├── Books ├── An Introduction to Statistical Learning using R (ISLR) Seventh Printing.pdf ├── Big Data Nowby O’Reilly Media.pdf ├── Foster Provost, Tom Fawcett Data Science for Business What you need to know about data mining and data-analytic thinking.pdf ├── Hands On Machine Learning with Scikit Learn and TensorFlow.pdf ├── Python Data Science Handbook - Jake VanderPlas.pdf ├── Ross_Introduction_to_probability_models.pdf └── Weapons-of-Math-Destruction-Cathy-ONeil.pdf ├── Data Analysis using Numpy and Pandas ├── NumPy Hands On.ipynb ├── Pandas Hands On.ipynb ├── SequenceData_Methods.pdf └── SequenceData_Operations.pdf ├── Data Visualization using Matplotlib ├── .ipynb_checkpoints │ └── Data Visualization using matplotlib (toyota) -checkpoint.ipynb ├── Advanced Matplotlib Concepts.ipynb ├── Data Visualization using matplotlib (toyota) .ipynb ├── Matplotlib Concepts Hands on.ipynb └── Toyota.csv ├── Data Visualization using Seaborn ├── .ipynb_checkpoints │ ├── Categorical Plots-checkpoint.ipynb │ └── Data Visualization using Seaborn-checkpoint.ipynb ├── Categorical Plots.ipynb ├── Data Visualization using Seaborn.ipynb ├── Distribution Plots.ipynb ├── Grids.ipynb ├── Matrix Plots.ipynb ├── Regression Plots.ipynb ├── Style and Color.ipynb └── Toyota.csv ├── Decision Tree and Random Forest ├── Decision Tree.ipynb ├── Decision Trees and Random Forest Project ( Lending Club).ipynb ├── kyphosis.csv └── loan_data.csv ├── Evaluation Matrix └── Confusion Matrix.ipynb ├── Exploratory Data Analysis └── Exploratory data analysis.ipynb ├── Geographical Plotting ├── 2011_US_AGRI_Exports ├── 2012_Election_Data ├── 2014_World_GDP ├── 2014_World_Power_Consumption ├── Choropleth Maps Exercise.ipynb └── Choropleth Maps.ipynb ├── K Means Clustering ├── College_Data ├── K Means Clustering Project.ipynb └── K Means Clustering.ipynb ├── K Nearest Neighbors ├── Classified Data ├── K Nearest Neighbors Project.ipynb ├── KNN.ipynb └── KNN_Project_DataSET ├── Linear Regression ├── Cloth Shop Linear Regression Project.ipynb ├── Ecommerce Customers ├── Linear Regression Project.ipynb ├── Linear Regression with Python.ipynb └── USA_Housing.csv ├── Logistic Regression ├── Advertising Logistic Regression Project.ipynb ├── Logistic Regression (Titanic Data).ipynb ├── advertising.csv ├── titanic_test.csv └── titanic_train.csv ├── My-Certificates ├── Applied-Data-Science-with-Python-Specialization Certificates │ ├── 1 Introduction to Data Science in Python Coursera.pdf │ ├── 2 Applied Plotting, Charting & Data Representationin Pytho Coursera.pdf │ ├── 3 Applied Machine Learning in Python Coursera.pdf │ ├── 4 Applied Text Mining in Python Coursera.pdf │ ├── 5 Applied Social Network Analysis in Python Coursera.pdf │ ├── Applied Data Science with Python (Specialization) Coursera.pdf │ └── README.md └── README.md ├── Natural Language Processing ├── SMS SPAM CHECK (NLP) .ipynb ├── Yelp Business Rating Prediction (NLP).ipynb ├── smsspamcollection │ ├── SMSSpamCollection │ └── readme └── yelp.csv ├── Pandas Build in Plotting ├── Pandas build in Plotting.ipynb └── df1 ├── Plotly-and-Cufflinks ├── Plotly and Cufflinks.ipynb └── plotly_cheat_sheet.pdf ├── Principal Component Analysis └── Principal Component Analysis.ipynb ├── README.md ├── Recommender System ├── Movie Recommedation System.ipynb ├── Movie_Id_Titles ├── u.data └── u.item └── Support Vector Machines ├── SVM Breast Cancer .ipynb └── Support Vector Machines Project.ipynb /Books/An Introduction to Statistical Learning using R (ISLR) Seventh Printing.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/An Introduction to Statistical Learning using R (ISLR) Seventh Printing.pdf -------------------------------------------------------------------------------- /Books/Big Data Nowby O’Reilly Media.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Big Data Nowby O’Reilly Media.pdf -------------------------------------------------------------------------------- /Books/Foster Provost, Tom Fawcett Data Science for Business What you need to know about data mining and data-analytic thinking.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Foster Provost, Tom Fawcett Data Science for Business What you need to know about data mining and data-analytic thinking.pdf -------------------------------------------------------------------------------- /Books/Hands On Machine Learning with Scikit Learn and TensorFlow.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Hands On Machine Learning with Scikit Learn and TensorFlow.pdf -------------------------------------------------------------------------------- /Books/Python Data Science Handbook - Jake VanderPlas.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Python Data Science Handbook - Jake VanderPlas.pdf -------------------------------------------------------------------------------- /Books/Ross_Introduction_to_probability_models.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Ross_Introduction_to_probability_models.pdf -------------------------------------------------------------------------------- /Books/Weapons-of-Math-Destruction-Cathy-ONeil.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Books/Weapons-of-Math-Destruction-Cathy-ONeil.pdf -------------------------------------------------------------------------------- /Data Analysis using Numpy and Pandas/NumPy Hands On.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 3, 15 | "metadata": {}, 16 | "outputs": [ 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | "[[1 2 3 4 5]\n", 22 | " [1 2 3 4 5]]\n" 23 | ] 24 | } 25 | ], 26 | "source": [ 27 | "a = np.array([[1,2,3,4,5],[1,2,3,4,5]],dtype=int)\n", 28 | "print(a)" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 4, 34 | "metadata": {}, 35 | "outputs": [ 36 | { 37 | "name": "stdout", 38 | "output_type": "stream", 39 | "text": [ 40 | "\n", 41 | "2\n", 42 | "2\n", 43 | "(2, 5)\n" 44 | ] 45 | } 46 | ], 47 | "source": [ 48 | "print(type(a))\n", 49 | "print(len(a))\n", 50 | "print(a.ndim)\n", 51 | "print(a.shape)" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 5, 57 | "metadata": {}, 58 | "outputs": [ 59 | { 60 | "name": "stdout", 61 | "output_type": "stream", 62 | "text": [ 63 | "[[1 2 3 4 5]\n", 64 | " [1 2 3 4 5]]\n", 65 | "(2, 5)\n" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "a1 = a.reshape(2,5)\n", 71 | "print(a1)\n", 72 | "print(a1.shape)" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 6, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "name": "stdout", 82 | "output_type": "stream", 83 | "text": [ 84 | "[[1 2]\n", 85 | " [3 4]\n", 86 | " [5 1]\n", 87 | " [2 3]\n", 88 | " [4 5]]\n" 89 | ] 90 | } 91 | ], 92 | "source": [ 93 | "a2 = a.reshape(5,-1)\n", 94 | "print(a2)" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 7, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "name": "stdout", 104 | "output_type": "stream", 105 | "text": [ 106 | "2\n" 107 | ] 108 | } 109 | ], 110 | "source": [ 111 | "print(a2.ndim)" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 8, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "name": "stdout", 121 | "output_type": "stream", 122 | "text": [ 123 | "(1, 15)\n", 124 | "[[1 2 3 4 5 2 3 4 5 6 9 7 6 8 9]]\n", 125 | "Wall time: 499 µs\n" 126 | ] 127 | } 128 | ], 129 | "source": [ 130 | "%%time\n", 131 | "l1 = [1,2,3,4,5]\n", 132 | "l2 = [2,3,4,5,6]\n", 133 | "l3 = [9,7,6,8,9]\n", 134 | "\n", 135 | "mul_l = np.array([l1,l2,l3],dtype=int)\n", 136 | "#print(mul_l.shape)\n", 137 | "mul_l = mul_l.reshape(1,15)\n", 138 | "print(mul_l.shape)\n", 139 | "print(mul_l)" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": 9, 145 | "metadata": {}, 146 | "outputs": [ 147 | { 148 | "name": "stdout", 149 | "output_type": "stream", 150 | "text": [ 151 | "[[1 2 3 4 5]\n", 152 | " [2 3 4 5 6]\n", 153 | " [9 7 6 8 9]]\n" 154 | ] 155 | } 156 | ], 157 | "source": [ 158 | "mul_l.shape = (3,5)\n", 159 | "print(mul_l)" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 10, 165 | "metadata": {}, 166 | "outputs": [ 167 | { 168 | "data": { 169 | "text/plain": [ 170 | "range(0, 24)" 171 | ] 172 | }, 173 | "execution_count": 10, 174 | "metadata": {}, 175 | "output_type": "execute_result" 176 | } 177 | ], 178 | "source": [ 179 | "r = range(24)\n", 180 | "r" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 11, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "name": "stdout", 190 | "output_type": "stream", 191 | "text": [ 192 | "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", 193 | "1\n" 194 | ] 195 | } 196 | ], 197 | "source": [ 198 | "a1 = np.arange(24)\n", 199 | "print(a1)\n", 200 | "print(a1.ndim)" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 12, 206 | "metadata": {}, 207 | "outputs": [ 208 | { 209 | "name": "stdout", 210 | "output_type": "stream", 211 | "text": [ 212 | "[[[ 0 1]\n", 213 | " [ 2 3]]\n", 214 | "\n", 215 | " [[ 4 5]\n", 216 | " [ 6 7]]\n", 217 | "\n", 218 | " [[ 8 9]\n", 219 | " [10 11]]\n", 220 | "\n", 221 | " [[12 13]\n", 222 | " [14 15]]\n", 223 | "\n", 224 | " [[16 17]\n", 225 | " [18 19]]\n", 226 | "\n", 227 | " [[20 21]\n", 228 | " [22 23]]]\n" 229 | ] 230 | } 231 | ], 232 | "source": [ 233 | "b = a1.reshape(6,2,2)\n", 234 | "print(b)" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 13, 240 | "metadata": {}, 241 | "outputs": [ 242 | { 243 | "name": "stdout", 244 | "output_type": "stream", 245 | "text": [ 246 | "1\n" 247 | ] 248 | } 249 | ], 250 | "source": [ 251 | "x = np.array([1,2,3,4,5], dtype = np.int8)\n", 252 | "print(x.itemsize)" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": 14, 258 | "metadata": {}, 259 | "outputs": [ 260 | { 261 | "name": "stdout", 262 | "output_type": "stream", 263 | "text": [ 264 | "4\n", 265 | "8\n" 266 | ] 267 | } 268 | ], 269 | "source": [ 270 | "x = np.array([1,2,3,4,5], dtype = np.int32)\n", 271 | "print(x.itemsize)\n", 272 | "x = np.array([1,2,3,4,5], dtype = np.int64)\n", 273 | "print(x.itemsize)" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 15, 279 | "metadata": {}, 280 | "outputs": [ 281 | { 282 | "name": "stdout", 283 | "output_type": "stream", 284 | "text": [ 285 | "[[1. 2.]\n", 286 | " [3. 4.]]\n", 287 | "[[5. 6.]\n", 288 | " [7. 8.]]\n" 289 | ] 290 | } 291 | ], 292 | "source": [ 293 | "x = np.array([[1,2],[3,4]],dtype = np.float64)\n", 294 | "y = np.array([[5,6],[7,8]],dtype = np.float64)\n", 295 | "print(x)\n", 296 | "print(y)" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": 16, 302 | "metadata": {}, 303 | "outputs": [ 304 | { 305 | "name": "stdout", 306 | "output_type": "stream", 307 | "text": [ 308 | "[[ 6. 8.]\n", 309 | " [10. 12.]]\n" 310 | ] 311 | } 312 | ], 313 | "source": [ 314 | "print(x+y)" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 17, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "name": "stdout", 324 | "output_type": "stream", 325 | "text": [ 326 | "[[ 6. 8.]\n", 327 | " [10. 12.]]\n" 328 | ] 329 | } 330 | ], 331 | "source": [ 332 | "print(np.add(x,y))" 333 | ] 334 | }, 335 | { 336 | "cell_type": "code", 337 | "execution_count": 18, 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "name": "stdout", 342 | "output_type": "stream", 343 | "text": [ 344 | "[[-4. -4.]\n", 345 | " [-4. -4.]]\n" 346 | ] 347 | } 348 | ], 349 | "source": [ 350 | "print(x-y)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 19, 356 | "metadata": {}, 357 | "outputs": [ 358 | { 359 | "name": "stdout", 360 | "output_type": "stream", 361 | "text": [ 362 | "[[-4. -4.]\n", 363 | " [-4. -4.]]\n" 364 | ] 365 | } 366 | ], 367 | "source": [ 368 | "print(np.subtract(x,y))" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 20, 374 | "metadata": {}, 375 | "outputs": [ 376 | { 377 | "name": "stdout", 378 | "output_type": "stream", 379 | "text": [ 380 | "[[1. 2.]\n", 381 | " [3. 4.]]\n", 382 | "[[5. 6.]\n", 383 | " [7. 8.]]\n", 384 | "[[ 5. 12.]\n", 385 | " [21. 32.]]\n" 386 | ] 387 | } 388 | ], 389 | "source": [ 390 | "print(x)\n", 391 | "print(y)\n", 392 | "print(x*y)" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 21, 398 | "metadata": {}, 399 | "outputs": [ 400 | { 401 | "name": "stdout", 402 | "output_type": "stream", 403 | "text": [ 404 | "[[ 5. 12.]\n", 405 | " [21. 32.]]\n" 406 | ] 407 | } 408 | ], 409 | "source": [ 410 | "print(np.multiply(x,y))" 411 | ] 412 | }, 413 | { 414 | "cell_type": "code", 415 | "execution_count": 22, 416 | "metadata": {}, 417 | "outputs": [ 418 | { 419 | "name": "stdout", 420 | "output_type": "stream", 421 | "text": [ 422 | "[[1. 2.]\n", 423 | " [3. 4.]]\n", 424 | "[[5. 6.]\n", 425 | " [7. 8.]]\n", 426 | "[[19. 22.]\n", 427 | " [43. 50.]]\n" 428 | ] 429 | } 430 | ], 431 | "source": [ 432 | "print(x)\n", 433 | "print(y)\n", 434 | "print(np.dot(x,y)) #matrix multiplication" 435 | ] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "execution_count": 23, 440 | "metadata": {}, 441 | "outputs": [ 442 | { 443 | "name": "stdout", 444 | "output_type": "stream", 445 | "text": [ 446 | "[[1. 