 567 |           "output_type": "execute_result",
 568 |           "data": {
 569 |             "text/plain": [
 570 |               "array([0.29765699, 0.98903906, 0.22353677])"
 571 |             ]
 572 |           },
 573 |           "metadata": {
 574 |             "tags": []
 575 |           },
 576 |           "execution_count": 101
 577 |         }
 578 |       ]
 579 |     },
 580 |     {
 581 |       "cell_type": "markdown",
 582 |       "metadata": {
 583 |         "id": "lMOeKfpY0xTV"
 584 |       },
 585 |       "source": [
 586 |         "Generate 5 Random Samples from Standard Normal Distribution."
 587 |       ]
 588 |     },
 589 |     {
 590 |       "cell_type": "code",
 591 |       "metadata": {
 592 |         "colab": {
 593 |           "base_uri": "https://localhost:8080/"
 594 |         },
 595 |         "id": "L6DuVu4B0ar4",
 596 |         "outputId": "c8b15458-3fa9-4592-bd43-57e05eb58921"
 597 |       },
 598 |       "source": [
 599 |         "np.random.randn(5)"
 600 |       ],
 601 |       "execution_count": 16,
 602 |       "outputs": [
 603 |         {
 604 |           "output_type": "execute_result",
 605 |           "data": {
 606 |             "text/plain": [
 607 |               "array([ 1.37710354, -2.01278539,  1.66007349,  1.88203505, -0.35254634])"
 608 |             ]
 609 |           },
 610 |           "metadata": {
 611 |             "tags": []
 612 |           },
 613 |           "execution_count": 16
 614 |         }
 615 |       ]
 616 |     },
 617 |     {
 618 |       "cell_type": "code",
 619 |       "metadata": {
 620 |         "colab": {
 621 |           "base_uri": "https://localhost:8080/"
 622 |         },
 623 |         "id": "9yL0TyEa03mQ",
 624 |         "outputId": "7e4ccca8-1b20-4d51-cded-b927c6f5fc07"
 625 |       },
 626 |       "source": [
 627 |         "#MAtrix containing all SND values. \n",
 628 |         "np.random.randn(7000,5)"
 629 |       ],
 630 |       "execution_count": 102,
 631 |       "outputs": [
 632 |         {
 633 |           "output_type": "execute_result",
 634 |           "data": {
 635 |             "text/plain": [
 636 |               "array([[ 1.96773329,  1.32788049,  0.12659496,  1.72309875, -0.07746591],\n",
 637 |               "       [ 1.82787214,  0.30832428,  0.64173028, -0.55261589,  0.90131923],\n",
 638 |               "       [-0.70942399,  2.5530326 ,  1.9371735 , -0.48668674,  0.17080277],\n",
 639 |               "       ...,\n",
 640 |               "       [-1.43510458,  0.30860086,  0.43482077,  0.42544275, -0.08406665],\n",
 641 |               "       [-0.67506255, -1.19003494,  1.74330016,  0.66187072, -0.73667764],\n",
 642 |               "       [ 1.14218215,  0.42645458, -0.59164283,  0.31159763,  0.93865275]])"
 643 |             ]
 644 |           },
 645 |           "metadata": {
 646 |             "tags": []
 647 |           },
 648 |           "execution_count": 102
 649 |         }
 650 |       ]
 651 |     },
 652 |     {
 653 |       "cell_type": "markdown",
 654 |       "metadata": {
 655 |         "id": "pn7fjP-r1JvA"
 656 |       },
 657 |       "source": [
 658 |         "'Randint' to generate random integers."
 659 |       ]
 660 |     },
 661 |     {
 662 |       "cell_type": "code",
 663 |       "metadata": {
 664 |         "colab": {
 665 |           "base_uri": "https://localhost:8080/"
 666 |         },
 667 |         "id": "dmAVjrmu1JO6",
 668 |         "outputId": "20df51b3-a46b-4fc0-8312-def84d2ca4b0"
 669 |       },
 670 |       "source": [
 671 |         "np.random.randint(low=1, high=100, size=10)"
 672 |       ],
 673 |       "execution_count": 103,
 674 |       "outputs": [
 675 |         {
 676 |           "output_type": "execute_result",
 677 |           "data": {
 678 |             "text/plain": [
 679 |               "array([25, 71, 64, 96, 74,  8, 60, 24,  6, 83])"
 680 |             ]
 681 |           },
 682 |           "metadata": {
 683 |             "tags": []
 684 |           },
 685 |           "execution_count": 103
 686 |         }
 687 |       ]
 688 |     },
 689 |     {
 690 |       "cell_type": "markdown",
 691 |       "metadata": {
 692 |         "id": "R3FUv1Gb1Fvr"
 693 |       },
 694 |       "source": [
 695 |         "## Shape and Reshape of an array"
 696 |       ]
 697 |     },
 698 |     {
 699 |       "cell_type": "code",
 700 |       "metadata": {
 701 |         "colab": {
 702 |           "base_uri": "https://localhost:8080/"
 703 |         },
 704 |         "id": "DPn3libE09jU",
 705 |         "outputId": "faf9885c-dbc1-45ba-8d7d-7b645e03e18e"
 706 |       },
 707 |       "source": [
 708 |         "# 1. Reshaping a 100 value arrray to a 10x10 matrix. \n",
 709 |         "v100 = np.linspace(0,1,100)\n",
 710 |         "v100.shape\n",
 711 |         "v100"
 712 |       ],
 713 |       "execution_count": 105,
 714 |       "outputs": [
 715 |         {
 716 |           "output_type": "execute_result",
 717 |           "data": {
 718 |             "text/plain": [
 719 |               "array([0.        , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n",
 720 |               "       0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n",
 721 |               "       0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n",
 722 |               "       0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n",
 723 |               "       0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n",
 724 |               "       0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n",
 725 |               "       0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n",
 726 |               "       0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n",
 727 |               "       0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n",
 728 |               "       0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n",
 729 |               "       0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n",
 730 |               "       0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n",
 731 |               "       0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n",
 732 |               "       0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n",
 733 |               "       0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n",
 734 |               "       0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n",
 735 |               "       0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n",
 736 |               "       0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n",
 737 |               "       0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n",
 738 |               "       0.95959596, 0.96969697, 0.97979798, 0.98989899, 1.        ])"
 739 |             ]
 740 |           },
 741 |           "metadata": {
 742 |             "tags": []
 743 |           },
 744 |           "execution_count": 105
 745 |         }
 746 |       ]
 747 |     },
 748 |     {
 749 |       "cell_type": "code",
 750 |       "metadata": {
 751 |         "colab": {
 752 |           "base_uri": "https://localhost:8080/"
 753 |         },
 754 |         "id": "gso5rcyp1z5T",
 755 |         "outputId": "c4950b2a-3be8-4c52-ce08-d84c2d20d1fb"
 756 |       },
 757 |       "source": [
 758 |         "m10_10 = v100.reshape(10,10)\n",
 759 |         "m10_10.shape"
 760 |       ],
 761 |       "execution_count": 107,
 762 |       "outputs": [
 763 |         {
 764 |           "output_type": "execute_result",
 765 |           "data": {
 766 |             "text/plain": [
 767 |               "(10, 10)"
 768 |             ]
 769 |           },
 770 |           "metadata": {
 771 |             "tags": []
 772 |           },
 773 |           "execution_count": 107
 774 |         }
 775 |       ]
 776 |     },
 777 |     {
 778 |       "cell_type": "code",
 779 |       "metadata": {
 780 |         "colab": {
 781 |           "base_uri": "https://localhost:8080/"
 782 |         },
 783 |         "id": "SIpEPWHu13X_",
 784 |         "outputId": "90836790-6455-41f0-ebbd-e615d6a7a129"
 785 |       },
 786 |       "source": [
 787 |         "m10_10.shape"
 788 |       ],
 789 |       "execution_count": 22,
 790 |       "outputs": [
 791 |         {
 792 |           "output_type": "execute_result",
 793 |           "data": {
 794 |             "text/plain": [
 795 |               "(10, 10)"
 796 |             ]
 797 |           },
 798 |           "metadata": {
 799 |             "tags": []
 800 |           },
 801 |           "execution_count": 22
 802 |         }
 803 |       ]
 804 |     },
 805 |     {
 806 |       "cell_type": "markdown",
 807 |       "metadata": {
 808 |         "id": "sC7qq7T21_Se"
 809 |       },
 810 |       "source": [
 811 |         "### max-min-argmax-argmin. \n",
 812 |         "\n",
 813 |         "1. np.max : returns max value in array. \n",
 814 |         "2. np.min : returns min value in array. \n",
 815 |         "3. np.argmax : returns positon (index) of max value. \n",
 816 |         "4. np.argmin : \" \" of min value."
 817 |       ]
 818 |     },
 819 |     {
 820 |       "cell_type": "code",
 821 |       "metadata": {
 822 |         "id": "iAYqGlEY17Kd"
 823 |       },
 824 |       "source": [
 825 |         "ranarr = np.random.randint(0,50,15)"
 826 |       ],
 827 |       "execution_count": 108,
 828 |       "outputs": []
 829 |     },
 830 |     {
 831 |       "cell_type": "code",
 832 |       "metadata": {
 833 |         "colab": {
 834 |           "base_uri": "https://localhost:8080/"
 835 |         },
 836 |         "id": "NT9Jkr402Wmy",
 837 |         "outputId": "59bb3805-8d83-4668-b835-6ef8f4bb2e5e"
 838 |       },
 839 |       "source": [
 840 |         "ranarr"
 841 |       ],
 842 |       "execution_count": 114,
 843 |       "outputs": [
 844 |         {
 845 |           "output_type": "execute_result",
 846 |           "data": {
 847 |             "text/plain": [
 848 |               "array([14, 24, 26,  8, 27,  9,  8, 43, 44, 10, 46, 42,  2, 27, 37])"
 849 |             ]
 850 |           },
 851 |           "metadata": {
 852 |             "tags": []
 853 |           },
 854 |           "execution_count": 114
 855 |         }
 856 |       ]
 857 |     },
 858 |     {
 859 |       "cell_type": "code",
 860 |       "metadata": {
 861 |         "colab": {
 862 |           "base_uri": "https://localhost:8080/"
 863 |         },
 864 |         "id": "y3W9soMg2Y-r",
 865 |         "outputId": "40f1e005-c9f6-4197-880a-a9377796882f"
 866 |       },
 867 |       "source": [
 868 |         "ranarr[np.argmin(ranarr)]"
 869 |       ],
 870 |       "execution_count": 116,
 871 |       "outputs": [
 872 |         {
 873 |           "output_type": "execute_result",
 874 |           "data": {
 875 |             "text/plain": [
 876 |               "2"
 877 |             ]
 878 |           },
 879 |           "metadata": {
 880 |             "tags": []
 881 |           },
 882 |           "execution_count": 116
 883 |         }
 884 |       ]
 885 |     },
 886 |     {
 887 |       "cell_type": "code",
 888 |       "metadata": {
 889 |         "colab": {
 890 |           "base_uri": "https://localhost:8080/"
 891 |         },
 892 |         "id": "Vza6x5tY2yKu",
 893 |         "outputId": "efdb662f-b559-4842-a4c6-1e786de74771"
 894 |       },
 895 |       "source": [
 896 |         "np.max(ranarr)"
 897 |       ],
 898 |       "execution_count": 111,
 899 |       "outputs": [
 900 |         {
 901 |           "output_type": "execute_result",
 902 |           "data": {
 903 |             "text/plain": [
 904 |               "46"
 905 |             ]
 906 |           },
 907 |           "metadata": {
 908 |             "tags": []
 909 |           },
 910 |           "execution_count": 111
 911 |         }
 912 |       ]
 913 |     },
 914 |     {
 915 |       "cell_type": "code",
 916 |       "metadata": {
 917 |         "colab": {
 918 |           "base_uri": "https://localhost:8080/"
 919 |         },
 920 |         "id": "4vBN3oi422e7",
 921 |         "outputId": "7b136890-cd62-49f6-cf80-0c97e7c78202"
 922 |       },
 923 |       "source": [
 924 |         "np.argmin(ranarr)"
 925 |       ],
 926 |       "execution_count": 28,
 927 |       "outputs": [
 928 |         {
 929 |           "output_type": "execute_result",
 930 |           "data": {
 931 |             "text/plain": [
 932 |               "4"
 933 |             ]
 934 |           },
 935 |           "metadata": {
 936 |             "tags": []
 937 |           },
 938 |           "execution_count": 28
 939 |         }
 940 |       ]
 941 |     },
 942 |     {
 943 |       "cell_type": "code",
 944 |       "metadata": {
 945 |         "colab": {
 946 |           "base_uri": "https://localhost:8080/"
 947 |         },
 948 |         "id": "SXGOQx1M25GJ",
 949 |         "outputId": "08f26e1d-7213-43f4-cd54-6df1992e4004"
 950 |       },
 951 |       "source": [
 952 |         "ranarr[2]"
 953 |       ],
 954 |       "execution_count": 113,
 955 |       "outputs": [
 956 |         {
 957 |           "output_type": "execute_result",
 958 |           "data": {
 959 |             "text/plain": [
 960 |               "26"
 961 |             ]
 962 |           },
 963 |           "metadata": {
 964 |             "tags": []
 965 |           },
 966 |           "execution_count": 113
 967 |         }
 968 |       ]
 969 |     },
 970 |     {
 971 |       "cell_type": "code",
 972 |       "metadata": {
 973 |         "colab": {
 974 |           "base_uri": "https://localhost:8080/"
 975 |         },
 976 |         "id": "zEqOu7x0267i",
 977 |         "outputId": "8c159e29-13fd-4b94-ee11-05af98c0d5c7"
 978 |       },
 979 |       "source": [
 980 |         "np.argmax(ranarr)"
 981 |       ],
 982 |       "execution_count": 30,
 983 |       "outputs": [
 984 |         {
 985 |           "output_type": "execute_result",
 986 |           "data": {
 987 |             "text/plain": [
 988 |               "8"
 989 |             ]
 990 |           },
 991 |           "metadata": {
 992 |             "tags": []
 993 |           },
 994 |           "execution_count": 30
 995 |         }
 996 |       ]
 997 |     },
 998 |     {
 999 |       "cell_type": "code",
1000 |       "metadata": {
1001 |         "colab": {
1002 |           "base_uri": "https://localhost:8080/"
1003 |         },
1004 |         "id": "jF96v_Sp2-_h",
1005 |         "outputId": "de4d5ec0-0c11-46d8-cdfc-b7f254552354"
1006 |       },
1007 |       "source": [
1008 |         "ranarr[8]"
1009 |       ],
1010 |       "execution_count": 31,
1011 |       "outputs": [
1012 |         {
1013 |           "output_type": "execute_result",
1014 |           "data": {
1015 |             "text/plain": [
1016 |               "48"
1017 |             ]
1018 |           },
1019 |           "metadata": {
1020 |             "tags": []
1021 |           },
1022 |           "execution_count": 31
1023 |         }
1024 |       ]
1025 |     },
1026 |     {
1027 |       "cell_type": "markdown",
1028 |       "metadata": {
1029 |         "id": "GYz7lOJq3Wxt"
1030 |       },
1031 |       "source": [
1032 |         "## Indexing, Selection and Broadcasting."
1033 |       ]
1034 |     },
1035 |     {
1036 |       "cell_type": "code",
1037 |       "metadata": {
1038 |         "id": "X9Qzrcu73BQC"
1039 |       },
1040 |       "source": [
1041 |         "arr = np.linspace(0,1,10)"
1042 |       ],
1043 |       "execution_count": 33,
1044 |       "outputs": []
1045 |     },
1046 |     {
1047 |       "cell_type": "code",
1048 |       "metadata": {
1049 |         "colab": {
1050 |           "base_uri": "https://localhost:8080/"
1051 |         },
1052 |         "id": "mZgFMkOZ3odi",
1053 |         "outputId": "8106a737-d8db-4125-8e80-84262c40b930"
1054 |       },
1055 |       "source": [
1056 |         "arr"
1057 |       ],
1058 |       "execution_count": 34,
1059 |       "outputs": [
1060 |         {
1061 |           "output_type": "execute_result",
1062 |           "data": {
1063 |             "text/plain": [
1064 |               "array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
1065 |               "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ])"
1066 |             ]
1067 |           },
1068 |           "metadata": {
1069 |             "tags": []
1070 |           },
1071 |           "execution_count": 34
1072 |         }
1073 |       ]
1074 |     },
1075 |     {
1076 |       "cell_type": "code",
1077 |       "metadata": {
1078 |         "id": "LBSn1Wjq3ugT"
1079 |       },
1080 |       "source": [
1081 |         "#indexing. Just like lists"
1082 |       ],
1083 |       "execution_count": 35,
1084 |       "outputs": []
1085 |     },
1086 |     {
1087 |       "cell_type": "code",
1088 |       "metadata": {
1089 |         "colab": {
1090 |           "base_uri": "https://localhost:8080/"
1091 |         },
1092 |         "id": "gup8Wkfj30ky",
1093 |         "outputId": "629f4ef9-ce43-4566-dc84-2e724c6cdb50"
1094 |       },
1095 |       "source": [
1096 |         "arr[7]"
1097 |       ],
1098 |       "execution_count": 36,
1099 |       "outputs": [
1100 |         {
1101 |           "output_type": "execute_result",
1102 |           "data": {
1103 |             "text/plain": [
1104 |               "0.7777777777777777"
1105 |             ]
1106 |           },
1107 |           "metadata": {
1108 |             "tags": []
1109 |           },
1110 |           "execution_count": 36
1111 |         }
1112 |       ]
1113 |     },
1114 |     {
1115 |       "cell_type": "code",
1116 |       "metadata": {
1117 |         "colab": {
1118 |           "base_uri": "https://localhost:8080/"
1119 |         },
1120 |         "id": "Hvs-4Ins31qp",
1121 |         "outputId": "802af69e-a0f7-4aea-8949-fdf0d2f49125"
1122 |       },
1123 |       "source": [
1124 |         "#slicing. \n",
1125 |         "arr[0:7]"
1126 |       ],
1127 |       "execution_count": 37,
1128 |       "outputs": [
1129 |         {
1130 |           "output_type": "execute_result",
1131 |           "data": {
1132 |             "text/plain": [
1133 |               "array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
1134 |               "       0.55555556, 0.66666667])"
1135 |             ]
1136 |           },
1137 |           "metadata": {
1138 |             "tags": []
1139 |           },
1140 |           "execution_count": 37
1141 |         }
1142 |       ]
1143 |     },
1144 |     {
1145 |       "cell_type": "code",
1146 |       "metadata": {
1147 |         "colab": {
1148 |           "base_uri": "https://localhost:8080/"
1149 |         },
1150 |         "id": "BIDCOlgy34fW",
1151 |         "outputId": "9ddcaaa7-9f3d-4ed0-edc7-3297a3b6e99e"
1152 |       },
1153 |       "source": [
1154 |         "#Note that a slice is the view of the same array than a new array. \n",
1155 |         "#Any changes made to the slice are reflected in the array. \n",
1156 |         "arr[0:3] = 1000\n",
1157 |         "arr"
1158 |       ],
1159 |       "execution_count": 38,
1160 |       "outputs": [
1161 |         {
1162 |           "output_type": "execute_result",
1163 |           "data": {
1164 |             "text/plain": [
1165 |               "array([1.00000000e+03, 1.00000000e+03, 1.00000000e+03, 3.33333333e-01,\n",