2.]\n", 447 | " [3. 4.]]\n", 448 | "[[5. 6.]\n", 449 | " [7. 8.]]\n", 450 | "[[0.2 0.33333333]\n", 451 | " [0.42857143 0.5 ]]\n" 452 | ] 453 | } 454 | ], 455 | "source": [ 456 | "print(x)\n", 457 | "print(y)\n", 458 | "print(np.divide(x,y))" 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 24, 464 | "metadata": {}, 465 | "outputs": [ 466 | { 467 | "name": "stdout", 468 | "output_type": "stream", 469 | "text": [ 470 | "[[0.2 0.33333333]\n", 471 | " [0.42857143 0.5 ]]\n" 472 | ] 473 | } 474 | ], 475 | "source": [ 476 | "print(x/y)" 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": 25, 482 | "metadata": {}, 483 | "outputs": [ 484 | { 485 | "name": "stdout", 486 | "output_type": "stream", 487 | "text": [ 488 | "10.0\n" 489 | ] 490 | } 491 | ], 492 | "source": [ 493 | "print(np.sum(x))" 494 | ] 495 | }, 496 | { 497 | "cell_type": "code", 498 | "execution_count": 26, 499 | "metadata": {}, 500 | "outputs": [ 501 | { 502 | "name": "stdout", 503 | "output_type": "stream", 504 | "text": [ 505 | "[4. 6.]\n" 506 | ] 507 | } 508 | ], 509 | "source": [ 510 | "print(np.sum(x, axis=0))" 511 | ] 512 | }, 513 | { 514 | "cell_type": "code", 515 | "execution_count": 27, 516 | "metadata": {}, 517 | "outputs": [ 518 | { 519 | "name": "stdout", 520 | "output_type": "stream", 521 | "text": [ 522 | "[3. 7.]\n" 523 | ] 524 | } 525 | ], 526 | "source": [ 527 | "print(np.sum(x, axis=1))" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 32, 533 | "metadata": {}, 534 | "outputs": [ 535 | { 536 | "data": { 537 | "text/plain": [ 538 | "array([0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", 539 | " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", 540 | " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", 541 | " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", 542 | " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", 543 | " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", 544 | " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", 545 | " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", 546 | " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", 547 | " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", 548 | " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", 549 | " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", 550 | " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", 551 | " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", 552 | " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", 553 | " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", 554 | " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", 555 | " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", 556 | " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", 557 | " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ])" 558 | ] 559 | }, 560 | "execution_count": 32, 561 | "metadata": {}, 562 | "output_type": "execute_result" 563 | } 564 | ], 565 | "source": [ 566 | "np.linspace(0,1,100)" 567 | ] 568 | }, 569 | { 570 | "cell_type": "code", 571 | "execution_count": 35, 572 | "metadata": {}, 573 | "outputs": [ 574 | { 575 | "data": { 576 | "text/plain": [ 577 | "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", 578 | " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", 579 | " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", 580 | " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", 581 | " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", 582 | " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", 583 | " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", 584 | " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", 585 | " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", 586 | " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])" 587 | ] 588 | }, 589 | "execution_count": 35, 590 | "metadata": {}, 591 | "output_type": "execute_result" 592 | } 593 | ], 594 | "source": [ 595 | "np.eye(10)" 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 38, 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "data": { 605 | "text/plain": [ 606 | "array([[0.70139299, 0.97053816, 0.76878328, 0.78243071, 0.6254806 ],\n", 607 | " [0.51445826, 0.61712515, 0.57078502, 0.0646338 , 0.59615391],\n", 608 | " [0.68245078, 0.02527595, 0.35008573, 0.70026666, 0.67735774],\n", 609 | " [0.94818667, 0.81106094, 0.39393055, 0.44454676, 0.62865448],\n", 610 | " [0.5143576 , 0.03422451, 0.93032803, 0.67588856, 0.40927504]])" 611 | ] 612 | }, 613 | "execution_count": 38, 614 | "metadata": {}, 615 | "output_type": "execute_result" 616 | } 617 | ], 618 | "source": [ 619 | "np.random.rand(5,5)" 620 | ] 621 | }, 622 | { 623 | "cell_type": "code", 624 | "execution_count": 40, 625 | "metadata": {}, 626 | "outputs": [ 627 | { 628 | "data": { 629 | "text/plain": [ 630 | "array([ 0.79874071, 0.32536748, 2.21222659, -0.50185226, 0.01328972])" 631 | ] 632 | }, 633 | "execution_count": 40, 634 | "metadata": {}, 635 | "output_type": "execute_result" 636 | } 637 | ], 638 | "source": [ 639 | "np.random.randn(5)" 640 | ] 641 | }, 642 | { 643 | "cell_type": "code", 644 | "execution_count": 44, 645 | "metadata": {}, 646 | "outputs": [ 647 | { 648 | "data": { 649 | "text/plain": [ 650 | "array([10, 17, 95, 19, 10, 64, 48, 34, 73, 12])" 651 | ] 652 | }, 653 | "execution_count": 44, 654 | "metadata": {}, 655 | "output_type": "execute_result" 656 | } 657 | ], 658 | "source": [ 659 | "np.random.randint(1,100,10)" 660 | ] 661 | }, 662 | { 663 | "cell_type": "code", 664 | "execution_count": 45, 665 | "metadata": {}, 666 | "outputs": [], 667 | "source": [ 668 | "arr = np.arange(25)" 669 | ] 670 | }, 671 | { 672 | "cell_type": "code", 673 | "execution_count": 46, 674 | "metadata": {}, 675 | "outputs": [ 676 | { 677 | "data": { 678 | "text/plain": [ 679 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", 680 | " 17, 18, 19, 20, 21, 22, 23, 24])" 681 | ] 682 | }, 683 | "execution_count": 46, 684 | "metadata": {}, 685 | "output_type": "execute_result" 686 | } 687 | ], 688 | "source": [ 689 | "arr" 690 | ] 691 | }, 692 | { 693 | "cell_type": "code", 694 | "execution_count": 47, 695 | "metadata": {}, 696 | "outputs": [], 697 | "source": [ 698 | "ranarr = np.random.randint(0,50,10)" 699 | ] 700 | }, 701 | { 702 | "cell_type": "code", 703 | "execution_count": 48, 704 | "metadata": {}, 705 | "outputs": [ 706 | { 707 | "data": { 708 | "text/plain": [ 709 | "array([ 5, 38, 25, 6, 28, 9, 33, 27, 49, 0])" 710 | ] 711 | }, 712 | "execution_count": 48, 713 | "metadata": {}, 714 | "output_type": "execute_result" 715 | } 716 | ], 717 | "source": [ 718 | "ranarr" 719 | ] 720 | }, 721 | { 722 | "cell_type": "code", 723 | "execution_count": 51, 724 | "metadata": {}, 725 | "outputs": [ 726 | { 727 | "data": { 728 | "text/plain": [ 729 | "49" 730 | ] 731 | }, 732 | "execution_count": 51, 733 | "metadata": {}, 734 | "output_type": "execute_result" 735 | } 736 | ], 737 | "source": [ 738 | "ranarr.max()" 739 | ] 740 | }, 741 | { 742 | "cell_type": "code", 743 | "execution_count": 54, 744 | "metadata": {}, 745 | "outputs": [ 746 | { 747 | "data": { 748 | "text/plain": [ 749 | "0" 750 | ] 751 | }, 752 | "execution_count": 54, 753 | "metadata": {}, 754 | "output_type": "execute_result" 755 | } 756 | ], 757 | "source": [ 758 | "ranarr.min()" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 53, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "9" 770 | ] 771 | }, 772 | "execution_count": 53, 773 | "metadata": {}, 774 | "output_type": "execute_result" 775 | } 776 | ], 777 | "source": [ 778 | "ranarr.argmin()" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 55, 784 | "metadata": {}, 785 | "outputs": [ 786 | { 787 | "data": { 788 | "text/plain": [ 789 | "8" 790 | ] 791 | }, 792 | "execution_count": 55, 793 | "metadata": {}, 794 | "output_type": "execute_result" 795 | } 796 | ], 797 | "source": [ 798 | "ranarr.argmax()" 799 | ] 800 | }, 801 | { 802 | "cell_type": "code", 803 | "execution_count": 61, 804 | "metadata": {}, 805 | "outputs": [ 806 | { 807 | "data": { 808 | "text/plain": [ 809 | "(25,)" 810 | ] 811 | }, 812 | "execution_count": 61, 813 | "metadata": {}, 814 | "output_type": "execute_result" 815 | } 816 | ], 817 | "source": [ 818 | "arr.shape" 819 | ] 820 | }, 821 | { 822 | "cell_type": "code", 823 | "execution_count": 60, 824 | "metadata": {}, 825 | "outputs": [ 826 | { 827 | "data": { 828 | "text/plain": [ 829 | "array([[ 0, 1, 2, 3, 4],\n", 830 | " [ 5, 6, 7, 8, 9],\n", 831 | " [10, 11, 12, 13, 14],\n", 832 | " [15, 16, 17, 18, 19],\n", 833 | " [20, 21, 22, 23, 24]])" 834 | ] 835 | }, 836 | "execution_count": 60, 837 | "metadata": {}, 838 | "output_type": "execute_result" 839 | } 840 | ], 841 | "source": [ 842 | "arr.reshape(5,5)" 843 | ] 844 | }, 845 | { 846 | "cell_type": "code", 847 | "execution_count": 62, 848 | "metadata": {}, 849 | "outputs": [ 850 | { 851 | "data": { 852 | "text/plain": [ 853 | "dtype('int32')" 854 | ] 855 | }, 856 | "execution_count": 62, 857 | "metadata": {}, 858 | "output_type": "execute_result" 859 | } 860 | ], 861 | "source": [ 862 | "arr.dtype" 863 | ] 864 | }, 865 | { 866 | "cell_type": "code", 867 | "execution_count": 63, 868 | "metadata": {}, 869 | "outputs": [], 870 | "source": [ 871 | "from numpy.random import randint" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": 64, 877 | "metadata": {}, 878 | "outputs": [ 879 | { 880 | "data": { 881 | "text/plain": [ 882 | "9" 883 | ] 884 | }, 885 | "execution_count": 64, 886 | "metadata": {}, 887 | "output_type": "execute_result" 888 | } 889 | ], 890 | "source": [ 891 | "randint(2,10)" 892 | ] 893 | }, 894 | { 895 | "cell_type": "code", 896 | "execution_count": 88, 897 | "metadata": {}, 898 | "outputs": [], 899 | "source": [ 900 | "arr = np.arange(0,11)" 901 | ] 902 | }, 903 | { 904 | "cell_type": "code", 905 | "execution_count": 89, 906 | "metadata": {}, 907 | "outputs": [ 908 | { 909 | "data": { 910 | "text/plain": [ 911 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" 912 | ] 913 | }, 914 | "execution_count": 89, 915 | "metadata": {}, 916 | "output_type": "execute_result" 917 | } 918 | ], 919 | "source": [ 920 | "arr" 921 | ] 922 | }, 923 | { 924 | "cell_type": "code", 925 | "execution_count": 90, 926 | "metadata": {}, 927 | "outputs": [], 928 | "source": [ 929 | "slice_of_arr = arr[0:5]" 930 | ] 931 | }, 932 | { 933 | "cell_type": "code", 934 | "execution_count": 91, 935 | "metadata": {}, 936 | "outputs": [], 937 | "source": [ 938 | "slice_of_arr[:] = 99" 939 | ] 940 | }, 941 | { 942 | "cell_type": "code", 943 | "execution_count": 92, 944 | "metadata": {}, 945 | "outputs": [ 946 | { 947 | "data": { 948 | "text/plain": [ 949 | "array([99, 99, 99, 99, 99])" 950 | ] 951 | }, 952 | "execution_count": 92, 953 | "metadata": {}, 954 | "output_type": "execute_result" 955 | } 956 | ], 957 | "source": [ 958 | "slice_of_arr" 959 | ] 960 | }, 961 | { 962 | "cell_type": "code", 963 | "execution_count": 93, 964 | "metadata": {}, 965 | "outputs": [ 966 | { 967 | "data": { 968 | "text/plain": [ 969 | "array([99, 99, 99, 99, 99, 5, 6, 7, 8, 9, 10])" 970 | ] 971 | }, 972 | "execution_count": 93, 973 | "metadata": {}, 974 | "output_type": "execute_result" 975 | } 976 | ], 977 | "source": [ 978 | "arr" 979 | ] 980 | }, 981 | { 982 | "cell_type": "code", 983 | "execution_count": 94, 984 | "metadata": {}, 985 | "outputs": [], 986 | "source": [ 987 | "arr_copy = arr[0:5].copy()" 988 | ] 989 | }, 990 | { 991 | "cell_type": "code", 992 | "execution_count": 95, 993 | "metadata": {}, 994 | "outputs": [ 995 | { 996 | "data": { 997 | "text/plain": [ 998 | "array([99, 99, 99, 99, 99])" 999 | ] 1000 | }, 1001 | "execution_count": 95, 1002 | "metadata": {}, 1003 | "output_type": "execute_result" 1004 | } 1005 | ], 1006 | "source": [ 1007 | "arr_copy" 1008 | ] 1009 | }, 1010 | { 1011 | "cell_type": "code", 1012 | "execution_count": 96, 1013 | "metadata": {}, 1014 | "outputs": [], 1015 | "source": [ 1016 | "arr_copy[:] = 100" 1017 | ] 1018 | }, 1019 | { 1020 | "cell_type": "code", 1021 | "execution_count": 97, 1022 | "metadata": {}, 1023 | "outputs": [ 1024 | { 1025 | "data": { 1026 | "text/plain": [ 1027 | "array([99, 99, 99, 99, 99, 5, 6, 7, 8, 9, 10])" 1028 | ] 1029 | }, 1030 | "execution_count": 97, 1031 | "metadata": {}, 1032 | "output_type": "execute_result" 1033 | } 1034 | ], 1035 | "source": [ 1036 | "arr" 1037 | ] 1038 | }, 1039 | { 1040 | "cell_type": "code", 1041 | "execution_count": 98, 1042 | "metadata": {}, 1043 | "outputs": [], 1044 | "source": [ 1045 | "arr_2d = np.array([[5,10,15],[20,25,30],[35,40,45]])" 1046 | ] 1047 | }, 1048 | { 1049 | "cell_type": "code", 1050 | "execution_count": 99, 1051 | "metadata": {}, 1052 | "outputs": [ 1053 | { 1054 | "data": { 1055 | "text/plain": [ 1056 | "array([[ 5, 10, 15],\n", 1057 | " [20, 25, 30],\n", 1058 | " [35, 40, 45]])" 1059 | ] 1060 | }, 1061 | "execution_count": 99, 1062 | "metadata": {}, 1063 | "output_type": "execute_result" 1064 | } 1065 | ], 1066 | "source": [ 1067 | "arr_2d" 1068 | ] 1069 | }, 1070 | { 1071 | "cell_type": "code", 1072 | "execution_count": 106, 1073 | "metadata": {}, 1074 | "outputs": [ 1075 | { 1076 | "data": { 1077 | "text/plain": [ 1078 | "40" 1079 | ] 1080 | }, 1081 | "execution_count": 106, 1082 | "metadata": {}, 1083 | "output_type": "execute_result" 1084 | } 1085 | ], 1086 | "source": [ 1087 | "arr_2d[2,1] \n", 1088 | "#or\n", 1089 | "arr_2d[2][1]\n" 1090 | ] 1091 | }, 1092 | { 1093 | "cell_type": "code", 1094 | "execution_count": 117, 1095 | "metadata": {}, 1096 | "outputs": [ 1097 | { 1098 | "data": { 1099 | "text/plain": [ 1100 | "array([[10, 15],\n", 1101 | " [25, 30]])" 1102 | ] 1103 | }, 1104 | "execution_count": 117, 1105 | "metadata": {}, 1106 | "output_type": "execute_result" 1107 | } 1108 | ], 1109 | "source": [ 1110 | "arr_2d[:2,1:] " 1111 | ] 1112 | }, 1113 | { 1114 | "cell_type": "code", 1115 | "execution_count": 118, 1116 | "metadata": {}, 1117 | "outputs": [], 1118 | "source": [ 1119 | "arr = np.arange(1,11)" 1120 | ] 1121 | }, 1122 | { 1123 | "cell_type": "code", 1124 | "execution_count": 119, 1125 | "metadata": {}, 1126 | "outputs": [ 1127 | { 1128 | "data": { 1129 | "text/plain": [ 1130 | "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" 1131 | ] 1132 | }, 1133 | "execution_count": 119, 1134 | "metadata": {}, 1135 | "output_type": "execute_result" 1136 | } 1137 | ], 1138 | "source": [ 1139 | "arr" 1140 | ] 1141 | }, 1142 | { 1143 | "cell_type": "code", 1144 | "execution_count": 122, 1145 | "metadata": {}, 1146 | "outputs": [], 1147 | "source": [ 1148 | "bool_arr = arr > 5" 1149 | ] 1150 | }, 1151 | { 1152 | "cell_type": "code", 1153 | "execution_count": 123, 1154 | "metadata": {}, 1155 | "outputs": [ 1156 | { 1157 | "data": { 1158 | "text/plain": [ 1159 | "array([ 6, 7, 8, 9, 10])" 1160 | ] 1161 | }, 1162 | "execution_count": 123, 1163 | "metadata": {}, 1164 | "output_type": "execute_result" 1165 | } 1166 | ], 1167 | "source": [ 1168 | "arr[bool_arr]" 1169 | ] 1170 | }, 1171 | { 1172 | "cell_type": "code", 1173 | "execution_count": 124, 1174 | "metadata": {}, 1175 | "outputs": [ 1176 | { 1177 | "data": { 1178 | "text/plain": [ 1179 | "array([1, 2])" 1180 | ] 1181 | }, 1182 | "execution_count": 124, 1183 | "metadata": {}, 1184 | "output_type": "execute_result" 1185 | } 1186 | ], 1187 | "source": [ 1188 | "arr[arr<3]" 1189 | ] 1190 | }, 1191 | { 1192 | "cell_type": "code", 1193 | "execution_count": 125, 1194 | "metadata": {}, 1195 | "outputs": [], 1196 | "source": [ 1197 | "arr_2d = np.arange(50).reshape(5,10)" 1198 | ] 1199 | }, 1200 | { 1201 | "cell_type": "code", 1202 | "execution_count": 132, 1203 | "metadata": {}, 1204 | "outputs": [ 1205 | { 1206 | "data": { 1207 | "text/plain": [ 1208 | "array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n", 1209 | " [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])" 1210 | ] 1211 | }, 1212 | "execution_count": 132, 1213 | "metadata": {}, 1214 | "output_type": "execute_result" 1215 | } 1216 | ], 1217 | "source": [ 1218 | "arr_2d[1:3]" 1219 | ] 1220 | }, 1221 | { 1222 | "cell_type": "code", 1223 | "execution_count": 134, 1224 | "metadata": {}, 1225 | "outputs": [], 1226 | "source": [ 1227 | "arr = np.arange(0,11)" 1228 | ] 1229 | }, 1230 | { 1231 | "cell_type": "code", 1232 | "execution_count": 135, 1233 | "metadata": {}, 1234 | "outputs": [ 1235 | { 1236 | "data": { 1237 | "text/plain": [ 1238 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" 1239 | ] 1240 | }, 1241 | "execution_count": 135, 1242 | "metadata": {}, 1243 | "output_type": "execute_result" 1244 | } 1245 | ], 1246 | "source": [ 1247 | "arr" 1248 | ] 1249 | }, 1250 | { 1251 | "cell_type": "code", 1252 | "execution_count": 136, 1253 | "metadata": {}, 1254 | "outputs": [ 1255 | { 1256 | "data": { 1257 | "text/plain": [ 1258 | "array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])" 1259 | ] 1260 | }, 1261 | "execution_count": 136, 1262 | "metadata": {}, 1263 | "output_type": "execute_result" 1264 | } 1265 | ], 1266 | "source": [ 1267 | "arr + arr" 1268 | ] 1269 | }, 1270 | { 1271 | "cell_type": "code", 1272 | "execution_count": 137, 1273 | "metadata": {}, 1274 | "outputs": [ 1275 | { 1276 | "data": { 1277 | "text/plain": [ 1278 | "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" 1279 | ] 1280 | }, 1281 | "execution_count": 137, 1282 | "metadata": {}, 1283 | "output_type": "execute_result" 1284 | } 1285 | ], 1286 | "source": [ 1287 | "arr - arr" 1288 | ] 1289 | }, 1290 | { 1291 | "cell_type": "code", 1292 | "execution_count": 138, 1293 | "metadata": {}, 1294 | "outputs": [ 1295 | { 1296 | "data": { 1297 | "text/plain": [ 1298 | "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])" 1299 | ] 1300 | }, 1301 | "execution_count": 138, 1302 | "metadata": {}, 1303 | "output_type": "execute_result" 1304 | } 1305 | ], 1306 | "source": [ 1307 | "arr * arr" 1308 | ] 1309 | }, 1310 | { 1311 | "cell_type": "code", 1312 | "execution_count": 139, 1313 | "metadata": {}, 1314 | "outputs": [ 1315 | { 1316 | "data": { 1317 | "text/plain": [ 1318 | "array([ 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])" 1319 | ] 1320 | }, 1321 | "execution_count": 139, 1322 | "metadata": {}, 1323 | "output_type": "execute_result" 1324 | } 1325 | ], 1326 | "source": [ 1327 | "arr * 100" 1328 | ] 1329 | }, 1330 | { 1331 | "cell_type": "code", 1332 | "execution_count": 140, 1333 | "metadata": {}, 1334 | "outputs": [ 1335 | { 1336 | "data": { 1337 | "text/plain": [ 1338 | "array([-100, -99, -98, -97, -96, -95, -94, -93, -92, -91, -90])" 1339 | ] 1340 | }, 1341 | "execution_count": 140, 1342 | "metadata": {}, 1343 | "output_type": "execute_result" 1344 | } 1345 | ], 1346 | "source": [ 1347 | "arr - 100" 1348 | ] 1349 | }, 1350 | { 1351 | "cell_type": "code", 1352 | "execution_count": 141, 1353 | "metadata": {}, 1354 | "outputs": [ 1355 | { 1356 | "ename": "ZeroDivisionError", 1357 | "evalue": "division by zero", 1358 | "output_type": "error", 1359 | "traceback": [ 1360 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 1361 | "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", 1362 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 1363 | "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" 1364 | ] 1365 | } 1366 | ], 1367 | "source": [ 1368 | "1/0" 1369 | ] 1370 | }, 1371 | { 1372 | "cell_type": "code", 1373 | "execution_count": 142, 1374 | "metadata": {}, 1375 | "outputs": [ 1376 | { 1377 | "name": "stderr", 1378 | "output_type": "stream", 1379 | "text": [ 1380 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n", 1381 | " \"\"\"Entry point for launching an IPython kernel.\n" 1382 | ] 1383 | }, 1384 | { 1385 | "data": { 1386 | "text/plain": [ 1387 | "array([nan, 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" 1388 | ] 1389 | }, 1390 | "execution_count": 142, 1391 | "metadata": {}, 1392 | "output_type": "execute_result" 1393 | } 1394 | ], 1395 | "source": [ 1396 | "arr / arr" 1397 | ] 1398 | }, 1399 | { 1400 | "cell_type": "code", 1401 | "execution_count": 143, 1402 | "metadata": {}, 1403 | "outputs": [ 1404 | { 1405 | "name": "stderr", 1406 | "output_type": "stream", 1407 | "text": [ 1408 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in true_divide\n", 1409 | " \"\"\"Entry point for launching an IPython kernel.\n", 1410 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n", 1411 | " \"\"\"Entry point for launching an IPython kernel.\n" 1412 | ] 1413 | }, 1414 | { 1415 | "data": { 1416 | "text/plain": [ 1417 | "array([nan, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])" 1418 | ] 1419 | }, 1420 | "execution_count": 143, 1421 | "metadata": {}, 1422 | "output_type": "execute_result" 1423 | } 1424 | ], 1425 | "source": [ 1426 | "arr / 0" 1427 | ] 1428 | }, 1429 | { 1430 | "cell_type": "code", 1431 | "execution_count": 144, 1432 | "metadata": {}, 1433 | "outputs": [ 1434 | { 1435 | "data": { 1436 | "text/plain": [ 1437 | "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100], dtype=int32)" 1438 | ] 1439 | }, 1440 | "execution_count": 144, 1441 | "metadata": {}, 1442 | "output_type": "execute_result" 1443 | } 1444 | ], 1445 | "source": [ 1446 | "arr ** 2" 1447 | ] 1448 | }, 1449 | { 1450 | "cell_type": "code", 1451 | "execution_count": 145, 1452 | "metadata": {}, 1453 | "outputs": [ 1454 | { 1455 | "data": { 1456 | "text/plain": [ 1457 | "array([0. , 1. , 1.41421356, 1.73205081, 2. ,\n", 1458 | " 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ,\n", 1459 | " 3.16227766])" 1460 | ] 1461 | }, 1462 | "execution_count": 145, 1463 | "metadata": {}, 1464 | "output_type": "execute_result" 1465 | } 1466 | ], 1467 | "source": [ 1468 | "np.sqrt(arr)" 1469 | ] 1470 | }, 1471 | { 1472 | "cell_type": "code", 1473 | "execution_count": 146, 1474 | "metadata": {}, 1475 | "outputs": [ 1476 | { 1477 | "data": { 1478 | "text/plain": [ 1479 | "array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,\n", 1480 | " 5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,\n", 1481 | " 2.98095799e+03, 8.10308393e+03, 2.20264658e+04])" 1482 | ] 1483 | }, 1484 | "execution_count": 146, 1485 | "metadata": {}, 1486 | "output_type": "execute_result" 1487 | } 1488 | ], 1489 | "source": [ 1490 | "np.exp(arr)" 1491 | ] 1492 | }, 1493 | { 1494 | "cell_type": "code", 1495 | "execution_count": 147, 1496 | "metadata": {}, 1497 | "outputs": [ 1498 | { 1499 | "data": { 1500 | "text/plain": [ 1501 | "10" 1502 | ] 1503 | }, 1504 | "execution_count": 147, 1505 | "metadata": {}, 1506 | "output_type": "execute_result" 1507 | } 1508 | ], 1509 | "source": [ 1510 | "np.max(arr)" 1511 | ] 1512 | }, 1513 | { 1514 | "cell_type": "code", 1515 | "execution_count": 148, 1516 | "metadata": {}, 1517 | "outputs": [ 1518 | { 1519 | "data": { 1520 | "text/plain": [ 1521 | "10" 1522 | ] 1523 | }, 1524 | "execution_count": 148, 1525 | "metadata": {}, 1526 | "output_type": "execute_result" 1527 | } 1528 | ], 1529 | "source": [ 1530 | "arr.max()" 1531 | ] 1532 | }, 1533 | { 1534 | "cell_type": "code", 1535 | "execution_count": 149, 1536 | "metadata": {}, 1537 | "outputs": [ 1538 | { 1539 | "data": { 1540 | "text/plain": [ 1541 | "array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,\n", 1542 | " -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849,\n", 1543 | " -0.54402111])" 1544 | ] 1545 | }, 1546 | "execution_count": 149, 1547 | "metadata": {}, 1548 | "output_type": "execute_result" 1549 | } 1550 | ], 1551 | "source": [ 1552 | "np.sin(arr)" 1553 | ] 1554 | }, 1555 | { 1556 | "cell_type": "code", 1557 | "execution_count": 150, 1558 | "metadata": {}, 1559 | "outputs": [ 1560 | { 1561 | "name": "stderr", 1562 | "output_type": "stream", 1563 | "text": [ 1564 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n", 1565 | " \"\"\"Entry point for launching an IPython kernel.\n" 1566 | ] 1567 | }, 1568 | { 1569 | "data": { 1570 | "text/plain": [ 1571 | "array([ -inf, 0. , 0.69314718, 1.09861229, 1.38629436,\n", 1572 | " 1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458,\n", 1573 | " 2.30258509])" 1574 | ] 1575 | }, 1576 | "execution_count": 150, 1577 | "metadata": {}, 1578 | "output_type": "execute_result" 1579 | } 1580 | ], 1581 | "source": [ 1582 | "np.log(arr)" 1583 | ] 1584 | }, 1585 | { 1586 | "cell_type": "code", 1587 | "execution_count": null, 1588 | "metadata": {}, 1589 | "outputs": [], 1590 | "source": [] 1591 | } 1592 | ], 1593 | "metadata": { 1594 | "kernelspec": { 1595 | "display_name": "Python 3", 1596 | "language": "python", 1597 | "name": "python3" 1598 | }, 1599 | "language_info": { 1600 | "codemirror_mode": { 1601 | "name": "ipython", 1602 | "version": 3 1603 | }, 1604 | "file_extension": ".py", 1605 | "mimetype": "text/x-python", 1606 | "name": "python", 1607 | "nbconvert_exporter": "python", 1608 | "pygments_lexer": "ipython3", 1609 | "version": "3.7.4" 1610 | } 1611 | }, 1612 | "nbformat": 4, 1613 | "nbformat_minor": 2 1614 | } 1615 | -------------------------------------------------------------------------------- /Data Analysis using Numpy and Pandas/SequenceData_Methods.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Data Analysis using Numpy and Pandas/SequenceData_Methods.pdf -------------------------------------------------------------------------------- /Data Analysis using Numpy and Pandas/SequenceData_Operations.