1166 |               "       4.44444444e-01, 5.55555556e-01, 6.66666667e-01, 7.77777778e-01,\n",
1167 |               "       8.88888889e-01, 1.00000000e+00])"
1168 |             ]
1169 |           },
1170 |           "metadata": {
1171 |             "tags": []
1172 |           },
1173 |           "execution_count": 38
1174 |         }
1175 |       ]
1176 |     },
1177 |     {
1178 |       "cell_type": "code",
1179 |       "metadata": {
1180 |         "id": "6Ki6ehJf4GXn"
1181 |       },
1182 |       "source": [
1183 |         "#In order to prevent that from happening. make a copy. \n",
1184 |         "\n",
1185 |         "# slice_array_version = array.copy()"
1186 |       ],
1187 |       "execution_count": 40,
1188 |       "outputs": []
1189 |     },
1190 |     {
1191 |       "cell_type": "markdown",
1192 |       "metadata": {
1193 |         "id": "omB4Lynr4YKV"
1194 |       },
1195 |       "source": [
1196 |         "### Indexing a 2D array/"
1197 |       ]
1198 |     },
1199 |     {
1200 |       "cell_type": "code",
1201 |       "metadata": {
1202 |         "colab": {
1203 |           "base_uri": "https://localhost:8080/"
1204 |         },
1205 |         "id": "wqu6_7c54S3H",
1206 |         "outputId": "49c50be6-f9b4-4d50-a89c-8dbfa4474f6b"
1207 |       },
1208 |       "source": [
1209 |         "a2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
1210 |         "\n",
1211 |         "a2d"
1212 |       ],
1213 |       "execution_count": 41,
1214 |       "outputs": [
1215 |         {
1216 |           "output_type": "execute_result",
1217 |           "data": {
1218 |             "text/plain": [
1219 |               "array([[ 5, 10, 15],\n",
1220 |               "       [20, 25, 30],\n",
1221 |               "       [35, 40, 45]])"
1222 |             ]
1223 |           },
1224 |           "metadata": {
1225 |             "tags": []
1226 |           },
1227 |           "execution_count": 41
1228 |         }
1229 |       ]
1230 |     },
1231 |     {
1232 |       "cell_type": "code",
1233 |       "metadata": {
1234 |         "colab": {
1235 |           "base_uri": "https://localhost:8080/"
1236 |         },
1237 |         "id": "AZFrG4G44dy_",
1238 |         "outputId": "b040fc20-b450-44e9-d404-30f59137a884"
1239 |       },
1240 |       "source": [
1241 |         "a2d[0,1]"
1242 |       ],
1243 |       "execution_count": 43,
1244 |       "outputs": [
1245 |         {
1246 |           "output_type": "execute_result",
1247 |           "data": {
1248 |             "text/plain": [
1249 |               "10"
1250 |             ]
1251 |           },
1252 |           "metadata": {
1253 |             "tags": []
1254 |           },
1255 |           "execution_count": 43
1256 |         }
1257 |       ]
1258 |     },
1259 |     {
1260 |       "cell_type": "code",
1261 |       "metadata": {
1262 |         "id": "tK2qqIMo4hRd"
1263 |       },
1264 |       "source": [
1265 |         "# a2d[ rowmin : rowmax+1 , :]"
1266 |       ],
1267 |       "execution_count": 44,
1268 |       "outputs": []
1269 |     },
1270 |     {
1271 |       "cell_type": "code",
1272 |       "metadata": {
1273 |         "colab": {
1274 |           "base_uri": "https://localhost:8080/"
1275 |         },
1276 |         "id": "c9mZA9p44n8Q",
1277 |         "outputId": "093b5799-26ac-4b15-815f-a74f3ddf8a5f"
1278 |       },
1279 |       "source": [
1280 |         "a2d[:2,1:]"
1281 |       ],
1282 |       "execution_count": 45,
1283 |       "outputs": [
1284 |         {
1285 |           "output_type": "execute_result",
1286 |           "data": {
1287 |             "text/plain": [
1288 |               "array([[10, 15],\n",
1289 |               "       [25, 30]])"
1290 |             ]
1291 |           },
1292 |           "metadata": {
1293 |             "tags": []
1294 |           },
1295 |           "execution_count": 45
1296 |         }
1297 |       ]
1298 |     },
1299 |     {
1300 |       "cell_type": "markdown",
1301 |       "metadata": {
1302 |         "id": "Bu811q-u4tgE"
1303 |       },
1304 |       "source": [
1305 |         "## Selection based on a condition"
1306 |       ]
1307 |     },
1308 |     {
1309 |       "cell_type": "code",
1310 |       "metadata": {
1311 |         "colab": {
1312 |           "base_uri": "https://localhost:8080/"
1313 |         },
1314 |         "id": "gmBIqJoe4qXR",
1315 |         "outputId": "a4a2ec1a-61ee-43be-d401-cb363dbf1ce6"
1316 |       },
1317 |       "source": [
1318 |         "Marks = np.random.randint(1,100,1000)\n",
1319 |         "\n",
1320 |         "Marks"
1321 |       ],
1322 |       "execution_count": 117,
1323 |       "outputs": [
1324 |         {
1325 |           "output_type": "execute_result",
1326 |           "data": {
1327 |             "text/plain": [
1328 |               "array([36, 63, 34, 66, 94, 26, 33, 68, 84, 46, 21, 44, 73,  5,  5,  1, 44,\n",
1329 |               "       48,  2, 71, 81, 55, 66, 27, 59, 55, 11, 55, 21, 11, 84, 34, 27, 37,\n",
1330 |               "       13, 31, 19, 36, 39, 81, 94, 23, 17, 22, 69, 65, 41, 36, 64, 12, 77,\n",
1331 |               "       66, 62, 11, 92, 53, 13, 75, 80, 20, 90, 69, 87, 41, 50,  2, 75, 76,\n",
1332 |               "       27, 82, 90, 25, 39, 82, 87, 41, 20, 80, 64,  9, 58, 35, 44, 83, 17,\n",
1333 |               "       30, 97, 25, 39, 27,  6, 81, 77, 67, 75, 69, 79,  6, 95, 26, 98, 71,\n",
1334 |               "       15, 60, 88, 62, 25,  1, 89, 81, 39, 23, 26,  5, 68, 74, 29, 29, 70,\n",
1335 |               "       98, 20, 31,  5, 53, 64, 11, 75, 80, 88, 73, 83, 36, 19, 14,  2, 13,\n",
1336 |               "       76,  4, 15, 79, 53, 15, 19, 72, 22, 88, 14, 11, 68, 12, 11, 81, 34,\n",
1337 |               "       80, 57, 68, 99, 57, 57, 77, 33, 46, 21, 73, 84, 18, 76, 49, 69, 70,\n",
1338 |               "        8, 96, 86, 93, 52, 22,  4, 26, 35, 24, 94, 23, 35, 11, 83, 15, 40,\n",
1339 |               "       20, 67, 96, 54, 67, 50, 47,  6, 88, 23, 93, 69, 96, 81, 82, 37, 26,\n",
1340 |               "       59, 72, 55, 52, 56, 45,  9, 14, 98,  5, 57, 54, 44, 77, 70, 69, 95,\n",
1341 |               "       75, 78, 12, 49, 55, 64, 65, 89, 59, 79, 90, 75, 19, 32, 20, 25, 21,\n",
1342 |               "       31, 78, 81, 34, 31,  3,  8, 68, 63,  9, 17, 84, 33, 66,  8, 27, 58,\n",
1343 |               "       30, 16, 96, 22, 49, 66, 51,  6, 63, 75, 28, 80, 43, 77, 30, 75, 63,\n",
1344 |               "       76, 88, 35, 55, 80, 20, 95, 75,  7, 11, 23,  4, 71,  7, 24, 11, 80,\n",
1345 |               "       11, 67, 96, 72, 56, 78, 11, 39, 63,  9, 21, 17, 89, 25, 99, 70, 78,\n",
1346 |               "       16, 28, 99, 58,  3, 30, 90, 48, 58, 21,  8, 83, 10, 24, 91, 89, 89,\n",
1347 |               "       30, 26, 44, 82, 14, 59, 45, 15, 83, 42, 44, 70, 93, 94, 38, 91,  6,\n",
1348 |               "       16, 52, 83, 19, 40, 70, 91, 82,  9, 82, 50, 49, 82, 90, 24, 87, 67,\n",
1349 |               "       63,  7, 35, 57, 36, 56, 27, 10, 70, 44, 45, 84, 11, 75, 64, 67, 33,\n",
1350 |               "       45, 79, 32, 91, 56, 57, 47, 95, 80, 67, 81, 27, 84, 10, 26, 83, 79,\n",
1351 |               "       79, 37, 40, 56, 27, 34,  2, 22, 76, 74, 24, 26, 32, 45, 80, 90, 16,\n",
1352 |               "       61, 35, 59, 65, 84, 36, 30, 51, 93,  6, 53, 57, 69, 32, 10, 63,  5,\n",
1353 |               "       58, 38, 79, 54,  9, 72, 47, 92, 34, 14, 10, 42, 26, 91, 24, 84,  8,\n",
1354 |               "       84, 65,  7, 15, 72,  6, 88, 53, 10, 66, 46, 91, 39, 50, 30, 56, 45,\n",
1355 |               "       14, 84, 31, 54, 59, 64, 24, 82, 19, 32, 49,  9, 19, 42, 63, 22, 11,\n",
1356 |               "       46, 67, 14, 48, 11, 45, 75, 75, 58,  5, 21, 55, 61, 97, 89, 20,  4,\n",
1357 |               "       86, 91, 87, 45,  9, 56, 94, 75, 65, 17, 85,  9, 75, 33, 40, 64,  8,\n",
1358 |               "       10, 93, 82, 78, 75, 41,  3, 46, 30, 11, 53, 61, 79, 86, 31, 75,  8,\n",
1359 |               "       68, 50, 36, 95, 95, 34, 86, 88, 74, 93, 57, 64,  4, 72, 69, 87, 39,\n",
1360 |               "       46, 74, 11, 46, 44,  9, 95, 99, 92,  4, 92, 36, 95, 36, 79, 50, 91,\n",
1361 |               "       88, 66, 63, 24, 98, 81, 25, 89, 65, 39, 61, 42, 10, 64, 98, 56, 69,\n",
1362 |               "       49, 72, 65, 39, 30, 57, 54,  7, 94, 45, 40, 58, 74,  8, 92, 91, 58,\n",
1363 |               "       21, 34, 90, 10,  4, 97, 31, 92,  8,  5, 72, 64, 81, 49, 57,  5, 97,\n",
1364 |               "       69, 91, 26, 42, 86, 16, 10, 75, 48, 38, 27, 10, 41, 48, 10, 99, 72,\n",
1365 |               "       55, 22, 88,  1, 44, 83, 36, 42, 85, 42, 67, 24, 61, 22, 83, 31, 61,\n",
1366 |               "       64, 34, 80, 44, 34, 69, 52,  5,  2, 47,  3, 49, 55, 50, 65, 87, 96,\n",
1367 |               "       41, 83, 21, 42,  8, 18, 72, 91, 30, 50, 26, 37, 62, 31, 37,  8, 67,\n",
1368 |               "       86, 52, 54, 65, 26, 89,  8, 84, 58, 98, 98,  4, 97, 53, 38, 18, 52,\n",
1369 |               "       19, 63, 10, 72, 98, 53, 81, 72, 85, 90, 25, 23, 29, 12, 17, 58, 56,\n",
1370 |               "       47, 81,  4, 53, 48,  2, 90, 63, 76, 51, 81, 20, 74, 29, 66, 75, 17,\n",
1371 |               "       56, 82, 97, 34, 23, 46, 77, 88, 97, 36, 55, 18,  5, 34, 40, 95, 31,\n",
1372 |               "       39, 63, 56, 79, 39, 73, 60, 85, 46, 67, 34, 81, 54, 32, 23, 39,  2,\n",
1373 |               "       58, 88, 67, 40, 90, 59, 39, 15, 80, 64, 78, 42, 32, 36,  7, 98, 26,\n",
1374 |               "       36, 35, 99, 94, 43,  8, 35, 88, 42, 50, 11, 71, 52, 39, 85, 70, 47,\n",
1375 |               "       55, 11, 51,  1,  9, 56, 88, 31, 77, 62, 83, 48, 61, 47,  8, 92, 73,\n",
1376 |               "       97, 44, 43, 91, 50, 10, 45, 13, 73, 83, 23, 15, 94, 34, 74, 41, 34,\n",
1377 |               "       65, 18, 33, 77, 66,  1, 76, 26, 54, 29, 62, 50, 24, 34, 61, 12, 60,\n",
1378 |               "       53, 46,  2, 96,  6,  1, 50, 98, 80, 55, 41, 23, 44, 31, 56, 11, 68,\n",
1379 |               "       86, 30, 87, 90, 58,  5, 34,  5, 28, 81, 91,  2, 58, 32, 72, 96,  6,\n",
1380 |               "       13, 47, 55, 12, 23, 54, 83, 12,  8,  8, 65, 44, 89, 57, 21, 48, 46,\n",
1381 |               "       67,  1, 63,  9, 75, 37, 17, 77, 71, 91, 16, 28, 96, 70, 50, 48, 13,\n",
1382 |               "       74, 57, 76, 98,  1, 32, 26, 99, 68, 41, 15, 53,  6, 62, 64, 39, 68,\n",
1383 |               "       87, 13, 75, 31, 65, 97, 71,  9, 52, 48, 91, 77, 42, 47, 48,  4, 14,\n",
1384 |               "       36, 77, 13, 25,  1, 13, 64, 24, 54, 98, 89, 98, 11, 16, 64,  3, 17,\n",
1385 |               "       61, 75, 43, 33, 69,  6, 23, 51, 25, 89, 51, 95, 15, 80,  9, 68, 12,\n",
1386 |               "       92, 37, 70, 51,  7, 90, 68,  2, 14,  8, 15,  7, 50, 93])"
1387 |             ]
1388 |           },
1389 |           "metadata": {
1390 |             "tags": []
1391 |           },
1392 |           "execution_count": 117
1393 |         }
1394 |       ]
1395 |     },
1396 |     {
1397 |       "cell_type": "code",
1398 |       "metadata": {
1399 |         "id": "_VqJx6yn4zDG"
1400 |       },
1401 |       "source": [
1402 |         "selected = Marks[Marks>75]"
1403 |       ],
1404 |       "execution_count": 50,
1405 |       "outputs": []
1406 |     },
1407 |     {
1408 |       "cell_type": "code",
1409 |       "metadata": {
1410 |         "id": "J0NPyAq5UtDs",
1411 |         "outputId": "25b0a3bc-3556-452c-b587-04df5d4bb6ba",
1412 |         "colab": {
1413 |           "base_uri": "https://localhost:8080/"
1414 |         }
1415 |       },
1416 |       "source": [
1417 |         "Marks[Marks>75].shape"
1418 |       ],
1419 |       "execution_count": 120,
1420 |       "outputs": [
1421 |         {
1422 |           "output_type": "execute_result",
1423 |           "data": {
1424 |             "text/plain": [
1425 |               "(239,)"
1426 |             ]
1427 |           },
1428 |           "metadata": {
1429 |             "tags": []
1430 |           },
1431 |           "execution_count": 120
1432 |         }
1433 |       ]
1434 |     },
1435 |     {
1436 |       "cell_type": "code",
1437 |       "metadata": {
1438 |         "colab": {
1439 |           "base_uri": "https://localhost:8080/"
1440 |         },
1441 |         "id": "ZZAlcNkR46en",
1442 |         "outputId": "a2a1dfc9-b508-47eb-e56a-390306f88885"
1443 |       },
1444 |       "source": [
1445 |         "selected"
1446 |       ],
1447 |       "execution_count": 51,
1448 |       "outputs": [
1449 |         {
1450 |           "output_type": "execute_result",
1451 |           "data": {
1452 |             "text/plain": [
1453 |               "array([96, 84, 79, 83, 97, 84, 92, 87, 78, 79, 83, 84, 78, 87, 80, 80, 86,\n",
1454 |               "       78, 96, 79, 82, 80, 77, 88, 77, 90, 93, 78, 91, 95, 90, 78, 86, 80,\n",
1455 |               "       88, 97, 99, 94, 97, 81, 81, 80, 99, 83, 84, 92, 95, 85, 91, 97, 89,\n",
1456 |               "       82, 99, 80, 77, 77, 89, 92, 80, 95, 99, 76, 76, 88, 97, 96, 85, 95,\n",
1457 |               "       98, 86, 87, 86, 83, 97, 88, 86, 93, 93, 86, 96, 89, 80, 88, 95, 82,\n",
1458 |               "       79, 98, 79, 77, 87, 79, 88, 85, 77, 95, 95, 76, 89, 97, 81, 89, 85,\n",
1459 |               "       94, 80, 82, 95, 93, 85, 92, 76, 86, 86, 91, 85, 91, 80, 80, 80, 93,\n",
1460 |               "       85, 85, 90, 87, 98, 96, 78, 99, 97, 99, 90, 93, 88, 86, 94, 83, 91,\n",
1461 |               "       85, 87, 82, 76, 82, 80, 90, 96, 96, 85, 85, 80, 97, 82, 82, 86, 95,\n",
1462 |               "       86, 76, 98, 90, 83, 85, 84, 88, 85, 85, 99, 80, 93, 83, 85, 77, 80,\n",
1463 |               "       91, 94, 80, 92, 90, 76, 91, 83, 93, 97, 93, 76, 86, 78, 79, 78, 93,\n",
1464 |               "       91, 98, 93, 99, 87, 92, 77, 99, 88, 97, 86, 83, 88, 94, 92, 93, 92,\n",
1465 |               "       93, 92, 91, 97, 94, 77, 79, 98, 77, 87, 98, 98, 91, 76, 95, 87, 92,\n",
1466 |               "       82, 91, 93, 93, 89, 77, 90, 82, 78, 79, 77, 91, 80, 91, 99, 95, 80,\n",
1467 |               "       78, 76, 83, 90, 77, 84])"
1468 |             ]
1469 |           },
1470 |           "metadata": {
1471 |             "tags": []
1472 |           },
1473 |           "execution_count": 51
1474 |         }
1475 |       ]
1476 |     },
1477 |     {
1478 |       "cell_type": "markdown",
1479 |       "metadata": {
1480 |         "id": "WC2m-5lR5Kr0"
1481 |       },
1482 |       "source": [
1483 |         "## Operations : Note that NumPy arrays operate on term by term basis. Each operation is applied to every term."
1484 |       ]
1485 |     },
1486 |     {
1487 |       "cell_type": "code",
1488 |       "metadata": {
1489 |         "id": "O9PIDIJS4-xq"
1490 |       },
1491 |       "source": [
1492 |         "A = np.arange(0,10)"
1493 |       ],
1494 |       "execution_count": 52,
1495 |       "outputs": []
1496 |     },
1497 |     {
1498 |       "cell_type": "code",
1499 |       "metadata": {
1500 |         "colab": {
1501 |           "base_uri": "https://localhost:8080/"
1502 |         },
1503 |         "id": "MPdoVsGL5VV4",
1504 |         "outputId": "6ff89b07-562d-4f79-fe20-9f34d6ec3458"
1505 |       },
1506 |       "source": [
1507 |         "A**2"
1508 |       ],
1509 |       "execution_count": 54,
1510 |       "outputs": [
1511 |         {
1512 |           "output_type": "execute_result",
1513 |           "data": {
1514 |             "text/plain": [
1515 |               "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
1516 |             ]
1517 |           },
1518 |           "metadata": {
1519 |             "tags": []
1520 |           },
1521 |           "execution_count": 54
1522 |         }
1523 |       ]
1524 |     },
1525 |     {
1526 |       "cell_type": "code",
1527 |       "metadata": {
1528 |         "colab": {
1529 |           "base_uri": "https://localhost:8080/"
1530 |         },
1531 |         "id": "twuY6ENr5WJM",
1532 |         "outputId": "49509091-8322-49fa-8c39-8d29689603cf"
1533 |       },
1534 |       "source": [
1535 |         "A + A"
1536 |       ],
1537 |       "execution_count": 55,
1538 |       "outputs": [
1539 |         {
1540 |           "output_type": "execute_result",
1541 |           "data": {
1542 |             "text/plain": [
1543 |               "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
1544 |             ]
1545 |           },
1546 |           "metadata": {
1547 |             "tags": []
1548 |           },
1549 |           "execution_count": 55
1550 |         }
1551 |       ]
1552 |     },
1553 |     {
1554 |       "cell_type": "code",
1555 |       "metadata": {
1556 |         "colab": {
1557 |           "base_uri": "https://localhost:8080/"
1558 |         },
1559 |         "id": "l4Gf9DQo5YrK",
1560 |         "outputId": "7c3cee6c-04c2-450f-d0fc-8686dba85619"
1561 |       },
1562 |       "source": [
1563 |         "np.sin(A)"
1564 |       ],
1565 |       "execution_count": 56,
1566 |       "outputs": [
1567 |         {
1568 |           "output_type": "execute_result",
1569 |           "data": {
1570 |             "text/plain": [
1571 |               "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ,\n",
1572 |               "       -0.95892427, -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849])"
1573 |             ]
1574 |           },
1575 |           "metadata": {
1576 |             "tags": []
1577 |           },
1578 |           "execution_count": 56
1579 |         }
1580 |       ]
1581 |     },
1582 |     {
1583 |       "cell_type": "code",
1584 |       "metadata": {
1585 |         "colab": {
1586 |           "base_uri": "https://localhost:8080/"
1587 |         },
1588 |         "id": "KXe5isMn5bCE",
1589 |         "outputId": "8e7a4cbd-b24b-4c08-c0d8-ee03b14405c5"
1590 |       },
1591 |       "source": [
1592 |         "np.cos(A)"
1593 |       ],
1594 |       "execution_count": 57,
1595 |       "outputs": [
1596 |         {
1597 |           "output_type": "execute_result",
1598 |           "data": {
1599 |             "text/plain": [
1600 |               "array([ 1.        ,  0.54030231, -0.41614684, -0.9899925 , -0.65364362,\n",
1601 |               "        0.28366219,  0.96017029,  0.75390225, -0.14550003, -0.91113026])"
1602 |             ]
1603 |           },
1604 |           "metadata": {
1605 |             "tags": []
1606 |           },
1607 |           "execution_count": 57
1608 |         }
1609 |       ]
1610 |     },
1611 |     {
1612 |       "cell_type": "markdown",
1613 |       "metadata": {
1614 |         "id": "ZtXFc-2v5efq"
1615 |       },
1616 |       "source": [
1617 |         "These are Universal Array Functions. \n",
1618 |         "\n",
1619 |         "Further reading : http://docs.scipy.org/doc/numpy/reference/ufuncs.html"
1620 |       ]
1621 |     },
1622 |     {
1623 |       "cell_type": "code",
1624 |       "metadata": {
1625 |         "id": "-wsnHETP5cNe"
1626 |       },
1627 |       "source": [
1628 |         "###################################################################"
1629 |       ],
1630 |       "execution_count": 58,
1631 |       "outputs": []
1632 |     },
1633 |     {
1634 |       "cell_type": "markdown",
1635 |       "metadata": {
1636 |         "id": "tAjzYbek5-xt"
1637 |       },
1638 |       "source": [
1639 |         "## 2. Python Pandas : The Ms. Excel of Python."