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Data Analysis using Numpy and Pandas/SequenceData_Operations.pdf -------------------------------------------------------------------------------- /Decision Tree and Random Forest/kyphosis.csv: -------------------------------------------------------------------------------- 1 | "Kyphosis","Age","Number","Start" 2 | "absent",71,3,5 3 | "absent",158,3,14 4 | "present",128,4,5 5 | "absent",2,5,1 6 | "absent",1,4,15 7 | "absent",1,2,16 8 | "absent",61,2,17 9 | "absent",37,3,16 10 | "absent",113,2,16 11 | "present",59,6,12 12 | "present",82,5,14 13 | "absent",148,3,16 14 | "absent",18,5,2 15 | "absent",1,4,12 16 | "absent",168,3,18 17 | "absent",1,3,16 18 | "absent",78,6,15 19 | "absent",175,5,13 20 | "absent",80,5,16 21 | "absent",27,4,9 22 | "absent",22,2,16 23 | "present",105,6,5 24 | "present",96,3,12 25 | "absent",131,2,3 26 | "present",15,7,2 27 | "absent",9,5,13 28 | "absent",8,3,6 29 | "absent",100,3,14 30 | "absent",4,3,16 31 | "absent",151,2,16 32 | "absent",31,3,16 33 | "absent",125,2,11 34 | "absent",130,5,13 35 | "absent",112,3,16 36 | "absent",140,5,11 37 | "absent",93,3,16 38 | "absent",1,3,9 39 | "present",52,5,6 40 | "absent",20,6,9 41 | "present",91,5,12 42 | "present",73,5,1 43 | "absent",35,3,13 44 | "absent",143,9,3 45 | "absent",61,4,1 46 | "absent",97,3,16 47 | "present",139,3,10 48 | "absent",136,4,15 49 | "absent",131,5,13 50 | "present",121,3,3 51 | "absent",177,2,14 52 | "absent",68,5,10 53 | "absent",9,2,17 54 | "present",139,10,6 55 | "absent",2,2,17 56 | "absent",140,4,15 57 | "absent",72,5,15 58 | "absent",2,3,13 59 | "present",120,5,8 60 | "absent",51,7,9 61 | "absent",102,3,13 62 | "present",130,4,1 63 | "present",114,7,8 64 | "absent",81,4,1 65 | "absent",118,3,16 66 | "absent",118,4,16 67 | "absent",17,4,10 68 | "absent",195,2,17 69 | "absent",159,4,13 70 | "absent",18,4,11 71 | "absent",15,5,16 72 | "absent",158,5,14 73 | "absent",127,4,12 74 | "absent",87,4,16 75 | "absent",206,4,10 76 | "absent",11,3,15 77 | "absent",178,4,15 78 | "present",157,3,13 79 | "absent",26,7,13 80 | "absent",120,2,13 81 | "present",42,7,6 82 | "absent",36,4,13 83 | -------------------------------------------------------------------------------- /Evaluation Matrix/Confusion Matrix.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "from sklearn.metrics import confusion_matrix \n", 10 | "from sklearn.metrics import accuracy_score \n", 11 | "from sklearn.metrics import classification_report " 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "actual = [1, 1, 0, 1, 0, 0, 1, 0, 0, 0] \n", 21 | "predicted = [1, 0, 0, 1, 0, 0, 1, 1, 1, 0] \n", 22 | "results = confusion_matrix(actual, predicted) " 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 4, 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "name": "stdout", 32 | "output_type": "stream", 33 | "text": [ 34 | "Confusion Matrix :\n", 35 | "[[4 2]\n", 36 | " [1 3]]\n", 37 | "Accuracy Score : 0.7\n", 38 | "Report : \n", 39 | " precision recall f1-score support\n", 40 | "\n", 41 | " 0 0.80 0.67 0.73 6\n", 42 | " 1 0.60 0.75 0.67 4\n", 43 | "\n", 44 | " accuracy 0.70 10\n", 45 | " macro avg 0.70 0.71 0.70 10\n", 46 | "weighted avg 0.72 0.70 0.70 10\n", 47 | "\n" 48 | ] 49 | } 50 | ], 51 | "source": [ 52 | "print('Confusion Matrix :')\n", 53 | "print(results) \n", 54 | "print('Accuracy Score :',accuracy_score(actual, predicted))\n", 55 | "print('Report : ')\n", 56 | "print(classification_report(actual, predicted))" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": null, 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [] 65 | } 66 | ], 67 | "metadata": { 68 | "kernelspec": { 69 | "display_name": "Python 3", 70 | "language": "python", 71 | "name": "python3" 72 | }, 73 | "language_info": { 74 | "codemirror_mode": { 75 | "name": "ipython", 76 | "version": 3 77 | }, 78 | "file_extension": ".py", 79 | "mimetype": "text/x-python", 80 | "name": "python", 81 | "nbconvert_exporter": "python", 82 | "pygments_lexer": "ipython3", 83 | "version": "3.7.4" 84 | } 85 | }, 86 | "nbformat": 4, 87 | "nbformat_minor": 2 88 | } 89 | -------------------------------------------------------------------------------- /Exploratory Data Analysis/Exploratory data analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import os\n", 10 | "import pandas as pd\n", 11 | "import numpy as np\n", 12 | "os.chdir(r'C:\\Users\\Pc\\Desktop\\Data Science Notes\\IRIS')" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "cars_data = pd.read_csv('Toyota.csv',index_col=0,na_values=['??','????'])" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 5, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "cars_data2 = cars_data.copy(deep=True) #deep specifies that new DF will not refer to previous DF, it's optional by default it True" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "# Frequency Table" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 11, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/html": [ 48 | "
\n", 49 | "\n", 62 | "\n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | "
col_0count
FuelType
CNG15
Diesel144
Petrol1177
\n", 88 | "
" 89 | ], 90 | "text/plain": [ 91 | "col_0 count\n", 92 | "FuelType \n", 93 | "CNG 15\n", 94 | "Diesel 144\n", 95 | "Petrol 1177" 96 | ] 97 | }, 98 | "execution_count": 11, 99 | "metadata": {}, 100 | "output_type": "execute_result" 101 | } 102 | ], 103 | "source": [ 104 | "pd.crosstab(index=cars_data2['FuelType'], columns = 'count', dropna=True)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "## Two way table" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 10, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "data": { 121 | "text/html": [ 122 | "
\n", 123 | "\n", 136 | "\n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | "
FuelTypeCNGDieselPetrol
Automatic
0151441104
10073
\n", 166 | "
" 167 | ], 168 | "text/plain": [ 169 | "FuelType CNG Diesel Petrol\n", 170 | "Automatic \n", 171 | "0 15 144 1104\n", 172 | "1 0 0 73" 173 | ] 174 | }, 175 | "execution_count": 10, 176 | "metadata": {}, 177 | "output_type": "execute_result" 178 | } 179 | ], 180 | "source": [ 181 | "pd.crosstab(index=cars_data2['Automatic'], columns = cars_data2['FuelType'], dropna=True)" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "## Two way table - Joint Probability" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 9, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/html": [ 199 | "
\n", 200 | "\n", 213 | "\n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | "
FuelTypeCNGDieselPetrol
Automatic
00.0112280.1077840.826347
10.0000000.0000000.054641
\n", 243 | "
" 244 | ], 245 | "text/plain": [ 246 | "FuelType CNG Diesel Petrol\n", 247 | "Automatic \n", 248 | "0 0.011228 0.107784 0.826347\n", 249 | "1 0.000000 0.000000 0.054641" 250 | ] 251 | }, 252 | "execution_count": 9, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | } 256 | ], 257 | "source": [ 258 | "pd.crosstab(index=cars_data2['Automatic'], columns = cars_data2['FuelType'], normalize=True, dropna=True)" 259 | ] 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "metadata": {}, 264 | "source": [ 265 | "## Marginal Probability" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 12, 271 | "metadata": {}, 272 | "outputs": [ 273 | { 274 | "data": { 275 | "text/html": [ 276 | "
\n", 277 | "\n", 290 | "\n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | "
FuelTypeCNGDieselPetrolAll
Automatic
00.0112280.1077840.8263470.945359
10.0000000.0000000.0546410.054641
All0.0112280.1077840.8809881.000000
\n", 331 | "
" 332 | ], 333 | "text/plain": [ 334 | "FuelType CNG Diesel Petrol All\n", 335 | "Automatic \n", 336 | "0 0.011228 0.107784 0.826347 0.945359\n", 337 | "1 0.000000 0.000000 0.054641 0.054641\n", 338 | "All 0.011228 0.107784 0.880988 1.000000" 339 | ] 340 | }, 341 | "execution_count": 12, 342 | "metadata": {}, 343 | "output_type": "execute_result" 344 | } 345 | ], 346 | "source": [ 347 | "pd.crosstab(index=cars_data2['Automatic'], columns = cars_data2['FuelType'], margins=True, normalize=True, dropna=True)" 348 | ] 349 | }, 350 | { 351 | "cell_type": "markdown", 352 | "metadata": {}, 353 | "source": [ 354 | "## Conditional Probability" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 14, 360 | "metadata": {}, 361 | "outputs": [ 362 | { 363 | "data": { 364 | "text/html": [ 365 | "
\n", 366 | "\n", 379 | "\n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | "
FuelTypeCNGDieselPetrolAll
Automatic
01.01.00.9379780.945359
10.00.00.0620220.054641
\n", 413 | "
" 414 | ], 415 | "text/plain": [ 416 | "FuelType CNG Diesel Petrol All\n", 417 | "Automatic \n", 418 | "0 1.0 1.0 0.937978 0.945359\n", 419 | "1 0.0 0.0 0.062022 0.054641" 420 | ] 421 | }, 422 | "execution_count": 14, 423 | "metadata": {}, 424 | "output_type": "execute_result" 425 | } 426 | ], 427 | "source": [ 428 | "#normalize='index'\n", 429 | "pd.crosstab(index=cars_data2['Automatic'], columns = cars_data2['FuelType'], margins=True, normalize='index', dropna=True)" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 15, 435 | "metadata": {}, 436 | "outputs": [ 437 | { 438 | "data": { 439 | "text/html": [ 440 | "
\n", 441 | "\n", 454 | "\n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | "
FuelTypeCNGDieselPetrolAll
Automatic
01.01.00.9379780.945359
10.00.00.0620220.054641
\n", 488 | "
" 489 | ], 490 | "text/plain": [ 491 | "FuelType CNG Diesel Petrol All\n", 492 | "Automatic \n", 493 | "0 1.0 1.0 0.937978 0.945359\n", 494 | "1 0.0 0.0 0.062022 0.054641" 495 | ] 496 | }, 497 | "execution_count": 15, 498 | "metadata": {}, 499 | "output_type": "execute_result" 500 | } 501 | ], 502 | "source": [ 503 | "#normalize = 'columns'\n", 504 | "pd.crosstab(index=cars_data2['Automatic'], columns = cars_data2['FuelType'], margins=True, normalize='columns', dropna=True)" 505 | ] 506 | }, 507 | { 508 | "cell_type": "markdown", 509 | "metadata": {}, 510 | "source": [ 511 | "## Correlation" 512 | ] 513 | }, 514 | { 515 | "cell_type": "code", 516 | "execution_count": 16, 517 | "metadata": {}, 518 | "outputs": [], 519 | "source": [ 520 | "num_data = cars_data2.select_dtypes(exclude=[object])" 521 | ] 522 | }, 523 | { 524 | "cell_type": "code", 525 | "execution_count": 17, 526 | "metadata": {}, 527 | "outputs": [ 528 | { 529 | "data": { 530 | "text/plain": [ 531 | "(1436, 8)" 532 | ] 533 | }, 534 | "execution_count": 17, 535 | "metadata": {}, 536 | "output_type": "execute_result" 537 | } 538 | ], 539 | "source": [ 540 | "num_data.shape" 541 | ] 542 | }, 543 | { 544 | "cell_type": "code", 545 | "execution_count": 23, 546 | "metadata": {}, 547 | "outputs": [ 548 | { 549 | "data": { 550 | "text/html": [ 551 | "
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PriceAgeKMHPMetColorAutomaticCCWeight
Price1.000000-0.878407-0.5747200.3099020.1120410.0330810.1650670.581198
Age-0.8784071.0000000.512735-0.157904-0.0996590.032573-0.120706-0.464299
KM-0.5747200.5127351.000000-0.335285-0.093825-0.0812480.299993-0.026271
HP0.309902-0.157904-0.3352851.0000000.0647490.0137550.0537580.086737
MetColor0.112041-0.099659-0.0938250.0647491.000000-0.0139730.0291890.057142
Automatic0.0330810.032573-0.0812480.013755-0.0139731.000000-0.0693210.057249
CC0.165067-0.1207060.2999930.0537580.029189-0.0693211.0000000.651450
Weight0.581198-0.464299-0.0262710.0867370.0571420.0572490.6514501.000000
\n", 670 | "
" 671 | ], 672 | "text/plain": [ 673 | " Price Age KM HP MetColor Automatic \\\n", 674 | "Price 1.000000 -0.878407 -0.574720 0.309902 0.112041 0.033081 \n", 675 | "Age -0.878407 1.000000 0.512735 -0.157904 -0.099659 0.032573 \n", 676 | "KM -0.574720 0.512735 1.000000 -0.335285 -0.093825 -0.081248 \n", 677 | "HP 0.309902 -0.157904 -0.335285 1.000000 0.064749 0.013755 \n", 678 | "MetColor 0.112041 -0.099659 -0.093825 0.064749 1.000000 -0.013973 \n", 679 | "Automatic 0.033081 0.032573 -0.081248 0.013755 -0.013973 1.000000 \n", 680 | "CC 0.165067 -0.120706 0.299993 0.053758 0.029189 -0.069321 \n", 681 | "Weight 0.581198 -0.464299 -0.026271 0.086737 0.057142 0.057249 \n", 682 | "\n", 683 | " CC Weight \n", 684 | "Price 0.165067 0.581198 \n", 685 | "Age -0.120706 -0.464299 \n", 686 | "KM 0.299993 -0.026271 \n", 687 | "HP 0.053758 0.086737 \n", 688 | "MetColor 0.029189 0.057142 \n", 689 | "Automatic -0.069321 0.057249 \n", 690 | "CC 1.000000 0.651450 \n", 691 | "Weight 0.651450 1.000000 " 692 | ] 693 | }, 694 | "execution_count": 23, 695 | "metadata": {}, 696 | "output_type": "execute_result" 697 | } 698 | ], 699 | "source": [ 700 | "corr_matrix = num_data.corr()\n", 701 | "corr_matrix" 702 | ] 703 | } 704 | ], 705 | "metadata": { 706 | "kernelspec": { 707 | "display_name": "Python 3", 708 | "language": "python", 709 | "name": "python3" 710 | }, 711 | "language_info": { 712 | "codemirror_mode": { 713 | "name": "ipython", 714 | "version": 3 715 | }, 716 | "file_extension": ".py", 717 | "mimetype": "text/x-python", 718 | "name": "python", 719 | "nbconvert_exporter": "python", 720 | "pygments_lexer": "ipython3", 721 | "version": "3.7.4" 722 | } 723 | }, 724 | "nbformat": 4, 725 | "nbformat_minor": 2 726 | } 727 | -------------------------------------------------------------------------------- /Geographical Plotting/2011_US_AGRI_Exports: -------------------------------------------------------------------------------- 1 | code,state,category,total exports,beef,pork,poultry,dairy,fruits fresh,fruits proc,total fruits,veggies fresh,veggies proc,total veggies,corn,wheat,cotton,text 2 | AL,Alabama,state,1390.63,34.4,10.6,481.0,4.06,8.0,17.1,25.11,5.5,8.9,14.33,34.9,70.0,317.61,Alabama