1640 |       ]
1641 |     },
1642 |     {
1643 |       "cell_type": "markdown",
1644 |       "metadata": {
1645 |         "id": "L4n61bBH6YML"
1646 |       },
1647 |       "source": [
1648 |         "### 2.1 Python Series : Like a Named Array/"
1649 |       ]
1650 |     },
1651 |     {
1652 |       "cell_type": "markdown",
1653 |       "metadata": {
1654 |         "id": "vOzjhc0tVJlt"
1655 |       },
1656 |       "source": [
1657 |         "NumPy -- Series --- DataFrame "
1658 |       ]
1659 |     },
1660 |     {
1661 |       "cell_type": "code",
1662 |       "metadata": {
1663 |         "id": "sG9noWeN6mjG"
1664 |       },
1665 |       "source": [
1666 |         "import pandas as pd"
1667 |       ],
1668 |       "execution_count": 121,
1669 |       "outputs": []
1670 |     },
1671 |     {
1672 |       "cell_type": "code",
1673 |       "metadata": {
1674 |         "id": "Nkd7C6_B57O-"
1675 |       },
1676 |       "source": [
1677 |         "values = [1,2,3,4,5]\n",
1678 |         "labels = ['a' , 'b' , 'c' , 'd' , 'e']"
1679 |       ],
1680 |       "execution_count": 59,
1681 |       "outputs": []
1682 |     },
1683 |     {
1684 |       "cell_type": "code",
1685 |       "metadata": {
1686 |         "id": "My4i_w0o6ihC"
1687 |       },
1688 |       "source": [
1689 |         "S = pd.Series(values,labels)"
1690 |       ],
1691 |       "execution_count": 62,
1692 |       "outputs": []
1693 |     },
1694 |     {
1695 |       "cell_type": "code",
1696 |       "metadata": {
1697 |         "colab": {
1698 |           "base_uri": "https://localhost:8080/"
1699 |         },
1700 |         "id": "RAIVZF2Q6k8R",
1701 |         "outputId": "843edabd-84cc-47ec-99ee-508787e43a71"
1702 |       },
1703 |       "source": [
1704 |         "S"
1705 |       ],
1706 |       "execution_count": 63,
1707 |       "outputs": [
1708 |         {
1709 |           "output_type": "execute_result",
1710 |           "data": {
1711 |             "text/plain": [
1712 |               "a    1\n",
1713 |               "b    2\n",
1714 |               "c    3\n",
1715 |               "d    4\n",
1716 |               "e    5\n",
1717 |               "dtype: int64"
1718 |             ]
1719 |           },
1720 |           "metadata": {
1721 |             "tags": []
1722 |           },
1723 |           "execution_count": 63
1724 |         }
1725 |       ]
1726 |     },
1727 |     {
1728 |       "cell_type": "code",
1729 |       "metadata": {
1730 |         "colab": {
1731 |           "base_uri": "https://localhost:8080/"
1732 |         },
1733 |         "id": "Ma2FfYof6rJt",
1734 |         "outputId": "c707da0e-7f06-4239-ba54-98e813fbe979"
1735 |       },
1736 |       "source": [
1737 |         "S['a']"
1738 |       ],
1739 |       "execution_count": 64,
1740 |       "outputs": [
1741 |         {
1742 |           "output_type": "execute_result",
1743 |           "data": {
1744 |             "text/plain": [
1745 |               "1"
1746 |             ]
1747 |           },
1748 |           "metadata": {
1749 |             "tags": []
1750 |           },
1751 |           "execution_count": 64
1752 |         }
1753 |       ]
1754 |     },
1755 |     {
1756 |       "cell_type": "code",
1757 |       "metadata": {
1758 |         "colab": {
1759 |           "base_uri": "https://localhost:8080/"
1760 |         },
1761 |         "id": "-MXIkPCE6udt",
1762 |         "outputId": "83f50dc0-9ecd-4389-a187-226a99e4df34"
1763 |       },
1764 |       "source": [
1765 |         "pd.Series(values,index = labels)"
1766 |       ],
1767 |       "execution_count": 65,
1768 |       "outputs": [
1769 |         {
1770 |           "output_type": "execute_result",
1771 |           "data": {
1772 |             "text/plain": [
1773 |               "a    1\n",
1774 |               "b    2\n",
1775 |               "c    3\n",
1776 |               "d    4\n",
1777 |               "e    5\n",
1778 |               "dtype: int64"
1779 |             ]
1780 |           },
1781 |           "metadata": {
1782 |             "tags": []
1783 |           },
1784 |           "execution_count": 65
1785 |         }
1786 |       ]
1787 |     },
1788 |     {
1789 |       "cell_type": "markdown",
1790 |       "metadata": {
1791 |         "id": "tEfNKHT166p1"
1792 |       },
1793 |       "source": [
1794 |         "## 2.2 : DataFrames"
1795 |       ]
1796 |     },
1797 |     {
1798 |       "cell_type": "markdown",
1799 |       "metadata": {
1800 |         "id": "OAq0xjVHWNYt"
1801 |       },
1802 |       "source": [
1803 |         "1. DF from a Dictionary."
1804 |       ]
1805 |     },
1806 |     {
1807 |       "cell_type": "code",
1808 |       "metadata": {
1809 |         "id": "aruf1_xa60k6"
1810 |       },
1811 |       "source": [
1812 |         "rock = {'phi': [0.05 , 0.20 , 0.25 , 0.08],\n",
1813 |         "        'lith': ['C', 'S', 'S','C']}\n",
1814 |         "\n",
1815 |         "rock_df = pd.DataFrame(rock)"
1816 |       ],
1817 |       "execution_count": 122,
1818 |       "outputs": []
1819 |     },
1820 |     {
1821 |       "cell_type": "code",
1822 |       "metadata": {
1823 |         "colab": {
1824 |           "base_uri": "https://localhost:8080/",
1825 |           "height": 172
1826 |         },
1827 |         "id": "hdWCCrZR7MlY",
1828 |         "outputId": "e088809c-6fd1-4829-a76b-3e67568528e3"
1829 |       },
1830 |       "source": [
1831 |         "rock_df"
1832 |       ],
1833 |       "execution_count": 123,
1834 |       "outputs": [
1835 |         {
1836 |           "output_type": "execute_result",
1837 |           "data": {
1838 |             "text/html": [
1839 |               "<div>\n",
1840 |               "<style scoped>\n",
1841 |               "    .dataframe tbody tr th:only-of-type {\n",
1842 |               "        vertical-align: middle;\n",
1843 |               "    }\n",
1844 |               "\n",
1845 |               "    .dataframe tbody tr th {\n",
1846 |               "        vertical-align: top;\n",
1847 |               "    }\n",
1848 |               "\n",
1849 |               "    .dataframe thead th {\n",
1850 |               "        text-align: right;\n",
1851 |               "    }\n",
1852 |               "</style>\n",
1853 |               "<table border=\"1\" class=\"dataframe\">\n",
1854 |               "  <thead>\n",
1855 |               "    <tr style=\"text-align: right;\">\n",
1856 |               "      <th></th>\n",
1857 |               "      <th>phi</th>\n",
1858 |               "      <th>lith</th>\n",
1859 |               "    </tr>\n",
1860 |               "  </thead>\n",
1861 |               "  <tbody>\n",
1862 |               "    <tr>\n",
1863 |               "      <th>0</th>\n",
1864 |               "      <td>0.05</td>\n",
1865 |               "      <td>C</td>\n",
1866 |               "    </tr>\n",
1867 |               "    <tr>\n",
1868 |               "      <th>1</th>\n",
1869 |               "      <td>0.20</td>\n",
1870 |               "      <td>S</td>\n",
1871 |               "    </tr>\n",
1872 |               "    <tr>\n",
1873 |               "      <th>2</th>\n",
1874 |               "      <td>0.25</td>\n",
1875 |               "      <td>S</td>\n",
1876 |               "    </tr>\n",
1877 |               "    <tr>\n",
1878 |               "      <th>3</th>\n",
1879 |               "      <td>0.08</td>\n",
1880 |               "      <td>C</td>\n",
1881 |               "    </tr>\n",
1882 |               "  </tbody>\n",
1883 |               "</table>\n",
1884 |               "</div>"
1885 |             ],
1886 |             "text/plain": [
1887 |               "    phi lith\n",
1888 |               "0  0.05    C\n",
1889 |               "1  0.20    S\n",
1890 |               "2  0.25    S\n",
1891 |               "3  0.08    C"
1892 |             ]
1893 |           },
1894 |           "metadata": {
1895 |             "tags": []
1896 |           },
1897 |           "execution_count": 123
1898 |         }
1899 |       ]
1900 |     },
1901 |     {
1902 |       "cell_type": "code",
1903 |       "metadata": {
1904 |         "colab": {
1905 |           "base_uri": "https://localhost:8080/"
1906 |         },
1907 |         "id": "y1G-g5gL7R9K",
1908 |         "outputId": "d3a2f111-e2a6-4768-acb4-b506d7ac6533"
1909 |       },
1910 |       "source": [
1911 |         "rock['phi']"
1912 |       ],
1913 |       "execution_count": 124,
1914 |       "outputs": [
1915 |         {
1916 |           "output_type": "execute_result",
1917 |           "data": {
1918 |             "text/plain": [
1919 |               "[0.05, 0.2, 0.25, 0.08]"
1920 |             ]
1921 |           },
1922 |           "metadata": {
1923 |             "tags": []
1924 |           },
1925 |           "execution_count": 124
1926 |         }
1927 |       ]
1928 |     },
1929 |     {
1930 |       "cell_type": "code",
1931 |       "metadata": {
1932 |         "id": "L7PnJwJS7mAp"
1933 |       },
1934 |       "source": [
1935 |         "#Creating a DataFrame from an array."
1936 |       ],
1937 |       "execution_count": 70,
1938 |       "outputs": []
1939 |     },
1940 |     {
1941 |       "cell_type": "code",
1942 |       "metadata": {
1943 |         "colab": {
1944 |           "base_uri": "https://localhost:8080/",
1945 |           "height": 203
1946 |         },
1947 |         "id": "GFOEQ0Dd7t7R",
1948 |         "outputId": "36084178-2f31-4e27-9f05-861cc6ac2d77"
1949 |       },
1950 |       "source": [
1951 |         "df = pd.DataFrame(data = np.random.randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())\n",
1952 |         "\n",
1953 |         "df"
1954 |       ],
1955 |       "execution_count": 125,
1956 |       "outputs": [
1957 |         {
1958 |           "output_type": "execute_result",
1959 |           "data": {
1960 |             "text/html": [
1961 |               "<div>\n",
1962 |               "<style scoped>\n",
1963 |               "    .dataframe tbody tr th:only-of-type {\n",
1964 |               "        vertical-align: middle;\n",
1965 |               "    }\n",
1966 |               "\n",
1967 |               "    .dataframe tbody tr th {\n",
1968 |               "        vertical-align: top;\n",
1969 |               "    }\n",
1970 |               "\n",
1971 |               "    .dataframe thead th {\n",
1972 |               "        text-align: right;\n",
1973 |               "    }\n",
1974 |               "</style>\n",
1975 |               "<table border=\"1\" class=\"dataframe\">\n",
1976 |               "  <thead>\n",
1977 |               "    <tr style=\"text-align: right;\">\n",
1978 |               "      <th></th>\n",
1979 |               "      <th>W</th>\n",
1980 |               "      <th>X</th>\n",
1981 |               "      <th>Y</th>\n",
1982 |               "      <th>Z</th>\n",
1983 |               "    </tr>\n",
1984 |               "  </thead>\n",
1985 |               "  <tbody>\n",
1986 |               "    <tr>\n",
1987 |               "      <th>A</th>\n",
1988 |               "      <td>-1.182853</td>\n",
1989 |               "      <td>-0.513556</td>\n",
1990 |               "      <td>-0.515698</td>\n",
1991 |               "      <td>0.494726</td>\n",
1992 |               "    </tr>\n",
1993 |               "    <tr>\n",
1994 |               "      <th>B</th>\n",
1995 |               "      <td>-0.577124</td>\n",
1996 |               "      <td>0.886354</td>\n",
1997 |               "      <td>-1.588290</td>\n",
1998 |               "      <td>-1.587124</td>\n",
1999 |               "    </tr>\n",
2000 |               "    <tr>\n",
2001 |               "      <th>C</th>\n",
2002 |               "      <td>-0.240799</td>\n",
2003 |               "      <td>0.123911</td>\n",
2004 |               "      <td>-1.001634</td>\n",
2005 |               "      <td>0.902924</td>\n",
2006 |               "    </tr>\n",
2007 |               "    <tr>\n",
2008 |               "      <th>D</th>\n",
2009 |               "      <td>-0.427675</td>\n",
2010 |               "      <td>0.080528</td>\n",
2011 |               "      <td>-0.625017</td>\n",
2012 |               "      <td>-0.778859</td>\n",
2013 |               "    </tr>\n",
2014 |               "    <tr>\n",
2015 |               "      <th>E</th>\n",
2016 |               "      <td>2.248956</td>\n",
2017 |               "      <td>0.264856</td>\n",
2018 |               "      <td>0.344202</td>\n",
2019 |               "      <td>-1.435114</td>\n",
2020 |               "    </tr>\n",
2021 |               "  </tbody>\n",
2022 |               "</table>\n",
2023 |               "</div>"
2024 |             ],
2025 |             "text/plain": [
2026 |               "          W         X         Y         Z\n",
2027 |               "A -1.182853 -0.513556 -0.515698  0.494726\n",
2028 |               "B -0.577124  0.886354 -1.588290 -1.587124\n",
2029 |               "C -0.240799  0.123911 -1.001634  0.902924\n",
2030 |               "D -0.427675  0.080528 -0.625017 -0.778859\n",
2031 |               "E  2.248956  0.264856  0.344202 -1.435114"
2032 |             ]
2033 |           },
2034 |           "metadata": {
2035 |             "tags": []
2036 |           },
2037 |           "execution_count": 125
2038 |         }
2039 |       ]
2040 |     },
2041 |     {
2042 |       "cell_type": "code",
2043 |       "metadata": {
2044 |         "id": "cXeuQriC7wZQ"
2045 |       },
2046 |       "source": [
2047 |         "# df"
2048 |       ],
2049 |       "execution_count": 126,
2050 |       "outputs": []
2051 |     },
2052 |     {
2053 |       "cell_type": "code",
2054 |       "metadata": {
2055 |         "colab": {
2056 |           "base_uri": "https://localhost:8080/"
2057 |         },
2058 |         "id": "e7yWUvZm7zrn",
2059 |         "outputId": "732dc5a8-de52-46f6-d8ec-925945d84bca"
2060 |       },
2061 |       "source": [
2062 |         "df['W']"
2063 |       ],
2064 |       "execution_count": 74,
2065 |       "outputs": [
2066 |         {
2067 |           "output_type": "execute_result",
2068 |           "data": {
2069 |             "text/plain": [
2070 |               "A   -0.709325\n",
2071 |               "B    0.239717\n",
2072 |               "C   -0.833494\n",
2073 |               "D    0.252288\n",
2074 |               "E   -0.012246\n",
2075 |               "Name: W, dtype: float64"
2076 |             ]
2077 |           },
2078 |           "metadata": {
2079 |             "tags": []
2080 |           },
2081 |           "execution_count": 74
2082 |         }
2083 |       ]
2084 |     },
2085 |     {
2086 |       "cell_type": "code",
2087 |       "metadata": {
2088 |         "colab": {
2089 |           "base_uri": "https://localhost:8080/",
2090 |           "height": 203
2091 |         },
2092 |         "id": "v7YdfZyG71gi",
2093 |         "outputId": "db5b4421-0fd5-43d2-d0b7-613c7620cbef"
2094 |       },
2095 |       "source": [
2096 |         "#Adding a NEw column. \n",
2097 |         "\n",
2098 |         "df['W_squared'] = df['W']**2\n",
2099 |         "\n",
2100 |         "df"
2101 |       ],
2102 |       "execution_count": 76,
2103 |       "outputs": [
2104 |         {
2105 |           "output_type": "execute_result",
2106 |           "data": {
2107 |             "text/html": [
2108 |               "<div>\n",
2109 |               "<style scoped>\n",
2110 |               "    .dataframe tbody tr th:only-of-type {\n",
2111 |               "        vertical-align: middle;\n",
2112 |               "    }\n",
2113 |               "\n",
2114 |               "    .dataframe tbody tr th {\n",
2115 |               "        vertical-align: top;\n",
2116 |               "    }\n",
2117 |               "\n",
2118 |               "    .dataframe thead th {\n",
2119 |               "        text-align: right;\n",
2120 |               "    }\n",
2121 |               "</style>\n",
2122 |               "<table border=\"1\" class=\"dataframe\">\n",
2123 |               "  <thead>\n",
2124 |               "    <tr style=\"text-align: right;\">\n",
2125 |               "      <th></th>\n",
2126 |               "      <th>W</th>\n",
2127 |               "      <th>X</th>\n",
2128 |               "      <th>Y</th>\n",
2129 |               "      <th>Z</th>\n",
2130 |               "      <th>W_squared</th>\n",
2131 |               "    </tr>\n",
2132 |               "  </thead>\n",
2133 |               "  <tbody>\n",
2134 |               "    <tr>\n",
2135 |               "      <th>A</th>\n",
2136 |               "      <td>-0.709325</td>\n",
2137 |               "      <td>-0.398991</td>\n",
2138 |               "      <td>2.255707</td>\n",
2139 |               "      <td>1.286425</td>\n",
2140 |               "      <td>0.503142</td>\n",
2141 |               "    </tr>\n",
2142 |               "    <tr>\n",
2143 |               "      <th>B</th>\n",
2144 |               "      <td>0.239717</td>\n",
2145 |               "      <td>-0.993057</td>\n",
2146 |               "      <td>-1.434999</td>\n",
2147 |               "      <td>-0.777527</td>\n",
2148 |               "      <td>0.057464</td>\n",
2149 |               "    </tr>\n",
2150 |               "    <tr>\n",
2151 |               "      <th>C</th>\n",
2152 |               "      <td>-0.833494</td>\n",
2153 |               "      <td>0.910116</td>\n",
2154 |               "      <td>-0.249891</td>\n",
2155 |               "      <td>0.342504</td>\n",
2156 |               "      <td>0.694712</td>\n",
2157 |               "    </tr>\n",
2158 |               "    <tr>\n",
2159 |               "      <th>D</th>\n",
2160 |               "      <td>0.252288</td>\n",
2161 |               "      <td>-0.240984</td>\n",
2162 |               "      <td>-0.607454</td>\n",
2163 |               "      <td>-0.266429</td>\n",
2164 |               "      <td>0.063649</td>\n",
2165 |               "    </tr>\n",
2166 |               "    <tr>\n",
2167 |               "      <th>E</th>\n",
2168 |               "      <td>-0.012246</td>\n",
2169 |               "      <td>-0.213262</td>\n",
2170 |               "      <td>-1.522099</td>\n",
2171 |               "      <td>-0.727474</td>\n",
2172 |               "      <td>0.000150</td>\n",
2173 |               "    </tr>\n",
2174 |               "  </tbody>\n",
2175 |               "</table>\n",
2176 |               "</div>"
2177 |             ],
2178 |             "text/plain": [
2179 |               "          W         X         Y         Z  W_squared\n",
2180 |               "A -0.709325 -0.398991  2.255707  1.286425   0.503142\n",
2181 |               "B  0.239717 -0.993057 -1.434999 -0.777527   0.057464\n",
2182 |               "C -0.833494  0.910116 -0.249891  0.342504   0.694712\n",
2183 |               "D  0.252288 -0.240984 -0.607454 -0.266429   0.063649\n",
2184 |               "E -0.012246 -0.213262 -1.522099 -0.727474   0.000150"
2185 |             ]
2186 |           },
2187 |           "metadata": {
2188 |             "tags": []
2189 |           },
2190 |           "execution_count": 76
2191 |         }
2192 |       ]
2193 |     },
2194 |     {
2195 |       "cell_type": "code",
2196 |       "metadata": {
2197 |         "colab": {
2198 |           "base_uri": "https://localhost:8080/",
2199 |           "height": 203
2200 |         },
2201 |         "id": "iMB8ZIXO7_PA",
2202 |         "outputId": "0d4dfb84-ab6d-4577-b875-7af14674e1b2"
2203 |       },
2204 |       "source": [
2205 |         "df['new'] = df['X'] + df['Y']\n",
2206 |         "\n",
2207 |         "df"
2208 |       ],
2209 |       "execution_count": 77,
2210 |       "outputs": [
2211 |         {
2212 |           "output_type": "execute_result",
2213 |           "data": {
2214 |             "text/html": [
2215 |               "<div>\n",
2216 |               "<style scoped>\n",
2217 |               "    .dataframe tbody tr th:only-of-type {\n",
2218 |               "        vertical-align: middle;\n",
2219 |               "    }\n",
2220 |               "\n",
2221 |               "    .dataframe tbody tr th {\n",
2222 |               "        vertical-align: top;\n",
2223 |               "    }\n",
2224 |               "\n",
2225 |               "    .dataframe thead th {\n",
2226 |               "        text-align: right;\n",
2227 |               "    }\n",
2228 |               "</style>\n",
2229 |               "<table border=\"1\" class=\"dataframe\">\n",
2230 |               "  <thead>\n",
2231 |               "    <tr style=\"text-align: right;\">\n",
2232 |               "      <th></th>\n",
2233 |               "      <th>W</th>\n",
2234 |               "      <th>X</th>\n",
2235 |               "      <th>Y</th>\n",
2236 |               "      <th>Z</th>\n",
2237 |               "      <th>W_squared</th>\n",
2238 |               "      <th>new</th>\n",
2239 |               "    </tr>\n",
2240 |               "  </thead>\n",
2241 |               "  <tbody>\n",
2242 |               "    <tr>\n",
2243 |               "      <th>A</th>\n",
2244 |               "      <td>-0.709325</td>\n",
2245 |               "      <td>-0.398991</td>\n",
2246 |               "      <td>2.255707</td>\n",
2247 |               "      <td>1.286425</td>\n",
2248 |               "      <td>0.503142</td>\n",
2249 |               "      <td>1.856716</td>\n",
2250 |               "    </tr>\n",
2251 |               "    <tr>\n",
2252 |               "      <th>B</th>\n",
2253 |               "      <td>0.239717</td>\n",
2254 |               "      <td>-0.993057</td>\n",
2255 |               "      <td>-1.434999</td>\n",
2256 |               "      <td>-0.777527</td>\n",
2257 |               "      <td>0.057464</td>\n",
2258 |               "      <td>-2.428056</td>\n",
2259 |               "    </tr>\n",
2260 |               "    <tr>\n",
2261 |               "      <th>C</th>\n",
2262 |               "      <td>-0.833494</td>\n",
2263 |               "      <td>0.910116</td>\n",
2264 |               "      <td>-0.249891</td>\n",
2265 |               "      <td>0.342504</td>\n",
2266 |               "      <td>0.694712</td>\n",
2267 |               "      <td>0.660225</td>\n",
2268 |               "    </tr>\n",
2269 |               "    <tr>\n",
2270 |               "      <th>D</th>\n",
2271 |               "      <td>0.252288</td>\n",
2272 |               "      <td>-0.240984</td>\n",
2273 |               "      <td>-0.607454</td>\n",
2274 |               "      <td>-0.266429</td>\n",
2275 |               "      <td>0.063649</td>\n",
2276 |               "      <td>-0.848438</td>\n",
2277 |               "    </tr>\n",
2278 |               "    <tr>\n",
2279 |               "      <th>E</th>\n",
2280 |               "      <td>-0.012246</td>\n",
2281 |               "      <td>-0.213262</td>\n",
2282 |               "      <td>-1.522099</td>\n",
2283 |               "      <td>-0.727474</td>\n",
2284 |               "      <td>0.000150</td>\n",
2285 |               "      <td>-1.735361</td>\n",
2286 |               "    </tr>\n",
2287 |               "  </tbody>\n",
2288 |               "</table>\n",
2289 |               "</div>"
2290 |             ],
2291 |             "text/plain": [
2292 |               "          W         X         Y         Z  W_squared       new\n",
2293 |               "A -0.709325 -0.398991  2.255707  1.286425   0.503142  1.856716\n",
2294 |               "B  0.239717 -0.993057 -1.434999 -0.777527   0.057464 -2.428056\n",
2295 |               "C -0.833494  0.910116 -0.249891  0.342504   0.694712  0.660225\n",
2296 |               "D  0.252288 -0.240984 -0.607454 -0.266429   0.063649 -0.848438\n",
2297 |               "E -0.012246 -0.213262 -1.522099 -0.727474   0.000150 -1.735361"
2298 |             ]
2299 |           },
2300 |           "metadata": {
2301 |             "tags": []
2302 |           },
2303 |           "execution_count": 77
2304 |         }
2305 |       ]
2306 |     },
2307 |     {
2308 |       "cell_type": "code",
2309 |       "metadata": {
2310 |         "colab": {
2311 |           "base_uri": "https://localhost:8080/",
2312 |           "height": 282
2313 |         },
2314 |         "id": "2cEIl-009GLc",
2315 |         "outputId": "a88b08d9-03f3-4816-e624-1975943788a9"
2316 |       },
2317 |       "source": [
2318 |         "df.plot()"
2319 |       ],
2320 |       "execution_count": 78,
2321 |       "outputs": [
2322 |         {
2323 |           "output_type": "execute_result",
2324 |           "data": {
2325 |             "text/plain": [
2326 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7fc40f148f10>"
2327 |             ]
2328 |           },
2329 |           "metadata": {
2330 |             "tags": []
2331 |           },
2332 |           "execution_count": 78
2333 |         },
2334 |         {
2335 |           "output_type": "display_data",
2336 |           "data": {
2337 |             "image/png": 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\n",
2338 |             "text/plain": [
2339 |               "<Figure size 432x288 with 1 Axes>"
2340 |             ]
2341 |           },
2342 |           "metadata": {
2343 |             "tags": [],
2344 |             "needs_background": "light"
2345 |           }
2346 |         }
2347 |       ]
2348 |     },
2349 |     {
2350 |       "cell_type": "markdown",
2351 |       "metadata": {
2352 |         "id": "Ki6MBGSI9QLK"
2353 |       },
2354 |       "source": [
2355 |         "## Loc and I-Loc. \n",
2356 |         "\n",
2357 |         "1. loc starts with 'l' : so it considers labels. \n",
2358 |         "\n",
2359 |         "2. iloc starts with 'i' so it considers indexes."
2360 |       ]
2361 |     },
2362 |     {
2363 |       "cell_type": "code",
2364 |       "metadata": {
2365 |         "colab": {
2366 |           "base_uri": "https://localhost:8080/",
2367 |           "height": 110
2368 |         },
2369 |         "id": "xyKNfQEQ9H9P",
2370 |         "outputId": "e3c22f35-f9db-4c41-e975-8fabf2363c85"
2371 |       },
2372 |       "source": [
2373 |         "df.iloc[0:2,0:2]"
2374 |       ],
2375 |       "execution_count": 129,
2376 |       "outputs": [
2377 |         {
2378 |           "output_type": "execute_result",
2379 |           "data": {
2380 |             "text/html": [
2381 |               "<div>\n",
2382 |               "<style scoped>\n",
2383 |               "    .dataframe tbody tr th:only-of-type {\n",
2384 |               "        vertical-align: middle;\n",
2385 |               "    }\n",
2386 |               "\n",
2387 |               "    .dataframe tbody tr th {\n",
2388 |               "        vertical-align: top;\n",
2389 |               "    }\n",
2390 |               "\n",
2391 |               "    .dataframe thead th {\n",
2392 |               "        text-align: right;\n",
2393 |               "    }\n",
2394 |               "</style>\n",
2395 |               "<table border=\"1\" class=\"dataframe\">\n",
2396 |               "  <thead>\n",
2397 |               "    <tr style=\"text-align: right;\">\n",
2398 |               "      <th></th>\n",
2399 |               "      <th>W</th>\n",
2400 |               "      <th>X</th>\n",
2401 |               "    </tr>\n",
2402 |               "  </thead>\n",
2403 |               "  <tbody>\n",
2404 |               "    <tr>\n",
2405 |               "      <th>A</th>\n",
2406 |               "      <td>-1.182853</td>\n",
2407 |               "      <td>-0.513556</td>\n",
2408 |               "    </tr>\n",
2409 |               "    <tr>\n",
2410 |               "      <th>B</th>\n",
2411 |               "      <td>-0.577124</td>\n",
2412 |               "      <td>0.886354</td>\n",
2413 |               "    </tr>\n",
2414 |               "  </tbody>\n",
2415 |               "</table>\n",
2416 |               "</div>"
2417 |             ],
2418 |             "text/plain": [
2419 |               "          W         X\n",
2420 |               "A -1.182853 -0.513556\n",
2421 |               "B -0.577124  0.886354"
2422 |             ]
2423 |           },
2424 |           "metadata": {
2425 |             "tags": []
2426 |           },
2427 |           "execution_count": 129
2428 |         }
2429 |       ]
2430 |     },
2431 |     {
2432 |       "cell_type": "markdown",
2433 |       "metadata": {
2434 |         "id": "Cx-u2gAH9b_v"
2435 |       },
2436 |       "source": [
2437 |         "### Pick first two rows using loc. "
2438 |       ]
2439 |     },
2440 |     {
2441 |       "cell_type": "code",
2442 |       "metadata": {
2443 |         "colab": {
2444 |           "base_uri": "https://localhost:8080/",
2445 |           "height": 110
2446 |         },
2447 |         "id": "tscoRC659a1N",
2448 |         "outputId": "c6380e5a-9b23-4321-857e-6db96f241429"
2449 |       },
2450 |       "source": [
2451 |         "df.loc['A':'B',:]"
2452 |       ],
2453 |       "execution_count": 80,
2454 |       "outputs": [
2455 |         {
2456 |           "output_type": "execute_result",
2457 |           "data": {
2458 |             "text/html": [
2459 |               "<div>\n",
2460 |               "<style scoped>\n",
2461 |               "    .dataframe tbody tr th:only-of-type {\n",
2462 |               "        vertical-align: middle;\n",
2463 |               "    }\n",
2464 |               "\n",
2465 |               "    .dataframe tbody tr th {\n",
2466 |               "        vertical-align: top;\n",
2467 |               "    }\n",
2468 |               "\n",
2469 |               "    .dataframe thead th {\n",
2470 |               "        text-align: right;\n",
2471 |               "    }\n",
2472 |               "</style>\n",
2473 |               "<table border=\"1\" class=\"dataframe\">\n",
2474 |               "  <thead>\n",
2475 |               "    <tr style=\"text-align: right;\">\n",
2476 |               "      <th></th>\n",
2477 |               "      <th>W</th>\n",
2478 |               "      <th>X</th>\n",
2479 |               "      <th>Y</th>\n",
2480 |               "      <th>Z</th>\n",
2481 |               "      <th>W_squared</th>\n",
2482 |               "      <th>new</th>\n",
2483 |               "    </tr>\n",
2484 |               "  </thead>\n",
2485 |               "  <tbody>\n",
2486 |               "    <tr>\n",
2487 |               "      <th>A</th>\n",
2488 |               "      <td>-0.709325</td>\n",
2489 |               "      <td>-0.398991</td>\n",
2490 |               "      <td>2.255707</td>\n",
2491 |               "      <td>1.286425</td>\n",
2492 |               "      <td>0.503142</td>\n",
2493 |               "      <td>1.856716</td>\n",
2494 |               "    </tr>\n",
2495 |               "    <tr>\n",
2496 |               "      <th>B</th>\n",
2497 |               "      <td>0.239717</td>\n",
2498 |               "      <td>-0.993057</td>\n",
2499 |               "      <td>-1.434999</td>\n",
2500 |               "      <td>-0.777527</td>\n",
2501 |               "      <td>0.057464</td>\n",
2502 |               "      <td>-2.428056</td>\n",
2503 |               "    </tr>\n",
2504 |               "  </tbody>\n",
2505 |               "</table>\n",
2506 |               "</div>"
2507 |             ],
2508 |             "text/plain": [
2509 |               "          W         X         Y         Z  W_squared       new\n",
2510 |               "A -0.709325 -0.398991  2.255707  1.286425   0.503142  1.856716\n",
2511 |               "B  0.239717 -0.993057 -1.434999 -0.777527   0.057464 -2.428056"
2512 |             ]
2513 |           },
2514 |           "metadata": {
2515 |             "tags": []
2516 |           },
2517 |           "execution_count": 80
2518 |         }
2519 |       ]
2520 |     },
2521 |     {
2522 |       "cell_type": "markdown",
2523 |       "metadata": {
2524 |         "id": "cZeZHXiB9l6W"
2525 |       },
2526 |       "source": [
2527 |         "### Pick first two rows using iloc."
2528 |       ]
2529 |     },
2530 |     {
2531 |       "cell_type": "code",
2532 |       "metadata": {
2533 |         "colab": {
2534 |           "base_uri": "https://localhost:8080/",
2535 |           "height": 110
2536 |         },
2537 |         "id": "wj8iJTMO9kYB",
2538 |         "outputId": "94f586db-43d9-4784-ad75-ae2dc3989d4f"
2539 |       },
2540 |       "source": [
2541 |         "df.iloc[0:2, :]"
2542 |       ],
2543 |       "execution_count": 81,
2544 |       "outputs": [
2545 |         {
2546 |           "output_type": "execute_result",
2547 |           "data": {
2548 |             "text/html": [
2549 |               "<div>\n",
2550 |               "<style scoped>\n",
2551 |               "    .dataframe tbody tr th:only-of-type {\n",
2552 |               "        vertical-align: middle;\n",
2553 |               "    }\n",
2554 |               "\n",
2555 |               "    .dataframe tbody tr th {\n",
2556 |               "        vertical-align: top;\n",
2557 |               "    }\n",
2558 |               "\n",
2559 |               "    .dataframe thead th {\n",
2560 |               "        text-align: right;\n",
2561 |               "    }\n",
2562 |               "</style>\n",
2563 |               "<table border=\"1\" class=\"dataframe\">\n",
2564 |               "  <thead>\n",
2565 |               "    <tr style=\"text-align: right;\">\n",
2566 |               "      <th></th>\n",
2567 |               "      <th>W</th>\n",
2568 |               "      <th>X</th>\n",
2569 |               "      <th>Y</th>\n",
2570 |               "      <th>Z</th>\n",
2571 |               "      <th>W_squared</th>\n",
2572 |               "      <th>new</th>\n",
2573 |               "    </tr>\n",
2574 |               "  </thead>\n",
2575 |               "  <tbody>\n",
2576 |               "    <tr>\n",
2577 |               "      <th>A</th>\n",
2578 |               "      <td>-0.709325</td>\n",
2579 |               "      <td>-0.398991</td>\n",
2580 |               "      <td>2.255707</td>\n",
2581 |               "      <td>1.286425</td>\n",
2582 |               "      <td>0.503142</td>\n",
2583 |               "      <td>1.856716</td>\n",
2584 |               "    </tr>\n",
2585 |               "    <tr>\n",
2586 |               "      <th>B</th>\n",
2587 |               "      <td>0.239717</td>\n",
2588 |               "      <td>-0.993057</td>\n",
2589 |               "      <td>-1.434999</td>\n",
2590 |               "      <td>-0.777527</td>\n",
2591 |               "      <td>0.057464</td>\n",
2592 |               "      <td>-2.428056</td>\n",
2593 |               "    </tr>\n",
2594 |               "  </tbody>\n",
2595 |               "</table>\n",
2596 |               "</div>"
2597 |             ],
2598 |             "text/plain": [
2599 |               "          W         X         Y         Z  W_squared       new\n",
2600 |               "A -0.709325 -0.398991  2.255707  1.286425   0.503142  1.856716\n",
2601 |               "B  0.239717 -0.993057 -1.434999 -0.777527   0.057464 -2.428056"
2602 |             ]
2603 |           },
2604 |           "metadata": {
2605 |             "tags": []
2606 |           },
2607 |           "execution_count": 81
2608 |         }
2609 |       ]
2610 |     },
2611 |     {
2612 |       "cell_type": "code",
2613 |       "metadata": {
2614 |         "id": "yZ__EWX09wh0"
2615 |       },
2616 |       "source": [
2617 |         "#Must Notice that Loc considers both ends \n",
2618 |         "#while iloc doesn't include the right extreme."
2619 |       ],
2620 |       "execution_count": 82,
2621 |       "outputs": []
2622 |     },
2623 |     {
2624 |       "cell_type": "markdown",
2625 |       "metadata": {
2626 |         "id": "9X5dNWZ0-Hu3"
2627 |       },
2628 |       "source": [
2629 |         "## Importing Data using Pandas/"
2630 |       ]
2631 |     },
2632 |     {
2633 |       "cell_type": "code",
2634 |       "metadata": {
2635 |         "id": "uzOSFwqhYjCS"
2636 |       },
2637 |       "source": [
2638 |         "A"
2639 |       ],
2640 |       "execution_count": null,
2641 |       "outputs": []
2642 |     },
2643 |     {
2644 |       "cell_type": "code",
2645 |       "metadata": {
2646 |         "colab": {
2647 |           "base_uri": "https://localhost:8080/",
2648 |           "height": 130
2649 |         },
2650 |         "id": "BqoPYsmc950C",
2651 |         "outputId": "ae50b48e-b4e0-4378-d94a-8ef85ab3666b"
2652 |       },
2653 |       "source": [
2654 |         "data = pd.read_csv('/content/sample_data/california_housing_train.csv')\n",
2655 |         "\n",
2656 |         "data.tail(2)"
2657 |       ],
2658 |       "execution_count": 132,
2659 |       "outputs": [
2660 |         {
2661 |           "output_type": "execute_result",
2662 |           "data": {
2663 |             "text/html": [
2664 |               "<div>\n",
2665 |               "<style scoped>\n",
2666 |               "    .dataframe tbody tr th:only-of-type {\n",
2667 |               "        vertical-align: middle;\n",
2668 |               "    }\n",
2669 |               "\n",
2670 |               "    .dataframe tbody tr th {\n",
2671 |               "        vertical-align: top;\n",
2672 |               "    }\n",
2673 |               "\n",
2674 |               "    .dataframe thead th {\n",
2675 |               "        text-align: right;\n",
2676 |               "    }\n",
2677 |               "</style>\n",
2678 |               "<table border=\"1\" class=\"dataframe\">\n",
2679 |               "  <thead>\n",
2680 |               "    <tr style=\"text-align: right;\">\n",
2681 |               "      <th></th>\n",
2682 |               "      <th>longitude</th>\n",
2683 |               "      <th>latitude</th>\n",
2684 |               "      <th>housing_median_age</th>\n",
2685 |               "      <th>total_rooms</th>\n",
2686 |               "      <th>total_bedrooms</th>\n",
2687 |               "      <th>population</th>\n",
2688 |               "      <th>households</th>\n",
2689 |               "      <th>median_income</th>\n",
2690 |               "      <th>median_house_value</th>\n",
2691 |               "    </tr>\n",
2692 |               "  </thead>\n",
2693 |               "  <tbody>\n",
2694 |               "    <tr>\n",
2695 |               "      <th>16998</th>\n",
2696 |               "      <td>-124.30</td>\n",
2697 |               "      <td>41.80</td>\n",
2698 |               "      <td>19.0</td>\n",
2699 |               "      <td>2672.0</td>\n",
2700 |               "      <td>552.0</td>\n",
2701 |               "      <td>1298.0</td>\n",
2702 |               "      <td>478.0</td>\n",
2703 |               "      <td>1.9797</td>\n",
2704 |               "      <td>85800.0</td>\n",
2705 |               "    </tr>\n",
2706 |               "    <tr>\n",
2707 |               "      <th>16999</th>\n",
2708 |               "      <td>-124.35</td>\n",
2709 |               "      <td>40.54</td>\n",
2710 |               "      <td>52.0</td>\n",
2711 |               "      <td>1820.0</td>\n",
2712 |               "      <td>300.0</td>\n",
2713 |               "      <td>806.0</td>\n",
2714 |               "      <td>270.0</td>\n",
2715 |               "      <td>3.0147</td>\n",
2716 |               "      <td>94600.0</td>\n",
2717 |               "    </tr>\n",
2718 |               "  </tbody>\n",
2719 |               "</table>\n",
2720 |               "</div>"
2721 |             ],
2722 |             "text/plain": [
2723 |               "       longitude  latitude  ...  median_income  median_house_value\n",
2724 |               "16998    -124.30     41.80  ...         1.9797             85800.0\n",
2725 |               "16999    -124.35     40.54  ...         3.0147             94600.0\n",
2726 |               "\n",
2727 |               "[2 rows x 9 columns]"
2728 |             ]
2729 |           },
2730 |           "metadata": {
2731 |             "tags": []
2732 |           },
2733 |           "execution_count": 132
2734 |         }
2735 |       ]
2736 |     },
2737 |     {
2738 |       "cell_type": "code",
2739 |       "metadata": {
2740 |         "colab": {
2741 |           "base_uri": "https://localhost:8080/",
2742 |           "height": 316
2743 |         },
2744 |         "id": "mNVcA8J6-Z7Z",
2745 |         "outputId": "03536a14-9dce-4baa-f28d-23583ef12984"
2746 |       },
2747 |       "source": [
2748 |         "data.describe()"
2749 |       ],
2750 |       "execution_count": 133,
2751 |       "outputs": [
2752 |         {
2753 |           "output_type": "execute_result",
2754 |           "data": {
2755 |             "text/html": [
2756 |               "<div>\n",
2757 |               "<style scoped>\n",
2758 |               "    .dataframe tbody tr th:only-of-type {\n",
2759 |               "        vertical-align: middle;\n",
2760 |               "    }\n",
2761 |               "\n",
2762 |               "    .dataframe tbody tr th {\n",
2763 |               "        vertical-align: top;\n",
2764 |               "    }\n",
2765 |               "\n",
2766 |               "    .dataframe thead th {\n",
2767 |               "        text-align: right;\n",
2768 |               "    }\n",
2769 |               "</style>\n",
2770 |               "<table border=\"1\" class=\"dataframe\">\n",
2771 |               "  <thead>\n",
2772 |               "    <tr style=\"text-align: right;\">\n",
2773 |               "      <th></th>\n",
2774 |               "      <th>longitude</th>\n",
2775 |               "      <th>latitude</th>\n",
2776 |               "      <th>housing_median_age</th>\n",
2777 |               "      <th>total_rooms</th>\n",
2778 |               "      <th>total_bedrooms</th>\n",
2779 |               "      <th>population</th>\n",
2780 |               "      <th>households</th>\n",
2781 |               "      <th>median_income</th>\n",
2782 |               "      <th>median_house_value</th>\n",
2783 |               "    </tr>\n",
2784 |               "  </thead>\n",
2785 |               "  <tbody>\n",
2786 |               "    <tr>\n",
2787 |               "      <th>count</th>\n",
2788 |               "      <td>17000.000000</td>\n",
2789 |               "      <td>17000.000000</td>\n",
2790 |               "      <td>17000.000000</td>\n",
2791 |               "      <td>17000.000000</td>\n",
2792 |               "      <td>17000.000000</td>\n",
2793 |               "      <td>17000.000000</td>\n",
2794 |               "      <td>17000.000000</td>\n",
2795 |               "      <td>17000.000000</td>\n",
2796 |               "      <td>17000.000000</td>\n",
2797 |               "    </tr>\n",
2798 |               "    <tr>\n",
2799 |               "      <th>mean</th>\n",
2800 |               "      <td>-119.562108</td>\n",
2801 |               "      <td>35.625225</td>\n",
2802 |               "      <td>28.589353</td>\n",
2803 |               "      <td>2643.664412</td>\n",
2804 |               "      <td>539.410824</td>\n",
2805 |               "      <td>1429.573941</td>\n",
2806 |               "      <td>501.221941</td>\n",
2807 |               "      <td>3.883578</td>\n",
2808 |               "      <td>207300.912353</td>\n",
2809 |               "    </tr>\n",
2810 |               "    <tr>\n",
2811 |               "      <th>std</th>\n",
2812 |               "      <td>2.005166</td>\n",
2813 |               "      <td>2.137340</td>\n",
2814 |               "      <td>12.586937</td>\n",
2815 |               "      <td>2179.947071</td>\n",
2816 |               "      <td>421.499452</td>\n",
2817 |               "      <td>1147.852959</td>\n",
2818 |               "      <td>384.520841</td>\n",
2819 |               "      <td>1.908157</td>\n",
2820 |               "      <td>115983.764387</td>\n",
2821 |               "    </tr>\n",
2822 |               "    <tr>\n",
2823 |               "      <th>min</th>\n",
2824 |               "      <td>-124.350000</td>\n",
2825 |               "      <td>32.540000</td>\n",
2826 |               "      <td>1.000000</td>\n",
2827 |               "      <td>2.000000</td>\n",
2828 |               "      <td>1.000000</td>\n",
2829 |               "      <td>3.000000</td>\n",
2830 |               "      <td>1.000000</td>\n",
2831 |               "      <td>0.499900</td>\n",
2832 |               "      <td>14999.000000</td>\n",
2833 |               "    </tr>\n",
2834 |               "    <tr>\n",
2835 |               "      <th>25%</th>\n",
2836 |               "      <td>-121.790000</td>\n",
2837 |               "      <td>33.930000</td>\n",
2838 |               "      <td>18.000000</td>\n",
2839 |               "      <td>1462.000000</td>\n",
2840 |               "      <td>297.000000</td>\n",
2841 |               "      <td>790.000000</td>\n",
2842 |               "      <td>282.000000</td>\n",
2843 |               "      <td>2.566375</td>\n",
2844 |               "      <td>119400.000000</td>\n",
2845 |               "    </tr>\n",
2846 |               "    <tr>\n",
2847 |               "      <th>50%</th>\n",
2848 |               "      <td>-118.490000</td>\n",
2849 |               "      <td>34.250000</td>\n",
2850 |               "      <td>29.000000</td>\n",
2851 |               "      <td>2127.000000</td>\n",
2852 |               "      <td>434.000000</td>\n",
2853 |               "      <td>1167.000000</td>\n",
2854 |               "      <td>409.000000</td>\n",
2855 |               "      <td>3.544600</td>\n",
2856 |               "      <td>180400.000000</td>\n",
2857 |               "    </tr>\n",
2858 |               "    <tr>\n",
2859 |               "      <th>75%</th>\n",
2860 |               "      <td>-118.000000</td>\n",
2861 |               "      <td>37.720000</td>\n",
2862 |               "      <td>37.000000</td>\n",
2863 |               "      <td>3151.250000</td>\n",
2864 |               "      <td>648.250000</td>\n",
2865 |               "      <td>1721.000000</td>\n",
2866 |               "      <td>605.250000</td>\n",
2867 |               "      <td>4.767000</td>\n",
2868 |               "      <td>265000.000000</td>\n",
2869 |               "    </tr>\n",
2870 |               "    <tr>\n",
2871 |               "      <th>max</th>\n",
2872 |               "      <td>-114.310000</td>\n",
2873 |               "      <td>41.950000</td>\n",
2874 |               "      <td>52.000000</td>\n",
2875 |               "      <td>37937.000000</td>\n",
2876 |               "      <td>6445.000000</td>\n",
2877 |               "      <td>35682.000000</td>\n",
2878 |               "      <td>6082.000000</td>\n",
2879 |               "      <td>15.000100</td>\n",
2880 |               "      <td>500001.000000</td>\n",
2881 |               "    </tr>\n",
2882 |               "  </tbody>\n",
2883 |               "</table>\n",
2884 |               "</div>"
2885 |             ],
2886 |             "text/plain": [
2887 |               "          longitude      latitude  ...  median_income  median_house_value\n",
2888 |               "count  17000.000000  17000.000000  ...   17000.000000        17000.000000\n",
2889 |               "mean    -119.562108     35.625225  ...       3.883578       207300.912353\n",
2890 |               "std        2.005166      2.137340  ...       1.908157       115983.764387\n",
2891 |               "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
2892 |               "25%     -121.790000     33.930000  ...       2.566375       119400.000000\n",
2893 |               "50%     -118.490000     34.250000  ...       3.544600       180400.000000\n",
2894 |               "75%     -118.000000     37.720000  ...       4.767000       265000.000000\n",
2895 |               "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
2896 |               "\n",
2897 |               "[8 rows x 9 columns]"