Beef 34.4 Dairy 4.06
Fruits 25.11 Veggies 14.33
Wheat 70.0 Corn 34.9 3 | AK,Alaska,state,13.31,0.2,0.1,0.0,0.19,0.0,0.0,0.0,0.6,1.0,1.56,0.0,0.0,0.0,Alaska
Beef 0.2 Dairy 0.19
Fruits 0.0 Veggies 1.56
Wheat 0.0 Corn 0.0 4 | AZ,Arizona,state,1463.17,71.3,17.9,0.0,105.48,19.3,41.0,60.27,147.5,239.4,386.91,7.3,48.7,423.95,Arizona
Beef 71.3 Dairy 105.48
Fruits 60.27 Veggies 386.91
Wheat 48.7 Corn 7.3 5 | AR,Arkansas,state,3586.02,53.2,29.4,562.9,3.53,2.2,4.7,6.88,4.4,7.1,11.45,69.5,114.5,665.44,Arkansas
Beef 53.2 Dairy 3.53
Fruits 6.88 Veggies 11.45
Wheat 114.5 Corn 69.5 6 | CA, California, state,16472.88,228.7,11.1,225.4,929.95,2791.8,5944.6,8736.4,803.2,1303.5,2106.79,34.6,249.3,1064.95, California
Beef 228.7 Dairy 929.95
Fruits 8736.4 Veggies 2106.79
Wheat 249.3 Corn 34.6 7 | CO,Colorado,state,1851.33,261.4,66.0,14.0,71.94,5.7,12.2,17.99,45.1,73.2,118.27,183.2,400.5,0.0,Colorado
Beef 261.4 Dairy 71.94
Fruits 17.99 Veggies 118.27
Wheat 400.5 Corn 183.2 8 | CT,Connecticut,state,259.62,1.1,0.1,6.9,9.49,4.2,8.9,13.1,4.3,6.9,11.16,0.0,0.0,0.0,Connecticut
Beef 1.1 Dairy 9.49
Fruits 13.1 Veggies 11.16
Wheat 0.0 Corn 0.0 9 | DE,Delaware,state,282.19,0.4,0.6,114.7,2.3,0.5,1.0,1.53,7.6,12.4,20.03,26.9,22.9,0.0,Delaware
Beef 0.4 Dairy 2.3
Fruits 1.53 Veggies 20.03
Wheat 22.9 Corn 26.9 10 | FL,Florida,state,3764.09,42.6,0.9,56.9,66.31,438.2,933.1,1371.36,171.9,279.0,450.86,3.5,1.8,78.24,Florida
Beef 42.6 Dairy 66.31
Fruits 1371.36 Veggies 450.86
Wheat 1.8 Corn 3.5 11 | GA,Georgia,state,2860.84,31.0,18.9,630.4,38.38,74.6,158.9,233.51,59.0,95.8,154.77,57.8,65.4,1154.07,Georgia
Beef 31.0 Dairy 38.38
Fruits 233.51 Veggies 154.77
Wheat 65.4 Corn 57.8 12 | HI,Hawaii,state,401.84,4.0,0.7,1.3,1.16,17.7,37.8,55.51,9.5,15.4,24.83,0.0,0.0,0.0,Hawaii
Beef 4.0 Dairy 1.16
Fruits 55.51 Veggies 24.83
Wheat 0.0 Corn 0.0 13 | ID,Idaho,state,2078.89,119.8,0.0,2.4,294.6,6.9,14.7,21.64,121.7,197.5,319.19,24.0,568.2,0.0,Idaho
Beef 119.8 Dairy 294.6
Fruits 21.64 Veggies 319.19
Wheat 568.2 Corn 24.0 14 | IL,Illinois,state,8709.48,53.7,394.0,14.0,45.82,4.0,8.5,12.53,15.2,24.7,39.95,2228.5,223.8,0.0,Illinois
Beef 53.7 Dairy 45.82
Fruits 12.53 Veggies 39.95
Wheat 223.8 Corn 2228.5 15 | IN,Indiana,state,5050.23,21.9,341.9,165.6,89.7,4.1,8.8,12.98,14.4,23.4,37.89,1123.2,114.0,0.0,Indiana
Beef 21.9 Dairy 89.7
Fruits 12.98 Veggies 37.89
Wheat 114.0 Corn 1123.2 16 | IA,Iowa,state,11273.76,289.8,1895.6,155.6,107.0,1.0,2.2,3.24,2.7,4.4,7.1,2529.8,3.1,0.0,Iowa
Beef 289.8 Dairy 107.0
Fruits 3.24 Veggies 7.1
Wheat 3.1 Corn 2529.8 17 | KS,Kansas,state,4589.01,659.3,179.4,6.4,65.45,1.0,2.1,3.11,3.6,5.8,9.32,457.3,1426.5,43.98,Kansas
Beef 659.3 Dairy 65.45
Fruits 3.11 Veggies 9.32
Wheat 1426.5 Corn 457.3 18 | KY,Kentucky,state,1889.15,54.8,34.2,151.3,28.27,2.1,4.5,6.6,0.0,0.0,0.0,179.1,149.3,0.0,Kentucky
Beef 54.8 Dairy 28.27
Fruits 6.6 Veggies 0.0
Wheat 149.3 Corn 179.1 19 | LA,Louisiana,state,1914.23,19.8,0.8,77.2,6.02,5.7,12.1,17.83,6.6,10.7,17.25,91.4,78.7,280.42,Louisiana
Beef 19.8 Dairy 6.02
Fruits 17.83 Veggies 17.25
Wheat 78.7 Corn 91.4 20 | ME,Maine,state,278.37,1.4,0.5,10.4,16.18,16.6,35.4,52.01,24.0,38.9,62.9,0.0,0.0,0.0,Maine
Beef 1.4 Dairy 16.18
Fruits 52.01 Veggies 62.9
Wheat 0.0 Corn 0.0 21 | MD,Maryland,state,692.75,5.6,3.1,127.0,24.81,4.1,8.8,12.9,7.8,12.6,20.43,54.1,55.8,0.0,Maryland
Beef 5.6 Dairy 24.81
Fruits 12.9 Veggies 20.43
Wheat 55.8 Corn 54.1 22 | MA,Massachusetts,state,248.65,0.6,0.5,0.6,5.81,25.8,55.0,80.83,8.1,13.1,21.13,0.0,0.0,0.0,Massachusetts
Beef 0.6 Dairy 5.81
Fruits 80.83 Veggies 21.13
Wheat 0.0 Corn 0.0 23 | MI,Michigan,state,3164.16,37.7,118.1,32.6,214.82,82.3,175.3,257.69,72.4,117.5,189.96,381.5,247.0,0.0,Michigan
Beef 37.7 Dairy 214.82
Fruits 257.69 Veggies 189.96
Wheat 247.0 Corn 381.5 24 | MN,Minnesota,state,7192.33,112.3,740.4,189.2,218.05,2.5,5.4,7.91,45.9,74.5,120.37,1264.3,538.1,0.0,Minnesota
Beef 112.3 Dairy 218.05
Fruits 7.91 Veggies 120.37
Wheat 538.1 Corn 1264.3 25 | MS,Mississippi,state,2170.8,12.8,30.4,370.8,5.45,5.4,11.6,17.04,10.6,17.2,27.87,110.0,102.2,494.75,Mississippi
Beef 12.8 Dairy 5.45
Fruits 17.04 Veggies 27.87
Wheat 102.2 Corn 110.0 26 | MO,Missouri,state,3933.42,137.2,277.3,196.1,34.26,4.2,9.0,13.18,6.8,11.1,17.9,428.8,161.7,345.29,Missouri
Beef 137.2 Dairy 34.26
Fruits 13.18 Veggies 17.9
Wheat 161.7 Corn 428.8 27 | MT,Montana,state,1718.0,105.0,16.7,1.7,6.82,1.1,2.2,3.3,17.3,28.0,45.27,5.4,1198.1,0.0,Montana
Beef 105.0 Dairy 6.82
Fruits 3.3 Veggies 45.27
Wheat 1198.1 Corn 5.4 28 | NE,Nebraska,state,7114.13,762.2,262.5,31.4,30.07,0.7,1.5,2.16,20.4,33.1,53.5,1735.9,292.3,0.0,Nebraska
Beef 762.2 Dairy 30.07
Fruits 2.16 Veggies 53.5
Wheat 292.3 Corn 1735.9 29 | NV,Nevada,state,139.89,21.8,0.2,0.0,16.57,0.4,0.8,1.19,10.6,17.3,27.93,0.0,5.4,0.0,Nevada
Beef 21.8 Dairy 16.57
Fruits 1.19 Veggies 27.93
Wheat 5.4 Corn 0.0 30 | NH,New Hampshire,state,73.06,0.6,0.2,0.8,7.46,2.6,5.4,7.98,1.7,2.8,4.5,0.0,0.0,0.0,New Hampshire
Beef 0.6 Dairy 7.46
Fruits 7.98 Veggies 4.5
Wheat 0.0 Corn 0.0 31 | NJ,New Jersey,state,500.4,0.8,0.4,4.6,3.37,35.0,74.5,109.45,21.6,35.0,56.54,10.1,6.7,0.0,New Jersey
Beef 0.8 Dairy 3.37
Fruits 109.45 Veggies 56.54
Wheat 6.7 Corn 10.1 32 | NM,New Mexico,state,751.58,117.2,0.1,0.3,191.01,32.6,69.3,101.9,16.7,27.1,43.88,11.2,13.9,72.62,New Mexico
Beef 117.2 Dairy 191.01
Fruits 101.9 Veggies 43.88
Wheat 13.9 Corn 11.2 33 | NY,New York,state,1488.9,22.2,5.8,17.7,331.8,64.7,137.8,202.56,54.7,88.7,143.37,106.1,29.9,0.0,New York
Beef 22.2 Dairy 331.8
Fruits 202.56 Veggies 143.37
Wheat 29.9 Corn 106.1 34 | NC,North Carolina,state,3806.05,24.8,702.8,598.4,24.9,23.8,50.7,74.47,57.4,93.1,150.45,92.2,200.3,470.86,North Carolina
Beef 24.8 Dairy 24.9
Fruits 74.47 Veggies 150.45
Wheat 200.3 Corn 92.2 35 | ND,North Dakota,state,3761.96,78.5,16.1,0.5,8.14,0.1,0.2,0.25,49.9,80.9,130.79,236.1,1664.5,0.0,North Dakota
Beef 78.5 Dairy 8.14
Fruits 0.25 Veggies 130.79
Wheat 1664.5 Corn 236.1 36 | OH,Ohio,state,3979.79,36.2,199.1,129.9,134.57,8.7,18.5,27.21,20.4,33.1,53.53,535.1,207.4,0.0,Ohio
Beef 36.2 Dairy 134.57
Fruits 27.21 Veggies 53.53
Wheat 207.4 Corn 535.1 37 | OK,Oklahoma,state,1646.41,337.6,265.3,131.1,24.35,3.0,6.3,9.24,3.4,5.5,8.9,27.5,324.8,110.54,Oklahoma
Beef 337.6 Dairy 24.35
Fruits 9.24 Veggies 8.9
Wheat 324.8 Corn 27.5 38 | OR,Oregon,state,1794.57,58.8,1.4,14.2,63.66,100.7,214.4,315.04,48.2,78.3,126.5,11.7,320.3,0.0,Oregon
Beef 58.8 Dairy 63.66
Fruits 315.04 Veggies 126.5
Wheat 320.3 Corn 11.7 39 | PA,Pennsylvania,state,1969.87,50.9,91.3,169.8,280.87,28.6,60.9,89.48,14.6,23.7,38.26,112.1,41.0,0.0,Pennsylvania
Beef 50.9 Dairy 280.87
Fruits 89.48 Veggies 38.26
Wheat 41.0 Corn 112.1 40 | RI,Rhode Island,state,31.59,0.1,0.1,0.2,0.52,0.9,1.9,2.83,1.2,1.9,3.02,0.0,0.0,0.0,Rhode Island
Beef 0.1 Dairy 0.52
Fruits 2.83 Veggies 3.02
Wheat 0.0 Corn 0.0 41 | SC,South Carolina,state,929.93,15.2,10.9,186.5,7.62,17.1,36.4,53.45,16.3,26.4,42.66,32.1,55.3,206.1,South Carolina
Beef 15.2 Dairy 7.62
Fruits 53.45 Veggies 42.66
Wheat 55.3 Corn 32.1 42 | SD,South Dakota,state,3770.19,193.5,160.2,29.3,46.77,0.3,0.5,0.8,1.5,2.5,4.06,643.6,704.5,0.0,South Dakota
Beef 193.5 Dairy 46.77
Fruits 0.8 Veggies 4.06
Wheat 704.5 Corn 643.6 43 | TN,Tennessee,state,1535.13,51.1,17.6,82.4,21.18,2.0,4.2,6.23,9.4,15.3,24.67,88.8,100.0,363.83,Tennessee
Beef 51.1 Dairy 21.18
Fruits 6.23 Veggies 24.67
Wheat 100.0 Corn 88.8 44 | TX,Texas,state,6648.22,961.0,42.7,339.2,240.55,31.9,68.0,99.9,43.9,71.3,115.23,167.2,309.7,2308.76,Texas
Beef 961.0 Dairy 240.55
Fruits 99.9 Veggies 115.23
Wheat 309.7 Corn 167.2 45 | UT,Utah,state,453.39,27.9,59.0,23.1,48.6,3.9,8.4,12.34,2.5,4.1,6.6,5.3,42.8,0.0,Utah
Beef 27.9 Dairy 48.6
Fruits 12.34 Veggies 6.6
Wheat 42.8 Corn 5.3 46 | VT,Vermont,state,180.14,6.2,0.2,0.9,65.98,2.6,5.4,8.01,1.5,2.5,4.05,0.0,0.0,0.0,Vermont
Beef 6.2 Dairy 65.98
Fruits 8.01 Veggies 4.05
Wheat 0.0 Corn 0.0 47 | VA,Virginia,state,1146.48,39.5,16.9,164.7,47.85,11.7,24.8,36.48,10.4,16.9,27.25,39.5,77.5,64.84,Virginia
Beef 39.5 Dairy 47.85
Fruits 36.48 Veggies 27.25
Wheat 77.5 Corn 39.5 48 | WA,Washington,state,3894.81,59.2,0.0,35.6,154.18,555.6,1183.0,1738.57,138.7,225.1,363.79,29.5,786.3,0.0,Washington
Beef 59.2 Dairy 154.18
Fruits 1738.57 Veggies 363.79
Wheat 786.3 Corn 29.5 49 | WV,West Virginia,state,138.89,12.0,0.3,45.4,3.9,3.7,7.9,11.54,0.0,0.0,0.0,3.5,1.6,0.0,West Virginia
Beef 12.0 Dairy 3.9
Fruits 11.54 Veggies 0.0
Wheat 1.6 Corn 3.5 50 | WI,Wisconsin,state,3090.23,107.3,38.6,34.5,633.6,42.8,91.0,133.8,56.8,92.2,148.99,460.5,96.7,0.0,Wisconsin
Beef 107.3 Dairy 633.6
Fruits 133.8 Veggies 148.99
Wheat 96.7 Corn 460.5 51 | WY,Wyoming,state,349.69,75.1,33.2,0.1,2.89,0.1,0.1,0.17,3.9,6.3,10.23,9.0,20.7,0.0,Wyoming
Beef 75.1 Dairy 2.89
Fruits 0.17 Veggies 10.23
Wheat 20.7 Corn 9.0 52 | -------------------------------------------------------------------------------- /Geographical Plotting/2012_Election_Data: -------------------------------------------------------------------------------- 1 | Year,ICPSR State Code,Alphanumeric State Code,State,VEP Total Ballots Counted,VEP Highest Office,VAP Highest Office,Total Ballots Counted,Highest Office,Voting-Eligible Population (VEP),Voting-Age Population (VAP),% Non-citizen,Prison,Probation,Parole,Total Ineligible Felon,State Abv 2 | 2012,41,1,Alabama,,58.6%,56.0%,,"2,074,338","3,539,217",3707440.0,2.6%,"32,232","57,993","8,616","71,584",AL 3 | 2012,81,2,Alaska,58.9%,58.7%,55.3%,"301,694","300,495","511,792",543763.0,3.8%,"5,633","7,173","1,882","11,317",AK 4 | 2012,61,3,Arizona,53.0%,52.6%,46.5%,"2,323,579","2,306,559","4,387,900",4959270.0,9.9%,"35,188","72,452","7,460","81,048",AZ 5 | 2012,42,4,Arkansas,51.1%,50.7%,47.7%,"1,078,548","1,069,468","2,109,847",2242740.0,3.5%,"14,471","30,122","23,372","53,808",AR 6 | 2012,71,5,California,55.7%,55.1%,45.1%,"13,202,158","13,038,547","23,681,837",28913129.0,17.4%,"119,455",0,"89,287","208,742",CA 7 | 2012,62,6,Colorado,70.6%,69.9%,64.5%,"2,596,173","2,569,522","3,675,871",3981208.0,6.9%,"18,807",0,"11,458","30,265",CO 8 | 2012,1,7,Connecticut,61.4%,61.3%,55.6%,"1,560,640","1,558,960","2,543,202",2801375.0,8.5%,"16,935",0,"2,793","19,728",CT 9 | 2012,11,8,Delaware,,62.3%,57.8%,,"413,921","663,967",715708.0,5.1%,"6,610","15,641",601,"15,501",DE 10 | 2012,55,9,District of Columbia,61.6%,61.5%,55.5%,"294,254","293,764","477,582",528848.0,9.7%,0,0,0,0,District of Columbia 11 | 2012,43,10,Florida,63.3%,62.8%,55.1%,"8,538,264","8,474,179","13,495,057",15380947.0,10.8%,"91,954","240,869","4,538","224,153",FL 12 | 2012,44,11,Georgia,59.3%,59.0%,52.3%,"3,919,355","3,900,050","6,606,607",7452696.0,7.2%,"52,737","442,061","24,761","311,790",GA 13 | 2012,82,12,Hawaii,44.5%,44.2%,39.9%,"437,159","434,697","982,902",1088335.0,9.2%,"5,544",0,0,"5,544",HI 14 | 2012,63,13,Idaho,61.0%,59.8%,55.6%,"666,290","652,274","1,091,410",1173727.0,4.6%,"7,985","31,606","3,848","28,584",ID 15 | 2012,21,14,Illinois,59.3%,58.9%,53.3%,"5,279,752","5,242,014","8,899,143",9827043.0,8.9%,"49,348",0,0,"49,348",IL 16 | 2012,22,15,Indiana,56.0%,55.2%,52.9%,"2,663,368","2,624,534","4,755,291",4960376.0,3.6%,"28,266",0,0,"28,266",IN 17 | 2012,31,16,Iowa,70.6%,70.3%,67.1%,"1,589,951","1,582,180","2,251,748",2356209.0,3.2%,"8,470","29,333","5,151","29,167",IA 18 | 2012,32,17,Kansas,58.2%,56.9%,53.5%,"1,182,771","1,156,254","2,030,686",2162442.0,5.0%,"9,346","17,021","5,126","23,493",KS 19 | 2012,51,18,Kentucky,56.2%,55.7%,53.4%,"1,815,843","1,797,212","3,229,185",3368684.0,2.2%,"21,863","54,511","14,419","65,173",KY 20 | 2012,45,19,Louisiana,60.8%,60.2%,57.0%,"2,014,548","1,994,065","3,311,626",3495847.0,2.7%,"40,047","41,298","28,946","90,881",LA 21 | 2012,2,20,Maine,69.3%,68.2%,67.0%,"724,758","713,180","1,046,008",1064779.0,1.8%,0,0,0,0,ME 22 | 2012,52,21,Maryland,67.3%,66.6%,59.5%,"2,734,062","2,707,327","4,063,582",4553853.0,8.9%,"20,871","96,640","13,195","85,285",MD 23 | 2012,3,22,Massachusetts,66.2%,65.9%,60.2%,"3,184,196","3,167,767","4,809,675",5263550.0,8.4%,"10,283",0,0,"10,283",MA 24 | 2012,23,23,Michigan,65.4%,64.7%,62.0%,"4,780,701","4,730,961","7,312,725",7625576.0,3.5%,"43,019",0,0,"43,019",MI 25 | 2012,33,24,Minnesota,76.4%,76.0%,71.4%,"2,950,780","2,936,561","3,861,598",4114820.0,4.4%,"9,383","108,157","6,006","72,712",MN 26 | 