2898 |             ]
2899 |           },
2900 |           "metadata": {
2901 |             "tags": []
2902 |           },
2903 |           "execution_count": 133
2904 |         }
2905 |       ]
2906 |     },
2907 |     {
2908 |       "cell_type": "code",
2909 |       "metadata": {
2910 |         "id": "HhJcnDKIX-qN"
2911 |       },
2912 |       "source": [
2913 |         "hma50 = np.percentile(data['housing_median_age'], 50)"
2914 |       ],
2915 |       "execution_count": 135,
2916 |       "outputs": []
2917 |     },
2918 |     {
2919 |       "cell_type": "code",
2920 |       "metadata": {
2921 |         "id": "r1r90DyRZ4oR",
2922 |         "outputId": "4f8bf24e-6c97-469a-a3a8-4e0c093df994",
2923 |         "colab": {
2924 |           "base_uri": "https://localhost:8080/"
2925 |         }
2926 |       },
2927 |       "source": [
2928 |         "data[data['housing_median_age']>hma50]['housing_median_age']"
2929 |       ],
2930 |       "execution_count": 138,
2931 |       "outputs": [
2932 |         {
2933 |           "output_type": "execute_result",
2934 |           "data": {
2935 |             "text/plain": [
2936 |               "7        41.0\n",
2937 |               "8        34.0\n",
2938 |               "9        46.0\n",
2939 |               "12       48.0\n",
2940 |               "13       31.0\n",
2941 |               "         ... \n",
2942 |               "16993    52.0\n",
2943 |               "16994    32.0\n",
2944 |               "16995    52.0\n",
2945 |               "16996    36.0\n",
2946 |               "16999    52.0\n",
2947 |               "Name: housing_median_age, Length: 8189, dtype: float64"
2948 |             ]
2949 |           },
2950 |           "metadata": {
2951 |             "tags": []
2952 |           },
2953 |           "execution_count": 138
2954 |         }
2955 |       ]
2956 |     },
2957 |     {
2958 |       "cell_type": "code",
2959 |       "metadata": {
2960 |         "id": "P3YlDPo_aBXx"
2961 |       },
2962 |       "source": [
2963 |         ""
2964 |       ],
2965 |       "execution_count": null,
2966 |       "outputs": []
2967 |     }
2968 |   ]
2969 | }


--------------------------------------------------------------------------------
/Pt.csv:
--------------------------------------------------------------------------------
 1 | time,pressure
 2 | 0.001,2743.96
 3 | 0.0021,2737.52
 4 | 0.0034,2730.16
 5 | 0.0048,2722.32
 6 | 0.0064,2713.83
 7 | 0.0082,2704.46
 8 | 0.0103,2694.23
 9 | 0.0125,2683.03
10 | 0.0151,2671.26
11 | 0.018,2658.36
12 | 0.0212,2644.71
13 | 0.0249,2630.14
14 | 0.029,2614.84
15 | 0.0336,2598.72
16 | 0.0388,2582.14
17 | 0.0447,2564.96
18 | 0.0513,2547.42
19 | 0.0587,2529.89
20 | 0.067,2512.19
21 | 0.0764,2494.89
22 | 0.0869,2478.16
23 | 0.0988,2462.14
24 | 0.1121,2446.97
25 | 0.1271,2432.97
26 | 0.144,2420.21
27 | 0.163,2409.04
28 | 0.1844,2398.75
29 | 0.209,2390.2
30 | 0.236,2382.79
31 | 0.266,2376.35
32 | 0.3,2370.96
33 | 0.339,2366.46
34 | 0.382,2362.35
35 | 0.431,2358.74
36 | 0.486,2355.31
37 | 0.547,2352.5
38 | 0.617,2349.73
39 | 0.695,2346.98
40 | 0.783,2344.54
41 | 0.882,2341.74
42 | 0.993,2339.53
43 | 1.118,2337.28
44 | 1.259,2334.67
45 | 1.417,2332.55
46 | 1.595,2330.48
47 | 1.795,2328.15
48 | 2.021,2325.9
49 | 2.275,2323.66
50 | 2.56,2321.42
51 | 2.881,2319.35
52 | 3.242,2317.33
53 | 3.648,2315.03
54 | 4.105,2313.02
55 | 4.619,2311.06
56 | 5.198,2308.95
57 | 5.848,2306.79
58 | 6.58,2304.87
59 | 7.404,2302.32
60 | 8.331,2300.59
61 | 9.373,2298.46
62 | 10.55,2296.49
63 | 11.86,2294.32
64 | 13.35,2292.28
65 | 15.02,2290.17
66 | 16.9,2288.11
67 | 19.01,2286.06
68 | 21.39,2283.99
69 | 24.06,2281.84
70 | 27.07,2279.76
71 | 30.45,2277.95
72 | 34.26,2275.56
73 | 38.55,2273.78
74 | 43.37,2271.54
75 | 48.79,2269.77
76 | 54.79,2267.69
77 | 60.79,2265.73
78 | 66.79,2264.46
79 | 72,2262.99
80 | 


--------------------------------------------------------------------------------
/Python_Crash_Course.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "Python Crash Course.ipynb",
   7 |       "provenance": [],
   8 |       "authorship_tag": "ABX9TyOs5lI5J16gWJ6k2t4rcgLc"
   9 |     },
  10 |     "kernelspec": {
  11 |       "name": "python3",
  12 |       "display_name": "Python 3"
  13 |     },
  14 |     "language_info": {
  15 |       "name": "python"
  16 |     }
  17 |   },
  18 |   "cells": [
  19 |     {
  20 |       "cell_type": "markdown",
  21 |       "metadata": {
  22 |         "id": "0sJXME0OwKuk"
  23 |       },
  24 |       "source": [
  25 |         "# Python for Petroleum Engineers. "
  26 |       ]
  27 |     },
  28 |     {
  29 |       "cell_type": "markdown",
  30 |       "metadata": {
  31 |         "id": "YgdmKzaOyl-U"
  32 |       },
  33 |       "source": [
  34 |         "> ## Divyanshu Vyas\n",
  35 |         "Machine Learning Engineer (Oil & Gas) @ DICELYTICS | Founder, Petroleum From Scratch. \n",
  36 |         "\n",
  37 |         "> Motivations : Kendrick Lamar | J'Cole | Eminem"
  38 |       ]
  39 |     },
  40 |     {
  41 |       "cell_type": "markdown",
  42 |       "metadata": {
  43 |         "id": "GsQ1gHHR0daB"
  44 |       },
  45 |       "source": [
  46 |         "## Why Python Though? \n",
  47 |         "\n",
  48 |         "1. Technical Edge and a smart one at that : Simulations | Automations | Quicker Problem Solving Mindset | Enhanced & Efficient Workflows.\n",
  49 |         "\n",
  50 |         "2. Oil & Gas Industry is very new to the Data Science and Machine Learning applications. \n",
  51 |         "\n",
  52 |         "3. Future of the Industry is hugely Data-Centric. \n",
  53 |         "\n",
  54 |         "4. Lot of Startups coming up that are solely based on DS/ML apps for bigger companies. They pay well. \n",
  55 |         "\n",
  56 |         "5. For someone who plans to diversify and smoothly transition to another industry/field - this is a very smart move. You can move into any domain. \n",
  57 |         "\n",
  58 |         "No. of Opportunities shoot up significantly!"
  59 |       ]
  60 |     },
  61 |     {
  62 |       "cell_type": "markdown",
  63 |       "metadata": {
  64 |         "id": "6GjhG5YT1VIo"
  65 |       },
  66 |       "source": [
  67 |         "In this Course I'll cover the steps that helped me efficiently transition from Core PE to a Data Analytics PE. "
  68 |       ]
  69 |     },
  70 |     {
  71 |       "cell_type": "code",
  72 |       "metadata": {
  73 |         "id": "XsKq8wqFwGiO"
  74 |       },
  75 |       "source": [
  76 |         "#######################################################################################################"
  77 |       ],
  78 |       "execution_count": 2,
  79 |       "outputs": []
  80 |     },
  81 |     {
  82 |       "cell_type": "markdown",
  83 |       "metadata": {
  84 |         "id": "9LKlBCZk227t"
  85 |       },
  86 |       "source": [
  87 |         "#Python Crash Course \n",
  88 |         "\n"
  89 |       ]
  90 |     },
  91 |     {
  92 |       "cell_type": "markdown",
  93 |       "metadata": {
  94 |         "id": "4vaszy7LYIS-"
  95 |       },
  96 |       "source": [
  97 |         "# Chapter 1 : Data Types."
  98 |       ]
  99 |     },
 100 |     {
 101 |       "cell_type": "markdown",
 102 |       "metadata": {
 103 |         "id": "nQsmrZO53CRU"
 104 |       },
 105 |       "source": [
 106 |         "## 1. Numbers & Mathematical Operators."
 107 |       ]
 108 |     },
 109 |     {
 110 |       "cell_type": "code",
 111 |       "metadata": {
 112 |         "colab": {
 113 |           "base_uri": "https://localhost:8080/"
 114 |         },
 115 |         "id": "2XCPgpPM210-",
 116 |         "outputId": "55eaaf55-b0cd-4eda-f288-28924373a2a1"
 117 |       },
 118 |       "source": [
 119 |         "1 + 1 "
 120 |       ],
 121 |       "execution_count": 5,
 122 |       "outputs": [
 123 |         {
 124 |           "output_type": "execute_result",
 125 |           "data": {
 126 |             "text/plain": [
 127 |               "2"
 128 |             ]
 129 |           },
 130 |           "metadata": {
 131 |             "tags": []
 132 |           },
 133 |           "execution_count": 5
 134 |         }
 135 |       ]
 136 |     },
 137 |     {
 138 |       "cell_type": "code",
 139 |       "metadata": {
 140 |         "colab": {
 141 |           "base_uri": "https://localhost:8080/"
 142 |         },
 143 |         "id": "CM0YuMF92LcI",
 144 |         "outputId": "5fb258e5-dff8-4dc9-a718-ca2b7e595e1c"
 145 |       },
 146 |       "source": [
 147 |         "1 - 1"
 148 |       ],
 149 |       "execution_count": 6,
 150 |       "outputs": [
 151 |         {
 152 |           "output_type": "execute_result",
 153 |           "data": {
 154 |             "text/plain": [
 155 |               "0"
 156 |             ]
 157 |           },
 158 |           "metadata": {
 159 |             "tags": []
 160 |           },
 161 |           "execution_count": 6
 162 |         }
 163 |       ]
 164 |     },
 165 |     {
 166 |       "cell_type": "code",
 167 |       "metadata": {
 168 |         "colab": {
 169 |           "base_uri": "https://localhost:8080/"
 170 |         },
 171 |         "id": "YdLOg-n23Qci",
 172 |         "outputId": "fa777df4-c1bd-4a99-c154-9ae22010d37b"
 173 |       },
 174 |       "source": [
 175 |         "1/2"
 176 |       ],
 177 |       "execution_count": 7,
 178 |       "outputs": [
 179 |         {
 180 |           "output_type": "execute_result",
 181 |           "data": {
 182 |             "text/plain": [
 183 |               "0.5"
 184 |             ]
 185 |           },
 186 |           "metadata": {
 187 |             "tags": []
 188 |           },
 189 |           "execution_count": 7
 190 |         }
 191 |       ]
 192 |     },
 193 |     {
 194 |       "cell_type": "code",
 195 |       "metadata": {
 196 |         "colab": {
 197 |           "base_uri": "https://localhost:8080/"
 198 |         },
 199 |         "id": "iQgLWbq03RtH",
 200 |         "outputId": "c04bb7f8-f607-45b3-e1e7-b04b6ab7ee59"
 201 |       },
 202 |       "source": [
 203 |         "70.6*2.303"
 204 |       ],
 205 |       "execution_count": 8,
 206 |       "outputs": [
 207 |         {
 208 |           "output_type": "execute_result",
 209 |           "data": {
 210 |             "text/plain": [
 211 |               "162.59179999999998"
 212 |             ]
 213 |           },
 214 |           "metadata": {
 215 |             "tags": []
 216 |           },
 217 |           "execution_count": 8
 218 |         }
 219 |       ]
 220 |     },
 221 |     {
 222 |       "cell_type": "code",
 223 |       "metadata": {
 224 |         "colab": {
 225 |           "base_uri": "https://localhost:8080/"
 226 |         },
 227 |         "id": "JX65x01C3ZnY",
 228 |         "outputId": "00033c6a-20c5-476e-fa65-c1a3baf9f62e"
 229 |       },
 230 |       "source": [
 231 |         "round(70.6*2.303, 2)"
 232 |       ],
 233 |       "execution_count": 9,
 234 |       "outputs": [
 235 |         {
 236 |           "output_type": "execute_result",
 237 |           "data": {
 238 |             "text/plain": [
 239 |               "162.59"
 240 |             ]
 241 |           },
 242 |           "metadata": {
 243 |             "tags": []
 244 |           },
 245 |           "execution_count": 9
 246 |         }
 247 |       ]
 248 |     },
 249 |     {
 250 |       "cell_type": "code",
 251 |       "metadata": {
 252 |         "colab": {
 253 |           "base_uri": "https://localhost:8080/"
 254 |         },
 255 |         "id": "_cMD3UV53fS3",
 256 |         "outputId": "374310fb-6caa-4d37-e8fe-514e539a6169"
 257 |       },
 258 |       "source": [
 259 |         "# 5^2 = 25\n",
 260 |         "5**2"
 261 |       ],
 262 |       "execution_count": 10,
 263 |       "outputs": [
 264 |         {
 265 |           "output_type": "execute_result",
 266 |           "data": {
 267 |             "text/plain": [
 268 |               "25"
 269 |             ]
 270 |           },
 271 |           "metadata": {
 272 |             "tags": []
 273 |           },
 274 |           "execution_count": 10
 275 |         }
 276 |       ]
 277 |     },
 278 |     {
 279 |       "cell_type": "code",
 280 |       "metadata": {
 281 |         "colab": {
 282 |           "base_uri": "https://localhost:8080/"
 283 |         },
 284 |         "id": "v-5hfmt63ke6",
 285 |         "outputId": "b1363fb0-6711-41b2-bc61-fdd239f2e869"
 286 |       },
 287 |       "source": [
 288 |         "# Finding the Remainder after Division. \n",
 289 |         "5 % 2"
 290 |       ],
 291 |       "execution_count": 12,
 292 |       "outputs": [
 293 |         {
 294 |           "output_type": "execute_result",
 295 |           "data": {
 296 |             "text/plain": [
 297 |               "1"
 298 |             ]
 299 |           },
 300 |           "metadata": {
 301 |             "tags": []
 302 |           },
 303 |           "execution_count": 12
 304 |         }
 305 |       ]
 306 |     },
 307 |     {
 308 |       "cell_type": "code",
 309 |       "metadata": {
 310 |         "colab": {
 311 |           "base_uri": "https://localhost:8080/"
 312 |         },
 313 |         "id": "_hMYwurF3q8Q",
 314 |         "outputId": "5a315eee-2ee4-4543-dbef-0f037787a625"
 315 |       },
 316 |       "source": [
 317 |         "4 % 2"
 318 |       ],
 319 |       "execution_count": 14,
 320 |       "outputs": [
 321 |         {
 322 |           "output_type": "execute_result",
 323 |           "data": {
 324 |             "text/plain": [
 325 |               "0"
 326 |             ]
 327 |           },
 328 |           "metadata": {
 329 |             "tags": []
 330 |           },
 331 |           "execution_count": 14
 332 |         }
 333 |       ]
 334 |     },
 335 |     {
 336 |       "cell_type": "code",
 337 |       "metadata": {
 338 |         "colab": {
 339 |           "base_uri": "https://localhost:8080/"
 340 |         },
 341 |         "id": "rlyK4Li033K0",
 342 |         "outputId": "6881a9b4-0325-42d7-9235-235f45df46c9"
 343 |       },
 344 |       "source": [
 345 |         "## Floor Division : Returns the Quotient after division. \n",
 346 |         "5 // 2"
 347 |       ],
 348 |       "execution_count": 16,
 349 |       "outputs": [
 350 |         {
 351 |           "output_type": "execute_result",
 352 |           "data": {
 353 |             "text/plain": [
 354 |               "2"
 355 |             ]
 356 |           },
 357 |           "metadata": {
 358 |             "tags": []
 359 |           },
 360 |           "execution_count": 16
 361 |         }
 362 |       ]
 363 |     },
 364 |     {
 365 |       "cell_type": "markdown",
 366 |       "metadata": {
 367 |         "id": "MoakU5jt41Ar"
 368 |       },
 369 |       "source": [
 370 |         "A // B -- Gives you the Quotient. \n",
 371 |         "\n",
 372 |         "A % B -- Gives you the Remainder. "
 373 |       ]
 374 |     },
 375 |     {
 376 |       "cell_type": "code",
 377 |       "metadata": {
 378 |         "colab": {
 379 |           "base_uri": "https://localhost:8080/"
 380 |         },
 381 |         "id": "HQoOxr6K33Im",
 382 |         "outputId": "62541a09-2291-4ee2-bc12-f84350421808"
 383 |       },
 384 |       "source": [
 385 |         "# Hence,\n",
 386 |         "\n",
 387 |         "# 5 = 2 * (5 // 2) + 5 % 2\n",
 388 |         "\n",
 389 |         "2 * (5 // 2) + 5 % 2"
 390 |       ],
 391 |       "execution_count": 17,
 392 |       "outputs": [
 393 |         {
 394 |           "output_type": "execute_result",
 395 |           "data": {
 396 |             "text/plain": [
 397 |               "5"
 398 |             ]
 399 |           },
 400 |           "metadata": {
 401 |             "tags": []
 402 |           },
 403 |           "execution_count": 17
 404 |         }
 405 |       ]
 406 |     },
 407 |     {
 408 |       "cell_type": "markdown",
 409 |       "metadata": {
 410 |         "id": "Rhq0NDFH5DYX"
 411 |       },
 412 |       "source": [
 413 |         ">### Subtopic : Variable Assignment"
 414 |       ]
 415 |     },
 416 |     {
 417 |       "cell_type": "code",
 418 |       "metadata": {
 419 |         "id": "GaA0nAIy33F7"
 420 |       },
 421 |       "source": [
 422 |         "# Suppose reservoir A has porosity 25 % and Reservoir B has 30%. \n",
 423 |         "\n",
 424 |         "phi_A = 0.25 \n",
 425 |         "phi_B = 0.35"
 426 |       ],
 427 |       "execution_count": 18,
 428 |       "outputs": []
 429 |     },
 430 |     {
 431 |       "cell_type": "code",
 432 |       "metadata": {
 433 |         "colab": {
 434 |           "base_uri": "https://localhost:8080/"
 435 |         },
 436 |         "id": "GDV_Yalz33DG",
 437 |         "outputId": "99dc401d-369a-499a-a407-39fae5988fa2"
 438 |       },
 439 |       "source": [
 440 |         "phi_A , phi_B"
 441 |       ],
 442 |       "execution_count": 21,
 443 |       "outputs": [
 444 |         {
 445 |           "output_type": "execute_result",
 446 |           "data": {
 447 |             "text/plain": [
 448 |               "(0.25, 0.35)"
 449 |             ]
 450 |           },
 451 |           "metadata": {
 452 |             "tags": []
 453 |           },
 454 |           "execution_count": 21
 455 |         }
 456 |       ]
 457 |     },
 458 |     {
 459 |       "cell_type": "markdown",
 460 |       "metadata": {
 461 |         "id": "vmNdD7Cy6SqU"
 462 |       },
 463 |       "source": [
 464 |         ">> #### Variable Name Rules. \n",
 465 |         "\n",
 466 |         "1. 1var : Invalid\n",
 467 |         "2. var1 : Valid\n",
 468 |         "\n",
 469 |         "3. Only special character allowed = Underscore _\n",
 470 |         "\n",
 471 |         "4. is Case Sensitive. A & a are different. "
 472 |       ]
 473 |     },
 474 |     {
 475 |       "cell_type": "code",
 476 |       "metadata": {
 477 |         "colab": {
 478 |           "base_uri": "https://localhost:8080/",
 479 |           "height": 132
 480 |         },
 481 |         "id": "Np4GNtM233AN",
 482 |         "outputId": "f112b0ba-4cda-4020-f8ea-e897a41c1028"
 483 |       },
 484 |       "source": [
 485 |         "dx&dt = 5"
 486 |       ],
 487 |       "execution_count": 22,
 488 |       "outputs": [
 489 |         {
 490 |           "output_type": "error",
 491 |           "ename": "SyntaxError",
 492 |           "evalue": "ignored",
 493 |           "traceback": [
 494 |             "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-22-fee09c0b3e60>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    dx&dt = 5\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
 495 |           ]
 496 |         }
 497 |       ]
 498 |     },
 499 |     {
 500 |       "cell_type": "code",
 501 |       "metadata": {
 502 |         "colab": {
 503 |           "base_uri": "https://localhost:8080/",
 504 |           "height": 132
 505 |         },
 506 |         "id": "IjBf9B616q_C",
 507 |         "outputId": "a663c780-ab40-4356-af4a-1e5398a7539e"
 508 |       },
 509 |       "source": [
 510 |         "# phi1 = 3\n",
 511 |         "\n",
 512 |         "1phi = 2"
 513 |       ],
 514 |       "execution_count": 27,
 515 |       "outputs": [
 516 |         {
 517 |           "output_type": "error",
 518 |           "ename": "SyntaxError",
 519 |           "evalue": "ignored",
 520 |           "traceback": [
 521 |             "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-27-8e8db5e216ea>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    1phi = 2\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
 522 |           ]
 523 |         }
 524 |       ]
 525 |     },
 526 |     {
 527 |       "cell_type": "markdown",
 528 |       "metadata": {
 529 |         "id": "fsWFD-TL309c"
 530 |       },
 531 |       "source": [
 532 |         "## 2. Strings"
 533 |       ]
 534 |     },
 535 |     {
 536 |       "cell_type": "code",
 537 |       "metadata": {
 538 |         "colab": {
 539 |           "base_uri": "https://localhost:8080/",
 540 |           "height": 34
 541 |         },
 542 |         "id": "ozV0gHUv3vpd",
 543 |         "outputId": "1d2593ca-bec9-4979-bf7a-1a553ffc0bf0"
 544 |       },
 545 |       "source": [
 546 |         "'single quotes are allowed'"
 547 |       ],
 548 |       "execution_count": 28,
 549 |       "outputs": [
 550 |         {
 551 |           "output_type": "execute_result",
 552 |           "data": {
 553 |             "application/vnd.google.colaboratory.intrinsic+json": {
 554 |               "type": "string"
 555 |             },
 556 |             "text/plain": [
 557 |               "'single quotes are allowed'"
 558 |             ]
 559 |           },
 560 |           "metadata": {
 561 |             "tags": []
 562 |           },
 563 |           "execution_count": 28
 564 |         }
 565 |       ]
 566 |     },
 567 |     {
 568 |       "cell_type": "code",
 569 |       "metadata": {
 570 |         "colab": {
 571 |           "base_uri": "https://localhost:8080/",
 572 |           "height": 34
 573 |         },
 574 |         "id": "TCoJzNJt7CHG",
 575 |         "outputId": "15100eda-da8d-4191-a62b-89210ba113e4"
 576 |       },
 577 |       "source": [
 578 |         "\"Double Quotes are also allowed\""
 579 |       ],
 580 |       "execution_count": 29,
 581 |       "outputs": [
 582 |         {
 583 |           "output_type": "execute_result",
 584 |           "data": {
 585 |             "application/vnd.google.colaboratory.intrinsic+json": {
 586 |               "type": "string"
 587 |             },
 588 |             "text/plain": [
 589 |               "'Double Quotes are also allowed'"
 590 |             ]
 591 |           },
 592 |           "metadata": {
 593 |             "tags": []
 594 |           },
 595 |           "execution_count": 29
 596 |         }
 597 |       ]
 598 |     },
 599 |     {
 600 |       "cell_type": "markdown",
 601 |       "metadata": {
 602 |         "id": "GLC50bmU7jNB"
 603 |       },
 604 |       "source": [
 605 |         ">> #### Printing."