2012,46,25,Mississippi,,59.3%,57.2%,,"1,285,584","2,166,825",2246931.0,1.5%,"22,305","30,768","6,804","45,416",MS 27 | 2012,34,26,Missouri,,62.2%,59.6%,,"2,757,323","4,432,957",4628500.0,2.5%,"30,714","55,470","20,672","80,785",MO 28 | 2012,64,27,Montana,63.5%,62.5%,61.6%,"491,966","484,048","774,476",785454.0,0.9%,"3,592",0,0,"3,592",MT 29 | 2012,35,28,Nebraska,61.1%,60.3%,56.9%,"804,245","794,379","1,316,915",1396507.0,4.7%,"4,466","14,260","1,383","13,407",NE 30 | 2012,65,29,Nevada,56.5%,56.4%,48.2%,"1,016,664","1,014,918","1,800,969",2105976.0,13.3%,"12,883","11,321","5,379","24,262",NV 31 | 2012,4,30,New Hampshire,70.9%,70.2%,67.8%,"718,700","710,972","1,013,420",1047978.0,3.0%,"2,672",0,0,"2,672",NH 32 | 2012,12,31,New Jersey,62.2%,61.5%,53.2%,"3,683,638","3,640,292","5,918,182",6847503.0,12.1%,"21,759","114,886","14,987","97,636",NJ 33 | 2012,66,32,New Mexico,54.8%,54.6%,49.8%,"786,522","783,757","1,436,363",1573400.0,7.3%,"6,553","21,381","5,078","22,963",NM 34 | 2012,13,33,New York,53.5%,53.1%,46.1%,"7,128,852","7,074,723","13,324,107",15344671.0,12.5%,"49,889",0,"46,222","96,111",NY 35 | 2012,47,34,North Carolina,65.4%,64.8%,60.1%,"4,542,488","4,505,372","6,947,954",7496980.0,6.1%,"35,567","96,070","4,359","90,843",NC 36 | 2012,36,35,North Dakota,60.4%,59.8%,58.7%,"325,564","322,627","539,164",549955.0,1.7%,"1,500",0,0,"1,500",ND 37 | 2012,24,36,Ohio,65.1%,64.5%,62.7%,"5,632,423","5,580,822","8,649,495",8896930.0,2.2%,"50,313",0,0,"50,313",OH 38 | 2012,53,37,Oklahoma,,49.2%,46.3%,,"1,334,872","2,713,268",2885093.0,4.5%,"25,225","25,506","2,310","41,053",OK 39 | 2012,72,38,Oregon,64.2%,63.1%,58.7%,"1,820,507","1,789,270","2,836,101",3050747.0,6.6%,"13,607",0,0,"13,607",OR 40 | 2012,14,39,Pennsylvania,,59.5%,57.2%,,"5,742,040","9,651,432",10037099.0,3.3%,"50,054",0,0,"50,054",PA 41 | 2012,5,40,Rhode Island,,58.0%,53.4%,,"446,049","768,918",834983.0,7.5%,"3,249",0,0,"3,249",RI 42 | 2012,48,41,South Carolina,56.8%,56.3%,53.6%,"1,981,516","1,964,118","3,486,838",3662322.0,3.5%,"21,895","34,945","6,116","46,532",SC 43 | 2012,37,42,South Dakota,60.1%,59.3%,57.6%,"368,270","363,815","613,190",631472.0,1.9%,"3,574",0,"2,761","6,335",SD 44 | 2012,54,43,Tennessee,52.3%,51.9%,49.4%,"2,478,870","2,458,577","4,736,084",4976284.0,3.3%,"28,135","64,430","13,138","75,421",TN 45 | 2012,49,44,Texas,,49.6%,41.7%,,"7,993,851","16,119,973",19185395.0,13.5%,"157,564","405,473","112,288","484,753",TX 46 | 2012,67,45,Utah,56.1%,55.5%,51.4%,"1,028,786","1,017,440","1,833,339",1978956.0,7.0%,"6,611",0,0,"6,611",UT 47 | 2012,6,46,Vermont,61.2%,60.7%,59.6%,"301,793","299,290","493,355",502242.0,1.8%,0,0,0,0,VT 48 | 2012,40,47,Virginia,66.6%,66.1%,60.7%,"3,888,186","3,854,489","5,834,676",6348827.0,7.1%,"36,425","52,956","1,983","66,475",VA 49 | 2012,73,48,Washington,65.8%,64.8%,58.6%,"3,172,939","3,125,516","4,822,060",5329782.0,8.2%,"16,355","88,339","8,895","72,070",WA 50 | 2012,56,49,West Virginia,,46.3%,45.5%,,"670,438","1,447,066",1472642.0,0.8%,"7,052","8,573","2,052","13,648",WV 51 | 2012,25,50,Wisconsin,,72.9%,69.5%,,"3,068,434","4,209,370",4417273.0,3.2%,"21,987","46,328","20,023","66,564",WI 52 | 2012,68,51,Wyoming,59.0%,58.6%,56.4%,"250,701","249,061","425,142",441726.0,2.5%,"2,163","5,162",762,"5,661",WY 53 | -------------------------------------------------------------------------------- /Geographical Plotting/2014_World_GDP: -------------------------------------------------------------------------------- 1 | COUNTRY,GDP (BILLIONS),CODE 2 | Afghanistan,21.71,AFG 3 | Albania,13.4,ALB 4 | Algeria,227.8,DZA 5 | American Samoa,0.75,ASM 6 | Andorra,4.8,AND 7 | Angola,131.4,AGO 8 | Anguilla,0.18,AIA 9 | Antigua and Barbuda,1.24,ATG 10 | Argentina,536.2,ARG 11 | Armenia,10.88,ARM 12 | Aruba,2.52,ABW 13 | Australia,1483.0,AUS 14 | Austria,436.1,AUT 15 | Azerbaijan,77.91,AZE 16 | "Bahamas, The",8.65,BHM 17 | Bahrain,34.05,BHR 18 | Bangladesh,186.6,BGD 19 | Barbados,4.28,BRB 20 | Belarus,75.25,BLR 21 | Belgium,527.8,BEL 22 | Belize,1.67,BLZ 23 | Benin,9.24,BEN 24 | Bermuda,5.2,BMU 25 | Bhutan,2.09,BTN 26 | Bolivia,34.08,BOL 27 | Bosnia and Herzegovina,19.55,BIH 28 | Botswana,16.3,BWA 29 | Brazil,2244.0,BRA 30 | British Virgin Islands,1.1,VGB 31 | Brunei,17.43,BRN 32 | Bulgaria,55.08,BGR 33 | Burkina Faso,13.38,BFA 34 | Burma,65.29,MMR 35 | Burundi,3.04,BDI 36 | Cabo Verde,1.98,CPV 37 | Cambodia,16.9,KHM 38 | Cameroon,32.16,CMR 39 | Canada,1794.0,CAN 40 | Cayman Islands,2.25,CYM 41 | Central African Republic,1.73,CAF 42 | Chad,15.84,TCD 43 | Chile,264.1,CHL 44 | China,10360.0,CHN 45 | Colombia,400.1,COL 46 | Comoros,0.72,COM 47 | "Congo, Democratic Republic of the",32.67,COD 48 | "Congo, Republic of the",14.11,COG 49 | Cook Islands,0.18,COK 50 | Costa Rica,50.46,CRI 51 | Cote d'Ivoire,33.96,CIV 52 | Croatia,57.18,HRV 53 | Cuba,77.15,CUB 54 | Curacao,5.6,CUW 55 | Cyprus,21.34,CYP 56 | Czech Republic,205.6,CZE 57 | Denmark,347.2,DNK 58 | Djibouti,1.58,DJI 59 | Dominica,0.51,DMA 60 | Dominican Republic,64.05,DOM 61 | Ecuador,100.5,ECU 62 | Egypt,284.9,EGY 63 | El Salvador,25.14,SLV 64 | Equatorial Guinea,15.4,GNQ 65 | Eritrea,3.87,ERI 66 | Estonia,26.36,EST 67 | Ethiopia,49.86,ETH 68 | Falkland Islands (Islas Malvinas),0.16,FLK 69 | Faroe Islands,2.32,FRO 70 | Fiji,4.17,FJI 71 | Finland,276.3,FIN 72 | France,2902.0,FRA 73 | French Polynesia,7.15,PYF 74 | Gabon,20.68,GAB 75 | "Gambia, The",0.92,GMB 76 | Georgia,16.13,GEO 77 | Germany,3820.0,DEU 78 | Ghana,35.48,GHA 79 | Gibraltar,1.85,GIB 80 | Greece,246.4,GRC 81 | Greenland,2.16,GRL 82 | Grenada,0.84,GRD 83 | Guam,4.6,GUM 84 | Guatemala,58.3,GTM 85 | Guernsey,2.74,GGY 86 | Guinea-Bissau,1.04,GNB 87 | Guinea,6.77,GIN 88 | Guyana,3.14,GUY 89 | Haiti,8.92,HTI 90 | Honduras,19.37,HND 91 | Hong Kong,292.7,HKG 92 | Hungary,129.7,HUN 93 | Iceland,16.2,ISL 94 | India,2048.0,IND 95 | Indonesia,856.1,IDN 96 | Iran,402.7,IRN 97 | Iraq,232.2,IRQ 98 | Ireland,245.8,IRL 99 | Isle of Man,4.08,IMN 100 | Israel,305.0,ISR 101 | Italy,2129.0,ITA 102 | Jamaica,13.92,JAM 103 | Japan,4770.0,JPN 104 | Jersey,5.77,JEY 105 | Jordan,36.55,JOR 106 | Kazakhstan,225.6,KAZ 107 | Kenya,62.72,KEN 108 | Kiribati,0.16,KIR 109 | "Korea, North",28.0,KOR 110 | "Korea, South",1410.0,PRK 111 | Kosovo,5.99,KSV 112 | Kuwait,179.3,KWT 113 | Kyrgyzstan,7.65,KGZ 114 | Laos,11.71,LAO 115 | Latvia,32.82,LVA 116 | Lebanon,47.5,LBN 117 | Lesotho,2.46,LSO 118 | Liberia,2.07,LBR 119 | Libya,49.34,LBY 120 | Liechtenstein,5.11,LIE 121 | Lithuania,48.72,LTU 122 | Luxembourg,63.93,LUX 123 | Macau,51.68,MAC 124 | Macedonia,10.92,MKD 125 | Madagascar,11.19,MDG 126 | Malawi,4.41,MWI 127 | Malaysia,336.9,MYS 128 | Maldives,2.41,MDV 129 | Mali,12.04,MLI 130 | Malta,10.57,MLT 131 | Marshall Islands,0.18,MHL 132 | Mauritania,4.29,MRT 133 | Mauritius,12.72,MUS 134 | Mexico,1296.0,MEX 135 | "Micronesia, Federated States of",0.34,FSM 136 | Moldova,7.74,MDA 137 | Monaco,6.06,MCO 138 | Mongolia,11.73,MNG 139 | Montenegro,4.66,MNE 140 | Morocco,112.6,MAR 141 | Mozambique,16.59,MOZ 142 | Namibia,13.11,NAM 143 | Nepal,19.64,NPL 144 | Netherlands,880.4,NLD 145 | New Caledonia,11.1,NCL 146 | New Zealand,201.0,NZL 147 | Nicaragua,11.85,NIC 148 | Nigeria,594.3,NGA 149 | Niger,8.29,NER 150 | Niue,0.01,NIU 151 | Northern Mariana Islands,1.23,MNP 152 | Norway,511.6,NOR 153 | Oman,80.54,OMN 154 | Pakistan,237.5,PAK 155 | Palau,0.65,PLW 156 | Panama,44.69,PAN 157 | Papua New Guinea,16.1,PNG 158 | Paraguay,31.3,PRY 159 | Peru,208.2,PER 160 | Philippines,284.6,PHL 161 | Poland,552.2,POL 162 | Portugal,228.2,PRT 163 | Puerto Rico,93.52,PRI 164 | Qatar,212.0,QAT 165 | Romania,199.0,ROU 166 | Russia,2057.0,RUS 167 | Rwanda,8.0,RWA 168 | Saint Kitts and Nevis,0.81,KNA 169 | Saint Lucia,1.35,LCA 170 | Saint Martin,0.56,MAF 171 | Saint Pierre and Miquelon,0.22,SPM 172 | Saint Vincent and the Grenadines,0.75,VCT 173 | Samoa,0.83,WSM 174 | San Marino,1.86,SMR 175 | Sao Tome and Principe,0.36,STP 176 | Saudi Arabia,777.9,SAU 177 | Senegal,15.88,SEN 178 | Serbia,42.65,SRB 179 | Seychelles,1.47,SYC 180 | Sierra Leone,5.41,SLE 181 | Singapore,307.9,SGP 182 | Sint Maarten,304.1,SXM 183 | Slovakia,99.75,SVK 184 | Slovenia,49.93,SVN 185 | Solomon Islands,1.16,SLB 186 | Somalia,2.37,SOM 187 | South Africa,341.2,ZAF 188 | South Sudan,11.89,SSD 189 | Spain,1400.0,ESP 190 | Sri Lanka,71.57,LKA 191 | Sudan,70.03,SDN 192 | Suriname,5.27,SUR 193 | Swaziland,3.84,SWZ 194 | Sweden,559.1,SWE 195 | Switzerland,679.0,CHE 196 | Syria,64.7,SYR 197 | Taiwan,529.5,TWN 198 | Tajikistan,9.16,TJK 199 | Tanzania,36.62,TZA 200 | Thailand,373.8,THA 201 | Timor-Leste,4.51,TLS 202 | Togo,4.84,TGO 203 | Tonga,0.49,TON 204 | Trinidad and Tobago,29.63,TTO 205 | Tunisia,49.12,TUN 206 | Turkey,813.3,TUR 207 | Turkmenistan,43.5,TKM 208 | Tuvalu,0.04,TUV 209 | Uganda,26.09,UGA 210 | Ukraine,134.9,UKR 211 | United Arab Emirates,416.4,ARE 212 | United Kingdom,2848.0,GBR 213 | United States,17420.0,USA 214 | Uruguay,55.6,URY 215 | Uzbekistan,63.08,UZB 216 | Vanuatu,0.82,VUT 217 | Venezuela,209.2,VEN 218 | Vietnam,187.8,VNM 219 | Virgin Islands,5.08,VGB 220 | West Bank,6.64,WBG 221 | Yemen,45.45,YEM 222 | Zambia,25.61,ZMB 223 | Zimbabwe,13.74,ZWE 224 | -------------------------------------------------------------------------------- /Geographical Plotting/2014_World_Power_Consumption: -------------------------------------------------------------------------------- 1 | Country,Power Consumption KWH,Text 2 | China,5523000000000.0,"China 5,523,000,000,000" 3 | United States,3832000000000.0,"United 3,832,000,000,000" 4 | European,2771000000000.0,"European 2,771,000,000,000" 5 | Russia,1065000000000.0,"Russia 1,065,000,000,000" 6 | Japan,921000000000.0,"Japan 921,000,000,000" 7 | India,864700000000.0,"India 864,700,000,000" 8 | Germany,540100000000.0,"Germany 540,100,000,000" 9 | Canada,511000000000.0,"Canada 511,000,000,000" 10 | Brazil,483500000000.0,"Brazil 483,500,000,000" 11 | "Korea,",482400000000.0,"Korea, 482,400,000,000" 12 | France,451100000000.0,"France 451,100,000,000" 13 | United Kingdom,319100000000.0,"United 319,100,000,000" 14 | Italy,303100000000.0,"Italy 303,100,000,000" 15 | Taiwan,249500000000.0,"Taiwan 249,500,000,000" 16 | Spain,243100000000.0,"Spain 243,100,000,000" 17 | Mexico,234000000000.0,"Mexico 234,000,000,000" 18 | Saudi,231600000000.0,"Saudi 231,600,000,000" 19 | Australia,222600000000.0,"Australia 222,600,000,000" 20 | South,211600000000.0,"South 211,600,000,000" 21 | Turkey,197000000000.0,"Turkey 197,000,000,000" 22 | Iran,195300000000.0,"Iran 195,300,000,000" 23 | Indonesia,167500000000.0,"Indonesia 167,500,000,000" 24 | Ukraine,159800000000.0,"Ukraine 159,800,000,000" 25 | Thailand,155900000000.0,"Thailand 155,900,000,000" 26 | Poland,139000000000.0,"Poland 139,000,000,000" 27 | Egypt,135600000000.0,"Egypt 135,600,000,000" 28 | Sweden,130500000000.0,"Sweden 130,500,000,000" 29 | Norway,126400000000.0,"Norway 126,400,000,000" 30 | Malaysia,118500000000.0,"Malaysia 118,500,000,000" 31 | Argentina,117100000000.0,"Argentina 117,100,000,000" 32 | Netherlands,116800000000.0,"Netherlands 116,800,000,000" 33 | Vietnam,108300000000.0,"Vietnam 108,300,000,000" 34 | Venezuela,97690000000.0,"Venezuela 97,690,000,000" 35 | United Arab Emirates,93280000000.0,"United 93,280,000,000" 36 | Finland,82040000000.0,"Finland 82,040,000,000" 37 | Belgium,81890000000.0,"Belgium 81,890,000,000" 38 | Kazakhstan,80290000000.0,"Kazakhstan 80,290,000,000" 39 | Pakistan,78890000000.0,"Pakistan 78,890,000,000" 40 | Philippines,75270000000.0,"Philippines 75,270,000,000" 41 | Austria,69750000000.0,"Austria 69,750,000,000" 42 | Chile,63390000000.0,"Chile 63,390,000,000" 43 | Czechia,60550000000.0,"Czechia 60,550,000,000" 44 | Israel,59830000000.0,"Israel 59,830,000,000" 45 | Switzerland,58010000000.0,"Switzerland 58,010,000,000" 46 | Greece,57730000000.0,"Greece 57,730,000,000" 47 | Iraq,53410000000.0,"Iraq 53,410,000,000" 48 | Romania,50730000000.0,"Romania 50,730,000,000" 49 | Kuwait,50000000000.0,"Kuwait 50,000,000,000" 50 | Colombia,49380000000.0,"Colombia 49,380,000,000" 51 | Singapore,47180000000.0,"Singapore 47,180,000,000" 52 | Portugal,46250000000.0,"Portugal 46,250,000,000" 53 | Uzbekistan,45210000000.0,"Uzbekistan 45,210,000,000" 54 | Hong,44210000000.0,"Hong 44,210,000,000" 55 | Algeria,42870000000.0,"Algeria 42,870,000,000" 56 | Bangladesh,41520000000.0,"Bangladesh 41,520,000,000" 57 | New,40300000000.0,"New 40,300,000,000" 58 | Bulgaria,37990000000.0,"Bulgaria 37,990,000,000" 59 | Belarus,37880000000.0,"Belarus 37,880,000,000" 60 | Peru,35690000000.0,"Peru 35,690,000,000" 61 | Denmark,31960000000.0,"Denmark 31,960,000,000" 62 | Qatar,30530000000.0,"Qatar 30,530,000,000" 63 | Slovakia,28360000000.0,"Slovakia 28,360,000,000" 64 | Libya,27540000000.0,"Libya 27,540,000,000" 65 | Serbia,26910000000.0,"Serbia 26,910,000,000" 66 | Morocco,26700000000.0,"Morocco 26,700,000,000" 67 | Syria,25700000000.0,"Syria 25,700,000,000" 68 | Nigeria,24780000000.0,"Nigeria 24,780,000,000" 69 | Ireland,24240000000.0,"Ireland 24,240,000,000" 70 | Hungary,21550000000.0,"Hungary 21,550,000,000" 71 | Oman,20360000000.0,"Oman 20,360,000,000" 72 | Ecuador,19020000000.0,"Ecuador 19,020,000,000" 73 | Puerto,18620000000.0,"Puerto 18,620,000,000" 74 | Azerbaijan,17790000000.0,"Azerbaijan 17,790,000,000" 75 | Croatia,16970000000.0,"Croatia 16,970,000,000" 76 | Iceland,16940000000.0,"Iceland 16,940,000,000" 77 | Cuba,16200000000.0,"Cuba 16,200,000,000" 78 | "Korea,",16000000000.0,"Korea, 16,000,000,000" 79 | Dominican,15140000000.0,"Dominican 15,140,000,000" 80 | Jordan,14560000000.0,"Jordan 14,560,000,000" 81 | Tajikistan,14420000000.0,"Tajikistan 14,420,000,000" 82 | Tunisia,13310000000.0,"Tunisia 13,310,000,000" 83 | Slovenia,13020000000.0,"Slovenia 13,020,000,000" 84 | Lebanon,12940000000.0,"Lebanon 12,940,000,000" 85 | Bosnia,12560000000.0,"Bosnia 12,560,000,000" 86 | Turkmenistan,11750000000.0,"Turkmenistan 11,750,000,000" 87 | Bahrain,11690000000.0,"Bahrain 11,690,000,000" 88 | Mozambique,11280000000.0,"Mozambique 11,280,000,000" 89 | Ghana,10580000000.0,"Ghana 10,580,000,000" 90 | Sri,10170000000.0,"Sri 10,170,000,000" 91 | Kyrgyzstan,9943000000.0,"Kyrgyzstan 9,943,000,000" 92 | Lithuania,9664000000.0,"Lithuania 9,664,000,000" 93 | Uruguay,9559000000.0,"Uruguay 9,559,000,000" 94 | Costa,8987000000.0,"Costa 8,987,000,000" 95 | Guatemala,8915000000.0,"Guatemala 8,915,000,000" 96 | Georgia,8468000000.0,"Georgia 8,468,000,000" 97 | Trinidad,8365000000.0,"Trinidad 8,365,000,000" 98 | Zambia,8327000000.0,"Zambia 8,327,000,000" 99 | Paraguay,8125000000.0,"Paraguay 8,125,000,000" 100 | Albania,7793000000.0,"Albania 7,793,000,000" 101 | Burma,7765000000.0,"Burma 7,765,000,000" 102 | Estonia,7417000000.0,"Estonia 7,417,000,000" 103 | "Congo,",7292000000.0,"Congo, 7,292,000,000" 104 | Panama,7144000000.0,"Panama 7,144,000,000" 105 | Latvia,7141000000.0,"Latvia 7,141,000,000" 106 | Macedonia,6960000000.0,"Macedonia 6,960,000,000" 107 | Zimbabwe,6831000000.0,"Zimbabwe 6,831,000,000" 108 | Kenya,6627000000.0,"Kenya 6,627,000,000" 109 | Bolivia,6456000000.0,"Bolivia 6,456,000,000" 110 | Luxembourg,6108000000.0,"Luxembourg 6,108,000,000" 111 | Sudan,5665000000.0,"Sudan 5,665,000,000" 112 | El,5665000000.0,"El 5,665,000,000" 113 | Cameroon,5535000000.0,"Cameroon 5,535,000,000" 114 | West,5312000000.0,"West 5,312,000,000" 115 | Ethiopia,5227000000.0,"Ethiopia 5,227,000,000" 116 | Armenia,5043000000.0,"Armenia 5,043,000,000" 117 | Honduras,5036000000.0,"Honduras 5,036,000,000" 118 | Angola,4842000000.0,"Angola 4,842,000,000" 119 | Cote,4731000000.0,"Cote 4,731,000,000" 120 | Tanzania,4545000000.0,"Tanzania 4,545,000,000" 121 | Nicaragua,4412000000.0,"Nicaragua 4,412,000,000" 122 | Moldova,4305000000.0,"Moldova 4,305,000,000" 123 | Cyprus,4296000000.0,"Cyprus 4,296,000,000" 124 | Macau,4291000000.0,"Macau 4,291,000,000" 125 | Namibia,4238000000.0,"Namibia 4,238,000,000" 126 | Mongolia,4204000000.0,"Mongolia 4,204,000,000" 127 | Afghanistan,3893000000.0,"Afghanistan 3,893,000,000" 128 | Yemen,3838000000.0,"Yemen 3,838,000,000" 129 | Brunei,3766000000.0,"Brunei 3,766,000,000" 130 | Cambodia,3553000000.0,"Cambodia 3,553,000,000" 131 | Montenegro,3465000000.0,"Montenegro 3,465,000,000" 132 | Nepal,3239000000.0,"Nepal 3,239,000,000" 133 | Botswana,3213000000.0,"Botswana 3,213,000,000" 134 | Papua,3116000000.0,"Papua 3,116,000,000" 135 | Jamaica,3008000000.0,"Jamaica 3,008,000,000" 136 | Kosovo,2887000000.0,"Kosovo 2,887,000,000" 137 | Laos,2874000000.0,"Laos 2,874,000,000" 138 | Uganda,2821000000.0,"Uganda 2,821,000,000" 139 | New,2716000000.0,"New 2,716,000,000" 140 | Mauritius,2658000000.0,"Mauritius 2,658,000,000" 141 | Senegal,2586000000.0,"Senegal 2,586,000,000" 142 | Bhutan,2085000000.0,"Bhutan 2,085,000,000" 143 | Malawi,2027000000.0,"Malawi 2,027,000,000" 144 | Madagascar,1883000000.0,"Madagascar 1,883,000,000" 145 | "Bahamas,",1716000000.0,"Bahamas, 1,716,000,000" 146 | Gabon,1680000000.0,"Gabon 1,680,000,000" 147 | Suriname,1572000000.0,"Suriname 1,572,000,000" 148 | Guam,1566000000.0,"Guam 1,566,000,000" 149 | Liechtenstein,1360000000.0,"Liechtenstein 1,360,000,000" 150 | Swaziland,1295000000.0,"Swaziland 1,295,000,000" 151 | Burkina,985500000.0,"Burkina 985,500,000" 152 | Togo,976000000.0,"Togo 976,000,000" 153 | Curacao,968000000.0,"Curacao 968,000,000" 154 | Mauritania,962600000.0,"Mauritania 962,600,000" 155 | Barbados,938000000.0,"Barbados 938,000,000" 156 | Niger,930200000.0,"Niger 930,200,000" 157 | Aruba,920700000.0,"Aruba 920,700,000" 158 | Benin,911000000.0,"Benin 911,000,000" 159 | Guinea,903000000.0,"Guinea 903,000,000" 160 | Mali,882600000.0,"Mali 882,600,000" 161 | Fiji,777600000.0,"Fiji 777,600,000" 162 | "Congo,",740000000.0,"Congo, 740,000,000" 163 | Virgin,723500000.0,"Virgin 723,500,000" 164 | Lesotho,707000000.0,"Lesotho 707,000,000" 165 | South,694100000.0,"South 694,100,000" 166 | Bermuda,664200000.0,"Bermuda 664,200,000" 167 | French,652900000.0,"French 652,900,000" 168 | Jersey,630100000.0,"Jersey 630,100,000" 169 | Belize,605000000.0,"Belize 605,000,000" 170 | Andorra,562400000.0,"Andorra 562,400,000" 171 | Guyana,558000000.0,"Guyana 558,000,000" 172 | Cayman,545900000.0,"Cayman 545,900,000" 173 | Haiti,452000000.0,"Haiti 452,000,000" 174 | Rwanda,365500000.0,"Rwanda 365,500,000" 175 | Saint,336400000.0,"Saint 336,400,000" 176 | Djibouti,311600000.0,"Djibouti 311,600,000" 177 | Seychelles,293900000.0,"Seychelles 293,900,000" 178 | Somalia,293000000.0,"Somalia 293,000,000" 179 | Antigua,293000000.0,"Antigua 293,000,000" 180 | Greenland,292000000.0,"Greenland 292,000,000" 181 | Cabo,285500000.0,"Cabo 285,500,000" 182 | Eritrea,284000000.0,"Eritrea 284,000,000" 183 | Burundi,282900000.0,"Burundi 282,900,000" 184 | Liberia,276900000.0,"Liberia 276,900,000" 185 | Maldives,267100000.0,"Maldives 267,100,000" 186 | Faroe,261300000.0,"Faroe 261,300,000" 187 | "Gambia,",218600000.0,"Gambia, 218,600,000" 188 | Chad,190700000.0,"Chad 190,700,000" 189 | "Micronesia,",178600000.0,"Micronesia, 178,600,000" 190 | Grenada,178000000.0,"Grenada 178,000,000" 191 | Central,168300000.0,"Central 168,300,000" 192 | Turks,167400000.0,"Turks 167,400,000" 193 | Gibraltar,160000000.0,"Gibraltar 160,000,000" 194 | American,146000000.0,"American 146,000,000" 195 | Sierra,134900000.0,"Sierra 134,900,000" 196 | Saint,130200000.0,"Saint 130,200,000" 197 | Saint,127400000.0,"Saint 127,400,000" 198 | Timor-Leste,125300000.0,"Timor-Leste 125,300,000" 199 | Equatorial,93000000.0,"Equatorial 93,000,000" 200 | Samoa,90400000.0,"Samoa 90,400,000" 201 | Dominica,89750000.0,"Dominica 89,750,000" 202 | Western,83700000.0,"Western 83,700,000" 203 | Solomon,79050000.0,"Solomon 79,050,000" 204 | Sao,60450000.0,"Sao 60,450,000" 205 | British,51150000.0,"British 51,150,000" 206 | Vanuatu,49290000.0,"Vanuatu 49,290,000" 207 | Guinea-Bissau,46500000.0,"Guinea-Bissau 46,500,000" 208 | Tonga,44640000.0,"Tonga 44,640,000" 209 | Saint,39990000.0,"Saint 39,990,000" 210 | Comoros,39990000.0,"Comoros 39,990,000" 211 | Cook,28950000.0,"Cook 28,950,000" 212 | Kiribati,24180000.0,"Kiribati 24,180,000" 213 | Montserrat,23250000.0,"Montserrat 23,250,000" 214 | Nauru,23250000.0,"Nauru 23,250,000" 215 | Falkland,11160000.0,"Falkland 11,160,000" 216 | Saint,7440000.0,"Saint 7,440,000" 217 | Niue,2790000.0,"Niue 2,790,000" 218 | Gaza,202000.0,"Gaza 202,000" 219 | Malta,174700.0,"Malta 174,700" 220 | Northern,48300.0,"Northern 48,300" 221 | -------------------------------------------------------------------------------- /Geographical Plotting/Choropleth Maps.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Choropleth Maps" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Offline Plotly Usage" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "Get imports and set everything up to be working offline." 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 251, 27 | "metadata": { 28 | "collapsed": true 29 | }, 30 | "outputs": [], 31 | "source": [ 32 | "import plotly.plotly as py\n", 33 | "import plotly.graph_objs as go \n", 34 | "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "Now set up everything so that the figures show up in the notebook:" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 252, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "data": { 51 | "text/html": [ 52 | "" 53 | ], 54 | "text/plain": [ 55 | "" 56 | ] 57 | }, 58 | "metadata": {}, 59 | "output_type": "display_data" 60 | } 61 | ], 62 | "source": [ 63 | "init_notebook_mode(connected=True) " 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "More info on other options for Offline Plotly usage can be found [here](https://plot.ly/python/offline/)." 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "## Choropleth US Maps\n", 78 | "\n", 79 | "Plotly's mapping can be a bit hard to get used to at first, remember to reference the cheat sheet in the data visualization folder, or [find it online here](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf)." 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 253, 85 | "metadata": { 86 | "collapsed": true 87 | }, 88 | "outputs": [], 89 | "source": [ 90 | "import pandas as pd" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "Now we need to begin to build our data dictionary. Easiest way to do this is to use the **dict()** function of the general form:\n", 98 | "\n", 99 | "* type = 'choropleth',\n", 100 | "* locations = list of states\n", 101 | "* locationmode = 'USA-states'\n", 102 | "* colorscale= \n", 103 | "\n", 104 | "Either a predefined string:\n", 105 | "\n", 106 | " 'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'\n", 107 | "\n", 108 | "or create a [custom colorscale](https://plot.ly/python/heatmap-and-contour-colorscales/)\n", 109 | "\n", 110 | "* text= list or array of text to display per point\n", 111 | "* z= array of values on z axis (color of state)\n", 112 | "* colorbar = {'title':'Colorbar Title'})\n", 113 | "\n", 114 | "Here is a simple example:" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 254, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "data = dict(type = 'choropleth',\n", 124 | " locations = ['AZ','CA','NY'],\n", 125 | " locationmode = 'USA-states',\n", 126 | " colorscale= 'Portland',\n", 127 | " text= ['text1','text2','text3'],\n", 128 | " z=[1.0,2.0,3.0],\n", 129 | " colorbar = {'title':'Colorbar Title'})" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "Then we create the layout nested dictionary:" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 255, 142 | "metadata": { 143 | "collapsed": true 144 | }, 145 | "outputs": [], 146 | "source": [ 147 | "layout = dict(geo = {'scope':'usa'})" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "Then we use: \n", 155 | "\n", 156 | " go.Figure(data = [data],layout = layout)\n", 157 | " \n", 158 | "to set up the object that finally gets passed into iplot()" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 256, 164 | "metadata": {}, 165 | "outputs": [], 166 | "source": [ 167 | "choromap = go.Figure(data = [data],layout = layout)" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 257, 173 | "metadata": {}, 174 | "outputs": [ 175 | { 176 | "data": { 177 | "text/html": [ 178 | "
" 179 | ], 180 | "text/plain": [ 181 | "" 182 | ] 183 | }, 184 | "metadata": {}, 185 | "output_type": "display_data" 186 | } 187 | ], 188 | "source": [ 189 | "iplot(choromap)" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "### Real Data US Map Choropleth\n", 197 | "\n", 198 | "Now let's show an example with some real data as well as some other options we can add to the dictionaries in data and layout." 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 258, 204 | "metadata": {}, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "text/html": [ 209 | "
\n", 210 | "\n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | "
codestatecategorytotal exportsbeefporkpoultrydairyfruits freshfruits proctotal fruitsveggies freshveggies proctotal veggiescornwheatcottontext
0ALAlabamastate1390.6334.410.6481.04.068.017.125.115.58.914.3334.970.0317.61Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...
1AKAlaskastate13.310.20.10.00.190.00.00.000.61.01.560.00.00.00Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...
2AZArizonastate1463.1771.317.90.0105.4819.341.060.27147.5239.4386.917.348.7423.95Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...
3ARArkansasstate3586.0253.229.4562.93.532.24.76.884.47.111.4569.5114.5665.44Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...
4CACaliforniastate16472.88228.711.1225.4929.952791.85944.68736.40803.21303.52106.7934.6249.31064.95California<br>Beef 228.7 Dairy 929.95<br>Frui...
\n", 342 | "
" 343 | ], 344 | "text/plain": [ 345 | " code state category total exports beef pork poultry dairy \\\n", 346 | "0 AL Alabama state 1390.63 34.4 10.6 481.0 4.06 \n", 347 | "1 AK Alaska state 13.31 0.2 0.1 0.0 0.19 \n", 348 | "2 AZ Arizona state 1463.17 71.3 17.9 0.0 105.48 \n", 349 | "3 AR Arkansas state 3586.02 53.2 29.4 562.9 3.53 \n", 350 | "4 CA California state 16472.88 228.7 11.1 225.4 929.95 \n", 351 | "\n", 352 | " fruits fresh fruits proc total fruits veggies fresh veggies proc \\\n", 353 | "0 8.0 17.1 25.11 5.5 8.9 \n", 354 | "1 0.0 0.0 0.00 0.6 1.0 \n", 355 | "2 19.3 41.0 60.27 147.5 239.4 \n", 356 | "3 2.2 4.7 6.88 4.4 7.1 \n", 357 | "4 2791.8 5944.6 8736.40 803.2 1303.5 \n", 358 | "\n", 359 | " total veggies corn wheat cotton \\\n", 360 | "0 14.33 34.9 70.0 317.61 \n", 361 | "1 1.56 0.0 0.0 0.00 \n", 362 | "2 386.91 7.3 48.7 423.95 \n", 363 | "3 11.45 69.5 114.5 665.44 \n", 364 | "4 2106.79 34.6 249.3 1064.95 \n", 365 | "\n", 366 | " text \n", 367 | "0 Alabama
Beef 34.4 Dairy 4.06
Fruits 25.1... \n", 368 | "1 Alaska
Beef 0.2 Dairy 0.19
Fruits 0.0 Ve... \n", 369 | "2 Arizona
Beef 71.3 Dairy 105.48
Fruits 60... \n", 370 | "3 Arkansas
Beef 53.2 Dairy 3.53
Fruits 6.8... \n", 371 | "4 California
Beef 228.7 Dairy 929.95
Frui... " 372 | ] 373 | }, 374 | "execution_count": 258, 375 | "metadata": {}, 376 | "output_type": "execute_result" 377 | } 378 | ], 379 | "source": [ 380 | "df = pd.read_csv('2011_US_AGRI_Exports')\n", 381 | "df.head()" 382 | ] 383 | }, 384 | { 385 | "cell_type": "markdown", 386 | "metadata": {}, 387 | "source": [ 388 | "Now out data dictionary with some extra marker and colorbar arguments:" 389 | ] 390 | }, 391 | { 392 | "cell_type": "code", 393 | "execution_count": 259, 394 | "metadata": {}, 395 | "outputs": [], 396 | "source": [ 397 | "data = dict(type='choropleth',\n", 398 | " colorscale = 'YIOrRd',\n", 399 | " locations = df['code'],\n", 400 | " z = df['total exports'],\n", 401 | " locationmode = 'USA-states',\n", 402 | " text = df['text'],\n", 403 | " marker = dict(line = dict(color = 'rgb(255,255,255)',width = 2)),\n", 404 | " colorbar = {'title':\"Millions USD\"}\n", 405 | " ) " 406 | ] 407 | }, 408 | { 409 | "cell_type": "markdown", 410 | "metadata": {}, 411 | "source": [ 412 | "And our layout dictionary with some more arguments:" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": 260, 418 | "metadata": { 419 | "collapsed": true 420 | }, 421 | "outputs": [], 422 | "source": [ 423 | "layout = dict(title = '2011 US Agriculture Exports by State',\n", 424 | " geo = dict(scope='usa',\n", 425 | " showlakes = True,\n", 426 | " lakecolor = 'rgb(85,173,240)')\n", 427 | " )" 428 | ] 429 | }, 430 | { 431 | "cell_type": "code", 432 | "execution_count": 261, 433 | "metadata": { 434 | "collapsed": true 435 | }, 436 | "outputs": [], 437 | "source": [ 438 | "choromap = go.Figure(data = [data],layout = layout)" 439 | ] 440 | }, 441 | { 442 | "cell_type": "code", 443 | "execution_count": 262, 444 | "metadata": {}, 445 | "outputs": [ 446 | { 447 | "data": { 448 | "text/html": [ 449 | "
" 450 | ], 451 | "text/plain": [ 452 | "" 453 | ] 454 | }, 455 | "metadata": {}, 456 | "output_type": "display_data" 457 | } 458 | ], 459 | "source": [ 460 | "iplot(choromap)" 461 | ] 462 | }, 463 | { 464 | "cell_type": "markdown", 465 | "metadata": {}, 466 | "source": [ 467 | "# World Choropleth Map\n", 468 | "\n", 469 | "Now let's see an example with a World Map:" 470 | ] 471 | }, 472 | { 473 | "cell_type": "code", 474 | "execution_count": 263, 475 | "metadata": {}, 476 | "outputs": [ 477 | { 478 | "data": { 479 | "text/html": [ 480 | "
\n", 481 | "\n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | "
COUNTRYGDP (BILLIONS)CODE
0Afghanistan21.71AFG
1Albania13.40ALB
2Algeria227.80DZA
3American Samoa0.75ASM
4Andorra4.80AND
\n", 523 | "
" 524 | ], 525 | "text/plain": [ 526 | " COUNTRY GDP (BILLIONS) CODE\n", 527 | "0 Afghanistan 21.71 AFG\n", 528 | "1 Albania 13.40 ALB\n", 529 | "2 Algeria 227.80 DZA\n", 530 | "3 American Samoa 0.75 ASM\n", 531 | "4 Andorra 4.80 AND" 532 | ] 533 | }, 534 | "execution_count": 263, 535 | "metadata": {}, 536 | "output_type": "execute_result" 537 | } 538 | ], 539 | "source": [ 540 | "df = pd.read_csv('2014_World_GDP')\n", 541 | "df.head()" 542 | ] 543 | }, 544 | { 545 | "cell_type": "code", 546 | "execution_count": 264, 547 | "metadata": {}, 548 | "outputs": [], 549 | "source": [ 550 | "data = dict(\n", 551 | " type = 'choropleth',\n", 552 | " locations = df['CODE'],\n", 553 | " z = df['GDP (BILLIONS)'],\n", 554 | " text = df['COUNTRY'],\n", 555 | " colorbar = {'title' : 'GDP Billions US'},\n", 556 | " ) " 557 | ] 558 | }, 559 | { 560 | "cell_type": "code", 561 | "execution_count": 265, 562 | "metadata": {}, 563 | "outputs": [], 564 | "source": [ 565 | "layout = dict(\n", 566 | " title = '2014 Global GDP',\n", 567 | " geo = dict(\n", 568 | " showframe = False,\n", 569 | " projection = {'type':'Mercator'}\n", 570 | " )\n", 571 | ")" 572 | ] 573 | }, 574 | { 575 | "cell_type": "code", 576 | "execution_count": 266, 577 | "metadata": {}, 578 | "outputs": [ 579 | { 580 | "data": { 581 | "text/html": [ 582 | "
" 583 | ], 584 | "text/plain": [ 585 | "" 586 | ] 587 | }, 588 | "metadata": {}, 589 | "output_type": "display_data" 590 | } 591 | ], 592 | "source": [ 593 | "choromap = go.Figure(data = [data],layout = layout)\n", 594 | "iplot(choromap)" 595 | ] 596 | }, 597 | { 598 | "cell_type": "markdown", 599 | "metadata": {}, 600 | "source": [ 601 | "# Great Job!" 602 | ] 603 | } 604 | ], 605 | "metadata": { 606 | "kernelspec": { 607 | "display_name": "Python 3", 608 | "language": "python", 609 | "name": "python3" 610 | }, 611 | "language_info": { 612 | "codemirror_mode": { 613 | "name": "ipython", 614 | "version": 3 615 | }, 616 | "file_extension": ".py", 617 | "mimetype": "text/x-python", 618 | "name": "python", 619 | "nbconvert_exporter": "python", 620 | "pygments_lexer": "ipython3", 621 | "version": "3.7.4" 622 | } 623 | }, 624 | "nbformat": 4, 625 | "nbformat_minor": 1 626 | } 627 | -------------------------------------------------------------------------------- /K Nearest Neighbors/KNN.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import seaborn as sns\n", 13 | "%matplotlib inline" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "df = pd.read_csv('Classified Data')" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "data": { 32 | "text/html": [ 33 | "
\n", 34 | "\n", 47 | "\n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | "
Unnamed: 0WTTPTIEQWSBILQEQWGFDJPJFHQENXJTARGET CLASS
000.9139171.1620730.5679460.7554640.7808620.3526080.7596970.6437980.8794221.2314091
110.6356321.0037220.5353420.8256450.9241090.6484500.6753341.0135460.6215521.4927020
220.7213601.2014930.9219900.8555951.5266290.7207811.6263511.1544830.9578771.2855970
331.2342041.3867260.6530460.8256241.1425040.8751281.4097081.3800031.5226921.1530931
441.2794910.9497500.6272800.6689761.2325370.7037271.1155960.6466911.4638121.4191671
\n", 143 | "
" 144 | ], 145 | "text/plain": [ 146 | " Unnamed: 0 WTT PTI EQW SBI LQE QWG \\\n", 147 | "0 0 0.913917 1.162073 0.567946 0.755464 0.780862 0.352608 \n", 148 | "1 1 0.635632 1.003722 0.535342 0.825645 0.924109 0.648450 \n", 149 | "2 2 0.721360 1.201493 0.921990 0.855595 1.526629 0.720781 \n", 150 | "3 3 1.234204 1.386726 0.653046 0.825624 1.142504 0.875128 \n", 151 | "4 4 1.279491 0.949750 0.627280 0.668976 1.232537 0.703727 \n", 152 | "\n", 153 | " FDJ PJF HQE NXJ TARGET CLASS \n", 154 | "0 0.759697 0.643798 0.879422 1.231409 1 \n", 155 | "1 0.675334 1.013546 0.621552 1.492702 0 \n", 156 | "2 1.626351 1.154483 0.957877 1.285597 0 \n", 157 | "3 1.409708 1.380003 1.522692 1.153093 1 \n", 158 | "4 1.115596 0.646691 1.463812 1.419167 1 " 159 | ] 160 | }, 161 | "execution_count": 3, 162 | "metadata": {}, 163 | "output_type": "execute_result" 164 | } 165 | ], 166 | "source": [ 167 | "df.head()" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 4, 173 | "metadata": {}, 174 | "outputs": [], 175 | "source": [ 176 | "from sklearn.preprocessing import StandardScaler" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 5, 182 | "metadata": {}, 183 | "outputs": [], 184 | "source": [ 185 | "scaler = StandardScaler()" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 6, 191 | "metadata": {}, 192 | "outputs": [ 193 | { 194 | "data": { 195 | "text/plain": [ 196 | "StandardScaler(copy=True, with_mean=True, with_std=True)" 197 | ] 198 | }, 199 | "execution_count": 6, 200 | "metadata": {}, 201 | "output_type": "execute_result" 202 | } 203 | ], 204 | "source": [ 205 | "scaler.fit(df.drop('TARGET CLASS', axis=1))" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 8, 211 | "metadata": {}, 212 | "outputs": [], 213 | "source": [ 214 | "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 47, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/html": [ 225 | "
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2-1.723391-0.7887020.3393180.3015110.7558732.031693-0.8701562.5998180.285707-0.682494-0.377850
3-1.7199270.9828411.060193-0.6213990.6252990.452820-0.2672201.7502081.0664911.241325-1.026987
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" 330 | ], 331 | "text/plain": [ 332 | " Unnamed: 0 WTT PTI EQW SBI LQE QWG \\\n", 333 | "0 -1.730320 -0.123542 0.185907 -0.913431 0.319629 -1.033637 -2.308375 \n", 334 | "1 -1.726856 -1.084836 -0.430348 -1.025313 0.625388 -0.444847 -1.152706 \n", 335 | "2 -1.723391 -0.788702 0.339318 0.301511 0.755873 2.031693 -0.870156 \n", 336 | "3 -1.719927 0.982841 1.060193 -0.621399 0.625299 0.452820 -0.267220 \n", 337 | "4 -1.716463 1.139275 -0.640392 -0.709819 -0.057175 0.822886 -0.936773 \n", 338 | "\n", 339 | " FDJ PJF HQE NXJ \n", 340 | "0 -0.798951 -1.482368 -0.949719 -0.643314 \n", 341 | "1 -1.129797 -0.202240 -1.828051 0.636759 \n", 342 | "2 2.599818 0.285707 -0.682494 -0.377850 \n", 343 | "3 1.750208 1.066491 1.241325 -1.026987 \n", 344 | "4 0.596782 -1.472352 1.040772 0.276510 " 345 | ] 346 | }, 347 | "execution_count": 47, 348 | "metadata": {}, 349 | "output_type": "execute_result" 350 | } 351 | ], 352 | "source": [ 353 | "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n", 354 | "df_feat.head()" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 50, 360 | "metadata": {}, 361 | "outputs": [ 362 | { 363 | "name": "stdout", 364 | "output_type": "stream", 365 | "text": [ 366 | "0.9496815136132967\n" 367 | ] 368 | } 369 | ], 370 | "source": [ 371 | "']))" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 14, 377 | "metadata": {}, 378 | "outputs": [], 379 | "source": [ 380 | "from sklearn.model_selection import train_test_split" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": 15, 386 | "metadata": {}, 387 | "outputs": [], 388 | "source": [ 389 | "X= df_feat\n", 390 | "y= df['TARGET CLASS']\n", 391 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=101)" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 16, 397 | "metadata": {}, 398 | "outputs": [], 399 | "source": [ 400 | "from sklearn.neighbors import KNeighborsClassifier" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": 17, 406 | "metadata": {}, 407 | "outputs": [], 408 | "source": [ 409 | "knn = KNeighborsClassifier(n_neighbors=1)" 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 18, 415 | "metadata": {}, 416 | "outputs": [ 417 | { 418 | "data": { 419 | "text/plain": [ 420 | "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", 421 | " metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n", 422 | " weights='uniform')" 423 | ] 424 | }, 425 | "execution_count": 18, 426 | "metadata": {}, 427 | "output_type": "execute_result" 428 | } 429 | ], 430 | "source": [ 431 | "knn.fit(X_train,y_train)" 432 | ] 433 | }, 434 | { 435 | "cell_type": "code", 436 | "execution_count": 19, 437 | "metadata": {}, 438 | "outputs": [], 439 | "source": [ 440 | "pred = knn.predict(X_test)" 441 | ] 442 | }, 443 | { 444 | "cell_type": "code", 445 | "execution_count": 20, 446 | "metadata": {}, 447 | "outputs": [], 448 | "source": [ 449 | "from sklearn.metrics import classification_report,confusion_matrix" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 21, 455 | "metadata": {}, 456 | "outputs": [ 457 | { 458 | "name": "stdout", 459 | "output_type": "stream", 460 | "text": [ 461 | "[[159 14]\n", 462 | " [ 14 143]]\n", 463 | " precision recall f1-score support\n", 464 | "\n", 465 | " 0 0.92 0.92 0.92 173\n", 466 | " 1 0.91 0.91 0.91 157\n", 467 | "\n", 468 | " accuracy 0.92 330\n", 469 | " macro avg 0.91 0.91 0.91 330\n", 470 | "weighted avg 0.92 0.92 0.92 330\n", 471 | "\n" 472 | ] 473 | } 474 | ], 475 | "source": [ 476 | "print(confusion_matrix(y_test,pred))\n", 477 | "print(classification_report(y_test,pred))" 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": 27, 483 | "metadata": {}, 484 | "outputs": [], 485 | "source": [ 486 | "error_rate = []\n", 487 | "for i in range(1,40):\n", 488 | " knn = KNeighborsClassifier(n_neighbors=i)\n", 489 | " knn.fit(X_train,y_train)\n", 490 | " pred_i = knn.predict(X_test)\n", 491 | " error_rate.append(np.mean(pred_i != y_test))\n", 492 | " " 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 30, 498 | "metadata": {}, 499 | "outputs": [ 500 | { 501 | "data": { 502 | "text/plain": [ 503 | "Text(0, 0.5, 'Error Rate')" 504 | ] 505 | }, 506 | "execution_count": 30, 507 | "metadata": {}, 508 | "output_type": "execute_result" 509 | }, 510 | { 511 | "data": { 512 | "image/png": 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\n", 513 | "text/plain": [ 514 | "
" 515 | ] 516 | }, 517 | "metadata": { 518 | "needs_background": "light" 519 | }, 520 | "output_type": "display_data" 521 | } 522 | ], 523 | "source": [ 524 | "plt.figure(figsize=(10,6))\n", 525 | "plt.plot(range(1,40),error_rate,color='blue',ls='--',marker='o',markerfacecolor='red',markersize=10)\n", 526 | "plt.title('Error Rate vs K Value')\n", 527 | "plt.xlabel('K')\n", 528 | "plt.ylabel('Error Rate')" 529 | ] 530 | }, 531 | { 532 | "cell_type": "code", 533 | "execution_count": 46, 534 | "metadata": {}, 535 | "outputs": [ 536 | { 537 | "name": "stdout", 538 | "output_type": "stream", 539 | "text": [ 540 | "[[165 8]\n", 541 | " [ 10 147]]\n", 542 | " precision recall f1-score support\n", 543 | "\n", 544 | " 0 0.94 0.95 0.95 173\n", 545 | " 1 0.95 0.94 0.94 157\n", 546 | "\n", 547 | " accuracy 0.95 330\n", 548 | " macro avg 0.95 0.95 0.95 330\n", 549 | "weighted avg 0.95 0.95 0.95 330\n", 550 | "\n" 551 | ] 552 | } 553 | ], 554 | "source": [ 555 | "knn = KNeighborsClassifier(n_neighbors=17)\n", 556 | "knn.fit(X_train,y_train)\n", 557 | "pred = knn.predict(X_test)\n", 558 | "print(confusion_matrix(y_test,pred))\n", 559 | "print(classification_report(y_test,pred))\n" 560 | ] 561 | }, 562 | { 563 | "cell_type": "code", 564 | "execution_count": null, 565 | "metadata": {}, 566 | "outputs": [], 567 | "source": [] 568 | } 569 | ], 570 | "metadata": { 571 | "kernelspec": { 572 | "display_name": "Python 3", 573 | "language": "python", 574 | "name": "python3" 575 | }, 576 | "language_info": { 577 | "codemirror_mode": { 578 | "name": "ipython", 579 | "version": 3 580 | }, 581 | "file_extension": ".py", 582 | "mimetype": "text/x-python", 583 | "name": "python", 584 | "nbconvert_exporter": "python", 585 | "pygments_lexer": "ipython3", 586 | "version": "3.7.4" 587 | } 588 | }, 589 | "nbformat": 4, 590 | "nbformat_minor": 2 591 | } 592 | -------------------------------------------------------------------------------- /Logistic Regression/titanic_test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. 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Michael J",male,,1,1,2668,22.3583,,C 420 | -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/1 Introduction to Data Science in Python Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/1 Introduction to Data Science in Python Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/2 Applied Plotting, Charting & Data Representationin Pytho Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/2 Applied Plotting, Charting & Data Representationin Pytho Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/3 Applied Machine Learning in Python Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/3 Applied Machine Learning in Python Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/4 Applied Text Mining in Python Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/4 Applied Text Mining in Python Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/5 Applied Social Network Analysis in Python Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/5 Applied Social Network Analysis in Python Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/Applied Data Science with Python (Specialization) Coursera.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/Applied Data Science with Python (Specialization) Coursera.pdf -------------------------------------------------------------------------------- /My-Certificates/Applied-Data-Science-with-Python-Specialization Certificates/README.md: -------------------------------------------------------------------------------- 1 | # Coursera 2 | Certification from University of Michigan 3 | 4 | Level: Intermediate 5 | -------------------------------------------------------------------------------- /My-Certificates/README.md: -------------------------------------------------------------------------------- 1 | # Certifications 2 | My Data Science Certifications -------------------------------------------------------------------------------- /Natural Language Processing/smsspamcollection/readme: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Natural Language Processing/smsspamcollection/readme -------------------------------------------------------------------------------- /Plotly-and-Cufflinks/plotly_cheat_sheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Plotly-and-Cufflinks/plotly_cheat_sheet.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Science-Study 2 | This repository contains jupyter notebook and other resources made by me during learning Data Science and Machine Learning... 3 | 4 | * books in "/books" directory 5 | 6 | I'm updating resourses on regular basis 7 | 8 | -------------------------------------------------------------------------------- /Recommender System/u.item: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliarslanansari/Data-Science-Study/41ee57d0d0ed4285325807328a8db3daa39b37c6/Recommender System/u.item --------------------------------------------------------------------------------