 606 |       ]
 607 |     },
 608 |     {
 609 |       "cell_type": "code",
 610 |       "metadata": {
 611 |         "colab": {
 612 |           "base_uri": "https://localhost:8080/"
 613 |         },
 614 |         "id": "WNN6pHeP7S-L",
 615 |         "outputId": "2a939e7e-c82a-47e6-efee-9670207e5a7f"
 616 |       },
 617 |       "source": [
 618 |         "print('This is Line1 \\nThis is Line2')"
 619 |       ],
 620 |       "execution_count": 35,
 621 |       "outputs": [
 622 |         {
 623 |           "output_type": "stream",
 624 |           "text": [
 625 |             "This is Line1 \n",
 626 |             "This is Line2\n"
 627 |           ],
 628 |           "name": "stdout"
 629 |         }
 630 |       ]
 631 |     },
 632 |     {
 633 |       "cell_type": "code",
 634 |       "metadata": {
 635 |         "colab": {
 636 |           "base_uri": "https://localhost:8080/"
 637 |         },
 638 |         "id": "hZ4ta1N47XSJ",
 639 |         "outputId": "40d8bb9d-deb8-4ef8-b3c1-aeccc135d009"
 640 |       },
 641 |       "source": [
 642 |         "name = 'Sandstone'\n",
 643 |         "porosity = 25 #percent. \n",
 644 |         "\n",
 645 |         "print('The rock type is name and the rock porosity is porosity')"
 646 |       ],
 647 |       "execution_count": 36,
 648 |       "outputs": [
 649 |         {
 650 |           "output_type": "stream",
 651 |           "text": [
 652 |             "The rock type is name and the rock porosity is porosity\n"
 653 |           ],
 654 |           "name": "stdout"
 655 |         }
 656 |       ]
 657 |     },
 658 |     {
 659 |       "cell_type": "markdown",
 660 |       "metadata": {
 661 |         "id": "YfckAzYv8DgI"
 662 |       },
 663 |       "source": [
 664 |         "##### f-Strings."
 665 |       ]
 666 |     },
 667 |     {
 668 |       "cell_type": "code",
 669 |       "metadata": {
 670 |         "colab": {
 671 |           "base_uri": "https://localhost:8080/"
 672 |         },
 673 |         "id": "_oNywnYY720j",
 674 |         "outputId": "7aa17db2-a3be-408b-d437-4a5b71e50f98"
 675 |       },
 676 |       "source": [
 677 |         "print(f'The rock type is {name} and the rock porosity is {porosity} %')"
 678 |       ],
 679 |       "execution_count": 38,
 680 |       "outputs": [
 681 |         {
 682 |           "output_type": "stream",
 683 |           "text": [
 684 |             "The rock type is Sandstone and the rock porosity is 25 %\n"
 685 |           ],
 686 |           "name": "stdout"
 687 |         }
 688 |       ]
 689 |     },
 690 |     {
 691 |       "cell_type": "markdown",
 692 |       "metadata": {
 693 |         "id": "VUWMIn2Z9VY2"
 694 |       },
 695 |       "source": [
 696 |         "Strings are Immutable/Cannot be modified."
 697 |       ]
 698 |     },
 699 |     {
 700 |       "cell_type": "code",
 701 |       "metadata": {
 702 |         "colab": {
 703 |           "base_uri": "https://localhost:8080/",
 704 |           "height": 166
 705 |         },
 706 |         "id": "46TODMw89PoH",
 707 |         "outputId": "c542efe8-59b9-4a73-907d-b10b82cc943f"
 708 |       },
 709 |       "source": [
 710 |         "name[4] = 'r'"
 711 |       ],
 712 |       "execution_count": 41,
 713 |       "outputs": [
 714 |         {
 715 |           "output_type": "error",
 716 |           "ename": "TypeError",
 717 |           "evalue": "ignored",
 718 |           "traceback": [
 719 |             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
 720 |             "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
 721 |             "\u001b[0;32m<ipython-input-41-e815363d64e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
 722 |             "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
 723 |           ]
 724 |         }
 725 |       ]
 726 |     },
 727 |     {
 728 |       "cell_type": "markdown",
 729 |       "metadata": {
 730 |         "id": "PKLi6ji19D35"
 731 |       },
 732 |       "source": [
 733 |         "## 3. Lists"
 734 |       ]
 735 |     },
 736 |     {
 737 |       "cell_type": "markdown",
 738 |       "metadata": {
 739 |         "id": "qv7DoNSgIafX"
 740 |       },
 741 |       "source": [
 742 |         "Number of objects stored in square brackets [ ]. \n",
 743 |         "\n",
 744 |         "Lists are Modify-able."
 745 |       ]
 746 |     },
 747 |     {
 748 |       "cell_type": "code",
 749 |       "metadata": {
 750 |         "colab": {
 751 |           "base_uri": "https://localhost:8080/"
 752 |         },
 753 |         "id": "guHq9kNm8X2J",
 754 |         "outputId": "1aabde45-4c0e-441e-97d1-25b8fc672c94"
 755 |       },
 756 |       "source": [
 757 |         "#list of integer type numbers. \n",
 758 |         "[1,2,3]"
 759 |       ],
 760 |       "execution_count": 43,
 761 |       "outputs": [
 762 |         {
 763 |           "output_type": "execute_result",
 764 |           "data": {
 765 |             "text/plain": [
 766 |               "[1, 2, 3]"
 767 |             ]
 768 |           },
 769 |           "metadata": {
 770 |             "tags": []
 771 |           },
 772 |           "execution_count": 43
 773 |         }
 774 |       ]
 775 |     },
 776 |     {
 777 |       "cell_type": "code",
 778 |       "metadata": {
 779 |         "colab": {
 780 |           "base_uri": "https://localhost:8080/"
 781 |         },
 782 |         "id": "rVjO_qveBc_0",
 783 |         "outputId": "344791bf-6a8b-40c9-ccae-30f8304ea0dc"
 784 |       },
 785 |       "source": [
 786 |         "#list of floating point numbers. \n",
 787 |         "[3.14 , 2.73, 0.87, 162.6]"
 788 |       ],
 789 |       "execution_count": 44,
 790 |       "outputs": [
 791 |         {
 792 |           "output_type": "execute_result",
 793 |           "data": {
 794 |             "text/plain": [
 795 |               "[3.14, 2.73, 0.87, 162.6]"
 796 |             ]
 797 |           },
 798 |           "metadata": {
 799 |             "tags": []
 800 |           },
 801 |           "execution_count": 44
 802 |         }
 803 |       ]
 804 |     },
 805 |     {
 806 |       "cell_type": "code",
 807 |       "metadata": {
 808 |         "colab": {
 809 |           "base_uri": "https://localhost:8080/"
 810 |         },
 811 |         "id": "M_o9LAUPBrTy",
 812 |         "outputId": "0236edcb-87b4-4ef2-f964-9cd5c6752dda"
 813 |       },
 814 |       "source": [
 815 |         "#mixed bag. \n",
 816 |         "['reservoir', 'drilling', 70.6, 141.2]"
 817 |       ],
 818 |       "execution_count": 45,
 819 |       "outputs": [
 820 |         {
 821 |           "output_type": "execute_result",
 822 |           "data": {
 823 |             "text/plain": [
 824 |               "['reservoir', 'drilling', 70.6, 141.2]"
 825 |             ]
 826 |           },
 827 |           "metadata": {
 828 |             "tags": []
 829 |           },
 830 |           "execution_count": 45
 831 |         }
 832 |       ]
 833 |     },
 834 |     {
 835 |       "cell_type": "code",
 836 |       "metadata": {
 837 |         "id": "aaCxBYw4Bz1G"
 838 |       },
 839 |       "source": [
 840 |         "#storing it into a variable. \n",
 841 |         "\n",
 842 |         "porosities = [0.2, 0.35, 0.4, 0.55]"
 843 |       ],
 844 |       "execution_count": 46,
 845 |       "outputs": []
 846 |     },
 847 |     {
 848 |       "cell_type": "markdown",
 849 |       "metadata": {
 850 |         "id": "hwiaLInLB_zM"
 851 |       },
 852 |       "source": [
 853 |         "#### Operations and Manipulations on Lists. "
 854 |       ]
 855 |     },
 856 |     {
 857 |       "cell_type": "code",
 858 |       "metadata": {
 859 |         "colab": {
 860 |           "base_uri": "https://localhost:8080/"
 861 |         },
 862 |         "id": "uAp8VYcoB6UP",
 863 |         "outputId": "d1f0fc1d-8d72-4452-ff7b-ccab11e2d7aa"
 864 |       },
 865 |       "source": [
 866 |         "# Joining 2 Lists \n",
 867 |         "[1,2,3] + ['a','b','c']"
 868 |       ],
 869 |       "execution_count": 48,
 870 |       "outputs": [
 871 |         {
 872 |           "output_type": "execute_result",
 873 |           "data": {
 874 |             "text/plain": [
 875 |               "[1, 2, 3, 'a', 'b', 'c']"
 876 |             ]
 877 |           },
 878 |           "metadata": {
 879 |             "tags": []
 880 |           },
 881 |           "execution_count": 48
 882 |         }
 883 |       ]
 884 |     },
 885 |     {
 886 |       "cell_type": "code",
 887 |       "metadata": {
 888 |         "id": "gOMXCFtgB7aM"
 889 |       },
 890 |       "source": [
 891 |         "#APPEND\n",
 892 |         "new_value = 0.40\n",
 893 |         "porosities.append(new_value)"
 894 |       ],
 895 |       "execution_count": 50,
 896 |       "outputs": []
 897 |     },
 898 |     {
 899 |       "cell_type": "code",
 900 |       "metadata": {
 901 |         "colab": {
 902 |           "base_uri": "https://localhost:8080/"
 903 |         },
 904 |         "id": "EsrMY6BNCxd_",
 905 |         "outputId": "6f2b445d-c9af-4113-bedd-ffb0a2f5d021"
 906 |       },
 907 |       "source": [
 908 |         "porosities"
 909 |       ],
 910 |       "execution_count": 51,
 911 |       "outputs": [
 912 |         {
 913 |           "output_type": "execute_result",
 914 |           "data": {
 915 |             "text/plain": [
 916 |               "[0.2, 0.35, 0.4, 0.55, 0.4]"
 917 |             ]
 918 |           },
 919 |           "metadata": {
 920 |             "tags": []
 921 |           },
 922 |           "execution_count": 51
 923 |         }
 924 |       ]
 925 |     },
 926 |     {
 927 |       "cell_type": "code",
 928 |       "metadata": {
 929 |         "id": "86BY4yYEC4wa"
 930 |       },
 931 |       "source": [
 932 |         "#Indexing. \n",
 933 |         "\n",
 934 |         "# Each Element in the list has an index starting with 0th index\n",
 935 |         "# for the first element. The same goes for strings as well. "
 936 |       ],
 937 |       "execution_count": 52,
 938 |       "outputs": []
 939 |     },
 940 |     {
 941 |       "cell_type": "code",
 942 |       "metadata": {
 943 |         "colab": {
 944 |           "base_uri": "https://localhost:8080/"
 945 |         },
 946 |         "id": "oIPEHavkDFCO",
 947 |         "outputId": "d73f9abb-9466-43f4-8225-5c38988514f8"
 948 |       },
 949 |       "source": [
 950 |         "porosities\n",
 951 |         "\n",
 952 |         "# porosities[0]"
 953 |       ],
 954 |       "execution_count": 56,
 955 |       "outputs": [
 956 |         {
 957 |           "output_type": "execute_result",
 958 |           "data": {
 959 |             "text/plain": [
 960 |               "[0.2, 0.35, 0.4, 0.55, 0.4]"
 961 |             ]
 962 |           },
 963 |           "metadata": {
 964 |             "tags": []
 965 |           },
 966 |           "execution_count": 56
 967 |         }
 968 |       ]
 969 |     },
 970 |     {
 971 |       "cell_type": "code",
 972 |       "metadata": {
 973 |         "colab": {
 974 |           "base_uri": "https://localhost:8080/"
 975 |         },
 976 |         "id": "IXCnbSqVDpVf",
 977 |         "outputId": "fb3a0fcd-c423-4cdb-9436-138826b75207"
 978 |       },
 979 |       "source": [
 980 |         "#Proof that lists are mutable/can be modified. \n",
 981 |         "poro = [0.20,0.22,0.34,0.27,45]\n",
 982 |         "\n",
 983 |         "#Notice that last porosity is mistakenly added as a percentage\n",
 984 |         "#We need it as a fraction. \n",
 985 |         "poro"
 986 |       ],
 987 |       "execution_count": 61,
 988 |       "outputs": [
 989 |         {
 990 |           "output_type": "execute_result",
 991 |           "data": {
 992 |             "text/plain": [
 993 |               "[0.2, 0.22, 0.34, 0.27, 45]"
 994 |             ]
 995 |           },
 996 |           "metadata": {
 997 |             "tags": []
 998 |           },
 999 |           "execution_count": 61
1000 |         }
1001 |       ]
1002 |     },
1003 |     {
1004 |       "cell_type": "code",
1005 |       "metadata": {
1006 |         "id": "pHnpQjFrENmh"
1007 |       },
1008 |       "source": [
1009 |         "poro[-1] = 0.45"
1010 |       ],
1011 |       "execution_count": 62,
1012 |       "outputs": []
1013 |     },
1014 |     {
1015 |       "cell_type": "code",
1016 |       "metadata": {
1017 |         "colab": {
1018 |           "base_uri": "https://localhost:8080/"
1019 |         },
1020 |         "id": "WY2Wlm2wEjEQ",
1021 |         "outputId": "85626bf7-923a-4fad-c627-b07ba391e5b9"
1022 |       },
1023 |       "source": [
1024 |         "# 2D lists. \n",
1025 |         "\n",
1026 |         "list_1 = [1,2,3] \n",
1027 |         "list_2 = [4,5,6]\n",
1028 |         "\n",
1029 |         "list_2D = [list_1 , list_2]\n",
1030 |         "\n",
1031 |         "list_2D"
1032 |       ],
1033 |       "execution_count": 63,
1034 |       "outputs": [
1035 |         {
1036 |           "output_type": "execute_result",
1037 |           "data": {
1038 |             "text/plain": [
1039 |               "[[1, 2, 3], [4, 5, 6]]"
1040 |             ]
1041 |           },
1042 |           "metadata": {
1043 |             "tags": []
1044 |           },
1045 |           "execution_count": 63
1046 |         }
1047 |       ]
1048 |     },
1049 |     {
1050 |       "cell_type": "code",
1051 |       "metadata": {
1052 |         "colab": {
1053 |           "base_uri": "https://localhost:8080/"
1054 |         },
1055 |         "id": "U_iVeXD3FbmW",
1056 |         "outputId": "6c2faa8e-6405-4c89-eb16-cee4270c108b"
1057 |       },
1058 |       "source": [
1059 |         "list_2D[0][-1]"
1060 |       ],
1061 |       "execution_count": 65,
1062 |       "outputs": [
1063 |         {
1064 |           "output_type": "execute_result",
1065 |           "data": {
1066 |             "text/plain": [
1067 |               "3"
1068 |             ]
1069 |           },
1070 |           "metadata": {
1071 |             "tags": []
1072 |           },
1073 |           "execution_count": 65
1074 |         }
1075 |       ]
1076 |     },
1077 |     {
1078 |       "cell_type": "markdown",
1079 |       "metadata": {
1080 |         "id": "bLGUZu7VF7uD"
1081 |       },
1082 |       "source": [
1083 |         "**List Slicing.**\n",
1084 |         "\n",
1085 |         "list_name[start:stop:step]"
1086 |       ]
1087 |     },
1088 |     {
1089 |       "cell_type": "code",
1090 |       "metadata": {
1091 |         "colab": {
1092 |           "base_uri": "https://localhost:8080/"
1093 |         },
1094 |         "id": "udaubIXKFfP4",
1095 |         "outputId": "a70910c3-6e34-423a-e7c9-74010de51592"
1096 |       },
1097 |       "source": [
1098 |         "#We can access a particular section or slice of a list.\n",
1099 |         "\n",
1100 |         "perms = [4,5,1,10,100,1000,3,40,50,60]\n",
1101 |         "\n",
1102 |         "perms[3:]"
1103 |       ],
1104 |       "execution_count": 68,
1105 |       "outputs": [
1106 |         {
1107 |           "output_type": "execute_result",
1108 |           "data": {
1109 |             "text/plain": [
1110 |               "[10, 100, 1000, 3, 40, 50, 60]"
1111 |             ]
1112 |           },
1113 |           "metadata": {
1114 |             "tags": []
1115 |           },
1116 |           "execution_count": 68
1117 |         }
1118 |       ]
1119 |     },
1120 |     {
1121 |       "cell_type": "code",
1122 |       "metadata": {
1123 |         "colab": {
1124 |           "base_uri": "https://localhost:8080/"
1125 |         },
1126 |         "id": "MbCmUgKdGG1o",
1127 |         "outputId": "e4ae81ab-f068-4484-c113-e8753113d2b5"
1128 |       },
1129 |       "source": [
1130 |         "perms[0:-1:2]"
1131 |       ],
1132 |       "execution_count": 69,
1133 |       "outputs": [
1134 |         {
1135 |           "output_type": "execute_result",
1136 |           "data": {
1137 |             "text/plain": [
1138 |               "[4, 1, 100, 3, 50]"
1139 |             ]
1140 |           },
1141 |           "metadata": {
1142 |             "tags": []
1143 |           },
1144 |           "execution_count": 69
1145 |         }
1146 |       ]
1147 |     },
1148 |     {
1149 |       "cell_type": "markdown",
1150 |       "metadata": {
1151 |         "id": "yVlpiqexG3kx"
1152 |       },
1153 |       "source": [
1154 |         "## 4. Tuples"
1155 |       ]
1156 |     },
1157 |     {
1158 |       "cell_type": "code",
1159 |       "metadata": {
1160 |         "colab": {
1161 |           "base_uri": "https://localhost:8080/"
1162 |         },
1163 |         "id": "Hs6n2hawGV7z",
1164 |         "outputId": "a3f8f466-ab6f-4774-fdd4-e2d1949d1a5c"
1165 |       },
1166 |       "source": [
1167 |         "t = (1,2,3)\n",
1168 |         "\n",
1169 |         "t[0]"
1170 |       ],
1171 |       "execution_count": 70,
1172 |       "outputs": [
1173 |         {
1174 |           "output_type": "execute_result",
1175 |           "data": {
1176 |             "text/plain": [
1177 |               "1"
1178 |             ]
1179 |           },
1180 |           "metadata": {
1181 |             "tags": []
1182 |           },
1183 |           "execution_count": 70
1184 |         }
1185 |       ]
1186 |     },
1187 |     {
1188 |       "cell_type": "code",
1189 |       "metadata": {
1190 |         "id": "c88BJGfeHJd1"
1191 |       },
1192 |       "source": [
1193 |         "#TUPLES are a SECURE way to store Data. \n",
1194 |         "\n",
1195 |         "#They help prevent modification. IMMUTABLE."
1196 |       ],
1197 |       "execution_count": 71,
1198 |       "outputs": []
1199 |     },
1200 |     {
1201 |       "cell_type": "code",
1202 |       "metadata": {
1203 |         "colab": {
1204 |           "base_uri": "https://localhost:8080/",
1205 |           "height": 166
1206 |         },
1207 |         "id": "FXXpB3sRHQ6N",
1208 |         "outputId": "a9b41597-0329-4ddd-a6cf-5b793937058c"
1209 |       },
1210 |       "source": [
1211 |         "t[1] = 2"
1212 |       ],
1213 |       "execution_count": 72,
1214 |       "outputs": [
1215 |         {
1216 |           "output_type": "error",
1217 |           "ename": "TypeError",
1218 |           "evalue": "ignored",
1219 |           "traceback": [
1220 |             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1221 |             "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
1222 |             "\u001b[0;32m<ipython-input-72-b9aa97991a78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1223 |             "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
1224 |           ]
1225 |         }
1226 |       ]
1227 |     },
1228 |     {
1229 |       "cell_type": "markdown",
1230 |       "metadata": {
1231 |         "id": "IAOzK1ibHcuS"
1232 |       },
1233 |       "source": [
1234 |         "## 5. SETS"
1235 |       ]
1236 |     },
1237 |     {
1238 |       "cell_type": "code",
1239 |       "metadata": {
1240 |         "id": "K8khvdy3HTtY"
1241 |       },
1242 |       "source": [
1243 |         "#MOST IMPORTANT application of Sets is that they help store \n",
1244 |         "#only unique information.\n",
1245 |         "\n",
1246 |         "set1 = {1,2,3}"
1247 |       ],
1248 |       "execution_count": 74,
1249 |       "outputs": []
1250 |     },
1251 |     {
1252 |       "cell_type": "code",
1253 |       "metadata": {
1254 |         "id": "iPe7vNEJHZLk"
1255 |       },
1256 |       "source": [
1257 |         "duplicates = [1,2,2,3,4,5,6,6,6,6,7]"
1258 |       ],
1259 |       "execution_count": 76,
1260 |       "outputs": []
1261 |     },
1262 |     {
1263 |       "cell_type": "code",
1264 |       "metadata": {
1265 |         "colab": {
1266 |           "base_uri": "https://localhost:8080/"
1267 |         },
1268 |         "id": "o5YUKhshHbe1",
1269 |         "outputId": "c2c9e08f-7960-46c1-f153-f956fb8ee31b"
1270 |       },
1271 |       "source": [
1272 |         "set(duplicates)"
1273 |       ],
1274 |       "execution_count": 77,
1275 |       "outputs": [
1276 |         {
1277 |           "output_type": "execute_result",
1278 |           "data": {
1279 |             "text/plain": [
1280 |               "{1, 2, 3, 4, 5, 6, 7}"
1281 |             ]
1282 |           },
1283 |           "metadata": {
1284 |             "tags": []
1285 |           },
1286 |           "execution_count": 77
1287 |         }
1288 |       ]
1289 |     },
1290 |     {
1291 |       "cell_type": "markdown",
1292 |       "metadata": {
1293 |         "id": "FbiACyKxIAgK"
1294 |       },
1295 |       "source": [
1296 |         "## 6. DICTIONARIES"
1297 |       ]
1298 |     },
1299 |     {
1300 |       "cell_type": "code",
1301 |       "metadata": {
1302 |         "id": "la10z29ZH8si"
1303 |       },
1304 |       "source": [
1305 |         "reservoir1 = {'phi':[0.2,0.3,0.25],'K(mD)':[120,124,140]}"
1306 |       ],
1307 |       "execution_count": 79,
1308 |       "outputs": []
1309 |     },
1310 |     {
1311 |       "cell_type": "code",
1312 |       "metadata": {
1313 |         "colab": {
1314 |           "base_uri": "https://localhost:8080/"
1315 |         },
1316 |         "id": "htcZljeQH_yA",
1317 |         "outputId": "f274d92a-cea9-4fc9-e653-d6260f4b742c"
1318 |       },
1319 |       "source": [
1320 |         "reservoir1['phi']"
1321 |       ],
1322 |       "execution_count": 80,
1323 |       "outputs": [
1324 |         {
1325 |           "output_type": "execute_result",
1326 |           "data": {
1327 |             "text/plain": [
1328 |               "[0.2, 0.3, 0.25]"
1329 |             ]
1330 |           },
1331 |           "metadata": {
1332 |             "tags": []
1333 |           },
1334 |           "execution_count": 80
1335 |         }
1336 |       ]
1337 |     },
1338 |     {
1339 |       "cell_type": "code",
1340 |       "metadata": {
1341 |         "colab": {
1342 |           "base_uri": "https://localhost:8080/"
1343 |         },
1344 |         "id": "9nv3vqJwI7-B",
1345 |         "outputId": "9e646f7d-80a8-458f-8269-44910984afb6"
1346 |       },
1347 |       "source": [
1348 |         "reservoir1.keys()"
1349 |       ],
1350 |       "execution_count": 83,
1351 |       "outputs": [
1352 |         {
1353 |           "output_type": "execute_result",
1354 |           "data": {
1355 |             "text/plain": [
1356 |               "dict_keys(['phi', 'K(mD)'])"
1357 |             ]
1358 |           },
1359 |           "metadata": {
1360 |             "tags": []
1361 |           },
1362 |           "execution_count": 83
1363 |         }
1364 |       ]
1365 |     },
1366 |     {
1367 |       "cell_type": "code",
1368 |       "metadata": {
1369 |         "colab": {
1370 |           "base_uri": "https://localhost:8080/"
1371 |         },
1372 |         "id": "Jzmku2G3I9aW",
1373 |         "outputId": "455ab534-ae2b-4e01-f04a-2368691bdedb"
1374 |       },
1375 |       "source": [
1376 |         "reservoir1.values()"
1377 |       ],
1378 |       "execution_count": 84,
1379 |       "outputs": [
1380 |         {
1381 |           "output_type": "execute_result",
1382 |           "data": {
1383 |             "text/plain": [
1384 |               "dict_values([[0.2, 0.3, 0.25], [120, 124, 140]])"
1385 |             ]
1386 |           },
1387 |           "metadata": {
1388 |             "tags": []
1389 |           },
1390 |           "execution_count": 84
1391 |         }
1392 |       ]
1393 |     },
1394 |     {
1395 |       "cell_type": "code",
1396 |       "metadata": {
1397 |         "colab": {
1398 |           "base_uri": "https://localhost:8080/",
1399 |           "height": 138
1400 |         },
1401 |         "id": "CUfHG4BnJGKP",
1402 |         "outputId": "ba44c748-ea48-45b8-de2a-40739afe95a9"
1403 |       },
1404 |       "source": [
1405 |         "##Optional. Converting a dictionary to a table. \n",
1406 |         "import pandas as pd\n",
1407 |         "pd.DataFrame(reservoir1)"
1408 |       ],
1409 |       "execution_count": 85,
1410 |       "outputs": [
1411 |         {
1412 |           "output_type": "execute_result",
1413 |           "data": {
1414 |             "text/html": [
1415 |               "<div>\n",
1416 |               "<style scoped>\n",
1417 |               "    .dataframe tbody tr th:only-of-type {\n",
1418 |               "        vertical-align: middle;\n",
1419 |               "    }\n",
1420 |               "\n",
1421 |               "    .dataframe tbody tr th {\n",
1422 |               "        vertical-align: top;\n",
1423 |               "    }\n",
1424 |               "\n",
1425 |               "    .dataframe thead th {\n",
1426 |               "        text-align: right;\n",
1427 |               "    }\n",
1428 |               "</style>\n",
1429 |               "<table border=\"1\" class=\"dataframe\">\n",
1430 |               "  <thead>\n",
1431 |               "    <tr style=\"text-align: right;\">\n",
1432 |               "      <th></th>\n",
1433 |               "      <th>phi</th>\n",
1434 |               "      <th>K(mD)</th>\n",
1435 |               "    </tr>\n",
1436 |               "  </thead>\n",
1437 |               "  <tbody>\n",
1438 |               "    <tr>\n",
1439 |               "      <th>0</th>\n",
1440 |               "      <td>0.20</td>\n",
1441 |               "      <td>120</td>\n",
1442 |               "    </tr>\n",
1443 |               "    <tr>\n",
1444 |               "      <th>1</th>\n",
1445 |               "      <td>0.30</td>\n",
1446 |               "      <td>124</td>\n",
1447 |               "    </tr>\n",
1448 |               "    <tr>\n",
1449 |               "      <th>2</th>\n",
1450 |               "      <td>0.25</td>\n",
1451 |               "      <td>140</td>\n",
1452 |               "    </tr>\n",
1453 |               "  </tbody>\n",
1454 |               "</table>\n",
1455 |               "</div>"
1456 |             ],
1457 |             "text/plain": [
1458 |               "    phi  K(mD)\n",
1459 |               "0  0.20    120\n",
1460 |               "1  0.30    124\n",
1461 |               "2  0.25    140"
1462 |             ]
1463 |           },
1464 |           "metadata": {
1465 |             "tags": []
1466 |           },
1467 |           "execution_count": 85
1468 |         }
1469 |       ]
1470 |     },
1471 |     {
1472 |       "cell_type": "markdown",
1473 |       "metadata": {
1474 |         "id": "ikyTEjC6Q2dG"
1475 |       },
1476 |       "source": [
1477 |         "# Chapter 2 : Operators & Statements."
1478 |       ]
1479 |     },
1480 |     {
1481 |       "cell_type": "markdown",
1482 |       "metadata": {
1483 |         "id": "e54swgeMROwI"
1484 |       },
1485 |       "source": [
1486 |         "1. ### Comparison Operators"
1487 |       ]
1488 |     },
1489 |     {
1490 |       "cell_type": "code",
1491 |       "metadata": {
1492 |         "colab": {
1493 |           "base_uri": "https://localhost:8080/"
1494 |         },
1495 |         "id": "wz15P9gnJTng",
1496 |         "outputId": "ea71f9de-f5ba-474c-99e0-4c5288c9217e"
1497 |       },
1498 |       "source": [
1499 |         "1>2"
1500 |       ],
1501 |       "execution_count": 86,
1502 |       "outputs": [
1503 |         {
1504 |           "output_type": "execute_result",
1505 |           "data": {
1506 |             "text/plain": [
1507 |               "False"
1508 |             ]
1509 |           },
1510 |           "metadata": {
1511 |             "tags": []
1512 |           },
1513 |           "execution_count": 86
1514 |         }
1515 |       ]
1516 |     },
1517 |     {
1518 |       "cell_type": "code",
1519 |       "metadata": {
1520 |         "colab": {
1521 |           "base_uri": "https://localhost:8080/"
1522 |         },
1523 |         "id": "bMElx0MbQyxW",
1524 |         "outputId": "20ebb9d2-8e39-46ca-f30d-48b0d0b089d6"
1525 |       },
1526 |       "source": [
1527 |         "2>1"
1528 |       ],
1529 |       "execution_count": 87,
1530 |       "outputs": [
1531 |         {
1532 |           "output_type": "execute_result",
1533 |           "data": {
1534 |             "text/plain": [
1535 |               "True"
1536 |             ]
1537 |           },
1538 |           "metadata": {
1539 |             "tags": []
1540 |           },
1541 |           "execution_count": 87
1542 |         }
1543 |       ]
1544 |     },
1545 |     {
1546 |       "cell_type": "code",
1547 |       "metadata": {
1548 |         "colab": {
1549 |           "base_uri": "https://localhost:8080/"
1550 |         },
1551 |         "id": "mWkcwNXrRSxo",
1552 |         "outputId": "f96f1a02-0070-4dda-9fe4-36740e6c296e"
1553 |       },
1554 |       "source": [
1555 |         "1 == 1"
1556 |       ],
1557 |       "execution_count": 88,
1558 |       "outputs": [
1559 |         {
1560 |           "output_type": "execute_result",
1561 |           "data": {
1562 |             "text/plain": [
1563 |               "True"
1564 |             ]
1565 |           },
1566 |           "metadata": {
1567 |             "tags": []
1568 |           },
1569 |           "execution_count": 88
1570 |         }
1571 |       ]
1572 |     },
1573 |     {
1574 |       "cell_type": "code",
1575 |       "metadata": {
1576 |         "colab": {
1577 |           "base_uri": "https://localhost:8080/"
1578 |         },
1579 |         "id": "wLKFybgdRTzh",
1580 |         "outputId": "f693854b-b511-43ab-ad40-5fa91070d732"
1581 |       },
1582 |       "source": [
1583 |         "3 >= 1"
1584 |       ],
1585 |       "execution_count": 89,
1586 |       "outputs": [
1587 |         {
1588 |           "output_type": "execute_result",
1589 |           "data": {
1590 |             "text/plain": [
1591 |               "True"
1592 |             ]
1593 |           },
1594 |           "metadata": {
1595 |             "tags": []
1596 |           },
1597 |           "execution_count": 89
1598 |         }
1599 |       ]
1600 |     },
1601 |     {
1602 |       "cell_type": "code",
1603 |       "metadata": {
1604 |         "colab": {
1605 |           "base_uri": "https://localhost:8080/"
1606 |         },
1607 |         "id": "M8MtRIdCRVuE",
1608 |         "outputId": "b93b7239-6118-43ba-cf5a-9184bc9eaf3c"
1609 |       },
1610 |       "source": [
1611 |         "1 >= 3"
1612 |       ],
1613 |       "execution_count": 90,
1614 |       "outputs": [
1615 |         {
1616 |           "output_type": "execute_result",
1617 |           "data": {
1618 |             "text/plain": [
1619 |               "False"
1620 |             ]
1621 |           },
1622 |           "metadata": {
1623 |             "tags": []
1624 |           },
1625 |           "execution_count": 90
1626 |         }
1627 |       ]
1628 |     },
1629 |     {
1630 |       "cell_type": "markdown",
1631 |       "metadata": {
1632 |         "id": "fHB0WUtORt2X"
1633 |       },
1634 |       "source": [
1635 |         "### 2. Logical Operators."
1636 |       ]
1637 |     },
1638 |     {
1639 |       "cell_type": "code",
1640 |       "metadata": {
1641 |         "id": "nr3-Z1sTRXUY"
1642 |       },
1643 |       "source": [
1644 |         "# and | or | not | is"
1645 |       ],
1646 |       "execution_count": 91,
1647 |       "outputs": []
1648 |     },
1649 |     {
1650 |       "cell_type": "code",
1651 |       "metadata": {
1652 |         "colab": {
1653 |           "base_uri": "https://localhost:8080/"
1654 |         },
1655 |         "id": "VVph2TeVR0ex",
1656 |         "outputId": "cb06ce86-a361-4b71-89b8-2590ebd99727"
1657 |       },
1658 |       "source": [
1659 |         "(1 > 2) and (2 < 3)"
1660 |       ],
1661 |       "execution_count": 92,
1662 |       "outputs": [
1663 |         {
1664 |           "output_type": "execute_result",
1665 |           "data": {
1666 |             "text/plain": [
1667 |               "False"
1668 |             ]
1669 |           },
1670 |           "metadata": {
1671 |             "tags": []
1672 |           },
1673 |           "execution_count": 92
1674 |         }
1675 |       ]
1676 |     },
1677 |     {
1678 |       "cell_type": "code",
1679 |       "metadata": {
1680 |         "colab": {
1681 |           "base_uri": "https://localhost:8080/"
1682 |         },
1683 |         "id": "31YS3PxMR3bx",
1684 |         "outputId": "03059976-8892-4fe1-bc09-ee06d34bdf55"
1685 |       },
1686 |       "source": [
1687 |         "(1 > 2) or (2 < 3)"
1688 |       ],
1689 |       "execution_count": 93,
1690 |       "outputs": [
1691 |         {
1692 |           "output_type": "execute_result",
1693 |           "data": {
1694 |             "text/plain": [
1695 |               "True"
1696 |             ]
1697 |           },
1698 |           "metadata": {
1699 |             "tags": []
1700 |           },
1701 |           "execution_count": 93
1702 |         }
1703 |       ]
1704 |     },
1705 |     {
1706 |       "cell_type": "code",
1707 |       "metadata": {
1708 |         "colab": {
1709 |           "base_uri": "https://localhost:8080/"
1710 |         },
1711 |         "id": "SBiwAOotR5sY",
1712 |         "outputId": "02e4f8ed-5039-40a3-e5d1-1f7a11f8aa0f"
1713 |       },
1714 |       "source": [
1715 |         "(1 == 2) or (2 == 3) or (4 == 4)"
1716 |       ],
1717 |       "execution_count": 94,
1718 |       "outputs": [
1719 |         {
1720 |           "output_type": "execute_result",
1721 |           "data": {
1722 |             "text/plain": [
1723 |               "True"
1724 |             ]
1725 |           },
1726 |           "metadata": {
1727 |             "tags": []
1728 |           },
1729 |           "execution_count": 94
1730 |         }
1731 |       ]
1732 |     },
1733 |     {
1734 |       "cell_type": "code",
1735 |       "metadata": {
1736 |         "colab": {
1737 |           "base_uri": "https://localhost:8080/"
1738 |         },
1739 |         "id": "Ly2LiwaoR7go",
1740 |         "outputId": "76b7a22c-fe5c-4ec8-af0c-f3bb195691a6"
1741 |       },
1742 |       "source": [
1743 |         "not 1"
1744 |       ],
1745 |       "execution_count": 96,
1746 |       "outputs": [
1747 |         {
1748 |           "output_type": "execute_result",
1749 |           "data": {
1750 |             "text/plain": [
1751 |               "False"
1752 |             ]
1753 |           },
1754 |           "metadata": {
1755 |             "tags": []
1756 |           },
1757 |           "execution_count": 96
1758 |         }
1759 |       ]
1760 |     },
1761 |     {
1762 |       "cell_type": "code",
1763 |       "metadata": {
1764 |         "colab": {
1765 |           "base_uri": "https://localhost:8080/"
1766 |         },
1767 |         "id": "W4JyXHwJR83t",
1768 |         "outputId": "e1406ecb-8195-4399-e0f1-e24cd66b52e8"
1769 |       },
1770 |       "source": [
1771 |         "1 is 2"
1772 |       ],
1773 |       "execution_count": 98,
1774 |       "outputs": [
1775 |         {
1776 |           "output_type": "execute_result",
1777 |           "data": {
1778 |             "text/plain": [
1779 |               "False"
1780 |             ]
1781 |           },
1782 |           "metadata": {
1783 |             "tags": []
1784 |           },
1785 |           "execution_count": 98
1786 |         }
1787 |       ]
1788 |     },
1789 |     {
1790 |       "cell_type": "markdown",
1791 |       "metadata": {
1792 |         "id": "VxLqMe8RStmi"
1793 |       },
1794 |       "source": [
1795 |         "3. ### if - elif - else Statements."
1796 |       ]
1797 |     },
1798 |     {
1799 |       "cell_type": "code",
1800 |       "metadata": {
1801 |         "colab": {
1802 |           "base_uri": "https://localhost:8080/"
1803 |         },
1804 |         "id": "U5fSdks1S1hp",
1805 |         "outputId": "0563e838-d5f6-4776-8939-c1fdde4ba47e"
1806 |       },
1807 |       "source": [
1808 |         "\n",
1809 |         "if 1 == 2:\n",
1810 |         "    print('first')\n",
1811 |         "elif 3 == 3:\n",
1812 |         "    print('middle')\n",
1813 |         "else:\n",
1814 |         "    print('Last')"
1815 |       ],
1816 |       "execution_count": 99,
1817 |       "outputs": [
1818 |         {
1819 |           "output_type": "stream",
1820 |           "text": [
1821 |             "middle\n"
1822 |           ],
1823 |           "name": "stdout"
1824 |         }
1825 |       ]
1826 |     },
1827 |     {
1828 |       "cell_type": "code",
1829 |       "metadata": {
1830 |         "colab": {
1831 |           "base_uri": "https://localhost:8080/"
1832 |         },
1833 |         "id": "Ru9jrpCsS8mb",
1834 |         "outputId": "f7005b40-d9b5-4dfd-fb61-db18bde11597"
1835 |       },
1836 |       "source": [
1837 |         "user_input = 'x'\n",
1838 |         "\n",
1839 |         "if type(user_input) == str:\n",
1840 |         "  print('Invalid Input')\n",
1841 |         "else:\n",
1842 |         "  if user_input % 2 == 0:\n",
1843 |         "    print('Even Number')\n",
1844 |         "  elif user_input % 2 != 0:\n",
1845 |         "    print('ODD Number')\n"
1846 |       ],
1847 |       "execution_count": 111,
1848 |       "outputs": [
1849 |         {
1850 |           "output_type": "stream",
1851 |           "text": [
1852 |             "Invalid Input\n"
1853 |           ],
1854 |           "name": "stdout"
1855 |         }
1856 |       ]
1857 |     },
1858 |     {
1859 |       "cell_type": "markdown",
1860 |       "metadata": {
1861 |         "id": "lbGHKtnGX6qf"
1862 |       },
1863 |       "source": [
1864 |         "# Chapter 3 : Loops / Iterations"
1865 |       ]
1866 |     },
1867 |     {
1868 |       "cell_type": "markdown",
1869 |       "metadata": {
1870 |         "id": "KfkB4t6ZYNZa"
1871 |       },
1872 |       "source": [
1873 |         "### 1. for Loops."
1874 |       ]
1875 |     },
1876 |     {
1877 |       "cell_type": "code",
1878 |       "metadata": {
1879 |         "colab": {
1880 |           "base_uri": "https://localhost:8080/"
1881 |         },
1882 |         "id": "KLRPOhjCTSCp",
1883 |         "outputId": "98eba13e-1e93-4f4f-daa3-740804a8e6a9"
1884 |       },
1885 |       "source": [
1886 |         "liths = ['sst', 'dolo', 'lst', 'shale']\n",
1887 |         "\n",
1888 |         "for name in liths:\n",
1889 |         "  print(name)"
1890 |       ],
1891 |       "execution_count": 112,
1892 |       "outputs": [
1893 |         {
1894 |           "output_type": "stream",
1895 |           "text": [
1896 |             "sst\n",
1897 |             "dolo\n",
1898 |             "lst\n",
1899 |             "shale\n"
1900 |           ],
1901 |           "name": "stdout"
1902 |         }
1903 |       ]
1904 |     },
1905 |     {
1906 |       "cell_type": "code",
1907 |       "metadata": {
1908 |         "colab": {
1909 |           "base_uri": "https://localhost:8080/"
1910 |         },
1911 |         "id": "NcdcQVq5Tsa8",
1912 |         "outputId": "c8a5fa9e-5c9c-4695-b080-c921ed313399"
1913 |       },
1914 |       "source": [
1915 |         "nums = [1,2,3,4,5,6,7,8,9]\n",
1916 |         "\n",
1917 |         "for n in nums:\n",
1918 |         "  if n% 2 == 0:\n",
1919 |         "    print('EVEN')\n",
1920 |         "  else:\n",
1921 |         "    print('ODD')"
1922 |       ],
1923 |       "execution_count": 113,
1924 |       "outputs": [
1925 |         {
1926 |           "output_type": "stream",
1927 |           "text": [
1928 |             "ODD\n",
1929 |             "EVEN\n",
1930 |             "ODD\n",
1931 |             "EVEN\n",
1932 |             "ODD\n",
1933 |             "EVEN\n",
1934 |             "ODD\n",
1935 |             "EVEN\n",
1936 |             "ODD\n"
1937 |           ],
1938 |           "name": "stdout"
1939 |         }
1940 |       ]
1941 |     },
1942 |     {
1943 |       "cell_type": "markdown",
1944 |       "metadata": {
1945 |         "id": "J2Lk7_1nY7gY"
1946 |       },
1947 |       "source": [
1948 |         "### 2. While Loops."
1949 |       ]
1950 |     },
1951 |     {
1952 |       "cell_type": "code",
1953 |       "metadata": {
1954 |         "colab": {
1955 |           "base_uri": "https://localhost:8080/"
1956 |         },
1957 |         "id": "2jGYd1FMYxeg",
1958 |         "outputId": "513eacef-ac23-452c-c5ae-09144298594a"
1959 |       },
1960 |       "source": [
1961 |         "#Keeps running as long as the condition is satisfied.\n",
1962 |         "i = 1\n",
1963 |         "while i < 5:\n",
1964 |         "    print('i is: {}'.format(i))\n",
1965 |         "    i = i+1"
1966 |       ],
1967 |       "execution_count": 115,
1968 |       "outputs": [
1969 |         {
1970 |           "output_type": "stream",
1971 |           "text": [
1972 |             "i is: 1\n",
1973 |             "i is: 2\n",
1974 |             "i is: 3\n",
1975 |             "i is: 4\n"
1976 |           ],
1977 |           "name": "stdout"
1978 |         }
1979 |       ]
1980 |     },
1981 |     {
1982 |       "cell_type": "code",
1983 |       "metadata": {
1984 |         "colab": {
1985 |           "base_uri": "https://localhost:8080/"
1986 |         },
1987 |         "id": "CzTNJiS2Y-3M",
1988 |         "outputId": "272b9084-163b-4b56-968b-88cf8b15e76a"
1989 |       },
1990 |       "source": [
1991 |         "#NOTE Range Function. \n",
1992 |         "range(10)"
1993 |       ],
1994 |       "execution_count": 116,
1995 |       "outputs": [
1996 |         {
1997 |           "output_type": "execute_result",
1998 |           "data": {
1999 |             "text/plain": [
2000 |               "range(0, 10)"
2001 |             ]
2002 |           },
2003 |           "metadata": {
2004 |             "tags": []
2005 |           },
2006 |           "execution_count": 116
2007 |         }
2008 |       ]
2009 |     },
2010 |     {
2011 |       "cell_type": "code",
2012 |       "metadata": {
2013 |         "colab": {
2014 |           "base_uri": "https://localhost:8080/"
2015 |         },
2016 |         "id": "zhXbaoOhZy0F",
2017 |         "outputId": "98cee505-3796-4087-b67a-cee4977c97e7"
2018 |       },
2019 |       "source": [
2020 |         "for i in range(10):\n",
2021 |         "  print(i)"
2022 |       ],
2023 |       "execution_count": 117,
2024 |       "outputs": [
2025 |         {
2026 |           "output_type": "stream",
2027 |           "text": [
2028 |             "0\n",
2029 |             "1\n",
2030 |             "2\n",
2031 |             "3\n",
2032 |             "4\n",
2033 |             "5\n",
2034 |             "6\n",
2035 |             "7\n",
2036 |             "8\n",
2037 |             "9\n"
2038 |           ],
2039 |           "name": "stdout"
2040 |         }
2041 |       ]
2042 |     },
2043 |     {
2044 |       "cell_type": "markdown",
2045 |       "metadata": {
2046 |         "id": "8EoLoKJxZ51Q"
2047 |       },
2048 |       "source": [
2049 |         "# Chapter 4 : Functions"
2050 |       ]
2051 |     },
2052 |     {
2053 |       "cell_type": "code",
2054 |       "metadata": {
2055 |         "id": "g8cqFNniZ10e"
2056 |       },
2057 |       "source": [
2058 |         "def func_name():\n",
2059 |         "  #All you want to do in the function. \n",
2060 |         "  #return values if applicable. \n",
2061 |         "  pass"
2062 |       ],
2063 |       "execution_count": 118,
2064 |       "outputs": []
2065 |     },
2066 |     {
2067 |       "cell_type": "code",
2068 |       "metadata": {
2069 |         "id": "J4LUmaR2aka0"
2070 |       },
2071 |       "source": [
2072 |         "#Example 1 : Take an input and return square. \n",
2073 |         "def squarer(x):\n",
2074 |         "\n",
2075 |         "  sq = x**2\n",
2076 |         "\n",
2077 |         "  return sq"
2078 |       ],
2079 |       "execution_count": 119,
2080 |       "outputs": []
2081 |     },
2082 |     {
2083 |       "cell_type": "code",
2084 |       "metadata": {
2085 |         "colab": {
2086 |           "base_uri": "https://localhost:8080/"
2087 |         },
2088 |         "id": "ChOycHO-ar2X",
2089 |         "outputId": "d495082a-4782-4a03-f824-395c11c6e905"
2090 |       },
2091 |       "source": [
2092 |         "squarer(2)"
2093 |       ],
2094 |       "execution_count": 120,
2095 |       "outputs": [
2096 |         {
2097 |           "output_type": "execute_result",
2098 |           "data": {
2099 |             "text/plain": [
2100 |               "4"
2101 |             ]
2102 |           },
2103 |           "metadata": {
2104 |             "tags": []
2105 |           },
2106 |           "execution_count": 120
2107 |         }
2108 |       ]
2109 |     },
2110 |     {
2111 |       "cell_type": "code",
2112 |       "metadata": {
2113 |         "id": "lAnAbpBNatkV"
2114 |       },
2115 |       "source": [
2116 |         "#Example 2 : Take in a number and power and raise number to\n",
2117 |         "# that power. \n",
2118 |         "\n",
2119 |         "def powerer(number, power):\n",
2120 |         "\n",
2121 |         "  result = number**power\n",
2122 |         "\n",
2123 |         "  return result"
2124 |       ],
2125 |       "execution_count": 121,
2126 |       "outputs": []
2127 |     },
2128 |     {
2129 |       "cell_type": "code",
2130 |       "metadata": {
2131 |         "colab": {
2132 |           "base_uri": "https://localhost:8080/"
2133 |         },
2134 |         "id": "FKIvbl8UbCxK",
2135 |         "outputId": "62eb293c-4c0a-42c4-f797-75f18cb1ecbc"
2136 |       },
2137 |       "source": [
2138 |         "powerer(625 , 0.5)"
2139 |       ],
2140 |       "execution_count": 122,
2141 |       "outputs": [
2142 |         {
2143 |           "output_type": "execute_result",
2144 |           "data": {
2145 |             "text/plain": [
2146 |               "25.0"
2147 |             ]
2148 |           },
2149 |           "metadata": {
2150 |             "tags": []
2151 |           },
2152 |           "execution_count": 122
2153 |         }
2154 |       ]
2155 |     },
2156 |     {
2157 |       "cell_type": "markdown",
2158 |       "metadata": {
2159 |         "id": "pBK6WvfnjHaC"
2160 |       },
2161 |       "source": [
2162 |         "# Mini-Projects 1 : Hands on practice with Functions"
2163 |       ]
2164 |     },
2165 |     {
2166 |       "cell_type": "markdown",
2167 |       "metadata": {
2168 |         "id": "UuH2oo31jLcF"
2169 |       },
2170 |       "source": [
2171 |         "#### Project 1 : Darcy's Law\n",
2172 |         "\n",
2173 |         "$ q = \\frac {kh} {141.2 \\mu B ln(re/rw) } \\triangle P $ "
2174 |       ]
2175 |     },
2176 |     {
2177 |       "cell_type": "code",
2178 |       "metadata": {
2179 |         "id": "8I-jY6FYbGI6"
2180 |       },
2181 |       "source": [
2182 |         "#Functions are very important. \n",
2183 |         "\n",
2184 |         "#1. Make the code clean. \n",
2185 |         "#2. Helps Prevent repetitive commands."
2186 |       ],
2187 |       "execution_count": 123,
2188 |       "outputs": []
2189 |     },
2190 |     {
2191 |       "cell_type": "code",
2192 |       "metadata": {
2193 |         "id": "ZT27GCZYjzWh"
2194 |       },
2195 |       "source": [
2196 |         "#So here the term except delP is important. \n",
2197 |         "#And we can use it again and again. So we can create a function of it. \n",
2198 |         "\n",
2199 |         "#function definition. \n",
2200 |         "def slope():\n",
2201 |         "\n",
2202 |         "  k = int(input('Enter K: '))\n",
2203 |         "  h = int(input('Enter h: '))\n",
2204 |         "  mu = int(input('Enter viscocity: '))\n",
2205 |         "  B = float(input('Enter FVF: '))\n",
2206 |         "\n",
2207 |         "  lnrerw = 0.5\n",
2208 |         "\n",
2209 |         "  m = k*h/(141.2*mu*B*lnrerw)\n",
2210 |         "\n",
2211 |         "  return m"
2212 |       ],
2213 |       "execution_count": 124,
2214 |       "outputs": []
2215 |     },
2216 |     {
2217 |       "cell_type": "code",
2218 |       "metadata": {
2219 |         "colab": {
2220 |           "base_uri": "https://localhost:8080/"
2221 |         },
2222 |         "id": "LQRDbhB1j1Pt",
2223 |         "outputId": "77e4ecc4-1708-4b4f-d436-d77cec6fd903"
2224 |       },
2225 |       "source": [
2226 |         "#function call to get the value function returns. \n",
2227 |         "#Note that this example also teaches how to take user input.\n",
2228 |         "delP = 500 #psia\n",
2229 |         "\n",
2230 |         "m = slope()\n",
2231 |         "\n",
2232 |         "q = m*delP\n",
2233 |         "\n",
2234 |         "print(f'The flowrate at {delP} psi drawdown is {q} STB/D')"
2235 |       ],
2236 |       "execution_count": 125,
2237 |       "outputs": [
2238 |         {
2239 |           "output_type": "stream",
2240 |           "text": [
2241 |             "Enter K: 100\n",
2242 |             "Enter h: 30\n",
2243 |             "Enter viscocity: 70\n",
2244 |             "Enter FVF: 1.1\n",
2245 |             "The flowrate at 500 psi drawdown is 275.928037967698 STB/D\n"
2246 |           ],
2247 |           "name": "stdout"
2248 |         }
2249 |       ]
2250 |     },
2251 |     {
2252 |       "cell_type": "markdown",
2253 |       "metadata": {
2254 |         "id": "E7D0d8DQkGp8"
2255 |       },
2256 |       "source": [
2257 |         "#### Project 2 : Function to find API gravity."
2258 |       ]
2259 |     },
2260 |     {
2261 |       "cell_type": "code",
2262 |       "metadata": {
2263 |         "id": "1uWwE3fDj4mi"
2264 |       },
2265 |       "source": [
2266 |         "def API(SG):\n",
2267 |         "\n",
2268 |         "  api = 141.5/SG - 131.5\n",
2269 |         "\n",
2270 |         "  return api"
2271 |       ],
2272 |       "execution_count": 126,
2273 |       "outputs": []
2274 |     },
2275 |     {
2276 |       "cell_type": "code",
2277 |       "metadata": {
2278 |         "colab": {
2279 |           "base_uri": "https://localhost:8080/"
2280 |         },
2281 |         "id": "F-u61-ZKkSRx",
2282 |         "outputId": "b07db1de-0717-45dc-e459-708455fccf50"
2283 |       },
2284 |       "source": [
2285 |         "API(1)"
2286 |       ],
2287 |       "execution_count": 127,
2288 |       "outputs": [
2289 |         {
2290 |           "output_type": "execute_result",
2291 |           "data": {
2292 |             "text/plain": [
2293 |               "10.0"
2294 |             ]
2295 |           },
2296 |           "metadata": {
2297 |             "tags": []
2298 |           },
2299 |           "execution_count": 127
2300 |         }
2301 |       ]
2302 |     },
2303 |     {
2304 |       "cell_type": "markdown",
2305 |       "metadata": {
2306 |         "id": "4vlXVxNfkWVm"
2307 |       },
2308 |       "source": [
2309 |         "#### LAMBDA Functions / Anonymous Functions."
2310 |       ]
2311 |     },
2312 |     {
2313 |       "cell_type": "code",
2314 |       "metadata": {
2315 |         "colab": {
2316 |           "base_uri": "https://localhost:8080/"
2317 |         },
2318 |         "id": "RV7jj3jKkTb9",
2319 |         "outputId": "8fe5e78e-8863-4247-c409-3e72e0daa5a0"
2320 |       },
2321 |       "source": [
2322 |         "API = lambda SG : 141.5/SG - 131.5\n",
2323 |         "\n",
2324 |         "API(1)"
2325 |       ],
2326 |       "execution_count": 128,
2327 |       "outputs": [
2328 |         {
2329 |           "output_type": "execute_result",
2330 |           "data": {
2331 |             "text/plain": [
2332 |               "10.0"
2333 |             ]
2334 |           },
2335 |           "metadata": {
2336 |             "tags": []
2337 |           },
2338 |           "execution_count": 128
2339 |         }
2340 |       ]
2341 |     },
2342 |     {
2343 |       "cell_type": "markdown",
2344 |       "metadata": {
2345 |         "id": "GZyP-tCQk0nZ"
2346 |       },
2347 |       "source": [
2348 |         "# Mini-Projects 2 : Hands on with Iterations."
2349 |       ]
2350 |     },
2351 |     {
2352 |       "cell_type": "markdown",
2353 |       "metadata": {
2354 |         "id": "oiWSFkTnlkvO"
2355 |       },
2356 |       "source": [
2357 |         "## Solving for a root."
2358 |       ]
2359 |     },
2360 |     {
2361 |       "cell_type": "code",
2362 |       "metadata": {
2363 |         "colab": {
2364 |           "base_uri": "https://localhost:8080/",
2365 |           "height": 269
2366 |         },
2367 |         "id": "cMKCVZ62kvJJ",
2368 |         "outputId": "6f9fa169-4a6c-4dba-896c-1809c5357520"
2369 |       },
2370 |       "source": [
2371 |         "#Can ignore on Day1. Can come back later and understand.\n",
2372 |         "import numpy as np\n",
2373 |         "import matplotlib.pyplot as plt\n",
2374 |         "\n",
2375 |         "x = np.linspace(-10,10)\n",
2376 |         "\n",
2377 |         "y1 = np.sin(x) ; y2 = x +1\n",
2378 |         "\n",
2379 |         "plt.plot(x,y1)\n",
2380 |         "plt.plot(x,y2)\n",
2381 |         "\n",
2382 |         "plt.axvline(root,color='red')\n",
2383 |         "\n",
2384 |         "plt.ylim(-3,3)\n",
2385 |         "plt.xlim(-5,5)\n",
2386 |         "\n",
2387 |         "plt.grid()"
2388 |       ],
2389 |       "execution_count": 151,
2390 |       "outputs": [
2391 |         {
2392 |           "output_type": "display_data",
2393 |           "data": {
2394 |             "image/png": 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\n",
2395 |             "text/plain": [
2396 |               "<Figure size 432x288 with 1 Axes>"
2397 |             ]
2398 |           },
2399 |           "metadata": {
2400 |             "tags": [],
2401 |             "needs_background": "light"
2402 |           }
2403 |         }
2404 |       ]
2405 |     },
2406 |     {
2407 |       "cell_type": "code",
2408 |       "metadata": {
2409 |         "id": "C-_q5YB4lWfm"
2410 |       },
2411 |       "source": [
2412 |         "# Analytical way :\n",
2413 |         "# sinx = x and solve."
2414 |       ],
2415 |       "execution_count": 133,
2416 |       "outputs": []
2417 |     },
2418 |     {
2419 |       "cell_type": "markdown",
2420 |       "metadata": {
2421 |         "id": "uSOL6iXRlxbb"
2422 |       },
2423 |       "source": [
2424 |         "### Using Newton-Raphson's Method. \n",
2425 |         "\n"
2426 |       ]
2427 |     },
2428 |     {
2429 |       "cell_type": "markdown",
2430 |       "metadata": {
2431 |         "id": "lf-uehoHmNiN"
2432 |       },
2433 |       "source": [
2434 |         "Step 1 : $ f(x) = 0 $\n",
2435 |         "\n",
2436 |         "Step 2 : $ x_{n+1} = x_{n} - \\frac{f(x_n)}{ f'(x_n)} $\n",
2437 |         "\n",
2438 |         "Repeat Step 2 till Convergence."
2439 |       ]
2440 |     },
2441 |     {
2442 |       "cell_type": "code",
2443 |       "metadata": {
2444 |         "id": "vKac8sHBlwcp"
2445 |       },
2446 |       "source": [
2447 |         "import math"
2448 |       ],
2449 |       "execution_count": 139,
2450 |       "outputs": []
2451 |     },
2452 |     {
2453 |       "cell_type": "code",
2454 |       "metadata": {
2455 |         "id": "9M6nwX7nmywQ"
2456 |       },
2457 |       "source": [
2458 |         "# f(x) = 1 + x - sin(x) \n",
2459 |         "# f'(x) = 1 - cos(x)\n",
2460 |         "\n",
2461 |         "f = lambda x_0 : 1 + x_0 - math.sin(x_0)\n",
2462 |         "fdash = lambda x_0 : 1 - math.cos(x_0)\n",
2463 |         "\n",
2464 |         "\n",
2465 |         "\n",
2466 |         "x0 = 1\n",
2467 |         "for i in range(2):\n",
2468 |         "  x0 = x0 - f(x0)/fdash(x0)\n",
2469 |         "\n",
2470 |         "root = x0"
2471 |       ],
2472 |       "execution_count": 150,
2473 |       "outputs": []
2474 |     },
2475 |     {
2476 |       "cell_type": "code",
2477 |       "metadata": {
2478 |         "colab": {
2479 |           "base_uri": "https://localhost:8080/"
2480 |         },
2481 |         "id": "wXjvAl4wm8z5",
2482 |         "outputId": "0e6c1f88-88ff-465f-a2d9-77b0c239bcf4"
2483 |       },
2484 |       "source": [
2485 |         "root"
2486 |       ],
2487 |       "execution_count": 154,
2488 |       "outputs": [
2489 |         {
2490 |           "output_type": "execute_result",
2491 |           "data": {
2492 |             "text/plain": [
2493 |               "-2.0242117630700327"
2494 |             ]
2495 |           },
2496 |           "metadata": {
2497 |             "tags": []
2498 |           },
2499 |           "execution_count": 154
2500 |         }
2501 |       ]
2502 |     },
2503 |     {
2504 |       "cell_type": "code",
2505 |       "metadata": {
2506 |         "colab": {
2507 |           "base_uri": "https://localhost:8080/"
2508 |         },
2509 |         "id": "XvvWzeghn0oz",
2510 |         "outputId": "103f2f3a-790a-48c6-cb1e-4e7ab8fb77af"
2511 |       },
2512 |       "source": [
2513 |         "math.sin(root)"
2514 |       ],
2515 |       "execution_count": 157,
2516 |       "outputs": [
2517 |         {
2518 |           "output_type": "execute_result",
2519 |           "data": {
2520 |             "text/plain": [
2521 |               "-0.8989562562324447"
2522 |             ]
2523 |           },
2524 |           "metadata": {
2525 |             "tags": []
2526 |           },
2527 |           "execution_count": 157
2528 |         }
2529 |       ]
2530 |     },
2531 |     {
2532 |       "cell_type": "code",
2533 |       "metadata": {
2534 |         "colab": {
2535 |           "base_uri": "https://localhost:8080/"
2536 |         },
2537 |         "id": "iVaPdDvvoSJQ",
2538 |         "outputId": "fb9897d3-27ed-4198-940e-51f554e35e26"
2539 |       },
2540 |       "source": [
2541 |         "1 + root"
2542 |       ],
2543 |       "execution_count": 156,
2544 |       "outputs": [
2545 |         {
2546 |           "output_type": "execute_result",
2547 |           "data": {
2548 |             "text/plain": [
2549 |               "-1.0242117630700327"
2550 |             ]
2551 |           },
2552 |           "metadata": {
2553 |             "tags": []
2554 |           },
2555 |           "execution_count": 156
2556 |         }
2557 |       ]
2558 |     },
2559 |     {
2560 |       "cell_type": "markdown",
2561 |       "metadata": {
2562 |         "id": "X4Cz_ur4olVH"
2563 |       },
2564 |       "source": [
2565 |         "# Homework Project :\n",
2566 |         "\n",
2567 |         "1. Finding Square Root Using Loops (Newton-Raphson). \n",
2568 |         "\n",
2569 |         "2. Klinkenberg Effect (Tarek Ahmed) using Newton Raphson.\n",
2570 |         "\n",
2571 |         "Source to Learn : https://www.youtube.com/watch?v=IHV7NO3akJ0&list=PLLwtZopJNyqZQ2n-UV0chlF3GXRL8f-ox"
2572 |       ]
2573 |     },
2574 |     {
2575 |       "cell_type": "code",
2576 |       "metadata": {
2577 |         "id": "qXSpELTsoVQE"
2578 |       },
2579 |       "source": [
2580 |         ""
2581 |       ],
2582 |       "execution_count": null,
2583 |       "outputs": []
2584 |     }
2585 |   ]
2586 | }


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/README.md:
--------------------------------------------------------------------------------
1 | # Petroleum-DS-ML-with-Python
2 | 
3 | This Repository consists of All the Codes/Notebooks/Presentations that I've either presented in various events in the past or will be presenting. 
4 | 
5 | The main aim is to put together all the Petroleum Data Analytics Projects at one place, and allow easy sharing. 
6 | 


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